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NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/scattering/scattering.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
import omni.graph.core as og
import omni.replicator.core as rep
# create a plane to sample on
plane_samp = rep.create.plane(scale=4, rotation=(20, 0, 0))
def randomize_spheres():
# create small spheres to sample inside the plane
spheres = rep.create.sphere(scale=0.4, count=30)
# randomize
with spheres:
rep.randomizer.scatter_2d(plane_samp, check_for_collisions=False)
# Add color to small spheres
rep.randomizer.color(
colors=rep.distribution.uniform((0.2, 0.2, 0.2), (1, 1, 1))
)
return spheres.node
rep.randomizer.register(randomize_spheres)
with rep.trigger.on_frame(num_frames=10):
rep.randomizer.randomize_spheres()
rep.orchestrator.preview()
og.Controller.evaluate_sync()
rep.orchestrator.preview()
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/scattering/scatter2D_with_limits.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
import omni.replicator.core as rep
# A light to see
distance_light = rep.create.light(rotation=(-45, 0, 0), light_type="distant")
# Create a plane to sample on
plane_samp = rep.create.plane(scale=4, rotation=(20, 0, 0))
# create a larger sphere to sample on the surface of
sphere_samp = rep.create.sphere(scale=2.4, position=(0, 100, -180))
def randomize_spheres():
# create small spheres to sample inside the plane
spheres = rep.create.sphere(scale=0.4, count=60)
# scatter small spheres
with spheres:
rep.randomizer.scatter_2d(
[plane_samp, sphere_samp],
min_samp=(None, None, None),
# Small spheres will not go beyond 0 in X, 110 in Y, 30 in Z world space
max_samp=(0, 110, 30),
check_for_collisions=False,
)
# Add color to small spheres
rep.randomizer.color(
colors=rep.distribution.uniform((0.2, 0.2, 0.2), (1, 1, 1))
)
return spheres.node
rep.randomizer.register(randomize_spheres)
with rep.trigger.on_frame(num_frames=10):
rep.randomizer.randomize_spheres()
og.Controller.evaluate_sync()
rep.orchestrator.preview()
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/surface_scratches/scratches_randomization.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
from pathlib import Path
import carb
import omni.replicator.core as rep
import omni.usd
from pxr import Sdf, UsdGeom
"""
Instructions:
Open the example scene file "scratches_randomization.usda",
located adjacent to this script, in Omniverse prior to using this script
"""
# Get the current Usd "stage". This is where all the scene objects live
stage = omni.usd.get_context().get_stage()
with rep.new_layer():
camera = rep.create.camera(position=(-30, 38, 60), look_at=(0, 0, 0))
render_product = rep.create.render_product(camera, (1280, 720))
# Get Scene cube
cube_prim = stage.GetPrimAtPath("/World/RoundedCube2/Cube/Cube")
# Set the primvars on the cubes once
primvars_api = UsdGeom.PrimvarsAPI(cube_prim)
primvars_api.CreatePrimvar("random_color", Sdf.ValueTypeNames.Float3).Set(
(1.0, 1.0, 1.0)
)
primvars_api.CreatePrimvar("random_intensity", Sdf.ValueTypeNames.Float3).Set(
(1.0, 1.0, 1.0)
)
def change_colors():
# Change color primvars
cubes = rep.get.prims(
path_pattern="/World/RoundedCube2/Cube/Cube", prim_types=["Mesh"]
)
with cubes:
rep.modify.attribute(
"primvars:random_color",
rep.distribution.uniform((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)),
attribute_type="float3",
)
rep.modify.attribute(
"primvars:random_intensity",
rep.distribution.uniform((0.0, 0.0, 0.0), (10.0, 10.0, 10.0)),
attribute_type="float3",
)
return cubes.node
rep.randomizer.register(change_colors)
# Setup randomization of colors, different each frame
with rep.trigger.on_frame(num_frames=10):
rep.randomizer.change_colors()
# (optional) Write output images to disk
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="~/replicator_examples/box_scratches",
rgb=True,
bounding_box_2d_tight=True,
semantic_segmentation=True,
distance_to_image_plane=True,
)
writer.attach([render_product])
carb.log_info("scratches randomization complete")
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/snippets/surface_scratches/scratches_randomization.usda | #usda 1.0
(
customLayerData = {
dictionary audioSettings = {
double dopplerLimit = 2
double dopplerScale = 1
token enableDistanceDelay = "off"
token enableDoppler = "off"
token enableInterauralDelay = "off"
double nonSpatialTimeScale = 1
double spatialTimeScale = 1
double speedOfSound = 340
}
dictionary cameraSettings = {
dictionary Front = {
double3 position = (0, 0, 50000)
double radius = 500
}
dictionary Perspective = {
double3 position = (-27.43726414861917, 32.59290269818252, 59.677509323096864)
double3 target = (-5.592588747006676, 8.168227082494138, 1.263151137001465)
}
dictionary Right = {
double3 position = (-50000, 0, -1.1102230246251565e-11)
double radius = 500
}
dictionary Top = {
double3 position = (-4.329780281177466e-12, 50000, 1.1102230246251565e-11)
double radius = 500
}
string boundCamera = "/OmniverseKit_Persp"
}
dictionary omni_layer = {
dictionary muteness = {
}
}
dictionary renderSettings = {
double "rtx:hydra:points:defaultWidth" = 1
double "rtx:post:dof:fNumber" = 5.599999904632568
double "rtx:post:dof:focalLength" = 35
double "rtx:post:dof:subjectDistance" = 5
}
}
defaultPrim = "World"
doc = """scratches_randomzation demo created by Paul Callender"""
endTimeCode = 0
metersPerUnit = 0.01
startTimeCode = 0
timeCodesPerSecond = 24
upAxis = "Y"
)
def Xform "World"
{
def DistantLight "defaultLight" (
apiSchemas = ["ShapingAPI"]
)
{
float angle = 1
color3f color = (0.36105606, 0.4226781, 0.66795367)
float intensity = 3000
float shaping:cone:angle = 180
float shaping:cone:softness
float shaping:focus
color3f shaping:focusTint
asset shaping:ies:file
double3 xformOp:rotateXYZ = (315, 0, 0)
double3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (0, 0, 0)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
def Scope "Looks"
{
def Material "OmniPBR" (
customData = {
dictionary ui = {
dictionary nodegraph = {
dictionary node = {
dictionary pos = {
double2 output = (1170.140380859375, 59.01169967651367)
}
}
}
}
}
)
{
token outputs:mdl:displacement
token outputs:mdl:surface.connect = </World/Looks/OmniPBR/OmniSurfaceBlendBase.outputs:out>
token outputs:mdl:volume
def Shader "data_lookup_float3" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:name", "inputs:default_value"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "data_lookup_float3"
float3 inputs:default_value (
customData = {
float3 default = (0, 0, 0)
}
hidden = false
renderType = "float3"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
string inputs:name = "random_color" (
hidden = false
renderType = "string"
)
float3 outputs:out (
renderType = "float3"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1719.1984, 136.92104)
}
def Shader "x" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "x(float3)"
float3 inputs:a (
customData = {
float3 default = (0, 0, 0)
}
hidden = false
renderType = "float3"
)
float3 inputs:a.connect = </World/Looks/OmniPBR/data_lookup_float3.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1218.8994, 141.73224)
}
def Shader "multiply02" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:a", "inputs:b"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "multiply(float,float)"
float inputs:a (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:a.connect = </World/Looks/OmniPBR/x.outputs:out>
float inputs:b = 360 (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1012.8081, 143.37856)
}
def Shader "z" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "z(float3)"
float3 inputs:a (
customData = {
float3 default = (0, 0, 0)
}
hidden = false
renderType = "float3"
)
float3 inputs:a.connect = </World/Looks/OmniPBR/data_lookup_float3.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1412.8395, 325.88718)
}
def Shader "construct_float2" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:x", "inputs:y"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "construct_float2(float,float)"
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float inputs:x (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:x.connect = </World/Looks/OmniPBR/remap.outputs:out>
float inputs:y (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:y.connect = </World/Looks/OmniPBR/remap.outputs:out>
float2 outputs:out (
renderType = "float2"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1012.624, 330.11096)
}
def Shader "remap" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:a", "inputs:start_old", "inputs:end_old", "inputs:start_new", "inputs:end_new"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "remap(float,float,float,float,float)"
float inputs:a (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:a.connect = </World/Looks/OmniPBR/z.outputs:out>
float inputs:end_new = 2 (
customData = {
float default = 1
}
hidden = false
renderType = "float"
)
float inputs:end_old (
customData = {
float default = 1
}
hidden = false
renderType = "float"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float inputs:start_new = 1.5 (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:start_old (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1221.7076, 330.11108)
}
def NodeGraph "file_texture" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:texture", "inputs:mono_source", "inputs:brightness", "inputs:contrast", "inputs:invert", "inputs:texture_space", "inputs:rotation", "inputs:translation", "inputs:scaling", "inputs:clip", "outputs:tex", "outputs:color", "outputs:mono", "outputs:r", "outputs:g", "outputs:b"]
float inputs:brightness (
customData = {
double default = 1
}
displayGroup = "Bitmap parameters"
displayName = "Brightness"
renderType = "float"
)
bool inputs:clip (
customData = {
bool default = 0
}
displayGroup = "Placement"
displayName = "Clip"
renderType = "bool"
)
float inputs:contrast (
customData = {
double default = 1
}
displayGroup = "Bitmap parameters"
displayName = "Contrast"
renderType = "float"
)
bool inputs:invert (
customData = {
bool default = 0
}
displayGroup = "Bitmap parameters"
displayName = "Invert image"
renderType = "bool"
)
int inputs:mono_source (
customData = {
int default = 1
}
displayGroup = "Bitmap parameters"
displayName = "Scalar mode"
renderType = "mono_mode"
sdrMetadata = {
string __SDR__enum_value = "mono_average"
string options = "mono_alpha:0|mono_average:1|mono_luminance:2|mono_maximum:3"
}
)
float inputs:rotation (
customData = {
double default = 0
}
displayGroup = "Placement"
displayName = "Rotation"
renderType = "float"
)
float inputs:rotation.connect = </World/Looks/OmniPBR/multiply02.outputs:out>
float2 inputs:scaling (
customData = {
double2 default = (1, 1)
}
displayGroup = "Placement"
displayName = "Tiling"
renderType = "float2"
)
float2 inputs:scaling.connect = </World/Looks/OmniPBR/construct_float2.outputs:out>
asset inputs:texture = @./scratches_randomization.png@ (
colorSpace = "sRGB"
customData = {
asset default = @@
}
displayGroup = "Bitmap parameters"
displayName = "Bitmap file"
renderType = "texture_2d"
)
int inputs:texture_space (
customData = {
int default = 0
dictionary range = {
int max = 3
int min = 0
}
}
displayGroup = "Placement"
displayName = "UV space index"
renderType = "int"
)
float2 inputs:translation (
customData = {
double2 default = (0, 0)
}
displayGroup = "Placement"
displayName = "Offset"
renderType = "float2"
)
float outputs:b (
renderType = "float"
)
float outputs:b.connect = </World/Looks/OmniPBR/file_texture/z.outputs:out>
color3f outputs:color (
renderType = "color"
)
color3f outputs:color.connect = </World/Looks/OmniPBR/file_texture/construct_color.outputs:out>
float outputs:g (
renderType = "float"
)
float outputs:g.connect = </World/Looks/OmniPBR/file_texture/y.outputs:out>
float outputs:mono (
renderType = "float"
)
float outputs:mono.connect = </World/Looks/OmniPBR/file_texture/construct_float.outputs:out>
float outputs:r (
renderType = "float"
)
float outputs:r.connect = </World/Looks/OmniPBR/file_texture/x.outputs:out>
token outputs:tex (
renderType = "texture_return"
)
token outputs:tex.connect = </World/Looks/OmniPBR/file_texture/file_texture.outputs:out>
custom token ui:description = "Allows texturing using image files of various file formats"
uniform token ui:displayGroup = "Texturing, high level"
uniform token ui:displayName = "Bitmap texture"
uniform token ui:nodegraph:node:expansionState = "open"
uniform asset ui:nodegraph:node:icon = @core_definitions.file_texture.png@
uniform float2 ui:nodegraph:node:pos = (-722.0467, 117.79854)
custom int ui:order = 30
def Shader "file_texture"
{
reorder properties = ["inputs:texture", "inputs:mono_source", "inputs:brightness", "inputs:contrast", "inputs:invert", "inputs:texture_space", "inputs:rotation", "inputs:translation", "inputs:scaling", "inputs:clip"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/core_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "file_texture(texture_2d,::base::mono_mode,float,float,float2,float2,float,bool,int,bool)"
float inputs:brightness (
customData = {
double default = 1
}
displayGroup = "Bitmap parameters"
displayName = "Brightness"
renderType = "float"
)
float inputs:brightness.connect = </World/Looks/OmniPBR/file_texture.inputs:brightness>
bool inputs:clip (
customData = {
bool default = 0
}
displayGroup = "Placement"
displayName = "Clip"
doc = "If set to true, texture will not repeat. Outside of the texture, color will be black and the scalar value will be 0"
renderType = "bool"
)
bool inputs:clip.connect = </World/Looks/OmniPBR/file_texture.inputs:clip>
float inputs:contrast (
customData = {
double default = 1
}
displayGroup = "Bitmap parameters"
displayName = "Contrast"
renderType = "float"
)
float inputs:contrast.connect = </World/Looks/OmniPBR/file_texture.inputs:contrast>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
bool inputs:invert (
customData = {
bool default = 0
}
displayGroup = "Bitmap parameters"
displayName = "Invert image"
doc = "Invert image"
renderType = "bool"
)
bool inputs:invert.connect = </World/Looks/OmniPBR/file_texture.inputs:invert>
int inputs:mono_source (
customData = {
int default = 1
}
displayGroup = "Bitmap parameters"
displayName = "Scalar mode"
doc = "Defines how the texture applies to scalar parameters"
renderType = "mono_mode"
sdrMetadata = {
string __SDR__enum_value = "mono_average"
string options = "mono_alpha:0|mono_average:1|mono_luminance:2|mono_maximum:3"
}
)
int inputs:mono_source.connect = </World/Looks/OmniPBR/file_texture.inputs:mono_source>
float inputs:rotation (
customData = {
double default = 0
}
displayGroup = "Placement"
displayName = "Rotation"
doc = "Rotation angle of the texture in degrees"
renderType = "float"
)
float inputs:rotation.connect = </World/Looks/OmniPBR/file_texture.inputs:rotation>
float2 inputs:scaling (
customData = {
double2 default = (1, 1)
}
displayGroup = "Placement"
displayName = "Tiling"
doc = "Controls the scale of the texture on the object"
renderType = "float2"
)
float2 inputs:scaling.connect = </World/Looks/OmniPBR/file_texture.inputs:scaling>
asset inputs:texture (
colorSpace = "sRGB"
customData = {
asset default = @@
}
displayGroup = "Bitmap parameters"
displayName = "Bitmap file"
renderType = "texture_2d"
)
asset inputs:texture.connect = </World/Looks/OmniPBR/file_texture.inputs:texture>
int inputs:texture_space (
customData = {
int default = 0
dictionary range = {
int max = 3
int min = 0
}
}
displayGroup = "Placement"
displayName = "UV space index"
doc = "Selects a specific UV space"
renderType = "int"
)
int inputs:texture_space.connect = </World/Looks/OmniPBR/file_texture.inputs:texture_space>
float2 inputs:translation (
customData = {
double2 default = (0, 0)
}
displayGroup = "Placement"
displayName = "Offset"
doc = "Controls position of the texture on the object"
renderType = "float2"
)
float2 inputs:translation.connect = </World/Looks/OmniPBR/file_texture.inputs:translation>
token outputs:out (
renderType = "texture_return"
)
}
def Shader "construct_float"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "construct_float(::base::texture_return)"
token inputs:a (
renderType = "texture_return"
)
token inputs:a.connect = </World/Looks/OmniPBR/file_texture/file_texture.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
}
def Shader "construct_color"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "construct_color(::base::texture_return)"
token inputs:a (
renderType = "texture_return"
)
token inputs:a.connect = </World/Looks/OmniPBR/file_texture/file_texture.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
color3f outputs:out (
renderType = "color"
)
}
def Shader "x"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "x(color)"
color3f inputs:a (
renderType = "color"
)
color3f inputs:a.connect = </World/Looks/OmniPBR/file_texture/construct_color.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
}
def Shader "y"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "y(color)"
color3f inputs:a (
renderType = "color"
)
color3f inputs:a.connect = </World/Looks/OmniPBR/file_texture/construct_color.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
}
def Shader "z"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "z(color)"
color3f inputs:a (
renderType = "color"
)
color3f inputs:a.connect = </World/Looks/OmniPBR/file_texture/construct_color.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
}
}
def Shader "OmniSurfaceBlendBase" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:base_material", "inputs:blend_material", "inputs:blend_weight", "inputs:enable_opacity", "inputs:geometry_opacity_threshold"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @OmniSurface/OmniSurfaceBlendBase.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "OmniSurfaceBlendBase"
token inputs:base_material (
displayGroup = "Materials"
displayName = "Base Material"
hidden = true
renderType = "material"
)
token inputs:base_material.connect = </World/Looks/OmniPBR/scratched_metal_v201.outputs:out>
token inputs:blend_material (
displayGroup = "Materials"
displayName = "Blend Material"
hidden = true
renderType = "material"
)
token inputs:blend_material.connect = </World/Looks/OmniPBR/scratched_metal_v202.outputs:out>
float inputs:blend_weight (
customData = {
float default = 1
dictionary range = {
float max = 1
float min = 0
}
}
displayGroup = "Blending"
displayName = "Weight"
hidden = false
renderType = "float"
)
float inputs:blend_weight.connect = </World/Looks/OmniPBR/multiply01.outputs:out>
bool inputs:enable_opacity (
customData = {
bool default = 0
}
displayGroup = "Geometry"
displayName = "Enable Opacity"
doc = "Enables the use of cutout opacity"
hidden = false
renderType = "bool"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float inputs:geometry_opacity_threshold = 0 (
customData = {
float default = 0
dictionary range = {
float max = 1
float min = 0
}
}
displayGroup = "Geometry"
displayName = "Opacity Threshold"
doc = "If > 0, remap opacity values to 1 when >= threshold and to 0 otherwise"
hidden = false
)
token outputs:out (
renderType = "material"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (714.2855, 60.34445)
}
def Shader "scratched_metal_v201" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:metal_color", "inputs:roughness", "inputs:glossy_weight", "inputs:anisotropy", "inputs:anisotropy_rotation", "inputs:normal"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/core_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "scratched_metal_v2"
float inputs:anisotropy (
customData = {
float default = 0
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Anisotropy"
doc = "Higher values will stretch the highlight"
hidden = false
renderType = "float"
)
float inputs:anisotropy_rotation (
customData = {
float default = 0
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Anisotropy rotation"
doc = "Changes the orientation of the anisotropy. A value of 1 will rotate the orientation 360 degrees."
hidden = false
renderType = "float"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float inputs:glossy_weight = 0.9 (
customData = {
float default = 0.9
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Reflection weight"
doc = "Intensity of highlights and glossy reflections and highlights"
hidden = false
renderType = "float"
)
color3f inputs:metal_color = (0.07411934, 0.11553271, 0.16988415) (
customData = {
float3 default = (0.9, 0.9, 0.9)
}
displayName = "Color"
doc = "The color of the metal"
hidden = false
renderType = "color"
)
float3 inputs:normal (
customData = {
float3 default = (0, 0, 0)
}
displayName = "Bumps"
doc = "Attach bump or normal maps here"
hidden = true
renderType = "float3"
)
float inputs:roughness = 0.17 (
customData = {
float default = 0.2
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Roughness"
doc = "Higher roughness values lead to bigger highlights and blurry reflections"
hidden = false
renderType = "float"
)
token outputs:out (
renderType = "material"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (265.7426, 21.59628)
}
def Shader "scratched_metal_v202" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:metal_color", "inputs:roughness", "inputs:glossy_weight", "inputs:anisotropy", "inputs:anisotropy_rotation", "inputs:normal"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/core_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "scratched_metal_v2"
float inputs:anisotropy (
customData = {
float default = 0
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Anisotropy"
doc = "Higher values will stretch the highlight"
hidden = false
renderType = "float"
)
float inputs:anisotropy_rotation (
customData = {
float default = 0
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Anisotropy rotation"
doc = "Changes the orientation of the anisotropy. A value of 1 will rotate the orientation 360 degrees."
hidden = false
renderType = "float"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float inputs:glossy_weight (
customData = {
float default = 0.9
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Reflection weight"
doc = "Intensity of highlights and glossy reflections and highlights"
hidden = false
renderType = "float"
)
color3f inputs:metal_color (
customData = {
float3 default = (0.9, 0.9, 0.9)
}
displayName = "Color"
doc = "The color of the metal"
hidden = false
renderType = "color"
)
float3 inputs:normal (
customData = {
float3 default = (0, 0, 0)
}
displayName = "Bumps"
doc = "Attach bump or normal maps here"
hidden = true
renderType = "float3"
)
float inputs:roughness = 0.78 (
customData = {
float default = 0.2
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Roughness"
doc = "Higher roughness values lead to bigger highlights and blurry reflections"
hidden = false
renderType = "float"
)
token outputs:out (
renderType = "material"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (252.82478, 283.53)
}
def NodeGraph "perlin_noise_texture" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:color1", "inputs:color2", "inputs:noise_levels", "inputs:absolute_noise", "inputs:noise_threshold_low", "inputs:noise_threshold_high", "inputs:object_space", "inputs:texture_space", "inputs:rotation", "inputs:translation", "inputs:scaling", "outputs:tex", "outputs:color", "outputs:mono", "outputs:r", "outputs:g", "outputs:b"]
bool inputs:absolute_noise (
customData = {
bool default = 0
}
displayGroup = "Noise parameters"
displayName = "Billowing appearance"
renderType = "bool"
)
color3f inputs:color1 (
customData = {
double3 default = (1, 1, 1)
}
displayGroup = "Noise parameters"
displayName = "Color 1"
renderType = "color"
)
color3f inputs:color2 (
customData = {
double3 default = (0, 0, 0)
}
displayGroup = "Noise parameters"
displayName = "Color 2"
renderType = "color"
)
int inputs:noise_levels = 3 (
customData = {
int default = 3
dictionary range = {
int max = 6
int min = 1
}
}
displayGroup = "Noise parameters"
displayName = "Levels"
renderType = "int"
)
float inputs:noise_threshold_high (
customData = {
double default = 1
dictionary range = {
int max = 1
int min = 0
}
}
displayGroup = "Noise parameters"
displayName = "Upper threshold"
renderType = "float"
)
float inputs:noise_threshold_low (
customData = {
double default = 0
dictionary range = {
int max = 1
int min = 0
}
}
displayGroup = "Noise parameters"
displayName = "Lower threshold"
renderType = "float"
)
bool inputs:object_space = 0 (
customData = {
bool default = 1
}
displayGroup = "Placement"
displayName = "Use object space"
renderType = "bool"
)
float3 inputs:rotation (
customData = {
double3 default = (0, 0, 0)
}
displayGroup = "Placement"
displayName = "Rotation"
renderType = "float3"
)
float3 inputs:scaling (
customData = {
double3 default = (10, 10, 10)
}
displayGroup = "Placement"
displayName = "Tiling"
renderType = "float3"
)
int inputs:texture_space (
customData = {
int default = 0
dictionary range = {
int max = 3
int min = 0
}
}
displayGroup = "Placement"
displayName = "UV space index"
renderType = "int"
)
float3 inputs:translation (
customData = {
double3 default = (0, 0, 0)
}
displayGroup = "Placement"
displayName = "Offset"
renderType = "float3"
)
float3 inputs:translation.connect = </World/Looks/OmniPBR/construct_float3.outputs:out>
float outputs:b (
renderType = "float"
)
float outputs:b.connect = </World/Looks/OmniPBR/perlin_noise_texture/z.outputs:out>
color3f outputs:color (
renderType = "color"
)
color3f outputs:color.connect = </World/Looks/OmniPBR/perlin_noise_texture/construct_color.outputs:out>
float outputs:g (
renderType = "float"
)
float outputs:g.connect = </World/Looks/OmniPBR/perlin_noise_texture/y.outputs:out>
float outputs:mono (
renderType = "float"
)
float outputs:mono.connect = </World/Looks/OmniPBR/perlin_noise_texture/construct_float.outputs:out>
float outputs:r (
renderType = "float"
)
float outputs:r.connect = </World/Looks/OmniPBR/perlin_noise_texture/x.outputs:out>
token outputs:tex (
renderType = "texture_return"
)
token outputs:tex.connect = </World/Looks/OmniPBR/perlin_noise_texture/perlin_noise_texture.outputs:out>
custom token ui:description = "Allow texturing with a random noise pattern"
uniform token ui:displayGroup = "Texturing, high level"
uniform token ui:displayName = "Perlin noise texture"
uniform token ui:nodegraph:node:expansionState = "open"
uniform asset ui:nodegraph:node:icon = @core_definitions.perlin_noise_texture.png@
uniform float2 ui:nodegraph:node:pos = (-900.64825, 692.84265)
custom int ui:order = 34
def Shader "perlin_noise_texture"
{
reorder properties = ["inputs:color1", "inputs:color2", "inputs:noise_levels", "inputs:absolute_noise", "inputs:noise_threshold_low", "inputs:noise_threshold_high", "inputs:object_space", "inputs:texture_space", "inputs:rotation", "inputs:translation", "inputs:scaling"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/core_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "perlin_noise_texture(color,color,bool,int,bool,float,float,float3,float3,float3,int)"
bool inputs:absolute_noise (
customData = {
bool default = 0
}
displayGroup = "Noise parameters"
displayName = "Billowing appearance"
renderType = "bool"
)
bool inputs:absolute_noise.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:absolute_noise>
color3f inputs:color1 (
customData = {
double3 default = (1, 1, 1)
}
displayGroup = "Noise parameters"
displayName = "Color 1"
renderType = "color"
)
color3f inputs:color1.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:color1>
color3f inputs:color2 (
customData = {
double3 default = (0, 0, 0)
}
displayGroup = "Noise parameters"
displayName = "Color 2"
renderType = "color"
)
color3f inputs:color2.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:color2>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
int inputs:noise_levels (
customData = {
int default = 3
dictionary range = {
int max = 6
int min = 1
}
}
displayGroup = "Noise parameters"
displayName = "Levels"
doc = "Higher amounts will add detail to the noise"
renderType = "int"
)
int inputs:noise_levels.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:noise_levels>
float inputs:noise_threshold_high (
customData = {
double default = 1
dictionary range = {
int max = 1
int min = 0
}
}
displayGroup = "Noise parameters"
displayName = "Upper threshold"
doc = "Lowering this value will create bigger areas uniformly colored with Color 1"
renderType = "float"
)
float inputs:noise_threshold_high.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:noise_threshold_high>
float inputs:noise_threshold_low (
customData = {
double default = 0
dictionary range = {
int max = 1
int min = 0
}
}
displayGroup = "Noise parameters"
displayName = "Lower threshold"
doc = "Increasing this value will create bigger areas uniformly colored with Color 2"
renderType = "float"
)
float inputs:noise_threshold_low.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:noise_threshold_low>
bool inputs:object_space (
customData = {
bool default = 1
}
displayGroup = "Placement"
displayName = "Use object space"
doc = "If off, UV space will be used. If on, 3d texturing in object space will apply. For applications that do not support object space, world space will be used"
renderType = "bool"
)
bool inputs:object_space.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:object_space>
float3 inputs:rotation (
customData = {
double3 default = (0, 0, 0)
}
displayGroup = "Placement"
displayName = "Rotation"
doc = "Rotation angle of the texture in degrees"
renderType = "float3"
)
float3 inputs:rotation.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:rotation>
float3 inputs:scaling (
customData = {
double3 default = (10, 10, 10)
}
displayGroup = "Placement"
displayName = "Tiling"
doc = "Controls the scale of the texture on the object"
renderType = "float3"
)
float3 inputs:scaling.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:scaling>
int inputs:texture_space (
customData = {
int default = 0
dictionary range = {
int max = 3
int min = 0
}
}
displayGroup = "Placement"
displayName = "UV space index"
doc = 'Only applies if "Use object space" is off. Selects a specific UV space'
renderType = "int"
)
int inputs:texture_space.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:texture_space>
float3 inputs:translation (
customData = {
double3 default = (0, 0, 0)
}
displayGroup = "Placement"
displayName = "Offset"
doc = "Controls position of the texture on the object"
renderType = "float3"
)
float3 inputs:translation.connect = </World/Looks/OmniPBR/perlin_noise_texture.inputs:translation>
token outputs:out (
renderType = "texture_return"
)
}
def Shader "construct_float"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "construct_float(::base::texture_return)"
token inputs:a (
renderType = "texture_return"
)
token inputs:a.connect = </World/Looks/OmniPBR/perlin_noise_texture/perlin_noise_texture.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
}
def Shader "construct_color"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "construct_color(::base::texture_return)"
token inputs:a (
renderType = "texture_return"
)
token inputs:a.connect = </World/Looks/OmniPBR/perlin_noise_texture/perlin_noise_texture.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
color3f outputs:out (
renderType = "color"
)
}
def Shader "x"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "x(color)"
color3f inputs:a (
renderType = "color"
)
color3f inputs:a.connect = </World/Looks/OmniPBR/perlin_noise_texture/construct_color.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
}
def Shader "y"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "y(color)"
color3f inputs:a (
renderType = "color"
)
color3f inputs:a.connect = </World/Looks/OmniPBR/perlin_noise_texture/construct_color.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
}
def Shader "z"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "z(color)"
color3f inputs:a (
renderType = "color"
)
color3f inputs:a.connect = </World/Looks/OmniPBR/perlin_noise_texture/construct_color.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
}
}
def Shader "multiply" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:a", "inputs:b"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "multiply(float,float)"
float inputs:a (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:a.connect = </World/Looks/OmniPBR/multiply03.outputs:out>
float inputs:b (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:b.connect = </World/Looks/OmniPBR/perlin_noise_texture.outputs:mono>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-17.815407, 598.25726)
}
def Shader "condition" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:x", "inputs:a", "inputs:b"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "condition(bool,float,float)"
float inputs:a (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:a.connect = </World/Looks/OmniPBR/perlin_noise_texture.outputs:r>
float inputs:b (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
bool inputs:x (
customData = {
bool default = 1
}
hidden = false
renderType = "bool"
)
bool inputs:x.connect = </World/Looks/OmniPBR/compare.outputs:out>
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-31.884296, 776.6469)
}
def Shader "compare" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:a", "inputs:b"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "compare"
float inputs:a (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:a.connect = </World/Looks/OmniPBR/perlin_noise_texture.outputs:r>
float inputs:b = 0.5 (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
bool outputs:out (
renderType = "bool"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-297.94135, 911.94666)
}
def Shader "multiply01" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:a", "inputs:b"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "multiply(float,float)"
float inputs:a (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:a.connect = </World/Looks/OmniPBR/multiply.outputs:out>
float inputs:b (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:b.connect = </World/Looks/OmniPBR/condition.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (237.34415, 591.09674)
}
def Shader "scratched_plastic_v2" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:diffuse_color", "inputs:roughness", "inputs:anisotropy", "inputs:anisotropy_rotation", "inputs:ior", "inputs:reflectivity", "inputs:normal"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/core_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "scratched_plastic_v2"
float inputs:anisotropy (
customData = {
float default = 0
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Anisotropy"
doc = "Higher values will stretch the highlight"
hidden = false
renderType = "float"
)
float inputs:anisotropy_rotation (
customData = {
float default = 0
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Anisotropy rotation"
doc = "Changes the orientation of the anisotropy. A value of 1 will rotate the orientation 360 degrees."
hidden = false
renderType = "float"
)
color3f inputs:diffuse_color (
customData = {
float3 default = (0.5, 0.5, 0.5)
}
displayName = "Color"
doc = "The color of the material"
hidden = false
renderType = "color"
)
color3f inputs:diffuse_color.connect = </World/Looks/OmniPBR/construct_color.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float inputs:ior (
customData = {
float default = 1.4
dictionary soft_range = {
float max = 4
float min = 1
}
}
displayName = "IOR"
doc = "Determines reflectivity"
hidden = false
renderType = "float"
)
float3 inputs:normal (
customData = {
float3 default = (0, 0, 0)
}
displayName = "Bumps"
doc = "Attach bump or normal maps here"
hidden = true
renderType = "float3"
)
float inputs:reflectivity (
customData = {
float default = 1
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Reflection weight"
doc = "Additional reflectivity control"
hidden = false
renderType = "float"
)
float inputs:roughness (
customData = {
float default = 0.2
dictionary range = {
float max = 1
float min = 0
}
}
displayName = "Roughness"
doc = "Higher roughness values lead to bigger highlights and blurry reflections"
hidden = false
renderType = "float"
)
token outputs:out (
renderType = "material"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (472.3509, 887.17596)
}
def Shader "construct_color" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "construct_color(float)"
float inputs:a (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:a.connect = </World/Looks/OmniPBR/x01.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
color3f outputs:out (
renderType = "color"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (126.76077, 842.6744)
}
def Shader "y" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "y(float3)"
float3 inputs:a (
customData = {
float3 default = (0, 0, 0)
}
hidden = false
renderType = "float3"
)
float3 inputs:a.connect = </World/Looks/OmniPBR/data_lookup_float3.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1369.051, 687.6842)
}
def Shader "construct_float3" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:x", "inputs:y", "inputs:z"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "construct_float3(float,float,float)"
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float inputs:x (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:x.connect = </World/Looks/OmniPBR/y.outputs:out>
float inputs:y (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:y.connect = </World/Looks/OmniPBR/y.outputs:out>
float inputs:z (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:z.connect = </World/Looks/OmniPBR/y.outputs:out>
float3 outputs:out (
renderType = "float3"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1111.6818, 685.6463)
}
def Shader "data_lookup_uniform_float3" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:name", "inputs:default_value"]
uniform token info:id = ""
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "data_lookup_uniform_float3"
float3 inputs:default_value (
customData = {
float3 default = (0, 0, 0)
}
hidden = false
renderType = "float3"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
string inputs:name = "random_intensity" (
hidden = false
renderType = "string"
)
float3 outputs:out (
renderType = "float3"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-887.7174, 1124.773)
}
def Backdrop "Backdrop" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
uniform token ui:description = "Preview perlin mask"
uniform token ui:nodegraph:node:expansionState = "closed"
uniform float2 ui:nodegraph:node:pos = (619.2451, 628.2425)
uniform float2 ui:nodegraph:node:size = (518.46063, 420.1667)
}
def Shader "multiply03" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:a", "inputs:b"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "multiply(float,float)"
float inputs:a (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:a.connect = </World/Looks/OmniPBR/file_texture.outputs:r>
float inputs:b (
customData = {
float default = 0
}
hidden = false
renderType = "float"
)
float inputs:b.connect = </World/Looks/OmniPBR/x01.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-390.1806, 491.07812)
}
def Shader "x01" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/aux_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "x(float3)"
float3 inputs:a (
customData = {
float3 default = (0, 0, 0)
}
hidden = false
renderType = "float3"
)
float3 inputs:a.connect = </World/Looks/OmniPBR/data_lookup_uniform_float3.outputs:out>
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float outputs:out (
renderType = "float"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-687.4775, 1120.0033)
}
}
def Material "Porcelain_Tile_6_Linen"
{
token outputs:mdl:displacement.connect = </World/Looks/Porcelain_Tile_6_Linen/Shader.outputs:out>
token outputs:mdl:surface.connect = </World/Looks/Porcelain_Tile_6_Linen/Shader.outputs:out>
token outputs:mdl:volume.connect = </World/Looks/Porcelain_Tile_6_Linen/Shader.outputs:out>
def Shader "Shader"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @http://omniverse-content-production.s3-us-west-2.amazonaws.com/Materials/Base/Stone/Porcelain_Tile_6_Linen.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "Porcelain_Tile_6_Linen"
color3f inputs:diffuse_tint = (0.086035006, 0.08880311, 0.071316786) (
customData = {
float3 default = (1, 1, 1)
}
displayGroup = "Albedo"
displayName = "Color Tint"
doc = "When enabled, this color value is multiplied over the final albedo color"
hidden = false
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float2 inputs:texture_scale = (20, 20) (
customData = {
float2 default = (1, 1)
}
displayGroup = "UV"
displayName = "Texture Scale"
doc = "Larger number increases size of texture."
hidden = false
)
bool inputs:world_or_object = 0 (
customData = {
bool default = 0
}
displayGroup = "UV"
displayName = "Enable World Space"
doc = "When set to 'true' uses world space for projection, when 'false' object space is used"
hidden = false
)
token outputs:out
}
}
}
def Mesh "Plane" (
prepend apiSchemas = ["SemanticsAPI:Semantics_6oAx"]
)
{
int[] faceVertexCounts = [4]
int[] faceVertexIndices = [0, 2, 3, 1]
rel material:binding = </World/Looks/Porcelain_Tile_6_Linen> (
bindMaterialAs = "weakerThanDescendants"
)
normal3f[] normals = [(0, 1, 0), (0, 1, 0), (0, 1, 0), (0, 1, 0)] (
interpolation = "faceVarying"
)
point3f[] points = [(-50, 0, -50), (50, 0, -50), (-50, 0, 50), (50, 0, 50)]
float2[] primvars:st = [(1, 0), (1, 1), (0, 1), (0, 0)] (
interpolation = "faceVarying"
)
string semantic:Semantics_6oAx:params:semanticData = "floor"
string semantic:Semantics_6oAx:params:semanticType = "class"
uniform token subdivisionScheme = "none"
token visibility
double3 xformOp:rotateXYZ = (0, 0, 0)
double3 xformOp:scale = (19.563743591308594, 1, 19.563743591308594)
double3 xformOp:translate = (0, 0, 0)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
def Xform "RoundedCube2" (
instanceable = false
)
{
float3 xformOp:rotateXYZ = (0, 0, 0)
float3 xformOp:scale = (0.1, 0.1, 0.1)
double3 xformOp:translate = (0, 10.140478753317057, 0)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
def Scope "Looks"
{
def Material "Material"
{
token outputs:mdl:displacement.connect = </World/RoundedCube2/Looks/Material/Material.outputs:out>
token outputs:mdl:surface.connect = </World/RoundedCube2/Looks/Material/Material.outputs:out>
token outputs:mdl:volume.connect = </World/RoundedCube2/Looks/Material/Material.outputs:out>
def Shader "Material"
{
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @OmniPBR.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "OmniPBR"
color3f inputs:diffuse_color_constant = (0.8, 0.8, 0.8) (
customData = {
float3 default = (1, 1, 1)
dictionary range = {
float3 max = (1, 1, 1)
float3 min = (0, 0, 0)
}
}
displayGroup = "Albedo"
displayName = "Base Color"
)
color3f inputs:emissive_color = (1, 1, 1) (
customData = {
float3 default = (1, 1, 1)
dictionary range = {
float3 max = (1, 1, 1)
float3 min = (0, 0, 0)
}
}
displayGroup = "Emissive"
displayName = "Emissive Color"
)
float inputs:emissive_intensity = 10000 (
customData = {
float default = 10000
dictionary range = {
float max = 100000
float min = 0
}
}
displayGroup = "Emissive"
displayName = "Emissive Intensity"
)
bool inputs:enable_emission = 0 (
customData = {
bool default = 0
}
displayGroup = "Emissive"
displayName = "Enable Emission"
)
bool inputs:enable_opacity = 1 (
customData = {
bool default = 0
}
displayGroup = "Opacity"
displayName = "Enable Opacity"
)
bool inputs:enable_opacity_texture = 0 (
customData = {
bool default = 0
}
displayGroup = "Opacity"
displayName = "Enable Opacity Texture"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
float inputs:opacity_constant = 1 (
customData = {
float default = 1
dictionary range = {
float max = 1
float min = 0
}
}
displayGroup = "Opacity"
displayName = "Opacity Amount"
)
int inputs:opacity_mode = 1 (
customData = {
int default = 1
dictionary range = {
int max = 3
int min = 0
}
}
displayGroup = "Opacity"
displayName = "Opacity Mono Source"
renderType = "::base::mono_mode"
sdrMetadata = {
string __SDR__enum_value = "mono_average"
string options = "mono_alpha:0|mono_average:1|mono_luminance:2|mono_maximum:3"
}
)
float inputs:opacity_threshold = 0 (
customData = {
float default = 0
dictionary range = {
float max = 1
float min = 0
}
}
displayGroup = "Opacity"
displayName = "Opacity Threshold"
)
float inputs:texture_rotate = 0 (
customData = {
float default = 0
dictionary range = {
float max = 360
float min = 0
}
}
displayGroup = "UV"
displayName = "Texture Rotate"
)
float2 inputs:texture_scale = (1, 1) (
customData = {
float2 default = (1, 1)
}
displayGroup = "UV"
displayName = "Texture Scale"
)
float2 inputs:texture_translate = (0, 0) (
customData = {
float2 default = (0, 0)
}
displayGroup = "UV"
displayName = "Texture Translate"
)
token outputs:out
}
}
}
def Xform "Cube" (
prepend apiSchemas = ["SemanticsAPI:Semantics_hnPa"]
)
{
string semantic:Semantics_hnPa:params:semanticData = "box"
string semantic:Semantics_hnPa:params:semanticType = "class"
float3 xformOp:rotateXYZ = (-90.00001, -0, 0)
float3 xformOp:scale = (100, 100, 100)
double3 xformOp:translate = (0, 0, 0)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
def Mesh "Cube"
{
float3[] extent = [(-0.9991834, -0.9991834, -0.9991834), (0.9991834, 0.9991834, 0.9991834)]
int[] faceVertexCounts = [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]
int[] faceVertexIndices = [137, 582, 648, 576, 582, 27, 642, 648, 648, 642, 83, 624, 576, 648, 624, 189, 191, 599, 649, 581, 599, 162, 635, 649, 649, 635, 109, 611, 581, 649, 611, 135, 82, 600, 650, 629, 600, 56, 617, 650, 650, 617, 163, 594, 629, 650, 594, 190, 1, 636, 651, 618, 636, 110, 630, 651, 651, 630, 164, 612, 618, 651, 612, 55, 29, 588, 652, 647, 588, 2, 623, 652, 652, 623, 54, 605, 647, 652, 605, 81, 0, 217, 653, 216, 217, 3, 218, 653, 653, 218, 6, 219, 216, 653, 219, 5, 3, 220, 654, 218, 220, 4, 221, 654, 654, 221, 7, 222, 218, 654, 222, 6, 4, 223, 655, 221, 223, 16, 224, 655, 655, 224, 17, 225, 221, 655, 225, 7, 5, 219, 656, 226, 219, 6, 227, 656, 656, 227, 9, 228, 226, 656, 228, 8, 6, 222, 657, 227, 222, 7, 229, 657, 657, 229, 10, 230, 227, 657, 230, 9, 7, 225, 658, 229, 225, 17, 231, 658, 658, 231, 18, 232, 229, 658, 232, 10, 1, 234, 659, 233, 234, 11, 235, 659, 659, 235, 14, 236, 233, 659, 236, 13, 11, 237, 660, 235, 237, 12, 238, 660, 660, 238, 15, 239, 235, 660, 239, 14, 12, 240, 661, 238, 240, 24, 241, 661, 661, 241, 25, 242, 238, 661, 242, 15, 13, 236, 662, 243, 236, 14, 244, 662, 662, 244, 17, 224, 243, 662, 224, 16, 14, 239, 663, 244, 239, 15, 245, 663, 663, 245, 18, 231, 244, 663, 231, 17, 15, 242, 664, 245, 242, 25, 246, 664, 664, 246, 26, 247, 245, 664, 247, 18, 2, 249, 665, 248, 249, 19, 250, 665, 665, 250, 22, 251, 248, 665, 251, 21, 19, 252, 666, 250, 252, 20, 253, 666, 666, 253, 23, 254, 250, 666, 254, 22, 20, 255, 667, 253, 255, 8, 228, 667, 667, 228, 9, 256, 253, 667, 256, 23, 21, 251, 668, 257, 251, 22, 258, 668, 668, 258, 25, 241, 257, 668, 241, 24, 22, 254, 669, 258, 254, 23, 259, 669, 669, 259, 26, 246, 258, 669, 246, 25, 23, 256, 670, 259, 256, 9, 230, 670, 670, 230, 10, 260, 259, 670, 260, 26, 10, 232, 671, 260, 18, 247, 671, 232, 26, 260, 671, 247, 27, 262, 672, 261, 262, 30, 263, 672, 672, 263, 33, 264, 261, 672, 264, 32, 30, 265, 673, 263, 265, 31, 266, 673, 673, 266, 34, 267, 263, 673, 267, 33, 31, 268, 674, 266, 268, 43, 269, 674, 674, 269, 44, 270, 266, 674, 270, 34, 32, 264, 675, 271, 264, 33, 272, 675, 675, 272, 36, 273, 271, 675, 273, 35, 33, 267, 676, 272, 267, 34, 274, 676, 676, 274, 37, 275, 272, 676, 275, 36, 34, 270, 677, 274, 270, 44, 276, 677, 677, 276, 45, 277, 274, 677, 277, 37, 28, 279, 678, 278, 279, 38, 280, 678, 678, 280, 41, 281, 278, 678, 281, 40, 38, 282, 679, 280, 282, 39, 283, 679, 679, 283, 42, 284, 280, 679, 284, 41, 39, 285, 680, 283, 285, 51, 286, 680, 680, 286, 52, 287, 283, 680, 287, 42, 40, 281, 681, 288, 281, 41, 289, 681, 681, 289, 44, 269, 288, 681, 269, 43, 41, 284, 682, 289, 284, 42, 290, 682, 682, 290, 45, 276, 289, 682, 276, 44, 42, 287, 683, 290, 287, 52, 291, 683, 683, 291, 53, 292, 290, 683, 292, 45, 29, 294, 684, 293, 294, 46, 295, 684, 684, 295, 49, 296, 293, 684, 296, 48, 46, 297, 685, 295, 297, 47, 298, 685, 685, 298, 50, 299, 295, 685, 299, 49, 47, 300, 686, 298, 300, 35, 273, 686, 686, 273, 36, 301, 298, 686, 301, 50, 48, 296, 687, 302, 296, 49, 303, 687, 687, 303, 52, 286, 302, 687, 286, 51, 49, 299, 688, 303, 299, 50, 304, 688, 688, 304, 53, 291, 303, 688, 291, 52, 50, 301, 689, 304, 301, 36, 275, 689, 689, 275, 37, 305, 304, 689, 305, 53, 37, 277, 690, 305, 45, 292, 690, 277, 53, 305, 690, 292, 54, 307, 691, 306, 307, 57, 308, 691, 691, 308, 60, 309, 306, 691, 309, 59, 57, 310, 692, 308, 310, 58, 311, 692, 692, 311, 61, 312, 308, 692, 312, 60, 58, 313, 693, 311, 313, 70, 314, 693, 693, 314, 71, 315, 311, 693, 315, 61, 59, 309, 694, 316, 309, 60, 317, 694, 694, 317, 63, 318, 316, 694, 318, 62, 60, 312, 695, 317, 312, 61, 319, 695, 695, 319, 64, 320, 317, 695, 320, 63, 61, 315, 696, 319, 315, 71, 321, 696, 696, 321, 72, 322, 319, 696, 322, 64, 55, 324, 697, 323, 324, 65, 325, 697, 697, 325, 68, 326, 323, 697, 326, 67, 65, 327, 698, 325, 327, 66, 328, 698, 698, 328, 69, 329, 325, 698, 329, 68, 66, 330, 699, 328, 330, 78, 331, 699, 699, 331, 79, 332, 328, 699, 332, 69, 67, 326, 700, 333, 326, 68, 334, 700, 700, 334, 71, 314, 333, 700, 314, 70, 68, 329, 701, 334, 329, 69, 335, 701, 701, 335, 72, 321, 334, 701, 321, 71, 69, 332, 702, 335, 332, 79, 336, 702, 702, 336, 80, 337, 335, 702, 337, 72, 56, 339, 703, 338, 339, 73, 340, 703, 703, 340, 76, 341, 338, 703, 341, 75, 73, 342, 704, 340, 342, 74, 343, 704, 704, 343, 77, 344, 340, 704, 344, 76, 74, 345, 705, 343, 345, 62, 318, 705, 705, 318, 63, 346, 343, 705, 346, 77, 75, 341, 706, 347, 341, 76, 348, 706, 706, 348, 79, 331, 347, 706, 331, 78, 76, 344, 707, 348, 344, 77, 349, 707, 707, 349, 80, 336, 348, 707, 336, 79, 77, 346, 708, 349, 346, 63, 320, 708, 708, 320, 64, 350, 349, 708, 350, 80, 64, 322, 709, 350, 72, 337, 709, 322, 80, 350, 709, 337, 81, 352, 710, 351, 352, 84, 353, 710, 710, 353, 87, 354, 351, 710, 354, 86, 84, 355, 711, 353, 355, 85, 356, 711, 711, 356, 88, 357, 353, 711, 357, 87, 85, 358, 712, 356, 358, 97, 359, 712, 712, 359, 98, 360, 356, 712, 360, 88, 86, 354, 713, 361, 354, 87, 362, 713, 713, 362, 90, 363, 361, 713, 363, 89, 87, 357, 714, 362, 357, 88, 364, 714, 714, 364, 91, 365, 362, 714, 365, 90, 88, 360, 715, 364, 360, 98, 366, 715, 715, 366, 99, 367, 364, 715, 367, 91, 82, 369, 716, 368, 369, 92, 370, 716, 716, 370, 95, 371, 368, 716, 371, 94, 92, 372, 717, 370, 372, 93, 373, 717, 717, 373, 96, 374, 370, 717, 374, 95, 93, 375, 718, 373, 375, 105, 376, 718, 718, 376, 106, 377, 373, 718, 377, 96, 94, 371, 719, 378, 371, 95, 379, 719, 719, 379, 98, 359, 378, 719, 359, 97, 95, 374, 720, 379, 374, 96, 380, 720, 720, 380, 99, 366, 379, 720, 366, 98, 96, 377, 721, 380, 377, 106, 381, 721, 721, 381, 107, 382, 380, 721, 382, 99, 83, 384, 722, 383, 384, 100, 385, 722, 722, 385, 103, 386, 383, 722, 386, 102, 100, 387, 723, 385, 387, 101, 388, 723, 723, 388, 104, 389, 385, 723, 389, 103, 101, 390, 724, 388, 390, 89, 363, 724, 724, 363, 90, 391, 388, 724, 391, 104, 102, 386, 725, 392, 386, 103, 393, 725, 725, 393, 106, 376, 392, 725, 376, 105, 103, 389, 726, 393, 389, 104, 394, 726, 726, 394, 107, 381, 393, 726, 381, 106, 104, 391, 727, 394, 391, 90, 365, 727, 727, 365, 91, 395, 394, 727, 395, 107, 91, 367, 728, 395, 99, 382, 728, 367, 107, 395, 728, 382, 108, 397, 729, 396, 397, 111, 398, 729, 729, 398, 114, 399, 396, 729, 399, 113, 111, 400, 730, 398, 400, 112, 401, 730, 730, 401, 115, 402, 398, 730, 402, 114, 112, 403, 731, 401, 403, 124, 404, 731, 731, 404, 125, 405, 401, 731, 405, 115, 113, 399, 732, 406, 399, 114, 407, 732, 732, 407, 117, 408, 406, 732, 408, 116, 114, 402, 733, 407, 402, 115, 409, 733, 733, 409, 118, 410, 407, 733, 410, 117, 115, 405, 734, 409, 405, 125, 411, 734, 734, 411, 126, 412, 409, 734, 412, 118, 109, 414, 735, 413, 414, 119, 415, 735, 735, 415, 122, 416, 413, 735, 416, 121, 119, 417, 736, 415, 417, 120, 418, 736, 736, 418, 123, 419, 415, 736, 419, 122, 120, 420, 737, 418, 420, 132, 421, 737, 737, 421, 133, 422, 418, 737, 422, 123, 121, 416, 738, 423, 416, 122, 424, 738, 738, 424, 125, 404, 423, 738, 404, 124, 122, 419, 739, 424, 419, 123, 425, 739, 739, 425, 126, 411, 424, 739, 411, 125, 123, 422, 740, 425, 422, 133, 426, 740, 740, 426, 134, 427, 425, 740, 427, 126, 110, 429, 741, 428, 429, 127, 430, 741, 741, 430, 130, 431, 428, 741, 431, 129, 127, 432, 742, 430, 432, 128, 433, 742, 742, 433, 131, 434, 430, 742, 434, 130, 128, 435, 743, 433, 435, 116, 408, 743, 743, 408, 117, 436, 433, 743, 436, 131, 129, 431, 744, 437, 431, 130, 438, 744, 744, 438, 133, 421, 437, 744, 421, 132, 130, 434, 745, 438, 434, 131, 439, 745, 745, 439, 134, 426, 438, 745, 426, 133, 131, 436, 746, 439, 436, 117, 410, 746, 746, 410, 118, 440, 439, 746, 440, 134, 118, 412, 747, 440, 126, 427, 747, 412, 134, 440, 747, 427, 135, 442, 748, 441, 442, 138, 443, 748, 748, 443, 141, 444, 441, 748, 444, 140, 138, 445, 749, 443, 445, 139, 446, 749, 749, 446, 142, 447, 443, 749, 447, 141, 139, 448, 750, 446, 448, 151, 449, 750, 750, 449, 152, 450, 446, 750, 450, 142, 140, 444, 751, 451, 444, 141, 452, 751, 751, 452, 144, 453, 451, 751, 453, 143, 141, 447, 752, 452, 447, 142, 454, 752, 752, 454, 145, 455, 452, 752, 455, 144, 142, 450, 753, 454, 450, 152, 456, 753, 753, 456, 153, 457, 454, 753, 457, 145, 136, 459, 754, 458, 459, 146, 460, 754, 754, 460, 149, 461, 458, 754, 461, 148, 146, 462, 755, 460, 462, 147, 463, 755, 755, 463, 150, 464, 460, 755, 464, 149, 147, 465, 756, 463, 465, 159, 466, 756, 756, 466, 160, 467, 463, 756, 467, 150, 148, 461, 757, 468, 461, 149, 469, 757, 757, 469, 152, 449, 468, 757, 449, 151, 149, 464, 758, 469, 464, 150, 470, 758, 758, 470, 153, 456, 469, 758, 456, 152, 150, 467, 759, 470, 467, 160, 471, 759, 759, 471, 161, 472, 470, 759, 472, 153, 137, 474, 760, 473, 474, 154, 475, 760, 760, 475, 157, 476, 473, 760, 476, 156, 154, 477, 761, 475, 477, 155, 478, 761, 761, 478, 158, 479, 475, 761, 479, 157, 155, 480, 762, 478, 480, 143, 453, 762, 762, 453, 144, 481, 478, 762, 481, 158, 156, 476, 763, 482, 476, 157, 483, 763, 763, 483, 160, 466, 482, 763, 466, 159, 157, 479, 764, 483, 479, 158, 484, 764, 764, 484, 161, 471, 483, 764, 471, 160, 158, 481, 765, 484, 481, 144, 455, 765, 765, 455, 145, 485, 484, 765, 485, 161, 145, 457, 766, 485, 153, 472, 766, 457, 161, 485, 766, 472, 162, 487, 767, 486, 487, 165, 488, 767, 767, 488, 168, 489, 486, 767, 489, 167, 165, 490, 768, 488, 490, 166, 491, 768, 768, 491, 169, 492, 488, 768, 492, 168, 166, 493, 769, 491, 493, 178, 494, 769, 769, 494, 179, 495, 491, 769, 495, 169, 167, 489, 770, 496, 489, 168, 497, 770, 770, 497, 171, 498, 496, 770, 498, 170, 168, 492, 771, 497, 492, 169, 499, 771, 771, 499, 172, 500, 497, 771, 500, 171, 169, 495, 772, 499, 495, 179, 501, 772, 772, 501, 180, 502, 499, 772, 502, 172, 163, 504, 773, 503, 504, 173, 505, 773, 773, 505, 176, 506, 503, 773, 506, 175, 173, 507, 774, 505, 507, 174, 508, 774, 774, 508, 177, 509, 505, 774, 509, 176, 174, 510, 775, 508, 510, 186, 511, 775, 775, 511, 187, 512, 508, 775, 512, 177, 175, 506, 776, 513, 506, 176, 514, 776, 776, 514, 179, 494, 513, 776, 494, 178, 176, 509, 777, 514, 509, 177, 515, 777, 777, 515, 180, 501, 514, 777, 501, 179, 177, 512, 778, 515, 512, 187, 516, 778, 778, 516, 188, 517, 515, 778, 517, 180, 164, 519, 779, 518, 519, 181, 520, 779, 779, 520, 184, 521, 518, 779, 521, 183, 181, 522, 780, 520, 522, 182, 523, 780, 780, 523, 185, 524, 520, 780, 524, 184, 182, 525, 781, 523, 525, 170, 498, 781, 781, 498, 171, 526, 523, 781, 526, 185, 183, 521, 782, 527, 521, 184, 528, 782, 782, 528, 187, 511, 527, 782, 511, 186, 184, 524, 783, 528, 524, 185, 529, 783, 783, 529, 188, 516, 528, 783, 516, 187, 185, 526, 784, 529, 526, 171, 500, 784, 784, 500, 172, 530, 529, 784, 530, 188, 172, 502, 785, 530, 180, 517, 785, 502, 188, 530, 785, 517, 189, 532, 786, 531, 532, 192, 533, 786, 786, 533, 195, 534, 531, 786, 534, 194, 192, 535, 787, 533, 535, 193, 536, 787, 787, 536, 196, 537, 533, 787, 537, 195, 193, 538, 788, 536, 538, 205, 539, 788, 788, 539, 206, 540, 536, 788, 540, 196, 194, 534, 789, 541, 534, 195, 542, 789, 789, 542, 198, 543, 541, 789, 543, 197, 195, 537, 790, 542, 537, 196, 544, 790, 790, 544, 199, 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rel material:binding = </World/Looks/OmniPBR> (
bindMaterialAs = "weakerThanDescendants"
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float3 primvars:random_intensity = (0.56797326, 0.91950375, 0.4426124)
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interpolation = "faceVarying"
)
int[] primvars:st:indices = [199, 601, 242, 116, 601, 260, 798, 242, 242, 798, 814, 483, 116, 242, 483, 693, 39, 813, 251, 543, 813, 739, 461, 251, 251, 461, 464, 521, 543, 251, 521, 407, 237, 583, 746, 696, 583, 810, 145, 746, 746, 145, 377, 736, 696, 746, 736, 565, 683, 62, 708, 130, 62, 538, 446, 708, 708, 446, 537, 283, 130, 708, 283, 682, 958, 698, 531, 160, 698, 243, 757, 531, 531, 757, 821, 172, 160, 531, 172, 755, 697, 816, 458, 441, 816, 851, 660, 458, 458, 660, 866, 255, 441, 458, 255, 159, 851, 411, 880, 660, 411, 652, 922, 880, 880, 922, 712, 763, 660, 880, 763, 866, 652, 638, 223, 922, 638, 627, 551, 223, 223, 551, 336, 657, 922, 223, 657, 712, 159, 255, 730, 8, 255, 866, 843, 730, 730, 843, 703, 153, 8, 730, 153, 824, 866, 763, 233, 843, 763, 712, 161, 233, 233, 161, 381, 574, 843, 233, 574, 703, 712, 657, 449, 161, 657, 336, 463, 449, 449, 463, 557, 720, 161, 449, 720, 381, 683, 403, 460, 376, 403, 365, 351, 460, 460, 351, 776, 112, 376, 460, 112, 275, 365, 119, 918, 351, 119, 488, 456, 918, 918, 456, 175, 875, 351, 918, 875, 776, 488, 384, 162, 456, 384, 333, 709, 162, 162, 709, 142, 481, 456, 162, 481, 175, 275, 112, 545, 90, 112, 776, 329, 545, 545, 329, 415, 317, 90, 545, 317, 422, 776, 875, 508, 329, 875, 175, 323, 508, 508, 323, 664, 713, 329, 508, 713, 415, 175, 481, 618, 323, 481, 142, 895, 618, 618, 895, 902, 48, 323, 618, 48, 664, 243, 0, 256, 564, 0, 5, 592, 256, 256, 592, 203, 350, 564, 256, 350, 623, 5, 293, 853, 592, 293, 676, 70, 853, 853, 70, 41, 893, 592, 853, 893, 203, 676, 433, 723, 70, 433, 824, 153, 723, 723, 153, 703, 960, 70, 723, 960, 41, 623, 350, 905, 593, 350, 203, 46, 905, 905, 46, 142, 709, 593, 905, 709, 333, 203, 893, 691, 46, 893, 41, 404, 691, 691, 404, 902, 895, 46, 691, 895, 142, 41, 960, 510, 404, 960, 703, 574, 510, 510, 574, 381, 684, 404, 510, 684, 902, 381, 720, 717, 157, 557, 952, 717, 720, 189, 157, 717, 952, 260, 3, 944, 241, 3, 927, 439, 944, 944, 439, 163, 539, 241, 944, 539, 864, 927, 665, 200, 439, 665, 954, 612, 200, 200, 612, 303, 753, 439, 200, 753, 163, 954, 635, 886, 612, 635, 374, 885, 886, 886, 885, 829, 498, 612, 886, 498, 303, 864, 539, 244, 123, 539, 163, 837, 244, 244, 837, 210, 204, 123, 244, 204, 750, 163, 753, 719, 837, 753, 303, 788, 719, 719, 788, 122, 867, 837, 719, 867, 210, 303, 498, 252, 788, 498, 829, 732, 252, 252, 732, 152, 917, 788, 252, 917, 122, 257, 804, 749, 570, 804, 948, 88, 749, 749, 88, 737, 147, 570, 749, 147, 769, 948, 956, 98, 88, 956, 108, 49, 98, 98, 49, 164, 198, 88, 98, 198, 737, 108, 701, 359, 49, 701, 548, 148, 359, 359, 148, 680, 259, 49, 359, 259, 164, 769, 147, 575, 704, 147, 737, 10, 575, 575, 10, 389, 228, 704, 575, 228, 733, 737, 198, 180, 10, 198, 164, 520, 180, 180, 520, 606, 47, 10, 180, 47, 389, 164, 259, 517, 520, 259, 680, 633, 517, 517, 633, 427, 9, 520, 517, 9, 606, 958, 43, 906, 914, 43, 584, 825, 906, 906, 825, 932, 868, 914, 906, 868, 174, 584, 925, 448, 825, 925, 454, 137, 448, 448, 137, 401, 819, 825, 448, 819, 932, 454, 805, 84, 137, 805, 750, 204, 84, 84, 204, 210, 60, 137, 84, 60, 401, 174, 868, 400, 387, 868, 932, 171, 400, 400, 171, 680, 148, 387, 400, 148, 548, 932, 819, 908, 171, 819, 401, 322, 908, 908, 322, 427, 633, 171, 908, 633, 680, 401, 60, 428, 322, 60, 210, 867, 428, 428, 867, 122, 870, 322, 428, 870, 427, 122, 917, 939, 443, 152, 450, 939, 917, 842, 443, 939, 450, 821, 656, 607, 361, 656, 667, 187, 607, 607, 187, 934, 688, 361, 607, 688, 582, 667, 576, 791, 187, 576, 898, 385, 791, 791, 385, 779, 73, 187, 791, 73, 934, 898, 877, 653, 385, 877, 229, 500, 653, 653, 500, 40, 452, 385, 653, 452, 779, 582, 688, 771, 742, 688, 934, 52, 771, 771, 52, 724, 426, 742, 771, 426, 313, 934, 73, 507, 52, 73, 779, 748, 507, 507, 748, 284, 889, 52, 507, 889, 724, 779, 452, 658, 748, 452, 40, 455, 658, 658, 455, 577, 109, 748, 658, 109, 284, 682, 379, 789, 272, 379, 822, 135, 789, 789, 135, 585, 312, 272, 789, 312, 547, 822, 282, 264, 135, 282, 856, 59, 264, 264, 59, 390, 536, 135, 264, 536, 585, 903, 167, 888, 419, 167, 930, 751, 888, 888, 751, 747, 556, 419, 888, 556, 904, 547, 312, 71, 224, 312, 585, 699, 71, 71, 699, 40, 500, 224, 71, 500, 229, 585, 536, 331, 699, 536, 390, 793, 331, 331, 793, 577, 455, 699, 331, 455, 40, 904, 556, 727, 434, 556, 747, 700, 727, 727, 700, 722, 218, 434, 727, 218, 644, 810, 96, 138, 287, 96, 68, 105, 138, 138, 105, 261, 65, 287, 138, 65, 2, 68, 911, 865, 105, 911, 42, 447, 865, 865, 447, 818, 362, 105, 865, 362, 261, 42, 878, 526, 447, 878, 313, 426, 526, 526, 426, 724, 876, 447, 526, 876, 818, 2, 65, 462, 124, 65, 261, 292, 462, 462, 292, 747, 751, 124, 462, 751, 930, 261, 362, 265, 292, 362, 818, 326, 265, 265, 326, 722, 700, 292, 265, 700, 747, 818, 876, 648, 326, 876, 724, 889, 648, 648, 889, 284, 541, 326, 648, 541, 722, 511, 262, 949, 234, 644, 218, 949, 262, 722, 234, 949, 218, 755, 254, 923, 957, 254, 423, 909, 923, 923, 909, 850, 802, 957, 923, 802, 27, 423, 420, 916, 909, 420, 571, 806, 916, 916, 806, 107, 628, 909, 916, 628, 850, 571, 207, 1, 806, 207, 353, 24, 1, 1, 24, 841, 408, 806, 1, 408, 107, 27, 802, 57, 620, 802, 850, 692, 57, 57, 692, 741, 715, 620, 57, 715, 215, 850, 628, 93, 692, 628, 107, 154, 93, 93, 154, 558, 342, 692, 93, 342, 741, 107, 408, 397, 154, 408, 841, 752, 397, 397, 752, 792, 230, 154, 397, 230, 558, 237, 269, 668, 12, 269, 86, 176, 668, 668, 176, 77, 933, 12, 668, 933, 860, 86, 451, 211, 176, 451, 694, 297, 211, 211, 297, 663, 307, 176, 211, 307, 77, 846, 95, 22, 775, 95, 641, 33, 22, 22, 33, 196, 208, 775, 22, 208, 245, 860, 933, 402, 79, 933, 77, 634, 402, 402, 634, 841, 24, 79, 402, 24, 353, 77, 307, 639, 634, 307, 663, 13, 639, 639, 13, 792, 752, 634, 639, 752, 841, 245, 208, 655, 799, 208, 196, 550, 655, 655, 550, 710, 494, 799, 655, 494, 26, 814, 705, 184, 103, 705, 887, 609, 184, 184, 609, 410, 66, 103, 184, 66, 625, 887, 388, 725, 609, 388, 453, 125, 725, 725, 125, 114, 194, 609, 725, 194, 410, 453, 266, 72, 125, 266, 215, 715, 72, 72, 715, 741, 568, 125, 72, 568, 114, 625, 66, 193, 412, 66, 410, 51, 193, 193, 51, 196, 33, 412, 193, 33, 641, 410, 194, 235, 51, 194, 114, 597, 235, 235, 597, 710, 550, 51, 235, 550, 196, 114, 568, 417, 597, 568, 741, 342, 417, 417, 342, 558, 339, 597, 417, 339, 710, 30, 226, 506, 919, 26, 494, 506, 226, 710, 919, 506, 494, 414, 567, 195, 716, 567, 358, 149, 195, 195, 149, 352, 835, 716, 195, 835, 852, 358, 496, 581, 149, 496, 796, 854, 581, 581, 854, 803, 99, 149, 581, 99, 352, 796, 640, 871, 854, 640, 513, 188, 871, 871, 188, 659, 535, 854, 871, 535, 803, 852, 835, 603, 899, 835, 352, 100, 603, 603, 100, 728, 869, 899, 603, 869, 202, 352, 99, 246, 100, 99, 803, 470, 246, 246, 470, 515, 16, 100, 246, 16, 728, 803, 535, 926, 470, 535, 659, 491, 926, 926, 491, 935, 882, 470, 926, 882, 515, 464, 610, 250, 97, 610, 907, 514, 250, 250, 514, 613, 924, 97, 250, 924, 44, 907, 786, 471, 514, 786, 807, 335, 471, 471, 335, 768, 689, 514, 471, 689, 613, 807, 61, 495, 335, 61, 485, 294, 495, 495, 294, 140, 849, 335, 495, 849, 768, 44, 924, 209, 695, 924, 613, 828, 209, 209, 828, 659, 188, 695, 209, 188, 513, 613, 689, 836, 828, 689, 768, 767, 836, 836, 767, 935, 491, 828, 836, 491, 659, 768, 849, 281, 767, 849, 140, 158, 281, 281, 158, 375, 591, 767, 281, 591, 935, 538, 629, 740, 308, 629, 477, 76, 740, 740, 76, 721, 429, 308, 740, 429, 800, 477, 127, 276, 76, 127, 177, 139, 276, 276, 139, 222, 589, 76, 276, 589, 721, 562, 921, 398, 936, 921, 202, 869, 398, 398, 869, 728, 630, 936, 398, 630, 890, 800, 429, 497, 170, 429, 721, 595, 497, 497, 595, 489, 341, 170, 497, 341, 81, 721, 589, 706, 595, 589, 222, 758, 706, 706, 758, 253, 946, 595, 706, 946, 489, 890, 630, 559, 883, 630, 728, 16, 559, 559, 16, 515, 594, 883, 559, 594, 600, 94, 221, 503, 271, 935, 591, 503, 221, 375, 271, 503, 591, 407, 522, 678, 549, 522, 31, 525, 678, 678, 525, 141, 608, 549, 678, 608, 185, 31, 743, 355, 525, 743, 360, 844, 355, 355, 844, 475, 673, 525, 355, 673, 141, 360, 28, 647, 844, 28, 920, 636, 647, 647, 636, 306, 560, 844, 647, 560, 475, 185, 608, 56, 649, 608, 141, 826, 56, 56, 826, 442, 855, 649, 56, 855, 731, 141, 673, 831, 826, 673, 475, 947, 831, 831, 947, 604, 523, 826, 831, 523, 442, 475, 560, 772, 947, 560, 306, 490, 772, 772, 490, 738, 555, 947, 772, 555, 604, 106, 432, 240, 478, 432, 435, 258, 240, 240, 258, 396, 950, 478, 240, 950, 316, 435, 897, 63, 258, 897, 286, 19, 63, 63, 19, 616, 579, 258, 63, 579, 396, 74, 540, 11, 524, 540, 879, 928, 11, 11, 928, 955, 213, 524, 11, 213, 940, 316, 950, 21, 413, 950, 396, 827, 21, 21, 827, 306, 636, 413, 21, 636, 920, 396, 579, 132, 827, 579, 616, 466, 132, 132, 466, 738, 490, 827, 132, 490, 306, 940, 213, 561, 117, 213, 955, 425, 561, 561, 425, 58, 14, 117, 561, 14, 181, 199, 857, 348, 383, 857, 631, 677, 348, 348, 677, 178, 762, 383, 348, 762, 754, 631, 586, 416, 677, 586, 247, 314, 416, 416, 314, 637, 101, 677, 416, 101, 178, 247, 436, 191, 314, 436, 942, 85, 191, 191, 85, 370, 151, 314, 191, 151, 637, 754, 762, 532, 527, 762, 178, 599, 532, 532, 599, 955, 928, 527, 532, 928, 879, 178, 101, 232, 599, 101, 637, 795, 232, 232, 795, 58, 425, 599, 232, 425, 955, 637, 151, 315, 795, 151, 370, 212, 315, 315, 212, 672, 368, 795, 315, 368, 58, 672, 666, 718, 368, 181, 14, 718, 666, 58, 368, 718, 14, 739, 367, 345, 459, 367, 115, 777, 345, 345, 777, 937, 605, 459, 345, 605, 661, 115, 327, 444, 777, 327, 518, 309, 444, 444, 309, 760, 165, 777, 444, 165, 937, 518, 169, 197, 309, 169, 298, 23, 197, 197, 23, 291, 900, 309, 197, 900, 760, 661, 605, 395, 782, 605, 937, 714, 395, 395, 714, 848, 619, 782, 395, 619, 468, 937, 165, 311, 714, 165, 760, 528, 311, 311, 528, 569, 356, 714, 311, 356, 848, 760, 900, 38, 528, 900, 291, 519, 38, 38, 519, 566, 155, 528, 38, 155, 569, 377, 516, 815, 146, 516, 941, 645, 815, 815, 645, 794, 111, 146, 815, 111, 296, 941, 764, 104, 645, 764, 505, 838, 104, 104, 838, 216, 53, 645, 104, 53, 794, 505, 18, 343, 838, 18, 624, 614, 343, 343, 614, 770, 347, 838, 343, 347, 216, 296, 111, 744, 501, 111, 794, 681, 744, 744, 681, 587, 121, 501, 744, 121, 305, 794, 53, 133, 681, 53, 216, 780, 133, 133, 780, 832, 325, 681, 133, 325, 587, 216, 347, 859, 780, 347, 770, 881, 859, 859, 881, 643, 405, 780, 859, 405, 832, 537, 611, 249, 493, 611, 726, 131, 249, 249, 131, 83, 572, 493, 249, 572, 670, 726, 274, 366, 131, 274, 406, 845, 366, 366, 845, 129, 346, 131, 366, 346, 83, 783, 473, 302, 418, 473, 468, 619, 302, 302, 619, 848, 862, 418, 302, 862, 15, 670, 572, 45, 231, 572, 83, 267, 45, 45, 267, 484, 817, 231, 45, 817, 102, 83, 346, 363, 267, 346, 129, 808, 363, 363, 808, 440, 787, 267, 363, 787, 484, 15, 862, 328, 809, 862, 848, 356, 328, 328, 356, 569, 858, 809, 328, 858, 273, 569, 155, 295, 858, 566, 36, 295, 155, 273, 858, 295, 36, 693, 225, 424, 910, 225, 686, 745, 424, 424, 745, 91, 938, 910, 424, 938, 67, 686, 120, 17, 745, 120, 578, 469, 17, 17, 469, 766, 190, 745, 17, 190, 91, 578, 372, 781, 469, 372, 277, 430, 781, 781, 430, 391, 502, 469, 781, 502, 766, 67, 938, 457, 467, 938, 91, 761, 457, 457, 761, 318, 654, 467, 457, 654, 6, 91, 190, 192, 761, 190, 766, 118, 192, 192, 118, 421, 54, 761, 192, 54, 318, 766, 502, 300, 118, 502, 391, 392, 300, 300, 392, 646, 839, 118, 300, 839, 421, 565, 573, 833, 166, 573, 344, 371, 833, 833, 371, 602, 834, 166, 833, 834, 173, 344, 373, 248, 371, 373, 621, 847, 248, 248, 847, 399, 487, 371, 248, 487, 602, 337, 530, 896, 790, 530, 321, 479, 896, 896, 479, 217, 891, 790, 896, 891, 369, 173, 834, 773, 263, 834, 602, 29, 773, 773, 29, 437, 774, 263, 773, 774, 959, 602, 487, 480, 29, 487, 399, 278, 480, 480, 278, 671, 285, 29, 480, 285, 437, 369, 891, 113, 580, 891, 217, 380, 113, 113, 380, 69, 674, 580, 113, 674, 542, 39, 87, 75, 299, 87, 183, 334, 75, 75, 334, 445, 632, 299, 75, 632, 364, 183, 931, 915, 334, 931, 512, 499, 915, 915, 499, 912, 872, 334, 915, 872, 445, 702, 7, 546, 626, 7, 6, 654, 546, 546, 654, 318, 901, 626, 546, 901, 492, 364, 632, 134, 861, 632, 445, 873, 134, 134, 873, 217, 479, 861, 134, 479, 321, 445, 872, 288, 873, 872, 912, 64, 288, 288, 64, 69, 380, 873, 288, 380, 217, 492, 901, 150, 894, 901, 318, 54, 150, 150, 54, 421, 707, 894, 150, 707, 268, 421, 839, 438, 707, 646, 320, 438, 839, 268, 707, 438, 320, 199, 116, 650, 857, 116, 693, 910, 650, 650, 910, 67, 472, 857, 650, 472, 631, 631, 472, 811, 586, 472, 67, 467, 811, 811, 467, 6, 35, 586, 811, 35, 247, 247, 35, 504, 436, 35, 6, 7, 504, 504, 7, 702, 143, 436, 504, 143, 942, 731, 759, 823, 649, 759, 512, 931, 823, 823, 931, 183, 136, 649, 823, 136, 185, 185, 136, 310, 549, 136, 183, 87, 310, 310, 87, 39, 543, 549, 310, 543, 407, 260, 601, 382, 3, 601, 199, 383, 382, 382, 383, 754, 830, 3, 382, 830, 927, 927, 830, 622, 665, 830, 754, 527, 622, 622, 527, 879, 598, 665, 622, 598, 954, 954, 598, 943, 635, 598, 879, 540, 943, 943, 540, 74, 884, 635, 943, 884, 374, 733, 279, 4, 704, 279, 286, 897, 4, 4, 897, 435, 615, 704, 4, 615, 769, 769, 615, 662, 570, 615, 435, 432, 662, 662, 432, 106, 92, 570, 662, 92, 257, 243, 698, 669, 0, 698, 958, 914, 669, 669, 914, 174, 553, 0, 669, 553, 5, 5, 553, 37, 293, 553, 174, 387, 37, 37, 387, 548, 820, 293, 37, 820, 676, 676, 820, 20, 433, 820, 548, 701, 20, 20, 701, 108, 304, 433, 20, 304, 824, 824, 304, 544, 8, 304, 108, 956, 544, 544, 956, 948, 812, 8, 544, 812, 159, 159, 812, 55, 441, 812, 948, 804, 55, 55, 804, 257, 219, 441, 55, 219, 697, 565, 736, 186, 573, 736, 377, 146, 186, 186, 146, 296, 474, 573, 186, 474, 344, 344, 474, 685, 373, 474, 296, 501, 685, 685, 501, 305, 588, 373, 685, 588, 621, 337, 785, 280, 530, 785, 298, 169, 280, 280, 169, 518, 227, 530, 280, 227, 321, 321, 227, 354, 861, 227, 518, 327, 354, 354, 327, 115, 201, 861, 354, 201, 364, 364, 201, 126, 299, 201, 115, 367, 126, 126, 367, 739, 813, 299, 126, 813, 39, 810, 583, 509, 96, 583, 237, 12, 509, 509, 12, 860, 590, 96, 509, 590, 68, 68, 590, 128, 911, 590, 860, 79, 128, 128, 79, 353, 34, 911, 128, 34, 42, 42, 34, 431, 878, 34, 353, 207, 431, 431, 207, 571, 529, 878, 431, 529, 313, 313, 529, 289, 742, 529, 571, 420, 289, 289, 420, 423, 214, 742, 289, 214, 582, 582, 214, 156, 361, 214, 423, 254, 156, 156, 254, 755, 172, 361, 156, 172, 821, 414, 349, 778, 567, 349, 106, 478, 778, 778, 478, 316, 729, 567, 778, 729, 358, 358, 729, 340, 496, 729, 316, 413, 340, 340, 413, 920, 651, 496, 340, 651, 796, 796, 651, 394, 640, 651, 920, 28, 394, 394, 28, 360, 301, 640, 394, 301, 513, 513, 301, 679, 695, 301, 360, 743, 679, 679, 743, 31, 238, 695, 679, 238, 44, 44, 238, 110, 97, 238, 31, 522, 110, 110, 522, 407, 521, 97, 110, 521, 464, 682, 283, 675, 379, 283, 537, 493, 675, 675, 493, 670, 220, 379, 675, 220, 822, 822, 220, 945, 282, 220, 670, 231, 945, 945, 231, 102, 270, 282, 945, 270, 856, 903, 617, 32, 167, 617, 624, 18, 32, 32, 18, 505, 144, 167, 32, 144, 930, 930, 144, 205, 124, 144, 505, 764, 205, 205, 764, 941, 533, 124, 205, 533, 2, 2, 533, 711, 287, 533, 941, 516, 711, 711, 516, 377, 145, 287, 711, 145, 810, 683, 130, 863, 403, 130, 682, 272, 863, 863, 272, 547, 482, 403, 863, 482, 365, 365, 482, 563, 119, 482, 547, 224, 563, 563, 224, 229, 797, 119, 563, 797, 488, 488, 797, 239, 384, 797, 229, 877, 239, 239, 877, 898, 734, 384, 239, 734, 333, 333, 734, 534, 593, 734, 898, 576, 534, 534, 576, 667, 386, 593, 534, 386, 623, 623, 386, 765, 564, 386, 667, 656, 765, 765, 656, 821, 757, 564, 765, 757, 243, 693, 483, 476, 225, 483, 814, 103, 476, 476, 103, 625, 378, 225, 476, 378, 686, 686, 378, 554, 120, 378, 625, 412, 554, 554, 412, 641, 330, 120, 554, 330, 578, 578, 330, 290, 372, 330, 641, 95, 290, 290, 95, 846, 324, 372, 290, 324, 277, 959, 236, 182, 263, 236, 694, 451, 182, 182, 451, 86, 80, 263, 182, 80, 173, 173, 80, 690, 166, 80, 86, 269, 690, 690, 269, 237, 696, 166, 690, 696, 565, 537, 446, 179, 611, 446, 538, 308, 179, 179, 308, 800, 465, 611, 179, 465, 726, 726, 465, 338, 274, 465, 800, 170, 338, 338, 170, 81, 784, 274, 338, 784, 406, 783, 929, 168, 473, 929, 485, 61, 168, 168, 61, 807, 552, 473, 168, 552, 468, 468, 552, 801, 782, 552, 807, 786, 801, 801, 786, 907, 596, 782, 801, 596, 661, 661, 596, 25, 459, 596, 907, 610, 25, 25, 610, 464, 461, 459, 25, 461, 739, 538, 62, 892, 629, 62, 683, 376, 892, 892, 376, 275, 913, 629, 892, 913, 477, 477, 913, 319, 127, 913, 275, 90, 319, 319, 90, 422, 642, 127, 319, 642, 177, 562, 756, 951, 921, 756, 627, 638, 951, 951, 638, 652, 687, 921, 951, 687, 202, 202, 687, 89, 899, 687, 652, 411, 89, 89, 411, 851, 82, 899, 89, 82, 852, 852, 82, 953, 716, 82, 851, 816, 953, 953, 816, 697, 332, 716, 953, 332, 414, 814, 798, 78, 705, 798, 260, 241, 78, 78, 241, 864, 840, 705, 78, 840, 887, 887, 840, 50, 388, 840, 864, 123, 50, 50, 123, 750, 486, 388, 50, 486, 453, 453, 486, 357, 266, 486, 750, 805, 357, 357, 805, 454, 409, 266, 357, 409, 215, 215, 409, 393, 620, 409, 454, 925, 393, 393, 925, 584, 874, 620, 393, 874, 27, 27, 874, 735, 957, 874, 584, 43, 735, 735, 43, 958, 160, 957, 735, 160, 755, 106, 349, 206, 92, 349, 414, 332, 206, 206, 332, 697, 219, 92, 206, 219, 257]
uniform token subdivisionScheme = "none"
token visibility
}
}
}
}
def "Environment"
{
def DomeLight "sky" (
apiSchemas = ["ShapingAPI"]
)
{
float intensity = 1000
float shaping:cone:angle = 180
float shaping:cone:softness
float shaping:focus = 0
color3f shaping:focusTint = (0, 0, 0)
asset shaping:ies:file
float specular = 1
asset texture:file = @https://omniverse-content-production.s3.us-west-2.amazonaws.com/Assets/Skies/2022_1/Skies/Indoor/small_hangar_01.hdr@
token texture:format = "latlong"
token visibility = "inherited"
bool visibleInPrimaryRay = 1
double3 xformOp:rotateXYZ = (270, 174.5894891995585, 0)
double3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (0, 0, 0)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
}
def Shader "data_lookup_float3" (
apiSchemas = ["NodeGraphNodeAPI"]
)
{
reorder properties = ["inputs:name", "inputs:default_value"]
uniform token info:implementationSource = "sourceAsset"
uniform asset info:mdl:sourceAsset = @nvidia/support_definitions.mdl@
uniform token info:mdl:sourceAsset:subIdentifier = "data_lookup_float3"
float3 inputs:default_value (
customData = {
float3 default = (0, 0, 0)
}
hidden = false
renderType = "float3"
)
bool inputs:excludeFromWhiteMode = 0 (
customData = {
bool default = 0
}
displayGroup = "Material Flags"
displayName = "Exclude from White Mode"
hidden = false
)
string inputs:name = "random_color" (
hidden = false
renderType = "string"
)
float3 outputs:out (
renderType = "float3"
)
uniform token ui:nodegraph:node:expansionState = "open"
uniform float2 ui:nodegraph:node:pos = (-1719.1984, 136.92104)
}
def Camera "OmniverseKit_Persp" (
customData = {
dictionary omni = {
dictionary kit = {
bool hide_in_stage_window = 1
bool no_delete = 1
}
}
}
hide_in_stage_window = true
kind = "component"
no_delete = true
)
{
float2 clippingRange = (1, 10000000)
float focalLength = 18.147562
custom uniform vector3d omni:kit:centerOfInterest = (-1.5448471651330086e-15, -2.4717554642128132e-15, -66.97754746649706)
float3 xformOp:rotateXYZ = (-21.38725, -20.503822, -2.4618642e-14)
float3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (-27.43726414861917, 32.59290269818252, 59.677509323096864)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
def Camera "OmniverseKit_Front" (
customData = {
dictionary omni = {
dictionary kit = {
bool hide_in_stage_window = 1
bool no_delete = 1
}
}
}
hide_in_stage_window = true
kind = "component"
no_delete = true
)
{
float2 clippingRange = (20000, 10000000)
float horizontalAperture = 5000
custom uniform vector3d omni:kit:centerOfInterest = (0, 0, -50000)
token projection = "orthographic"
float verticalAperture = 5000
float3 xformOp:rotateXYZ = (-0, 0, -0)
float3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (0, 0, 50000)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
def Camera "OmniverseKit_Top" (
customData = {
dictionary omni = {
dictionary kit = {
bool hide_in_stage_window = 1
bool no_delete = 1
}
}
}
hide_in_stage_window = true
kind = "component"
no_delete = true
)
{
float2 clippingRange = (20000, 10000000)
float horizontalAperture = 5000
custom uniform vector3d omni:kit:centerOfInterest = (-1.5407439555097887e-33, 0, -50000)
token projection = "orthographic"
float verticalAperture = 5000
float3 xformOp:rotateXYZ = (-119.99992, -0.00011739155, -0.00003145489)
float3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (-4.329780281177466e-12, 50000, 1.1102230246251565e-11)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
def Camera "OmniverseKit_Right" (
customData = {
dictionary omni = {
dictionary kit = {
bool hide_in_stage_window = 1
bool no_delete = 1
}
}
}
hide_in_stage_window = true
kind = "component"
no_delete = true
)
{
float2 clippingRange = (20000, 10000000)
float horizontalAperture = 5000
custom uniform vector3d omni:kit:centerOfInterest = (0, 0, -50000)
token projection = "orthographic"
float verticalAperture = 5000
float3 xformOp:rotateXYZ = (0, -90, -0)
float3 xformOp:scale = (1, 1, 1)
double3 xformOp:translate = (-50000, 0, -1.1102230246251565e-11)
uniform token[] xformOpOrder = ["xformOp:translate", "xformOp:rotateXYZ", "xformOp:scale"]
}
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/22_Change_Textures.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
# create new objects to be used in the dataset
with rep.new_layer():
sphere = rep.create.sphere(
semantics=[("class", "sphere")], position=(0, 100, 100), count=5
)
cube = rep.create.cube(
semantics=[("class", "cube")], position=(200, 200, 100), count=5
)
cone = rep.create.cone(
semantics=[("class", "cone")], position=(200, 400, 200), count=10
)
cylinder = rep.create.cylinder(
semantics=[("class", "cylinder")], position=(200, 100, 200), count=5
)
# create new camera & render product and attach to camera
camera = rep.create.camera(position=(0, 0, 1000))
render_product = rep.create.render_product(camera, (1024, 1024))
# create plane if needed (but unused here)
plane = rep.create.plane(scale=10)
# function to get shapes that you've created above, via their semantic labels
def get_shapes():
shapes = rep.get.prims(
semantics=[
("class", "cube"),
("class", "sphere"),
("class", "cone"),
("class", "cylinder"),
]
)
with shapes:
# assign textures to the different objects
rep.randomizer.texture(
textures=[
"omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/aggregate_exposed_diff.jpg",
"omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_diff.jpg",
"omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_multi_R_rough_G_ao.jpg",
"omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/rough_gravel_rough.jpg",
]
)
# modify pose and distribution
rep.modify.pose(
position=rep.distribution.uniform((-500, 50, -500), (500, 50, 500)),
rotation=rep.distribution.uniform((0, -180, 0), (0, 180, 0)),
scale=rep.distribution.normal(1, 0.5),
)
return shapes.node
# register the get shapes function as a randomizer function
rep.randomizer.register(get_shapes)
# Setup randomization. 100 variations here from 'num_frames'
with rep.trigger.on_frame(num_frames=100):
rep.randomizer.get_shapes()
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(output_dir="~/replicator_examples/dli_example_22", rgb=True)
writer.attach([render_product])
rep.orchestrator.run()
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/hello_replicator.ipynb | {
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "7925aad5",
"metadata": {},
"source": [
"<a href=\"https://www.nvidia.com/dli\"> <img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/> </a>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9d578eea",
"metadata": {},
"source": [
"# Hello Replicator!\n",
"\n",
"The goal of this tutorial is to provide an introduction to the basic Omniverse Replicator functionalities. In this notebook, we'll step through:\n",
"1. Creating a simple scene with a few predefined 3D assets\n",
"2. Applying randomizations\n",
"3. Writing the generated images to the disk for further processing\n",
"\n",
"<span style=\"color:red;\">TODO: add image that characterizes the notebook</span>\n",
"\n",
"**[1.0 Hello World Replicator](#1.0-Hello-World-Replicator)<br>**\n",
"**[2.0 Basic Randomizations with Replicator](#2.0-Basic-Randomizations-with-Replicator)<br>**\n",
" [2.1 Background Randomization](#2.1-Background-Randomization)<br>\n",
" [2.2 Change Textures](#2.2-Change-Textures)<br>\n",
"**[3.0 Advanced Randomization and Challenge](#3.0-Advanced-Randomization-and-Challenge)<br>**\n",
" [3.1 Importing multiple assets from designated folder at once](#3.1-Importing-multiple-assets-from-designated-folder-at-once)<br>\n",
" [3.2 Add variety of furniture and semantically label them](#3.2-Add-variety-of-furniture-and-semantically-label-them)<br>\n",
" [3.3 Exercise: Choose a variety of 5 furniture with labels, randomize their rotations, placements and the camera](#3.3-Exercise:)<br>\n",
" [3.4 Suggested Solution to 3.3](#3.4)<br>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "fe698c3d",
"metadata": {},
"source": [
"---\n",
"# 1.0 Hello World Replicator\n",
"\n",
"[Omniverse Replicator](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html) is a highly extensible framework that enables physically accurate 3D synthetic data generation to accelerate training and performance of AI perception networks. You can run Replicator from within the Omniverse Code application, which can be run from a desktop RTX GPU workstation. \n",
"\n",
"For the purposes of this workshop, we'll use a cloud-based instance of Omniverse Code running in an RTX GPU instance on the DLI (Deep Learning Institute) platform. Running \n",
"\n",
"First, let’s create a new USD layer using `rep.new_layer()` in which we will place and randomize our scene assets. Layers let us isolate the changes we make to the base stage.\n",
"\n",
"We will create a camera in the scene at a desired position. Cameras give us an eye to see into the stage."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b383deec",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
" camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9339adad",
"metadata": {},
"source": [
"Now, let’s create basic 3D shapes for our simple scene. Users can also bring any custom 3D objects they may have already created. The tutorial [Using Replicator with a fully developed scene](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator/apis_with_fully_developed_scene.html) demonstrates the process to bring user generated assets within the scene. Notice when creating the assets, we add semantic data, using the flag `semantics`. These semantic labels are added to the 3D shapes and used for generating annotation data to be used in ML model training."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d31fc43a",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
" camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))\n",
"\n",
" # Create simple shapes to manipulate\n",
" plane = rep.create.plane(\n",
" semantics=[(\"class\", \"plane\")], position=(0, -100, 0), scale=(100, 1, 100)\n",
" )\n",
" torus = rep.create.torus(semantics=[(\"class\", \"torus\")], position=(200, 0, 100))\n",
" sphere = rep.create.sphere(semantics=[(\"class\", \"sphere\")], position=(0, 0, 100))\n",
" cube = rep.create.cube(semantics=[(\"class\", \"cube\")], position=(-200, 0, 100))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a36d878e",
"metadata": {},
"source": [
"We have created a camera, several 3D assets and a plane to constrain those assets. At this point, we are ready to define randomizers with specified distributions (uniform random in this case). In this tutorial, we randomize the position and scale of these assets within the scene.\n",
"\n",
"Notice that these randomizations are triggered at every frame event. We will generate 10 frames with randomizations in this example."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5de0567",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
" camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))\n",
"\n",
" # Create simple shapes to manipulate\n",
" plane = rep.create.plane(\n",
" semantics=[(\"class\", \"plane\")], position=(0, -100, 0), scale=(100, 1, 100)\n",
" )\n",
" torus = rep.create.torus(semantics=[(\"class\", \"torus\")], position=(200, 0, 100))\n",
" sphere = rep.create.sphere(semantics=[(\"class\", \"sphere\")], position=(0, 0, 100))\n",
" cube = rep.create.cube(semantics=[(\"class\", \"cube\")], position=(-200, 0, 100))\n",
"\n",
" # Randomize position and scale on each frame\n",
" with rep.trigger.on_frame(num_frames=10):\n",
" with rep.create.group([torus, sphere, cube]):\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),\n",
" scale=rep.distribution.uniform(0.1, 2),\n",
" )"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "cfe48d39",
"metadata": {},
"source": [
"Lastly, data generated for each frame can then written to the disk. For this purpose, Replicator provides a default `Writer` that can be used as demonstrated in this example. In addition to the default writer, users can also create their own custom writer. This is demonstrated in one of the [subsequent tutorials](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator/custom_writer.html).\n",
"\n",
"First we will create a `render_product` that attaches to our scene camera. We will set an output resolution of 1024x1024 for our output images.\n",
"\n",
"The default Writer is initialized and is attached to the renderer to produce the output of the images, semantic segmentation, and annotations with 2D bounding boxes. Note the user can specify a specific output directory for the writer to use."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b454e27-d8f2-4128-a4da-5ef4fabfb033",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
" camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))\n",
"\n",
" plane = rep.create.plane(\n",
" semantics=[(\"class\", \"plane\")], position=(0, -100, 0), scale=(100, 1, 100)\n",
" )\n",
" torus = rep.create.torus(semantics=[(\"class\", \"torus\")], position=(200, 0, 100))\n",
" sphere = rep.create.sphere(semantics=[(\"class\", \"sphere\")], position=(0, 0, 100))\n",
" cube = rep.create.cube(semantics=[(\"class\", \"cube\")], position=(-200, 0, 100))\n",
"\n",
" with rep.trigger.on_frame(num_frames=10):\n",
" with rep.create.group([torus, sphere, cube]):\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),\n",
" scale=rep.distribution.uniform(0.1, 2),\n",
" )\n",
"\n",
"render_product = rep.create.render_product(camera, (1024, 1024))\n",
"writer = rep.WriterRegistry.get(\"BasicWriter\")\n",
"writer.initialize(\n",
" run_id=1,\n",
" output_dir=\"replicator_example\",\n",
" rgb=True,\n",
" semantic_segmentation=True,\n",
" bounding_box_2d_tight=True,\n",
")\n",
"writer.attach([render_product])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "c8d59574",
"metadata": {},
"source": [
"Now, let's see that script in action. To connect, you'll need to know the IP address of the remote server. \n",
"\n",
"Execute the following cell to get the IP address and connect via the Omniverse Streaming client. If for some reason, you cannot connect you will find the way of running headlessly below. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "aa0c45db",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This instance IP address is: 34.219.32.45\n"
]
}
],
"source": [
"from requests import get\n",
"\n",
"ip = get(\"https://api.ipify.org\").text\n",
"print(\"This instance IP address is: {}\".format(ip))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "acb65429-c864-4ea5-a5cd-872e8c050e4b",
"metadata": {},
"source": [
"After connecting to the instance, go to the Script Editor and you type-in the script below, or use the included `Snippet` from the drop-down menu of the Script Editor."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e806b61-bd71-49d5-90ce-992b79f1de8f",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
" camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))\n",
"\n",
" plane = rep.create.plane(\n",
" semantics=[(\"class\", \"plane\")], position=(0, -100, 0), scale=(100, 1, 100)\n",
" )\n",
" torus = rep.create.torus(semantics=[(\"class\", \"torus\")], position=(200, 0, 100))\n",
" sphere = rep.create.sphere(semantics=[(\"class\", \"sphere\")], position=(0, 0, 100))\n",
" cube = rep.create.cube(semantics=[(\"class\", \"cube\")], position=(-200, 0, 100))\n",
"\n",
" with rep.trigger.on_frame(num_frames=10):\n",
" with rep.create.group([torus, sphere, cube]):\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),\n",
" scale=rep.distribution.uniform(0.1, 2),\n",
" )\n",
"\n",
"render_product = rep.create.render_product(camera, (1024, 1024))\n",
"writer = rep.WriterRegistry.get(\"BasicWriter\")\n",
"writer.initialize(\n",
" run_id=1,\n",
" output_dir=\"replicator_example\",\n",
" rgb=True,\n",
" semantic_segmentation=True,\n",
" bounding_box_2d_tight=True,\n",
")\n",
"writer.attach([render_product])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "d6ae1024",
"metadata": {},
"source": [
"To preview our randomizations, we can use `rep.orchestrator.preview()` in the Script Editor, or we can choose `Preview` from the `Replicator` drop-down at the top of our Omniverse Code toolbar."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6af24cc2-caa3-49c9-91f6-d7e970899d82",
"metadata": {},
"source": [
"To generate all ten frames, change `rep.orchestrator.preview()` to `rep.orchestrator.run()`, or select `Run` from the `Replicator` drop-down menu. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9c6b89f",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
" camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))\n",
"\n",
" # Create simple shapes to manipulate\n",
" plane = rep.create.plane(\n",
" semantics=[(\"class\", \"plane\")], position=(0, -100, 0), scale=(100, 1, 100)\n",
" )\n",
" torus = rep.create.torus(semantics=[(\"class\", \"torus\")], position=(200, 0, 100))\n",
" sphere = rep.create.sphere(semantics=[(\"class\", \"sphere\")], position=(0, 0, 100))\n",
" cube = rep.create.cube(semantics=[(\"class\", \"cube\")], position=(-200, 0, 100))\n",
"\n",
" # Randomize position and scale on each frame\n",
" with rep.trigger.on_frame(num_frames=10):\n",
" with rep.create.group([torus, sphere, cube]):\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),\n",
" scale=rep.distribution.uniform(0.1, 2),\n",
" )\n",
"\n",
"# Initialize render product and attach writer\n",
"render_product = rep.create.render_product(camera, (1024, 1024))\n",
"writer = rep.WriterRegistry.get(\"BasicWriter\")\n",
"writer.initialize(\n",
" output_dir=\"replicator_example\",\n",
" rgb=True,\n",
" semantic_segmentation=True,\n",
" bounding_box_2d_tight=True,\n",
")\n",
"writer.attach([render_product])\n",
"rep.orchestrator.run()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9f90c76a",
"metadata": {},
"source": [
"Below you can also see how you would run replicator headlessly. This may be needed if your are running at scale, or if for some reason you cannot connect to the client, or you want Omniverse to exit after completing.\n",
"\n",
"```!cd / && ./code_startup.sh --allow-root --no-window --/omni/replicator/script=/dli/task/Replicator_scripts/hello_world.py```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a9b2744-ac0a-4186-89ec-a3d2fdf3932d",
"metadata": {},
"outputs": [],
"source": [
"!cd / && ./code_startup.sh --allow-root --no-window --/omni/replicator/script=/dli/task/Replicator_scripts/hello_world.py"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "77129951-7e75-4188-9551-6667b82fbe1c",
"metadata": {
"tags": []
},
"source": [
"---\n",
"# 2.0 Basic Randomizations with Replicator"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "7242ff5f-2d4e-42c5-80b4-10b27aef77dc",
"metadata": {},
"source": [
"## 2.1 Background Randomization"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "3075a900-7ed7-4992-8562-f4eda193836e",
"metadata": {},
"source": [
"The goal of this tutorial is to provide an introduction to the basic randomizations that can be done using Replicator. \n",
"\n",
"In this case, Omniverse Replicator is running in an `RTX GPU` instance on the `DLI` (Deep Learning Institute) platform. Omniverse is accesible through Omniverse Remote client or can run headlessly. For the best experience we recommend the client as shown in the Getting started notebook. \n",
"To run the script follow set up instructions in Setting up the Script Editor.\n",
"\n",
"Building on what we have learnt in 1.0 Hello Replicator which is the basic scene set-up before randomizing placements of the basic shapes in the scene. Here, we guide you on how you can start to randomize your environments. First start by loading in the files of the various environments that you want to use for this series. In this case, we are using the 4 environments found within the Omniverse content browser, but there are many more to choose from. You can even import in your own .hdr files if you're trying this after you've completed the course."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a68bceed",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
"\n",
" def dome_lights():\n",
" lights = rep.create.light(\n",
" light_type=\"Dome\",\n",
" rotation=(270, 0, 0),\n",
" texture=rep.distribution.choice(\n",
" [\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/champagne_castle_1_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Clear/evening_road_01_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/kloofendal_48d_partly_cloudy_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Clear/qwantani_4k.hdr\",\n",
" ]\n",
" ),\n",
" )\n",
" return lights.node\n",
"\n",
" rep.randomizer.register(dome_lights)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "be4cf920",
"metadata": {},
"source": [
" Then we go ahead and create the object that we want to see in our scene, and in this case, we're choosing the torus that we used in 1.0. So we create that, then we go ahead to create the surface on which the object will reside, followed by the camera for this scene "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d76688d0",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
"\n",
" def dome_lights():\n",
" lights = rep.create.light(\n",
" light_type=\"Dome\",\n",
" rotation=(270, 0, 0),\n",
" texture=rep.distribution.choice(\n",
" [\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/champagne_castle_1_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Clear/evening_road_01_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/kloofendal_48d_partly_cloudy_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Clear/qwantani_4k.hdr\",\n",
" ]\n",
" ),\n",
" )\n",
" return lights.node\n",
"\n",
" rep.randomizer.register(dome_lights)\n",
" torus = rep.create.torus(semantics=[(\"class\", \"torus\")], position=(0, -200, 100))\n",
"\n",
" # create surface\n",
" surface = rep.create.disk(scale=5, visible=False)\n",
"\n",
" # create camera for the scene\n",
" camera = rep.create.camera()\n",
" render_product = rep.create.render_product(camera, resolution=(1024, 1024))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "13735df0",
"metadata": {},
"source": [
"Lastly, data generated for each frame is then written to the disk. Just like in the first example, we will use Replicator's built-in `BasicWriter` for this.\n",
"\n",
"In the following code, we use the BasicWriter which is initialized and is attached to the renderer to produce the output of the images and annotations with 2D bounding boxes. Note the user can specify a specific output directory for the writer to use."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f8e5ea3",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
"\n",
" def dome_lights():\n",
" lights = rep.create.light(\n",
" light_type=\"Dome\",\n",
" rotation=(270, 0, 0),\n",
" texture=rep.distribution.choice(\n",
" [\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/champagne_castle_1_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Clear/evening_road_01_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/kloofendal_48d_partly_cloudy_4k.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Clear/qwantani_4k.hdr\",\n",
" ]\n",
" ),\n",
" )\n",
" return lights.node\n",
"\n",
" rep.randomizer.register(dome_lights)\n",
" torus = rep.create.torus(semantics=[(\"class\", \"torus\")], position=(0, -200, 100))\n",
"\n",
" # create surface\n",
" surface = rep.create.disk(scale=5, visible=False)\n",
"\n",
" # create camera & render product for the scene\n",
" camera = rep.create.camera()\n",
" render_product = rep.create.render_product(camera, resolution=(1024, 1024))\n",
"\n",
" with rep.trigger.on_frame(num_frames=10, interval=10):\n",
" rep.randomizer.dome_lights()\n",
" with rep.create.group([torus]):\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-100, -100, -100), (200, 200, 200)),\n",
" scale=rep.distribution.uniform(0.1, 2),\n",
" )\n",
" with camera:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)),\n",
" look_at=surface,\n",
" )\n",
"\n",
" # Initialize and attach writer\n",
" writer = rep.WriterRegistry.get(\"BasicWriter\")\n",
"\n",
" writer.initialize(output_dir=\"replicator_example_02\", rgb=True)\n",
" writer.attach([render_product])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9d58bfa0-3cf7-44b6-a4ac-c950b7261e3d",
"metadata": {},
"source": [
"## 2.2 Change Textures"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "75aa9e0e-3645-4345-8573-7c61f8d74eb5",
"metadata": {},
"source": [
"So you've learnt how to change the background environments of your scene for randomization! \n",
"\n",
"Now to the next level!\n",
"\n",
"Let’s create basic 3D shapes for our next scene. Users can also bring any custom 3D objects they may have already created. The tutorial Using the Replicator with a fully developed scene demonstrates the process to bring user generated assets within the scene. Notice when creating the assets, we add semantics, using the flag semantics. These semantics labels are added to the 3D shapes and used while generating annotations for the data."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f5d6e7ac",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"# create new objects to be used in the dataset\n",
"with rep.new_layer():\n",
" sphere = rep.create.sphere(\n",
" semantics=[(\"class\", \"sphere\")], position=(0, 100, 100), count=5\n",
" )\n",
" cube = rep.create.cube(\n",
" semantics=[(\"class\", \"cube\")], position=(200, 200, 100), count=5\n",
" )\n",
" cone = rep.create.cone(\n",
" semantics=[(\"class\", \"cone\")], position=(200, 400, 200), count=10\n",
" )\n",
" cylinder = rep.create.cylinder(\n",
" semantics=[(\"class\", \"cylinder\")], position=(200, 100, 200), count=5\n",
" )"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "814be623",
"metadata": {},
"source": [
"After we have created our objects - note that users can create as many as they want - we are going to create our scene camera, and attach it to our render product. This is similar to what we have done above. By attaching it to the render product, we're ensuring that the randomized elements of our scenes, are captured by the camera and sent to be written by the Basic Writer. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "42531185",
"metadata": {},
"outputs": [],
"source": [
"# create new camera\n",
"camera = rep.create.camera(position=(0, 0, 1000))\n",
"\n",
"# create render product and attach to camera\n",
"render_product = rep.create.render_product(camera, (1024, 1024))\n",
"# create plane if needed (but unused here)\n",
"plane = rep.create.plane(scale=10, visible=True)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e08f4815",
"metadata": {},
"source": [
"The lesson for this section comes here, and we now want to not only create our various objects in the scene, but we also want to make the textures randomized. So here we've added 4 textures for Replicator to choose from when alternating between the textures used for the randomization. Users are once again free to add their own textures, and even more textures if they want a higher degree of randomization to occur for this part. \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b0498e4",
"metadata": {},
"outputs": [],
"source": [
"# function to get shapes that you've created above \n",
" def get_shapes():\n",
" shapes = rep.get.prims(semantics=[('class', 'cube'), ('class', 'sphere'), ('class', 'cone'),('class', 'cylinder')])\n",
" with shapes:\n",
" #assign textures to the different objects \n",
" rep.randomizer.texture(textures=[\n",
" 'omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/aggregate_exposed_diff.jpg',\n",
" 'omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_diff.jpg',\n",
" 'omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_multi_R_rough_G_ao.jpg',\n",
" 'omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/rough_gravel_rough.jpg'\n",
" ])\n",
" \n",
" # modify pose and distribution \n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 50, -500), (500, 50, 500)),\n",
" rotation=rep.distribution.uniform((0,-180, 0), (0, 180, 0)),\n",
" scale=rep.distribution.normal(1, 0.5)\n",
" )\n",
" return shapes.node\n",
" # register the get shapes function as a randomizer function \n",
" rep.randomizer.register(get_shapes)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "13c9ca6d",
"metadata": {},
"source": [
"Similar to the previous sections, here we run our randomization function, and proceed to write the data generated for each frame. For this purpose, Replicator provides a default Writer that can be used as demonstrated in this example. In addition to the default writer, users can also create their own custom writer.\n",
"\n",
"In the following code, we use the basic Writer which is initialized and is attached to the renderer to produce the output of the images and annotations with 2D bounding boxes. Note the user can specify a specific output directory for the writer to use."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d3e00aa",
"metadata": {},
"outputs": [],
"source": [
"# Setup randomization. 100 variations here from 'num_frames'\n",
"with rep.trigger.on_frame(num_frames=100):\n",
" rep.randomizer.get_shapes()\n",
"\n",
"# Initialize and attach writer\n",
"writer = rep.WriterRegistry.get(\"KittiWriter\")\n",
"# my own directory but use your relevant directory here\n",
"writer.initialize(output_dir=\"replicator_example_22\", rgb=True)\n",
"\n",
"writer.attach([render_product])\n",
"\n",
"rep.orchestrator.run()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "0ed63b20-55ed-4f35-88f2-0755f37abe47",
"metadata": {},
"source": [
"---\n",
"# 3.0 Advanced Randomization and Challenge [WIP]"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "57e34946-5d43-40fb-982b-5708ac6714bf",
"metadata": {},
"source": [
"# 3.1 Importing multiple assets from designated folder at once"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a6ba65ce-8f32-44a2-bee2-c8e7657f835d",
"metadata": {},
"source": [
"The goal of this tutorial is to help users apply what they have learnt in the previous sections to do a more advanced application of Replicator.\n",
"\n",
"Combining what we have learnt previously, now we seek to combine everything we have learnt and make a randomized dataset of furniture placements in various environments!\n",
"\n",
"Here we show you how to load in assets using the localhost, and collect all the objects that are within a designated folder. \n",
"\n",
"In the first part of this code, we create our environments that we want to vary throughout our run of Replicator.\n",
"\n",
"``` python\n",
"\n",
"import omni.replicator.core as rep\n",
"\n",
"\n",
"with rep.new_layer():\n",
"\n",
"\n",
" def dome_lights():\n",
"\n",
" lights = rep.create.light(\n",
" light_type=\"Dome\",\n",
" rotation= (270,0,0),\n",
" texture=rep.distribution.choice([\n",
" 'omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCGcom_ExhibitionHall_Interior1.hdr',\n",
" 'omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCG_com_WarehouseInterior2b.hdr',\n",
" ])\n",
" )\n",
" return lights.node\n",
" rep.randomizer.register(dome_lights)\n",
"```\n",
"\n",
"Next, we will import assets from the Omniverse localhost library, more specifically, conference tables from the ArchVis library. Then, similar to what has been done previously, we will randomize the instance of the conference table chosen in this folder , then randomize the rotation and position of the folder. \n",
"\n",
"Note that here, the mode that is used is 'scene_instance' instead of the default, as this is needed while we work towards building a more complex scene.\n",
"\n",
"``` python \n",
" conference_tables = 'omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Conference/'\n",
"\n",
"\n",
" # create randomizer function conference table assets. \n",
" #This randomization includes placement and rotation of the assets on the surface.\n",
" def env_conference_table(size=5):\n",
" confTable = rep.randomizer.instantiate(rep.utils.get_usd_files(conference_tables, recursive=False), size=size, mode='scene_instance')\n",
" with confTable:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),\n",
" rotation=rep.distribution.uniform((-90,-180, 0), (-90, 180, 0)),\n",
" )\n",
" return confTable.node\n",
" # Register randomization\n",
" rep.randomizer.register(env_conference_table)\n",
" \n",
" # Setup camera and attach it to render product\n",
" camera = rep.create.camera()\n",
" render_product = rep.create.render_product(camera, resolution=(1024, 1024))\n",
" \n",
"\n",
" \n",
"\n",
" surface = rep.create.disk(scale=100, visible=False)\n",
"\n",
" # trigger on frame for an interval\n",
" with rep.trigger.on_frame(5):\n",
" rep.randomizer.env_conference_table(2) \n",
" rep.randomizer.dome_lights()\n",
" with camera:\n",
" rep.modify.pose(position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)), look_at=surface)\n",
"\n",
"\n",
" # Initialize and attach writer\n",
" writer = rep.WriterRegistry.get(\"KittiWriter\")\n",
" writer.initialize(output_dir=\"furniture_rand_edit3_labelled\", rgb=True)\n",
" writer.attach([render_product])\n",
"```\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5dc7c04f-1c7c-4dd9-a082-48e089d39054",
"metadata": {},
"source": [
"## 3.2 Add variety of furniture and semantically label them"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "017d9a43-9027-4e8d-8500-291dd7e999ed",
"metadata": {},
"source": [
"So in this section we are going to show you how to add various furnitures from different locations, and to label them semantically using Replicator's labelling capabilities. \n",
"\n",
"First, we are going to do something similiar to what we have seen earlier - to add new layer, and add the environments that we want to see changing in the scene. Then, we go ahead and import the objects from 5 different locations, pasting in their links and assigning them to their respective objects. Users can also go ahead and import objects from scenes that are pre-existing, as well as locations outside of Omniverse's localhost domain.\n",
"\n",
"``` python\n",
"\n",
"import omni.replicator.core as rep\n",
"\n",
"\n",
"with rep.new_layer():\n",
"\n",
"\n",
" def dome_lights():\n",
"\n",
" lights = rep.create.light(\n",
" light_type=\"Dome\",\n",
" rotation= (270,0,0),\n",
" texture=rep.distribution.choice([\n",
" 'omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCGcom_ExhibitionHall_Interior1.hdr',\n",
" 'omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCG_com_WarehouseInterior2b.hdr',\n",
" ])\n",
" )\n",
" return lights.node\n",
" rep.randomizer.register(dome_lights) \n",
" \n",
" # Add 5 different objects\n",
" bar_stools = 'omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Furniture/BarStools/'\n",
" reception_tables = 'omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Reception/'\n",
" chairs = 'omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Seating/'\n",
" normal_tables = 'omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Tables/'\n",
" sofa_set = 'omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Furniture/FurnitureSets/Dutchtown/'\n",
"```\n",
"\n",
"In this next part, we are going to instantiate the folder that we read in for barstool objects, and load them in. \n",
"\n",
"Then, we go ahead to add semantic labels to this object, and in this case we are calling this object 'chair_barstool'. You are free to choose whichever label you would like to have for your specific folders and objects, and it will be the label that will follow all instances of such objects from the same folder, in your whole generated dataset.\n",
"\n",
"```python\n",
"\n",
" # create randomizer function conference table assets. \n",
" #This randomization includes placement and rotation of the assets on the surface.\n",
" def env_bar_stools(size=5):\n",
" barstool = rep.randomizer.instantiate(rep.utils.get_usd_files(bar_stools, recursive=False), size=size, mode='scene_instance')\n",
" rep.modify.semantics([('class', 'chair_barstool')])\n",
" with barstool:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),\n",
" rotation=rep.distribution.uniform((-90,-180, 0), (-90, 180, 0)),\n",
" )\n",
" return barstool.node\n",
" # Register randomization\n",
" rep.randomizer.register(env_bar_stools)\n",
" \n",
" # Create functions for each object type / folder that you are importing assets from\n",
" # function_2(.......)\n",
" # function_3(.......)\n",
" # function_4(.......)\n",
" # function_5(.......)\n",
" \n",
"\n",
"```"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "b2be0c3e-c99d-456a-8ecc-fde08cf22566",
"metadata": {},
"source": [
"## 3.3 Exercise: Choose a variety of 5 furniture with labels, randomize their rotations, placements and the camera\n",
"### Bonus Challenge: Add an additional randomized element e.g sphere_lights or vary the textures of the furniture in the scene"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4a79cbdb-a2f5-4bde-bbff-5d91936cf7d4",
"metadata": {},
"source": [
"So now, for this exercise, it's your turn to create the script!\n",
"\n",
"Here are the guiding steps for this exercise.\n",
"\n",
"1) Define your function for your environment - how many environments HDRs would you like to change between?\n",
"2) Register this new function you've created - you'll need to call it later in the rep.trigger.on_frame() function\n",
"3) Decide on which are the 5 different furniture types you're planning to use, and what you will be labelling them as.\n",
"4) Copy their file paths, and import them into the script\n",
"5) Create randomizer functions for each of these 5 furniture types\n",
"6) Within these randomizer functions, you should initiate the path, then semantically label it, followed by modification of the poses of those objects - namely the placement and rotations.\n",
"(Bonus Challenge) If you would like to attempt the bonus challenge, create the sphere_lights function or a texture_randomizing function here\n",
"7) Register all these functions, so that you can call them later in the rep.trigger.on_frame() function\n",
"8) Create your viewing camera, as well as the surface that the camera will focus on\n",
"9) Attach your camera that you just created, to the render product, for writing data later on\n",
"10) Call the with rep.trigger.on_frame(..) function, with all the relevant functions you've created, called within it\n",
"11) Write the data that is generated in each frame, to a specific folder, using either KittiWriter or BasicWriter.\n",
"12) Run the script, and view your very own generated synthetic data!\n",
"\n",
"Refer to the previous sections to get hints on how to complete this exercise!\n",
"\n",
"You can view the suggested solution in Section 3.4 below."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "4b7a9083-26a0-45d7-ad36-ae994b8188a7",
"metadata": {},
"source": [
"## 3.4 Solution for Exercise 3.3"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "04909e45-4672-4fe1-bc22-bf86c3531984",
"metadata": {},
"source": [
"### Congratulations! We believe you tried out the Exercise 3.3 and experienced great success in creating your own synthetic data! To give you more options and ideas on how such a script would look, below we have attached our suggested script on how we would complete Exercise 3.3!\n",
"\n",
"### Happy experimenting!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5bab6c35",
"metadata": {},
"outputs": [],
"source": [
"import omni.replicator.core as rep\n",
"\n",
"with rep.new_layer():\n",
"\n",
" def dome_lights():\n",
" lights = rep.create.light(\n",
" light_type=\"Dome\",\n",
" rotation=(270, 0, 0),\n",
" texture=rep.distribution.choice(\n",
" [\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCGcom_ExhibitionHall_Interior1.hdr\",\n",
" \"omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCG_com_WarehouseInterior2b.hdr\",\n",
" ]\n",
" ),\n",
" )\n",
" return lights.node\n",
"\n",
" rep.randomizer.register(dome_lights)\n",
"\n",
" bar_stools = (\n",
" \"omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Furniture/BarStools/\"\n",
" )\n",
" reception_tables = (\n",
" \"omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Reception/\"\n",
" )\n",
" chairs = \"omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Seating/\"\n",
" normal_tables = \"omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Tables/\"\n",
" sofa_set = \"omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Furniture/FurnitureSets/Dutchtown/\"\n",
"\n",
" # create randomizer function conference table assets.\n",
" # This randomization includes placement and rotation of the assets on the surface.\n",
" def env_bar_stools(size=5):\n",
" barstool = rep.randomizer.instantiate(\n",
" rep.utils.get_usd_files(bar_stools, recursive=False),\n",
" size=size,\n",
" mode=\"scene_instance\",\n",
" )\n",
" rep.modify.semantics([(\"class\", \"chair_barstool\")])\n",
" with barstool:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),\n",
" rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),\n",
" )\n",
" return barstool.node\n",
"\n",
" # Register randomization\n",
" rep.randomizer.register(env_bar_stools)\n",
"\n",
" # create randomizer function conference table assets.\n",
" # This randomization includes placement and rotation of the assets on the surface.\n",
" def env_reception_table(size=5):\n",
" receptTable = rep.randomizer.instantiate(\n",
" rep.utils.get_usd_files(reception_tables, recursive=False),\n",
" size=size,\n",
" mode=\"scene_instance\",\n",
" )\n",
" rep.modify.semantics([(\"class\", \"reception_table\")])\n",
" with receptTable:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),\n",
" rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),\n",
" )\n",
" return receptTable.node\n",
"\n",
" # Register randomization\n",
" rep.randomizer.register(env_reception_table)\n",
"\n",
" # create randomizer function conference table assets.\n",
" # This randomization includes placement and rotation of the assets on the surface.\n",
" def env_chairs_notable(size=10):\n",
" chairsEnv = rep.randomizer.instantiate(\n",
" rep.utils.get_usd_files(chairs, recursive=False),\n",
" size=size,\n",
" mode=\"scene_instance\",\n",
" )\n",
" rep.modify.semantics([(\"class\", \"office_chairs\")])\n",
" with chairsEnv:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),\n",
" rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),\n",
" )\n",
" return chairsEnv.node\n",
"\n",
" # Register randomization\n",
" rep.randomizer.register(env_chairs_notable)\n",
"\n",
" # create randomizer function conference table assets.\n",
" # This randomization includes placement and rotation of the assets on the surface.\n",
" def env_normal_table(size=5):\n",
" normTable = rep.randomizer.instantiate(\n",
" rep.utils.get_usd_files(normal_tables, recursive=False),\n",
" size=size,\n",
" mode=\"scene_instance\",\n",
" )\n",
" rep.modify.semantics([(\"class\", \"normal_table\")])\n",
" with normTable:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),\n",
" rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),\n",
" )\n",
" return normTable.node\n",
"\n",
" # Register randomization\n",
" rep.randomizer.register(env_normal_table)\n",
"\n",
" # create randomizer function conference table assets.\n",
" # This randomization includes placement and rotation of the assets on the surface.\n",
" def env_sofaset(size=5):\n",
" sofaset = rep.randomizer.instantiate(\n",
" rep.utils.get_usd_files(sofa_set, recursive=False),\n",
" size=size,\n",
" mode=\"scene_instance\",\n",
" )\n",
" rep.modify.semantics([(\"class\", \"sofa\")])\n",
" with sofaset:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),\n",
" rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),\n",
" )\n",
" return sofaset.node\n",
"\n",
" # Register randomization\n",
" rep.randomizer.register(env_sofaset)\n",
"\n",
" # Setup camera and attach it to render product\n",
" camera = rep.create.camera()\n",
" render_product = rep.create.render_product(camera, resolution=(1024, 1024))\n",
"\n",
" # sphere lights for bonus challenge randomization\n",
" def sphere_lights(num):\n",
" lights = rep.create.light(\n",
" light_type=\"Sphere\",\n",
" temperature=rep.distribution.normal(6500, 500),\n",
" intensity=rep.distribution.normal(35000, 5000),\n",
" position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)),\n",
" scale=rep.distribution.uniform(50, 100),\n",
" count=num,\n",
" )\n",
" return lights.node\n",
"\n",
" rep.randomizer.register(sphere_lights)\n",
"\n",
" # create the surface for the camera to focus on\n",
" surface = rep.create.disk(scale=100, visible=False)\n",
"\n",
" # trigger on frame for an interval\n",
" with rep.trigger.on_frame(5):\n",
" rep.randomizer.env_bar_stools(2)\n",
" rep.randomizer.env_reception_table(2)\n",
" rep.randomizer.env_chairs_notable(2)\n",
" rep.randomizer.env_normal_table(2)\n",
" rep.randomizer.env_sofaset(2)\n",
"\n",
" # rep.randomizer.sphere_lights(10)\n",
" rep.randomizer.dome_lights()\n",
" with camera:\n",
" rep.modify.pose(\n",
" position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)),\n",
" look_at=surface,\n",
" )\n",
"\n",
" # Initialize and attach writer\n",
" writer = rep.WriterRegistry.get(\"BasicWriter\")\n",
" writer.initialize(output_dir=\"replicator_example_33\", rgb=True)\n",
" writer.attach([render_product])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f37c78e",
"metadata": {},
"outputs": [],
"source": []
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e94641b1",
"metadata": {
"tags": []
},
"source": [
"---\n",
"<h2 style=\"color:green;\">Congratulations!</h2>\n",
"\n",
"In this lab, you have learned about:\n",
"- What is Omniverse Replicator\n",
"- Creating simple shapes, cameras, lights, and 3D assets from a Nucleus server\n",
"- Using Replicator to make randomizations on objects\n",
"- Writing images and annotations to disk"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a5e94c0f",
"metadata": {},
"source": [
"<a href=\"https://www.nvidia.com/dli\"> <img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/> </a>"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a3f083e1",
"metadata": {},
"source": [
"Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES.\n",
"All rights reserved.\n",
"\n",
"BSD-3-Clause\n",
"\n",
"Redistribution and use in source and binary forms, with or without\n",
"modification, are permitted provided that the following conditions are met:\n",
"\n",
"1. Redistributions of source code must retain the above copyright notice, this\n",
"list of conditions and the following disclaimer.\n",
"\n",
"2. Redistributions in binary form must reproduce the above copyright notice,\n",
"this list of conditions and the following disclaimer in the documentation\n",
"and/or other materials provided with the distribution.\n",
"\n",
"3. Neither the name of the copyright holder nor the names of its\n",
"contributors may be used to endorse or promote products derived from\n",
"this software without specific prior written permission.\n",
"\n",
"THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n",
"AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n",
"IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n",
"DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n",
"FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n",
"DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n",
"SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n",
"CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n",
"OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n",
"OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
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"vscode": {
"interpreter": {
"hash": "a61027dd82f138c11773bdf35712ca03ae38c9dcb5e13e07ad518c836e552bc0"
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}
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"nbformat": 4,
"nbformat_minor": 5
}
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/getting_started.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "ad8724fc",
"metadata": {},
"source": [
"<a href=\"https://www.nvidia.com/dli\"> <img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/> </a>"
]
},
{
"cell_type": "markdown",
"id": "3e0d5ee0",
"metadata": {
"tags": []
},
"source": [
"# Welcome!\n",
"In this workshop, you'll learn how to generate synthetic data with Omniverse Replicator Framework. Omniverse Replicator is a highly extensible framework built on a scalable Omniverse platform that enables physically accurate 3D synthetic data generation to accelerate training and performance of AI perception networks. \n",
"\n",
"The best experience is to run Replicator from a local desktop RTX GPU workstation. However, for this workshop you won't need a GPU RTX on your desk. Instead, a GPU RTX instance is provided in the cloud, which you can connect to from your computer by installing the Omniverse Streaming Client, which does not require an RTX workstation."
]
},
{
"cell_type": "markdown",
"id": "2315d834-ad09-4165-99c3-a4a270ac0799",
"metadata": {
"tags": []
},
"source": [
"### Getting Started - Connecting to Omniverse Code\n",
"In this notebook, you'll learn how to connect to an Omniverse Code instance GUI in the cloud using a local Omniverse Streaming Client installed on your own Windows or Linux computer. "
]
},
{
"cell_type": "markdown",
"id": "1bd7bb6c-1d69-445d-a8ac-ed7686c02619",
"metadata": {},
"source": [
"<!-- TOC -->\n",
"**[Notebook Prerequisites](#Notebook-Prerequisites)<br>**\n",
"**[1.0 Getting Started](#1.0-Getting-Started)<br>**\n",
"**[1.1 Connect to Omniverse Nucleus](#1.1-Connect-to-Omniverse-Nucleus)<br>**"
]
},
{
"cell_type": "markdown",
"id": "3e5007b1",
"metadata": {},
"source": [
"---\n",
"# Notebook Prerequisites\n",
"You must install Omniverse Launcher and Omniverse Streaming Client on your Windows or Linux computer. If you have not done so already, please:\n",
"1. [Install Omniverse Launcher](https://docs.omniverse.nvidia.com/prod_launcher/prod_launcher/installing_launcher.html)\n",
"1. [Install Omniverse Streaming Client](https://docs.omniverse.nvidia.com/app_streaming-client/app_streaming-client/user-manual.html#using-omniverse-streaming-client)"
]
},
{
"cell_type": "markdown",
"id": "c66bbba7",
"metadata": {},
"source": [
"---\n",
"# 1.0 Getting Started\n",
"\n",
"Now that you've installed the Omniverse Streaming Client on your local machine, let's make sure the Omniverse Code server is up and running before trying to connect. We can do a quick check by looking at the server logs. Execute the following cell to find the logfile name. (You can execute the cell by either clicking the triangle in the menu above or by pressing [SHIFT][ENTER] on the cell.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e06ef52",
"metadata": {},
"outputs": [],
"source": [
"# get logfile name - wait 10 seconds for loading\n",
"import glob, time\n",
"\n",
"LOADED_STRING = \"switching to 1x1 layout\"\n",
"LOGFILE = \"\"\n",
"while LOGFILE == \"\":\n",
" try:\n",
" LOGFILE = glob.glob(\"/root/.nvidia-omniverse/logs/Kit/Code/*/*.log\")[0]\n",
" except:\n",
" print(\"Wait 10 seconds...\")\n",
" time.sleep(10)\n",
"print(\"Logfile is {}\".format(LOGFILE))"
]
},
{
"cell_type": "markdown",
"id": "61c3627f",
"metadata": {},
"source": [
"Execute the following cell to wait for the server. Estimated time for this step is **2 minutes** after the initial course launch."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "943d4397",
"metadata": {},
"outputs": [],
"source": [
"# function to test for string in a file\n",
"def check_file(filename, my_string):\n",
" with open(filename) as f:\n",
" if my_string in f.read():\n",
" return True\n",
" return False\n",
"\n",
"\n",
"# server wait loop\n",
"import time\n",
"\n",
"while check_file(LOGFILE, LOADED_STRING) is False:\n",
" print(\"Not ready; trying again in 30 seconds\")\n",
" time.sleep(30)\n",
"print(\"Server ready!\")"
]
},
{
"cell_type": "markdown",
"id": "4197654e",
"metadata": {},
"source": [
"To connect, you'll need to know the IP address of the Omniverse Code server. Execute the following cell to get the address. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d698bfe",
"metadata": {},
"outputs": [],
"source": [
"from requests import get\n",
"\n",
"ip = get(\"https://api.ipify.org\").text\n",
"print(\"This instance IP address is: {}\".format(ip))"
]
},
{
"cell_type": "markdown",
"id": "02b1f1dc",
"metadata": {},
"source": [
"Once the server is ready, open the Omniverse Launcher on your computer and navigate to the \"Library\" tab. Select the Streaming Client and click the launch button. A window will open with a form. Paste your server IP, select the resolution you prefer, and click \"CONNECT\".\n",
"\n",
"<img src=\"images/streaming_client_form.png\">\n",
"\n",
"The Omniverse interface will open up!"
]
},
{
"cell_type": "markdown",
"id": "b1ef55f8-9351-456e-8475-791671a9dee9",
"metadata": {
"tags": []
},
"source": [
"---\n",
"# Connect to Omniverse Nucleus\n",
"[Omniverse Nucleus](https://docs.omniverse.nvidia.com/prod_nucleus/prod_nucleus/overview.html) is the database and collaboration engine of Omniverse. With Omniverse Nucleus, teams can have multiple users connected together live with multiple applications all at once. This allows people to use the application they are the most comfortable and quickest with and really opens a lot of doors for rapid iteration. For this course, you'll connect to a read-only instance with access to standard assets. To connect, click \"Add New Connection\" in the GUI (Content/Omniverse). Type in the URL address of the DLI Nucleus Server: **ove-nucleus.courses.nvidia.com**<br>\n",
"\n",
"<img src=\"images/add_nucleus.png\">\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "129ed796-2553-48b7-b1dd-cdf5e0426cb9",
"metadata": {
"tags": []
},
"source": [
"---\n",
"<h2 style=\"color:green;\">Congratulations!</h2>\n",
"\n",
"In this notebook, you learned how to:\n",
"* Connect to Omniverse Code in the cloud from Omniverse Streaming Client on your computer\n",
"* Connect to Omniverse Nucleus from the Omniverse Code GUI\n",
"\n",
"Move on to the [\"Hello Replicator!\" notebook](hello_replicator.ipynb) to learn how to use Omniverse Replicator."
]
},
{
"cell_type": "markdown",
"id": "2c04528c",
"metadata": {},
"source": [
"<a href=\"https://www.nvidia.com/dli\"> <img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/> </a>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.6 64-bit ('3.10.6')",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython3",
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"vscode": {
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|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/03_replicator_advanced.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
def dome_lights():
lights = rep.create.light(
light_type="Dome",
rotation=(270, 0, 0),
texture=rep.distribution.choice(
[
"omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCGcom_ExhibitionHall_Interior1.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCG_com_WarehouseInterior2b.hdr",
]
),
)
return lights.node
rep.randomizer.register(dome_lights)
conference_tables = (
"omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Conference/"
)
# create randomizer function conference table assets.
# This randomization includes placement and rotation of the assets on the surface.
def env_conference_table(size=5):
confTable = rep.randomizer.instantiate(
rep.utils.get_usd_files(conference_tables, recursive=False),
size=size,
mode="scene_instance",
)
with confTable:
rep.modify.pose(
position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),
rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),
)
return confTable.node
# Register randomization
rep.randomizer.register(env_conference_table)
# Setup camera and attach it to render product
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
surface = rep.create.disk(scale=100, visible=False)
# trigger on frame for an interval
with rep.trigger.on_frame(5):
rep.randomizer.env_conference_table(2)
rep.randomizer.dome_lights()
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)),
look_at=surface,
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(output_dir="~/replicator_examples/dli_example_3", rgb=True)
writer.attach([render_product])
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/01_hello_replicator.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
# Create a new layer for our work to be performed in.
# This is a good habit to develop for later when working on existing Usd scenes
with rep.new_layer():
# Create a simple camera with a position and a point to look at
camera = rep.create.camera(position=(0, 500, 1000), look_at=(0, 0, 0))
# Create some simple shapes to manipulate
plane = rep.create.plane(
semantics=[("class", "plane")], position=(0, -100, 0), scale=(100, 1, 100)
)
torus = rep.create.torus(semantics=[("class", "torus")], position=(200, 0, 100))
sphere = rep.create.sphere(semantics=[("class", "sphere")], position=(0, 0, 100))
cube = rep.create.cube(semantics=[("class", "cube")], position=(-200, 0, 100))
# Randomize position and scale of each object on each frame
with rep.trigger.on_frame(num_frames=10):
# Creating a group so that our modify.pose operation works on all the shapes at once
with rep.create.group([torus, sphere, cube]):
rep.modify.pose(
position=rep.distribution.uniform((-300, 0, -300), (300, 0, 300)),
scale=rep.distribution.uniform(0.1, 2),
)
# Initialize render product and attach a writer
render_product = rep.create.render_product(camera, (1024, 1024))
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="~/replicator_examples/dli_hello_replicator/",
rgb=True,
semantic_segmentation=True,
bounding_box_2d_tight=True,
)
writer.attach([render_product])
rep.orchestrator.run()
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/physics.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
# Define paths for the character, the props, the environment and the surface where the assets will be scattered in.
PROPS = "omniverse://localhost/NVIDIA/Assets/Isaac/2022.1/Isaac/Props/YCB/Axis_Aligned_Physics"
SURFACE = (
"omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Basic/display_riser.usd"
)
ENVS = "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Interior/ZetCG_ExhibitionHall.usd"
# Define randomizer function for Base assets. This randomization includes placement and rotation of the assets on the surface.
def env_props(size=50):
instances = rep.randomizer.instantiate(
rep.utils.get_usd_files(PROPS, recursive=True),
size=size,
mode="scene_instance",
)
with instances:
rep.modify.pose(
position=rep.distribution.uniform((-50, 5, -50), (50, 20, 50)),
rotation=rep.distribution.uniform((0, -180, 0), (0, 180, 0)),
scale=100,
)
rep.physics.rigid_body(
velocity=rep.distribution.uniform((-0, 0, -0), (0, 0, 0)),
angular_velocity=rep.distribution.uniform((-0, 0, -100), (0, 0, 0)),
)
return instances.node
# Register randomization
rep.randomizer.register(env_props)
# Setup the static elements
env = rep.create.from_usd(ENVS)
surface = rep.create.from_usd(SURFACE)
with surface:
rep.physics.collider()
# Setup camera and attach it to render product
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
# sphere lights for extra randomization
def sphere_lights(num):
lights = rep.create.light(
light_type="Sphere",
temperature=rep.distribution.normal(6500, 500),
intensity=rep.distribution.normal(35000, 5000),
position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)),
scale=rep.distribution.uniform(50, 100),
count=num,
)
return lights.node
rep.randomizer.register(sphere_lights)
# trigger on frame for an interval
with rep.trigger.on_time(interval=2, num=10):
rep.randomizer.env_props(10)
rep.randomizer.sphere_lights(10)
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-50, 20, 100), (50, 50, 150)),
look_at=surface,
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(
output_dir="~/replicator_examples/dli_physics",
rgb=True,
bounding_box_2d_tight=True,
)
writer.attach([render_product])
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/more_information.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "7925aad5",
"metadata": {},
"source": [
"<a href=\"https://www.nvidia.com/dli\"> <img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/> </a>"
]
},
{
"cell_type": "markdown",
"id": "9d578eea",
"metadata": {
"tags": []
},
"source": [
"# Additional Help and Information\n",
"\n",
"Here is a list of recommended reading and videos:\n",
"- [Documentation and tutorials](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html)\n",
"- [GTC Talk](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html)\n",
"- [Replicator landing page with blogs](https://developer.nvidia.com/nvidia-omniverse-platform/replicator#:~:text=DRIVE%20Replicator%20is%20a%20purpose,the%20NVIDIA%20DRIVE%20Sim%20platform.)\n",
"- [Omniverse Synthetic Data Forum](https://forums.developer.nvidia.com/c/omniverse/synthetic-data-generation-sdg/595)"
]
},
{
"cell_type": "markdown",
"id": "a5e94c0f",
"metadata": {},
"source": [
"<a href=\"https://www.nvidia.com/dli\"> <img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/> </a>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.10.6 64-bit ('3.10.6')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
},
"vscode": {
"interpreter": {
"hash": "6e75d9671bde9e237d0449067a77b1b23886bc055a4cc69b493f52615e9e42e3"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/02_background_randomization.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
def dome_lights():
lights = rep.create.light(
light_type="Dome",
rotation=(270, 0, 0),
texture=rep.distribution.choice(
[
"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/champagne_castle_1_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Clear/evening_road_01_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/kloofendal_48d_partly_cloudy_4k.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Clear/qwantani_4k.hdr",
]
),
)
return lights.node
rep.randomizer.register(dome_lights)
torus = rep.create.torus(semantics=[("class", "torus")], position=(0, -200, 100))
# create surface
surface = rep.create.disk(scale=5, visible=False)
# create camera & render product for the scene
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
with rep.trigger.on_frame(num_frames=10, interval=10):
rep.randomizer.dome_lights()
with rep.create.group([torus]):
rep.modify.pose(
position=rep.distribution.uniform((-100, -100, -100), (200, 200, 200)),
scale=rep.distribution.uniform(0.1, 2),
)
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)),
look_at=surface,
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(output_dir="~/replicator_examples/dli_example_02", rgb=True)
writer.attach([render_product])
# Run Replicator
# rep.orchestrator.run()
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator/tutorials/fall_2022_DLI/33_replicator_solution.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import omni.replicator.core as rep
with rep.new_layer():
def dome_lights():
lights = rep.create.light(
light_type="Dome",
rotation=(270, 0, 0),
texture=rep.distribution.choice(
[
"omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCGcom_ExhibitionHall_Interior1.hdr",
"omniverse://localhost/NVIDIA/Assets/Skies/Indoor/ZetoCG_com_WarehouseInterior2b.hdr",
]
),
)
return lights.node
rep.randomizer.register(dome_lights)
bar_stools = (
"omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Furniture/BarStools/"
)
reception_tables = (
"omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Reception/"
)
chairs = "omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Seating/"
normal_tables = "omniverse://localhost/NVIDIA/Assets/ArchVis/Commercial/Tables/"
sofa_set = "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Furniture/FurnitureSets/Dutchtown/"
# create randomizer function conference table assets.
# This randomization includes placement and rotation of the assets on the surface.
def env_bar_stools(size=5):
barstool = rep.randomizer.instantiate(
rep.utils.get_usd_files(bar_stools, recursive=False),
size=size,
mode="scene_instance",
)
rep.modify.semantics([("class", "chair_barstool")])
with barstool:
rep.modify.pose(
position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),
rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),
)
return barstool.node
# Register randomization
rep.randomizer.register(env_bar_stools)
# create randomizer function conference table assets.
# This randomization includes placement and rotation of the assets on the surface.
def env_reception_table(size=5):
receptTable = rep.randomizer.instantiate(
rep.utils.get_usd_files(reception_tables, recursive=False),
size=size,
mode="scene_instance",
)
rep.modify.semantics([("class", "reception_table")])
with receptTable:
rep.modify.pose(
position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),
rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),
)
return receptTable.node
# Register randomization
rep.randomizer.register(env_reception_table)
# create randomizer function conference table assets.
# This randomization includes placement and rotation of the assets on the surface.
def env_chairs_notable(size=10):
chairsEnv = rep.randomizer.instantiate(
rep.utils.get_usd_files(chairs, recursive=False),
size=size,
mode="scene_instance",
)
rep.modify.semantics([("class", "office_chairs")])
with chairsEnv:
rep.modify.pose(
position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),
rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),
)
return chairsEnv.node
# Register randomization
rep.randomizer.register(env_chairs_notable)
# create randomizer function conference table assets.
# This randomization includes placement and rotation of the assets on the surface.
def env_normal_table(size=5):
normTable = rep.randomizer.instantiate(
rep.utils.get_usd_files(normal_tables, recursive=False),
size=size,
mode="scene_instance",
)
rep.modify.semantics([("class", "normal_table")])
with normTable:
rep.modify.pose(
position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),
rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),
)
return normTable.node
# Register randomization
rep.randomizer.register(env_normal_table)
# create randomizer function conference table assets.
# This randomization includes placement and rotation of the assets on the surface.
def env_sofaset(size=5):
sofaset = rep.randomizer.instantiate(
rep.utils.get_usd_files(sofa_set, recursive=False),
size=size,
mode="scene_instance",
)
rep.modify.semantics([("class", "sofa")])
with sofaset:
rep.modify.pose(
position=rep.distribution.uniform((-500, 0, -500), (500, 0, 500)),
rotation=rep.distribution.uniform((-90, -180, 0), (-90, 180, 0)),
)
return sofaset.node
# Register randomization
rep.randomizer.register(env_sofaset)
# Setup camera and attach it to render product
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
# sphere lights for bonus challenge randomization
def sphere_lights(num):
lights = rep.create.light(
light_type="Sphere",
temperature=rep.distribution.normal(6500, 500),
intensity=rep.distribution.normal(35000, 5000),
position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)),
scale=rep.distribution.uniform(50, 100),
count=num,
)
return lights.node
rep.randomizer.register(sphere_lights)
# create the surface for the camera to focus on
surface = rep.create.disk(scale=100, visible=False)
# trigger on frame for an interval
with rep.trigger.on_frame(5):
rep.randomizer.env_bar_stools(2)
rep.randomizer.env_reception_table(2)
rep.randomizer.env_chairs_notable(2)
rep.randomizer.env_normal_table(2)
rep.randomizer.env_sofaset(2)
# rep.randomizer.sphere_lights(10)
rep.randomizer.dome_lights()
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-500, 200, 1000), (500, 500, 1500)),
look_at=surface,
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
writer.initialize(output_dir="~/replicator_examples/dli_example_33", rgb=True)
writer.attach([render_product])
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_randomizer_materials.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Creating materials, and applying them to objects in a scene with a randomizer
# Create the materials to apply to the objects
mats:
create.material_omnipbr:
diffuse:
distribution.uniform:
lower: [0, 0, 0]
upper: [1, 1, 1]
count: 100
# Create the objects in the scene
spheres:
create.sphere:
scale: 0.2
position:
distribution.uniform:
lower: [-1, -1, -1]
upper: [1, 1, 1]
count: 100
plane:
create.plane:
semantics: [["class", "plane"]]
position: [0, 0, -1.5]
visible: true
scale: 100
# Create the camera and render product
camera:
create.camera:
position: [5, 0, 0]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialRandomizerMaterials/"
rgb: True
writer_attach:
writer.attach:
render_products: render_product
# Register a randomizer that sets the materials of the spheres
register_materials:
randomizer.register:
get_spheres:
inputs:
spheres: null
mats: null
with.spheres:
randomizer.materials:
materials: mats
# Set the trigger as on_frame, setting subframes to accumulate frames for a
# higher quality render
trigger:
trigger.on_frame:
max_execs: 20
rt_subframes: 3
# When the trigger executes, apply the randomizer
with_trigger:
with.trigger:
randomizer.get_spheres:
spheres: spheres
mats: mats
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_randomizer_scatter_surface.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Scattering objects on a plane
# Create the objects in the scene
light:
create.light:
light_type: distant
sphere:
create.sphere:
semantics: [["class", "sphere"]]
position: [1, 0, 1]
cube:
create.cube:
semantics: [["class", "cube"]]
position: [1, 2, 2]
plane:
create.plane:
semantics: [["class", "plane"]]
visible: true
scale: 10
# Create the camera and render product
camera:
create.camera:
position: [13, -6, 9]
rotation: [0, -35, -27]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialRandomizerScatterSurface/"
rgb: True
writer_attach:
writer.attach:
render_products: render_product
# Register a randomizer that scatters objects on a plane
register_scatter2d:
randomizer.register:
get_scatter2d:
inputs:
input_plane: null
shapes:
get.prims:
semantics: [['class', 'cube'], ['class', 'sphere']]
with.shapes:
randomizer.scatter_2d:
surface_prims: [input_plane]
# Set the trigger as on_frame, setting subframes to accumulate frames for a
# higher quality render
trigger:
trigger.on_frame:
max_execs: 20
rt_subframes: 12
# When the trigger executes, apply the randomizer
with_trigger:
with.trigger:
randomizer.get_scatter2d:
input_plane: plane
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_randomizer_dome_light.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Randomizing the a domelight
# Create the camera and render product
camera:
create.camera:
position: [5, 0, 0]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialRandomizerDomeLight/"
rgb: True
render_products: render_product
# Register a randomizer that sets the HDR texture of a domelight
register_dome_light:
randomizer.register:
dome_lights:
dome_light:
create.light:
light_type: "Dome"
rotation: [0, 0, 90]
texture:
distribution.sequence:
items:
- "omniverse://localhost/NVIDIA/Assets/Skies/Cloudy/champagne_castle_1_4k.hdr"
- "omniverse://localhost/NVIDIA/Assets/Skies/Clear/evening_road_01_4k.hdr"
- "omniverse://localhost/NVIDIA/Assets/Skies/Clear/mealie_road_4k.hdr"
- "omniverse://localhost/NVIDIA/Assets/Skies/Clear/qwantani_4k.hdr"
# Set the trigger as on_frame
trigger:
trigger.on_frame:
max_execs: 10
interval: 1
# When the trigger executes, apply the randomizer
with_trigger:
with.trigger:
randomizer.dome_lights: null
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_basic_functionality.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Moving a group of objects to varied positions using a distribution
# Create the camera and render product
camera:
create.camera:
position: [5, -1, 4]
rotation: [0, -30, -27]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the objects in the scene
light:
create.light:
light_type: "distant"
torus:
create.torus:
semantics: [["class", "torus"]]
position: [1, 0, -2]
sphere:
create.sphere:
semantics: [["class", "sphere"]]
position: [1, 0, 1]
cube:
create.cube:
semantics: [["class", "cube"]]
position: [1, 1, -2]
group:
create.group:
items: [torus, sphere, cube]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialBasicFunctionality/"
rgb: True
bounding_box_2d_tight: True
writer_attach:
writer.attach:
render_products: render_product
# Set the trigger as on_frame
trigger:
trigger.on_frame:
max_execs: 10
# When the trigger executes, modify the poses of the group
with_trigger:
with.trigger:
with.group:
modify.pose:
position:
distribution.uniform:
lower: [-1, -1, -1]
upper: [2, 2, 2]
scale:
distribution.uniform:
lower: 0.1
upper: 2
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_randomizer_textures.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Randomizing the textures on materials of prims in the scene
# Create the objects in the scene
sphere:
create.sphere:
semantics: [["class", "sphere"]]
position: [1, 0, 1]
cube:
create.cube:
semantics: [["class", "cube"]]
position: [1, 2, 2]
count: 10
plane:
create.plane:
semantics: [["class", "plane"]]
visible: true
scale: 10
# Create a group of objects with these semantic labels
shapes:
get.prims:
semantics: [['class', 'cube'], ['class', 'sphere']]
# Create the camera and render product
camera:
create.camera:
position: [5, -1, 4]
rotation: [0, -30, -27]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialRandomizerTextures/"
rgb: True
writer_attach:
writer.attach:
render_products: render_product
# Register a randomizer that sets the textures on the materials of prims
register_shape_textures:
randomizer.register:
randomize_texture:
inputs:
prims: null
with.prims:
randomizer.texture:
textures:
- "omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/aggregate_exposed_diff.jpg"
- "omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_diff.jpg"
- "omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/gravel_track_ballast_multi_R_rough_G_ao.jpg"
- "omniverse://localhost/NVIDIA/Materials/vMaterials_2/Ground/textures/rough_gravel_rough.jpg"
# Set the trigger as on_frame, setting subframes to accumulate frames for a
# higher quality render
trigger:
trigger.on_frame:
max_execs: 20
rt_subframes: 3
# When the trigger executes, apply the randomizer
with_trigger:
with.trigger:
randomizer.randomize_texture:
prims: shapes
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/randomizeCameraPositionList.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Moving a camera to positions defined in a list
# but rotated to look at the center of the the scene, showing a cube on a plane
# Define camera positions list
camera_positions: [[2,4,5], [-5,-5,5], [-3,4,5], [3,-2,3], [6,7,4]]
# Create the camera and render product
camera:
create.camera:
look_at: [0, 0, 0]
position: [10, 10, 10]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the objects in the scene
cube:
create.cube:
semantics: [["class", "cube"]]
position: [0, 0, 0]
plane:
create.plane:
semantics: [["class", "plane"]]
position: [0, 0, -0.75]
visible: true
scale: 10
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/RandomizeCameraPositionsList/"
rgb: True
bounding_box_2d_tight: True
render_products: render_product
# Set the trigger as on_frame
trigger:
trigger.on_frame:
max_execs: 10
# When the trigger executes, modify the poses of the camera
with_trigger:
with.trigger:
with.camera:
modify.pose:
position:
distribution.choice:
choices: camera_positions
look_at: [0,0,0]
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_randomizer_light.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Randomizing light positions in a scene
# Create the objects in the scene
sphere:
create.sphere:
semantics: [["class", "sphere"]]
position: [1, 0, 1]
cube:
create.cube:
semantics: [["class", "cube"]]
position: [1, 2, 2]
plane:
create.plane:
semantics: [["class", "plane"]]
visible: true
scale: 10
# Create the camera and render product
camera:
create.camera:
position: [13, -6, 9]
rotation: [0, -35, -27]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialRandomizerLight/"
rgb: True
writer_attach:
writer.attach:
render_products: render_product
# Register a randomizer that sets light positions
register_sphere_light:
randomizer.register:
sphere_lights:
inputs:
num: 1
sphere_light:
create.light:
light_type: "Sphere"
temperature:
distribution.normal:
mean: 6500
std: 500
intensity:
distribution.normal:
mean: 35000
std: 5000
position:
distribution.uniform:
lower: [-3, -3, 2]
upper: [3, 3, 3]
scale:
distribution.uniform:
lower: 0.5
upper: 1
count: num
# Set the trigger as on_frame
trigger:
trigger.on_frame:
max_execs: 10
rt_subframes: 50
# When the trigger executes, apply the randomizer
with_trigger:
with.trigger:
randomizer.sphere_lights:
num: 10
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/multipleCamerasWithBasicWriter.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# A multi camera scenario, with 2 cameras positioned in different locations
# but rotated to look at the center of the the scene, showing a cube, a sphere and a cone.
# Create the objects in the scene
sphere:
create.sphere:
semantics: [["class", "sphere"]]
position: [0, 0, 0]
cube:
create.cube:
semantics: [["class", "cube"]]
position: [2, 0, 0]
cone:
create.cone:
semantics: [["class", "cone"]]
position: [-2, 0, 0]
# Create cameras and attach render product
camera:
create.camera:
look_at: [0, 0, 0]
position: [0, 5, 1]
camera2:
create.camera:
look_at: [0, 0, 0]
position: [0, 0, 5]
render_product:
create.render_product:
camera: camera
resolution: [512, 512]
render_product2:
create.render_product:
camera: camera2
resolution: [320, 240]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/MultipleCamerasWithBasicWriter"
rgb: True
bounding_box_2d_loose: True
bounding_box_2d_tight: True
bounding_box_3d: True
distance_to_camera: True
distance_to_image_plane: True
instance_segmentation: True
normals: True
semantic_segmentation: True
writer_attach:
writer.attach:
render_products: [render_product, render_product2]
# Set the trigger as on_frame
trigger:
trigger.on_frame:
max_execs: 10
# When the trigger executes, modify the poses of the cameras
with_trigger:
with.trigger:
with.camera:
modify.pose:
position:
distribution.uniform:
lower: [-5, 1, 5]
upper: [5, 5, 5]
look_at: [0,0,0]
with.camera2:
modify.pose:
position:
distribution.uniform:
lower: [-5, 1, 2]
upper: [5, 5, 5]
look_at: [0,0,0]
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_randomizer_pose.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Randomizing the pose of objects in a scene with a randomizer
# https://docs.omniverse.nvidia.com/extensions/latest/ext_replicator/randomizer_details.html#randomizing-pose-position-rotation-and-scale
# Create the objects in the scene
sphere:
create.sphere:
semantics: [["class", "sphere"]]
position: [1, 0, 1]
cube:
create.cube:
semantics: [["class", "cube"]]
position: [1, 2, 2]
# Create the camera and render product
camera:
create.camera:
position: [13, -6, 9]
rotation: [0, -35, -27]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialRandomizerPose/"
rgb: True
writer_attach:
writer.attach:
render_products: render_product
# Register a randomizer that sets the pose of the objects
register_move_shapes:
randomizer.register:
move_shapes:
shapes:
get.prims:
semantics: [['class', 'cube'], ['class', 'sphere']]
with.shapes:
modify.pose:
position:
distribution.uniform:
lower: [-5, -5, 0.5]
upper: [5, 5, 0.5]
rotation:
distribution.uniform:
lower: [0, 0, -180]
upper: [0, 0, 180]
scale:
distribution.normal:
mean: 1.0
std: 0.5
# Set the trigger as on_frame
trigger:
trigger.on_frame:
max_execs: 20
# When the trigger executes, apply the randomizer
with_trigger:
with.trigger:
randomizer.move_shapes: null
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/randomizeCameraPositionUniformly.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Moving a camera to positions using a distribution
# but rotated to look at the center of the the scene, showing a cube and sphere on a plane
# Create the objects in the scene
sphere:
create.sphere:
semantics: [["class", "sphere"]]
position: [0, 1, 1]
cube:
create.cube:
semantics: [["class", "cube"]]
position: [2, 2, 1]
plane:
create.plane:
semantics: [["class", "plane"]]
visible: true
scale: 10
# Create the camera and render product
camera:
create.camera:
position: [10, 0, 0]
render_product:
create.render_product:
camera: camera
resolution: [512, 512]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/RandomizeCameraPositionUniformly/"
rgb: True
bounding_box_2d_tight: True
render_products: render_product
# Set the trigger as on_frame
trigger:
trigger.on_frame:
max_execs: 10
# When the trigger executes, modify the poses of the camera
with_trigger:
with.trigger:
with.camera:
modify.pose:
position:
distribution.uniform:
lower: [-5, 2, 1]
upper: [5, 5, 5]
look_at: [0,0,0]
|
NVIDIA-Omniverse/synthetic-data-examples/omni.replicator_yaml/tutorial_randomizer_colors.yaml | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# Default scene settings. This script default is 1 (IsaacSim), so if you change
# the meters per unit or up axis, you need to alter the coordinates and rotations.
# IsaacSim default is 1, Code is 0.01
stage_unit_setting:
settings.set_stage_meters_per_unit:
meters_per_unit: 1
# IsaacSim default is "Z"", Code is "Y"
stage_up_axis_setting:
settings.set_stage_up_axis:
up_axis: "Z"
# This YAML script example demonstrates:
# Randomizing the colors of a cube and sphere's materials
# Create the objects in the scene
sphere:
create.sphere:
semantics: [["class", "sphere"]]
position: [1, 0, 1]
cube:
create.cube:
semantics: [["class", "cube"]]
position: [1, 2, 2]
plane:
create.plane:
semantics: [["class", "plane"]]
visible: true
scale: 10
# Create the camera and render product
camera:
create.camera:
position: [5, -1, 4]
rotation: [0, -30, -27]
render_product:
create.render_product:
camera: camera
resolution: [1024, 1024]
# Create the writer and initialize
writer:
writers.get:
name: "BasicWriter"
init_params:
output_dir: "_output_yaml/TutorialRandomizerColors/"
rgb: True
writer_attach:
writer.attach:
render_products: render_product
# Register a randomizer that sets the semantic label and randomizes the color
register_colors:
randomizer.register:
get_color:
shapes:
get.prims:
semantics: [['class', 'cube'], ['class', 'sphere']]
with.shapes:
randomizer.color:
colors:
distribution.uniform:
lower: [0, 0, 0]
upper: [1, 1, 1]
# Set the trigger as on_frame, setting subframes to accumulate frames for a
# higher quality render
trigger:
trigger.on_frame:
max_execs: 20
rt_subframes: 10
# When the trigger executes, apply the randomizer
with_trigger:
with.trigger:
randomizer.get_color: null
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/CLA.md | ## Individual Contributor License Agreement (CLA)
**Thank you for submitting your contributions to this project.**
By signing this CLA, you agree that the following terms apply to all of your past, present and future contributions
to the project.
### License.
You hereby represent that all present, past and future contributions are governed by the
[MIT License](https://opensource.org/licenses/MIT)
copyright statement.
This entails that to the extent possible under law, you transfer all copyright and related or neighboring rights
of the code or documents you contribute to the project itself or its maintainers.
Furthermore you also represent that you have the authority to perform the above waiver
with respect to the entirety of you contributions.
### Moral Rights.
To the fullest extent permitted under applicable law, you hereby waive, and agree not to
assert, all of your “moral rights” in or relating to your contributions for the benefit of the project.
### Third Party Content.
If your Contribution includes or is based on any source code, object code, bug fixes, configuration changes, tools,
specifications, documentation, data, materials, feedback, information or other works of authorship that were not
authored by you (“Third Party Content”) or if you are aware of any third party intellectual property or proprietary
rights associated with your Contribution (“Third Party Rights”),
then you agree to include with the submission of your Contribution full details respecting such Third Party
Content and Third Party Rights, including, without limitation, identification of which aspects of your
Contribution contain Third Party Content or are associated with Third Party Rights, the owner/author of the
Third Party Content and Third Party Rights, where you obtained the Third Party Content, and any applicable
third party license terms or restrictions respecting the Third Party Content and Third Party Rights. For greater
certainty, the foregoing obligations respecting the identification of Third Party Content and Third Party Rights
do not apply to any portion of a Project that is incorporated into your Contribution to that same Project.
### Representations.
You represent that, other than the Third Party Content and Third Party Rights identified by
you in accordance with this Agreement, you are the sole author of your Contributions and are legally entitled
to grant the foregoing licenses and waivers in respect of your Contributions. If your Contributions were
created in the course of your employment with your past or present employer(s), you represent that such
employer(s) has authorized you to make your Contributions on behalf of such employer(s) or such employer
(s) has waived all of their right, title or interest in or to your Contributions.
### Disclaimer.
To the fullest extent permitted under applicable law, your Contributions are provided on an "as is"
basis, without any warranties or conditions, express or implied, including, without limitation, any implied
warranties or conditions of non-infringement, merchantability or fitness for a particular purpose. You are not
required to provide support for your Contributions, except to the extent you desire to provide support.
### No Obligation.
You acknowledge that the maintainers of this project are under no obligation to use or incorporate your contributions
into the project. The decision to use or incorporate your contributions into the project will be made at the
sole discretion of the maintainers or their authorized delegates. |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/README.md | # Synthetic Data Generation and Training with Sim Ready Assets
This project provides a workflow for Training Computer Vision models with Synthetic Data. We will use Isaac Sim with Omniverse Replicator to generate data for our use case and objects of interest. To ensure seamless compatibility with model training, the data generated is in the KITTI format.
These steps can be followed on a Cloud/remote GPU instance or locally
## How to use this repository
- [Guide](local/README.md) for running the workflow locally
- [Guide](cloud/README.md) for running on a cloud/remote instance
## Workflow Components:
* Generating Data: Use Isaac Sim to generate data
* Training: We will use TAO toolkit, however users can train a model in a framework of their choice with data generated
### SDG
- Using the `palletjack` assets from the Warehouse Sim Ready Asset collection
- Carry out Domain Randomization in the scene with Replicator:
- Various attributes of the scene like lighting, textures, object pose and materials can be modified
- Important to generate a good quality dataset to ensure model detects objects in the real world
- Data output KITTI format
- We will use the KITTI Writer for generating annotations
- Possible to implement a custom writer (can be useful when data is expected in a certain format for your model)
- Sample generated images:
<p>
<img src="images/sample_synthetic/21.png" height="256"/>
<img src="images/sample_synthetic/653.png" height="256"/>
</p>
<p>
<img src="images/sample_synthetic/896.png" height="256"/>
<img src="images/sample_synthetic/1545.png" height="256"/>
</p>
### Training
- TAO: Outline of steps
- Generating Tfrecords
- Model training and evaluation
- Model backbone selction
- Hyperparameters specified via `spec` file (provided with repo)
- Running inference with trained model
- Sample real world detections on LOCO dataset images:
<p>
<img src="images/real_world_results/1564562568.298206.jpg" height="256"/>
<img src="images/real_world_results/1564562843.0618184.jpg" height="256"/>
</p>
<p>
<img src="images/real_world_results/593768,3659.jpg" height="256"/>
<img src="images/real_world_results/510196244,1362.jpg" height="256"/>
</p>
<p>
<img src="images/real_world_results/1574675156.7667925.jpg" height="256"/>
<img src="images/real_world_results/426023,9672.jpg" height="256"/>
</p>
### Deployment
- Perform Optimizations: Pruning and QAT with TAO to reduce model size and improve performance
- Deploy on NVIDIA Jetson powered Robot with Isaac ROS or Deepstream
## References:
- Real world images from the [LOCO dataset](https://github.com/tum-fml/loco) are used for visualizing model performance
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/LICENSE.md | SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: MIT
Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/cloud/README.md | # Requirements
- Access to a cloud/remote GPU instance (workflow tested on a `g4dn` AWS EC2 instance with T4 GPU)
- Docker setup instructions are provided in the notebooks
- Entire workflow can be run in `headless` mode (SDG script and training)
## Synthetic Data Generation
- Use the Isaac Sim docker container for running the Data Generation [script](../palletjack_sdg/palletjack_datagen.sh)
- We will generate data for warehouse `palletjack` objects in KITTI format
- Follow the steps in the `cloud_sdg` notebook
- This generated data can be used to train your own model (framework and architecture of your choice), in this workflow we demonstrate using TAO for training
## Training with TAO Toolkit
- The `training/cloud_train` notebook provides a walkthrough of the steps:
- Setting up TAO docker container
- Downloading pre-trained model, we will use the `DetectNet_v2` model with a `resnet_18` backbone
- Running TAO training with `spec` files provided
- Visualizing model performance on real world data
- Visualize model metric with Tensorboard
<img src="../images/tensorboard/tensorboard_resized_palletjack.png"/>
## Next steps
### Generating Synthetic Data for your use case
- Make changes in the Domain Randomization under the Synthetic Data Generation [script](../palletjack_sdg/standalone_palletjack_sdg.py)
- Add additional objects of interest in the scene (similar to how palletjacks are added, you can add forklifts, ladders etc.) to generate dataUse different models for training with TAO (for object detection, you can use YOLO, SSD, EfficientDet)
- Replicator provides Semantic Segmentation, Instance Segmentation, Depth and various other ground truth annotations along with RGB. You can also write your own ground truth annotator (eg: Pose Estimation: Refer to [sample](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_replicator_offline_pose_estimation.html) These can be used for training a model of your own framework and choice)
- Exploring the option of using Synthetic + Real data for training a network. Can be particularly useful for generating more data around particular corner cases
### Deploying Trained Models
- The trained model can be pruned and optimized for inference with TAO
- This can then be deployed on a robot with NVIDIA Jetson |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/cloud/cloud_sdg.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1: Synthetic Data Generation and Training Workflow with Warehouse Sim Ready Assets\n",
"\n",
"This notebook is the first part of the SDG and Training Workflow. We will be focusing on generating Synthetic Data for our use case\n",
"\n",
"A high level overview of the steps:\n",
"* Pulling Isaac Sim Docker Container \n",
"* Using Replicator API for Data Generation with Domain Randomization\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of Contents\n",
"\n",
"This notebook shows provides an overview of generating synthetic data using Warehouse Sim Ready assets with Isaac Sim and Omniverse Replicator. We will generate data for the `palletjack` class of objects. \n",
"\n",
"1. [Set up Isaac Sim via Docker Container](#head-1)\n",
"2. [Generate Data for Detecting Palletjacks](#head-2)\n",
"3. [Deeper dive into SDG script](#head-3)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Set up Isaac Sim: Docker Container Installation <a class=\"anchor\" id=\"head-1\"></a>\n",
"\n",
"### This step can be skipped if the Isaac Sim Docker container has already been set up on your Cloud/Remote Instance\n",
"\n",
"* Follow the [instructions](https://docs.omniverse.nvidia.com/isaacsim/2022.2.1/install_container.html) for Isaac Sim Container Installation\n",
"* Ensure that `docker run` command on Step 7 works as expected and you are able to enter the container. \n",
"\n",
"We will use `./python.sh` in the container to run our SDG script. Please make sure you exit the container before running the next cells "
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## 2. Generate Data for Detecting Palletjacks <a class=\"anchor\" id=\"head-2\"></a>\n",
"\n",
"* We can find the Palletjack USDs in the Warehouse Sim Ready asset collection (`http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks`)\n",
"* First, we will mount our current local directory while running the docker container. This will ensure that we can run our scripts inside the Isaac Sim container. Data generated in the container will also be saved in this mounted directory."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/karma/Downloads/getting_started_v4.0.1/notebooks/tao_launcher_starter_kit/detectnet_v2/sdg_and_training/sdg-and-training/palletjack_sdg\n"
]
}
],
"source": [
"import os\n",
"\n",
"# This is the directory which will be mounted into the Isaac Sim container. Make sure <path_to_repo_cloned> is updated correctly\n",
"# os.environ[\"MOUNT_DIR\"]=os.path.join(<path_where_repo_cloned>, \"palletjack_sdg\")\n",
"os.environ[\"LOCAL_PROJECT_DIR\"]=os.path.dirname(os.getcwd())\n",
"os.environ[\"MOUNT_DIR\"] = os.path.join(os.getenv(\"LOCAL_PROJECT_DIR\"), \"palletjack_sdg\")\n",
"print(os.getenv(\"MOUNT_DIR\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"# Make sure the MOUNT_DIR location is correct, it shold have the scripts needed for SDG there\n",
"\n",
"!docker run --name isaac-sim --entrypoint bash -it --gpus all -e \"ACCEPT_EULA=Y\" --rm --network=host \\\n",
" -v ~/docker/isaac-sim/cache/kit:/isaac-sim/kit/cache/Kit:rw \\\n",
" -v ~/docker/isaac-sim/cache/ov:/root/.cache/ov:rw \\\n",
" -v ~/docker/isaac-sim/cache/pip:/root/.cache/pip:rw \\\n",
" -v ~/docker/isaac-sim/cache/glcache:/root/.cache/nvidia/GLCache:rw \\\n",
" -v ~/docker/isaac-sim/cache/computecache:/root/.nv/ComputeCache:rw \\\n",
" -v ~/docker/isaac-sim/logs:/root/.nvidia-omniverse/logs:rw \\\n",
" -v ~/docker/isaac-sim/data:/root/.local/share/ov/data:rw \\\n",
" -v ~/docker/isaac-sim/documents:/root/Documents:rw \\\n",
" -v $MOUNT_DIR:/isaac-sim/palletjack_sdg \\\n",
" nvcr.io/nvidia/isaac-sim:2022.2.1 \\\n",
" ./palletjack_sdg/palletjack_datagen.sh\n",
" \n",
"# Make sure $MOUNT_DIR is set correctly from the cell above"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"The data generation will begin in `headless` mode. We will be generating 5k images and using a 90:10 split for training and validation. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Once the data generation is complete, list the folders in the data directory\n",
"\n",
"!ls -rlt $MOUNT_DIR/palletjack_data\n",
"\n",
"# There hould be 3 folders -> 1. distractors_warehouse 2. distractors_additional 3. no_distractors "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Deeper Dive into SDG Script <a class=\"anchor\" id=\"head-3\"></a>\n",
"\n",
"* The `standalone_palletjack_sdg.py` is the Python script which runs and generates data in headless mode inside the container.\n",
"* The overall flow of the script is similar to the `standalone_examples/replicator/offline_generation.py` file provided as a starting point with Isaac Sim\n",
"\n",
"\n",
"* We will be carrying out specific randomizations targeted to our use case. Some of them are:\n",
" * Camera Pose Randomization -> Should be similar to a robot perspective in the scene\n",
" * Palletjack Color Randomization -> To ensure model is robust to variations in Palletjack colors\n",
" * Distractors Pose Randomization -> To enable the model to *focus* on the right object (Our object of interest: Palletjack)\n",
" * Lighting Randomization-> Model robust to lights and reflections/shadows in the scene\n",
" * Floor and Wall Texture Randomization -> Model more robust to changes in background textures and features <br> <br>\n",
" \n",
" \n",
"* We are only interested in the `palletjack` object class, all other semantics are removed from the stage with the `update_semantics()` function\n",
"\n",
"* You can use a model of your own choice to train with this data (Pytorch/Tensorflow or other frameworks)\n",
"\n",
"* The data is written in the KITTI Format, this allows seamless integration with TAO to train a model. Refer to `training/cloud_train.ipynb` notebook (Part 2) for training with TAO\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"vscode": {
"interpreter": {
"hash": "f23a2831654361cfd8b219e05b5055fdda3e37fe5c0b020e6226f740844c300a"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/cloud/training/cloud_train.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2: Synthetic Data Generation and Training Workflow with Warehouse Sim Ready Assets\n",
"\n",
"This notebook is the second part of the SDG and Training Workflow. Here, we will be focusing on training an Object Detection Network with TAO toolkit\n",
"\n",
"A high level overview of the steps:\n",
"* Pulling TAO Docker Container\n",
"* Training Detectnet_v2 model with Data generated in Part 1\n",
"* Visualizing Model Performance on Sample Real World Data\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of Contents\n",
"\n",
"This notebook shows an example usecase of Object Detection using DetectNet_v2 in the Train Adapt Optimize (TAO) Toolkit. We will train the model with Synthetic Data generated from Omniverse Replicator.\n",
"\n",
"1. [Set up TAO via Docker container](#head-1)\n",
"2. [Download Pretrained model](#head-2)\n",
"3. [Convert Dataset to TFRecords for TAO](#head-3)\n",
"4. [Provide training specification](#head-4)\n",
"5. [Run TAO training](#head-5)\n",
"6. [Evaluate trained model](#head-6)\n",
"7. [Visualize Model Predictions on Real World Data](#head-7)\n",
"8. [Next Steps](#head-8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Set up TAO via Docker Container <a class=\"anchor\" id=\"head-1\"></a>\n",
"\n",
"* We will follow the [instructions](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#running-tao-toolkit) for using TAO toolkit. Make sure that the pre-requisite steps are completed (installing `docker`, `nvidia container toolkit` and `docker login nvcr.io`)\n",
"\n",
"* The docker container being used for training will be pulled in the cells below, make sure you have completed the pre-requisite steps and `docker login nvcr.io` to allow pulling of the container from NGC\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"%env DOCKER_REGISTRY=nvcr.io\n",
"%env DOCKER_NAME=nvidia/tao/tao-toolkit\n",
"%env DOCKER_TAG=4.0.0-tf1.15.5 ## for TensorFlow docker\n",
"\n",
"%env DOCKER_CONTAINER=nvcr.io/nvidia/tao/tao-toolkit:4.0.0-tf1.15.5\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Download Pretrained Model <a class=\"anchor\" id=\"head-2\"></a>\n",
"\n",
"* We will use the `detectnet_v2` Object Detection model with a `resnet18` backbone\n",
"* Make sure the `LOCAL_PROJECT_DIR` environment variable has the path of this cloned repository in the cell below\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# os.environ[\"LOCAL_PROJECT_DIR\"] = \"<LOCAL_PATH_OF_CLONED_REPO>\"\n",
"os.environ[\"LOCAL_PROJECT_DIR\"] = os.path.dirname(os.path.dirname(os.getcwd())) # This is the location of the root of the cloned repo\n",
"print(os.environ[\"LOCAL_PROJECT_DIR\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!wget --quiet --show-progress --progress=bar:force:noscroll --auth-no-challenge --no-check-certificate \\\n",
" https://api.ngc.nvidia.com/v2/models/nvidia/tao/pretrained_detectnet_v2/versions/resnet18/files/resnet18.hdf5 \\\n",
" -P $LOCAL_PROJECT_DIR/cloud/training/tao/pretrained_model/"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## 3. Convert Dataset to TFRecords for TAO <a class=\"anchor\" id=\"head-3\"></a>\n",
"\n",
"* The `Detectnet_v2` model in TAO expects data in the form of TFRecords for training. \n",
"* We can convert the KITTI Format Dataset generated from Part 1 with the `detectnet_v2 dataset_convert` tool provided with TAO toolkit\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for palletjack warehouse distractors dataset\")\n",
"\n",
"!mkdir -p $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/distractors_warehouse && rm -rf $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/distractors_warehouse/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/cloud/training/tao/specs/tfrecords/distractors_warehouse.txt \\\n",
" -o /workspace/tao-experiments/cloud/training/tao/tfrecords/distractors_warehouse/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for palletjack with additional distractors\")\n",
"\n",
"!mkdir -p $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/distractors_additional && rm -rf $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/distractors_additional/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/cloud/training/tao/specs/tfrecords/distractors_additional.txt \\\n",
" -o /workspace/tao-experiments/cloud/training/tao/tfrecords/distractors_additional/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for kitti trainval dataset\")\n",
"# !mkdir -p $LOCAL_DATA_DIR/tfrecords/july/distractors_palletjack_warehouse && rm -rf $LOCAL_DATA_DIR/tfrecords/july/distractors_palletjack_warehouse/*\n",
"!mkdir -p $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/no_distractors && rm -rf $LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords/no_distractors/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/cloud/training/tao/specs/tfrecords/no_distractors.txt \\\n",
" -o /workspace/tao-experiments/cloud/training/tao/tfrecords/no_distractors/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Provide Training Specification File <a class=\"anchor\" id=\"head-4\"></a>\n",
"\n",
"* The spec file for training with TAO is provided under `$LOCAL_PROJECT_DIR/specs/training/resnet18_distractors.txt`\n",
"* The tfrecords and the synthetic data generated in the previous steps are provided under the `dataset_config` parameter of the file\n",
"* Other parameters like `augmentation_config`, `model_config`, `postprocessing_config` can be adjusted. Refer to [this](https://docs.nvidia.com/tao/tao-toolkit/text/object_detection/detectnet_v2.html) for a detailed guideline on adjusting the parameters in the spec file\n",
"* For training our model to detect `palletjacks` this `spec` file provided can be used directly\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!cat $LOCAL_PROJECT_DIR/cloud/training/tao/specs/training/resnet18_distractors.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hyperparameters can be set in the `spec` file. Adjust batch size parameter depending on the VRAM of your GPU \n",
"\n",
"* You can increase the number of epochs, the number of false positives in real world images keeps decreasing (mAP does not change much after ~250 epochs and usually results in the best trained model for the given dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Run TAO Training <a class=\"anchor\" id=\"head-5\"></a>\n",
"\n",
"* The `$LOCAL_PROJECT_DIR` will be mounted to the TAO docker for training, this contains all the data, pretrained model and spec files (training and inference) needed\n",
"\n",
"#### Ensure that no `_warning.json` file exists in the `$LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords` sub-folders (`distractors_additional`, `ditractors_warehouse` and `no_distractors`)\n",
"* Delete the `_warning.json` files before beginning training\n",
"* TAO training won't begin if the structure of the `tfrecords` folder directories is not as expected "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Setting up env variables for cleaner command line commands.\n",
"%env KEY=tlt_encode\n",
"%env NUM_GPUS=1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* TAO Training can be stopped and resumed (`checkpoint_interval` parameter specified in the `spec` file)\n",
"* Tensorboard visualization can be used with TAO [instructions](https://docs.nvidia.com/tao/tao-toolkit/text/tensorboard_visualization.html#visualizing-using-tensorboard). \n",
"* The `$RESULTS_DIR` parameter is the folder where the `$LOCAL_PROJECT_DIR/cloud/training/tao/detectnet_v2/resnet18_palletjack` folder which is specified with the `-i` flag in the command below"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 train -e /workspace/tao-experiments/cloud/training/tao/specs/training/resnet18_distractors.txt \\\n",
" -r /workspace/tao-experiments/cloud/training/tao/detectnet_v2/resnet18_palletjack -k $KEY --gpus $NUM_GPUS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Evaluate Trained Model <a class=\"anchor\" id=\"head-6\"></a>\n",
"\n",
"* While generating the `tfrecords` part of the total data generated was kept as a validation set (14% of total data)\n",
"* We will run our model evaluation on this data to obtain metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 evaluate -e /workspace/tao-experiments/cloud/training/tao/specs/training/resnet18_distractors.txt \\\n",
" -m /workspace/tao-experiments/cloud/training/tao/detectnet_v2/resnet18_palletjack/weights/model.tlt \\\n",
" -k $KEY --gpus $NUM_GPUS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Visualize Model Performance on Real World Data <a class=\"anchor\" id=\"head-7\"></a>\n",
"\n",
"* Lets visualize the model predictions on a few sample real world images next\n",
"* We will use palletjack images in a warehouse from the `LOCO` dataset to understand if the model is capable of performing real world detections\n",
"* Additional images can be placed under the `loco_palletjacks` folder of this project. The input folder is specified with the `-i` flag in the command below "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 inference -e /workspace/tao-experiments/cloud/training/tao/specs/inference/new_inference_specs.txt \\\n",
" -o /workspace/tao-experiments/cloud/training/tao/detectnet_v2/resnet18_palletjack/test_loco \\\n",
" -i /workspace/tao-experiments/images/loco_palletjacks \\\n",
" -k $KEY"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/jpeg": 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SmRISxJ+1JgZHrW2ABmuesHdryPn5Qa6EjHPetmZjR1xmnL6ZoUY5p6gA+9QA4HnFS/wg1HjrinrjHPaqQEYHzk9BmmzLjPFSqPnzipLhOOB+NWhMymPvUJADZqeUbW6VXfOeKYE8JwSP51KCCelRwj5elSLjOetADFX/AEoeuKvxg81TiAN6DWiBt7UmFhuPanIMDnvTsndjFKq+vFIBR7UjDHOaftG3mlCDPNCQxoGOBThwOtIBtp3Q5xVAKDijI55zR1HA5pVQ5NJDEzindMelAXtS7SVpiYABulSKvy9OaaBjAqQD5hQIQDB5oIyelPIz1NIPX1oBAFxSqueacAcYpVUk4607DDp/Wjoc1dt9Mu7liI7eQ++OK0U8KXsp+dkiU+p5qlEXMYYGetGMnGK7G38J28QBmuGbHZRitOHTNOtcbIFY+rc1XITzHAxWU82BHC7E+imtS38L6hMQTGI17lzXXm4ij+4FUD0GKrXGtwQD55APxpqKFzGdB4SjHM8569FFaMGh6ZbZYQhm9WOayLnxbaR9Hyax7nxmS2IYyapJCuzuQ1rbfcjRT7CoZdSRDy6qPc15zN4iv7gn+EH3rPlmvLj78zH6HFILM9Cu/EdtED++XI9DWPceMUjz5al65RbGWVgAjMfpVuPRbgrl1VB/tHmmHKXZ/FN1Op2ArntWXJql9K5zIwU9q0k0mBAPNnz7IKmFraxkYhL47saLlJGEUmnI+ZmpyaXPJ0jIz68V0ILgbUjC/QYxR5Ejnk4ouBkxaMT/AKyVVAHOOalSxsoh8oeQ+9af2QY5PFPSFFHC0hlCOEBgYoACfUVbWCZuWbb7CrSqM8CpNoHBNFhXK6WYI5Y1Zjs4lHC5pysijr+VSx7mHyRsfenYVxViUdFAqQR/L0qVLecgZAFW4NGu7kAokjqfQcU7AZxCjgkUKQThQTXR2/hK4bDNGiH/AGmrRj8JjBElwB7IlF0FjjFSRjhEOPU1aispnxk/kK7q28PWFvj920hHdzmr8dtDCMRxIo68LijnCxw0OhzyjIilYfTFaMHhicjLKiHP8Rz/ACrrqKXOx2MKLw1CP9ZKT6bRirsejWUf/LHcf9o5rQopczCxFHbwxf6uNF/3VxUtFFSMiuIVngeJlBDDGDXBCHy5pARyDg16Ea4d+bqcf7bfzq4CZzurp+9Uj0rOHHtWrrAInX6VlgcmuiJLLFuBur0nw2m3Roz/AHmJ/WvN7cfOK9M8PDGjW+evP8zU1RRNWiiiuY0CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5mzk1GzkdP1qTk81Gx/GvBR2DWYnjNLubIxTc4GTSNz9KLAPF1NHwsjA/WpRqtyP4wwHqOtUyMk0h7AY4quVBc0E1ljw8YB9jUq6lGV5znNYze1IX2jvilyIDaa+jbOHBHpTGuVAyvP41inmkDFT1NPkA2GuMjqPxpn2ggc9Par3g7To9V1lYJxuTGcHpXYah4M02OcxsHTPQocVnOahoy+U4MTrjBoEqtj5hzXQXHghVcrBePntuGRUdp4QkW4IuZ1dR0VR1qPax3BQM6zspbmUbB8ufvdq6/R/Dy7/lAdgclz0FaNrpkcVuYfLCqB0xW3p8AjtkCjaAKxlXT0RdrEtnbJbnnBfHWsPxtHv0xGx0cfhW9FCftJLHPHFZPjKL/iQyOCflYEispK0kzKTPOWGR05HSkHlhCZIz9aUHgkn5TTkkUKRtDZ7Gu+IiKSCKS3Lh9uOxpkUKyRhiVqSdIpIT/CfaoYoUkiA3kbTWysyHuNlslbIIqs9ikeS3PFa6KoZQzZBFRXMP3gOnrS1uVc5wrHKTs3HBxV3S57nStRgvIV3mNg2M44qHS1JuLhDjKtWt5PAz1qnKyBI7T/AIS7VfETPb6fbMuFyR1Nc3rHhjW5I2uJIGJHJHeuj+G0YTWLg7esdemzRCaMqQDxVpJK5D0PlacMjsrBlYcEEYwaqls8MK9a+IPhHdGL60iUSKfnA4yK8omi2sVfgitYu473KzryWFJHLt4IzStwKQlWXk4NaATrIHGAacBk5PSqa7lI9/SrsLKV5PWkWmKAARgcU4LhuanwFjyORUDH5uKRaLSAOBnio5h5a5xmlibBGeSKfMcgYFKSuZzRly4Y80sRA7Uy4OzlhioYpAzg9qIoiDszUGHxubAHakZlBGOc1GgyAamEQYCqVje5n3kfmrkrWGy7Gx0FdVNGAvNYV5b/ADlhxWkWjCorlAjORUf3amYYqJgDz3rQ52IaaevWlxS4pghppMjHWnfhRtzQAntR0qTymY+lL5RoGQ5FL+FSrHz9Kl8vHagRXXFL34NTbBR5ftQBFt9qAOelSEUgBGKAGjHTNOA96XHtQI8jpiiwXDkDtSgeuPwpRBz1NCxH1IoAXb6CgLzyKUJz96jYT/EcU7BccFwKDkUqqMc0uAR9PWgQwn5c1F5gJ71b8tSABwTUTQjdz+dAFcnnNGARUpjFL5fyjvQMhCUxkx2q0qc4pHiwKLCKe31pNvpVryjimmMimMrYINOB4qRk54FJ5eaLgMBp6uBjNHl47VGeDiq5hWJGYMOaj4/Kgg5zR26UcwWHq59eKvW0hcDtis/bjpUkDlGFbUqjTJaNGQgcmmFQ65NNkcOnB6UQnjBruumZ2Kki7SRUa/eq7Om7gcVWjT58GuSpBqRaY9c44q7bENgd6qTYjAApLWQidcGqpys7CkWbwBW6fWqDjmtO9G7Ycdaz50wBV1o9QiV24pBzTiKbXBLc0QvBNGaTGKAKkZZtWxKM13XhjTEudSgkYZU+1cXplm91dJGgzk8+1ew6Lpy2C2iqPmxgms61TkiXGJrzRCO1kCDGBxT9LjAcNuJbFSXqlbeTHcUzSixKHZjgd68qDfMdDLGo6Lpd4jPdWcTt/exg/nXLXvgi3YFrC6eE44SQbh+dd+8KujIehpUWMQiN4wyj1ruiZ3PH7mx1jSpD5lu5iH/LRPmU02DXCMBwOOtevfZYW4Rse1YGseDtP1AkyWwjY/8ALWHg1Vg0OWtPErQODHcEA9ia3oPEtrdpi7iVlP8AEOtcrqHgK8gkK2NykwHISThvzrAmm1LRpvLvLd4z6OOPwNNomzPVV+zzKG0+9VMdUY1ZF3NGAHUkdzXkkXiDPVNp9VNammeMLqzbABmT0Y9KloSudiZ9+tStjCuuaj1WRUsvmYZ3DrTLHV4NYjMklsIiDjIrO1xEW9to1l3pjcc1k0aIntj/AKYla8kYJz6iswW9zBKJfKO3AIyOtbCEFVY+lJIbKMkAUfKSD3qBmlQ8jcPWtWVFYHNUpYcjgn8KBXKbTxsMSDFOzGyjaRVS7ieM5ALZqsUYfMmQfSmmOxdcEVXkAIPrUC3jpMI2BbNSvcRnIPyn3obFYglAMLelcvq6/vR9K6s7WiOD0rmtYj+cEDrWlPcmRQ07/XpnpmugOc9KwdPGJlJ9a3iR0B5raRmOUZoQZagDHelB29KkBwBzg1IuOaYOgzzipUAP+FMBFB8welW5ovlquuN4NaUiZjB7YrSJLOauc+YR2Bqt3IzV+8TbMfeqTLlvamBNGMI1PjGaSJflOelSonOc0FDYlxecDtWkoyOetUYhi8Bx2rRBpMBuwbsZxTguKXuDTgOcZqQFC8YppHNSbTmjbTuBHjJp23NOAx+FKmBye9MBVXjOKdjnFPCM/ABFWrfT7i4P7uJm+gp2C5SK96cF47VuQeF9Qmb/AFYjUdS5rSi8HqDme5x7KKpRbJckcgVxjNSLC5Pyox9ABmu8t/Dul2wy6GZvVzV5WtbcARRRpj0WqUBORwtroOo3QBjtX5PVuK0ovB95vBnlRFHYcmujl1qGDhpFGPes258WafDktLuPoKfKieYIPDdjFjzGeT1ycVoxW1hbf6u3jGOhIya5O98ZxOSLeFz7nisiXxHqE3CjaDVWQXZ6S99HGMZVR+VZ1zr1tCpJnT8DXnrXF5Ofnmb86YtjJK3RnP0zSCx2F34vgjHyHcfSsefxddvxFHj3qqmjzNgtGFHqxqwmkwhiHmH/AAEUx2M+XWtTuHIaUge1V2S5uD8zu2PeugjsbK3OFQyn1apyxAAjjVfoKAsc7HpUr8+U2T36Vci0VsDzJEQfnWoEmc8mnrbE9TQMorp1pHyXZ8dhwKmVIUXENsn1IzWhFZ84VCT9KuRaNPL0XaD0zQFzHzMTx8v0qRbN2Xc7EiuqtfCtw5BJJX0ArYh8JjIMhGB2PNArnAx2iliMZNWUsXb7sTEewr0aHw5aR9Rn1wMVfi0+2hHyRL+NGgXPNINDu5W+WIgfStW28IzyYaTK+3Su+VFX7oA+gp2KVwPNdV0RNOniRjkMM1z115i3RjiT5cda9R8SWAurDzlA3wnd9V71wYtS90wAyTwBTWo9ilbafPMwG5mJ/hUZNb1n4SvJQGMDAeshx+ldrpOlw6XarFGAZCPnfux/wrRpNgcla+DghzLMi89EXP61qQ+G7CLBdXkI/vH/AArZopXYytDYWkAHl28a46HbzVkAAYFFFIAooooAKKKKACiiigAooooAKKKKAA1w8n/H5L6b2/nXcGuKK/6TN/vt/OriI57Wj+/GOMCstfetbXABcgf7NZCk7sGuiLEy7aryDXpWgf8AIGt89wf5mvNLcndXpXh8k6JbE9SD/M1FXYSNWiiiucsKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPmATqcjJpBIG+XIFaLWMLfwiq8mnxn7pIrwOZHZZlXeCuO4pOPwNSnT2H3W5qGS1uUHAyKtNCsN5wT+VNIpMTJ96M49ajMp3HK0wHucA1GQSMflR5inPr6U7OdqgcntVbDSJLe1e4l2IBk1eTQ7oTbXClcZ3A1b0WzlW7V2GB6V0FxCVcbTjNc9Ws4uyNYxRb8E2iWmqqqrztPzV1mu5SRSOtZfh2ARTq+Oa19fjY7HXHFclSbluNmKWaWUBFIPcmtOztYowzsctUEPLDircGzzG3dcdKzUknqIlcB4zgYqeyw1qDzxxUErHaSAOnSnadKHt8d8kVjzXd0IlQkXOc8dM1Q8VKG0C4yCTitEr5NwpI4IqrritLotyqYwUJp9UTI84tjGyMpUHFQ3QTblBjHtRaMyOWI6inG8hdjGyHPSvcp+9Gxi2ykGDcEVLZohcrgE9hQUCkjimwvDHIQ3HoaiGjKaLjwo3OGU+vaop9yptJBBHWnGYxnMb7l9DUcrtKB0x7Vo5IVmYWmnbqNypPU9q2jjg1i2RKaxcIwwDzitr+Ltis2WjqfAT7fEGzcQGQ8V6oteQ+DZWXxJbqQMNkV68Pu1tHYiZn6lZC8jKMqkds14/498FyQodRs4/nHMqIOD717XKuV461z3ip7m20Cae2VTMMDkZHPFUvIzvY+aN2SQQQfeo3iI59a7HV/BuprZS6oEVwWyyr6VyQbDEHp0wa6EzRO5CjjO1qtRADpyKgmiz8y8UyOco2DQxo1SwMeO4qnIx3YHSrMbKwyDTJsBgcZPakWhIZMNzVtZARn0rPzzzxVmOTjGKCiveXCkkvGDWako80kAYrTu0Vo/rWQVKsapIyaszZt5A44q6sbHkdKxLeYoRW3aTq5+Y0mi0x0lrIV4OfwrCvkMbHPSuxjQPGeaw9ZsPkLU4ky2OUlYZqPmnOhRyDTa2OViEYpvT607HvSqh7jpQJDUXcasLCQKfBF8wJ71ecIANp5ouMqbcAU09elTlN1Q7ecUxXDbg8CnYyaMHIFOPDUBcjKetAjx06VIwAIwKQgEUAM8vvigIBxingEA4NKDkc0AR7fanbcDkdacc9MUbuMEUEjDgGkyCaf8AK3Sk2imCGHkmk2k8Cnhc08KKBkYBApoJzyKsgd6DtzimBCfXNG4kc81IyfKaYRtFACdfag8cimkn0pc0CFUZPAqUpkdKYrANVkBSORTQEAjB7VG0eD0qds54NQSNjIxQwItuT6U4J2xk1GXNPRySKQyQQZGexqu8HzkAVpQEEU2aMLkpgn0q0hXMp0K/WpYot/GKlk5HKgVJZoXkwMdaLBcqyW7KOmKr8jqK6O+tCtuH9etZEUW9sY70WHcrB2FSJOR1rR/s4MoOMD1qtNYtG3HIrRSaJshqyBuc0x5I0OQOaiZSp4puQ3WqdVvcLBIxds9qfbcTL61GQR0qW1/165FKGskD2NO6BCRnGapXi4RcVp3Wwwoc8iqV+o+yh+hzXoVYrlZlF2ZlNQaXOeaQ9K8d7m6ACpLeIyygAZqMHNdn4N0A3s4uZlIiXkZqZSUVdlxVzd8KaAIohNLHtY+1djgJcQAf3x1prNHaQgDntxUEd2ZriLKbdsg79a82rUdR3RulY2LvLQyDHamaWMBMjnFSTOGjk9hUdlhVjz61lTfvFPY6BBl8HpUwjUZwKYo3OAKnaGRVyBmu+KMG9SBohnOPxFRsWUdeKf57CTYVIPvUhCvwRTsFzNe1immExJBxg+9Q3OkxXCFWEcinqsi5FabQDPynionjIPcUDTOA1X4d6dPKzwl7Rj/zz5XP0rmZvB+q6YCdqXMWeGjPP4ivX5YjImCc1VFuhO05pMdzgvDakLKroyMrYORVfxGzJf2+04ypOfxrrL+0MGoRRcbZOcisXxNpbywRXEYLPE23aB1BrKxdzZs9Rf7HH5vzjYODSC4srvIWbYT2JrPSRRaLG/yuFAri/wC05ra5ljchtrkcVaSIdz0BY7iFyAwdO3OaUzA5DDFcvY+InXADY+tbcGrwXIxKoB9RUuIJkzKHkznikeCJx06U/wAqJ8PC4pUVwxyvFKwzPls1B3Bckd6qXEAK421sFwTx+tRyxBlpWKTMW3XaroOlY+sJxXRbFikI7tWTq0X7st3rSnuTNmBYx/vVHvW2R0x1rMsYxuBPXNapGCPWtmZCjFOVaRVz1qRcYPFKwxEGKkUdwMUgHPtThzwKLCFxh1+tbbIDCB7VijLOMVvhMxr9K2giGc5qEJBJrLCkt+NdJf25YZHasMKQ2MU2hpjolAUqATT0XFOjHPTmpI0Z2woz+FTYZCABdjHpV9Bkc8Ug0u6bEqQvjpnFbNn4X1G5AKx4U924p2C5lgc09U5z1FdXB4Lbj7Rcqv8AujNaUHhnTLfBfdKw7k4o5Q5kcQsTMQACc9OKtw6Nf3HCWrkHuRiu8iis7Vf3UEYA77eaSbVYYV+eRVA96pQRLmcvb+D7xz+9dIl9zk1oQeELKJw81w7+w4FNvPFllDn96D9KxrnxsjZEETN7mq5UhXZ2EVlptqPkt06dW5qVr2GFRt2qo9MCvNp/E+oSn92AoPqaoyTX90f3lw49gcUxas9JuPEFrAMvcID9ax7rxraKMRyFz/siuIaykdgFWSVz6c1ow6PNhcoqDvuNMfKaM3jC6kBEKED1NZ02sarctnziq+gq2NNjT78y/RRmrKWtqgASJ5D7mlcLGEY7i4OZJXZj1yami0mRjkRN9SMV0USTAYjiRR64p/2aaTG+X8BRcdjGTSWXbuKKO/NTrY2yEFmLY9BWmLSNep3GrMVg8o/dwMR64oAykjhjP7u3BPYtzU6tORxhR7CtcaPcHA8vHvWlZ+Fp5sEq5H0wKYjl/KZhlmP51JHa7jhVZvoM13tv4QjXBcKD1+Y5rVh0G3j6nP8AujFGgXPO4dJuXI2wsB6nitODw3cy4yQo9hmu/jsLePpGPqeasBQBgAAewpXA4u38JHgsrH3JxWpB4Yt4x8yoD7DNdDijFFwsZEOh2ySZaMEVox2sMQwkSr+FTYpcUrgkNxilxS0Uh2ExS4oooGFFFFAFe9XdYzj1jb+VcPpsIOr2/tIp+td5cf8AHtL/ALh/lXFaWP8AiawcdXFUhHcClpBS1IwooooAKKKKACiiigAooooAKKKKACiiigAooooAQnArjUOWlc92JH512L/cb6GuMVso2PWqiBz2vnF0P92slSAa0tdfffDPZQKye9dERM0LXhq9M0EY0S1/3f6mvL7bBYZzXqmj/wDIHtP+uS1FTYk0KKKKwLCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDwdrNOcDFQNYkE4JxWmY9vGDVdy6ZI5+or5NSPTM9rRw3B4qGSGRf4c1pRSmVjuXBp7oWUhl/EVamyWjGaM91/Co2tg3WMGtYp82ME0ySLCgnitY1GS4mMlrb+eqlMHPNadzaxRvGRGoyMcCqk6hZVY9c1rzQkRRHr70Tm3Yq1h9lbyK4c8elavlPNKoK81HbQ7woBxnFdGLIQBGxzisaumrHclsIhBsPcda0NWUS24x6VVhG51AwOauagpW34Bx7VzSd0JmVbEK659KsIwaYHA96p2+WmA7ehq2i7JcHrng1EncZacjpjjvTNNiyZD0AbilkB2c/hSaUsivKc5XNTHQTLzAMyhhyKq6kjmxmRSAChzn6VZn3GRCKj1EpHZSmVlUbD1PtV3uQzxq5nuomXyEDgHkE8024J85XCEbhz7GrcgjyQDx60mT5ZAIJHrXr0p2Rm1ciZgpGRkEdaiONx5x6cU6RnbAwD9KcMKRuHOKvzALb95MFPQ+lXWttv3WI+tVrU5uQMCtB92COoNaRinuS2YT6Zcw37ToBIjdcHkVcQOSBtbI6gipGkliJxnHqauWMjsxZ14/vDtTlBdAUi54UPl+JLQnjLYr2UDIrwTSdYtofGdrafNuMnyk9DXvq9B9KcU7ahNkbAAVj+IftDaJOttGZJGGAuM5rcKg1FNuC/IAfaqWhnuedpY6mukNazxNC8p4J5ArgvGPgx4LxJNOUNuX51B6n2r3K7tpZ4CZTj2qsNFtJysjxBpFHyk9qOYtHy6zGN2jcYZThlPY1A8XOVr2rxr8M/wC0pTe6cEjuv4l7PXkN9pmp6RctDf2M0JU4JKkr+BrWMh3KsEjI2D0rQDCVcqcEVQMYcblPT0pVkKtwaq1y0yeRe+8GmRyFcgmopJGwfSog9FhqRadtyEkGs+VcMTVtXyQM0ktsWGVqkKRTUkHk8VahudhHNVJYmQ89Kj24+tFiNTp7XVQBhvyqzc3cU8JAPB7GuUV2jI7gUkt64BIFJIG9CLUVVZzjGKp5yKWSUyNuNRZJrVbGD3Jol3NirgjAXNV7UHcOa0AMjH6UxDIx04xSsOetOJ2r0qu0jA0XESsDnOaiOQOafExOMjpT3GTQBCPenEBu9P257c+tNKjIzxTEIaNp/Cg98UozjNO4xvSlBBNHJ5NBJ4pCY7Bx1puKUcg4pVyeaY0M25PSk24OAcVKaTAIwcUAR4bpTucUYNKDzhhQAKwpetJhM9KTAHQ9aYgYU0g+lOORSFsY55oAaffmkOKcaMAD2NIYIADU6njioV4OOtTrkCqQmRSZA4qu7EmrM3Aqo3UmhgMY80qHvSE5pynJqRl+2+ZeabMCknOaW34ANSyyAnBFaokqgqwxxzU1tHJHcKQOM1GYVJ3L0NShzEQVNAzopkM9kQR2yMCuTQiO6Kj+9XZadJ51nnPIFclqCeVqDc85zmqEdLBCj26ttzxVae1ViOAAa0NMw9kuSOlVb4+UR3FaxV0Q3qZt1o7GPegG7HSseSykViuw5rtrcCaAHviqUkYS4+Yd6Tpoakce8UiHBGD70sJCSAmu+bRrW+t/3kYB7MOtcxrXh6bTkNxETJCDyQORSUJR1RV0yBmMsYweKm1O2ePSd/b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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image \n",
"\n",
"results_dir = os.path.join(os.environ[\"LOCAL_PROJECT_DIR\"], \"cloud/training/tao/detectnet_v2/resnet18_palletjack/test_loco/images_annotated\")\n",
"# pil_img = Image(filename=os.path.join(os.getenv(\"LOCAL_PROJECT_DIR\"), 'detecnet_v2/july_resnet18_trials/new_pellet_distractors_10k/test_loco/images_annotated/1564562568.298206.jpg'))\n",
" \n",
"image_names = [\"1564562568.298206.jpg\", \"1564562628.517229.jpg\", \"1564562843.0618184.jpg\", \"593768,3659.jpg\", \"516447400,977.jpg\"] \n",
" \n",
"images = [Image(filename = os.path.join(results_dir, image_name)) for image_name in image_names]\n",
"\n",
"display(*images)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Next Steps <a class=\"anchor\" id=\"head-8\"></a>\n",
"\n",
"#### Generating Synthetic Data for your use case:\n",
"\n",
"* Make changes in the Domain Randomization under the Synthetic Data Generation script (`palletjack_sdg/standalone_palletjack_sdg.py`\n",
"* Add additional objects of interest in the scene (similar to how `palletjacks` are added, you can add `forklifts`, `ladders` etc.) to generate data\n",
"* Use [different](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#downloading-the-models) models for training with TAO (for object detection, you can use `YOLO`, `SSD`, `EfficientDet`) \n",
"* Replicator provides Semantic Segmentation, Instance Segmentation, Depth and various other ground truth annotations along with RGB. You can also write your own ground truth annotator (eg: Pose Estimation: Refer to [sample](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_replicator_offline_pose_estimation.html). These can be used for training a model of your own framework and choice\n",
"* Exploring the option of using `Synthetic + Real` data for training a network. Can be particularly useful for generating more data around particular corner cases\n",
"\n",
"\n",
"#### Deploying Trained Models:\n",
"\n",
"* After obtaining satisfactory results with the training process, you can further optimize your model for deployment with the help of Pruning and QAT.\n",
"* TAO models can directly be deployed on Jetson with Isaac ROS or Deepstream which ensures your end-to-end pipeline being optimized (data acquisition -> model inference -> results)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"vscode": {
"interpreter": {
"hash": "f23a2831654361cfd8b219e05b5055fdda3e37fe5c0b020e6226f740844c300a"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/cloud/training/tao/specs/training/resnet18_distractors.txt | random_seed: 42
dataset_config {
data_sources {
tfrecords_path: "/workspace/tao-experiments/cloud/training/tao/tfrecords/distractors_warehouse/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
}
data_sources {
tfrecords_path: "/workspace/tao-experiments/cloud/training/tao/tfrecords/distractors_additional/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
}
data_sources {
tfrecords_path: "/workspace/tao-experiments/cloud/training/tao/tfrecords/no_distractors/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
}
image_extension: "png"
target_class_mapping {
key: "palletjack"
value: "palletjack"
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 960
output_image_height: 544
min_bbox_width: 20.0
min_bbox_height: 20.0
output_image_channel: 3
}
spatial_augmentation {
hflip_probability: 0.5
zoom_min: 0.5
zoom_max: 1.5
translate_max_x: 8.0
translate_max_y: 8.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.20000000298
contrast_scale_max: 0.10000000149
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: "palletjack"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00499999988824
dbscan_eps: 0.15000000596
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 20
}
}
}
}
model_config {
pretrained_model_file: "/workspace/tao-experiments/cloud/training/tao/pretrained_model/resnet18.hdf5"
num_layers: 18
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
arch: "resnet"
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 5
minimum_detection_ground_truth_overlap {
key: "palletjack"
value: 0.5
}
evaluation_box_config {
key: "palletjack"
value {
minimum_height: 25
maximum_height: 9999
minimum_width: 25
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: "palletjack"
class_weight: 1.0
coverage_foreground_weight: 0.0500000007451
objectives {
name: "cov"
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: "bbox"
initial_weight: 10.0
weight_target: 1.0
}
}
enable_autoweighting: true
max_objective_weight: 0.999899983406
min_objective_weight: 9.99999974738e-05
}
training_config {
batch_size_per_gpu: 32
num_epochs: 100
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 5e-06
max_learning_rate: 5e-04
soft_start: 0.10000000149
annealing: 0.699999988079
}
}
regularizer {
type: L1
weight: 3.00000002618e-09
}
optimizer {
adam {
epsilon: 9.99999993923e-09
beta1: 0.899999976158
beta2: 0.999000012875
}
}
cost_scaling {
initial_exponent: 20.0
increment: 0.005
decrement: 1.0
}
visualizer{
enabled: true
num_images: 10
scalar_logging_frequency: 10
infrequent_logging_frequency: 5
target_class_config {
key: "palletjack"
value: {
coverage_threshold: 0.005
}
}
}
checkpoint_interval: 10
}
bbox_rasterizer_config {
target_class_config {
key: "palletjack"
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 1.0
cov_radius_y: 1.0
bbox_min_radius: 1.0
}
}
deadzone_radius: 0.400000154972
}
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/cloud/training/tao/specs/inference/new_inference_specs.txt | inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "palletjack"
# Inference dimensions.
image_width: 960
image_height: 544
# Must match what the model was trained for.
image_channels: 3
batch_size: 32
gpu_index: 0
# model handler config
tlt_config{
model: "/workspace/tao-experiments/cloud/training/tao/detectnet_v2/resnet18_palletjack/weights/model.tlt"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: false
overlay_linewidth: 2
classwise_bbox_handler_config{
key:"palletjack"
value: {
confidence_model: "aggregate_cov"
output_map: "palletjack"
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
coverage_threshold: 0.005
clustering_algorithm: DBSCAN
coverage_threshold: 0.005
dbscan_eps: 0.3
dbscan_min_samples: 0.05
dbscan_confidence_threshold: 0.9
minimum_bounding_box_height: 20
}
}
}
classwise_bbox_handler_config{
key:"default"
value: {
confidence_model: "aggregate_cov"
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.005
dbscan_eps: 0.3
dbscan_min_samples: 0.05
minimum_bounding_box_height: 20
}
}
}
}
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/cloud/training/tao/specs/tfrecords/distractors_warehouse.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/cloud/training/tao/specs/tfrecords/no_distractors.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/cloud/training/tao/specs/tfrecords/distractors_additional.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/local_train.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Synthetic Data Generation and Training Workflow with Warehouse Sim Ready Assets\n",
"\n",
"This notebook is the second part of the SDG and Training Workflow. Here, we will be focusing on training an Object Detection Network with TAO toolkit\n",
"\n",
"A high level overview of the steps:\n",
"* Pulling TAO Docker Container\n",
"* Training Detectnet_v2 model with generated Synthetic Data \n",
"* Visualizing Model Performance on Sample Real World Data\n",
"\n",
"`This notebook is very similar to the cloud training notebook, only mounted directories and paths for the docker containers are changed. The data, model and training, evaluation and inference steps are identical` "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### If Isaac Sim is installed locally, ensure that data generation is complete. Run the `generate_data.sh` script in this folder. Ensure the path to Isaac Sim is set correctly in the script (`ISAAC_SIM_PATH` corresponds to where Isaac Sim is installed locally on your workstation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table of Contents\n",
"\n",
"This notebook shows an example usecase of Object Detection using DetectNet_v2 in the Train Adapt Optimize (TAO) Toolkit. We will train the model with Synthetic Data generated previously.\n",
"\n",
"1. [Set up TAO via Docker container](#head-1)\n",
"2. [Download Pretrained model](#head-2)\n",
"3. [Convert Dataset to TFRecords for TAO](#head-3)\n",
"4. [Provide training specification](#head-4)\n",
"5. [Run TAO training](#head-5)\n",
"6. [Evaluate trained model](#head-6)\n",
"7. [Visualize Model Predictions on Real World Data](#head-7)\n",
"8. [Next Steps](#head-8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Set up TAO via Docker Container <a class=\"anchor\" id=\"head-1\"></a>\n",
"\n",
"* We will follow the pre-requisites section of [instructions](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#running-tao-toolkit) for using TAO toolkit. Make sure that the pre-requisite steps are completed (installing `docker`, `nvidia container toolkit` and `docker login nvcr.io`)\n",
"\n",
"* The docker container being used for training will be pulled in the cells below, make sure you have completed the pre-requisite steps and `docker login nvcr.io` to allow pulling of the container from NGC\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: DOCKER_REGISTRY=nvcr.io\n",
"env: DOCKER_NAME=nvidia/tao/tao-toolkit\n",
"env: DOCKER_TAG=4.0.0-tf1.15.5 ## for TensorFlow docker\n",
"env: DOCKER_CONTAINER=nvcr.io/nvidia/tao/tao-toolkit:4.0.0-tf1.15.5\n"
]
}
],
"source": [
"import os\n",
"%env DOCKER_REGISTRY=nvcr.io\n",
"%env DOCKER_NAME=nvidia/tao/tao-toolkit\n",
"%env DOCKER_TAG=4.0.0-tf1.15.5 ## for TensorFlow docker\n",
"\n",
"%env DOCKER_CONTAINER=nvcr.io/nvidia/tao/tao-toolkit:4.0.0-tf1.15.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Download Pretrained Model <a class=\"anchor\" id=\"head-2\"></a>\n",
"\n",
"* We will use the `detectnet_v2` Object Detection model with a `resnet18` backbone\n",
"* Make sure the `LOCAL_PROJECT_DIR` environment variable has the path of this cloned repository in the cell below\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# os.environ[\"LOCAL_PROJECT_DIR\"] = \"<LOCAL_PATH_OF_CLONED_REPO>\"\n",
"os.environ[\"LOCAL_PROJECT_DIR\"] = os.path.dirname(os.getcwd()) # This is the location of the root of the cloned repo\n",
"print(os.environ[\"LOCAL_PROJECT_DIR\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!wget --quiet --show-progress --progress=bar:force:noscroll --auth-no-challenge --no-check-certificate \\\n",
" https://api.ngc.nvidia.com/v2/models/nvidia/tao/pretrained_detectnet_v2/versions/resnet18/files/resnet18.hdf5 \\\n",
" -P $LOCAL_PROJECT_DIR/local/training/tao/pretrained_model/"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"## 3. Convert Dataset to TFRecords for TAO <a class=\"anchor\" id=\"head-3\"></a>\n",
"\n",
"* The `Detectnet_v2` model in TAO expects data in the form of TFRecords for training. \n",
"* We can convert the KITTI Format Dataset generated from Part 1 with the `detectnet_v2 dataset_convert` tool provided with TAO toolkit\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for palletjack warehouse distractors dataset\")\n",
"\n",
"!mkdir -p $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/distractors_warehouse && rm -rf $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/distractors_warehouse/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/local/training/tao/specs/tfrecords/distractors_warehouse.txt \\\n",
" -o /workspace/tao-experiments/local/training/tao/tfrecords/distractors_warehouse/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for palletjack with additional distractors\")\n",
"\n",
"!mkdir -p $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/distractors_additional && rm -rf $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/distractors_additional/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/local/training/tao/specs/tfrecords/distractors_additional.txt \\\n",
" -o /workspace/tao-experiments/local/training/tao/tfrecords/distractors_additional/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"print(\"Converting Tfrecords for kitti trainval dataset\")\n",
"# !mkdir -p $LOCAL_DATA_DIR/tfrecords/july/distractors_palletjack_warehouse && rm -rf $LOCAL_DATA_DIR/tfrecords/july/distractors_palletjack_warehouse/*\n",
"!mkdir -p $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/no_distractors && rm -rf $LOCAL_PROJECT_DIR/local/training/tao/tfrecords/no_distractors/*\n",
"\n",
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 dataset_convert \\\n",
" -d /workspace/tao-experiments/local/training/tao/specs/tfrecords/no_distractors.txt \\\n",
" -o /workspace/tao-experiments/local/training/tao/tfrecords/no_distractors/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Provide Training Specification File <a class=\"anchor\" id=\"head-4\"></a>\n",
"\n",
"* The spec file for training with TAO is provided under `$LOCAL_PROJECT_DIR/specs/training/resnet18_distractors.txt`\n",
"* The tfrecords and the synthetic data generated in the previous steps are provided under the `dataset_config` parameter of the file\n",
"* Other parameters like `augmentation_config`, `model_config`, `postprocessing_config` can be adjusted. Refer to [this](https://docs.nvidia.com/tao/tao-toolkit/text/object_detection/detectnet_v2.html) for a detailed guideline on adjusting the parameters in the spec file\n",
"* For training our model to detect `palletjacks` this `spec` file provided can be used directly\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!cat $LOCAL_PROJECT_DIR/local/training/tao/specs/training/resnet18_distractors.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hyperparameters can be set in the `spec` file. Adjust batch size parameter depending on the VRAM of your GPU \n",
"\n",
"* You can increase the number of epochs, the number of false positives in real world images keeps decreasing (mAP does not change much after ~250 epochs and usually results in the best trained model for the given dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Run TAO Training <a class=\"anchor\" id=\"head-5\"></a>\n",
"\n",
"* The `$LOCAL_PROJECT_DIR` will be mounted to the TAO docker for training, this contains all the data, pretrained model and spec files (training and inference) needed\n",
"\n",
"#### Ensure that no `_warning.json` file exists in the `$LOCAL_PROJECT_DIR/cloud/training/tao/tfrecords` sub-folders (`distractors_additional`, `ditractors_warehouse` and `no_distractors`)\n",
"* Delete the `_warning.json` files before beginning training\n",
"* TAO training won't begin if the structure of the `tfrecords` folder directories is not as expected "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Setting up env variables for cleaner command line commands.\n",
"%env KEY=tlt_encode\n",
"%env NUM_GPUS=1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* TAO Training can be stopped and resumed (`checkpoint_interval` parameter specified in the `spec` file)\n",
"* Tensorboard visualization can be used with TAO [instructions](https://docs.nvidia.com/tao/tao-toolkit/text/tensorboard_visualization.html#visualizing-using-tensorboard). \n",
"* The `$RESULTS_DIR` parameter is the folder where the `$LOCAL_PROJECT_DIR/local/training/tao/detectnet_v2/resnet18_palletjack` folder which is specified with the `-i` flag in the command below"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 train -e /workspace/tao-experiments/local/training/tao/specs/training/resnet18_distractors.txt \\\n",
" -r /workspace/tao-experiments/local/training/tao/detectnet_v2/resnet18_palletjack -k $KEY --gpus $NUM_GPUS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Evaluate Trained Model <a class=\"anchor\" id=\"head-6\"></a>\n",
"\n",
"* While generating the `tfrecords` part of the total data generated was kept as a validation set (14% of total data)\n",
"* We will run our model evaluation on this data to obtain metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 evaluate -e /workspace/tao-experiments/local/training/tao/specs/training/resnet18_distractors.txt \\\n",
" -m /workspace/tao-experiments/local/training/tao/detectnet_v2/resnet18_palletjack/weights/model.tlt \\\n",
" -k $KEY --gpus $NUM_GPUS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Visualize Model Performance on Real World Data <a class=\"anchor\" id=\"head-7\"></a>\n",
"\n",
"* Lets visualize the model predictions on a few sample real world images next\n",
"* We will use palletjack images in a warehouse from the `LOCO` dataset to understand if the model is capable of performing real world detections\n",
"* Additional images can be placed under the `loco_palletjacks` folder of this project. The input folder is specified with the `-i` flag in the command below "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"!docker run -it --rm --gpus all -v $LOCAL_PROJECT_DIR:/workspace/tao-experiments $DOCKER_CONTAINER \\\n",
" detectnet_v2 inference -e /workspace/tao-experiments/local/training/tao/specs/inference/new_inference_specs.txt \\\n",
" -o /workspace/tao-experiments/local/training/tao/detectnet_v2/resnet18_palletjack/5k_model_synthetic \\\n",
" -i /workspace/tao-experiments/images/sample_synthetic \\\n",
" -k $KEY"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/jpeg": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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SmRISxJ+1JgZHrW2ABmuesHdryPn5Qa6EjHPetmZjR1xmnL6ZoUY5p6gA+9QA4HnFS/wg1HjrinrjHPaqQEYHzk9BmmzLjPFSqPnzipLhOOB+NWhMymPvUJADZqeUbW6VXfOeKYE8JwSP51KCCelRwj5elSLjOetADFX/AEoeuKvxg81TiAN6DWiBt7UmFhuPanIMDnvTsndjFKq+vFIBR7UjDHOaftG3mlCDPNCQxoGOBThwOtIBtp3Q5xVAKDijI55zR1HA5pVQ5NJDEzindMelAXtS7SVpiYABulSKvy9OaaBjAqQD5hQIQDB5oIyelPIz1NIPX1oBAFxSqueacAcYpVUk4607DDp/Wjoc1dt9Mu7liI7eQ++OK0U8KXsp+dkiU+p5qlEXMYYGetGMnGK7G38J28QBmuGbHZRitOHTNOtcbIFY+rc1XITzHAxWU82BHC7E+imtS38L6hMQTGI17lzXXm4ij+4FUD0GKrXGtwQD55APxpqKFzGdB4SjHM8569FFaMGh6ZbZYQhm9WOayLnxbaR9Hyax7nxmS2IYyapJCuzuQ1rbfcjRT7CoZdSRDy6qPc15zN4iv7gn+EH3rPlmvLj78zH6HFILM9Cu/EdtED++XI9DWPceMUjz5al65RbGWVgAjMfpVuPRbgrl1VB/tHmmHKXZ/FN1Op2ArntWXJql9K5zIwU9q0k0mBAPNnz7IKmFraxkYhL47saLlJGEUmnI+ZmpyaXPJ0jIz68V0ILgbUjC/QYxR5Ejnk4ouBkxaMT/AKyVVAHOOalSxsoh8oeQ+9af2QY5PFPSFFHC0hlCOEBgYoACfUVbWCZuWbb7CrSqM8CpNoHBNFhXK6WYI5Y1Zjs4lHC5pysijr+VSx7mHyRsfenYVxViUdFAqQR/L0qVLecgZAFW4NGu7kAokjqfQcU7AZxCjgkUKQThQTXR2/hK4bDNGiH/AGmrRj8JjBElwB7IlF0FjjFSRjhEOPU1aispnxk/kK7q28PWFvj920hHdzmr8dtDCMRxIo68LijnCxw0OhzyjIilYfTFaMHhicjLKiHP8Rz/ACrrqKXOx2MKLw1CP9ZKT6bRirsejWUf/LHcf9o5rQopczCxFHbwxf6uNF/3VxUtFFSMiuIVngeJlBDDGDXBCHy5pARyDg16Ea4d+bqcf7bfzq4CZzurp+9Uj0rOHHtWrrAInX6VlgcmuiJLLFuBur0nw2m3Roz/AHmJ/WvN7cfOK9M8PDGjW+evP8zU1RRNWiiiuY0CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD5mzk1GzkdP1qTk81Gx/GvBR2DWYnjNLubIxTc4GTSNz9KLAPF1NHwsjA/WpRqtyP4wwHqOtUyMk0h7AY4quVBc0E1ljw8YB9jUq6lGV5znNYze1IX2jvilyIDaa+jbOHBHpTGuVAyvP41inmkDFT1NPkA2GuMjqPxpn2ggc9Par3g7To9V1lYJxuTGcHpXYah4M02OcxsHTPQocVnOahoy+U4MTrjBoEqtj5hzXQXHghVcrBePntuGRUdp4QkW4IuZ1dR0VR1qPax3BQM6zspbmUbB8ufvdq6/R/Dy7/lAdgclz0FaNrpkcVuYfLCqB0xW3p8AjtkCjaAKxlXT0RdrEtnbJbnnBfHWsPxtHv0xGx0cfhW9FCftJLHPHFZPjKL/iQyOCflYEispK0kzKTPOWGR05HSkHlhCZIz9aUHgkn5TTkkUKRtDZ7Gu+IiKSCKS3Lh9uOxpkUKyRhiVqSdIpIT/CfaoYoUkiA3kbTWysyHuNlslbIIqs9ikeS3PFa6KoZQzZBFRXMP3gOnrS1uVc5wrHKTs3HBxV3S57nStRgvIV3mNg2M44qHS1JuLhDjKtWt5PAz1qnKyBI7T/AIS7VfETPb6fbMuFyR1Nc3rHhjW5I2uJIGJHJHeuj+G0YTWLg7esdemzRCaMqQDxVpJK5D0PlacMjsrBlYcEEYwaqls8MK9a+IPhHdGL60iUSKfnA4yK8omi2sVfgitYu473KzryWFJHLt4IzStwKQlWXk4NaATrIHGAacBk5PSqa7lI9/SrsLKV5PWkWmKAARgcU4LhuanwFjyORUDH5uKRaLSAOBnio5h5a5xmlibBGeSKfMcgYFKSuZzRly4Y80sRA7Uy4OzlhioYpAzg9qIoiDszUGHxubAHakZlBGOc1GgyAamEQYCqVje5n3kfmrkrWGy7Gx0FdVNGAvNYV5b/ADlhxWkWjCorlAjORUf3amYYqJgDz3rQ52IaaevWlxS4pghppMjHWnfhRtzQAntR0qTymY+lL5RoGQ5FL+FSrHz9Kl8vHagRXXFL34NTbBR5ftQBFt9qAOelSEUgBGKAGjHTNOA96XHtQI8jpiiwXDkDtSgeuPwpRBz1NCxH1IoAXb6CgLzyKUJz96jYT/EcU7BccFwKDkUqqMc0uAR9PWgQwn5c1F5gJ71b8tSABwTUTQjdz+dAFcnnNGARUpjFL5fyjvQMhCUxkx2q0qc4pHiwKLCKe31pNvpVryjimmMimMrYINOB4qRk54FJ5eaLgMBp6uBjNHl47VGeDiq5hWJGYMOaj4/Kgg5zR26UcwWHq59eKvW0hcDtis/bjpUkDlGFbUqjTJaNGQgcmmFQ65NNkcOnB6UQnjBruumZ2Kki7SRUa/eq7Om7gcVWjT58GuSpBqRaY9c44q7bENgd6qTYjAApLWQidcGqpys7CkWbwBW6fWqDjmtO9G7Ycdaz50wBV1o9QiV24pBzTiKbXBLc0QvBNGaTGKAKkZZtWxKM13XhjTEudSgkYZU+1cXplm91dJGgzk8+1ew6Lpy2C2iqPmxgms61TkiXGJrzRCO1kCDGBxT9LjAcNuJbFSXqlbeTHcUzSixKHZjgd68qDfMdDLGo6Lpd4jPdWcTt/exg/nXLXvgi3YFrC6eE44SQbh+dd+8KujIehpUWMQiN4wyj1ruiZ3PH7mx1jSpD5lu5iH/LRPmU02DXCMBwOOtevfZYW4Rse1YGseDtP1AkyWwjY/8ALWHg1Vg0OWtPErQODHcEA9ia3oPEtrdpi7iVlP8AEOtcrqHgK8gkK2NykwHISThvzrAmm1LRpvLvLd4z6OOPwNNomzPVV+zzKG0+9VMdUY1ZF3NGAHUkdzXkkXiDPVNp9VNammeMLqzbABmT0Y9KloSudiZ9+tStjCuuaj1WRUsvmYZ3DrTLHV4NYjMklsIiDjIrO1xEW9to1l3pjcc1k0aIntj/AKYla8kYJz6iswW9zBKJfKO3AIyOtbCEFVY+lJIbKMkAUfKSD3qBmlQ8jcPWtWVFYHNUpYcjgn8KBXKbTxsMSDFOzGyjaRVS7ieM5ALZqsUYfMmQfSmmOxdcEVXkAIPrUC3jpMI2BbNSvcRnIPyn3obFYglAMLelcvq6/vR9K6s7WiOD0rmtYj+cEDrWlPcmRQ07/XpnpmugOc9KwdPGJlJ9a3iR0B5raRmOUZoQZagDHelB29KkBwBzg1IuOaYOgzzipUAP+FMBFB8welW5ovlquuN4NaUiZjB7YrSJLOauc+YR2Bqt3IzV+8TbMfeqTLlvamBNGMI1PjGaSJflOelSonOc0FDYlxecDtWkoyOetUYhi8Bx2rRBpMBuwbsZxTguKXuDTgOcZqQFC8YppHNSbTmjbTuBHjJp23NOAx+FKmBye9MBVXjOKdjnFPCM/ABFWrfT7i4P7uJm+gp2C5SK96cF47VuQeF9Qmb/AFYjUdS5rSi8HqDme5x7KKpRbJckcgVxjNSLC5Pyox9ABmu8t/Dul2wy6GZvVzV5WtbcARRRpj0WqUBORwtroOo3QBjtX5PVuK0ovB95vBnlRFHYcmujl1qGDhpFGPes258WafDktLuPoKfKieYIPDdjFjzGeT1ycVoxW1hbf6u3jGOhIya5O98ZxOSLeFz7nisiXxHqE3CjaDVWQXZ6S99HGMZVR+VZ1zr1tCpJnT8DXnrXF5Ofnmb86YtjJK3RnP0zSCx2F34vgjHyHcfSsefxddvxFHj3qqmjzNgtGFHqxqwmkwhiHmH/AAEUx2M+XWtTuHIaUge1V2S5uD8zu2PeugjsbK3OFQyn1apyxAAjjVfoKAsc7HpUr8+U2T36Vci0VsDzJEQfnWoEmc8mnrbE9TQMorp1pHyXZ8dhwKmVIUXENsn1IzWhFZ84VCT9KuRaNPL0XaD0zQFzHzMTx8v0qRbN2Xc7EiuqtfCtw5BJJX0ArYh8JjIMhGB2PNArnAx2iliMZNWUsXb7sTEewr0aHw5aR9Rn1wMVfi0+2hHyRL+NGgXPNINDu5W+WIgfStW28IzyYaTK+3Su+VFX7oA+gp2KVwPNdV0RNOniRjkMM1z115i3RjiT5cda9R8SWAurDzlA3wnd9V71wYtS90wAyTwBTWo9ilbafPMwG5mJ/hUZNb1n4SvJQGMDAeshx+ldrpOlw6XarFGAZCPnfux/wrRpNgcla+DghzLMi89EXP61qQ+G7CLBdXkI/vH/AArZopXYytDYWkAHl28a46HbzVkAAYFFFIAooooAKKKKACiiigAooooAKKKKAA1w8n/H5L6b2/nXcGuKK/6TN/vt/OriI57Wj+/GOMCstfetbXABcgf7NZCk7sGuiLEy7aryDXpWgf8AIGt89wf5mvNLcndXpXh8k6JbE9SD/M1FXYSNWiiiucsKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPmATqcjJpBIG+XIFaLWMLfwiq8mnxn7pIrwOZHZZlXeCuO4pOPwNSnT2H3W5qGS1uUHAyKtNCsN5wT+VNIpMTJ96M49ajMp3HK0wHucA1GQSMflR5inPr6U7OdqgcntVbDSJLe1e4l2IBk1eTQ7oTbXClcZ3A1b0WzlW7V2GB6V0FxCVcbTjNc9Ws4uyNYxRb8E2iWmqqqrztPzV1mu5SRSOtZfh2ARTq+Oa19fjY7HXHFclSbluNmKWaWUBFIPcmtOztYowzsctUEPLDircGzzG3dcdKzUknqIlcB4zgYqeyw1qDzxxUErHaSAOnSnadKHt8d8kVjzXd0IlQkXOc8dM1Q8VKG0C4yCTitEr5NwpI4IqrritLotyqYwUJp9UTI84tjGyMpUHFQ3QTblBjHtRaMyOWI6inG8hdjGyHPSvcp+9Gxi2ykGDcEVLZohcrgE9hQUCkjimwvDHIQ3HoaiGjKaLjwo3OGU+vaop9yptJBBHWnGYxnMb7l9DUcrtKB0x7Vo5IVmYWmnbqNypPU9q2jjg1i2RKaxcIwwDzitr+Ltis2WjqfAT7fEGzcQGQ8V6oteQ+DZWXxJbqQMNkV68Pu1tHYiZn6lZC8jKMqkds14/498FyQodRs4/nHMqIOD717XKuV461z3ip7m20Cae2VTMMDkZHPFUvIzvY+aN2SQQQfeo3iI59a7HV/BuprZS6oEVwWyyr6VyQbDEHp0wa6EzRO5CjjO1qtRADpyKgmiz8y8UyOco2DQxo1SwMeO4qnIx3YHSrMbKwyDTJsBgcZPakWhIZMNzVtZARn0rPzzzxVmOTjGKCiveXCkkvGDWako80kAYrTu0Vo/rWQVKsapIyaszZt5A44q6sbHkdKxLeYoRW3aTq5+Y0mi0x0lrIV4OfwrCvkMbHPSuxjQPGeaw9ZsPkLU4ky2OUlYZqPmnOhRyDTa2OViEYpvT607HvSqh7jpQJDUXcasLCQKfBF8wJ71ecIANp5ouMqbcAU09elTlN1Q7ecUxXDbg8CnYyaMHIFOPDUBcjKetAjx06VIwAIwKQgEUAM8vvigIBxingEA4NKDkc0AR7fanbcDkdacc9MUbuMEUEjDgGkyCaf8AK3Sk2imCGHkmk2k8Cnhc08KKBkYBApoJzyKsgd6DtzimBCfXNG4kc81IyfKaYRtFACdfag8cimkn0pc0CFUZPAqUpkdKYrANVkBSORTQEAjB7VG0eD0qds54NQSNjIxQwItuT6U4J2xk1GXNPRySKQyQQZGexqu8HzkAVpQEEU2aMLkpgn0q0hXMp0K/WpYot/GKlk5HKgVJZoXkwMdaLBcqyW7KOmKr8jqK6O+tCtuH9etZEUW9sY70WHcrB2FSJOR1rR/s4MoOMD1qtNYtG3HIrRSaJshqyBuc0x5I0OQOaiZSp4puQ3WqdVvcLBIxds9qfbcTL61GQR0qW1/165FKGskD2NO6BCRnGapXi4RcVp3Wwwoc8iqV+o+yh+hzXoVYrlZlF2ZlNQaXOeaQ9K8d7m6ACpLeIyygAZqMHNdn4N0A3s4uZlIiXkZqZSUVdlxVzd8KaAIohNLHtY+1djgJcQAf3x1prNHaQgDntxUEd2ZriLKbdsg79a82rUdR3RulY2LvLQyDHamaWMBMjnFSTOGjk9hUdlhVjz61lTfvFPY6BBl8HpUwjUZwKYo3OAKnaGRVyBmu+KMG9SBohnOPxFRsWUdeKf57CTYVIPvUhCvwRTsFzNe1immExJBxg+9Q3OkxXCFWEcinqsi5FabQDPynionjIPcUDTOA1X4d6dPKzwl7Rj/zz5XP0rmZvB+q6YCdqXMWeGjPP4ivX5YjImCc1VFuhO05pMdzgvDakLKroyMrYORVfxGzJf2+04ypOfxrrL+0MGoRRcbZOcisXxNpbywRXEYLPE23aB1BrKxdzZs9Rf7HH5vzjYODSC4srvIWbYT2JrPSRRaLG/yuFAri/wC05ra5ljchtrkcVaSIdz0BY7iFyAwdO3OaUzA5DDFcvY+InXADY+tbcGrwXIxKoB9RUuIJkzKHkznikeCJx06U/wAqJ8PC4pUVwxyvFKwzPls1B3Bckd6qXEAK421sFwTx+tRyxBlpWKTMW3XaroOlY+sJxXRbFikI7tWTq0X7st3rSnuTNmBYx/vVHvW2R0x1rMsYxuBPXNapGCPWtmZCjFOVaRVz1qRcYPFKwxEGKkUdwMUgHPtThzwKLCFxh1+tbbIDCB7VijLOMVvhMxr9K2giGc5qEJBJrLCkt+NdJf25YZHasMKQ2MU2hpjolAUqATT0XFOjHPTmpI0Z2woz+FTYZCABdjHpV9Bkc8Ug0u6bEqQvjpnFbNn4X1G5AKx4U924p2C5lgc09U5z1FdXB4Lbj7Rcqv8AujNaUHhnTLfBfdKw7k4o5Q5kcQsTMQACc9OKtw6Nf3HCWrkHuRiu8iis7Vf3UEYA77eaSbVYYV+eRVA96pQRLmcvb+D7xz+9dIl9zk1oQeELKJw81w7+w4FNvPFllDn96D9KxrnxsjZEETN7mq5UhXZ2EVlptqPkt06dW5qVr2GFRt2qo9MCvNp/E+oSn92AoPqaoyTX90f3lw49gcUxas9JuPEFrAMvcID9ax7rxraKMRyFz/siuIaykdgFWSVz6c1ow6PNhcoqDvuNMfKaM3jC6kBEKED1NZ02sarctnziq+gq2NNjT78y/RRmrKWtqgASJ5D7mlcLGEY7i4OZJXZj1yami0mRjkRN9SMV0USTAYjiRR64p/2aaTG+X8BRcdjGTSWXbuKKO/NTrY2yEFmLY9BWmLSNep3GrMVg8o/dwMR64oAykjhjP7u3BPYtzU6tORxhR7CtcaPcHA8vHvWlZ+Fp5sEq5H0wKYjl/KZhlmP51JHa7jhVZvoM13tv4QjXBcKD1+Y5rVh0G3j6nP8AujFGgXPO4dJuXI2wsB6nitODw3cy4yQo9hmu/jsLePpGPqeasBQBgAAewpXA4u38JHgsrH3JxWpB4Yt4x8yoD7DNdDijFFwsZEOh2ySZaMEVox2sMQwkSr+FTYpcUrgkNxilxS0Uh2ExS4oooGFFFFAFe9XdYzj1jb+VcPpsIOr2/tIp+td5cf8AHtL/ALh/lXFaWP8AiawcdXFUhHcClpBS1IwooooAKKKKACiiigAooooAKKKKACiiigAooooAQnArjUOWlc92JH512L/cb6GuMVso2PWqiBz2vnF0P92slSAa0tdfffDPZQKye9dERM0LXhq9M0EY0S1/3f6mvL7bBYZzXqmj/wDIHtP+uS1FTYk0KKKKwLCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDwdrNOcDFQNYkE4JxWmY9vGDVdy6ZI5+or5NSPTM9rRw3B4qGSGRf4c1pRSmVjuXBp7oWUhl/EVamyWjGaM91/Co2tg3WMGtYp82ME0ySLCgnitY1GS4mMlrb+eqlMHPNadzaxRvGRGoyMcCqk6hZVY9c1rzQkRRHr70Tm3Yq1h9lbyK4c8elavlPNKoK81HbQ7woBxnFdGLIQBGxzisaumrHclsIhBsPcda0NWUS24x6VVhG51AwOauagpW34Bx7VzSd0JmVbEK659KsIwaYHA96p2+WmA7ehq2i7JcHrng1EncZacjpjjvTNNiyZD0AbilkB2c/hSaUsivKc5XNTHQTLzAMyhhyKq6kjmxmRSAChzn6VZn3GRCKj1EpHZSmVlUbD1PtV3uQzxq5nuomXyEDgHkE8024J85XCEbhz7GrcgjyQDx60mT5ZAIJHrXr0p2Rm1ciZgpGRkEdaiONx5x6cU6RnbAwD9KcMKRuHOKvzALb95MFPQ+lXWttv3WI+tVrU5uQMCtB92COoNaRinuS2YT6Zcw37ToBIjdcHkVcQOSBtbI6gipGkliJxnHqauWMjsxZ14/vDtTlBdAUi54UPl+JLQnjLYr2UDIrwTSdYtofGdrafNuMnyk9DXvq9B9KcU7ahNkbAAVj+IftDaJOttGZJGGAuM5rcKg1FNuC/IAfaqWhnuedpY6mukNazxNC8p4J5ArgvGPgx4LxJNOUNuX51B6n2r3K7tpZ4CZTj2qsNFtJysjxBpFHyk9qOYtHy6zGN2jcYZThlPY1A8XOVr2rxr8M/wC0pTe6cEjuv4l7PXkN9pmp6RctDf2M0JU4JKkr+BrWMh3KsEjI2D0rQDCVcqcEVQMYcblPT0pVkKtwaq1y0yeRe+8GmRyFcgmopJGwfSog9FhqRadtyEkGs+VcMTVtXyQM0ktsWGVqkKRTUkHk8VahudhHNVJYmQ89Kj24+tFiNTp7XVQBhvyqzc3cU8JAPB7GuUV2jI7gUkt64BIFJIG9CLUVVZzjGKp5yKWSUyNuNRZJrVbGD3Jol3NirgjAXNV7UHcOa0AMjH6UxDIx04xSsOetOJ2r0qu0jA0XESsDnOaiOQOafExOMjpT3GTQBCPenEBu9P257c+tNKjIzxTEIaNp/Cg98UozjNO4xvSlBBNHJ5NBJ4pCY7Bx1puKUcg4pVyeaY0M25PSk24OAcVKaTAIwcUAR4bpTucUYNKDzhhQAKwpetJhM9KTAHQ9aYgYU0g+lOORSFsY55oAaffmkOKcaMAD2NIYIADU6njioV4OOtTrkCqQmRSZA4qu7EmrM3Aqo3UmhgMY80qHvSE5pynJqRl+2+ZeabMCknOaW34ANSyyAnBFaokqgqwxxzU1tHJHcKQOM1GYVJ3L0NShzEQVNAzopkM9kQR2yMCuTQiO6Kj+9XZadJ51nnPIFclqCeVqDc85zmqEdLBCj26ttzxVae1ViOAAa0NMw9kuSOlVb4+UR3FaxV0Q3qZt1o7GPegG7HSseSykViuw5rtrcCaAHviqUkYS4+Yd6Tpoakce8UiHBGD70sJCSAmu+bRrW+t/3kYB7MOtcxrXh6bTkNxETJCDyQORSUJR1RV0yBmMsYweKm1O2ePSd/b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pTtYSNKiiisSwooooAKKKKACiiigChrTbNHuT/sYrhoPukj3rs/EbbdCuj7D+Yrirc/ufrVxA5LVH/wCJjMMk/NyaqKxBzU2pMBqE3P8AEarI53ADpWqLNaxI85M9Mjj15r2yP/VrxjjpXiemEG6iGP414/EV7aKiozIdRRRWRQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRSZoAWikzQWwOTQK4tFQPdQRD95NGv1YVXfWLBTj7UhPoOadmFy/RWNL4js4x8u9j9MVnTeLCgykCH0BanysTkdVSZrkv+EslZf9XGp9smo5PEt2+AhVfotPkYudHY5ozXEtrt45/1rD6cVA+p3Uv35W/On7NhzndtKi/eYD6monvLdOsyfnXCm5lYjL5+tMM7ZznmqVMnnO3bVLRDgzA/TmoW1y0BwCx/CuM85v71NMmeM5p+zQuZnWt4hhGdsZP1NV38Rtj5VQVzW/FJvJ7U+SIczN9/EFwehH4Cqz6vcv1lf86yQxJOaUMxNOyFdl172VycsT9TTPOc/wAVVhnPBoPX71MCdrhuu40CY+tQYHQmlyqjrTuIkaTPrSCSo/MTrR5y46UXAeWPbrRliajMw9O1J55PQUXCxqsoxjNReUqsGQ4b2NTSFQMsQDUJiWQh0bH0r8816HuaEp84A7X5pWlnCjIVuMcimgHb941k6l4itdLlVZSXJ6qnJFVCMm9CXYmubOOeQtPYW8oP96MGq1rpVjbXRkg0yGB2/ijQLUUXi3TZf+WzLz0dcVeg13TZn4vIQB3ZsVqlWjo9gui42nhlLA49iKdYW8W2RGVc9OlWPtdrKg8u5hbjs4osFWSWReCcetP0JbK0VssbFYkVTnnAq/CpijOTk1FHEyTng8VYIZs/MK15V7PmJ5jOeXk896nspNzOO+KqSIRIy4PWp7BSsxzxkVzRLKF7Epu95HzZqWABkPrT9QXbJketNtCSTyKG3awIfFGFn69RVgBkPr71Su9RtbA755AoAzjPJqC21qLVrC5e2LIY1PNZKLY7mtd6na6fCJLmYIPU1yGr+MWS6iSzK7XP3m9KfratdeHIWmYuwkHOK5a50Y3DwuZNqr2711UqUbXZNrnSeJlSeO0lkHzMnWuYNkpJyPpijX9Y1Bo4IY2CrCMZZetYg1/UFI3wxS49Plr0KMHy6Gb0Nd7LI+VyKj8iZeBLkelUYvEfmMBNatH7qc4qZtXttygOefatVBktlhpLtDtTDDtmnG5lUZkhB9ahGo2yNteQKeuWq0ssUqB1dWXswORQ4gmipc38TIUdChPTirGlyCGEhzgMc1VvVUpng81pWSrLaoWQfd60rBoTrJG0mdw213GhajbiySESyZXOeM1wwt4i4wMD2rtdGWNNPVYxgjqawrtxiaR1JjrHh6wvGlYSG4/iBB5rO1Hxcs25LWIRKehPJqt4g003Nu0sKBpk7etcWVk4DqUfuDWFOPOtSWjburx58+dcOwPOC3FS6De2w1WOHd96smJGaL549w9afplzDBqKvFC0kinhApJ/Cun2GmgHc6nO8CK0UZYZ5xWbdxxXEPm7SrHsahv9b1myiEo0j90T1kzXUaHYHVdPjn1CCOMvztSinSktRNnlms4EDxqCzdAFGSaxdL8DeK9aYG10aVYmPElx+7X9a+kLLQtLsDvt7OIP/eIya0txx1rti0kZtni2jfBG+Mizaxq6W5HISyyWH/Ajj+VdE/wo0OzV5RNd3DgZ3Tvuyfyr0Qvjiob0g2r/AEp813cTkzgH8OaasYDWMOcY3KuD+lVW8OWg/wBT5sR/2XNdX5YkiU+1QNAR24rujZoybOW/svUrfm11R/8AdlXNN87XIf8AWW1pdfTg/rXSvDjtimGEZp8iFc5C8ktBFu1Twnu9TFGHrEl0zwFft80c9hIfQMuP5ivSHjwpA9KzJbKCcbZreOQf7SA0nSQ+Y8+k+HukXXOl+IVJJ4WbDVVuPhlrVum+3azu/QK+0/rXoLeHtMb/AJco1z/cyv8AKkXw9bxndby3UB/2JDU+zHzHklz4W8TW7ESaLcADr5eG/lWY8M0RKzQyxt3V0INe6rY6hCAYdUlPtIoOa5zxNbXslrIJ1gmXHULg0KLQnI8hcEHFOiJA4p0q7ZD160kQz061ZKZMJiTtanEfL9aYUOc9Kev3cUhMjYcU0jjk1Ix56Ux8dKLAM7UAflQRjijFMBO9HPSgUuM5pAIGO6nHvRt70ZzQNiDFBA7UuBnOaaCaAuIRgZpePWl+ozQRxQCGk4HNGTijHc0hNAxwYqBS5yaY2DSYI70CJCRkUqrz1pmO9PXORTAt2qZbIGDVuZcRkVBaZzkVZkAK/Ma0toSzGmHNQFQSe1WrlRk44qt/nNZtFIYBj3pygdaMe9OCZpgNJ9qdB/rRzSOu0cHin20ZLU0I0ZBug4IzWc07xk4OB6VPds8SqM9aotljyOabYISSYvy3Wmx8uBQRx0p0K/vB60luB0tqoWzz2xWRMD5p9O1aq5SxPPbmsWRzvq3sIeo9aUiog59aC3NZlEgUg13PhNMQ1wyNhueldr4UuMDYwxnoa1pbkyNW6BW54POapar4et9TaOW4uRDtHYVp3q4nBGKfLoy6nHH5krhAc4XvVYmXLTbCkveOB1fS10+XbHJ5kWOGPeqUC7lPoelek+KfD/2rSImsowJIe3ciuDNoYXWMjk9favLpVOZHY0Z0kexsVa0yVoryNux4Ipt3GRjiorcsjgjjFdCYi9rzIL9UHdASfel0++ktXHPArNvZC10GPPGK1tNubFgEvI2wf4kPSqtcOax6f4Qvl1aBoixLp69q0pFCaiwYZYcVzngx9Msbppba53q4xtLYIro5cT6i0iMwB9K5cXT0UluNTTdjrLBCbQHjgcLirAtJnTfHLgn+E9KZor7rDnGRxVsXIjO0HpW8VeKMmzD1HREviRqGk2t2o/jKAt+fWuU1P4ceHrgF4Bd6fJnOFYsv5HmvTVuQfSkYxSDDBSPQ81XILmPC3+G2s5drSa2uEB+UFtrMP5Vkajo1/oq7tQ065i9WCbl/McV79eWMTxEwBUf1FVore4ZcxzhiOz81NiuY+do9VibiCKRj7jFSO1+373yDCn94V7te6BZ3Kk3elW8hPJeNcH8xzXPan4I0+7t2jtrya0ZugcbgP61LuNWOB0LWNVg1G2VdSuREXAKF8gj6V6lrGoTroN2yMGxEfvCuRt/h/f6dqME5uoLiCNgSU4P5V1OtMj6Jdrt5MRFIbsZGj6XZ6loVtPMjeYQcsD1qZtavNIuUsbV/PiC91+5T/C6yLoUCM2AAcL+NPuLMF5HJxnqayleOw1qX9E1WKdZzqF9HAV+6GHWorjX9OildTcoyjo4PWuBubB5ZnZ5nfnpniq729sqbZPKjA/2uTUqq7aj9mjtJ/F2nLGyxh3OOoUmud/4SDyZVlhE7kNkEClTxVHZWItIrmFYgNu2NASaqC4ikj83DBDyCRipnNjUDopPG2uXK8qqL26Cs9tW1e8f97eCJfY5NZSXf2q5WG28piTjLPgCk1NdR0+6WJHgfcu7dGN2PahSm1cfIjRP2gzbmvpZF9uKuW8yW0iyhSWHdjWLZTXaofNVmc/xFcAVOkl95xEiwyRE8hvShNp3bHbQ6azj1TWs3NjZy3CodvmZ+UewJODWVqGqXC3zWlwkwuYztMbJgrXe6T490Kz06G1eJrUxIF8uFNyj6Vw/inVrXXdffULe2uNmxY0wvJx3Ndc1Dl1ZlG/Noijf3T2Co1yjIH6Zru/BSxaroDPYXu50YiVWGNh9K4VIGvwBdQybE+75tejeBbbTLXRpokk8qaSQmTacfSsKKi5WLnorlAWPjbR5HktXF3bs2QqMG4+hqFvElp9oMfiHRnimbgsEKkV30UkqIBavFcRrxgPzSzfZLxDFfWq46YmQEfga7eU57nMWE1pIgfQ9eMJ/54TkEfka1o9T1q14vNL+0Jj/XWrZz/wAB61Rvfh9oOoEy2wktZD0e3k4/I5rLl8KeLdIbOh6950K9ILg/45FFmg0Z21lqUN9HuVZImHBSVdpFXQQehzWJoMmpz6cRrdskd0pwSoGGHrxUMV7YC8dIrma2wcYI+U07isdDRUELuy5Ekcq/3lNT1QgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAExxVG/0iz1CMrNEuezKMEVfqveXlvYWslzcyCOJBkk00xHjniK3k07VJrSVCnlnII6Mp6EVgtIvPBNbniTVW1vVZLxhtTASJD1VB0/E9fxrBkIBwBitObQpIQtnkU0sQvNMZ8Djn6VA8zdv1qLlEjvntUJKoMnFMMnBG6qUzybiByKaYiYzSSOdp4qZGbFV4gyJnj3p4bI5PFVzE2LayDHWpA4OCKpI4z1q7ZGN7qMS/dz0qbgS/OMbgQO1bmi2YnO9m5UjArQbTo50T92DnhRitHTtDayk3NuJPb0p8wrGhaW4kIwMGuw0VXWx2uOjnH0rn7e2liZTtPzHj3rr4kEcSoB0FJsLD6KKKgYUUUUAFFFFABRRRQBj+J22+HrtvQL/AOhCuJh4tyxPWuu8ZTiHw3Ov8UrKi/XOf6VwsdyBaY745q0NI5W9cm6l/wB41CjY6U26kBmcg85NRo/erRRv6Qw+2wn/AG1/mK9vrwrSG3XsA6/vF/nXuoqZmXUdRRRWZQUUUUAFFFFABRRRQAUUUUAFFGaQsBySB9aAFoqMzxr1kX86YbuEH7+fpzQFyeioRPuGQjY9TxTJL63gGZp4k9iwoEWaKxp/EunxA7JDIw/uisybxkqD93b59y1OwHWUVwc3jG+YkJ5SD2XNUZfEmoSKd10/P904osM9HeRIx87qv1OKqTavYQZ33KEjspzXmz6hK5+aRm9yc1ELlvXigLHoreItPXo0h5xwtSalqy2FtHKE8wyfdGcV5yty2772D2r0uK2hutPhWaMOCg6/SmrEswZPFNxnKQRKPQkmqkniLUW5EqpnsEFXdS8ONErS2hLr1KHqPpWB5MryBF6k4xVaCaZYbVtQkBDXcvPXBxVaS4mIw0rt9XJp0tu8MhR25HWoTGDzu/WncmzHFt3LHimb1Xo1IVj6FqjJiHAI5ouOxIzBhzTQU6AUGVAMYyKZ5wB+7T5hWJcYPApRuqL7SSeF6UnnueQKXMxWLGGOOaU7sY3VUM0nTpRvfP3jzT1Atexb8aPl45qrhjkFjShORk/rVXCxZLIO9J50YqE+WD94fnTWaIE/OKaYix569hTTcegNVzcwgYDdKjN1GTgAmgC15zMaXe9UxdYPCGj7VJ2TAoAuBn9aQMx6nmqnnTMOwpnmSjq4GKAsaAU45PWkI45IqjvJ6y4o3IM5ckUAXSU6FhxSCWMfxVR3JngE+tOJBGFQmi4Fo3ER6NxSC7jxwCapgPyfKNPVZcfdAFMdjsmRXyCn5is+9urTTo908ip6Duarz6jqWpP5Wnw+WvQyNUlp4RWWYTahM079SCeK+CjC+561zHutUvNUt2TTI3TPG81Angq4W2M1zJulIzk16Nbafa2cW2GJVGOwqKdUkt3Q/L6H0rqmlBe6RzHj7wqAYyo3KSM1XFkG+8gwepq/c23k31wpfOJDikA2rkmtY6hcqtpi4/dqwz6GkhS5syfLuZ1J77zxVwQysoaNqZcTSxqoK810RgrEORCmqa3A37vVJ+PXmtC28T+JI23NdRyjHG5RVN7iUohSFSD1yKmEDSpuACt3FEqd0NMsL401CCRmuLaGb1AO01ctfiLbiQGXTJR6hHzXNzIUzvWqdmALp+mCM4rH2UOqKudrP440q6J/18R9HTp+VPt/EemuwCXac+vFclNChywUEfSmxWEc6klFVqhYeLeg9jW16GK5lea1uRJntuyPwqbw3dHT7K6juIz+8UgEGsBdLaJmYBgOoKng09lmI4kcZ9K0eH92yFzmlc+IXgsmtASUzkAr0rOstUNzfxoTyTxmoHt52X5pA3sRVV/NtHDpGpccgikqNilI3PFSFLVGIXr3rjXIYegraub661+DypoRGV7g1myaFebDtl/MVvQXKrSJkzAlmeGU7JOM9Ki/tWeNgNyn6rV+48NagQWWRCfSs+bw3qqfN9nLL6qwNdkXHqzB3HvrbzY80KccdKlgvEcbQrDPYHFZb6dfQ/620lX/AIDmpLctCRuVl+oIptLoCRtCHC5V3APbNNS51GIhLe7kVAeh5FEd5H5WCwJp8U8WQNw56ZqUkUXodevo02SCKV/72cGvUPCuo+foaT3MsERHB+cV4ZrbmKdPLbII5weldH8NYX1LxElnMzNC2CUJOKirQVSI4S1sevwX1jdzGKC8jdyegNRT+Bb3Upi6TQwqxzvIyfyra07wfa6bfSTQooB6DHSuohj2IBXNChGI3I5rTPAmm2EYW4ke6Yc/NwPyro7exsrIf6LaQxe6oAfzqfHrSMeOK6L9iG7mVq1mLrDOMj0PSrtrbpFCiKoAA6Ckuh+7GasJxGPpQK48cUHpTQ1IWGKAFzioL3m0k+lSFsc9qoajqdlbwSCe4jQ7TwTS5rEspW2DbRnPalYeorhYfG2oi4e0g0uOVVYhX3kZFaS+L7qI4vNDnUd2ibdXdTd43IZ0pUHtTTGCelYsfjDSnfEpmg9pIz/SrsWt6ZOP3d7Fz03HH8611ETvCMZFQLAeTirSPHKvySI30YGmgOknIODTuIr+QxPSl8hgOlWt2DzQXGaVxlXyDisbXLTfaPwDxXRA5HSs/VI99u30oEz521aEw6hPGQAQx6VWg57Vs+LofJ1ubjGeaxoCCRSYiyx7Ug6elK/BpmDt+tIBjNk5FMI6elOZTik780DGgfjSHjg1Jjn0ppAz15ouIQgdTScmlK0mCDQMU7hyTwaUfdxmmk8UmcUwsLSd6O1JwKAFznIxRnoKMUh9qNAE3AGjINJ3I7U3nOCeKQx46ZpeCab2xSikIdnPGKfG2OozTOQPanKTnFUgNO0GccDFTzLhDUNnwAe9W5CCmRWy2JMO4zznmqbGr94Pm75qiRyazYxUNTqMrUCj5sVbRcpikBA+CcVbsovlJI/GqzJ8wFaFsAkII71SQFS9w0oAPSqj1NcYaViD3quxzUsaGEVJAP3o+tR9+Kt2Me6celOO4M1pyBZj3rGbBNa1+cQqvY1jMCKchJCgY57UdaTtR1qbjHqDXU+GXYT9eD2rl0OOK3vD7n7UoFaQeomdpdkFlya0La9ggiUSPgVnTjIUnuKzNXvVsLTzHBIB7VrVgpwaZEJNPQ7h7yB7NmjmQkD1rzfUpluLtmVcY7+tFnqQulyiMFbjpVe8lVSxHavKVGNN6M6lNvczdQcsF44FUUPerVyxeJaqqccc1si0NlBZskUquVGBSyDAHXNMCt07etXEzmaenTkHvur0DwdfzyXTiZy8Kj7rHpXn1mFRcng13/hKLFlLMCBnvVtGaR6fo13FJaSGNNgDY/GnyXA8zGeawdGuTHbyJ23VcZ93JNYuaLsaYlyODTxK6jg/nWPuZfuyY+tSLPKo55+lNSFY1fPZkKkdfSobLzYJm3N8h6Zqot8FGWU/lTH1BmkAQDHei4HRLcIR1Bpshhl+8in6jNZiNuUHPNKzMvRjVC1K3iXToW0S4mgkaKWMblKNirmjQw3Xhq2S4jWQvF8xYZJNZmszyHRrpMA5TrVzQJQNBtUJ/gqNL2LVzBOrwaTiwFl5pQnlTjFbCWiXdiWeMwtIMY6la4zV5VXWJSMg+Zziu/0l0lso3bGMVEbSk0Xsjx/xJot7Y6r9mjuhPFjJP3cUmmf2bpLiaeO2ll7iY7gPwrqvFMtoNbnLjOAMBa5CZ9MS4by9HmuZG5yTxXLKVptI0i7o09Q1bwffREjTY4bnu8I7/QUzTpIbhGihjkMSjH7xeDVSK6uF/wCPbR7S1x0Z8E1PHfXkrbWuYy/dIgOKiTZRM2nCJt1raRBs9+KilivEO641C1t/Ycn9aqahOsYAupJhuP8ABnJqnAlu03mWujXt0/ZpMhaUbg2aKG1eUCTUZp27BF2g1qrPCu1FtWftktWP5etPKgOm29rFnnc4OB+FaR2IpV7hQQP4ecU5WuLcdPPeW/yw6dDg87maoxey+Tm81K3tm7KpHFZE/wDZxmP2i8v7hs/cXpSKNOjBNtoTyn+9O+P50wNnT5PtnmLFd/aSh5YcAV6X4BVhY3MRjhKrJyf4ua8nXUbiG12QQQ2meqxgcV3vw++w2mjPLNcyG7mkLO6seB2regveuRPU67UdJsml3ot3ZSf897YkD8as6Wl3GjxyalHqEX8O9drj6+tZkWqS31w39ieIbS5kU4a3mxwf51KNR1K2OdR8PEjPM1mQ344HP613JnOzdSVYMh7ZoR6oMj9KjVJpLv7RDdLJFjAiIxj8aqWniDTJ22pdmJ+6Tgrj8Tx+tagVJAHCq4PRkNUIaLh0z50LIB3B3CnFLe6T5kjkB7EZqRQccMf+BU14Uf7yc+q8GgDOuNGTG6yka2lznKk4NJANZgJWUQzIo+9nBNacahF27i3+8eaC7A8oSPUUDMLTvF2n6hdrabZYZycbXXAzXQ1TNvYzzq7QxNMhypKjcDVygQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUANJCgkkADkk15X4x8QJquoKltKWs4AVB7O3c/0rqvGmutp9mLG3I+0XKncf7idCfx5FeS3c2DgdBwKuK6ghs0gBJLGqbzDdimO5Y8d6rMGyaTLJWuDg9qrNIfXmlKFjyacIBgljUgV97buaUfPTyqKc8UhnRV7UXFYVstwO1IsbNwOlM+1J25zS/atvK8mldhYnEQXGTUkbBXBC9DmqLXbE0C5kK5zVIDrP+Emv8IsexNgGDtrc0nxDdgebdSiVj2PauAtGaadQWIHetgxywuNpO01RLPWdF1FNZvYx5mzZzs9cV2FeE6dq8tjMroxSRfusO1d5Y+P9sAF3b+a/wDfjIGfwpNEnd0Vzdt4xtLlN6wTKCe+KSfxbGrERQlj2zSsM6WkzXFz+LLw5EcSr7mqE3iPU5Bjz9n0p2C56HuA6moXu7dM75oxj1YV5pc6peyDMl0xOO5qkZ2cczMT9aLCuemya7psWd12mR2HNUZ/GOlQqcSO59AK87cxg5Z+frTGkt1HXNFkNF/xFrjapIZGYhE4jjXkL/8AXrLjlb7NyuOKa88O7OKY1x+7bAwMd6LlJnOSsfNb0zTQwz1xUMshaVvrSbuc+tUmUbukSMl9EwGSGBA9a97hfzIkfGNyg/nXzxpV0IL1JCMhTmvRdJ8e3MluIjbRN5fyhsnpRLUzluej0VxEni+8b7kcSj2BNQSeJtTkXIlCAf3UFTYLne5pC6qMlgMdcmvNpNdv5GybuXPs2KpS3kr5LyF2PUlqLCuenPqFnGu5rmED/fFVX1/TEXP2lT7KCa8yNy/Tp7AUnmSMv8WPSnZBc9Cm8V2iNiNGcepOKrv4ygAJWAn/AIFXDgTOBiMmpUhmY42AUtAOrfxhK5/dxIB75NVn8UXjHCv+SisHyJF4yKDHs6yUXQGvLrl468zyY9N1QC8nlYBpTz6sTWYzJn5pOB71NFLCGX5xRcDVu9UWxIjTDNjJNZsniS8Vf3coX6DmszVrgSXx2E7VAAPrVDf71DZaia0mtXU+fMuJWz2LVXNyx5P6mqG4HnNL5ny0uYdi59oY8E0hmPeqgfmjzPzpcw7FlpSaTzCR71XD0ZouBPvPc0vnEA1AW+XmkQ45ouFi0s3zA+lew2RzY259Y1/lXjKEMwxXsGnSq+l28mRgRjOD7U0zN7l1hkYrzaFZLfxLJFI5G2Q8GvRYJvPiDgYz0rkPE1kbfWYryNeJBhvqKtFIytUcS30pRyFz+dUDtAwXJqO5WVp3JlABPSq5TJw01UQy0xjUZJ/WoVnjDnJXH1qu8ceOXZvpUYiH9xjSuI0PtMKn7wz7U03kfvn6VXCNgYt/xNSLHL2jX8qLgP8AtIzkRmj7TJ1WOkaOfH8IHc1A8mMjzBkdqaYrEplnb+ECm+ZPjllFRRYmmWMOxLVqDSUx8zE0+awJGf8AOT801L8ozulJ9hUd6y202xFzVqzRJIwxA3U7gyuzJngM1Ku1jkRtx61pGNB/CPypVQDtijmJsZ+D/wA8qcI5Qc7QK0DjPSk2jHJo5gsUDHMT2FOFrIcZf8qtgA9xmlLBRjIzRcZVFmQfmc0osl9SasGRM8kU03CgUcwDFtU6d6eLdVHCg01p1B69aVrrA4ouwsPWNAT8tLsGDgVELgYqNrwgYCmncCyMkjinAY4NUTcytwFNHnXBPAOaAR6AnlpCFVBx7VKqMfmzUUQygGKmA2x4zxXxk+W90ekOMnynFVpCdjFlz7VKfl5AyKgkkDo69xWc5toVjy/X/l1i48vCjd90VSRnCYfp61qarBnXrknGMg81SvMKmFwQa7aC5ooHoRJLj7rkY7VGWaSQDP51KY0a23quCKjSMlQe+a3ScWS9SyqXAHyrke1QTzXCuD90VbtxOFyFzUF2sjkEqQe/FVJvoCI7iYy2+No3YrIhyt23HOK1ADwpqkU2XhI71ndvcosBzjbjirEcW8YJ2+lV9uV461OjEJg80KVhsSeKWO3ZYnXcfeoUFz9nxGRvHXPehpA8pH5VLBE0gIV8NXTCXMZNWHquAouMc96bPZBwWjAIqSSxumXHytz61WJlgVlBYN6UpopGfaHyrqRB+VaS3HGNuay7T5r5t3UjvWoYgv0NYSTKB5VEYUpyajV7YlY5ZCpY46dKsMyG32sufeoEgt3A3Lk5yK1TViOpJstYpPL8zcR7U37JHKTujVhnpirKWyOwZYc+4NWooym4lcD0qnHqiro56bRbB2YvBHuPotUJfD1i+cwgH1U4rqJI4pCSAQ4qmydfXPeo5pLqTa5zU3g+1mGDLLjtg10Hw98OrpPiuKZZ2YHjawqYFt64H0rb8Ol11qEyKAN3p0rWFSXcqyWp61x17UueKj3cVG08aDc8iqPc0NmTLGabmse78SabaZ33SsQcYQ5rBuvHqBitpaNIezMcCkB1904Crn1p8tzDAm6WREUDqxrzW98R67qKbEQQqenlpzUcGi6xqDZuTM4P8Tt/SmFmdpd+MNItcgTmVvSMZrCu/HM8mVsrPA/vSHmi18FEbTM5B/2a14PCNlHgsrOf9o0rBY4yfWdav8h7t1U9o+Krx6Tc3Ll2illY92716bFolpDwsKj8KtLZxoAAoFFh6Hnlno9xbOrmHbWsYsDn8a6qe2TYflFYs0ABOBXZQlZWM5GY0EbrhkVh7jNQPpdo/LW0ZP8Au4rS8vFAjwOa6jMx/wCw7QHciPG/Yq54qQWdyiER6hcr6ZbNamykEfFAGWi6xC2U1ESD+7IlTLqGrxj57e2lH+ySDV7y8gcUGKjQCqNYuFXMlg49djZqG71uDyD5kE6f7y1e8sYqlqFu72zBBmlYTPG/G0sNzqfmQ5+7zmubtxhua6DxfbXUOqkzQskbLwccGsGAcjAzSBFplHPrUfQGnPw5zUZIHFTYBpGaYVOc1L9aYTxzTBIaTyRSA0dTmkDZOKGAnpSck04AHNIRjtSGIeRSDPenAcUZ5NADTkGmknHvSseKTggYphYUNgYpCc9elHFAHHWkAfypMkjkUAEUmT07UALkZ9qXvTMccilHIosOxKAfWnL1461GOCKkXrTQrGrZAH1zVt0BU1XsQCBnirsi/J7Gt1sQzBugQxGTVInnrV+8GM1Rxn61nLcpCAEHNWomyMVXIOetSJwfekgJ2iwQfWrKjbDye1RxqG6nmpLk7YWxV2EZDsCTUZwac2eab0FZsYnrWlpkZaQH0rOA55rc0lAELE/SqiDE1RiAB7Vkbj3rQ1KTMxFZwY5pSBB0pcZHpSZz9aAecVIyVBW5obbLxKxExxmtvQsfbEOe/WtIbiZ28wLQptrnfEqt/ZzAjncMV1MiDylwRWHrkXnWxjyMkjmt5/CRH4jhob+4tCVSRlQ9VFK98XUjPWlv7MW82zeGOM8VnupU5FcOh1NF+KcNkNVi2UFqxldg1XIrraAD1FJoE7GhdwjGcVSXKHnOKsfaPNABNN8tmYACnG6FJ3LNvwPUGvUNAgEOjxpj7wzXmlvAWaNB1LDivWLKMxwwRDsgzVzdoNkx3L1mPLV1XpmpJZ3jOM8VBExDyLVe8doyD1FebCehvYure54Paplu1IxnBrGWbK5weaVrtUxk1rzicToUuE28moS0G/PHXrWSkwIzu608y7uMg1SkS4mwrsOUkIFPN3Mnoaxxdsox/KkfUCF7jHrWnOTyljVb+T7BIrIQpGDirel3nlaTCo4wuMVjTXwktmU4OaWKR/LUA4UDtWTlrctIx9WLHUJG67mPSu20i526VEOnGOa4PUpGFwSvr1ro9IudthGGYsaUJJSKaViprlil5qTSCXaCOcDNcneWSQXbLN4g8mMdEQfMPyrZvpY11KbN2uSc7N3IrIvLnRLeXdO/70/3Vya55c3OylsZxttDMxElxqV6e5HQ/nW1pbWFtlrXTZIv9qQ5NQi5naDzLLRrmZMZUnjNRLq98FKz6f8AZWHRWBzTlGbWw00as91fygi0SNW9WXOKoy2mvTYVr51H/TP5KoXWragY9tuXRj1KrVYxaldrulupjnsz4pQg7XbGzUXSzC5lurpmb+9LLViNrRBkTRnHcc1zSaL9nl8+6v12jorGtW1jUqskZZ07bRwatxV9xXLpvbDc3zyuR12JVC48SaVbMFMNzIR2birF1qKWSeabXLdMdM1JZIupwi5kt4ULdFK5ND5UgJba9R7f7QLKJI2GV38mvR/h7p811pr6nNDCIpSVjQDnA7muDS2IGMZ9scV6V4EtVbR2UXUiyq5yqngCroSTlYmaaVzA8S/Dma91iXUdNmNnK/IAUhd34VQ8v4neGo96y/brZB03CXj6HmvRpdO12KYyWWspIuc+TcRDH5jmo31HW7UEX+ircoDzJZyZz/wE813o57nBwfF6HcIPE/h142H8apnH/AW/xrZsPFHgzVH3WGtS6XMf4Wcxgn3zx+tdEdS0DUQIr62WNiMeVeW+MfXIx+tZuo/DfwbryeZHaRxnGN9lJt/QZH6Uw0Og0x7xsMNQt7+3I4kTAb9OKvy3KxMA6SAf3gMgVyHhbwFH4Qv5XsNQupraUcxykHafwqTXdR8XaXqe/TdOS/08rkrj5we/Q5ouKx18c8M33JFb6GpK4iLxlbG3361od9p3OC7xErn68GtW017S7wqlhrMZZuiSH9OeaoVjocDOcDNLVNJbxSPMhjkU/wAUbf0NXKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACg0UUAcr4r8MHWwt3attvIk2hWPyuvXHsea8jvIvKkZJQEdSQynqCOor2jxbq0+jeHprq12faCVSMt0BJ6+/Ga8KvCzOWdyzHksTyT61cXoUiJ5okOc5qs90oHAprRnJ5qHYo6npUsYNcMc44qFp5GfA7VKdinqKiMi5Jx+VSA6OJpASSR7moWgZX2ntVuOb9wcDkVGzmZCRjcKLiIREeKkEY2c8VHubGM8il+8CSSaBjgir34NPUxjjPFQhRjpTxgDpRcDQ00I12m0E810zRP5gBXjFcnZytFMrBsEGu2tpY5okZjkkU0yTNnh3ngc1qaLb28bP9ryTj5c9BV6GzillBwM1Bf27Q3BxyKYF5tQgh+SMDaOmBUD6qMfKOaxWkdSSzVEZQ3RqeotDVm1JieMDPqaqSXrnvVRvLJOWphdBjuaVxXLDXZzy5JpoucnGTUIIX+A5pyRuzbglFwJ/NVRwDURkkJPHFSJC5HOKf9lz96UA0rgQFXYg5HFLKu2Fi7dBVowWyLzNk/WoLk2y2zgEu2OKLjW5yzPucketG/nrzURc7iRxntQCcZ60zVFuF8Pwa2NEkcl8GueWTbz3rQ0aa4dnWM7RTIkjtI5JNvJFP8xnG0vgVlxk+WRJPSxGJ2yZCF9z1qbkM09sAzuf9aBcWgOAQcelVCbQfxA/jTTd2kfQDPoBSuI0Bd24Hypk+wpTf5OFhOPpWd/aKY+SIn6CkGoTs2EgOMdaYGkLm4Y4SPH1pjz3Y4yij1qgLm9KnEYU9uaaxu2GXZV/GkBbFxIQSZgMdTUCt5023zGYD0qmIJJHAe5ATuBVtZILXBjOfegC2tpk/dP41XaSOG/jjIzn0pP7UXB+ZqitJmmvTKtqz8YBI4pXGg1Jx9qbbwvaqRfsKvX0MrStK649R6VSCg55wazcjaKDgLk8Ubhx2pdoPel2r6ZpXCwBuKUHIzQASvT8Keq8AUrgxvJ6CnBGx9akCgGn9PeqJuRCMnrShakHrnmjvkY4HWmFx6LsrvvCMLTaa5aRiobGM1wStuIBrvvB0j/2fJhfk3U4ETOniXYgX0rO1uGG8tDEWG8HII6irbzEITjmubntNQS8lu5XBjYcKOwraxKZhzafbo+CDkcZzURtoVPCD8ahudRTzW+bPPNQnUUIOOaTBlsxxjkqB+FIFHoOapG9J6An2phupz92Mj3qRGmFpAAOtZomum42kUEXcgOSVFMDQl27DzWC8TmRiBxmtBbe4cYZ8CmizcEgvTQEVihhuRI5AArYbUYwKz1sgRyxp/wBkUcnJFFgK10sdxKXZuTS28iQLhQTVryIgOlIsKj+EYpgN+2FuAnNH2iQ/KAanCKOiijg+lFxWIPPmzjHNMJnY9CKtbRSbh60XArCObnnFHlS9261Y3jON1KrKepyBQmBXW1dhy9SC0A4Lc1J5qDvQ0yccimA0W6Lyaf5Uf51H9oUjvTHuPTk0CJ9iL/CKUIo7VV+0Mf4TSidxnC0xlrbt5pQAeap+bKT0o3zUAd7ZyEpgnNWHwBgnrVEfJkrUyt5gG45NfDu56TH7ggODmoDtZyR360k+5eO1RRMGcg9cUtWByGuWwXWJCpzuANUJLB3i+Ubj1rW17cmoDYmXZep6Vl6Xc6kLtkuIIzGfu7TyK9LBLQmZHb2zLHJHIpH1FRtAqDOTgGujcNIp3JjIrnZi6XLJxtrpqPUzTLNtNbrHjzChHrVsqJQGDRyD3rEcSoS20ECtuwtYri1Dlfm7gGrp1HJ2BqxkXkKecQF2HHasqSH/AEpec11r6fbBiXByfVqZ/YunykMVYMOhD0Og27oFNHPmIq2F6VNbwow/enFbD6VAhbHme3OazLlY4lYM4GPU4rCcXB6l8yZDNpnBdCCPWoreVYGOQH9qkjuGC7Q+VI7GovIKlmPOfStFNLYmSLy3UTt/qmH41TulAk3DOMUwFo1BDD6EUssryLggUTqtgomVEf8AiZAEYX1rpYrOJoA2TisOKNWvUJGecV06tFDb5fhQOaIWZTMW5RIZCqngjjNVwp61szRW9xF5keD3qkYsZwOBRLQSY9HaGEFoHYHoy1YDRbd53L+NRRzOhCn8qsyJ5g+7+VWqumgnHW5SklALMgzUGd8ZJ60+VWjchVOPpQkDPg4NZ2uUium4PyOKuJfTWFxHPEQxU52t3qdI4goVl2n1qrd2+0jDZHaqtbUL3OnOv67qEQaAoigchRmq6aFrOpNvmmfDHJy1avgfLRypIOR0rsljUdBVohqxxtn4HRSGnlLeorctvDVhbYxCCfU1s4FLQK5QuLWGONQsagD0FTxKAgpLs5UCnREbaYFgdM0U0GlzQApppkxxilpr8imhCSnKGsqVMmtV/uVnOME100iJFFo+TUe05q6VBqNo8nium5BW2UoT2qfZilCdKLgRrHSlB6VJjmkxSAhMYz0ppiBXFTnpTSMUxHD+M9Ja702YKu4quQMV5BbxeW+1hhs4NfRV/aie3ceoxXjXibRW03UmdQRG5yOO9Ajn7gKM+tUmJOM1bn5NVZBx/KkA04ODTGfmkJOKjJNAEhOaZ0pc8Dmjr9aAA9KOvvTuOlNxxxSGJ3oJOeKQcHmg+1AXGsOc0KCehoJ9qT6daYxxyOtN6jNHOOSaQcUguOLcUmKP4KBSAXPc0BePamk9s9KXnHFMLj846VInLdKiDVLFgviqS1EzbsVwoJ6VfkC+WcelVbOM7BjpippFwCM9q3S0M2YN3zKR2zVYqMjirl5GS2R17mqexgM5rOSKTF25pypls96ZuIHNPjcBvmpJBcvQJk+tQ6hIEjxnmrdtwu6srVJA0u3PStGtBFPeM0u7Paojg8ClA9KysUTxrlhW/ZQ7IN2O1YVurF1x1rolYx2hyO1WloDMC8bdMSD3qsD61PccueeKhBGc9KiQIXHegDHPem96XtUDJk55rX0bIvI+pwax0J71r6RgXUZPUGtIbiZ6KvNunGa53xRK0VgxXglgK6WBN1sCDXKeMTixxgj5xk10S+Ez6nKBluGyWw3vUUsJXORUAfDcHkehqdbjcu1z+NcUtzpjLuQNFik2nHSrBeEHl8+wpPOiA+WMk+9CKuiurujZ7dq2dLkiubhI5ZPL3cAnpWaWMmMgD2Faek6cLq4QvkIvJNNMOU7Kz8KXSyxXEUsUsQOTiutiEgmXcMEDFZfhS5ju4pYoWJjibaD68V0U8O0R7eGzWGMqqMOXqx0463IIsebIOc+tQXLAMAakiGLiQGq94fnAPArhg9DVjd4PAIppAcfMoNV8YPGab5kinrkVaYixtAPDke1LnHeoBIducCmGVZehIx1xVJgWt5NU5JJQ+Sfl7Cpg64/1lQzE44wabkAzzd0RyAD7VrQyYtV9MVh5BPIwK1lbFooHcVCeo2jJ1I/vTWtp0n+hj1FYt85MmMd61NPbNoDVR3AxNQ0pLvVZbkIyNnrnrWbd6HagtLN5kjDnG6uluZkWYghifrWTqOsfYQGWzWU/7RrKc5c9kUkrGbpurSwP5dvHcxBeAWY4qze3N9dAufNmkH3RszUFv4k1jVbsWthYwtJjIVVyQKbdzeKIdwuLO5ix3ji/rXRyVHuS2kTaTaaqZmk1CIeVj5VJA5rcVVCbVSJP1rmNKvbqXUdlzJO2B0fP9a6RB37VhVunqUtSjqVvayKPtUpKg8hVqBdT0yCMRxCQqOMdBWi8AuGK9axtV0oQ20koX7q5q6UeZakydh7a9axk7bZGx03nNRSeJJ5HAjMcY9AtccHY81LCztKiKRksAAa6PYxtczU3c9MidniUluo7V3/gaYwabKc8+ZyTXnC3Aht0VgMgDoa9I8B2z3mgvOjRMrSHChufxrKive0Lk9Bvj/W57HwxJPZzvFMZVXejYIFcVp/xT1+xVVa4jukU8idck/iMGuw+Inh/V9S8Jy2+m6c09z5qMERhkgHmvELyw1LTJTFqWn3VpJ3E0RUfh6126mFrnstp8YLOeLZqulZB/wCeRDD8jWhD4g8CavJuW5awuT0I3REfTHy14RETIMq447CtKzgEvMv50OTQ+Q+ldJtPIjMkWqTXtvIMoZHD/k1Sz3d7BOdtgZof76SDP5VzPgZ/sugQxIrlQeTnOKl8WeItQ0qW0+xKoVyS+8Zz7U1K5NjafVtPnXyL2F4w3WO5i4P8xTbbQNA8z7RbWFmWPO5FBrl4fiE0nyXmmI8fco/9DWja+JvC91j9/wDYpfQ5TH5cVSkhWZPqnh/WX1H7XpOsm1QgA2zLlOP8+ldHbiYW0YuCpm2jeV6Z74qtaSxzKDaaglwmP7wf9RV8Zxz1qriCiiigAooooAKKM0maAFoqrNqFnb7vOuoI9v3t8gGKx7vxr4ftBzqCStjIWEF8/iOPzNOzYrnRUVx6/EbR2GfKvP8Av2P8a1tP8UaRqWBBfRhz/BIdjfr1/CnysLm1RTVdXUMrAqehHNOqRhRRRQAUUUUAFFGaKACiijmgAoqN54oz88qL9WAqpJrGnRAl72EY9Gz/ACoAv0VizeKNKiTcJzIf7qKc1nz+NbZeILaV/dyF5p2Fc6qiuBm8eXEZO5LdPqelZN18SLkAhbqBB6qvIosBd+KGpxhLHTkKs4YzyAHlcDC/nk/lXldzKXJPSr+r61FfXclw8rPI/LOe9Yc1/F02E0y0Ocserce1QFTnPJqJr5uixgVJGZJEeRztRR27moYDvJbbnHFRsuMc9aiM8xXHmHGelNBY8ljzSAtIVCMpNQq21iRTCdtNU5JoAnZwx/nTlcZ5xtqvnHQfjU9rhrhQx+UnFG4GhJ9nkt0SKPDDq3rUtrYxySLubitEaT02qcHocVMdGmjX5VIPakxmbqWnCzlWSE7429O1a2mYkgRg2CDVJ2kibybgHB71ctGWCERxruUnJxQhM7qKKCZI8EKQByKztcVbUKA24svWpobaE20b2lzhwoypPesLXpZiVM8bIRwGHQ1aEjnZJCZGG9jk0+N0jI8zJzQjRhuuar3bMZV8v86LiNRZYgOI+vrSC454QAVnb5gmOvvTUWUnllA9zQxWNN5HLZ3KBSrIV+UzflVNYsjLTce1PWCHI+dyakRZeaNEJMjH8ari9gBJAZj7mntbRbR+7Z6dFbqGAW2OfcU7jIH1JQMJF9c09tSZ7ZwkHVcA4qy9rKx4iRfrUVzBcR2UmWVAFJOBSuNHMM3PoaM44JqMt1yKM5FO5skSHpnParmkBmV/3uwfzrOY5Tg84rZ8PxRyIS4yc8CnciRpRQREZknkYnsKnjhh3gbJHrVigj2DCAe9XIVVQBgCpuZmbHCgUBbY596f9nlI+WFVHvWpwD2Ao3of4hQIz1tLjAHyj8Km+wykHEvPsKsGaIHlx+dN+2RA/e4oAiGnHPzSsfWnHT4hgHJ+tO+3xeuaY+op2FAE8Nlbr/yyXPrSz20e3HljA9qppqLdkOKSS9ncECNvyoAgkRF4wAM1s2pQQrgcYrAfzPJOQd2eBV23jvCqqQACPWk9hmvqsCR6A0xAJdwBiuUYAiul1e6j/sOK2D5cMCR9K5rODntWDN47Ce+aXp160mcg/wBaAc9+lFyh6n1p6HOT2zUWfzqZeExQmJj/AKUvPemjntSgkmnciw4DAxmlJ20gNJnrRcLFizj8y4Vcck8V6bodpNY2Co64B5ry+CZoZUljbEinINep6TqT3mlRyy48wryR3rWm7szmTzShpFA6CmXro9lJuYABTmqlySMkNXNSi8N3O81w32cD5V9a3ISMd7aEStherZ5qSOCIdFFUnvGZ2IQ4JpyzzD/lmalu4zQ8pMjgUoQCqIkuiThR7U//AElv4gDUCLgAP0oZ0AAqp5Ux6yYpv2djy0hoAteYAcZGKaZVHUj61CLcY5Y5p62iA5bmmBIJB1B4pklwijGefamHAODwPam7FPJFMBjXQ/hBNBuj0CE0/CjoMUZBXngigCITykY2EUhaduQMVLuwDxmgEMOtMCEm4K43YNIsMpPMtS5HegFRznmhgIIAOC5o8gf3zil3oOSwpBKo/iFJAL5KrwSaXy0HToKjadAQN2TSfaEzn0qrgTbB3FG0KMYqBrscgKfamG5Y9FNK4FnnPFOzjGRVPzpT0jJxTvMmbA2YqrgXMqaRSB9Kq5uCegpdtwRycUXA7+36MCKa2Q529qASjcZwe9TTTRQRb5CAuOpr4pps9FsiMqkYbrUCuY5dxwRWbqfiS0s49ywPMT0KjiuUn8S3N1qUEZzHEx5UVUabbFubXiK+shdowuBvxyPSsuK+to5FkEyc+/WtvUdGsby2hk8kbiMlh1NYzeG7LoN645610U5qOwFxtZjOApTBHrWZK/2ibdhB9KbL4bhcDEsgqAaA1s/mR3LED+E1qqjk9WS4lrgZVgMVq6Sg2sq8Z5rnZNGup5DJFdFB6HNa/h+0vbSZxO/mKemTW1GymDWhsvAzLhtpqAWTg5DVfLMAf3bVF56qcEEGvSjsczRUljYAqePpXKa3pjXayxMzbW6H0rsZJkPPb6VzeuXaW06KY3O8dVHSuaumaQMLTdNazgETSFj61oojqxVmPtTRqFtsVSjHPqKkiuIGlOwkketcjTbuasjkRywAPTsaZLCVIJ4zVmS4jlUgOoYU0yhlGSrCkNFJECXUZPQmtmS4iK/ZyDkjjjisuRo1nRg3Ga3FTgOpVjjpVRlZg1cyYJDGXG0kDjFJDeRsHOCADjB5xWnJCgUssYLdSKo+WoBYQkHOSAK1lKMtiLWZWmu4o2DBse5FaljIZ7cSAj8KqrHGwO5SCemRWlpako6ELtHTFTT1lYp7EMhaNuUyKfZyo0gXbirptUZySM037LEvAXmujlsZEOqNFHGqBVJbuO1ZLEFBwT6Gr+qWVzGsckG10/iBPIrlrk65DrKCP5rNgMjjArOcbspM9H8GMRK4I4xwa7UHrXCeE5mS72HbyPxruQfWi1gZIKTNRu+0ZrJTXoZr2W0jVvMTrnpTIbNG7YbKWI/JmuJ13xRfkyWlnEkdxG2Dnn8azbFPFF5co13fERHnYDgfpTC7PSpLqGEgSSomemT1qlZ+IdOv7yS1tp98qfeGMVwq6O2uLNLJeyo9uxCjPGa2vCdvbWd3JDLGv2tl3CUfxCgaO07Ux+3NLQQDTQDSfkqjIPmNXzwMVSlHzGt6bIkQ01ulPI600Cukkbig9qdikI5piGGs7UtbsdJ2G9mMSscBtpP8q0iBXK+M7WyurFI7h/Lk3fI56Z96a3Jk7GpB4j0i6YLDfwszcBScE1ollC5JGK8ttLLSYr62tZJgLkkHzQflzXpNzp8F7YtaTndG67SVOKuUbCi7oskZBxyD6VyHiyyiuLFw69OQcVyeqa1qHgya60B7h54XG+3mV/njBqG61LV7nS4JmvTJGyYYM3JrMbucbd4WZh6GqTtlvWrN3/rWJ9apEjccUxCMPyph/SnnBB55qM5ApMA7U7OeelIOnNIWPpikMfxSbsDmm5peo5NMBQc4xR1pAcDpSDrg0gGnPNIKVjjoaTNBSFJzxSD3FIT6UA80APzx7Uw8daUZFIxJPNADQaeOKZinDpQA7jPvVm2ALVVPWrdqPm6c9quO4mb9qdqfWp5D8mSKhtsBBzU8ihkzXTHYye5kyoGY+1UpFIatCVdueapyNyTUSHEqNkHGKdGmWAHWlHPJp6J84PapRRoICkPJx6Vz9zJvnfnPNbdydluewxXOseTSkwQ7HNKPTFIpNKvJqCi/Yx5kGOvvWzcuI7QrkdKzNPj+bn8Ksak+I8CtFsSzJcjd1qImlY89abnisnuUh4we1KFz3poOKdSGSqpA44rX0hN92gOBzWTEOM1q6b8twjd81cNyWemWke22Ap62MEzbp4klH91hkU3TX8yzGe1a1rH5i9cU8XJxotoKVnMof8Inod/GTNpkIJPVBtP6Vh3vw00rzsW89xCDzy2QK7uFCgwTkVFcJvlPc44rwFVqLqdrSbueOeJfBg8P2i3YvxOrNtC7cEVgQwfut/U16F4+tbtdHMkrqYhIMD0rzqC5CDYwJFelQk5Ru3cwejLlpZS3cqxxLkk4rf1q3k0Pw7ujYCSQhGPpms7w9dtJrltEg2x7unc12Ou6XHdSWwnQtA0oVkPQ1206d1cmdXohnwqffpVwm4HZJ2r0KYf6okVm6FolloNtJFZQIgc5JHJq/c5Ij5715mYK0kjWk7q5RfC3MnFVL0hsFqlunMc7nPUd6plvNbBJyK547GhASOxP0owR3496e8BGcN9M1BL5qLnbux6VVwuTBioI2jFRBVU5ximeYXjDbWX2oEq9D196oQ4kHkdKik4Gal3KeOMVDMFK8cU7CRAZMHvWwjf6GuTWJwD15rZhO61XdULcsy7wgMS34Vd0+T9xwOM1namDu47dqvacpa2A6VaAhupF+0vkisPU2EgwO3etu4sQ10zsHJxjrxTHsotmCI0z3dql6SuBl+C4TF4mSRDjKEGvWYbtY8GSRR/vMK8sjsrG1cu+rwRt/wBM25/nRLc6HGD5upzzn2YmumNdLSxk43V7no/i6WE+GZpI4YPNLDD7Rn8MV5v58zjlWOOwottZ0gOLeBLmQnpvPFXxdxL9y3T8ayrz53sXTSSJtHVtztNFwRwWPNP1OCS5je3ghfDjBI5rOutYvIYiYljXjj5c1gS+KdZBObgIP9lBxUU1NqyG2jXs/A1oTi5W4CY6h8Gr8PhvTdLjxDEsmTnfMQWFcXN4jvpuJLmU/wDAqjXVEJLTyN9S1dHJVas2RzI7sQ2j9ShHfvXoHw91S2topNMhjBQEvuA5zXlGlzRSWSOjZQ967zwBMDq8qggjy/TmopwcZjk00ervdQJA80kvlRoMsxOABVODUtM1mELb3dnext/CWDZ/Cub8czRReDdQe5Z1hCDfs64zXi8L6H9mNza6zKjqMogU5z+Fd2phY901bwR4c1JW+06KkbHrLaqEP6VzEvws0RiRp+s3Vs46JPhgD+lee6L4r8YmbytEn1K7Cn7oQyL+OQa9E07WviRdRBbvwvazk/xyssZH60Ndx3aO18O6LPomlraNcrcc53hcZrmviFo+u6o9kNKt2aOPd5jKRn8qZeaD45ZYp9P1C2tJCMvCjnaD6cjmsXVV+KtpHh7oSRgctbKmf5Zo5Qv1Ktr4I8TzptaFo1xzuYL/ADNP17wPfaFoJ1GS6gcRsvmR85AJxwe5ya5C68U+J3kMb65qKsDggTlcH8Kgv9d1rVYI4b/Uri5RDkLI2Rn1+tJJIOds67wXrmn6Nq4ub668pNhU4BOfwFekWfj7w1fS+WmpxxN0Hngxg/ieK+ehFITy5qYIOjNu+tXGxLR9D3XjLw5Z7fN1mzO7p5Unmf8AoOcVj3XxQ0C3Lrb/AGm6K4AMUWAfxOP5V4izxwJv2AEegquNSLMAq8Z71okiT1u7+K9w0uLLTY0T+9O5JP4DGKqN8UtcJ+S0078Vf/4qvPVlLJniniSTbSbih2OuuvHniG7JzfCBG6pBGFx9G6/rWRc6vf3Thri/upiBgeZKxwKyQznqacGPfOaOdBylnzFweM59eTQsoAwBgVXXcT0I9KlVCTgDNP2gWJvNI5B4qv8Aa5A5DAn61cihOeVqveMsE6hozk01UE4mzpuqXUIjENxJGc9EcjFe42p32sTE5LIpJ9eK8FsSiuhVR0zmveLBt2n2resSn9BSlK4rFijNB4FQyPHjlwD9alIGyYGuf1rxQNDfFzYTmI8LMuCp+vpU6m6F0Sl1G8efu96fdTLNC0FxAkkTcMp5Bq1AnnOXk8fmeTFt5aDtu70o8VapL9141PslSap4S0zUVH2dVtSo6KMVxWq+Gda0keZa3BlQ9lPIqnAdzrpNZ1aVvmu2HsoxUD3F3MxMl1O5/wB41yWj6nrSThXt/MboPMWuvi1XVIgFe0gjc8ncMVjK6KRGLSSQ/clcn1BNSppdyw4t2A9+Kn/ti/ZCHeFP90VVbU5R9++IzxxgVF2A6TSpwHZwiKoyWJ6V5/rGrgXTpbzeYinG5T1rr9Q1G2azm33jMdp+Xd1ry15BuPTqelVFlIkubqSdgTn86rEHB7CmyTqPrUZm68dapyCw5hxyaQqKZvPNN3k561PMMcB6VanYR2qJ/e5qmMgjJ4qS75RMEYApXAi3c9aQvjPFNHQGkPtSAczEgUoJNNOcAik3Me3FMZJkk8Vf0i3+0ahEpIXDZOazOalt5JI5gwYg56+lIR6ZAJxMi8MM4zWq6sFG5PwrntFun8iOXzd7jrXXQahHcQgzRj60wRzt1JFD5pkgB47iuM0i/YaqU3kxM5wK7rVrRZGbyJfv9mrz02s+l6yiy7c785HTFNbAz0OCNbk/eMZA421k+IP7QW3RPME0K8471Yt5ON0T7sjrVa8ml2sGJxilcEcstzOXIVQPrVmCCSVg0kn5VFKy+Y5GPqKdBdrHjOaZLNNLNMYLMfxqeK0g6bcn3qkt8McKTUq38n8EJ+tIRqx20SjAjFTxxIpHyjNZC3N6+Nse0evrUgbUXGAQvNAG48ahOgpimPsRmslra/kOGnIHpmiPTJf+WkzHNAjSkuIVHLqB9azdTvYGsZAHBJGAPWn/ANixkjezY69ag1HTLeCxlkCnKrx70ionIkml5FNBI78UdT1pG6EkcBCe/vWpo08iRfu0JJNZMv3DW94d/wBWNw4qiJm2k2oMB8mBUyx6g7j5xgdq0IdgUZxUqugyAy5pGNyobW7PHm5p/wDZ0u35pTmrouIVGDIKQ3kHrk0AVo9KCrlpGY1MLCEDBGfrSm+Uj5UY49qZ9rds4ibNArk0dlCONuaGt4V6KM1GLi4I+WLBPemlrpuoVaBlmONQBgClYYz6VVVLkk/vABSGGY8NKaYEUhzJx61o71WE5bnFZEiEShMnrya0BbRLEfvHjvSew1uY0zFpW5yM0zOBSyH94wHTNIcEH+lc8tzdbCHlaT6UE/LTQOaRRIoB69alBA6mq+aXJHr7cUWEWhIMfSkaRQetQhJGAO1j+FO8iVjxGxz7UxWHNMSMCm7yeM1KtjcuuVibFOFhcAZKgD3NJoZGudwOea9P0RANJiH+zXnUVk+5SXRfqa7mz1SysrCKKS7h34wRuFbUdHdmNRXNG8ykR2jJrEvG/csDjpUt5rumvGR9vjyOwNZVzrWlPGVNzuJHYV0SmiEmY24lzgVJHnNM/tDSI87TKT9KaNZsl5SBz9ay5kPlZdU9qeTzgcVnnX4hgi0PPq1RN4hfOVto/wAzS5kPkZrDJ7UYY9FrG/t+5P8ABGPwpp12+I4dB9Eo50HIzb8qTOdpqRIJWbGOK5ttXv3/AOXhvfFRtqV7/FcSEfWjnQ+RnVmyYkZ4+tQNbjON6j8a5f7TM3JlkP1Y1F5j8ncxz6mlzj5Dprow20O951znhR1NUDfoTgDGetYxOec05flwc03MOQ3Be2wXLyH8BTTqNkM43k9hisbcMkGkXG/OaXOwcUbJBlQPu2qegpFt+Dl2pEC/Z1LNgVJ56BfvVabM2KLVCeScUC3jViO1KLhcDDDFNa5i3ZzTESLFHu+7T9qDPAqt9o3NlamVHIyFY0AKQpPSnADFRLFcE/LC5/CnrbXrY/cMM+tMY8EDjNG4c05NNvnBJVV/GpF0a7Y5MiL+tAiHeo69advwKsx6HNuy84x9KsLoMYfLTtii4Gb4k8R6np1y9rbhUXsx5zVnRLy51LRpTcMXbb3NGr6SNTuW3Nj0I7Ve0PQ5tMgaMSGRW7EV8rFXWp6OhG9oZ9HaMICcgjisdfC93NexSbljReTnrXb28Hlqdw47gU8yx52hapK2wr2M27drWyjjABZazwZ3XcUQfjWhrQJtwyNjB54rPilEsRG4HFQ0BWa4ZWwYxx6Gq1xeomA6FS1XthQHam4+9RFlZCJolI+lbU6bauJsr29wg/hb8utXrS8ha4Vc4Y+tVY7mJUISIFgOBSRTpJtkkt9hXuBW8KfLK5N7nSjleKgMalslQTVdNb0wqB9rjBA5B4NB1bTuv22DB9WxXpKSsYPcmaCM5+UVTubeMsPkUj3FWBfWzrlLiNgehDDmoLsRzINkgz7GsamqKiVpLGB/vQof+A1RuNLgUl0iCH2FTLb35nKxTHb1wTUzw3e07n+oxXJqmaszV0a2lB3R8k9RxUb+HoFXCs4H1rQBnjTKYP4UpmuQuWjGPUVLu2NHGNoYGtpE0zmJunPIq+/hydGJiv5AO2c1bvl8q6juwOV6qRV+K+82EOECg9iahtotGE2mapHjZqB/GnsupR23yyqZh3I4NbLT7gPkz9KiJDc4IpxkyZK5kJLrSnMkEbnHOK2NDe7nYrcQeX7ipI5AAcocfSrlncJ54AI/KtaU7S1B7Fh7dugdsiqhguN5G7jtWrJjPHWmqPXrXU2YlcQyNFh+cDkVzlzeWSTNG022VTjaRXZoMiua1XSLaS/Z5IRubnNZtXA0fDMsbX64lBJHCnvXeKeM155oVrHFqsbKOV4FegqflFMYSH5TXLW13bReIJ41QFyeSa6liMc1mvHYQ3LSrChnbqccmhMlo4XxJbS3OtNdLbyugAX90DnNbMVlrP8AZ1o2nxqsgPzCc84rbup2ePEURUdTxitC1JaBSww2ORTBI5mLwbcNIZpb9omkOZo4+hrW03wva6be/aY5pncDADngVtCnA0ihc0Z7UmaUdKBA3Sqcv36tk+lVJeG5raluTIiPNJ0xSnk0V1kDSPSk6detOz6UnOaYDSM1k6xo1jrEHkXrlVzkEHBBrXPSq1zZw3SBZVJA9DincVjkH+HunCVHi1CcbDkZwRXTvHObAx2kiNMF2ozdM9s03+xbYdDIPT5qdJp7eVtjuHRvUUOTe4rWOKi+Hc8kN7eatMl3qMuShBO0elcnf6Zd6ZpkUd9bbXQkZDcCvVJdOvxFj+0GZe6kVwPjwyLFHyWAGDzSBux57PtkzVEx7TxViTPPPNVSTu74oJuI2RxTDkj6U9iab70xiE8UbsilIzTWPtSAXjpS4GOtIBTh9KBgMihqU/Sg/SgdiPI9KaV5pxHNNJOaTENbNAOBQQQaCfTFAw3Eg4oDZpAaVVGM0AL1xxQRjmnAAdelHFAgB71btc9aqDGauWq5I5q47gzct/ug5qwxHl1DbKGUDvVgxfKa6IszkZ0wzkdfU1QlUcmtKePAPHSqJTJIIpSFErqOalhU7vmHFP8ALAA4qRUG0mpSKbKupShYcZ61i5yKvam+XCis8ZHWpkUiQGpF65qJRmp4lyRUDNSxwBn0qLUJdxxnpU8BCR5NZ1026U4NW3oSVyKAcUE802sWWSZB5xTwOKjGRT85AFMCeKtTT8+avoDWVH1rX07l17GrhuSz0fRyPsfNbdoxGCo471zemylbfB6Yraj1G2tog0syJ9TU43+C0OkveNxSevFQzZ+0DHpWdb65a3GTDIXAPUDiq+p62ttH56xlyONucZr56zvZnaZfxEX/AIpmTPZhg1453ru/FPimbVLFrV7fyU/MmuDGN2BXqYSLjDUwqbmx4c/5DdqR1D9a9M1N5BPa7iCplXivLtDyusW23rvFenapgvbKSP8AWrzmvUpbHPI7AzlIThetZd9qYgjBZH4bsM1bmcpAfm7dawtSLSIOTXmZgv3iOmh8JmTa15140kkjbc8LjpWjFeQSqGSWMtjkZxXJazIbQKydzzmslNWPRgMVhGGmho2ehtcowIDgkdcGnLLuGBgj1rgE1NCDhmUn0NWYNXnQAC5YAdjzTcGFzsirZyCMUpHHIGa5VdauQcB0YGrMesv1dSB7GlysZuFFyTjFV5vu5DEYqpFq8TNh3IHutJLf27DAkBz+FKzBD1baSWOTWxbuGtF9Kw4ZIW53A1sQuv2QbenY1KWoyne/fz3qzpjHy29M9Kp3bgttJFWdMbELfMOuKuO4ivqTsLpgGbkdM1y2shpMZJOOnNdFqhnNw3kwszetZc2mXV7tAUA9yTindKQ7aFDwhpdrqOtvFeRGSNYyducc12r/AA+0m6UGGSe2Y+h3CsLwtBJpmvM8kZYbdpAFd6uq7CFWMH69q6FVgt2YuLOVm+H0ulI16NTSWNP+WZiwxpIdNvJv9VbSsP8AdrshqsrkBthX6dKvR3Jdc+YMe1Z1Jwk9Bq6ORt/BeoX3M8kdsnofmJ/Cnn4TyXgZG1MoD3SLJrrm1aG0h3ykKucbjUNpq13dTqtjceYXbAAHQVpCcUtETJmFY/A3SIl/0y/v7g9xkKP5V0tl8LvCOkwh20ZJyf4rhy/6HitoPqkcZjdPMIHJU5NV1Fw3EttOW7byR/Or9u+xBNHYaJCgSKxtEVeAgiXAq9ANOt2DxxQJJjHyIAcVTis3SFpWSNfc9az5b0yXHlQxdOC5PBp+1YXNPVP7O1Ozks7uMS28ow6f3h6VnwW/hzQrTdb6RYWkSDl/JXP4nFXLW0ecDypY8nse1ZniHSZZrWW3kkG08ZWp9qwJP+FhaPuFtp7rI+P4U2ikk8TXEiEhyrEdFNcO2kJaX8UMFudmPmk25zWlLKYgIzxxz60OoxpXK+t6zqIG46jcxY+ZdjkYrL0X4o+ILV/Ju5VulPyhnGSKTUUM0MhYkkg4zXHW0bJcLyM7qunUbBxR3x0O1upHupmZpJmLkZwMnmqtxocartjVM+ua7DSPDMV7YQz3V75akAhU61autE0qxtjJJNI47ZbrWHNJsd0eeHQ1QEMwzVZtKOcIC3viuyupI1TfFbKF6BiM1RaSeYbAQo9hirU2Byl3pEzQsqxnOKxI9OmWYb9ij3au3uI5HUjcc49a4tUdL2VJTyGOMmtoyJaNy2sl8vJdT9Kux2aGIgDd9ah05CIeavghUb2qXIaIPsSJjoWNTSWCRx5c/Oei46UxdQFuysqBn7bugpftklwTIwJYnmpbYEa2gLgHpWnFbRJD5SqpZuSaqxlgCSvP1qaGQmQcYNNSY2iwIFToBxXHavqZvL1o44wscRKj1PvXcJ8/UgL3NcXqVvHFdTvEB8znA9KuLJNbQIzLZb26EkDNe7aUFXSLQKcjyV5/CvENGiEOmx7ickE17XohB0OyIyB5S9atEsvkgDJrPvfIflnxj0qzPJjCjvWbeRecMJwB1IreESGzhRqH2nxnLZwu6RxcnnrXTSzOmCrk1xNjEifEG7MTZUId31xXf2enw6jasxd1YNgEVs3YzKbXpA+YVj3+q28cyQs5DSHgV0FxoVyn+qdZFHY8GvNvFETDxFYoxMe1sMT25o5kwsztNGVBqccoUEK3ORWz4ytYbjRTKZBDMrDZJ39xXN2sDrIvlXQLfwgHvWzq6Xv9lRvfsDhhgehrGojSJxDWEbnLX1w4/wBkU9bCxyP3c8hH944rSMiBeMCozKg6sK5XuWUbixhmtZYYbIKzKRuJrzW5ha2uHhbqhwcV6DrupSwadIbWQLIeM157PO0jl3yzE8k1SKIMZPSm7TSmRs5xTgXI54FAxpznjFAHXJ+tIw75pFG44zxQAZAIw1PZwwAPOKjKhWNLux2pAOAB6CgKMEGmFyowv403LZBNAWJSRjimlgBTeSOopGz60rgSBgRwKA5U54xUOznqanjj3NjGTTuB1/hy9tntjbllVz0PrXZJaRmzVRLh8da81s1WEK2MYrpdM8T28Tx21y5Jc7VOOlFxD9We+tWA2B4h/EpridRvPteofKpLDg5Neh69brKgFrM20jJyODXnF6JLfUQNgBz971pjOks7gxrGgcggdKs3F8rIyyL26iqkXktGu7IYjrVe9hkVCY23Ke1Aiq4jkcmNsg1PbpGB0BNZkTMhcY5qxC8vqKCWbcKqCOBVtXTGMCsaPzT/AMtD+FWEQk4Z2/OmI10eMdCKlW4jH8agetZiWykj5ic+9WY7eIkAqDQBfW8g5G8E/wAqab2AZwWJ9hSJDEg4RfyqRVXsopAQm/8ARHP4VT1e8lbSpl+zsARyx7VqdOMVna1If7LnH+zQxx3OI3elG7BxSAjFLwelI6Bspyh+lbOgK0iAbyBWHPzGfat3w+6pGC3TvTM5nWw2qlRlmP41ajtIQcYP4mqsV2u35VJ/Cp1u2PSFqZgWktokbJQVKsaY4QflVYzzkcRY+tCvc9cKKQFoqAOAKAMLmqv+knqwo2Tkcy4HsKALi9KazAZ5FVhAeMyN+dN+yoWyS350AWSy4yXA/GmG4jXncKjFtH0xmg28aZJUfjSApT3CeerHpmr32uJotoDcis91RrhcgYBrTwvlkqOcU3sUtzNCWhJ8242nPTFLjTkGWmdh7CsyUDznLetNbkHniuduxukaTT6Zu4EzUv26wBytqzY9TWWCqIT3pkeSCfyqeYdjX/tK2HC2ijHTmkbV8crBGDn06VmZwetBw2OMUuZhymm2sXBIOEUdsCom1K6PR8CqWc5JpFzTcgsWzf3PIEzD8ai8+Vzkytke9QlTu4FADdhxRzAPZ2bqxP1ppAPOOaXaTz+tGxgaSbCwmABx39Kdx3NAQ9xTvLxySKq7FZEfRs04Nx6U4IQTyKTZyOlF2Mbzjg00ZzipSqpkbhRsQ8lulAXEIwtMB4p4eIZ+cY+tMM8CkDev509QugyRxSliDTDd2g6yoB7mon1OxVgBKp+lNJiuiwWytB6D0qm2sWIGfMHFRN4gslHUk+y1XKxXRbYkP708FiMnpWW3iC2LcITjrTG8RxgZCE+1VysLo1wGyeM1IgO8Ag1g/wDCRjJxHirlnrRmYEx9O9LlZLkdvYaUl3CrSSEAdAKvr4es1HO9vq1RaBcGaxDlcVsK1WjLdlGPRrJCSIs/U1MunWiDiFPyqxu5pC5JxiqHYaIIEHEa5+lPCDHAAFJnmgnPegByqPwp3B7CoxmlHXmkApPNJyO9BxRjNMByvTtxx0qIrk0DI4oEX/JituQu4n1q9E6vH0x6VXHzgZFWQFCYBwa+ZvodzGhvvDFMMKht3FLGTvIJpSOeuKV2IzdTwIGAXINYawiL51P4Guh1LC259cVghHeI46HpSaBDd7HDxuAfQ96R596kFV96ryJIMgLke1Qoku0ggj61UJOOxTjoW7dQGJ2jFWTFJNEyxKnSq1vDI8eEYZ9zV6B/JXbMm1vUGuyLb+J6GdrGYNPSNWeS2RXB5yKzb6KFhgxKB6AV1gdJOA6sO4NZGqRxocogBHYVM4crumUpGZYLCIcRrjB5rbt4Q8ZLNj0rKtFaZCUXAFXRcyRLsKZFYzb5rlj2h8uUsJhk96rxx36XMkpmDROMYz0FDTwu23aSfQVLDF5mV3soraNRy0JaIoYmBcrJ68E0+G6lB2sFZfcUkVmryMPOAPv3qQ2xt2ILAg9MVVRTjqkTFoz9XmEtsVCAe9VdOh3WgL9RUupvmJ1AApuiOPs2056nrWCbluXexbCbACoFNXaG+ZM5pZ3Xzdo6e1ShNsiADqO9bwpOLu2Q5D2nsbfaHLKznAHWiWW1S4VBIdxGQQKdJbxSyKJIgW7E1YFpsIPlBselaqEW7oSfcg+1jdtaVwR321UnuboTAQXHHfK1auIwxyV21UlVVZT39aylWlezK5Uxtw2qmMhLthnptGKzEsb64uRJPeXBIOMhq22YiPOeMVBBh2I34zQqjbsLkW5e0iGSK8QklsHrXcqflFcRprTQXICkEeua7SI5iXPXFbMglBpgiTcW2jPrS0vSkhFe7wI+KSOaNVGWAqvqZIjzjP41Wi+YKcZ+lMZtRTRy52MDjrUmfSq8CALlVwTVjFAC0optOHSmAN0qtKMmrRHFVpeDitqZEiA8UnWlNJmutEDcc0nNL7UhFMApO1Ljim9qAAik7c0tNb2oAhuDiI15Z49c+UqgHO6vULo/ujXlPjx8qgOR82eKLGbPPZTtPPFVWkG7ip58nvzVRlKnPemNEhPNIw9aaCcZzSFj1zSuAcUh+91oOc0mOc0APU+tPyCvNRheOKcMimAucdaODSkcVGx70hiMSDSE0Gm9qQxDQRzR1PJooBigU7pTcYp4H4igBaQinlce1M5HamABc9qu2wIIxVNMk1o2ozjI6VUdyWa9sxCgDirW7jrVaFcKOOalzgeldKWhmyC7zsOPSs9MgjPIq7O+8VTLAMflOPWk0JDi249KWRhHF+FNTk1U1GbZGQCc4pNWQ0ZNzKZJic5pg5GKjGc5NPHFYt6miJADVm3HNVk5IFXYFB6UkDLchKxZHpWXISWNaM7AJjPNZzdacmCGHpSc5pTRUFCjkYpw9O9NB4wacOtAFiE81t2C4kUgdaxYF5HFdDpaEMvp71pAlnYWWBa59BXJ6/fldS8vnCrXWx4Szz0+tYVzpnh6a7e4vdSkErdY0bp+lGJaUNQo/EWvB9yZ4bgkg7WrZ1ZVaEZxwc1W8OR6JCJo9LSWRjy5kJq1q99e6fGGTT4ZQTx3NeDUSc9DvTOI8QW+LQyBTjPXFcgowTXc65q2r3umSJLpzwW5HzN5eB+dcODmvQw11GzOerpI0NGJXVbduuHFejaoFZ4ZDnKuDXm+lkpfREf3hXot++beNiDwQa9Gkc8jrE/ex8uAMVDewWhhX94zSenpS2Q86QBuhHAq3eWUJUHYufWvNzF2qI6MPrE4TxPpUc+nl45ZGlTkKB1rgHS4T78Mi/VTXt5SOJvK2g55yap3tuDg7Vx34rClV0NXG54zvK+o+tL5zdc/jXqE2m2zBt1vG2fVRWfJoGmTH5rRB/u5FaquuqFynA/anHc1Il869zXVz+FNPzuUzJjsDkVRk8NxE/upiP8AeWrU4sVmZS6rL9alXU2Jw0dW28NXABKPG2PfFRnR7yM/NCRReI7Mkt7ppSFRCCe4NXzcXkGAsr7R2zTbSweIDKEGrpgbaSQelZO19CjPiuppBI7ncxOOas22qS24AWL5R1GetFvBkMAvf0qxFpjyNwOKelgNmwvxc24lkjIz2NU9T1z+zQPJsxIT6npU0Mf2WII3WsbWyHxisFG89RsZF4q1S8l8q1t7eM1fsLnXJL9PtYQRd9uKxvD2hy6xrAt4VYtjPBxXbReCL7THN1Kx8tOSDJ2+lbyjBdCNS8rYXjn1q1FdGNchR9M1RRxgbeRVy2liGQyFmPesbCZBf3Hn2+yRFKk55FR2dtLbTxyQXIRmOB5fGK0L+GWSxeSGDJRcgZArF0z+0WRneAqT90lhit4Gcj0XTrqSODy4pzI69SDyTS3V1dSPunEoI4BbgVl6E99aRoZBEJGOcqMmtS7u5pJsTvNIOoCjgU73JG2k0kauZGeReu0npTohqFwu5bTEB6EkDAq3aJaz2pIguxIP4tvFatvt+wtbxsZHZT8rdqdmFjnGu3sXUJGhYnnnpU91dpdJumVlb0FNubN7Mjz7c7jyuDmuZ1a7uY2LqzKfUnoKEhnMatquoJdy26uIm3YBTqBTtIaSaBzLKzkH7zHJqnquyRmInxM4zluprR0SCRNKRCuHOWJFOSBIS8UeRJyAADzXJQfNKPrXUXUAm3K7H86woLcrM3y5G6rp6FM9E8MarHZ3MX2hfNj24Cdce9dNqGs6FkM1i9w/UKVworjNMawiRGkLLKRjIrqI9LW9jVIZd24cn0rNtXJsc5q2sxXLHyrMxjsqnhazYblsfcGT69q7NfBjGOR5IWGOhDZJ/CsyXRrS3EkcobzeoAyDTsxnNXUjbGK9QOorgfma4d5GJYv369a9QmtolTaY2xXEyWYm1F5FjOwPiriJnRaRbNcCOMDjAycdKnubU200sZGADx7iobad4IisYK5p73EkzEysW+tS2NFcwbjyoNShVUBQMYp28HI7U0Mo70rlChN2cVoWWnGcH94AQM1ml1Hc1NBfGInazDNPUDeNhawWZD3O+TuFridZgFreIyjEUhwAeua3Y7pC5aQ9f1qhepDf3lu7HiNs1UWSy9awqlssYznFev6HxoVnlt2IhzXn220hdI7d0mG0FvrXeaC4n0OHaAvBGB25reFzOQ+8mDMAOg71n3FxiM4bAxU11FJH2J96zLnLAjPauyOxkcbo8hj8UaluUc87u9d74flP9nyMvUyGuLtoCmoXc6ofnON3rXZ+FQf7LYEc+YetOWwmaqzk8NwcV5T41tXl1y2fHBmr1iTA5K5NeQeI7qdvFuOqI3Q9BUocTRhWdZVZFYsp6rWprVzqN1aRW9xKyKMHHc/WsNdUl89FkuTFFn5iq84qz4n8V2/9mommWpbawzLL1NRItIqfY2P3pGIp32NCfvE1zJ8UagwIVIVz3xnFV49c1i55jOf93ArjnNRdmdlDCVK8XKFrK17tLe9t2uzOl1HT4JLGRXYKoGSa83mlRJGUHcAeD61v6hqmo3dp9mlUoM/Mdw5qha+G9TvkMkFhcSp/eVSB+dCqxff7mb/2fVXWP/gcP/kjIabJwFo81iDnAzWzN4Q1cAYtTGf9pk/lmmL4evLckTxIzAf89kGP1p+0j5/c/wDIX9n1e8f/AAOH/wAkZAbPelDY9c+la7Wy20ZaSKJcd/tCVkS/YmmLvcNyeFEgwP0pKon3+5/5B/Z9XvH/AMDh/wDJEyQyMM+WaetlPIeFxWtayvLbgwwK6DuJlzS/bSrsptvmHUeaP8KxlXa6P7maLL6jW8f/AAOH+ZlvYSRxM8hUBeaz4J1uLhYI1Ykn0ro2ulI+ayBHoZB/hVdtctoSQlrFGRxkgn+lEa9+j+5jeX1O8f8AwOH+Yq6WqYycj2pZNOj8o7EJb3qB/EakABo1+imqsmptcLIUnBKgsQFI6UKpbe/3MX9nVnpFxb8px9e/kx1tpd0bgGXYqA8jdW3DYgDjZXHnUZcn5jU0F7I8yhnOD71rKLscCaOpltowcPcouOwNR2tpbyajCqkyEuPmpscMXlqQvNTWjiK8hdeNrg1lGTuaNaHpeqtFp2mGSaLfFGgJUD2rlA/h/VsEfu5W7EdK7fVs3HhS6JUFntzg49q+fU1CZEHz/MCRxXTbQyR6PPobsQbTEigdjWHqdrd2ud8UiY9qy9E1fVW1CGG3nYKzAEHmvRfFl21loyl0DnADHHSkriPMo5WLsWP1q3FKu0c8inLc2F1kFQhbqemacIYY2xGwZfrVXJaLCTqCBzVhbgZ+6fyqvCy54xVpCvai5JILpuMRkVPHcyEgCPmoQwABzUsUqg5yKBFpp7hk+UKKfHLc4ABAphuYwgG4Uq3US/xii4XHslxIwJlI+lZ+tWzLp8jvIxx0GeKujUIR1b9Kzdbv4305413Fj7cUFQ3OX3euKQdMA0xSDkHrSnHTNI6UhkxOw5rpvDuGgQY471y1x9znOR0ro9C87yE2EKKZlUOziUbRgcVaUqBWTDHclf8AW/lU8cMpJDStxQYGl5qgdsGk81QMkgVR+xsSSZW/OnLaDGSSaQFpriMdWFRteRAcP0qIWiEjNP8AsqdQopoYn2+I+uBSHUUzwrE/SpBbqOijmgwjcPlH1oER/b27RtTJLqV1P7s81cMYx2pjoChxQNGRLJIHTC5YmtGFp8Zc4GKqKcXC59a0ZHiS3ZncDA9etDGtznJVkkmcrnbk07y2wADXNan4jvIbqSKLYqA4yRk1lv4g1CQc3BUf7IrF0m2bcyO+gsY3JaWX8BTNsSkhW4rgRql46HNxJz71XF9Pn/WuT/vUexYc56P+7Ayx4prXFqp5lQEe9ebm8nbrK/8A30aYJmI5Y89yafsRe0PRDqVmOsyfTNRHW7BW/wBcOPSvP9x65xSbiO9V7EOY71/EVio+8T+FQnxLag8BiMVxO8kUqtxQqSFzHXt4ph5xEwz0zUUniw4CrBz65rlc5Jo38c9atU0LmZ0zeKZc8Rrj61H/AMJNcEklVx2rnc5570HJAp+zQORut4kumJAx71A/iC9IIEgxn0rI3c07vQoRDmNFtWu2JJlbn3qNtQuGHMzZ781RLHilJyavlQrlv7XNjhzTPPkbqelV9xHQUBifwo5UBOZWccnPpTd5LcVHmjJDYoshXJd55z0pAxK9eaj5zjNKDge9MB+STz2pC5Bx1NNzgUdeCaBkqk45ra0/iIY71ipnjnJFblmP9GXFTIk9R8N/LpietbYbisPw+w/s2MZ7cmtkNxWQIk3UmaaDk0uadwFAzzSnmmqcUuc00wFBpc57U3NIelMB2etOBzUeaAeaQiTPNB9qZnml3UDNYE+WCOlS/eSoIi3lAghlqZd5HGK+YudrG2/yuQaeTufFR5xJyecUMxBzilcRW1AD7M4Y5GKxY428oFWIx2rZv9z25Azn1rAn1K00yz33kvlqDgnGaa1WgEhxklhg1C2CDziqMviCzu7dpNPkEwH4GsTTvFCahdvAInR1655FVCErjub5m8pTtYqc9akEzSKC77vSsy4uE6EmlS6JQbZBgdilaNMaRqxkBwQwz9aLskoSDk4rKS5xu3EZ7YqRL0yLgDBFLUVibTrx2LoYiuD1PerLyr5oBGTVO1l8zeCu1h3qaNsHL4JpST6FaF0WtoJfOwVk+tV57k2Uw2MGDdyM1DPdjeqZHNOns/OjBDgfWtFU0WmpLQ9NRdZCyohPcEVML0zuA8IQ+oPFZhCwsAX/ABxU3nEAsjhh9K0lUk1YSSQuoOjQupUZ9xUOgxLLbuuBkHpUM0ryI4xn3pvh3zDLKEzwwzWVJa2KsaMlnItwTtOPpUrFfPUdwK2Bkx4bGa526gkj1AujEqe3pXbV6IyW5pATK+/ytwp/2yPPzoyH1qAXE0cYwelQSXBn++vPtWKq8uxVrlm6njlTarbj61nXAAH9akEipwQabOVePg81lJubux7DliHl9SSaiaKRSPlxz6VJG8qqAApH15qx9q4AYZH0pRiuo22OseLqMfzrtYv9WMelcVCQ10hHAzXaWg3QLjniumL0sZslHSipPLIxigxY74qhFO7jDp0zSwIoXAUCpbny1TlhSxNCoBznimgJUX0FSbSRUZu416Lmozetn5VAoEWhGacsdUvtUh/i/KjzWPO40XAmvLqCyj3ytx7DNQs6zRiRMkHmqOpHMQzzzU6N+4Ug/wANbU2SxGO3rxTN3oagmYljmoS/uRXWiC5nHWjOBwaol2xwTR5rj+I1QF0mkzxVP7Q470v2h/QUAWutJmq/2lvTNIboAH5aAEu22wsa8g8fTN5sag9TXql/dqsJyD0rxvxpcrPfBR/DQQ9zkpDuPXmoWOTzSyOVPWoGk5pgPJ/Kl429c03IIopAHPfr6UA/NQaVeW4pjJAO1OxtNMUnOaeeRSARjkYxUbHinNkioz05oATORQTx1pCeaaT60mMUdaXvSdaeOO1AMULmnhccUgzTxwMUIQhzioyTUpOeMVGwpgCDnrWrZrjHNZkZ5HFaloc4q4ITNePhcd6STAFLGflFMkzzmupGTIWAYdaqygrnHHtVwDjmqdxy3WgLhGvyVjarI3mbe1ajOUiJrDu5PNmPcCs5scSBexNOFN6U8c1zmo9BV2AdO1U4/vc9avRthPemkJjbhhVUn2qSWTefeosgH60mCDOTz+FJg5pSO9IP5UhigY96euC1MBzxT0x+NAy7AeRXR6UA0iZxzXO26ksK6jSYiXQCtoEM6G8Ii08sTxT9H8MaRPbLdyWweaTJJYn+VSapbsNAmcdQua2/DkaN4ctZMZcpXJj3JwtE1oWTuyfTtKtLRSkESpn0qzeRIQgK9PUU+3J8zB4NPuRu2+teG2zsOX8Xp/xTV3k9FyK8TRsnJ6mvdPF0GfDV4STxGTgV4WvtXo4H4WYVXqXdPbF7Hj+8P516FqU3+iRnryO9eeWBzeR4z94V3d+W+xKADkY6161M5pHZaW/mSpg/w/0rXuseWOO9c9ocrPOnAztH8q6Sdc4BPGa8zMvjR0UPhMiaMfahkcYqG6K+WPrVy/4mCg9RVC52rCAea5IbGxUOM+1RLEq57ZpHdVPU4pA27ODxTAacAEGomRDxtFSHrRhT1qkwIfLRRkcUpCsAOtPIB4qvJ8ozmquJ3I5EZWJVuPSrMUYa3y45NUWkbcBjitGNs2nAxzScgKtvCsF5vk5g7gda1oJ9NkdsRSj61lu/JGKdA37zg1XMBYurS8upc26iOL3PWoH8PSzxt5txEjDoWbpWiGfYDuP51xniQO93uDyA46buDWcbylYbZ01jY22jZf8AteGOY9WVwKmnubW6kRDrDXEnZRJnNch4V06wvrqdb5Wcqvygmuuh8P6Zav58Me116c5rp9g7XuZ85fiZYx06U9WUTCRXAHcVULEGhLeSUnaDWQGne3K/YJZDNGuxScFsZrk9M1S+m1K3imn2wu33VHQV0E+lbrNzLjbjkE1N4X0awF7JPfupVV/dqDVxZLOotrlAUSIFgOncmuvtrmeS3RTbKygfxDFZFrfaZahTZJFEVGN2MmrP/CRWy5Ml0CR2UVSlYzN22BMWDCsYP8I6VJFbwwsTHGFJ6kVy58WlF3qqPH2zwaa3ii4lBwgjBHBWt41IpaiNHXr60ttq3lo8qn7pVsVyWq67oSWEtotkIftHyNI7Z2++abqd1Pcyn96zyEcbhmuS13TL65s2wNxznbjrWcp3KSHal4QQlbyzuTcQqPnJYfpWzbSR21pGjpghcAY6U3w7oN2kCC4QmNV3EK3eqOoXDNO6qDlO1Q3cpE93BawRGWZ8ZGcCuR83blk4DN3q1dXbnIck/WksdPuNQGLdFLBv4mAFXB23Bo1YgAiNnnFdRo+tpZPG043KP4VqinhnUzGg2QDjG7zOKe/hua2OLi5Tcf8Annzis2rsOh103jzTolAjgndsdMYArnL/AMUz6hKWMUaDoAByPxqoumpbP5rTAqvJzVS5ntJWJjdS2edorRzbWpNkJNfzYO5Qa5azaRbyeSQYy5wO1brvEvJUmqzyRjJSMfjU8xaQiuTz2oLgZqNpm2gHAFBkFFx2Jd64z3ppf/Zq9pFgmpTsjuUCjJIFbP8AwjNoOs8p/IU0mxNnKMSegpQpOPm5rqx4e05fvNKSP9uj+xdKXtJ/38NXysXMjlhDuHzPSrbqp+9XVrpGnAMUiY4Hdia5guqTsOwYgUrWC9zoPDAgOqQRXK7o2OK9VRI4YwkaqqDoAMCvFItVOlSJepGshQ8Ka9D0vXJdW05ZpP3TsMlB2rroLmiY1HY3LxxKhUMQBXPmJpC+SQtWJ7hjFjceKzlkme8QeZ8pPSuyMbGVyBLOVFlfbhf4RV/RdRXTbWRLyORQzZDYzViZmhjBArOvJt0e0sT3IHShpCOgXU7W4XMc6/icV5XrTeZr0zAj75PFdQJIcDzABWfqmkWdyVm0/f54+8GPBpNWKicvdyhRwcmqtxL5mmFMD72as3VtIkpjuF2npUNxZyJYSPGCyL1PpXPK5ujGAI7Va0b7i5HAB/nVFpMg4J/On2FwIYixJwB0rmkv3i9H+h30v9yqf4oflM3Zdb0bRbY39/CLmcNiC0X+M+re1crq/jfxVq65lvmsbck7YLX92APQkcn8azrWNtS1SW4mJKKSRn9Kk1V1ZgAR9BWkWo7HC48zuzNF7fFtxu529Szk043M8hG6R/rmolhkOWIwvvVpLKZtuExn1qnLuS0UZgwYryR7mmrGxOB0NSXBZJivpUIYtIAScZpthY2bIvYyLMMnjketdHf6eJdNg1SFvlkwGFc+0qLbrHnnFW7fVpRpf2NmJiVshai+mpS0ZOhJibJ7ViXSkZJrSF5lCqxHPqTWfIfNyhB3VjHctsy92TVi0YH7R/1xanf2dMx4HH0qzbadLD5hYH50KDj1p1muR/11R15an9ah8/8A0mRm5O3OKdHKfMXvg1qQ6FcyxliAoHTceTVmPRGUAeXlu5q3JHEos14SXt0OQAR3p8ELSX0EEciCR2AHPSqP2G7KqpfCgetX9MtWs7lLgsrOtYJK5bvY9OnF/a6S8DNHLmLHPTpXid5omoWvmSPaOYi5w6LkV7qWtbnw8HuW2KU5Oa5qLUlW32WdzBPCvG04NdC2ITPLtGuPs2pQOOofkGvTvFM/2jw1Nlhnys/Xis26XRbiYG508RyD/lpHxz+FS3iW11pclvb3ilmXADnmgR5Yk3IxWhZTOSflzTLjQ76zc74wyj+JTmlt1ZAcjBoYzQtt7OecD0q+IW7uay7WZVc7m47VoJdqB3/KgzaLItsjHmN+dSx2i8ZLH8arrdrnIVj+FTrdSEcRN7UxFtbVB/hViK2QD7o/xqis90f+WI/Op45b1iP3SqB70hFv7Mi87Ris7XIgmmuQOKtt9tbjcgNZeuR3I08+bOCmfugUFwOVzzxTt5NM+ppwPtQzcSZspyOK6rw+QLdCxGMetclN9yuo0C0jeFCQTx0zTRlM6xLqBEyXA/GnjUbUf8tBUEdpAAB5a4q0kEAIAiQd+lBkMOpQn7mT9BR/aS4+WNz/AMBqyqRrnCqPwpwCgZAFIRT+3SZ+WBzQbu6YALb457mrecHpTm5HHFUMqB709I1A+tPxfEdUGKtZAX/69NV1Y8sOPelcRVEV3glphyeMCgwTFTumNWnniTgyLnsM1A93EEILD8KECM3yi90qCQgetaD6fGYyWcnjqTWZ9qiW7BLYFavnpNCyK2BjqaGXHc8m1jB1CYZGNxHFZp9R0rQ1Vs30w4wHPP41nk1SKY4SELgCkGck5xSdBzS9V96ZIv0NHak5AoGSaAADJz2p5HakH1xQDjvSuAHIoAJpM54xxTqYCkYXrSbcjB7UZGPek4zQMd79qMelNBpe9AC+/SjOTxTc80ueSaYCk8YNKfypBgGjIxSuIUk5Bo+hpOgoPtzTAcM/j3oGcUgBzTu2cUAIaM9B60nXgCjGaYC4pRwfekOc0ozimMlTqMV0ukw+aEDfd9K5iPhhjmuv0VtqqCMjrWVRgeg6SpS1VcAAdBWkG4rL02XdDWgvTrWaJJt3egScUwE4oHBOTmmBLv74p26og2e9KW4pgSbsUoOelQFqUMfXigCbOaQHBpgbFNLHqeBQgJS2OaA2ecVAH3ZPJFSq3ANAG1FkW42n8KkjZyOvNZ2lXn2mwVyME1aSY7uBXzNrHdLQk2M04yanaPbgnms+e9WGdcsqn0JrL1jxfZadlJJC0mM7EGSaCTduSBAwXFcrcS28qOkkaSDPKsMiqth4u/tgSpBE6AcZbrmuXuYdZa4kzuZSeCo61cYa6uwNM2ks7a3YusKR7j0UYzUf2Oyt5mlht1RjySoxk1zUlvqwdWHnjB49KlXUtbiQxvCGwerrzWypdpFX0N1cM+WBPPpWiMCMeUoJ965qbVL6Gy81LcF+681my+ItXcALH5WfRa0dK+lyFJnVTRtLKMrtPsKsraJBDkAk1xSeJdaiP3o5AOu5BV+HxjOq4ubUE+qdKHQ00Y3I6HLtE6Rttf1rPsrLVIrlnmvxKvdGWskeIrhnMkUAAPXcauW/iUhA01uS/fHSpdKSWjEnrYSXRLqTXBffaisZ6x1ujeZMLKduOlY512znYl2aI56YqN9TtS2VvSn0zU8re5ZvT2+xcqWJ9M1U8yYAqVbH0qvbapAqgrePMe+e1PbxBaFSDOo9fWrlCSV0jNSV7AZzbg8Ek96l0GcQvPgkMTn61Va/tJELrcRlfrTdJvbNb2TfMgU9yeKhJrWxpoajeKZYbw2rWc7LnHmBciknv7n7bGoizG5+9jpU73FmxBiuYif98Uzzkz/rEYZ/vZrRScuhDRbMjFdxTj61VLyNJwpxn1xUpkDNhXTHrmpSdyjAVvcHNS4sEyo+8McowPbnNIkocEHg+9WH3joMD3pjouCTjNKxRKkaiPe7AYHUnFRTSrFAZXZRGOd2abLbW95amG4G5COgOKpxaXbW9u8EKssTZypYkUrJgdPodtY6lbLcLcFwOQErpopxDGEQcD1rkvCNpBZW0kUGQuc810rda6dFsZyLJu5CetRtMx6k1FmgnilckiuSSmc9KkgbMSn2qOb/AFZ9KLVh5QHvVXGWM0u6m5pfagQ8EU8GohjNSrgCnYZXvFMkfCk06PItlyMYFSyt+6ODzWfJKzRBcmt6SuyJA7AsSKiPPWm5OKXJ4rrRmJ1oJo5NNzgUAHajrRn1pKBhSHpQTzSHpmmIytWk2QsM9q8X8QXBk1CXvg9a9W8Q3Jjt5WHYV4xfTia4d/UmhEFJwTUDKAetWeDkGonSmMapOOKdjjFNHCmjnPApAP6kU5Rg00HBFSLz2oGAXHtTs/lSj0pjEZ60CGnrTG9qU9frTGPOBQMRvakP86M0d6VhigHPNSY9KaAakU8+tADwOKMc9aUdOlLj8qAbGsRjioyew4qU49PwqNh0oJCPhhj9a1rUE+lZUaktWxZLkZraAM1Is7AD1pJFJxzUkYO0ChwdtdCMmVnXjpVORCecValJB56elV2dSMntTYjPvZQkRGKxN247quanNvYqPWqC5xXNNmkUSHmlX2po5p6+tZlEiD2qxu+XpUMeDUjHA4pgRv161GOuKe2DTfakMM0opoNO70gF7Uq8Ed6QdKemBQMv2pHWur0bJdfeuTtBlgPeux0RTkHAzW0CWdZqMDN4duW80KojOR68Vc8KIBoNs5m3fKflx0qO+t0m8LXG7IIjJGKm8Ixxjw1CRneM1w4iV20aU0bKlS3GM0kwVNr9ajhBEhNSXXMa8V4jZ1IxPFRDeH7xfWI446V4GB0Oeele++I8nQrobRgxN/KvA+h9K9LAO6ZjU3LVh/x8pz3Fd3dndYgk8gA8muCsv9ev1rtpPmsgG9K9emznkdZ4ZG9lYcgL1zXSXeQoPTmuY8Ik7gB8qheK6m7OYuPWvMzL40dFD4TPvQA6nvjrVC5YGKrt6G8xMjHFZt2f3OO9ccHobFJsHjimfMp4Aph3EZ54pA5P3s1dgHF25IWo1kcZ3KRSGUE8dqTzCTTSAVpMjIyKryvxwxqYNnjP51EzbDzjNUBXUkj0rTgLG1J9KztwLZrSg/49qjqBTWbeCSMc96khYeZxzUMrDzgo6VJbkBzwasRYOoAKYwPmHGMZrndWi1C5lLrbAxKPvZArp4rcY3oMP61kavqY05lSWBpN3vSTtLQdjP8ADc8trcyyPZ5UjBJOK6621FLpliEDrnv6Vy2n6tdalO0VlYRDaMksegrqbUyJEpYKHx822t3VnFaojlRs/YLZUBeQIMdepNC3q267LcADpuIyayZLiQKSSeKwY7u8acsZXAZ8BayvcTVjoNQuM27giRmemWyyJEo2YwPWhW4Gcn1qZcHtS5rBYuwTTRqSWC8d+asWkNvMDJdXpBzwicGqiRKwy+OPetLSo7Ybi0SsexNNSE0i3EljFGJI4WK9iwJqzb3BvHPlxSSbP4VXgVKbqKWHY7x7VHClq5+b4kpZebZx6Jt8tivmRsBmndFU6M6nwq500t9B5PlG2bzc/wAK9PxpIEsnGJ7aRz65wBXGQ+P42Rk/ssgnnd5uTR/ws27hhaKHSrby2P8Ay0Yn86dzb6nV3sdrda/a6TE6WNrvk6HPQVxd9r9xIskSpEgf7xC81jXvjLUb3JaO1iT+5CpH8zXPjW55rn5o1WMtjg81cbvYipQlS+I0HtXndmaXAzxV3TYGiDIXJ96pmYr0GeavWjbuckewraSXIYLc7LQdRNuot5ELBjgHqRWxfeVHlo4pJBjJLdBXJaZNNbzhoCA54GeTXWXFnfJFHNf30abhzGT835VzobOf1Ga3aECRnQZzhVJzWLarDNO8kcRjA7ueTXVXsNldW6RQPK8+eSSAKwH01oZnXzMn/Z5Aq7k2VyrMoHAyR9aqPgCpZTtbaWOfeql1u8lwv3iKi+praxFPdQpgGRck9M1MHBUEHiuLa0aKF2l3Cdm+WusteLaMHJO0datqxJ1Hhds3EuP7tdOQSOuK5rwqMyz8dFFdMYwTk1cSJFdoeuX5qLYc1bMa+lJ5QPYVZNiNAFiYk1xEyq0z4b+I120jxRxujSIGxnGea4pULzEjpuNRIqKJI7VZFVXPy5B5rstBctDM23Cg4H0rmI4mJAFb2mSObLHQ5INdWHlYiauajXICOO4rn9L1h7zXGiKFVTOM1oLEwLM78GsawnYa48qIoABAruUkYcp0GuXdyukXPkMRLsO0jtWJoeoSvpg+0P5jjgse9W9U1BlsJwBhmU1h2NwkWnKFHB5qWykjoYpY5YSzKCc1k3d2kTMBuH0NQNqMNrbAzMVGccVlX1yJXZ0bC44pNjSKdtqk9xc3HmvuUOQFbsK07bX47WN4XhEkbjkVzlkypPL3yaWeRQ5559K5pSNUipdyxfaJCi7EJJC+lVxIRG6BeSODVe4lYTtx3qeEs0pRRgnuawk/3i9H+h6FNf7FU/xQ/KYulW7Lay4GctioPsIEzLEhZ+7N0WnmV7CZlJbDH8Kr3t7Nt3QyFQeuKfocWxI+kzsyvvAGQeTXQCBBaAlVBC4zXDm7nklAMrZz613CKZNNTPQLXPiW1bUNDnxaQuzNKVGT3qJ9LtdxZrkDvxWbqMrC5YZ71VTfK33jtHU10pNoG0dGRZx27P5m7aPzqnYyJhmfOT0Aqkpe42xp/q1q4sRQhRjFNokvLJEDkRk03+2rSElRa5I4JqEF1H3uKyJhl29zUcl9yuY2z4iVAdlun401dce6Z/3aL5amQbfUVzm7Bq1YEn7Uf+mDf0qatOKg/wCuqO3LZP61D5/+kyNBtfu3PL4HsKautXTMAZDjNY+TgmnxHLgk4FauETg5mdbHdySqPmOKktJzHexkk4yOPWq1uQkQJHUU+OVvODxxnjpx3rJJXHd2PTNanFz4TcQ7lxHz+VeLR3UsJ+R2U57GvaII7yfwm8TIpuJYyEU8YryW88O6vYlvtFk+F6snIrYRb0rVbua7jgd2kDHuOlaGu3MFsEb5vMPHHasXQGEWqKDnOD2q74pB2xsSM5oEUl1mZR8srEehOaZJqLOPmQc+lZK9RxVnB2jnk0WA1tP2uxYrxWvGBjGKxLJZVGQRWkivj/WYFIk0VwB04qeMgY54rPjjYjJc/nVhIhkfMT+NMLmgHHQ8c1ZR1z94fnWelurHBJP41MtsvYH86RLLbTop5dR+NYviK7iax2K2SSOlaq2sPdMkVkeIkRbABUA5HOKCoHL5yOaAcn3phHGe9OBJoOgZM3H1rq9EmeO1iAiPSuTm7fWu00Ef6NEWPbimjKZtR3E2OIefrUyy3RbiNR9TSpIgIUsoP1qQ3UKD76/nTsZCCS4PB25p6+eefMAqBr23X70qZPvTP7St1bAlHHpSsBcEM7g/vsfQUotH7ysaqNq9vGv+s/Kga3bnkMxH0piLZs8nmRz+NPWyiHJBP41QbW4z91HP4U3+2uCVhfj2pAbcGhfaE8wIoHbPeluNBeOBnVAQOuBWlpEzTWCOy7Sexrr9OtUmsCGAOaa1GeT6ZojX+os7uqRxfqa1tR0uO2s3YN82PStp9L/s/VJyv3H7VjeJp/KsSAcFjipldOxcUeK6hzdzY4G4/wA6oc9quagf9KkHUbjVPHPWrQ5B74pw6daYQQAc0q8c07iFIOQacATxTckmlU4pBcMY4NHtjig9eKU5oAF9O1KRx3pAenelKknigBeg6Ug5PHWlpQetK4DSM9R+NOB4IpMelC8U7gIPelx60Y596PvZx1p3EAz9aUgfjQeR3pAec0gHZA6mm5NLletGAeQOlMYoJ64pBknPelByaO4xQITkcUoIoOQ2ccUfQYouMRutKp7HrSH3oAGc5pgTw/frsNHH7pD2rk7RSz4HNdjp6eXEuTjjmsZsLHV2E2Ixg1qpLnv0rCtCfKU4xWhFJtx1oS0JZqK5HSl3Z61Xjk3DrUuehzxQA8kgjinZBpAwoyc9OKAH0gXnrTRn0pwx1xTuAvXpS7Cw5xj0oDUo4FAxNoxilB5xjNGPelFAHNeCNfnvNCEIhAkUcHNY174l1yPVlja58uMPgqorsBe2FsSGeGE/7IArktXk0i5ulkS9Tzg3Qd6+cTU581judm7nQiN79IZ5WJcc7qpeItL+0eW6Id4XqO9Z91rj6ZpmUXeFGV5rjpPG2qXVyoEgSM8beuKcKM5SvETaudfoCPpBlNzGQhP+TVw69p5YnzSRnsKox3izaWHZzvK965WW/ZJWDIhH0rSMG5O42ztP+Eh0svhpyv8AvIRUo1bSpQc31uD/ALT4Nec3GtLbrzboTnvWfJr8Mj/PZoce9dEaMnsjOTsepyXVoq+YZowg/i3cU+O6sHXIuYCD/tCvNF8XQC2Nu1iCmMcNUaeILDbg2bD6EVTw8iVM9V+zWrJnbCQe/FVJbLTy2WjiJzXndrqGnzT5VJ1JPXdxWyTGUB3MRjjmspU2tDRand21pZPbKphibnAqQ6JYnj7KufYV52hjIP72ZT7MRiqs179lB/4nN7AT3EhojTv1CWh6VJ4dsWOTbgEfWq83hzT2GGiP5mvPI9dZeviO4f03Oasx+JDyRrsmR6k1fspIjnOyGhWlrlolYZ9TUK+FrOYlyZAScn0rmbTxPObgrc6xG8XYPirEvia9RmW01FNo6cA0/ZyFc17zwnbG3fEsm4cjNV9M0S2ltHimViVP3ga5i58ba+H8priEgnnMQq1pfifUoSyokLh+TuFEqUrXuVGVzoT4UsiOJJvzp0Ggw2jkpLIQexqsviq5GN8EQ9QM8U5PFLTEqYUH0NRCMhtpbiHw9507uLqZcnOAeKF8N3Sk7L+VR9TULeMpILhoDpRbb0fzcZ/Spf8AhLWBV/sPBHI8z/61W4zWwk4tDl0nVFyq6mwx6k80r2WtxAmO/LH0HenDxlZbsS2FwPdSDTx4usF+b7NPx9KhRnfVD5kMWHxIF4uEJHUMBSb/ABKgxmJvT5RVoeMdMYD93Orf7makHi3Ql4luJEb0MRqrSXQRu+DrjUS8kd+ip6YFdmD1ri9F1ixuH82GQsg745rdudaigQOkUsqn+6KlzS3BxZrZBpNwrEHiDcvy2smT0z2qpFqt/wDaibi3Ih7bRS9rEXIdJIQVI68VXtriMRuC6jB5ya5zUxe6jDi0vWs2B5YnqPSs2zuJbBnt7rUbZ/V2brU+2XYagztpdWsoh811CPbeKzrrxXpdspYzlsf3VJrkbuy8Ktc/abvVD5p/uNxSvrfhqJQgvBMgGNoFNVbrRByGtJ8StFR/LSSSRz2C4qO9+IrwwmSGxBUf3361z03iDwfb4aPTYWk/vYGaozeNNGCnytPQgdPlzVpyeyDlRpn4nardyBYrBYR034JAq7p3inUZZS10waPsFQDFZGheILfWNQFuIEVDjgLiuuvbC3htmaONQccYqJYh03qivZpoaviOLu0in3Soj4pjSXa1xEiju4xXM3Ms6PtEbbfpWLrjPFpzSsoB963hWnIh00j0pPEUL/curVvo/wD9epxq+7GGtz9JBXgn9psuAODSf2o27/69dSczJpHvo1J+8IYez0f2sMkG3kGPQivB11qdfuyyAD/bNOXxBdKci6mB9nNNTn1Hyo92XV4c8xTD/gNKdWtsHJccd1rxFfFepqMLfXGP96pYvF+rF+bxyPRlBqlNkuJ2virVoHt5VifLEYwRivLZfvEmtm81e5vIz5zA/RQKw3OSRmtomTVhOtMc4pyjg85wajdyOcc1QCU5cdKYr7qlx70hgBmpF45BpgBJ6U4Z6YOaBDj+OahfPbpUp6c1C5pjGnim9eaTJNGRSYAelOXgUAZpyjrxSHccBn6VIi+lIoHpUoWmSwK8Cg5FO6UxqQDSTTBkmnZpVANFih8a/PWxaDaoFZsCZI4rXtkOOlbwRDZdiWiX5AM0+NTiork4HPaulGVzPupMNgVQnlKREkirU7Zckmsa+l4Kg1nNlIz5nMkhPrTVyPpSd6dt6YNczZoPHtT1FMAxUiikBIhxTy/y9aRSMUPz06UANzkikyAeKTpQaBg3WlB4zTetL0pAOU80oPOKQHPNOGB3pgXLT74+tdvovCfh1riLbbvX1ru9EOLY/StqZDOueWI+GLlGfBMZqTwrdRnw3FFu+cZGAK5ifxNpkFhd2Utwi3O0gIau+EdYsl0lIZLy1jlBPyvIFNeZiIVG5WR00+XudhEcSAc1Jcj92uR3qKx3XRzFPbuv+zICatXNncGEBFDEHswrznhqu/Kzbnj3MLxCu7RLnavPlnmvAT1Ne+66HXSLpXDKfLYfSvAv4ia7MCmrpmdXQktD+/XPrXYGQm0+gzXH2/8Arl+tdcHD2eBxha9aBzSOi8LXUjXiBum3ArubogQZPFeeeGFZ7iPymG/Ga9Au8mz+b7w615mY7o6aC0M+8nQsnzE8VlzSK6EjJANPvNzbVzzUAjMMOC3FcVN6G7RAxGO/NMcjGM4p7HI60ny55rW5JXCCMk5zUbSc1YYZ9KiMZJzxii4ERbPaoJHJPIq06gDgVXlU7elVcCuJMNycVrW7E2rHNZIjycmtO3GLUjOKnqMqTD/SFx3qWAlZDkmkkQbwwJJp0XD9aq4jYtmjWMs5A+tcv4j8mSdV3hsD16VqMshbgnB7Zqnc6ZBOGyqLIf42bpSi0mDRmeGvLtr6Us+0MuBz1rso2HUdDXOWmlWtmAZbuB3/AN+t+IqEG0gjHBq6lXmVhcth0udrYrLt4zPc7ifumtdgCp+lUrG3aIzMwIJbI+lZpgy4pYGql9qUNgA07kZPAA61cDDnNY+sWQuZIpeyZ49acVdks0rK9S9jLRhj9a1bESsTzhR15rB0GCQRyvtKhm4ycVv26pESZGAz23VUlbYRoqEIILAcdq4PUlEmoXKsuR5hxXZiSLnbKOnIzXIXltcNdzymM7GkJU+tS72PTy6cISfO7BpemQ3eow22fLEpwzDrXWN4A0ZGx512/wDwP/61ctps1xZajFefZZJI4jkjBFdQ/jKWQfJphGemXrSn/eNsdXba9jLQxvEvhuw0ezie1abdI+0hzntXImLbJEijIBzmuw1bU7nXPIhlhSCOI7ic8muVVz/asluF+QHINaJq+h51WpKUUpO7JXuRuK8gitjSZfPXCj25rmLuTbeMD0roPDzA9ckZ7VtP4DkW51+n28KyBriIH0YN0qS5ktopS6FmbPrmq4lViFWJh+NaNl9kALzwOcdBuArkKZiX8E2pwMlqpVuu9mwKREvIbQJI2do2lhW9H4jhtb4ymzi2Rj5UcZFQat4wu9UtTBst4bc/wxrk/mf6VqrWJtqcywLPk5PvUUoOMDFPaYljg1C7sTxjFRpc0MG6VXutkhy4PFbkQCRqo7Csu8tnN6koxjvWkp4FaN6EnS+HbyC0WZpnVcjjJrXPiCwXrMn51wwBPTNKB60lOwONzspfEFoWwkg/AUxtetwMFwD9K5JGIbHrT5DjtV+0FyGrNdWr3Ek4mO5u201n27/OcGqxk9BU9uQxzipcrlWNG1LvcKpbAJrpIIBBAEznnNc1Zf8AH1HxnmuleXA6GumgtLmUiOYsEbDdq5eyE39rSZY9/pXRyyZzwaxnRI74FeCfeuq5FiW98ySFlDday9zW0IQYOKv3Em1Tg1kTOzseeBSuwsV9Rv8AFqFMSk54zVQXBMKl+OOlQ6k5wBnqe1NORFyc1EpMdhsTLG7k96rTzr5meeaR2O7PSq8hDMNxrLcpIozykztz3rRt5FjuyTWRM4WdgDxWpboj6gwkbC4/pWcl769H+h30v9yqf4oflMt3fl3Klf1rAmPlSNE3IroG2q52oSO2aoXFsjbm8sbjTjucTMNotjhl5Fd/bt/xKkx0KVxZieFhuI2k9K7KD/kGLxwUrnxb0Qo6HE3se+6cngZ5NV3fgRpwvr61Ynk+0XRjLBVzjce1WDBp6YBuC3riu1S0E1qRQtJGgVF4+lSO8xPAxVuNoOikkDpxTsxk8CocilEoBZnU7m4pi2xatBpI4wfkzVeTUEQECKjmFYiTTYWbLOfoKsQ2kMZk2A4KFT9KqnVuyxgZ70+3vpJfOzgbYmYVnVvyP+uqO7LbfWofP/0mQ2LSHnY7fkXsTVm20RoJsyssi+grOGozg5DkfSlGoXDtzMxP1rT3jh0OoRFAXgYFadpNBE67kUnI4rjVu3I5c5+tWdPnaW7RC2csByajldxtqx7R9vsY7JGlYIAoyaqNdWlxGVguomVuo3CsnWysekOABhYv6V5Kl5IrZWRgT6EitUQes3GmWDN5gjjDjo61zmt6Ml8uPMI29DWDoWoXUt+kTTSOuPuk5rd1XWfsYHyZJpajOSm0qWB+pIFR7djc1ek1zzshk6moTsnw20AmqEyxb3CKgAq0t4gx1P4VWhiQDGKnWJd3Cighlhb8Z4Un6VOl8SQFjY1DGFGPlq1GF4AoEPXUpFOFhOanGo3LcLD9c0IVHXGKsK6jjjFAEBv74nKxKKytYlvZLdGmAWMnoPWt8MvqKyfELKLRQGBYnse1DLic2W560g+9nPFBxj3poPHBpG1xsvLACup0iwluIEJnKj0Fcq5CsNxrsdF1CCOBAXBwPypoykaUWgfNuNxI341P/YqYG52yOnNTLq1sq8Pn6Cg6vARwGP0FBmNXQ7bfvfcT25qT+x7ZBnbwf1ph1eL+6x9sUq6lJIu5LaRlB6gUATLp1vn7lPNlD08sYqv9uuWJ22b8eppyzajKyrHacn/aoAurbRqoGxRgelSLFHuA2D8quWuhapKEM0lvGD1AOSK1k0u2DCLknoWpgPs0CWicdq6Cw1PyLcRhB9ayrqOG08uGNicLnmmRS4I5oTsOxb1KUSSb8c1wfjKUi2Qe9dhdSbiK4Xxqw8uIknrxWbd2aRR5VeH/AEhz71U5znrVu4jczP8AKetQiCQkDY2PpVpoGmRnn604DH1qT7NIf4G49qUW0oJxG/5UOSFYi9RRUwtpj0jf8qd9jm/55MfwpcyHYr496B71aWxuT0iYD6U4adc8fum56cUcyCxVHFJnBq8NMuieIiSacNKuupSlzIOVlEjNAGfrWiukXJ520o0a43DIGKOdByszskUmeua020afPanrocxx8wzRzxFysyuDilPTrW0ugtwN4zTjoHzff/Cj2iHyswh9aXHGa6BdCjPDOfwFPGhwd2NL2iDkOcx69KMc8Guk/sS3CjJYnPWnf2LbkDOc+1HtUPkOYAFA579K6gaLbehzTxpNqG+VOtP2iDkOV5OeOKXGOK6v+yrYLxH0pw022wDsBpe0DlOQ2/jTgpPQZrrf7PtQT+6FPFlbg48lRx1xR7UOQwdNi3XCfKeT2rr4Rgj2qK1t4ozkIB24qyFCtwOKzcrsbjZGvaE+SDVoEiqlsSIABVhWArWOxkyykpXA6VajkDYrO3DOetTJJtPWqEaiuSMCn7jnFVY5Rt4qUNUiJ91B55qNTzTyeKYxw5pVbqKQdPakoESA0FqYDwfWkFAHn3i2BLuJnjDIfauAtwY70K7Hr3r1TWoBdTOHkwD2UViJoWlI4klDO47lq8OjVUFY9BIbcoJNKAOWBT8a5I6JfSXCm1tnZSepFesaRZW15DsidQAMdM1bbSJY2wsyD04qY1+RscrbnIw29wmlLFLGEkC8iudmsJzK2Ubk8cV6DL4eupn3Nd4X0ApjaQkY2Fst6miNZJi1Z5hqOlTNbs6RszKOgFc19lu8/Nazj/tma9smsktVLyQ5A9DVA2k90N0EUhX/AGRmuuniUlsRKm27nkZt5QM+TKD/ALhqHaynlHH1U17Za6ZNj97HMp/2lqydEF0QMurDuBW31nTVEOCTPHdOB3A4I+oxXSxSuIwAvFd8PCgPDysQfRRVW48KpCCRNluwZKwlXjLVlapaHGl225Zf1rE1wjyM4P412409Uu1hn2FSfSrraDYSERvCrqT0K0Qqxi7hyuSPHFj3AMDTSCvQ17NJ4M0lf+XWHn+ECqzeDNG3gSafHj2JFdCxUGQ6djyEYJGecetbVhyAe9egv8PdGdiUtWQenmGnw+BtPjO1BIuPR6JV4PYIo84vSS4IFbuiMrKAwGa6a48AQzPl55QO23FZ2r6bH4bijfLSKTjJHNZualGyKVluXTEu37inI7iuB8QF7XViI2ZFKg4BxXe6dH/acPmxFlGM/MKoaj4Fj1G4806hJG3TGzIooyUXaQVItrQ89+2XOc+fIR/vGpF1C5UYE8n/AH1XXv8ADhkH/IVGPeL/AOvTX+HMqJvTVUb2MRH9a6uemzKzRzUWoXLsFMzYrbtblvLAc5PvViDwRdI2RdRP/wABNLLoGoWrYa3dh2Ze9YzlB7FxF3hwccE1k6jeS2zDawP1FaX9m6gvS1m/Kqtz4e1C+GVCqf8ApoCKULX1Kkn0Ox+HuoXF9HIjrHgDrjmt3Wdb1bS3UW3k+Wf7y5rF+HunzaRJILySMEg425rotbhFzyiGQY7CvNxCtW0Whv8AZRzD+MtZJOZYh/upilj1zULpP3t5KPYNgVi6hbTRylVglJz/AAoTVjT7DXpsfZtBv5h6iEgfniulUotXSMZNlk3Msr4kllkGecsTVhbKK5OFQ5PrT4PC3jabcYdAZATkebIq4/Miui0Twr4itZVfU7KNHzwEkDfyNTKm0romEnfU5K70HUQ3yREx45JFc28JjkMb8EHBFe8TaHq9zH5fnW8MZ685NZD/AAn067kEt7qlzu7rCAM/iadFy2kaNo8rTSLN498kmG9cgCoH07TlOPtyD/gQr2u3+FvhKHG+3ubj182Y/wBMVtW/g3wnaLiLw/ZE/wDTVN/863TMnc8P8JrZ22tKIrrfIxAx2r03U7xIrZlKyO2OkaEmuyi0fRLchrfRdOhYdGjtlU/oKaTEtz8kSIcfwqKznh1Vkm2UqrUbHksl+wOfst2q/wC1CaxfEFyt7YPFDb3DluwiNe8pMQc5P51Mbtjz8v8A3yK6VhYqzRLqtrU+SJrGeP5pLWdPrGf8Kqsu0fccenymvrt5N55RD9VFQvb20g+a1gb6xKf6V0cpm2fIwZT3IP0NKZFUcsBj1r6z/szTWHz6ZZv7tCtRyaHokwxJo1gw94FNFgTPk9Z1JxvX86sW7FpDtIOOuDX07L4Q8Ly/f8PaafXEAFUrvwV4USAldAs0Pqi4pWBs+fj/AKo+uKovnd0rufG+mafpsqrYwCFWPIB4rh2HatImV7iKOaGi3d6coyafkDkiqGVxCFp2MU4EMfSl25oEA6+9KfXNKAMU1h60gGsxqFjk9KWTOaYc0MA69DQKQcUvU0DHDp2qReaYgqZeKAHL71KpHemAGnYIpAKx9qjfg5FLhu9MYUAIDup8a5bpUYGDU0Qww5qkMv2qAcnitSBRgELVK0QnB9a1IkIUcV0QMpEqD5RkVTuep5q4WKgjiqM/JyeAK36GdzMuztU5rnrht0uM5rU1O5wSATzWN1bNc1SRrEAMGnKVzyKSlUn0rEsk96cKYpzUqDPFFwHZ4pcgfWkx603IFACim8YzS7jTetIBT0pR0oxxR070wHYGPelTjBNNB96eD60gLdr98c13mkJiyB9q4K2HzjnFd5pMhFgBW8CJHCeIEb/hIJ3VeMiqso4BZFz71c1s/wDE4uT33VRmbcBmok9SkMJU9QMfUitLS9butIlL2jBWI7kkfzrIPX0pN1HOgsdjP4+1m5tXgleEowwTs5rkifmJ9ajByevFP71m1FbIq76ksJxIM+tddC+6xP8Au1x8X+sWuohLC0IA7VpAlm14PfbqsWM56V6Xe5+ynFeZ+Ej/AMTSHv8ASvTZ/wDU8815eY/Ejpo7HPXURUxsepNRTnMeCK0dXYiGAKnfrWXMdycmuOm7o2bKpH92mMOaVuDSYPXtWghCMmmH5TmnEEVE7N5gVQatANdjjFQuSV61MQR1qGUenSnYCDJDetaNsPMt2HTvWbk44q/aviBhUtAQyttYAd6RXPnY/Wmyjcc+lRiUiYfLxVIRofMRmub8QQ73XGfzrd83qMmsrUrd53yOFA6mlGylqUY+hRK+pgOgbAJ5r0GLGxQAB7CuM0i1uf7R22qgvjlmHygV10TfwF1Mi9QK3qyTWhCumW8jGKAfamLuxk085UZ6VylChsdqo6sZltDJGMFec1c3E4pswWVDG4yD2qosloNJ8mSySR5iGYZIxVtBFvb5twHT5etR24SCJI1j4HSrUc4DcHb9BVbiD7Q7DaLcKo7heTVq1X99G5t1lAOQjdDUMhURlhdNu/ukVmyTyM2C7fgaLgjbvy8rjzLSKBBwVjrJult1B2tj2pkl3J5OwHI+tUiNwJNPmGrjGZc9apLZot8brJ3YxirQ6nNNPQ00waOaviPtz56Zre0AjGVxXPXwJu3ArW0PMcMhUfMR610v4DO2p3QiHkhhKob0VTUL3LxuU3qTXN2Ul0y/PJJy3A31sCAKcuSp+tczKQjTfvtzxq+OcMeKWZ1mjZtsUQHZFprlQcDmq0zZB/WgEQk005oBx1oznnFAyCfORTxwBzzUc7EAHrzSM5EZYc4FWlcCdXwetL5nPvXH3GoSJM7tJJ14ANbemakLpRGQVcDJBq3Ssriua8bneB1qafpuJ/Cq8bAYI61MzKy5x0qAIC4yKuWrAAn1qmSN3K/hV2JlVBgAUwL+nFW1GIMTszzXS3GwZI4UCuVsjm6Tnv2rX1a6MGnzyDJKocV10XoZyRFFqdpdyyRwTK5Q4bHY1SuGxeKRiue8JvmS4Lj52+ZvzrZmf/SVIbNbkDrljs4FVLRFmu4o5RlWbBFSySFie1VY033KqScZ7HmiO4NmzqWh6VNOqmEoR/dauQv1+zzPGnKhsCt8yiKdkSKZcfxNzXN3yss8h2nJJPNOa0FFmdPcbAWIzVb7QJACFouslGzVWM4jFYlkMhAlOeua0gCb1iGAAxyT7VjTP+9PcVduX23sg5HT+QrOS/eL0f6HbT/3Kp/ih+Uy3PdOCVV+npWdc3Ep53n86RmbknNMkYSL8oq1E47lVp5NwyxPNbaeJLhbQQKq4AxnHOKwJBg0qg8U3CMtybklwcSbs9eaiVsEE80+fJK8dqiUEnABqrCubdrKPLHrVjzFHH41lRlgvepQ7dRnBrNxLTLz4Kk1mXHIJFTPMETnJ9qjVllGNpppWE2Z/erunnm5B/54N/SmtbZYgcVYtINnn45LQMP5VNZ+4/66o7ctX+1Q+f8A6TMoN0pqE7+K0LWzSZv3pI9hVxdPto24yTVuRwpGckbMRxWnpdtI15GVxkMCM04QxIeAc1bsYt8+ELIw5B9KnmuNo7nWop5NEmjSMy3DR4VRXlL6VqNsWFxZypt6nbxXUz31/p1wHbU2OT0Y5pL3Xb/UoTA1zGUYc7QATV3EZfhmZLfUi0idVwCe1WvEricLsI+gNVU0h87vtCAYzgHmpF01SCJLlQB0FQ5DsY9lpt5qM4gs4HmlP8K1rnRNRsYna7s5YinBLDitfQphoN8bmGdC2MVsa54pOt2Rs5JI40Y/NjqaOcLHErI6tyR+dSLOzHAdauG005VG6fcfY0iw6cBtU7qSmHIadnoGqXlmLiAxkHoKpahDc6Q6R3blZG5AA61rWniYaZbiFJti+hHSqV3rNtqM3mz5mbsSOlNzBQMwakMHLvQNUXPImz6VbM9pniBffinfa4e0Iz9Kj2o/Zo3vB2kweJbmRLlpYo0GeuCaseLfBVlpVk9zDebypysbHLH2rm11hrckRZU+qnFQ3XiF1wJVLt2yapTbBRsZXlSnLfZ2x2yKVbebORAc59KuHxAJGAEairP9pqI9xZc46VLlIvQ6Pw/pWiTaXIdVRPPPr6VFqWnaXb2hXTH2t246Vyv9tzZwMHmlfVrhhk8CnzSJsidbe+ydrkD1z1qQQ3q8NKcH3qpHfSyKTv5NPFxLjJc1Lkw5UTtHcgkmU89810ul6pHaaaqTNwOpNci8rsMsxwfep52/0Dkk+1VFsTiegQ3lhcx5inTJHTNT2zBbpQG4ryqzldJkCZzu6ZrotPv7r+37eIy/Ie1W2Q42PW45DgH2oRv3oOe9Z0dwwQbjVgS5GQam+okTX0u+b6CmwP8AMOarSShpOuadA2ZDTuBancDkkYrh/FTrPcxAcha667wRg5rjtcZFuVUKckcVDNImB9nizyi/lQIU/ug/hV826hQaj8gZ4rLU0uisI0XooFHkoCTtFWmgwMGk8njJNK7C6KxUeg560uxRgYFT/ZznOaTyG3Uaj0ItgxShR0FSNEaQIwHSlqGgzAGMcYpMY5p5RifajYe4o1DQaAMZoPIp2COopuCMnHFPUNBhGDihOtI4PUDmnIGznmhoQ/G7JpT2ppyKCfbmkMU9MZ5oxj0NIORSmgGKOmaTJ7Dikzx7dqTcM80wuL1xTs7fekyMD1oIOM9TQIQHBpcnqKNvekzgYpoAIzQpJGKCcfSjHoPxouBat8Y61ITnOKjgxtNP4A60ReonsatrkRAHFT4wMVWt2/dL61Ox5Ga6lsc97jwcGpARmoQecmnKcAetMC2j4NW1kzgdazgc96lR8YpWEaQbOMU7d+NVUkzipgw7DA70AT7iaUfWogwA4708OMUWAXuaUHmkHI4pFGCcmkMi+xQtncm4n2qo2hWEjsZAy/7pxWkjMR6HvS4V2/eH6Yr5dSsj0WU9P0+Kwk/csSvvV5pCM4XLfSn28aA8GpZF2HNLnSEUVMrsWPHbiqVwmJ+TnFaqK5YlTke9Z9ww+04cc5xVRs3oBUnbzPl25X3FOtY22FYcD2HFPndVTOzg+9RiNXiEseVPfmu2nTcPeIcr6FiWS6ityqqWOKrQNdtAQrbJO9XVEnlDac8etQwxyu5XftPrW8Kik9EZPRksUtzGgErbvepLkRyRbt3zUxrWePl23r6ioptoUc4PpWFWbTehojkr9wuppuyOfSt+CEuVbd8uO9ZF8if2ghYZGc10CSJ5a4GKxcro1T0JNqjnZuIqN5mfIeFeOnFSvIsKhnYKO2aR5HlQMpBGO1a0pLqZSIYNSVy0LWw3joccU6O6mGS1qnB7U+J2CfJHk+1NeaYDDDBPtW7kkiUyyJorhNx+QjtXE+OEilswqyBiDyPSukQStJhSBz3rG8V25FhlwjEnt1rGM03qNxIPDp22KJHwAtb0Ubsu44P4VzvhwnYBjArpRgjaufwq5bmj2JWIKBGt0I9xTfPhjlCrbgjHTHFK07wxc5I9xUUc4l5UgHtW0JxaMpLUsI85c/uIwnbC094HmTeQDjtVKO41IEhlR1zxt61NHLdROWKMo68ihtSewWIJd2ShBFI0a+Vyw6d6nmleVizqB6YpsZXbgpmsmND9NtLeaYCTJ/3K7Cy0vT7UBkhLE8/Oc1y+mII7wOvHPQ12KHMYNNJA2ydJBF/qoo1HsooFxLnhiPpxUfekqkQSl3bJJJqozkzgZqZsjnNUnk23CnHene4i8pp4GKrxzB24BFWM80wHil7U0HmjNCGOzwc1nsf9LxV4n5azSf8ATsVpTepMizg0uCB60o+lO7V2mY3mgZBpee9HWi4BzRzRS9ulACc1VvR+4NW6rXo/cHIpCex4p8QgftMeenOK4LaD1r0D4hoxuYyOg7VwOMk9eatEAqjtSyKccVIi88UrkDg0wKSo2c1KATT/AL3pS7DQO40Db3qNyRk05yRx6VCxz1pANPNNPSgtzSZLHFILCd6UCg9cU5QT2pjHoMipkFNRTjOKmAIoADkU7rR157UwnFIQNTCexp7EAUw880xiGpYhzUR6fWrEAy341UdwNS0B45xWtEPlrPtVB6jFaagBa6YIykyGRuKzbyULGQavzEc4rnNTuDyAcZq5uyIijKu5PNlPtUAXHegA7sk0/GBXJJ3ZukM704E44NIetKuO9SAq5qVTTABTlx3oGSAhhzxTSaQ4zmjk0CEH0peB1pBS89aBgTS+lNx3pw6fSgBwFL/OkHPSlUUAWbc/vBnpXa6W2LL61xMHEgJNdjpTZtTg1rEmRx2tyY1iYerVUnfCrkYq7riL/a83HOapTLuQZArOb1KiV/MBNBdeucU5Qi/wg1ox6nbxRKn9lWkhH8Tjk1FwbMtXXdgMM/WpATWo/iD5CsemWEeRjcIuRWXncxai4XJI8+Yv1rq4I2+z8HPy1ySE7xj1rsLB91scrn5eK1gJl7wjMU1iJTg/NXqF02YcA15d4TdP7WjLADk4r0m5uURCSVAA65rzMxV2jpobMj1FA1imTyOlYU2QhAP1q5e61byqlum0t3bd0rLu7pI4WbeD9DXFTizUiZnyMEfjTTJIvGBUay+ZGGUgg+9IHZhzj8635QHGeQfwimtNJnOwfTNB3DB4xSZ3dqaVgGtOxBLJ+tQyXGF+4afK2F5GKhOXHCkimK5EtwG+6px9K0bVgYWbbg1nf6sEYxVy0lzA4pNDI5HA6dahDM0vCmllYdT60gdc9cU0hM3tPWwMW6aIPJnv2pmsXiWEbSW1tE/GQGFQaeFwWOSe9Qa7OPI+YgDGBgUSSEmzJj8aam0qxJb20YPHTpV+wdpb3fkEnliOlcs0YE6v7811WkSwNkxkZ9D1rRqKjogvqbiPjtTmbPWoVJpGJzwa59yiwGA6mlRgXJwDVcZI5poyO+KBF2RyB8oUVm37Xa2reQPmPcVIxPdjWTrFzLHYyiJ2UnjI7VpATRYsXuFnCtKzZHIJrSKsw561zGgu4uwm4uWXLMW6V04kApzSEiFkOcMSKVlVV6k1IzgncwFM8yNlJY4qUBXc4HHrQgyc018Z+XkUqEg9KpDZymrMUu5CK0fC8ryxTCQZwc/WqGqgfapPlyKueFCW8/jocCul609DN3NSbUrOzvIhLEXIOdiGugsbm0vkafy9iA4Ks3NchPbkarLOYztRevqa0NL3CNyAcselYtJrQZ0Us1qTsSLYPU81BPChjzGkh99uBUCrNjdszinSyu8eXZh2xmp6AVW44ppOBSMcGmZP4Ui9xkw7mnHBjxUcxLDmnLwn1p7A0cvqkavKCqgBD8w9au6Mu+6LhQBtxVW/R1v3A5HU8Vp6YQ4Lrx2NdDfukpGvxkVM+BEORVZSc4HSpWyF5Nc7LG55zVxGOwc1Q3AfSrUT5HvQBftGxcR+ua19RxJZSK3IK1iWmPPQn1rUvpStq+DzjiuygjKZz+hKEmuCFOTx07VYuHIu1xwKNM3rE0hGN3t1qG4cm5BxXQzIkbGSdxrJ1PVZdKMU0KK77uhq87HdgVga6S8aKBls/lU3sOxv2Xi6W5UPNZR8jPBxWLfzmeV5WOC5zj0plnGY4EB54pl0fyocmwSsZtyQUOOtVQMLTrhzu4PFJuGPwpDKMmPNOavXPN7J+H8hVCU5fpV28Yi8kHTpz+ArN/xF6P8AQ7af+5VP8UPymQkOxxmkC7cjApC3z/Kar3JbdnODVnEDRFmJyAKa2A3HNV9xz1NSKeAatIktDb5eCwGaREiBGHyfaqsx6UttkzKKTBGp5QK8kmnCDjvipRkcZ4qwseYi5OAO9ZuRqkrFCaERpv2kgVSN2FPCVp3MoNoy456ViMuapEMsm+bHCgVYsrpn+0cD5YGNZR4q5p2P9K/693/pUVl7j/rqjsy1/wC1Q+f/AKTMRb6RTkHrWlpkr3EhVyTWCMg1saLv3MVB+tVNaHGmbqRoH+7yK1dNjDk4UfWsz5sZxzWnpVywDJtyfWsYlSOZ8VkDU1RRgKP1rJhZh0JzWxr9ndXOoF47d3XHUVlrBJHnfGykeorckVZJDlS5P41MisecmolIVquR8gH17Vmy0RTb47cvk81nmRjyWNbl7bSyWoVIyT3rHaznAOYmwO9UkiWRLIxP3jU63UsIGD1qBI234xin3EbEgAHinoGoj3Ukh5Oa0LFiUGcYzWX5bA8qRWjYiQqdiEge1KVrArmmvJNSEgDjrVZTMFP7lz9BSn7ScYibnpxWDNUOI+bp+NZ2p83AweQK0Yobl5RvjYAdTipJ9JEsm4llNaxaRLMFFIOc4p05O0ZJrUl0mWNwEG4etRS6TcSDhcD60e0j1Fysy0kKuCOavowlUbjU1v4b1CWNpY41Kr71JbaLqHmZeEooPUmrclYmzHQLHHwDxUxkQjGRx0q6uiTshOVFMHh2Rgd0oHpxWDnHuUkyg7r03Dmrl0VGninPoGzDeaTj0FbVvYRraDzEVhjoRVRknsN3OQgYrcKwrb0RhJ4lhLHjFR6jHa7jsiCEf3af4cSJtbiIYlh2rW+hmz0wNgDB5qcsRHntVXstSyyYjxWbJJlxjNPtmzMTmoEYmLPals3/AHjA8ikBcmXcciuK8RDZqcAII4Ndk8mDx0rjfETA6lHzkkUiosquxCgVGZWQ5AFK54FRuc8CkaCiUs2GqTGBwMmmxxO5XAH41LKvlHG4H6UriEQFjz1q0JII48Mo9yap+cVX096qyymTIzzTuBbmniPCd6i3KAPmyaqH7vvSoNp5ouMt7snr3pW475phIYA55pcgkYNAEixyOhYISo5JxUbZIxjirT3rm2FsoCoOpHeqZB3daLiDrxipMYXleaZuA75+lTQPmUAnA96dwGfKPvDFNymSP1qxcxliWRSVHcVWVVxyTjvRZDHbVPbikCp0xn61es4YbuQIG2r3Y02/FvHMI7fJVeM+poshXKRjQUgRCuc0Sgg1oaJaQXFxmdHZF5IHA/GgLmfsBH3qNgK8Gr2otbtcsttGEjHHXrVJM+YABRyoLgy9x1qPae9XZY3RQXXaMcE1ULAn196LIExApJOOlJkg4zS7mJ9qa5x9KloZbtyNhp4BY4XmqkTEr1xV62kCDJGaILUUnoX4gdg+lS7srUUbbk4qRRkc11JaGHUerA0oJ3cVFgqeKevvTAmDU5GBqLtTlOKBFqN/m64q2jZ96oIecmrKHAGaQXLI49akDA9KhBJGexpR1FICYHFKTxUY608N0FIYK/cGpFOGBPNRxxAtndUpby3AGMV8lqeiThwo4FKWZ+pApDhhuBoDKxwzbalMQ1SQ/wApFZt8T54OPmz1q8MRTDcw5NVdSi3FWBrSLsxlWeGSWMEL0qSFSsGGTIq7ZTBYtuwtU8pXYTsxj1FeyvgOeW5Vjlh8rGwg49aqC6MV0fl3LTZZJpCfKi4FVhK6v+8G36Vzxqa3Q+U3I7qJwSJNmOoNUL+RZGAXafcVUhuF8/DHcp9q1jFAYg6RrjHattasQ2OM1T91OpJA54rcgdmt0JA6davsNPlcefbxOR0LLmrQW2CARIgHoFqVhbrVj9qkrGa0RuoSuMmqUdvPEGDh1AroGAVCVAH0FUpJnWNhu3A8UpQjTWrDmuR6fII/vMR71oh0k6SxsPRhWAzsknyjNHnMH4kXHpVxr8q2FymzLGgIYKoPtXLeKB5tqQBggHmtlJGbB3ZArL18iSwlGADtNYt3lcZjeHJsBRkNjrXVSSAQkrxXEeFpMTlZSRn0r0CK2juIyqvzj0roppcwS2KkEhe3feMgCiCaAnmLoatjTWgife4I9qzlBVtqdQepq6rtLQhGhvhiYPEOPSr6XMLpjz1Hs9Ye4q+WYHHvUiQtd4Eag496VOpJPRDaNG9CGDKmMnsVrMVW65qcabdL91AvsTQ2nXgOSE2/71OcJzd7AmkOskzcrhicHvXZRN+7Ue1cnBDPEd4CnHvVl/EckS4+yklf9qo5GtwbudMWx0pA3NcY3jC7OQLFR9XNQP4t1R/uwwqPpmpuSdvKWI4qi8ckk6gHbXHt4j1luVkRfolUJtY1d5gWvXBz/CMU0B6UkckZ+YgipxIoHLD8TXljXmpzP+8vpyPTdSMlxJkPPKQf9o0DPUGvYIzlpox9WFV5db06Hh72Ee26vNfsgAJO4/U5o+yIT93mqQHoEnirSYly12p9lGa5vWPGDwzpNpccc3PIkB5FYn2VR/DzU0dqhYfLVJ2YWNrTvFXiDUifLsbNPqD/AI1oG/8AEmOYrEfn/jUGjosZwFrb4rtg7ozkjLGpeI1U5trI/n/jTDq3iUHizssfj/jWtgd6QirJsZJ1nxKv/LlZn8/8aadb8RjJ+w2mfx/xrXwDzSHHWgDGOv8AiIn/AJB1ufpn/GobnxFrywndpkBHqCf8a3iAO1V7sfuSMcUCZ5D4u1C8vZQbmBYiOy1ySj5q7bxtnevPc8VxiDnNNEEmz8qhmO3jFWhx9KikCHrimMqISW9qlY/LnPNKVRRkVGzZ+lICKRyT0qEkjIqRyMntUJI3c0wDA60g69KXsaBwaQxQtSqB+VIoGeakRQDxTAkRc8GpQoAweaaE4FPAIFIBpGKjapWBqM8cUgIzz1prH2p7Yph4pgAJyKuW/UDiqSqc1dthg896uO4mbtmvA4q+5CqRiqdoflFWZWIXOM11w2MZGddyFVbJ7Vyt++6XFb+oS/KT3rmpDvkYn1rKpIqCGqKUrSgHpQ9c5qRk0AjuDRgYpOh64oAkGM1IozUa/XNPGQKAFIGaMYFBPNGM96AG5peo60UYGKBCYxTh70nHFKOnFAxQOR6VIOB70wDFPA4pAWYQGIrsNHQfYyK463++Mmuz0gEWxGelbQ2Jkch4gH/E4lx3ANZ8gOwcYrS8QqRq8hPdRiqL5MYyO1Z1NxxKpFMNPYUxh371BQ3nOKevUU2nLTAkQ/vB9a63Tn2wA46rXILwwNdZp2WtRx2q4CZZ8Ps39pjkdTXb6lHutGJz06Vxfh2LzNVRMgZbqTivQ7+wzZvm5hGB6152Ybo6aL0POL2IIjSDIIrCOpFTgg/nXbXGkGaJlZ1KkcmuOmsdOhmdGE2VOOOlKgoyWo5uxF/arZ43D6GkOqSDnc//AH1TZ4NPWFmiM28dARxWc31rp5Ikc0jVGsOF4kk/76NKNbmB4mlH0asZiMYzSxgNIqs2ATjPpT9nETbNk65cPgNPIR705NduEBAnbFbr+FfDsGnid/EH70R7thQcnHSuSjCtJxHlc4odNIFJmkusXEhP7zPuRVqLVb+KJim0q3qtQRWKkqyAY74rQMDGLaE6VhLlRorlFdTuJplWTGPYVPLfyRvuVVIHrUL25jmDuuFp8sYZMgcGhJNiehv6DeNdQMzDDZ6DpV64jj27p3jCju3asvws26GYcYDYpfEqhoMZ+XvWE0/acpSatchddJRzI19GSP4QM06zSJLuNoHyW56dq5ErsfA6ZrrNGZZRGxXDqOa3dPljuRfU6ZCCKUkKahB4oLE1ys0JwwGCRmhpUJ+6RUAYhelKrjoaLCLSywlP9WcnvVC7hilhlATnB4NWQd5wOKcbKRw3ybuO1UgZk6FaIHZypJxg7a2gke7GGA+lZOmafcwz3G+GRFLccdq2E06Z4jJvIXpg9atkpkTeR8wV2/Gq7rEqn95uNTvYMnfr7U7+ygAHmvIYd3QEEmoSKuZ2ewpQcd6lntvKJ2OHUdwOtQYJFUDZzWr5e5f5uKveFMqZQD1/SqWqRN9qPoa0PDcYjL7RyeTXT/y7uQdBqkYitVlLRgA/NTreJYrZW81cOM8CjUIPtFk0UZXcfWq8FmqWyI1xGHAxjmudDLXmoqttkbP5VUklYtjdmr0emWm0NPq0Uef7qFjUM9hF5m2zlluR67Mc1STC6KhJPem5PrTpYXgJWRSpqJTuPBpPQdxs2RzmlB4FNlI29e9G8YHBxVIVyGW3Rt7bcsRiotOikt4Ssg5J7Vb3hh0/GgPjjFFwJEc54FPLMw74qASY9gKlEgPO6lYdwyfSp4Gw2CM/SqTuw+7kk0+KV1OTkGiw7m1bt+9XjvWncxrKm0nqKx7OQsVJYdela0jYGRjFdeHMZFdlWCELurKnb9+DVyWfzCwHas6d0WQbnA/GulohDJZgqnJrEvm8yRAOme1ak53KwrN8tS/JzUNDJQQEAB4qncMT9BUtxMsEWSM4rPW9WcbgpHtUjKlxy44xTTyOKlmHmMO1QTgohxwaVwKkhbeauXoLXsg7cfyFVEVmbBq1fbjfuoPXH8hWcv4i9H+h20/9yqf4oflMb5W1PvDNVrnnA6mtIWfyA7uaa0MUYzI+2nzHJYw2BU9Ken3cGtKVbNyCZOKpy+WJSIySvY1opENEbxu4BC8U2MMkmcVO0jFABwBUsYZgSFJ/ChscUiVZy2ODTnuHI27Tj0zTV3KcHj61Oqg8YzWbZrydiq7Suv3eKqMrMcCto7QhGwVT+2Rwyf6oHHrTUjNoq/YnIHFW7OzaMXHJ+aFl6UHV/SICpLXUGl8/5QNkTN+VRVb5H/XVHZlv+9Q+f/pMygtk744NX7YTWqFU6VU/tOQcjFPiv5ZZApbgmrdziLhvJ/71XLO/mgf5XIU9SamjtY3QZHNQXKKgwKzUtSuVmq+oqibmlA9zVYz21+4jBEhPpXOXsvybNxPtU+gvtvA2Oa0l8NxJanRJokDycxDH1q6mjW8bBhEOOlSozMuc44qtdzOI2w5B9Qa4OeT0NklYuPZlkAAFVzpZZeCAK5Se9utxzPJjt81VzeXB58+T/vo10RpztuS2jsP7GhzldnvxT20qJv7gA9q4Z725/wCer/8AfRpDf3A481/zNHspvqLmR3sOl26sQzISaswWFugKxsnPpXmpupyMeY+D/tGut8MZ+z53lnPc81NSnKMdwUlc6ZbNFHUYPtTWsojySfwq1BaTSDcelE9u0IJJ4xxXLaRqmimYrWLmRwo9TVaa+0oHm4Td061BqQPlEn8q4y6P7wn+Kt6UG1uKTsdqb7SVP+vQn2NNOpaWvLHI/nXCbmx1qFnYnknH1rZYddWZe0O+TxTYRrsQMFHbFI3ieybB2Nj3FcBuPY09MsQCTmr9ghc56Da6/b3TiOONsmthSjLkrXBaFGBdg812omwuM4rkrQ5HoaRlcdO6iMgIKw9WvpYbYLG5BPGRWpcSZXFc/q5ygI6Cqo7jkygk7yITI5Yd81oeD5PN1/hOg4NYjNiM4OBW54JTOrMwHRa7Hsc8melg5YCppztiqJTyKW5bMYFZgh6sRAMelLYn529aaoP2cemKLHqx96YMsufnrjNdY/2wBngLXYscMfeuK1xx/bHP90UiokTfM4GacY8dOtRockGpd5J4pFibZmICZJ7ACrENldsNzwOP94U2O5ltFZ4zhuxxVZ9W1Fm+a6f8KTshpXJp7a4Jx5TACoDaTHkJQLq4bh5WJPfNL9ol28sayc0PkE+yynH7s0fZZgfu8CnCaU/xNn6015JepZselNTTHyjjazbeFIzTBa3S8BaieeXdjzCR9aj+0SEj943HvV8wcpfSynIDdPrUqafMWJOD+NZYmmJ/1jfnSxzzF8CR8/71NSRDRv2+hXrZaOFGHbLUyTRdRiLSSRoB3w1VYLm4UAedJ/30auS3cxgwXYjHc1egkipJqLNCbbYABwcd6rbx0AxUWT5h9SakIK8sKzb1GxfNKjapI9aVJBj5s5pmMnJFPVlx0/CqQkOAV3GeKtS3ZihEFuxCkfNjvVJzzk8CkWQCThSaLMBWLKASOtSwzNFIrxgbhzyKOZmwzBR79qhmkCN5cTZUd/WmIkur6W5f94Rn2quOT7U0ZGeOtA4HWgpDwQGNRsx7Uu/I6ios/NWbYWLUJJWrIyFB3ADNV48KvB4qTaWKntVQ3JkayE7RzxUgbHNQxkhQO2KkGMiulGJIHJHSjBNIOvqKcD6dqBChiOop4JNM4NKAc9aYFlOlWY+nWqaNz71Zj5+lSItL0p49ahUgd6k3UDJQRQGGetRq3B4o689KQ2TRqQ3XNTsgYg5ANeeaJ4ya+vBAeQenGK7p3Z0VgO3NfL1KUoaM9HpcuOhEeV6+1Vi5PJU8U6OV/LxtbBpDDM/3Qeay5bgDsHAJUcVBetKEAxwelTtbvu+YqP8AgVF/LAlr87oMcDLVtGLQmzLGpXViQUtFlB/28Yp13q9zNasFtAkhHHz5qJ7q2cBTPHnt8wpjXdsvy+fGSP8AaFdarTSskQ0mMsUvooXlmcqTzg00M9zKd7/Sny38TxlFuY+nXcKrRXEW7d56MB1xRBXd2Sy5FC8cg/eNjPHFb0cZ8gYINc419bq2BdJ/31W3ZzNJbgphx9a6aD3JdwmtBJyTj6VNbQKgIJOKZK83/PMY+tQiWfkhDxXSmZFi7SZI2EZHTvXEQX2t/wBpzQXCAwA/Kdvb612TXUpT50I7ZrGlnhjuWLTx5POCcGuavfoi4bkSGVtwJAb1FIiOH+d159RUf2qIsxDqfpRHJHuDfaIh7FhWDTtsaJl9tyQ+ufSsm/fzLORCCOO9aDXcJTH2mHI7bhWbfXVu0R/0iIe24c0KLBnPaCqrclVbBB4zXe6fJJ5oR+Af4ulef6VNFBdPI7AIG6jniupXxJo8kewXeG90Nbx5k9EKT0Oi1PzI7FzC+XweprlLKe8Kb5wFOcc1oG+tZYsx3seO2TVSSaBjj7RG2D2NXKXMRaxcBZ0yzLnvkVf0lJEfJKsvtWQbzToIt014gPoOauaJqdnPN+4uQwB6YxTpRdxtnQSyEgBSVNRPNMEwQfripZ5Y8ggjNOS6iKgEivQRi2V5pXjtGdVy+OnrXK6Zqt7e3MsdxbJHsYjiusnmiKnDAVy82rafBqQtzIBMzdAprCtFtWRUHqbEumO6q4XqKItEc8t0NdLaRhrRCcEYqwsYA6VwtWNbnNx6ACPmNNk0KNHBwTzXU7AO1QTrgUIRjRaJDk/L16ZqcaNDtwFrVjUGplAxjFUFzE/saHH3KadGix92t7aPSjaKEBzD6QoblRUq6Qka8Ct5o1zyKd5Q64pgZFva+SwOavHgVN5WD701+nTmuqk9CJEWDSEHNLk0meK3JENJt96Xvz1oNJsBCOOKq3ZxCT2q1mqt6MwMMUXEzyjxm5M6rXKogzkjNdX4vjJuVrBWHC5xgVaZmVm2heBzVGUEkkE1dmIGcc1RZjkk0xjOQnNRs2D0p7EnofwqJiSfagBhNNx6UrHPFJgmkAhGetKopRT1GDTAegJ4qdFAqNFNTL0oAeBgU1ieOaXnsaaQSOvSkAMeMCot3PApxJ6GmtxQxjWNNIANDE54PNAyetAWHBckc1o2qDOP1qnCuTWtaxnAHetYITNS2XCgjpikuGIFSw5VOnSqt3J1IArqWiMXqYGozbd3pWOBk1fv5NzkY5qko55rlqPU0ihQOKjY81K3AzUJOT9azKADIpuDnApaBweKBjgpp44GKaue5p1AgzRRikzge9Aw6mgnj2oHvRjmgApwPFJjilxxQA4GnKeaYM09RyO1IZbgXLA11+jtiIiuStslvpXV6Tnyj/WtoESOa8TKP7XyO6gmsyUnyxwK1fEvGpg+qCsmQnywMVnMcSAtTSM80pFBHFSURd+tPWm+pFOWgBwPzCuw0vH2MHP8PFcd/EMCuq052+ygY7dauImWtGfbqYJycMcV2M771wTXFaKc6lg/3vzrs3t5mY7Y2z6Vw422lzeiJHsK43fXNQyMm87UTH0py2N1124p/wBgmcHAPHpXnpnQyq3lMPmij/75FRmC2xkwxn/gIq2dLnweQMe9M/syRgcyAH61qnYVkUxBZ55gi/75FNezs2HEEec5ztFXv7IAxmdT+Ipx0tcD9/inzi5UZcljZOMtGjexFQPYWpX5Y0H4VbulS3kCB9wHU1DlCC2Sfar5mxcqRGLWFV4wPpTTHDAu9nAHqTUZlJbGDSSGIH9786n+Gna4yKWGO5IwxK+oNXIYLeNAsiBx71WikiztjTYo7CnNIXYBdwH0pttbCtcswPFabxbwiMH0qtqLyXMQzg9yKnMxtE+Yhs9qz7vVZw2YGVR7rmpXM3cdkjNWyed8KjKAepFdJplo1qAxweKyP7Sv/LVjeAg/wiMV0NpJvt0Zxlsc1pKbFyotib2o8wcnApAI8fdpwRMfdBrLRk2DzQV6Uwyqp5A/E1OmzAGwVmaiGa4GABGBTSA043DrnZ+VWIT82NsoP+yM1n6a8nkEkDGePpWlBe3CZWNgv4Ch2Qty0kc4PymUH3qRLO4kY75pMnoBTFkuWG9pstUirNINwuHDei0rhYvW2h3rht82O6qwqwmh3YAd9jsDwOK5q+8QHR5Y1uhdTb+BtbmrFt4rt54SYI7lCDghz0NaJKxDvc6r/hEL26i33mqWkUbdI/Qe9YGteFjYyDyLq3lQDkq3SsfUfEMtnEXRmZ3OPmbOKwRq1zcyGKR2KsMkZquW49Se00S31zVpbeTXNP08L1kuJAB+GeCa25fDui6HAq2Xie21a+dsGO3C4UeuQxri7nTbaZWZ15z2qTQbRLW8YouMjFdPMlC1hbs7y30a7cZW3Vz6tIAKmt/DGpXjlY0to2B5y9Z9sASAZtqnrkmtRFUNiJw4HpXGmhms/gu0t7dAbmW5uT1WNwFFT2PhiP5wscMZT/nrMefyrEDXucI6R+5NZt74n1ewk+zpPE2PTGa0RDv1NTUfCup3d6fJS2SPGPlJwfzrA1LQbrSX2zSwbj2V+asxaprmsp+4N/Mw6rbox/kK5zV7C7sr4y38NzHcOuQlwpDY9eaFa+pSuWBErgkyrx1AqUpDtHOfrWVZFkhJbjcavqSy9aTdikP2rnjgCmlUJ60ueMdaOvapuVYBDGy8viobq4t7SLe7Nj2qb6jFZusQNNAAq5waqOomOt9TtbiQIok56EitFliVdzdBzmuZhULcwoing4OK35QWt2XnpTaQkGm6vaTakluqOfmAJ7V6RJpNt5AY7l+XOK8o0yBLa5RAMSPIOfxr155SI0Xbn5ACfwrtw6VjGbaZy09tDGzBSTzXGazA0msWyxsV5xya7S8bFxIBxzXOXqh9RtyegcGtXuLoaN7o62VkZnl3MFyRiufiAcEng1q65qt0dQ+zRpiAIM5HJrLC7R7UmCuJcaVJc2zMrgLjvWHbRqquueQcZrq3nVLLbycjtXJgiPPGMnvUSKLCWjMMg8D1qpcJlSCelaUM2IP0rLuXO5gO9ZjKRIDgZqzeE/2q2Pb+Qqlv2yc1bvZCmqucen8hWcvjXo/0O2n/ALlU/wAUPymasfCciqN+v7skmhbvK89elQXMrSDAGaaWpy30Mtyc4pydKJEOeRzQoIFaozZraVNbQybp49w+ma6CLVNNVRiNsDsFAzXIw8H61eUkDpUSlY3pU1LctX88d1dNJGgRT0FQwqeajOc1GZGQ9cZqNzaaSWhe25U1jXQxKatNcMFJzzVXY0r57mqirHLJlXNXdOx/pX/Xu/8ASlFiWX7wFTWdt5QuSG6wMKK0lyP+uqOvLV/tUPn/AOkzMsjmprU4nT60qWzu+MH8qtxWRjbcDk1TkjiSNbz8YIeobiVT1PNVWjbIqOdnUYxkVmkrl30K1wM9OauaOdtxkiqRLdSMVqaVCJG3E1crWJW500FxGI/maoLuZDCxWoPs55wTSmzDxhSTXGkkzW+hg3AyMjpVM/KenSt650yQ4EKE+tNtdHbzP9ITPtmuhTSIauznnJJphBrs20WzB4j/AFqOTRotn7uME9hT9tETgceBnrXW+Hplt7YFuOaqDw/PkEqAM961o9NWNAo4wKmpNNWHGJur4g2IFRQT6mo7jWTMFVsfWsv7GABzzT1teBnH51zXNUrEOo3gMZ5GD0rn2s5J0LgqPbNbF3pEly3yyqnsaS20KS3JMlwhGK6ISUVcmWpzUkbRnHWo/s8jc7DiuwfSE4YutIdOjQnLhfrVqsmRyo4toyM5BqSGGRhkITiunOk2z5PnrVmGzt0TYJVPvQ6yJ5TF0uSaGbcVKgVtLfszdCQalW0hIO2RW+lCwxI3XNYymps0irEL3zqPmGaryS28sH7+Qg5q1cRqMYPWsHVH2OEUjFXTSCQtyIACEfINbngdR/aTnP8ABXGyH5Rz1rsPAKg3U3HbrW72MGejry4HalusBBTU6imXzbVGRzmshosAkwD6Uthj5uO9MLZtuTzilsCCpJoAnc5ciuF1whtaPP8ADXbuDvY9q4TWW3a3Jg9BQ9iojdxH0p6Tqo9ahJIU4qEHB9qzT1LNN5lkhO0VSxzyKI2lkAjjGcnFakfhvUHRWJjGR3bpRJNjUkikmMcipleJUyRk+laK+Gb7bgPEPfdTv+EXvj/y1hHHNRyMrnRlbwWyMA0xst1PStZvDd2MZlj/AAzS/wDCOXTDBkjz60ezaDnRzzYycUzgE+lXNSsZLGTy5CCfaqRZgOn4UFbkicKT0pIiGfOPypgYlcEdafHlecU09SGjWtnAIBUfjViZv3ZxVG3LkjABHvVi5eRYiOPwrVMkzixEhI65qZCZmyx4qru5pzTMV2gYFVoTqE8vzbUOFFRhirAk5JpuM9e1MKnilcdi8j5UDqKBnJbGBVWOQxnnOKsSXI8oqoGTRcLETyuxwDgUg3dajTOf604OeeKm4x2WDc+lJvJzijJP0pu7r0xSAQn0pAx3ClzntSDJkoAtpnA+taaRBoM96zYxkc1vWyL9kySOnU1dN62JmyOM/LzT1OSKZtAJAP5U4HHtW5iS5IoHIpnvShv0oAk3YNOBx061HwacDx70ASI2DjuatRHtVNDg5zViN+gHFAi4pp4PNRK3fFPzxkGkCJdwHSlBytRBuc08nOKBnjOl6T4j0y6jni0m4yD/ABx8V6jJqetR6Sbjy4EdAMqU5rWktpdwO9yB71HOUaBomY5IxyK+fqYn2vxI7uWysjjZPHXiEMsYktsE4wIq6K4ub3UdHaTzmV9o5Q4rFu/Cl1cyhreWJBnJ3cV0djC2nWPkT/MfVaUpQ5dClscQhulvoxLPMQrc7nJzXcHTor/TkBJyOhqjcaBBcyi4QzKc5wBxWnb3ktnB5Yt9+O5PahSuiWZw8MgHmX/x2oz4YUOcSHnnkVpy6vcsG8uCMHtnNQQ6jqDOfMWDGOMA1V7klL/hGwrf6zj/AHaiGjCBvv8Ay/StKW/vP+Wfl591NV5Lud8CQICfQU4pjZWTw/G5Lbz8x9K6TSLb7NF5YbIrKW4uY1GxR+Iq3Y3s4YmZSR/srW9FvmJexsSCmZYAkc+1UJtXRVJaGY49qyIfHGn/AGz7IYLnzD7DFdXMjHdnSsUlQqykVyGq+HBc6gJUlK59RXRHVVwCsR596qXN3M7bkVQPzrGpOL2LUWjCi0JoFKvJuycZxUZ8KRseZyfwrTS4v5ZSJfK2dsLg1bUzgABQffFZtO2hasjEXwfGBkTH8v8A69ZHiHwssNg06yklVJxiuzxqEzYjlSMf7SZqG5067ngaK5mSZCDkBcU1GaVweqOL8G2aXtiyFNxHc1sN4SmaQsGUDPcUy1sl8PygWWVyeQxzXTR3c5jVy6cipnLW6K0ZzH/CJSf3lBPQgGnjw69qCfMXH0roTcyyH5CM+lRyx3Dod4BFKMpt6EuxzS+HPOkZhKoB9RWvo2ifYbtZfNDevFTx21yrEhML/ezUiJdxTBjINo/KtE6kZrsGy0OjMaH+EH8Kb9liPOKrrqNuqKZJ41xwcmmtrGmjg3sOfY16kHoc8kTvaRlSAK4+48OLPrpuC5HzDArq01S0lyscwY+oBqg6ztclopUxnvUVn7ug4KzudTYLstETPAGKtCqVgW+zjewLD0q2Dx1rz3oascSFPWq106qm4soHuayPFME9xprCCVo2GDkHFZk1g9x4XdZpi7iPls9xTUdLkNnUR3MAUbp4QcZ5kA/rUy3lqRkXMLfRwa8UttLtNRcr9qlSVeCMHFdEPD0emae5ind+ACT707DR6Q+o2MSl5LyFQoycuOKfb3dvdRiS3lWVD0ZTkGvLp/CzLbx3MV2ziU4kUnsa7zw3a29ppaRQE/JwQadhmu/XpUinjBpAM0o4NIAbAqBuRUz9KgJxW9MiQwimkCnMc80nSui5IhB9KYelSA4NNOCTSAid8KSe1czqHjLRbaeS0nndZl6gIf510z8ISRmvLPEsOjSa6zvK0UyjlT/FVwV3Ylsq63e2upTebbEso7kVkSgiLmtWNYpVIjjVVHAwKo6gBGm2qtYgw7g7QccVmvN82DWhOu7OTVFrcA5NMY3O6m+tPxjIpjECgBh45pDn1pxGTSYoGAHqeanRfSmhASOKnRefegQ5F4p64BpSO9MZ8ZoAcTz0ph+tMLDHXmmk56GkCHMTjFMJxSE000DF4zQAaTI4FOUDOM5oGy1bgHjJ+lbVsmOgrJtoycVuWybVzXTTiZyZZLbUqB4XmiYjqKWVv3Zyea09JiE8RzVVJcsbkwV2cFfwtHMQRyaqBa6XxStv9vEURzIg+fHSueC4PNcilzamlrEUh4xUFSzE76i78UwAUYJNLS59KBiotOwfSkXNOJ44pAN6GlI70YpOlMAGaB15pM04CgBaXtSYzSj60AOFOFMHWpVGWGKBFyzAyK6zTAGjOAelctBGeDXWaQu2A57itYITOV8UnGpAZz8grHdtsXNa/ikD+1P+ACseQZj6VMxoh872NBlB7Um0Hik2CsxhvpQaaQPSnL1ouMf3FdRpYU2+ME8Vy38XSuo0iY/Z8bcjFVEGP04N/aBVchtx2810eiyasmuf6ddZtjnALcCuWtpSmolzlQGJOK6Ky1bTr2+FuS7N0I2kfrXPirNbGtJ2Ovu7i0MDL9qRD0yr8157qi6lFOxi1Oby88YYiutli0pYsCAq45BzzXOX11HI7ARHHSvOgknobpye5z1zf6gibTeSkf73JqkNRuwwJnkOPVjVrUMlyAjD8DVDymblUY49FNdsUrakSujT0pri+1m2UzsuXGea9ajsEKA7s4HpXkWhyvaarBIYHbDf3TXq8OqoIxu2rx0JxXNiVqrFRuZWrWyQ3AAIPHpWHqNy1rbM8agtjpWjq2pLLcZUrgehrDvZluYTHuVSfeppp9SmZA8SXIGGgiz680v9vyuOYIyajfSULAmcAd+KRdPhUk/aOBXZaPQy1NMXcxtPNVUBx0xVOPWrrO1RHn6VI0ka2/k+YKrRQwRNuMvIpcoXNQ3MjRqZQpY03as3BAx7VUa7jZMK4qS3uBnG4A0uVlXILtvImQKOK6ixbfbIec4rAmhW4ZSW5HpW/ZArboCegxWc7NDLq96yNSvrqO7SKBwvGTxmtVTx04qhfqpYcAMeAazjvqJl7T53ntgz8t0zUN8cTKpPBqxpsfl2yqTyKjvIGnuYyvCg8mmxE0ClY1Ge1TIrHJz3pyqAoANKsZUEtz6Ck0NDxI44zxSLcSxnKEj6Gjt0IqSOIOvLY/CkkDMrUrl2lSZ1DFemRUOluziR3XBZs4q7q9oBZGRXG5CPxpum20ksChBktzg4rVbE6FXU5AY8Mo46VStI90jOwwQOtamtaXPFZyFtgIXPDcis7TVJsVduSapCI522rwcDNTaa6m66dqyL27kWVkAGAavaBIbi7OSAcVq/hEjpMt/DGW+nNPS5mt2ykRUg5wQavWsMkZDRytux2FSyW9w78ykt3JrmKKZ1e8kfIiQZ9EJrPm1XUNOlmvIooxIRgFoOa66xtHjiAklBz6CqmsQo+1ndlVByHIArSOxD3MbSPGfiQ2J/4mLR5JwEiUY/SsPXNSvb/UlmvbqW4kbC7pDnA9B6VvXFqIrdXBUq3I2nrWLPaiadHJ4U5xTbKSSFEQWBVA/Spo0IFMkfAxzToy23vUspEgXaTmlBH1puD3zSDgYqRjs9wPwqC5y0TADnFS54o2qynJ/CnERj2RWWcDbhkPNarglGAx0qhaWhhvJn24RjkZrQPzDHSm9RFHSgkmooj8usgP616S99DvH704A9K870qxZNZac/d7GuqcADg812UZWRlNEd7IJJ3ZT8pNYkwX+0bdnYqqtk1qyheT3FZs4BbJFbNisQavcxS3vyEtkcms8vk+1LP/x9+1ROyjvUNhYsm4j2BS2OK56+A3fK3GavyOCeO1Zt2SSOeKVwLEU6CMLmqs3Lk01TggCnORj3qWx2IIoQ0nIBq7exI05JPPHT6VUgIEnXvV665nP8655N869H+h6FJL6lU/xR/KZClqMA1HMyQ8E1cUEhTjjvWfqQBPHHFaI4miAy27NliSaryMpb5OlVzweacvNbJGZaibjkVdjYkcVRRuAKsRyAd6iSOmi0idmNQt8zU4yBqYDtaoRpUaaJhEoj3YBIqubpUPCDNTtkxnB4NZ83DGrRyssi/wAZ/dip7O78w3Hyj5YGb+VZGauacMm7/wCvZ/6VNWPuP+uqOvLX/tUPn/6TMd/abp0Rali1KR3AwvNZRqa2P74Vo4nEmdEi7lyT1qpcH5unSrEcoKDtiql0yjkNWXUtvQozyZGAPqa2NH/1ROawpDvPFbmlDZCCaqfwkxNkSt61nX+o3EP3GwPpVtphtyCKy9QbehzjPrWEYpvU1bKraxe5OZTmk/ty+Bz5v6VnscE0wnmulQRk5GmNevBn95z9Kf8A8JDe7NolwfUCsc0dKPZxFzM1k1zUHIU3L10NrPM0Ks7FuOpri4vvg11tq5Fsp9qzqRViotlwyt/e/WlEr4PzVUWXIOetTK42ZxXPymtzN1K4mQ/LIwPqDWN9uuNxPnSE/wC8a0dSyc569qxmBzxXVTSsZSbJmv7ntM+f940x7uaQfPM5PuxqI9Kj554rSyIuyYXEgPDkVIJ5CfvnNVsECnqTxRZBdnU6OzC3ILd6vGSQOTgVmaPIBbH1q/5gzxXLNamsWOmkLJ83QVzt8c3BrblfJwM1kXdu/mFmwBV00KRmyGu18AAebMc81xkyc4zXb+Al+WZsd8VtLYxZ3sZyRUV78zJn1p8Z+YCobw/vEHvWRSLUnFtz6U/ThiM1HMcWw+lP08nyTQwJnP3hXAakP+J3L/Ou+kPDGvP78f8AE7nJPfFS0VEVj8pqvzn2qVzweaiY5AwOPSoNDQ00ZnTjvXfxrlFx6VwWkkfaU4713kRwijtiriZSJgMGlZs8A4FJnC9M1XYMM8Yz71ZI542/v0LGQpy2ai5A5/nUsbfIe9AanHeI/wDj9PPQdKxOuc9a19fcG/YZ6d6yAc1hM6o7Dh05qSMYI71D0xzxU8XJzSSJZo24xj09KfdsREfpSW5zgY6U2/OI60QjLLAc9/Sk3jHXmmnnmjaNppgCgdaO9IDk0nehoBWOccU4KRScgf1pwORSQAAc4xzRt5x3FSK3HvS55p2EREFeDTDj0qWT7w9KQnI6VLGN4ApoGW60N096EB3gAfnUgXox8qknirH2o7o4l5FV16YPamof9KGOtXB6kSRrqeeKdyCDUa9etOUkH1966TIlDfrQOtR7iBg0u7HNMVyfPOe1G4GolJIOP1pwU+tAEqkcVZQ9MVUVOeTViP0zmgRbGdoxzUgbC81EgOCAakBI61IIcCccDNO5OOcU0HFHfrigdzamijZDtkUH61lfZn80lyCM8YNLICRwx+lLGCDgnIr5h1E+h38pIDECBg59qlMSyAAfrTYkUt0qxwKiUl0KIZ90CDbyKhiAlB3VbkYbSNufeqqL8x4pqRJUmjVZOOKqylkPPT2qxeTpG+GJFVVK3HIOcVtECaGRyuUQH3xT55v3eWgUsO+KInaBcIOKjkmMuQ0g/CuqNSNrESTH+fcSQAQooYeoqQXE8UY83bn6UWcDMp3Sso9hRcafcMMq/mj24Nbu6jeKJVupFPdyNGQqIeMZxXDTp5WvoWypx0rsyHiO1lKt3zXI6yfL1iEvjDdawU3fUpJX0OxhBeFe/FWFhIXPSsu1uGSGMgHGOtX1mMo5rJ6MtkcxuEJIzjHFMtr3UxJsKKU6ZxUhc5wScfWr1vFG8X/LT8K7aLU1ZGMrpleKO6LOZJsgnpStO8alQ2R71beCMJx5n5VnuNrHBPHqKiu5Rdgi7mLqTFpQT61oQMXgTCYXFZ+q9BkHIq/YyK1qoPHFYGg9AN4+fYalKPHE22QHPvUToCDk5A71G8h2bQCR9K0pVOXSxE1dCxRSzoySTEKeeKm2/ZogNxY+maZYyxiXBYA+hrZbyyv/ACx/KuxLnj2M1dGBLMj/ACuhH1FFraQSTb9ik9jtq/qMqEDOw+m2qsAVmGD37VztuDtc0sWjEqHrgewpjxopBEh+tPeNyvB+WoiVQYc/nVKbCx02kp5drjzN+ec1fBrO0oj7OAKvg1DdxMztc0+41GweC1lWORhwTWVDod+mivZyXKCVkwXHNdHPGZY9ocqfUVRl05mQ5uX+opqWlibHL6Z4Uu4pMNqkLqOCgTmtObwhc3MzFtYkWFsZiCcVesLNopSRJnDZPHWtlTRcZzC+CY8kf2ndBe2PWtnRdIGkQGP7TJPk53PWhnijPPFFwJfxpc81GDS7sUJAOZsCoGOalY8ZqBjya6KZEhD0pKaTijNbkik46U0kYpC9ITSuANgjnpXM6h4e8NXd0096ga4x18wj9K6Mk1Xls7aRi7QoW9SKak0Kxw+pWOnWaH7Gfl7DOa43UJCzHNej63aoIXCoo44wK8z1M4kODWidyHuZkzc5zzVZyTzSzy4bgVXE2Tg1QDieDg1E3JpzNmm5560AJ+lPVaZ3qaMZPpQMkjGDnGanH3c96avAxTiMHrQITOFPNRHnPNKxxk1ESaQAxx9KTnFGQQeKXGeaYCEflSYOc0/FIeBSGM609Mbqj3c4q1aReY2O3rVRWoM1bGPd2rWACR4qGzt9iDdUly2BwMAV1wVkYyZVmfe2CeK29LlMCg/uwmOCzYrnGbkkk1XkutoIDH6HvWdVXVmVB2Oi1uSxmhfbJCZD12Dn864y4URg9sdKsiQsST+FUL2TJ25rnSSLuU2bJpO1IaTnNAxTS84oPr2pN2DQBIuSOaU03eaM5PSgBTSE8UZpcZ5pgGKVaSndqAFHFKelN5HSlzxzQIUcVIjfN6VGBxSg0hmrZklhmuw09dtru6VxNjJ+9X0zXc2bL9hznBraDIkcR4kffqjAdgBWW2fKHSrutNv1OVgc81SY5ixxUTKiQGkpSKTHesxiZpyk0lC9aLAO6c10GmMRb4J7Vz3euh0pCYTVIGybT5Y47/zJVLgHpXSWviS3ublooNE2sBgyYx/SuYtm8u/yRnDdK6S51qTdGtvbxxY6kc5rDERUkaU3YZPGZZHlKMvtmqtp4jGmlx9gRye7mrT6ncyYDIo9TWZfwrIpYAZ71xRjZ2kdClfYuN4/dQc6ZbAH3/8ArVWHj1xJvXT7UH0zXLXiENgDpVLac8A10xowauZSbO/tPH93d3KQnT7YBjjKk8VcvopruXzBDjA5wK4TRSU1W3J/vCvVA8e0gkD8a5sRFQkrGtN6anFz2c/mkBaoz2TR/vJDtA9a6u5aITn5hmsfWgHsWVfmJ7CnTk3oEjFe4hIx5yn6VEs0EYP70VnC1nP/ACyb8qUWVwefKbH0rrUUZXZohElXzd+VFNLRv8oYnPtS26SR25jaJtxp9rblJN0i4FJjVyaOzSPBbdg80siHzAYzxU09wjEBTwBUQlUY9ahtlIsIHyAxOfauhtcrAvNc2s4MgIIrorVswKBz9KymVctbj68VU1CJpYhtJ4OeKsZC5J6CkhvLeWQxq6sw6ioSYmy3bArCoI7VNyf4aiDYIGKevXqRTQiVAxPIxTlDbiNuajBH97mnIzqT3FIB7qVGCDSxsCQCxA9qa0mPvHNKksWeSQPpQJlue1srq28pnYlupzWaIIbUiEM3ljjPfFasVxYiEgONw6ZyM1k3UivISCKpMRHeJasdluJDGRz5h5NVVVY4tgACjsKk6Go3wAaa3Gcvekm6cj1rS8O489utZl6Abl8etaPh/m5P0rofwk9Tso5I1HO8fQ0puE3jDyD/AIFUK/dAxTjE2AxiP5VyW1KJRcNJcAm4YAdDuqe4s7C7tHWaWZ5G6turOuLtUdd8Sxgei1ZhmSdC0RUqOprTYkrBLe2Cx7XkQdAWqGRo3kYqioD0A5xUlzMD0I6+lVGLd6BiSkAYzT43OAAKrSscHpSq52DLflTAnYnr70ZLcYqMt6NzTd/vUjRMeoOaB3qLIx1zSq3HBosFx2MmlHLYzTDn6Uq5DdaLhYvWyqHHOOa0pJAB0zWZC2whj2pINYhvbqSGJWGzgk11UOpnJFmVuTVGYk1adsg8VTmfnjpXSSZs6qrM7HFU94cZUhh61LqY8y3cGqFkMQYHbtUMY5xyfWqF0G2jitCVscVRmOAcnIqQKkTHcSac7nGMUQ4bdx3qRwApyKm4FeNgHzjmrWoTmK5br2x+VVoWQTDNWdTZPNwRzkfyrCXxr0f6HfS/3Kp/ih+UyEXspGMn8qimLzHoT9avQQqUDbe1PkEUK5Za0ucdjAkiZWwackfByR9KtXN1FIpCR4Y96pgnrWsWZsnWPK8nFNw4OATTI5GB4q4snrjNNlRIFYg808ucZFTOVKVAODUGl7BucrwDUTIznGK04nSOPJQGom1KNG/1AzSuQ0VBZPjrVqytWjFznPzQMKRtX5+WMAVPaaiZjcfIPkiZ/ripqt8j/rqjty231qHz/wDSZGWLV3YKFPPtV5dNMWDnJo/tkjgRLT01l3YDy1PNVJs4kkSpasT1NRy2g53k4rdgXdEGwBuFMu0VYCPXvWXPqPlOaZVjJ2qQPerkMkgi+RetVLg7WIGetaFvfeXEqmMdOuK1buidmNWSQZ3KailWaYcggelXP7SXOfLGMUn9uiDO23Rj6mpSKuZMlpIWwqs30FSxaczrl1ZT9KujxJNnIijH4Uh8QynnykzVXkToV207gfK2fpUL2JT/AJZt+VaH/CTzA5EMfFL/AMJNIGz9njNF5BoZsdtKJBiFyPpWr591tCiMgfSnReIZLmUL5CDPHFXxfMhB8taiUmUkikoueuxvyqRTOR/q2/KrR1dlOPLXNA1dznCLzWepRk3kVxI+PKY/QVHbaU8zESq6j6Vbn8QSRuR5KE461X/4SSYDAiStFzWIdiU6KmMBWIoGiIqklScVCfEkwJ/drTW8R3DKRtUfhRaoGgp0pnf5Ijj6VIuivjiJyfpUK+IrlCCoXP0qeLxPelxjafwpvnDQmTT7mNNiqyj0qQWV4oztb2zWtb3s0sYZwMkVLJcSeXgdawc3c0UUYDiaEkzErWXeXZlYgMSBWlqe8qSzZ9qwSMk1009TOegjEdSea9A8Brm2kYcCvPWHNeieBR/oD4/vVcjFnZp97qKguSGnQVMmAeahuOLlKxuWi1cj/R8Z7VJYAfZ+agumzDU1jnyKAJj91q89vTu1y56/er0Jz8jYrzd2b+2rnJGNx70FxJJCF68mo/MwDxUjnPaogvzcDg1BaNTRm33ceO5r0CNRgfSuC0bi8Qgcg/lXctIVC4Gc1pEymWQoNDRI3XrVbz2JxipPMbAOKZA8wR+lMIRRgDFRvcZHAwaFOYyxNA1ucPruTqUgGMAdKyj3zWlrLA6pIx6HtWcfXisJ7nUthFyTirMWciqmfpU0MwxQiWa8Iz0ODUOoNhOtSW0gbaF5PSp7vRNQugvlxKFPPzNiqWpN7GAGHY0A8njrW/a+DryTHn3cEQ74GTWovguyUATaow+iAVoo3FzI47Az7UYwcg126eDNJJ41SVvYAVKvgXTXIH9oTbfoKrkC6OFB9e9KBzXog8CaKoBe+lP40yXwhokXCXMjN6s9HIFzz9UyenFKF5PavQE8HaUU3G9Kf8C6U+TwloSQBI71zIers9PkFzHnvllu1RmJ8njgV6HH4P0gjB1Hn/eok8JaTG3y6hn6sKXIhc55w42kUITvHpXolv4F0y6ly9/lfQMK0ovh1oCj95dTH0w9P2Nw9ojzZfX8qZGc3q12+ueGND0mzklhvJppeiRg5A+tcNHn7YBnpS9k4vUJSutDXUjHBpwbBz1qIcGne4rQyRIMnuKcuPeo19aduJ46UDJM4+hpwPJqMH2pVf8ACgRMDxjHWp4zz7VW3VMp6elAi4hwc9qlVjUCMAaep9KQIlznpTlBByaiVqeHFA7FhmTdjP60g+XkE1GFbd8yIKemd2ABivk7HpFqKTI6808Av35FVlzvwDirS5U+ppE3F3sFw1QbsP15qSR2X5cg1U3ky4xg01YLFfUMhh3/AAqoJXGNoA9avXhBU7zis9QWYgdO1bx1AsxyO4wEJ9qhmglVtwjYfhT4XeBtwb8KdcXtyejgD3rop8iWu5EmFnfTQMFHK9wRWoL2IrllMZ9q53q5Z3Kk/wB3pTG8wsSty5z2Na+2lHREqNzSu7ovKTuyPpXIa1t/tGFiOd3A9a2ZfMK43k1hawpDxM56NxUKV2Vax2Wm3ZjtkUxKVxzxVyQQvGZI12n0FYullntEGOgrRO8LgYFKUtAa6lSZiG/GrENxOkeElYfQ1XkIJ5xmpFYBMk06c2tiWrlpbufaQXJ9jUbOT1OM1WZ1PQkfQ1AZlDcsc+maUpSluykivqmQM56VPpzb7ZelQ6kxe0yE/Gk0wobfIbBHWnZgaMiYB+Y0kU/l5XANQtKGHzdPWoVuLZGISRC/pnmqheLuJ7F8GPaxCrv9agMlwWxuUCqkt0uM98VEl1k8soHrVScpO4opGj5L5y21gaRV+fb0HqKqNqEKKoLbgfSrcEiybShGKSQyyrMvy7mxU3mQ+XtljDZ7kVG01ugCmRAx7bhWTdeIbK1uRbSSHzD0AGauLaFudppJURkK2RWlmsLQLlJ48p0IrbJoZLFzSO3yEe1JnikPINAjOju1Sdl2nNWRqChsGNseorPa1ka9LhRtxxzVyO3lVgcLirSQi/FKJF3AED3qTrUa8Dmn54qWA4HJxTuM0zIHSl5600xjz9Kgcc1LmoZTnjFbUyZERpM4HFDGm5710EC9aQmkPSmk8UgHE8e9MJwKTNNLce1IDI1k/u2I9K8j1kj7U4Bzg9K9P167EcbktwB0ryS9n824dvU1rAze5mTE7jUcfParjbCCSMmoG24OK0AjZQaiK+lSFsnpQBk0AIoYdBVmIZ7Usa45xmp+AvI60AJwOM1G7c02Qk9KjIwetAD24pmOelOGc04UANCilA49qcSM4yKjLgfSgBSec1G7grSO46VEWJ4oAQ9eK19JI34J4rHJx9KuWM+yRSBmqhuDO2VQYhjFVp045zUlhIHjHOakuEyhIJrtjsYPcxXQlWOKqLbNOx27eOuTWxLA7WxKDkVjbNkxycN6Vy1Ja2NIjZwsMRB7ViyncxNal/J8oT1rIIrIsYcg9aUDNLjJp4WmMaeBzUZOTUrcjHeogMUhDgRjinA00NkdKVRwKYx44oNLznIpDnNAAKUUfhxSjigLi9KMZpy0vegBoXNO20v8qVTzikIsWgxKPrXY25DWQXJHHauRtBmUeldCdQtbS3AknCHtmtIsErlSbw9bTM8zXM3zHJAApY/DNlMu37TOB64FQy6zArELPkewNOj1m1CDNxj8DTkkOxK/hKwXAF3cn8Fobwnp4zi4uvyX/CrMfim0gQBZFb6oajuPFVtL0lx/uoayW5pyxtuQHwlZsmUmueOudtRR+GrVm2iSb8xUb+IkcbRK+3PpTYtXjMnys+fpWiiurJbj0L//AAiliMBnn477xV6y0iBHEUIlYDrzmsOXW03gSGQ/QVbstdeGYNHvG7ihRSW43Z6JF/UNBSzZJ7aR2BOXV+30psSRNOoeUIPU9K6Ty3vNPzKV3EZ4rjtTQxhkznBrkqy5tDZ0+RnTyaPCkInnv1WLGd22qbWuhsoD6ynPoKnnbzfD0ceMjYOtcTcw+W5A6dq44avUuzOjfRvDDuS+sMCff/61RDR/Bwb5tXkI9n/+tXLM2/gnmqrgg10wi11MpHdRaX4IhlWZdYfepyNz5H8q1IZdE1C4Fta6n5rnptHNeWE1v+DW2+I4cYyQQTSqUrq4Rkzrrrw/HHP8k8rjuSKoXWiXxkH2Xbt77ziuvuyFb8Kp7ia5os01MGz0SZYz9qIMnonQVBqGhXLhfscgT13V0oPfNMl35BHSr9ow3OVttDvYmJuJEk+lLdaHJIPll8s/SujPQkmoHbIx+tPnbJOfg0c2+S5Eh9xRJYgjCouT7VqzMTx0qtnDdarUDm5dPmEpUK2PaursIzFZxqc5xzVV2Ibr0q5A26MZHFTJsaHTbmjYD0rKtIViuEUff3c4rWY4U+mKpWJDzsxX7ppRYNGvvfHGOKUTuPTFMVzn7tNlc+UwA5xR1ABqMJkMYljLjqM1ajuS3BHFcdHEsEwLghy1dTbvHsHOfeqaQrl0OhPNNuJ7eK2dzwQOKYChblqJ7aCSBgZV6dCamK1BsztMvY5rmMXUmyNj1revILJVJt5A49c1j6V4ZfUdQUm6t0gUZGX5rcvNGFgP+PmKVe21qppCWpkNtxXPX+o3KX3kQBcAZJIropQATyKwtRiAlZ0P7xhiqja4MxnkaZyW+9nk1raD8l1/nmsVtwYgnnPNaWiSOL5Ru4x0reS91kLc7iKTn7mana/kXClSV9jVNJNo61Kt2IZA+xXI7EVxrc0MvV7u4luk52QAcg+tJpbTbJP3gEZPAFaGq+JYcpHJpNo4PG7b0qnDd74tywrGGPCr0Fa30IsSO2Oq/TNQuwz05oZixOetMbjnmo5tSrGTrF+9qyJGoy36UaffPNIEkGDjPFO1VVZFZuo6Gqmm5NxyK2VrE9Te+Q9aXEfvimLjvTsZPtWTepZmaxcyW8A+znBY4z6VXsLqZZ0jmdiW5q5fKmAWGearafCTetI+OBxWiasI2gA2OaljQFutVxgGrEe1sncQaybHYshNwwrCs6xgEGqSIOvUmribfMADUy22NqcnXcBXTQe5Ei1IQqktnArLjvobkt5Z+6cVp3fMTrnGRXNaeBHJIuM8811mZauiDE5xgYrLhIVMDpWlc/PGwB5xWbGuEx3rJoY2bGckis65fjFOvC32hQDgCo5gWxUsZHbEhjUsrZBxTI025NRuxL4FTuMZGf3mferWpn/TD+H8qqRuBLyKm1STOoMgHQj+QrOS/eL0f6HdS/3Kp/ih+UzRt3KQgEgVDeH5c9c9qYsEwAYjcMU9rSefggIKNEzk1sYjAh6dsTbncS1Xr3Tjax7zIDz0rOyfzreLujJoMlDxTlbLZNNbnApAD0piHlznqcVNE2etVcmnq5UikM0kIMZFZ8wAY4qwjMVzTfKZuACc1OwylVzTeDd/9ez/ANKupohaMMZgCe2OlTwaULcTkSg74mQcdM1FWa5H/XVHdlsX9ah8/wD0mRzpNTW4/fL9anaycHAyecDitC30T92sjyc9cCrlNWOFRZuwyAQovbFRXjr5RJOaqmZ0wqocCoJp5XUjy6wsmzToZUx3SHB4qUPtUd6rMrCQjHU1YWFgoJ710aJEdR+QQM9aqygbjjpU/luW6UjWzNyTSTQNFE9eKM4qc27E4CmpY7Ld984quZE2KWc03nNaZ08epqJ7Tacc0cyCwyzYrMMda30dSo3Gsi2tXVt5BAq+qMMY5qJNFJMmYKTkGoiyqTk/jUgikI6AZqOW1kweRUJooyrpsyHnIqqW7VcktnJxg59qlXS3YZJrVSSIszMI65pMVrf2Ux/iqGSx8vvT50HKygAc9Kntv9eoPTNTx2LPzghfWrUen+Wwbd0pOaBRZ0Nu2I1A9KnaUbfesqOQhcAdKJZZFXJ6VyuN2bJ2I9RcFDjrWCVbPI4raMmUJZRj3rLuH8wnGK6IKyM5FUj5gc16T4KXZppPqa81P3wK9O8GrjSVzwPSqkZM6mL1NV5m/wBKRe9WI+M+lVGYG+XjpWRRbuuIjnrip7EHyBjFVr3/AFVWbM/6MOecUAPlO2Nq8/hgWTVrlsZJY13kx/cvk9q423heG5lYclmJpPYqI/yFJzjpSeRntVtQ/ChRmrMNoWcb3C5rK5ZBpce2+T0BrsVB6Vz5t7a2v4ljuN7fxYHArdSZMAbgK1izOSJRFubOal2jGKrm5VeAQaUTgrk4/OrFZiyRBsAcUzaY4yOvBprXWFJx0rFXVbuSdlYKqjOMUDUWc5qx3ajL7GqBYDrVvUXP2uU4xk81SP3evNYS3OlbCM2O3FRNKRzSuTtqnK5APNOKEzb0i4aW9jRWxzXoSQzCMEv2ry3w5NjW4ix4r05rx2G1GAHrV2sc8nqODSrnC5I9qilaZ/vcYqZHYKS0oz7VyuvG7kvQILqVFA5CnFXFXBK50KyyggL/ACrRtjO0WS7D8K8+slvzKTLf3GwHpu612en3EgthmRzgd6JqzBxsUL+yvJbhj9qmCegOKzH0u4Lg/aJ25/vV0E14S2CxNQG6ZcAYNCkwuXLO2AtVRw2cc5PWlaMowVVJqv8A2hMBgAEVGLqZm+ZgDUtskufZSeWUmqN/JZ2x/wBKmSPPTcauR321dpkFcnr/ANmub8NISWHSqhq9SopMtnWtLRsJMzMOyA102kai0kAxuCnoGPNeeW621tN5obkeorrdMuopoBsJJrVtxegSikW9amLQFRiuNhRxebiMCuovGkEZO35feucafNztA471cnckthuDzS5KjrUYOR0pxyOKklEynpmlz2qFWycU4NkUhkucjNOzzUWcAUoY7c0CZYT+VWEqmh3N16dqtKRSuCLKCpRyKhjPy9aeOnFFwbJAB+Ip6j5+tRDgg0/OKALkjHOcikSTnikfoMnP4U1cqeM18pqekyZCWbng1YXO7BIPvVRY3Z8jn6U8pKDgqaTixF54xs3Z5rPcETbhip1Vz1J+lMdGUj5ec9aErOwrlDUJTsJbk46CudOtzx3GxNNuXHTcorqrz5Uztz+FZrMGf0rppW6oVzFv9S1RWR7a3dVJ+YMmcVahvp3jXzfvEcjFX5dxB2njpVOWA53bsfhWqt2JZJuY8g4X6Um5W6SMD9KYspTjI49ad5qk5aRMfUVUk2JMGDLyr5PvWHr5kKDACsPat4T26r/rY8/7wrD16aOePZDcRlu4DZojF3KubGkNK+nrsI346npTkGsF2W5a2MZ6bOtV9Fu7e3tFSe4RGA7mrsup6aG5uox7g0NMTMiLSrmPUXuHuGKE8LnpWxCEI27iR71SudV07kC4DcZ+XNQ2+rWigsm5h34q4JvchuxrGFFH3AajaGLcDjmqA8SabuwGkPr8hqOTxJYD7ol/790lF9Sk9C3fMTbkYPFMsE3WjJuKn1FZV74itZY9ipMXP+zgU611oWsA3W7sH6c1ai0FzRttN8h38zUJ5Af4X6Cs86NapfPcrIxc9s8U4+I8k4szjHGW61X/ALdYvuFoAf8Ae/8ArU4pks04UA44/EVcXbjAROfasWTWpYYTIkCv7GoRr94wGLWEfnTaCLNt4kaQAgbfYVbiVI+OQtc3/bl/likMYA77CarNr+q7wpEa56Dy6SiN67G/caPYXN6J2Qs/+8aLrSLN5VlNvukXo3esT+1dWK7tqg/9c6ktrzVpbqESMxQt8wC44rTREq53Phr5GMQDY9xXT7D6VU0s2sFqjRWqq5HzHuavm+Uf8shSkJkew+lL5ZIpx1DH/LJaUX68DyRUiM9oyLjpVxUJAqFrxftAJiHWrf26MDAiFNDGeWafsPpQL9c/6kEfWnDUE3Y8kfnTEJ5ZFLsPanm+jI/1QpwvY8/6ofnTQyMoe9VpflPNPvdREYAjQKT61TknLqCR+VawRMhzHmm7u1QtMAOhqNp+cgVsQWC3FNJz1qubjI6U03BAGQKALJNV7ubZCeajNw2axdXv/LhbJxgUJAzlvFOoDJjDfWuGdwW4rR1e8866bJJFZROTwOK3ijIU8A8VA2M8dKe8qjg5zTV2sOKoZGoqZYznkcVIkeAMDNS4AwcUCGrx0FBJI5oznJprY6k9aAExmkwOpPFMaTA9aiaUfgKBk7EAdaYXAqsZMg803cT0pATNJUZkqLJzS8nrTGOY85zTd1KBml8vNAhuQakR9pGKbsxRt9KaA6XSr4cKTzW/uWSLg9a4WzdkkA7V1thOGjCmumEtCJI1Y4VMOGzg1j32kW0chmS+i+bqhPIroFTNr17Vxc/F1IfeuabvNlIzr8ATEA5HY1RKc1oXa5bNVSMmkMiwMZApCCfanspFMJ5oAYwqPac5qZuaZzmgAA9RQBj6UvNAOKBi5OKTrSck0ox+NMQ7tQvWkFLg0APXGetLkZxTB0pw4pDHZxzSjOabjIpy5BoQi7asUBYn2qldzGaYgnIHFaawAW249cZrDYjzCfeqdwQjoVH1o52U+U5UUzrHUlDKDSZopCFXqBUyPtPFVwfm5qXPFUgHO2+TnrW3p2GkiRlGM9awU5etuylAeJdpOT1FVbQ0g/eR6LG3kxIqrgYxiud1qzd7gBRy/IrpbYIIFAboO/NZ2swsyJIrYwa85no1Y3imHlFdLS3AJdVxWDc6VcTZUIN5HGTV+YP9lIWUq2Oua5i4nu4WK/aJT77jWUVd6HO5WJP7Avs5wg9eaRvD15IuRs/Os2TVLuLK+c/13Uz+1r0ni5kH410qMzJyTNL/AIRi86ZUfjWpoGiXOn6vFdSunlJ1x1rM0fUNRurtYzcsw/2q6lIb4MD5y7fSsqs5x0ZaimX9W8V6bFcbEErkdcLWWfFNuXKJbzE444FNltd8xzGhPc1kalcvpoXEKMW7+lRCKegPQ128SOoJW2J9MmqsniG8mUN5ARR6nrXPNrshPEMdRNrVxtAG38q29kTzI3Zdcv3wqwrjPUZpH1S6PBZfwFYC6pcPkbsZqe1MtxMAW70+SwXuaa3lw6gyyc+1Rm5mWUFZTtHUUrRqrlfSnRIDJ0GMUtAsNa9aQnJatrTpna0JY/SsvYCeABWpaYEHA/CpkUkZ0l/d/bHG4CMHGMVqWqGKJpCRkjNULiENPtXA5ya1FYCHZ7UtBWKNvqc8suJI1CZ4xWuHj2896xBGTcDHQGtNSMYPShgZkzRC7bcCWByDU1rqCGQhjIFzjgUk1ozXfmAjbVVYz9p+T7oNUrWJZ1LPaLAW3Sl8d8YrmW1W6kkmBRfKzgcc1rhiY9uM8VkTRlpAiDCE80lZAXdCtfOSWR5kRUPAJqZ9QhMhRcsoPDDpSCNEgMYAAI7VmFf3ixp9wNSeo0jbALYNZurKVi3qcEelaKDC9aZcRiVCrDIpJ2YNHFzO/nYPfmtDTZpYLgSJg465qpqKKt4yqMAVNYBjIAO9dT+EzW52tvOzxq23JIqU3BUZaLPrVa23RxKB6VYdXaFt0oXIx0rk6mpzmo679sk8pLRUhjb5n7mnjXGeTYkAEagc5qvdWkduhjSUPvbJwOaYYFhXZGDluprXSxBvxTCWJX9ak3DPJqraJtgVT6VMSM81i1qWUtV2tatgcjuaxLK8kR8leOlb14qvbuD3rCCGMhFGcnritovQlo6KFi6BhxmpDgEnnpUEBIiX2FSk5U881DeozA1PU5HleBIjgd6ntNQSC3VnUszccVDdQiGaV87mf9KSK0XyEcnpzitFawjYt71bhtoUqfQ1fjGe9YWnkvdM5HArbVhis5FInTb5owQeazxczJ4hZFwIiuDx1q5Hy4x61FJERqSbQOOSa3oWIkT6vNLb6fNJGPmCnaa57SJJJIS0pG8nmtzVMvZyAk/d7Vy+nRslxzkZ6DNdZkbN0xEbbeuKxbVmLNvYk1qTyERnJ4xWTCTuc1DGU52JvDznnvUzuc8qKq3e7z8jikO4rycmpYyzEwYkZ5pHUbicc1Haqd+amkbDEVmO5XiAMh3DmpNS41Fj9P5Co0OZDT9SYDUmB9R/IVD/AIi9H+h3Uv8Ac6n+KH5TN+ziDQIzHqKbeu0ERaPGfenWjqLZBnHFUNZuQiBVYe+KzSbkc19DKvdRlucK2AB6VT3Ajk01n3E8daY3riuqKsjCTHhsNmnE55AqJSvpUqt3ANWIRVz1pXXAFO8zHGKekgA+YZFSWrDYX2kA1cS5eJSVAP1qm2DkgfSpI5Ny4PBpNXHYmOqTbhwKtWeotN5+VA2RM/5VkzIRzVjTj/x9/wDXs/8ASsqsFyP+uqOzLW/rUPn/AOkyLP8AbTKuPKT60f23KxGFT6Vjsc0sX3x9a0cFY4eZnaWgM8IcgDPNOmjVEOBzimWJxbIM9qfcsPLP865nuarY5yTm5wfWteK3zGMYxWO7A3XXvWzFNhBWjvYlbgyCPkLn61SmvxG5VoxV6aT5cVg3ZHmHmnBXCTLY1MA/6taX+1Ix/wAshWQW9qNw6dBWvKZ3NY6wn/PIYqRdXhP3oxWGRzSUcg+Y6qyuVvvuxYAq+0QQDKg1i6HJtU81tmUEc9K55qzNI6j4wh6oOKLiSOJcmLIpkbjr0qC+fMePWoS1Ksiu+q2iDHk4PsKYNZtVXPlk+tYlyfnIqsTXQoKxm5WOlGuWZB3RHNRvrFixB8jn3Fc4TxTc+3NUqaJ5zpV1izPVPwxVmC+gunCRxEt9K5EetbWgsRcEg1E4W1GpHSxwKR9wfSorqABOEFWVfnPSoLufMRHSudXua9DBvQVUjtWYx+Wr9++T/Ws5uR9K64bGUyLq4+tep+ExjSYyemK8rH3ueBXq/hlQujQAdMdKcjI6FG496qHm+GTirKEAYqknOoVmWW7w4j61btf+PZee1UL5v3Y+tXLckW6n2pMBJyQjfSueEuyVgAOvWtm6kHlP1xiuPe7be2PWokXE3FuAD0yafFPhy5UN9TWD9rZQcGmLfnfhiazNLF7ULyX7WZFO09tvamR6leDH79qpTS+aSwNLHyBmk20Uoo1Bqt3t4l/Sn/2tdcHfxWcvQ4FPyQM9qSmx8qLw1a6XJDDn2qJtUmUZGMkdcVV61Ex6juKfM+4WGSuZXZmJLHrVds4PGalY4OMc1ExPNNARE5U561QmPORVtzlTiqMp6jNaRJZc0DadYj3MAvfJr0gTQJgGVPwNeV6f/wAfqn3rrQ+7vROTRny3Oma+hVsCdfpWPqVvHe3Ak+3FAB0WqXfJ7UZ59RUqpbUahYf/AGfbowxeSEfWtW3u7a3hCb2Y9yaxlI5FBOKHVbG43Nl9QtRztNN/tG3B6GsgYPPWncD61PtGHIjWOpw9AhpP7Sh5/dHNZORn5utLnnBpe0YciNRdVjDEiGq8tzazS75LcMao5AOaO/qDQqjGok7PZYOLNCc9Sa0tOuc4SONUHtWLxnFaGlHEp9M1oqkm9yZxVjV1A5h5bIxzWA2zcdqgVr6pLttzziufjlLDmujmZkWg3PFODetQdBS7z6VZBYBpQTxzUQ5ApwOO9AXH7vWpFOahyOlPBIIAHNAPUnjPJGKsxcnNVIznqatJxxSEi0OvBp461GvA6ZqRRmkOw/I7Uozj1pgAB6U8ckYNAyR72FQcvVc6jCSPn4J4ryVtR1oggyR8dcmr+mrqc80cs1zEEQ9Aea8V4FrVs7PaJna+II5XszLb3EsbKcgoxFZfhy7uWvXWS9uHxggNITU83iXSraA211uDevUVR0e60g37TWly0hPO30pxpOKasSp3On1yS7S1d7e5kjbH8J6VzGmaxqjX6Ry6hOyg4IZs5rev9d0/aYpQyFhjJ6Vhra232wTwXsG3OdueaUFbdBpc6bUfts1kjRvJnPODWOLXUmJ/1zA+9bia7YQ2qxyO4I6kLkVWk8S2C8qJn+iUrPoMzDp2oSf89P8AvvFB0m9wepI9Xq43iuzxtW3uGJ9gKqv4qdM7NLuZB2ORVRpz3sLmEXT58bWyOMcmhdFmYYYgj0z0pw115hv+xvG3dWOajbxRcAlY9OG73ercJvYlSRJ/YL4A3AVhalphs7xSSME9h1rWOv61nIsbRF/2nz/Ws2+ury/YGWOFCP7rcU4wl1HzGxHoiXMSsDtOOOKefDwA5kz+FZkeralGoVZLcAD+Lmql14mv4XAOs28HsIwaFRmxuSN0eHl6mT9KUaOkRwDxmuWPi2Yff1gSH1RMf0qJvF0JUiW+mceqg1pGhUT3M5TR16eHoCOd2TzSHQ7VU5yeeua4ZfEVqku9dQ1Fv9kk4p8+rzXceYZptvucUSw0r7lKaaOmv9OtIwr/AHcdTmr9pLpElkivcQuQORu5Fee219PzHLKzjPRjWhbRRytkAA+1DpWW407na7tKHJeJR2JqvLf6Lbj5pYcey5rmp4NsRYsxA7HpXPvrkkLtH9njOD3NVClcmUrHfyalpqIJQ6mM9SFqX/hI9DiXO8k+girzc+IrnaVEMIU8Yxmok1u4V+Eh/wC+a1+r9yVM9Rh8S6TMcxCTI45jwKhn1zTxcD9wxI5BC5rjNOv7i5kHmbAM9lxXRoY9mcDPrispQsbXVjSfXbeIZW2kkx0wQKIPFERmQNp8qnP3sg4rEvsm3YoxU+orim1TUI5igvJQFb1qqdHm1M3OzsfSWnXImtFZRj2q1u9a5/wlcefo8ZLFmwMk1vE571nJWGxxbBpN2R15pvGKBwOtQIglcLKMmrIbiqlx1HrU6HKA54xTAmDmlDelRqPbFOHA5pASbjjIpyvzzUW7mnCmgIb7kCowcxgn0qa5GUFVt3yD2reBLEfPSoj6mnO/pTGkBGMVsQNamnOMUFsUhJ7igYjfKK5PxExEEnPWuplY7DiuP8RSExMD3qokSOBmGZWzzzUZUY4q00fzHjvUbg7TW6IKbRAnJNPjjAp4QE1LhVx/OgYgUAU12FI8wGcYqrLPnpigCVnHaoZJTjmoDIT3phbIoAkd8io8k0AZGKeF9RQBGATTwmTUirx0qQL1oAi8rt3pwjHepCcfWomJ9aBhtA5pc8c0zBNISaBDyRSZyaZzSjd2pjJ0YIc1qWN6BIqg85rF+tTWrFZQfTmqUrCPS7Vw1oSOeK5C7Qm6cjAGT3q1b69FFb+UfMyRyRWdIxludw6E1nrcCG6T5RiqDDB61tS2pMWfasx7Z88CmIrHFR456VYaMg8imFNozQMiIA60zAz0pZGqPJHegY/jtSUbfek4xQICKd2pg+tOAJxmmMcQeKWkzg0o56UAL2pw6UnHBpfegBwHpU8MJd1HvTYkJPI4rUsbYM44xzVRQmy5PCUsCSuMLXH5Bfj1rtNYJg02XnquK4hOSBTkJE7gsgxQEbZ1FSMPlApBkccVkUQeWc9aPL4qTPJzSE+9AyLGDmlPFOJzSE8YpoBU65rTtXKSxEetZq4xWhbkFo89jW0XoT1PSbeNzbKVYEkdqnaAPasJCNwFZ1nOiWyNuOAOlXYrtJ+nX0rgcZXueupJwsYLxqZCrEjBrPv9NklBMKlz7V3f/CJXd9bC8jktkjIzlpOayJrD7IzI0yE/7JyKiUHF3OJvoea3VjOjnzIXXHtVYW5x91vyr0tvJQYdkaqU5tt5cKo9cCqjUsieU5jw/G/2/AU8dSa7ZXIxnoKyFuLMvhSqsfQ4qOVZLq4ENtctGB94g5rGo3N3ZadjTeZQzHBIzXPa9tuSgLhcU6TTLkOytcTOP97rVW4sRbJvn3BexNVTikxSZnrpabvmnAFSLpttjmf8qUy2iAnzcn0waj+2W2Pl3flXRqZaFkWWnrjDOSeuasQvbWwJjJ/GstrqMjO00LJv6Ck0VzGo17FuzjOfU1H9vRHJGPTFQR2Ms8RYJgDvUUloyjtxU8qC7Lf9ojrxW9YS+baq6965jR7dLrV44JeUJ5HrXpEemwxIESEBR0ArKq1HQ0jdnL3CMt/HwcGtMYI96tXtldBl+z2oYepIFFpYXTn/AEi3KL2wai4zIjgcXrYIx71oLEQvJH51rf2coOQpzVSWGVWKpAzAd9tF7iZV8piM4H51RjtWN+Rgge1bEcch+9Bt9Mip47Us2cKDTuK5WijmjPyIW9tuazZoL19WVfsr7GHRUIroAb+F8W8bE9sEVdt21vHntGEYHjkGlcLmE1nMvDQSj/gBrNtdP1CfUnjisblkXkkRH/Cu9l1zV0QblhQf7mSagg+IviLTy8VtBC0YPO9P5U4tdRNs56W0uLcgS28yHGcNGRUDHg8Guvi8SXniF/M1NZMgYChQox7VVv7ayJBiiMYx3PWk7XHc8uv42+3MMcVLpwf7YihSAT1xXRG3tYNQacw+Y47N0rQhjk1V1Y+UhXhQB0rqulC7JtqV8kYwacdzrt3detao0EKQJbtFXuVGcU8ad4fjdVl1e6JPB2xD/CuWLTY3KyONeNo9R8kkEEZzTmlWG72ld2eldheeDdMnvYptN1lSSvInIz+lXbHwBoYu0l1y/eYdktn2/nWnkJNPVHLRziOEjy1JPc9RUeS9eiXvhbQPtUcWm2DvkYHmXBI/nWVeeC7y1V5TJYRR9QomywH5VLQ1I4u4B8sismPcLrZjjNdRNpU7q2wBsdxVBdHfz/M3DI6iqQCI2F4Wn7xzxzStAUOCentU0NoZlJTB96lrqNO+hz+oSGO7Xj71Tzjy7RTjpWhe6FPcujRlQV606409hblGYKSMZNNSCxUsAGj8zoTV9BkgdKq2tuEiEXmgsPTvWgli6YJP0pSGhyKQ6855qMnOpEdsVdtrN5COO9TyaPIsxk6cdK2oESMrUWzauB6Vh2qAyKT1rqZNEnkRmdgFPQVW/sPyVDFgAK60jIwL0sqEKKzYiVBx3rpbu2gjOHkUnHrWayW4bAxUyYzCuUZpc4JqMI3ocVs3UiQpkIKzJLgucBcVN7jGQqytnoKsYDckZNVfMbvUkbZxz1qGNDZ02YZTik1Nc6k578fyFSzgGI81HqI/4mcnXt/IVm3769H+h3U1/sVT/FD8plmNnSIMWwAKybuczSE5zV67ctCIox9TWcbeRT0zWkVqcbTsOhQbcmmTEZqZSUTBGKruMtmtDNiRLuYCrZxGvGKrwDa+amkJxzzQSQtzkikGB1Oad1wBTGU7qCkx+8YxmmqxBzTRG/pSlSBwKQ9S3w6ZHSp7KNR9qwOTbuP5VTi3LjitCzU4uDn/AJYtWVX4H/XVHflv+9Qv5/8ApMigtruPIIFSpaorA571It0Vj2kAimw3O6ULsGDVts4TSW8eOADHAFRS3zSRYx1qx9mV19OOtVZ4lhQ96z0uV0M6IPJcAKMkmtY2d2qjIx+NUNOKi9VmHGeldUt3CcAg0pyaCMbmObK8ZRkZ9OaqPpN0+W2/nXTtcwheuMVSk1iyUkFj+AqIzkU4nLSWrRttPWrEWk3Dru28Gtf7ZpjPlh83XJGasx6pYAbfMIHbitJVJW0RCijC/se4H8NQy6fLD9/Arqo9U0zGGnwT0yDUbyaPcHc9wGx1FSqsuqK5UYtgkiJ8ik+taCvOeNv6VrWaWbRbbYgrntVhraEHJbFZyq6lqJg+bcKR8uajmedlOUOMelb7W0OeGpsttEYSGfapHWkqiDlOKlRnk6c04adcsoIhbn2rp4dP06F96zAn1ZquqYAcean/AH1WrrWWhDicZ/ZV2BnyWP0qGSxli++mK78TQbeJI/xIqhdWVtdybpLhfwNEaze6DkRxiW0jn5FJ+grSsIpbV9zIRmultbO1gTbG6n6mrBhgl7qaUq/SwKCMgXbbckYqGW6LqRitw6bGRwRVaexjgQsoGfWojNFuNjnZYmk528VnyrgnB/Cte+kKQHB21jMS3J611Q1RjIjH3sV614eAGkw8YBXivJ1Qqw6GvWtEyNNh/wBwU5GXU0RIBnHSqsDb9QPpVzGUOMVnWr/8TFvasyy3qDYC896vwH/Rl+lZWpNjbj1rQj4tVPtUgV9QlWO1kPTiuIWe3LOfNUY7E11eqyqtnIW5+U15LI3+kOxOMsarluUnY6t7mM/dcEUQmJjuZx9M1m2NnLOAsUZckdR0raj8P3QAZlArOSSLTuOG3+H7tSR4570C3MXyMBxUyoB1rGRsh207etLjt2pyggYppbJxUDGkYFRtxn+dSsB+NRMM96YiBuuT2qEnAqcrjOePaoSMCrQFd2wDx1qhOV59KvzEAc1nTAbuelaxJY/TQTdqK6tBkVyumc3i4rqkGRU1GStx38Wfzpe9N9/zp3B78VgWNyOgpSeRS7cGkZeSaAsHUUAnNJ6HoKXqeKBCZBpc88U1cAnIo4PX8KYIdx1pOhxSc4xRnB96LDYrcVf0z/W89Kz87mxV6wOKuC1M5FjWm3WxC9qwIQAtX9euWt7JnGOvGaxLC6M0YLda7LGT0NbcpXHek3bWGBUIYAjmptwYVfQzHhqepzUC/K2KerdeaQicMM5pytz1qtk54xipVJ70XGWoyBzVuMkDJ6VSiNW48d+lIC2rZFSA8YqAEGpgQMAd6TAepHfpThwfao9wHHWnLnqTSCx5T/wit/g7pefrVux0Ca1kLu5I9K7j7FOePJk4/wBg0xtOuHGPIl54+4a891pM6eU8/wBYtlaU8HHrU+k6THEwlRmDd+a7A+H7oPkW05z38smpH0O+CYNhMM9xEaHPSwRVjj9eQNDgsSR6mudijXeozjntXoc3h/zAVuraQjOPmBFQnwtpB+UWhz14kNXCaSsxdR2nSGPTFw244zk81mz38gkJVwMcdK15tOeK38u0QqOgBNZX9iXzEsYxyefmFRZXuNs5/UtSvUbMdwV9doFZTaxqR4N7Pj2bFdLfeGNQujiPykHcs1VY/A9/xvnhBPpmuyDjYyd7mFJqN0eTczE/75qF7udvvTSH6ua6b/hB7no10v4KaevgmTd891/3ylVzxQrM5q1aR5ANzH6sa6O2jYoCc1ai8HCI/wDHzIT67BVweGyqgC5l49BWcpJlK6KTxt61zesRlXGQBiu2Xw+qjiaU/U0yXwxbzk+eXkH1xSjJJiabPOM8UhBJ4r0ZPCWnJkfZ8/U1KvhmwUcWq+2a29pEXKzzVBg4Nb1nNGIcdyK7JfD9moIFvHz7VImjWi9IEz9KiVRMpKxwQfbO2RxmtO1udpwEOa61dOhX/lin/fIp4so84EajHtUNplJs5q4uWltyiqxbFcvNpt9LMWS1lOfavTvsiD7oAHtR9nAIOMU4SUSXdnmC6NqDdLVx9akTQdRdhi3P4mvTPIpVtxjJrT2qFY4e103UrcZES5H+1WgItW2ZAQAdQa6nyF9AaXYAO1ZtpjuzmzBfyR4Kjmsl/Ck00hd5tpbnAFdydo7imnZzlhTjKwDPD9xr+kWnkWkaXCgYww6Vsf8ACUa/F8s2iH3KZqLS5sMRG3PSodf17VtKi82zeM4PKuua4a1R+05UbxStqa//AAkV/wCSZDZc4zt5zUMPjRndo5rGWFh/eFcd/wALQ1hGUtZ2Zx1yCKsJ8RNSvlw+kWjj6mqVKqldoPdOjufHESusf2WU57qM1sW/iGJrRX2MDjOK4yLxnesBEdCtlQ9SrV01pHbTWon+ygOeSM1jOU4LVFJJosQ+MLKSYxMJVbPUrxRc+MtOtjiR369kzXO6l4vs9PnNvPoxdR0ZSOaonx7ozcPoj/oauHPJXsTZI7y18U6ZcQiQXOAfUGnHxVpasFNyM544Nefr4m8OTn57K8gU9lQYNX4vEHg+QpH9nn8xuAWjOaH7RdA9w7o65YSxZFwvPY8VTOr2rZCvkfSstdO06dPNhEmO2Tiqv9nFZTt3Gs44h3KdNG79vh7vx9KT7bD6k/hWBdWF2YmMLFSB1JrzjVNR1VdTkg/tGSPacY3YFdNKcqj3MpJI9jN7GTwrUhvUx0P4mvEme+bmTWMnuPNNVJGfGTeux/66E10ezl3I5lfY9yl1GFQQXQZ9XFcprVzFKrAOhJ7Bs15mgDSAEsefU10unwD72O3NVCDTu2KbT2EYZPtSbUxyc1PLH1xVJ42A9K6kYiTFByOlU5H9DTpZQFxmqzsOPagQ1znvULCpC2TTO+fSgY0Dil20ucnipFUECgBoTNSImetOVR3qVVGM0AMCe1Kw44p+cVEz8e1ADQOcGpFiUioQR1NSLJj6UwAx84xxTPL56GpvNoDAjjimBX204AVMRke9NCHODRYCMjPapYkIGcUGMnitGztVZMN0xT2Dcq2lstxcBGLAZ7VvxWESnhWyPU1Ha2FrFIWUtv8ArWh/qwfmGK5p1Ly0L5dCGQKg2kDAqjNJFggAVLdSjOQeaw7q5O7FaRIe4t1KoHGM1mySFu9JJIWPWmdaoBp4680DBHJpG96bkZzQBJwBQOtAHFOxQAmPSjvSjFIetMAz60oNIKdmgBwPpViCBpiMCq6DLYrqNKs0MYOMmrjG4mypHaeUoJGTVq3fY445FaBtGJPAqobcoxwpzWvLZEXuZ/iC6drbaDwx5rmohzmtzVLO9uJdq2zlQOtU4dEv3GfKVR7uKxkzVJorysQg55qNfmHWtNtCvSOfKH1epY9Af+K4jDewNRYpIw3YqcYphdvSulbw2sgXF0F9TtzUieFY1TL3jHPogpbD5GzlQx3ZJqQcmusi8HW8vJupj9FAqyvg+yRgGnuDn3FLnSKVGb2Rx49q0LZc7Seea6mLwlYq+d0jfVqtxaDaIgWNNoHqc0/bRRaw877E9pAk9mgYnAHahbUW826Mk/WrUNvFEABnAp8gTIOBWKneWh1yjaN2WYTbeUGnvxF/0z55rG1q+siCtsSzjqQMVT8RamlisSqhLt6CuXa+e4kyc1rLRanBe7NsXIIzUcsqyZxxUEaHyC2O1RAknj8q5dLmvQqJG9xdsq44rqdFshErEjDHrXP2CBb5ieldVYSKSRnis5vQEJcfIx4wB3rnPEUrvbxorHaDk+9dLcFWJy3HpWPqekzamqLbuikf3uBSp6PUJHFEYphPNdR/wht0OZbqEfTNIfC9tCMzagufRQK7FNGVjnklRR8y5NaFhF9qcBVwKu/2Lpyrlrtm+gqxALKyAEJc/h1qZz00GlY1DCI7DaOwrEnXr1zV2XU1ZNgDVSZy3OwmsoKW7G2iPw5GP+EiiyOhzXp6ybl6DArgNLd1vo8RgDPUda68zDK8msq2+pcSvq+qtZlSp/KsweK5YY2aRsr2AHNJ4hXzdoU4x1rj5yyscniqpRTWopM7OLxbHImRvB9xUkfi1dxBkJHpjmuBSdomytMkmZm3dCfSt1SRHMz2fSL211KDzN5BA5yKtFbXcXSbIHtXB+DjnTZP3hLlsYzXQwQmNWDSYX0zXHVkoyaNIq6L82tW1uTuuYht7Ec1QbxzZwsULx57Z6Vx2sptuZSjkgmucl3sxyTW9OEZq5EnY9bTxrbTKABaEe5pra9HPJwsPqdprx/y/XFLyOMkfQ1r7CJPMz2uwvhqMjCNoVVe7NitMyabHaN59mk0wzhxIcV5v4JVpYZVTcxQ8118jkKVxj2NctRcrsaRV0c5eTpLcyZjwueADxW94cSyNqd8bIwPGD1rnLsMkzEAcmtHRpJDbNlu9VKTcLFWOqeazV9rJuA65ojvNBB3y7VZT93HNY0mVjLb8t71yuoXDRStvJwTxis6UU3qE07aHpUWo6S9yCsGM9G2itkXWlx4Z2jQY+9tya8Ek1GYP8sjAezVBLqLEcSyZ7/Oa6vYp6mN2fQsd9p8mTFOCP722ojeWUe5IY1b/aMdfPJ1O5XlLmZcf3XNWrPX9V8+JP7QuChYAqXNJ0WCep7lI0s+VEhVPQLism4tYodxZzWQt7IkKbmkJx3Y0S3MrQs/zdOma5HPU3UdCyE01v8AWO30wakt4dMAbyZQB3ycV5zqus37s6iUxqDwFHNYR1G9yf8ASpf++q6Y0nJbmbdj2QLbRyHNwu3tzUE9jpt2f3swJHT58V5H9vvcj/SJCPrUw1O8JBMrHFX7AXMerroOiRAObtBL1VfMzVuOziH3ZUPoDXk1tqVwbqIs38QBzXpsHmqkbY3EqOBWFWDiXFmkluYmVg69afqM8y2rNGuSB6Vnxs5uFBBHPftWzqKN/Zsm0ZOw1eGbT1FNHnsuv6jLJsN2UHTC1curCZ7ZHe9ZmYZbJrDtbGWa5YCPaN3OetXdestSj08C3clB95R1xXp6WMOpQnR0P39wHeoYDulO4nFQWz3vklfs0j/UVcSCUYeRdp9MVzVJI0SI9RC+Vk8VjsPlO3nFbF0nnrzwB61TW2RDywwamMgaMyJmd8EYqwmclVBJqy8MCnKjn1qa0m+zS7xGrD0am2SUpGBi9DQ2pO0m5ra3YnqSmT/OtC7mt7jOIAh74rPMaDgAUnCMt0dNHF1qCapytffbp6pk/wBsB5MEH/fFNkvlQZMEH/fFQtHkYxUT28hHtU+xp9vz/wAzo/tTFJfF+Ef/AJEl/tAHpa23/fuoW1HB/wCPOz/79f8A16gaFlH0qLa2ehqlRh2/P/Mh5nin9v8ACP8A8iXE1I5/487QfSL/AOvUp1Dj/j1tf+/f/wBes4I+7pTsMKfsYdvz/wAyf7SxX8/4R/8AkC4L85/487T/AL9f/Xpw1EbubS1/791T+c9jSCBz2NJ0Ydvz/wAylmeKX2/wj/8AImoL6PoYbb/vini+ixzFb/8AfFZPkSelJ9nc9BS9hDt+f+Zos1xK6/hH/wCRNU38ecCG3P8AwCon1LCsqQwLuUqSqYODWf8AZ5COBR9mkJ6UKjDt+f8AmTLNcU9p/hH/AOREeUYOKdaMfPBNOWyZsZqxFZMjAg81qzz7mwko2AZqneOCpxTUgnzwOtI9jK/U4rPl1uVzaFG3OJwR61sIWY5XmqQtFiOd+T6VPHMU6ClJDiyzISEOTzisKc4kNassrOMc1myROznA4pxQ5Mq5NJuqU20n904oNu/pWuhmQZNKCc9al+zuO1AgfPIo0FqdToMhS1JIHqa1vMWTuOa5yyJjt9pO2raTbWOG4FcVSF3c6IS0NnavTvVXUyfsrY6YqmbxychqgvrtpLcqTUxp6lOSOflY7uPWozI2c7jmnyAselR7G5wK7YpWMJSYplc5+Y0gmcA4YijYcdDTQrDtVWRN2PFxIP4zWxosjly28/iaxRGa2dHRsnAqKi90qLdzo1kcEfMaJ5C8RGKqrKUf5qlluYVgIU5Y1xqNmbtmBqB5xnis4nA4rVuUDr8x/Cs5oSeg4rsp7GE2QISZlzzzXrek/Lp8QH90Zry2CBllViOAa7611+yhto4yzBsAEY4pyMzpckISOlZVk2dQfH41Yj1G3a33GRMEZ+9WRpmoQ/bpmeUBckDNZlGpqL4kTritNGH2YDPauY1fVbcTRCOQPzzt7VdTWrXYq+cMmlYQmtuBYy+u01zvh7T9GZDLeQ/aH7KTwK1dYvYJLVgHHIxjNUdKhtUswectyaJy5Ylxjc66C40yGJUhhjjT+6opz6zpUJ/eSoM9ia51pIYxwrHisbVDDLASI/nPeudNyepry2OwnuNCuGLhkznqHxULxaQ5+S524/2q83CsGx0qCdpI+d3FaqjclzsekvbWh+5dL/30KgNkmcLdIT25rzTznHR2/OnC6mz/AK1/++jVfVyfaM9JNk+f9aDQNOkIysimvOxf3I/5bP8A99GlGpXYGRcSD/gVL6u+4e0Z6C+lzsOJE/OmPpEoxl1/OuD/ALYvQBi5k/Opotbvwf8Aj6k/Ol7CXcpVDsjoUkgP7xc9s1XPhuR+sqD6iqejalf3U+17h2X0NdCTOPunNZTcoOw1K5mWvh17WYP5oYjoAMVpG1mA4xSCS653Vz+q61fW8wRJAoA9OtCTmGx0HkTAZ4/OjyZwPug5964k+I9RB/1w9uKVfFGpoMecpHoVq/YSJ5ztDFcf3RR5U542Vxo8Uahnllz9KB4q1IZw6H/gNHsJC5zsBFN02cU3bMD/AKokCuObxNqRfd5oB7YWkbxRqbf8tk/74FHsJD5zrnmdOGjIpqzFuimuP/t7UJOGmGD2CinDWbwH5ZT+QqlQYe0Ow8xh/wAs2pBKxb/Vt+VcrHrWoMwAl5+grotNj1KSNXnm4PIAWplDl3Gp3LHntuKmNs/StK0WbyyyQsfw4qARTKQTJ9eKW4e6+zNGsr4I5A4qYtXGzlNf1ua6na2+6iNgj1NP0lv3fuO1Zt3Zssz9SScmnQvPANqMRXatjCR0ykHvT1fsPWucF5dAfeNPW+ucnnpTJsdKHB6/nS8dVNc6NQuQBjFKmoXWTxxQwsdEj461Mj+tc4NUuQR8gIqUarOV/wBUp96QHTRycDircbA5Ga5KPWplODECPc1Zi8RSKTm3AH1oYWOuVgBTkPrwK5geJmHH2fH0NKPFI/59nz9RUjOqBHTvS545rmf+EpiPBt5B+Ip48VQY/wBRJz7ikwPa/Mk5At/xwKTfN3gUfjWT/b/iFyALGxXP+wf8ali1DWpSPtFvAB/sKRXnyjHozdX6mmTMeyj8aTNxziRR9eaqebOw5Q59M0uJz/CAPesLljpIHlBEkkfP+zVWTSbZxhxEfogqUiToXUU1sKPnuEH/AAICr1FYoTeHbN+VfafYVQm8LAElb6ID0Za2GkshkyXiAe7ioHv9FiP7y8gP/A809RWMN/DUgBxdW7fnVdvD92MYWNx/stXQHxB4fiH/AB9Qj6KTUMnjDQUU7bksR2WM1XMwsc7Jpdyhy0Rx0zVcwAHGAMVvSeM9FZT+8lx/1zNQ/wDCReGnAZ5OT/0zoTYrMxWRUHJFNIXvXRHW/DkkY8ue3x/tJj+lULm+0mRsQGFiP7vGaLyvsOxl4Qds03EeeE5qeTY/zIABUJIGB3p3ATcmPuGlO1ui8Uu91zjj8Ka0kmeMUnIQ0qCc4pCncCnFnYcHmoyGzjdRzMBpHrSDANKRk4pu0H60c7HYRgOxpp5HLCnY+YDH40mzuVzRzCI8Fj1pChP8RqdRj+HGfWlOc8AflRzMLFTyie5pvkEt1NXcEjpSFCRRzMLFBrdiSaaYGA4FXhEQSO1BiI+lHOwDTFMMuSKj8RgXNu0aYyR3qUJk9wailhWX72TWTheXMac2ljza8sJ4H+bHPvU+n30VmMSxsf8AdNdzJpNvIctGrH3FV30Cwb71sn4CuuNZW1MmjAi8QWiSqfs8mCeuRXo+k3sc+nIw+VSPWuUHhrTzh/s+GHTBrUh3W8QjUAKKwxCjNaGkHZWMHxege6JiYOM84rjzuVu4NehT2ouMnC5PtWVceGll+YSsD9BWtF8kbMiRyX2u5UbVPH0pbaa5kvYsPg7h2roX8JOxwt03TPK1HH4RuEnSQXQwDkgLzW/tIWJitT07SiRp8ZkbPHWralckgVhWUzW9osbOxx7Uj6hNGSF34+leV7NuTZ1SkuhuTnMDLgmvE/E0RXXrjPU9K9Gl1O7bhRKf0qoGzIXksldz/EwBrqofu3c55anl4gdiMI5+impUsLlvu20x/wC2Zr0/7QwUBbZBj0FBvLllAWMD8K6XXfRC5Tz2y0u9aVSbSYD3XFdPbxG3iO9Nh961jJPjJA4rH1G6coQa0pzcjOWhBPeKDgYNZlxc7846U11YndVVlIOa6EZjHPPuajPH1pzDJpCMVQDSR1puc0uO1OSPn2pAIqnHFTKhpyIPwFTBcAUAMC+tKxUetKzbearySZH1oAV3x0qEuelBzTDnpQAu7oKCxzx0oIzRyBQMdu4pRIcUzpRnNAidHGeanXBqmnB5qxGC2MdqtAyzFHuYc1tWcBC9KzrSMs44710VtCQnTNKo+WNxxRCEA5HWmT7ymccetXzF8pOMVSvHMcR+lcS1Zo9jn7y8wxX0rJkk3sTU9y2+RiOOar4NdS2MhgGRS7SadgYozVARt700KKeep4o69sUDFHPFB5470EGkGc+1ABSZPtS9aQikIXJpcmm5wOKUHJxTGSRkhhxXZaExlVQcdK4sHDCus8Pz4kUE4OK2pbky2OoktvkyFqokGH5xWwMvEPpVG5AQYH3q0ru0NBUVeeoxrRJM7jmoVsY4s7V/WnxzMnU5FSCYHJ215fvo9ZOm0U3s97cnI9qiOnqj8kY9KvebHvz09qQ4kboAKuMpESVMrrbRjgHAqdLVAATyfrTSgDcGnKxqnGTIVSKLCjao+UAU5drNUJk2gClRgecVn7Js0+sRWxO+A428/SkdyDgZA+lRREu5xxVrau05IJq1h+7M3iddEQFiw9T9KdFEzsCVPBpokKSeg9QKuw3C5xjJrSFFRdzGpXlJWMjWtIsbt4Wv5PLVenzYrLk07w7bqfKYO/rvJrS8TqswiLr05Fc6I06bazrRvLczjIuNc2KptSFiMduKgM9upHlWgz6k0iorDladt5wBWPKkXzFLy5fNZk4z2FWIpLpCNspX8KmCnnilAwM96NAuME1yDkyE/UUjSzv1kYfQ4qT6g0hGecUaCuyEru+87HPqaaYU75qfbSCPPY00BD5UZB2pQIxnO0flU4U5woOKULxjmi4yAxgnoM/SlEWR0wasBMGjbgmi7FYijj2HIODU4ldT95vzpQvOaMc9Klq+47kUymY/MxP1NVnsEYj5c1fK8mlwc4AppWDczDpcRP3Kb/ZEWT8taoQ96kEZAHy1V2LQraaDpqMtuAN3Wrx1CdlIIUioxD3Awad5JAyTis5QUndlKRm3MDT9TjNUJNIBPU10H2f35o8ng8Vcbol6nNHRVPXPtSf2IDnBNdL9nPSl8ogVXMxEOgyto1u8UceWY5LZrSl1WSU5KD3qkIWo8lj65rOUOZ3ZSdipcs80jMAAPrV3TrkWsWHyTUf2c7j6etPWA9qbgrWGpFia/WVgCGx64rC1KNpnLICfrWubdm/pSfZG71MYWBzuci9jcMThaiOnXBBwtdn9kI/hzTWsSCODit7tEHGHTbjGdpxUtpp8wu4y6EKGBJrrhZHpinCxyuelHMwNNJ7cxqplTgdzimzXyGNlWRRxiqAsPfNONiB1rleG1ua+1Obvo97nPzfQVmNDjpE35V2v9nJ1pRpUR+9+FdUU0ZydzhSkgJxGfyppSU9FP5V3baPbeWflJPqTUS6Va7tu1ye+au5LZxlukouI/lOdwNetWeolIIS8Y4AyRXPLpduhBEeKurEQAAWx9awq05T2KjJLc33v4JZFIjKnPNdJPcQGySJECqy/Mz1wCwkHIY/nVl3mlVRI7MBwATTo0nF3YSmmaU6aTp86zKUZ88gHJzUTa1ZK5BjLB++3pWd5Az92lFsMZK11amZBd6puZlhgUL6kdayJvOmctjH4Vu/Z4+65zQIE/u4qXBMfMcxJaSSDnNRnTCema6r7OP7tAtu+OafIhXOW/sx/Tij+zH6gGup+y8njmkW3P9w0cgXOYOmPzkc0n9lMOdtdUlurMS6nFJ9mJY7VwKOQOY5VtMI9aY1ix4xwK642K4ye/ak+wRnt0pcoXOQGnOeQvFIdKPUrXYGzQLjbgelH2JSPlFPlC5x39lseNgGPaj+yzjJTj6V2IsCSaBpxJ+Y/gKOVhdHGnTpMA7aP7Pkz9wkV2g05e4pfsCDtxT5GHMcSunScnYR+FPGmyFsbDiu1NivGB+lBsemRx70cgcxxY0xxyVNS/wBmkHletdcbBuNq5FL/AGfjqOaOQVzkxZhesfSnCFVONn6V1X9mhjyKP7L44UUuQLnKsMcbT+VV5c5xiuuOj5PK0xtGQgArS5GPmOOMeRyOaDCMZC12A0OPnK801tFjXgL19aPZsOY48RHk4pRCM5IrrW0eIDOOajOjgnhMil7Nj5jlTGvoaaYRyMda6ptByOFxSHw6x6mjkYcyOXWJfSniFMcgYrpR4eAP8Rp/9hLwCKHBhzI5kRrj1xS+UBxiul/sDsB+dL/YRxik6bBSOZ8pf/rU14lPBWukOhnJGDTDohIPB4pezY3I5n7Mo6qMUC1TnK/pXRNokijgH6Un9iyjqOKrkYuZHO/ZVI+6KRrVTxsFdGNFfA56e1O/sduvP5UcrFc5v7Gh7VPHC0Q+QbRW7/ZDZPGMU8aYwxx1ocGx3RhhJDjOTTjaOxIGa3RpzD+GlFo44AqVTHzmAbIBuQTSGzHXHNdB/Z7dcZposTn7tVyk3ME2gUcCni0A7VufYmPG3il+wt1xRytjuYPknkYPFJ5BHQGt4WXHSk+w5FHIHMYLRHHTn1oMDbc1ttYbhxUbWJ6YP1xS5BXMXyy2O+K6HT0j8gAiqh0/g8YNJiaEgcjHpWNWDasaU3ZmwYomHIFZWrJGkfGB7VIJ5APvcfSsbVZppuFyfesYU3c1lNGfeTRx/c5NUGl8373IqRrSUn5gQKQWkg42mu2KsYN3K5VaNq/Sp/ssh4xSNauO1VcRBhfWgqvNS/ZXHOOtBtpOu2i4iHaM0KuD1qUwP3BpPJfng0IZ0XhgYmJzXZowwOlcToBMJJOa6YX8SDLnkVw14uUtDWFkaTr8ufWuG8Rgi7I/Oukl1u3CcM30xXJ6vdLcT7kBA96KMGncqTRjlOeaTZz15pzA0m1sdDXaYMXaccUKvODRhhRg0CuPkWMqAn5moPLOelSZY8AU7JFO4EYVl7U/BxkZoywPOacJGHakA+03rOh2k816hp43WkeR0FebWQee6RUHOa9Hs8pbopPOK5MS9jWBeCp3xUdwo8psCkVwT1zSSS7kKnFcsU7mxyk1sGlboTmmix9QK2XtVLkjHNOS2wMZr14R0OOTuzFNiBj5eKaNOO7pzXQpaEjG2pPsXfFPlJuc8unkDBAzThY4/hxXRLaKee9P+xj8qOULnNfYv9npSmxyBx+FdKbPBzgYpRZgY4o5Quc0bE4+7zQbEn+HFdL9iUnilFkvIxRyjuc0LPDcrxQLIf3a6Y2AHbNIdPB74NHKHMcybPB3FaabLOPlzXUf2cMZHWmiyI4xS5AbLbeOtWYcSRKe+EqB/GGru3/H4QPRQBXOg7vxpMEHrjNeXyo6zbfxPqzA7r6bPqDioG13UZAd15MSfV6zAMg0m6lyoLls6ldHJa4mb6uaj+0McNvYn3JqFuKQHrxV2QXJPNI5yTz35pCxHf8AIU3HGc4pQO2akQgdl5zxTWlJGM9aGHGKYFHSqsMazkMetVzPtPBqz1J5zVa4iI5xVJILjlkVzyQVParcMjRzKR16A1kxPsl2n9a0Y/mOc07AdVZyCQEEcYqVlX6VUsP4OeD1rUaNTWNTQTKeAM5NAUc9asFFH8NGADjFZiKxX0NKEAPrVhoxTCg5pjuQgAduaNo4IFSbATmlC/jSsFyLbSlPaptmAaQrnJHSgCHy8gfyp3lGnhCD3p2CB1osIiMJ7fnQLfHvU2GPfFIAxOM9KLDIzbnPWk8gA9Km2t03Gk2nPU0WAjMQ6U4QqVxgVII+ec5pwhwaYiEQpTTGg4OKsmADtwaPJGCNtMLFXykHWmNEjcdqt+WM420eWPQZpICiYoVHCikCLzxzV7yhx8tL5YyMCqYFAIDyBg0bAMYWr5iPpR5eDikFymFPHAxSsGx0FWygXjioyuPvGiwXKu0jrio2QHr2q7gHGTxTdg55p2ApeXjoKTaBj5auAIBzRhPSqAz51BU4Fcxqq/PxXXXCrsyK5bU1BZiO1dFJGMzEY7QRVaTnODTp+CarqCea6kZoQ5HXmk/nT+RkkUmKYxACegqVE7kcUsS+9PJx16UAJwB0pNwxTHYZNRM4zyaAHSNxwc1FikLc8GkDE45oAcFxyadtUZNJv9aC2RmgYvy44HNIy96PQUuc8UCG49qbjHapCO1JigBmMGrNsCTx1qEDJq/aQ5b3rSKBmvpsfzAsK6SFQF6VhWXDfStyKTK89KyxKsioMWT7prB1SQhWUZPFdAzA+lc/q2WLYx0rkprUuRyrn5iTUeOasSxkE1Dt9a7EZXGEeoo79M0pOKQGgBCOw603kHkc0rZzxQOnJoGN5PejNOIo25PFMBv86TqKcR6Uhz2oENHAxSgHPFGKU8UAC9a39EkInjA5OawAOK1tHYC6T2rSm9RM9KhkAtgSeRVCaYGU7mqWGRPs4yR+dRCOJ2LdfxrWrJExTK8ksfTrUL3SIMCtHyYsH92DQkcUZ3eUhPuK5m0a3Zjm7G/OefSnjUQx27cGtIvAVcTWkTn+E4xis+K1Ebk5GD04pO1jSG+o1p5QM7cUqyyk8Z/CrbCMoQetSRSxRqAR09qxdSS6HR7Om38RXUO33sj61p2VpZuhNxeshH8KjrWNqU8k3EKlQO4rn7o6nu2Hz5ExzxVwqN7mVWEIu0Xc7/7HEjEo7FeoORmmsiHpvrzf7TqEBzGLhCO4DVKPEGtIQBdSr7Mtac5lY9ELW+0KbYk9yWp01nCIle0kYSnqjdBXnv8Awkuq4ybkE+6ipIvGOqQtw8DfVKpSJ5TrtVspJlRn5wOxrINofSqUnja9ni2yWtufdciqf/CRSuQTEo9gaxmrlI2fsx560ptfQmsgeIWHVDntzTxrxyMg1nyDNUQADHPNKLYD14rPXXYywDEgfSrUer27HJlUfWk4Bcm8j60rQDHSpY722dsRyo341J5tszYEsf03ClyhcqiLPahYxnha0FjR+i5+hoMccY+YEU+ULlAxkdBx3pBCvpmtBY4m9akFunXP50coXMzyMnJ6U4WoPfir5hjHRhSiHjginYVygbYL3JpVgGOaveQ/XjNJ5MmOgosBTEHHSpFgU9etWRDJjH6UnkyDqpPvRYCIW6getO8gkcflUu1wOAcClCtnmiwEH2c0fZnxVsKaUqTRYCp5DY7UghNXVRuuOKQoc9KdgKhiGMGlCgfw5q15Zzgin+WcdPxosBS5IA2DFAXtiruz2p20YzgU7CuZ+w5Awc08DGMrmrW3H1pNmRx1qlEZAcDjFGBjOKsCFu5pRE3QUWAr7eCcCjBJ4xVkRN09acLdvajlC5V8s9qQxt26VdFv704W/HJp2FcopGwOBTzCTx3q35BHOaPKYDjFOwXKEkUqj5VDU1Fmzgx8VpeUfX8qeIRRYVygEKjmMn6U4QMQDjFaHlgelOCiqSC5QFszGlFtKGI4rQCEjjrShWp2EUltnGTjNSCJ/wC7mrJVv7pp3GPnBGKLAVgjdCtKVbH3TVsFD3/OnhExnePpmmkBneWwOdhp4iBPKn8q0Vj3d84p4iH0qrCM3y19DTlRD7VoCKlEAJ4HFFguUfJU8/lTjCo4q+IQB93imtbg9j+FFgKXkpuHFBgJHHFWDYc5V2BpBaTAkib8xTEVhb5FAtsfSrW2WPqN3rgVG12kbYdCM0gGfZx1xmk+zHqABVmO4jcYBAqRcE4BBzTAp/ZzjOeaRYWHU1o+Vxk0piPaiwFAQZHIpfs4AGFq6I2xwRRsbHJFNICiIJN33RinmLJ6GrW1u/IpcEc0+ULlbyRkcUvk99tWQCTTtuaOUVyoI1/umk2L6Groh7mlEYFHKFymI+AcU7yD/dq2Iz6YqRU564o5RXM7yOeRR9nUn5gKvvGCOOtMVAfvNzRYdyr9mj9BTxbxjsKtFVA4pVUEdKdguVBEvYCkMAPpVzy160oiUiiwrlIW604WUZ5PWrflKTT/AClosCZT+yR5xjmkFqvpV7ye4JpfJIGQaLIGUDZgHgU37GuPu1faJ9uc03DDtT5UJlL7ChHIo+woVPy1c2P6U8K2KOUEZn9nDHC0f2bz0rVCHjNOK9Bmp5R8xj/2b/s4pn9l4/hrcZMjrTVjJ6/nT5Q5mYZ04Z6U3+zwD92t/wAhSeelKbdeopOIcxg/YeOnFJ9gAP3a3RDz0FP8lR1FHKK5zx08Z4Wk/s856cCuj8hAOlKsK46UcocxzJ01vTij+zv9nP4V07QgjGOKT7L7UuUfMcudNJYnZ0phsMtgIfyrrPsw9qPsq+nNHKHMcp/ZhH8FRPpWeqDNdb9kyTQ1oPb8qXIUpHGtpIGfl6+1QPowI5Qce1dv9jTutMayQ5GKXs0HMcM2ioT/AKviojoik5EY/Ku7+wIe1BsEHan7MXMcCdBQgnyh+VRnQFK4aPrXoH2JMYxTfsS55SlyIfMedtoIzjZ+lMOh7QQI8ivQzZJzlKT+z0P8NLkDmPOjobFfufpUX9huSSEGDXpX9lrjOKYdLUkZApcgc550mkzx8KDnvih9PuehU816GdJQc4pp0pMdKn2Q1M87fTZieUP5VWk0qRjzEc/SvThpUeMEUDSoRncoJo9kPnPLf7JPaM8e1MOlkYxHz9K9V/smH+6M/SmnRo8Z2D8qfs2LnPKzpeekZ59qQ6Uw4MZz9K9TOixE5Cj16Uf2LGOdgz9KPZsXMeWLpTH/AJZnr3FL/ZDbgPLbn2r1E6KnXb09qQaMmc7entS9mx8x5cdHc/8ALM/lT/7Dk4+Q4r0w6QgYfJz9KUaYq9EqvZsOc85tdJuLaQmOLDVrQrqWwboua7Iaah52c1KunLt4XFQ6Ce41M5JIr4jLR4qzBbXDON+FH1rpvsGeAKb/AGeoPQ1Kw8UP2jMdbIYxnmpBZ/NWoLAA9xThaBR3NdCRnczvs2MYNO8gitEW3GR0pDbnHenYVyktuKd5IAq2YCBmm/Z/c0hlQoADkZNOSMEfNkVYa1BP36UwhRy1ICv5KDpRsTHWrHlA9DTBbkewoC4iRr1JpxhQnAqRIyOO9TiP5eRzTsIqGIKOtMEYPer5gBPtUb2wHc4p2A863dqXfyDmm5wDnNIGyMY5rxzubJCeM0g+bgCjOAc9KbnJ+XNFhMeMcmhSBzg0wt2706mCFY59qbk4yaXOBxS7sgY6GkAgYnv+dNYHjGOafswCc0hG4DFO4EZUhs0ybLCpWGDjOaQjKnPei4WM2VcODjmrdo5HDGq8oPQnFOgbaT71SYHV6edyrjt3raEe7HzGsHSnBVRmujUDAxUT2GRNCMcZzSCHHJ5qyq8Um3isLisVymTSiPI6VM0fHBpDEB/EaAIWjwOF5oCYGKnEe0/ezSMo6YoAj8sdqNmDjtTwoHegjnpSERlRmgxr6dadnHXigEnvVANMHvzSeXz1zUhb160gI2570DGlD/e5pArZxyadvxjilLc5OaAGhG3c08E5poIJ+lPBB6VVhDcds0mPenge1L+dFgGdKaUJ6U8jselNYEDg0ABU9zTegIB5p3PNLgUgGYfjJxTvLJpwGaX5QKYEfl/NTDCD1qxuHUGms3PamgK/krjgGkMY6YqYyhR6Gm+dg9qoRF5fTK0bB0xUhlJ9KbuYdAKAZTuk2xGuP1Rysh5/CuwvGYoelcTqrYkO5q6aRlIyJdp685qv8qinyNzgHkVFy1dJA05bNPSLpk8UqqBSs4xQA5nCg46Cq7zE98Gmu5ORioWOaAHM+R7009aQCnbeetAxoHqead19qMUCgA/ChcUvIFNNADs04kVGDTgO9AD8k0DrQOtSIhZxjpVJCJreEsckVq28GwFjgCorOHAGRU9xL5a7RiuiMbIzbLlrtMmFPWt2JQIwCK53Sfmk3E/nXTLyBjmuHFSuzaCEbZt+7WBq7KoO1a6CRDt4zXL64xXPBrCnqypGDLJyarMxJ60rv82TUfeuszELe9GKTFOH1oAYacOR0oI96BwetAC9DRgdqCcmlFMY3OaSnAZpMUCG0nalOPSjjtSGC9avaaw+0p6g1Q71bsCRcLj1qo6CZ11zN/o2A3aq9uQg+VmX8arS3HyAswFKk6MB8wrOtKTLgkaQu5kGFlb86cL6UDl8/WqHmDHUfnTN568Gua8i9DTF+wPzKDTxqKd06Vk+ZQXqryEbA1KPPK8VJ9tt2xk4/CsHeScZoD4HNF2BvC6ticbhxUgngxkMv51zwkoEueMYpqTA6ldpXJKle2DUkcHmj5Yww+ma5LzwMgE1NFf3AhKRzyID/dYiq5hHTnT4skmBD9UFMGi2bAbrOI/VK56G9u41H+kzE9clyauprmoKOLljj1ANHOBpTeHdKkUE2Man24qofCulNwImUd9shpyeJb4YDrBIP9qPr+VWo/FK7l8/R7aVR1CEoT+NJyAz5fB2kkfLLcqf94GqD+D4jny71x9UrqD4m0SVdq6DPCf7y3Of0qeHWvDbJ86ahG+O6AijmYHEt4Rmz8l3Gfqppg8KageBJCf+BYruWvNBxuTUGDHkK8Rz/KrEJ06WMOmo2wz2Y4NHMwOBt/D2qQF2MSn/AHWzVKTSdSjkLPZyde3Neoi085h5MsMnptcc06TSL2MZa0Y+m0g/yoTA8qMN7GeYrhPpmmebcKSS04PuTXpkttLGpaSCUH0KGsC91iC3byv7NupTnqE4qhNnN2+o3cHzCeQduef51ah1u7WQP52/HYqCDV9tR1Ccbbfw+Nh7yEClb7YIi76RbZA6KwzRYWpKvjWeLAk0vTnHqYyDVgeKbSdQ39lQJ/uSEZ/Suek1izVyl5orBh2Vs1NBf6PN0sruM+y5FFho2LrX7NwnlWDxnviXNMTWLY4DI6Z75zVMDTpZAALheOjpVltDWVN0PT34pWAstqdmBlZmJ9MUf2tbAEC4CnHcGsmfQryMAwwlh7N0rLuILuAlZYJF+q00gOhNzdTuRDqdsin1UGrNtaXLtl9T871CIK4sSuTwce1bOh3MsV2u1sL3GetOwGxew6nEpNlMjSD+CRarQXHiEkLLBaD1JNdjatHcINyD8RUslrZjOYlx6gVooCuc9C13t/fGEN6KamieRj84A+hrZexsGTcsf5MazsWXnFFDBgehOaTixphtO3NRuSGC8sx6ADNP1XWUt7dIbW3VW7swrY8JeNpdKspEGmwzTFtxmJ2nHpSirjbM1tM1KK3NxNp13FAvWR4Sq8+9VsYrqdU8cX+u2MtoUhghf74jySw9MmuaxzwauSsTuRbMilVeacQCc5oAwKkCRVoUGmYz3xT1dVUjcM0APGT2qX5cVX87tmnB+uTTAnwAOKTgCo1JYZBpwzjn8qLAxevIpdpNADUo96YgCYHFOCCnBgBTg+elOwCeWDTggAxijB7Cl2ZFMBQnpShOenNAVlPFB30xDgpx/SghcfMRSDeaDA7DLNxQO5E4Vvu1EIHYjaD9auxwouDgn61YUdABxTSERwxEJhutSqgFO2t2FBVvUVSELtGKAB0AoUccnNPAX3osA30zQBg9c0/Ck04BQPenYVyLPPSlqcRqRwRSFB13U7AQ4/OoJrRH+Z8Zqw0mOFQn3qJw0g5FJoLmVKihsIOlWLbcrgBauC1HVhViOJVHApWGR7DjLUm3jrzU+w5oER5zVWEQbRjPelwq9xVgRAnGKGijU8qKBXIFKnIBFO2ZqT5P4VAoAGaAGKgHanlAe3NSAIMZpSymmA1Yx1NL5aUeYvcUodDSENOB/DTguR0p42kYpwxjnNAEQjHU0jImeAKl28etIF74NMCExgDgCmIkhfjpVgxsT0NPVGx90gU7AReTjqacseKnERxSiI9zQIhCHuKXb7VYCkdaXbQUVwpxzijGB1qcx1G0LMOCBQIibAFJww61OIDt6ZpBDjjFMRGFBGKTAB5qYIPSgxj0oAiyBQVB5FShVA5FKqg0gIdg6Zo244qx5Qp3lDvTGVdvPFLhwc4qzsAo25GBQBVywPSngjvVgIKQxj6UCIlUGpRGfSl8v06UZZeM0gDYQOlJ9RT/ADfxoDs3agBvHJo4HWnGNm6HFJ5JIpDG554FDZPSniPbTgpxwKYrkO0nr0pDHxU4U+lLgDqKLDZV8pvWmlCKuMoPQUzYD2oEUishzgUhEgHTmrpHtSGk0BSKuQcjmo9rjpV7jkUoQdcUWBlIbh1FSKFPWrBReeKaEUnkU7CIxCM+tKIAealEXzccVJ5R7GlYCt9noaADjFWQjDvS7T9aLDKnkqvOMUFR2q3tHTGaQxrjOOaLAUxGQaCoJGKtNGCelGwZzRYCt5XBoERz0q2FGOlOCLQFip5HsKT7OOuKvBARzSGMUwKfkc9BR5APSrflgn2pSgHSlYCp5AI6U3yParnlntSbD70WC5TNv/s0nkjpirxX8aQxknpQBRMPbFILf1PFXjHmgRdaYFL7OCenFBt144q2y47Uw5A4pWC5Va2Rl+7TTZx/3RVobj1HNPCn0osFyiLND0GKPsS8jk1f25PSnBfalYLmetkqk8Gn+R2FXtgI6Unl57UWHcpGDPfmoWtGPG85rSaPJBpAgJPNFgPG26UAdxQDwcjmgAZz2rxjuYE7jilwccUY6GkHDE54p2AePWl69RxTTznA5FKGUfxA0NMLilSWoIxik83P0oAdvuo7fRTQoMVx3zdCKcBtXk9alW3upPu20zewQ1OukanIwC2ExOMjK4p8kg5kZ5XLZxTWUd62o/DerOMizwf9pqsDwlqMq8+Qh93qlTbDnRyksK856moV4OBXXyeDrhcb7qEeuATUEvhFEjL/AG9dw7BKr2bRPOivpLsMYAwa6qIsyDgCuYt7YWcwQPv966K2ceXyelZVFpqXe5Ptfv1pDuHfmmiSgycVzCHgMF6/Wk+bFMDj05prM+eBTHclZyByM1EHYn7pow5HNNCsOpNAhwZgfu0jPIeQtLtwO9HsKAG5cjmk5PHFPxkdTSCPHRqYDSWAx3pcE9TT9hB5pcE9ulAEZBzxTl6dadyOwoHBIwKAE5OBxSlWB4pccdBT13YA20XAblgOKOfWpDkjsKY8THnfgfSi4Dcc80uMnNRNal+TK/4U+O2RcZZz9TQA/oOlGBjkHNP+QDoaeMYJosBCBxwKQo2On4VP34oyMUAVQh5xQUOeanbGMVAzccEU0A0oMcjJqJlHQVNtPUnNIw2kCqAh2ADnFKEAHrUuQf4TSY3HoRQBn33CkYxxXCauT5xHbpXd6kGVWPPSuF1BlMjZ65rpo3MJmEYzvyelSAcdKe5AJNQs/vXUSOZgPpUTNTSSeuaaDxQAE55NNIpSeKTgmgAHIpcEdaQHpQfSgBNuD1pQvr0oBzTwDjmgBuM0FRTivNLQAwLg9KWjnHtQMmmgHqvNX7WDLg4qGzg8wjitqOJIlHFbwgRKVhwARMDis+5JaSpriYHOKgQ72ArSWxMdzW0cbcEr1roVlGAApzWZpNuCFzW75QIwBivLxL1OqBXZsISTXIa/JuJAI612c6bYm47Vw2uN+/IxWdFO4TMI/MaQ8GnGmn1rtMxtKOlJjuaeOh7UAN5zRjBpCSDTwR+NIBOMe9Lxijb1pcDNMQg6UlPxSEY4oAjwaTbTyKbg0DEAxU9rnzVxUPSp7XJmXHrQBJqAk2ZyQM1RSaYdGbArureyiltxvQH6il/se2brEv0xWVSok7FJHFi9lxgsxxUq6gQPvNmurPh+yf8A5YgfSoZPDdm2cIw+jVn7SJVmc+uog8FzUy3oI/1gxWpJ4Vt/4ZZB+VQN4VOCUn/MU+eItSst3nGCpA96k8/dzj8jUb+G7pThZFqBtD1CIZAB/wB1qd4MC4sv+yad53PfB9qzja6lDyUk4phnvox8yP8AitO0QNPzE7nFSwupBwwArH+3zL99Qfwpy3yAHdF+VDigN5ZF7EHFBcHjIFYa3sOT8jD8akF9Dx8zAUuQVzb3L60u7jishb6LI/e/nTxfdgy0nAdzTDfTNOB681ni7YDO3NSi9XupFTysC4D2HWnKc8VUW9iI9KmS5iYDDUagWVfgYJH04q/b6pdwkeXeTx+wkNZqSp2YGplkXPzDFKwG6niHUs83sj/72D/Sp18TXZwsqQy4/vL0rBRkGeMmn7kxjbg+1K7HY2DqEco3PHhz1I6U1pYivOMe4rLVskY4qdgSOc4qk2IseTatJ5myEv6sBmrALgAiOMj24rOAUkZqTcEXk8VaYF8vKoyYgR7GrmnWdzqblYVRdvJLtgVzVxqq2mN2/afSp/7VdrfMEzKrDqOKANq8aGxu/s0tzAz/AOw2QK3YNB0eSFJtT8R2EKHkxK6sxHp1ryiX/XbySxJ5JouLskLsTp1wK1p26iZ6B4+07wTB4ZeTQoUN95inz0ZunfOePyFecWN1awzAySov410F/FLqXg2QRRktt4Hc4rzv+zL0YBt3x34qp2JR67YappwiG3ULYcd5AKvLe2k0gQXluSf+mgrxQ2Mw5MD5/wByozaSY5hfP+6aFUQcp79HaKpBe6gVG/6aCm3WhWw/frcpwM8MOa8DFuw+UrIAPc1t6Xq8Gnx4EExc/eYtT50wsdlrEeG4IIz1HNP0sMYWO7ANZEGv28p/1bj6ite3vxIB5ak56cVK3uNsvW1vsz1JNTeSfXFNimmbGY8CrIDEc1TdwREIMAc4pwhqYKT2p4Q9weakCIQL35pRCuR8ox9KseWcdKcEPpQBXMaE8KKTywB0FWiuBnFIQD/DQBVAUntTio98+wqbyYxyVpQmBwKYEXksRw2KXyzjrUyqzUohz1amIh2EelJ0q0sK92zTxEmKNQKvPangMTjBqwAAOBUinHamIhAJ42mnrFxUucdqXsMLTQEapgU7YQOoqXbg8gU/HAGKYiBY2xxzTtr+1TKnPXFPRPpzTsBW+bIBNOEZJzU/lKeoFPEQC9adgIPL54p5hJUc81OkIxxUgiXPJxTsK5UW3bHWneS47Z96tFCOFUn8aciEn5sj8aaAomGQninJbuuQck1oeWh/ixQ+1OlMVyisRzyppfL2D7tWAwPPIpTmgZUAJPI4p2AOq4qcgdTTSAetAiEtk9OaT86m+TsRSFlA6UgI9pJ4JxTtnrTftCBjwaetwmOnNAWFEIP8NKIl9BS7/QUFs07CGlAD0zTTGx7YqT5iOFpw3Y6UDIPLzSiAk54qXY5NP+YcY5oAYIdo+8KUrk9RinZJ4K08DnIFAiNV96dsPapBsHOOaeCD2pgQZkXouaVJHP3lwamP0pAtMBu5vTing8elLggdKjfJ6CkFiQgEZoBA601ckUFTnrQAHJ4FGGA4FJtbPWnAMB1oGIS69qT94e1Pw470YbPWgRGN46inEvjpTiT0pcH1oAiPTJGKcMDrinbQepprgDpTBjsrSbl4pvOOBk09I+OetAhwVT6ml2jsKcqcU7FAEflj1pfLGOtPxSg470AReWenajyTmpMnPSm72z0pDI2iINKgIHNOyWPSlHoaYAKCe3NOLKvagEsaAEAz1FLxTgMUYJoERlgKiZiTU5Sk8vFACJkjBNOKjI5pvlnqKXyz1oACvHakCDpijZ70oVuuaAAxr6c0hiBp4VvWgo46GgCPyRQYhnNOJcdRSZY9qAG7Ao605QtIRnrmkwo4yaAHbAeKcEGMZoUADrQyqe9AB5WelIYz0o5B4NKJCOtADDHgdKT6jipRIGPPFPAVqBFfinAL6VLsU/w01l9qLDuMC8+1GCOgpSCopQeaAG8+lLu4+7TsZpQB3FAERYntijdjrUuB6UoRfSgRECKMHqOlS7BngUhj4x3oGRbcilCcUpG09DTS7DotAClMjgUGMY6AUAuemKCpzyc0ANwoPrTWf0U1IEXrijaD0FAEWT/cpykn+Gn7cDrSAH6UhDQGP8NPVCetJhvWjDdjQApj564NRlMU/B6k0HkGgZ5vH4RmOfMu4+fRTUy+DkBy16x+iV14VRxgUpA9K4fZxN3NnMp4SstoDTTPj0GKsr4Y0xRzbyMPd63gPSl7c0+RC5mYqeHdKVgfsa+4JNWBpumQDixhyP8AZzV5jgf/AF6pzyyDACA0+VBzMQGyU/Laxj/gAqYXkS/dgUAf7IqoPPf+BVH0pdj55cAU+VCuywdRbJxFj6UrXUzcj9aptGxPE2KkQqoxJODVJIGxWu7kNgOAPSk+0yk/M+M+gqQWcUxBDEg1MtgoGAp/E0rCKzOrr97cayr75EJAroPsQXsBVe4sI3hOcUmM4aWRlnU+/Nb9kQ0QY8VmajAkU4PoavWT/u8Vx1TeGxd45wetISKAV3Ypcp6k1yMoZuA6inhhSHbjofypmPqKQE2T60HAPWoweOpoJosBKKO+eKiWQZxzmnhh0o1Qxdx7AUuRmmk807IxgUAGRSZyaN2OooU80xCcDOaUEHtTsjOcUoIHagBo54xTwe3Sjf7dKcrY7UDF9MClwfSgP6A0jOfSkAYJxgcUbcEZxTfMbPK4oDgimgH4pO3GKbTTncD2piJDSEDuaYWxxkUm7PbNFgFZVY1G0ag8LThuPSgqfxppAN3bRjbSFh6U7YT1pBHnjvVJCE3DtRv4G0U7YM9QKaUPrxVWC5l6ox2HI7V53qcmJ37YNehakh8tsnivPtSgDSOS2STXTSRjIzN+7JNRkjrT3QIMCoWJ710EiFqbkkUuMnOKDgHpQAU3HFOOM0daAEH3elJnin7c03igAWnjIFIo9KfjI4oAQFs9KXJHalAP0pDQAhPNOVMmmAEtVy3hORx1q4K7EzRsIcKOcCrV0+BjoKSBdoGBziqt7K2D0rq2Rk9WUZZRvPNWbMhnGfWs2Qlm4PNaGmAtKox35rGTNEjutKiCoMCtTaRzxWfYBvKAGOnerhEn/PQD8K8+qtTeOhFdHEZzjFcDrZDXD49a7e+yIfvn8q4PUsmdufxqqasRNmOwwaYeDzUrDmoiPmrckdtyabJxUgGBUMhyxFADQ3NPDVHgU8UwJV5p2DjmmR8nGasquRzTAi28jNBGFPenN1pCeDxRYCJgcUw8VIaYeaQxuDnmrVkf9JT0zVc1Ysh/pMY65PemB6FY24kgXAJ4qz9jPoQal0uP/Rhj07Vf8n34rmqRVy0zI+zuDxk00wyf3a2DD6daYbc55NY8pVzHaJ88im4IPQ1rm3B45pjW4bqOlLlGZbH1FIqLWl9mUnoKa1qgyKLMDMKLuzjNNMan/wCvWkbVelJ9iByAaWoOxktZwOTuiU+uVqu+l2cnW2T8q3/sTL/FTTZtjjrTTkhaHOnQrBgcQlT7Gq7+G7Zj8rOPxrqWtHHbpUf2ZuTtOafPILI5NvCw/gnx/vCom8LXI+7PGfzFdgY2XA20nlZ6Ue0kKyONPh7U4gCroR7PSf2Zq6dbdZMf3WFdkyNnuaaFK+tV7RhY4xob5AN9hKPcDNRmVlzvhlX/AICa7feR60p2OOVB+ope1fYLHDC7hyfnK1KL1cf8fOPfNda1lbOctbxN/wABFV30fT5Cc2kXPoMVXOgsc8utqn/LfdgelSHxIijOwuew6Vqv4c05wcQbfTacVXbwpZEDbJKD9c0+aIrFFfFhXGbX6kNU/wDwmIfA+yuAPRhSt4QiY/u7ll/3lqJvCUwyI7lCPdTzVKURakw8XQD/AJdpfrkVMPFenOm2SKcH2AIrNPhO76iaP8jUR8LX/OApI96tOIHSWvifSJj5LQTMD0GzNT3ctrJg2yFV9DxiuXi8P61btmGHB9c1oQ6Z4gUktEpHfJp6BcuxW6nJbqfepF0tJWB88L6jFPttO1H5fOhUfQ1opprkcg5qhXEgtXjtzCtx8h7Zxmnx6TnkMKsw6acgkMaurakccgelRJXGjO/scHuKcNGTvjFaa27ZzkinG3YfxGlYZmf2JERny1/KpF0a2U8xJ/3zWj5JPG8jFKIiB97NNCZTjsbdP+WScf7Iq5FBGDkKo/Cgx5HXmnAMP4qu4rEmwcbQMU3ZjPFMLMOd1JvbHXNO4EuPXFAbHFQ7n9KcBIe1ICXzAp6Zo8wnpUe180DzPUGgCXJPfFLwepqL94O4pzCT1FACksR8uPxoVnBwQDQA+Mk4pdjHvVIB+71peuMUwRsep4pwiP8AepgOpc8dM0nlAH7xp3lE9CaBACo9c0pYA5z+FHk+5NOEC980CAMMcninhwB1pBEgOSOKeFjP8JqgAOO5pwOT1NOBQD7gp3mDPCgUxCZ7AE04FgOmKQHJ4NPAOR6UwGhmPJBqRSzA09QB71Mqgg4XFNAQqsmevFPKOTyTUyqcZ21IGI6gVRJEiN0yalEJ/iJpyyOW4UYqQknqKYyIxgdBmoishP3Ksjg9RTwuec8UCsUgsoPKinZc9atNGnXvSBE7mgCqYyTyaTyVHXk1aZFI4PFRlTQMgMYH8NBVccipvLJ5zTTGoOc5oFqQ+WhPSnCFc5qQjA6VG8u0HaMmgB2wCkOMVV8ydzyAB6U75sfMwouBIH5qUSCq8e3OdwqcPF60AO3Z9M0AnPTNOXaW4ANSD2FADArHtinCJhyadtOKQ7x3pgKsR7inqi4puWx0NKGYCgQ4xDrSbcfSgOx4Ap4BYcimA3rQRkdKeYzilWM0AMEYIpPLHrUpjakKY70gI9gApAQO9ObHQ00IOvNAxwUHvTvLHrTdoxxSrjPJoEIVAFJ5dSbR60uQB1pgQmOkK4H3amyOtPBGOlAFdf8AdqQL6in4B7YpygdKBDNmM0m2p9meKURZ+tAiHZTdh64qyIOeTTvJ/GmBT8tjR5Rz0q6Iz3FNKZ4NIdyttbGBjNKsZzyKsCIDoeaMBeKAuQmEfxChQqdBU4CnvzQVUjNAiIsuORRvXjilZBTVWgAJQ57VGVz0NTbR6CmrHzjHFADFB9ak2ccmn+TjvQIsd6AItoHbNL19qkKYFNORwKYEZApOV75qQkdMUhK0gGc0ox3pflPBpwjB6dKYDflal2J6CnbBjgUxlYUgEKe3FIVxTWLDvSb2yBz70DHECjYDQRnpR8w9BQAhjPYUm1wadubHBpQx7mgBQ7AYxzRv9VpyjJ4p4XimIgLA9uKAoPU4q0F9RSNGmeaYit5PGd9Gw561P5a461E0ZGcGlYYBe3el2kUwAjvS5agBwJNOCg96hySeTzSq2DQBOV9DUZjJPanCRe9PGw85pgQBMdMUYIPIqxtXqKaaVgIgoJpSgxgU7aPSgEZ60gGFABimlam4NJhc4JoAgKnNAz0qx5amm+R6NigZFgfxUfJipzbkjk1E0GOCKAOa+2A55HtUcl/sPzGkithnGamaxjc/NiuQ1Kv2/d0Y4p32xy2KsDT4gMCmvaqnI60hD4GaTqBxVsIMcgVSiIRsHAq550YAJdR+NMBs5REy1Yd1eQK5XJB+la91JE6Y8wViTWKzTD96B+FAEY1CFUyFYnPPNI2pJwqxfrU6aKojIyzDPpTG0+CEjIc/U0DEi1eaNcqqjFDazesTibbn0Ap0cNsrBfLBB9TWlDpsL8iNOncZpoDEl1K9cjNxJj61o2jSSRHczHjua1F02MdgPYCiSzVY2wSDjtSYHHa0h3g571asGUIOO1V9Yj8vkdc1LYnco9cVx1kbwZpHGaZvwcAU4EAdM0h5NchY4N60hfIOBSYUd6TiiwhdxPXiikyCD1o7ZGaLAOJIAxTcnPPWm89+1IwOODjNFhIlDDHSgHmqxVyfvUqiTPLfnRylXLG89xS5z9aiA96XByOtFhEhYg+9JubJzimfmTSkUWESCQ46fjS+YRg4qMKO+acq/lTsMeJ8ckUhbI5FJt54FKVOORRYAzuHHWm+/NLtweKcV49TTsA3t3pwT1JpyqemKkAP4CkBEI1I5zmneWQOKlCjoKRlbJxTQhmyhlIPSn5IxSYY8DpTGNZTjjpUe01NsZRyaADyKaEVwrbulDq+MCp9pAwetKRgdapEs5zViUiOSc4rzvUJ8ysM8E16H4gYJGxB/CvMboM0zH3rppoykMPzc5zUT+tO5HemFjurcQ3d6Uduoox6UmCKAHDmjOPrSDOPekP0oAfgEYpu05xThinAZNAwVDmn7DzTlU9e9I7EfSgQjZAqMtu4ApGfPFOj64NNK4EsMWTWlbxkEE9BUEKYA4q+iKF4NdEFYzkyVnEceSe1Y11MSxzV+6l2rt4rGmfcxPJonIIoRWy1b2jR5lBxk5rAj5aup0KPc+Txisr6GiO2sYl8kErzirPlDGMVFbFVhGSBUjXEI43Z+lcU9WalLUowITxXneqZEznHGa73UrhSmBmuK1KLcScVdNGcmYDg0irk0+RSG5/Knhdq7jWwiKQ4FViMmpXbcxqMg+uKAEA56U8HFJg9zR2oAkRsGr8Q3DNZqkZ96uW8uD1q4sGSvFznFQspya00VZE61BNDjoOKtq5NzNPSmmp5E56VERg1m1YobjirmmKGvY8jIzVM81f0xc3cQzg7qaQHpumxhbcDrV8ADgZqppxAt1Gaubl9elc01qaITBHY03aaf5mDmmM4YnmosMO5pNooxzgHmjacYBosAbUHQDNM2qTzQdwPQmk+oNFhAUUGkZUbpThzwRQAuOaLAMCr070vyDvSuAwwOCe9Q/Z2JyJDT5UBPhW+lL5Y6imqjovDk08F+5osgGmFSeVzSG1TsOacGZT0o8wiiyAjNoM9KRrMHORU3netPWQHkiiyApmyU9Vpps06hfrV/OegpwViT8vFHKgMo2SjOcikNghP3jWx5QJwy0GFD2o5UFzI+woB97igWCkYB5rXEK+lHlKBnHWjlQGUunj15qUaevUEcVoeUOwpvlHPcU+VAVRYqDnHNPFmlTmNvXGKNp6k00gGLAo6Cl8kN0p+0k8Hil8txyP50ybDQgXtmlVB1pu2XJI6U5FkBFO4FgLhKTHAOPrQu8DkjFKWoYwAFKVAHrTfOUeuaa0p9DSuNDyp9KNmepqPzXPQYFL53XK0gFKDHBppjIxzxQJx3BpDOuc7TVpiAqvU0vyjvTfPUjlTTWlQn7pp3HYlBXsc0ocDHIquZ1BwFNMMwJ4U0rktF3I9RQApPWqiyE8ngVIsseOM5pgifaM9aMelRebjtTfP9jTAs4JHWjDY9ahWZiOhpDPjHWmBPuI6mjfgZxUKyE9s0vmnGCtMCUS81Iko9DUG7gcUuSe1MC0LhQenWl89c1XUEnG3in4IP3RTQicSg04MvpUChscCnCOQ85wKoRZUKRnNGYy1QGFm434qRYgnU5NMCZUBPGRUmw9hUKysDgKBTt5fG5yPpQInCY5NSrjpVZDHj7+T71YjCMfvCqQClsd+KAR3zUmEXjANOH0FMQgdAOQaXzY8dDS/VMinhl6bMe9NAMM8Y7HP0pDOGHyg1OI1foBSiBQ3C0AVjvYZ5pvlSH+LFaAtkIzyKQxAcUWEUlgc8lqkWPbxmpypHFMaNiPamAwqcdajK5+6DQYnJ+9xSGOQdGxSGHPQg03YT/DTtsh/izS7JfWkIjMWf4ajMAB+6TVkCTPODTsNTsMofZMnIU5qUQ4xlSPpVocUZFAiNFVe1PzjpTsKenWnbAOuaAEV89RTsY56UB0Uc04OrDimAxpCBxmmqW71MFUntSlfcUCBVzS429DTcE8AkU5YWPU0AODEHk5p2/BzigQBmHOKeYcfxUxEbTgfw0nnqeoqXy1HU5FIVQDigZD5y55XinCRGHA4pGA9BTCUAz0pASfLTcA8VB52cY4qTax+bNMRIIC3O7mk8hgeTRGzbuak3DNADVt2J+9UnlbaUOq0GdaAuIQey5qNt2fu1L5y4pplxzzigQKW96cGZe1RiU9QtAmOefyoAsBjwTUi7uvaokbNS5GDzTAeG7YoK57VXLkH73NSKzkdaAF2MKcY88mm+YR160huAB6mkAuwCkIHaojIx5xS7sjrQMcVJppRx6GlGcfepPMI4yM0xDSGApisQeQasKcjkUpUYPTmkBDuyelPD5pREM5Jp+0dqYDC3pSbqcU96iY7T60ANYc803ZuzzzTmdmNN8t+1ADo4j6VZCYHPWq/7xAMGniVscjJpASEAVXkznkcVIrux6Ujbz1AFMCq7kEYWmPIccrVtYmcn5c0NbE/eSgCisxHGKmjcN61IbVc8nFSrHGgwCSaQDVh3jil+yEGnbyvQClaaQelMBBAw708DbUTTnHzcU37TGo65NAFjOaa3401Jw3Sh3YjjincBC645IqMnPQ0mx25wDTGZl6p0pXAkIHXNJkdqj84+lSIQ3UYoAYSAeacMHj1qUJGeTmnBIxzQBFheKcIwByakIjPSmsg680CExgcUm7BNSLHxjkU4w5Ge1MZBuNROOc5qfgAgCq7REkndSAaGx9KUtTGDA8Cnrlu1IYqtjqaerH1qHZk9KeFI7UxFlZWqXzVP3lqkHcdqcJWHUUAYAt3ByGAqTy3I+/+lPOelL0PNcRsReVz941DNb8/ebH1q0TS4DdaYGDMhUtgk+mTUUTHB4NbVyiCMkIM+uK567u5YpgEO0H2oA0MFkyVOPpTRE4fOzg9zWS13cspBlfHpmmbp3brIcj3oEdILhETDSIpHYtVGdraRwxkTPoGrI+yTPkeW2OvNPS0m28rx70AaImsoWH70H22k1ai1u2iOFV2/DFZi6a7HlgM9Ktw6EWbJlGPpQMsS+JAqnZb8g92qa31OW7Rt0apn0NQnw+vdiT7VYt9La2XCnj3NAHP65EdhJNQ6exKDjirWto+1skcVR0/hQM8CuaqbQNjPGeBTdwB96AVxzg03cCeMCuNrUocXGMkU1nPQUFuMcUmc8UWAAxzQWY9aCGFNG/vTQD1J280uMdTxTdrHvUeJg52qpHqTQBNgZ60pYUiLIRhgB9KkSPHWgY3ORnFKckdKceO1AG7PakIYuS3PIqUAc4FIEx35oGKVwDAPanrHkjnmlXr61MuD2ouMaIf9qpBHx1FMKue4xShcdXpWAcIlFBUZ4HFHPY0ZxzmmAqhR0FGBjJFIxwcg0iyA54zTsAqsopcg8gU0SZ/hoDY7UIQrgntSDAHGaQsc84zSMwUfeFMYpPBpu7aODUZc9iDTSSe1UhEjPkjmmSNwe1JtyORUdywSIkccVSRDOZ8QTiONz14rz+d8uSfWul8SXzK5X1rlHfc3zcV1wRk9xjE54qPnPPSnZFNx3rQBc4ppPpS9aTPNAC9aUAkjilUVKoBoAbtFSKmBzTwq4pCcDAoACQBkVAzZOaV37YqItk0CBRz1qxEpzzUSL0PrWhbp7VpBCbLFtHkAkfnVmSRY0piARpmqlzMHFb7Ijdle4kLEkn6VTZgTUjnLZqLHzVhJ3NErD4B84Fdn4fiI5POTXI20e6QY9a9A0K12xqaluyKRvrCPLAxUZs0JyGxVoKcD0oK4PSuRydyzDv7NI1J3M1cteJksBxiu01Mr5RB61ysyKzN6Z61pFmbOea3+Ysw4qncyAfItaWo3KxAquM1hsxZia1EJ1pDnil7ZozQMTjFGccU7vTTwaADmpYyQ3FRjNOBwapAbNmxIHIxV5ody8c1iWkpVx6V0dswePoK6IamcjJuLc/3eaovEQeBXTPbhic1Ukss/wANOVO4lI58oc4xV3TCVuo+O9Wm07J6GrFlpsi3KMPWs3BotSR3Wn5+zjjj1qdhmorGN44ApBq2Eyea457myINpU5zTh096fsUZ5pAikZFQAnbOcUFh2JoZCRgUmx17fjQA4Fh1OaMnHOBTMNnk0HIHSncCTI79aNg6imAM2CKeOmM80ALsPtijy9wOeKjDN609mJ4znNMA8vaeXzTto9eKYB+dPCtQFxflHuaT5T1U08RZHWl2Y70gGbY/SmH0CiphGM0AKDSAjAlPYfhT181epp27b2pWO4ZpgALZ5NG7nmojkdKCcjmgCcyelG4kVAXXHP8AOmiZR3pgWRuxQWI5FVvtOOAOKY1wxPSmBcZ+OabvH4GqnnMwxzQCfegC2HGcZoLgd6qjcT0NO2seopCJvMB7mnguKgCtnoalUN/d/OnYBcue9OAc9elM+YZ+WnAyHpTAk2HHSlAwMGm5fjml+Y8cUWAeu30p20dhmotpxSjKd6LAP2r/AHaQouOlMEhUc0byT14ppDYYG7/61GBzgDP0pwbI65pDj1p2EJ5ecHaM0eXj+AUhOOhNIJADgk0WEPMaFfSmKijjFLvz64pVK+hpgHyrxS5UDhc0F19KTdz0oAUEDqMU/apweKYzjAyBSqVP1qhD1Uck05QpPao/xpcECmBKNucAClJQfWmKuRkmn7R6GmhAAmc45p+3PekVMn0qXZnvTsA0JgU4RsR14p6ocVMi9iKYiuseT1qURBMHk1NgdAopNuDk9KYDQhOTtpQpzzFxTwVJHNSBBnJamAzywB90U5RH2zUmxccHmpI4hjrmqQiIRr1z+dSoq45YfnT/AC19qkWFeyg07CIwoxwDQM56GrAVUOCMU/cgHA5osFxiBgMgCgSt0wKVnwvGBVZpDn5SKLgWfOb0pnnMeoNVzcMvGATQL0KOU/Ki4Exc9c00uSO+KjN9HjJjbPtSLfwsdpVgfcUxj8nHJpjHPWnhoHH3qXy4iOGH50gGDcOQOKdgnr/OlMQx8rfrQIif4qBC4VRgU7aSucU4REDrmnAEdTTGQhD3qQAj+Higuo7ZpBJnsaBDshf4BS7t38NIXB4zT1UMPvUCIjGp/go8vAwOKnMZPAIpBEw5zmiwFZopOzU3yZj0kq5sY0hV05AzRYdyp5NyOnNPIuV6Amp1nYNgqM1MjSMegxTsTcpK8oPORT1mfOC1aYVQMuoqN0hbogz7UrDuU/tCg8sKDdRY5IqVrNWPK7R7Un9mx9V/WmBDjzDlcAUn2MtyScfWpGtJFJAqMxzL2akO437IR0Y09YnGMk4HemNJIPvZFKtxwBmmJkwj9+KUR5PFCyqR1pwk5wKBDPL96MBepqYKG7UySFT2NAXGF1A4IpN1OW3DHC0ptWB60AAYHtxS/IBmkELdMVILfI5BpiGrIM8VIWqMxKpxzRt9DQAcZ7UbnHSgIR2o2F+oxQA4yE9cUBx3AFJ5O3vTSgPagB+VPemMue9Kse1SeaQDJ4pAIA/QU8RuDnj6U5Syn7oNSFz3XmmBCSy9VpVkJ9RUjNkdMU0NjtQA/dgetCnv0pvmego3HoBQA5o2Y/KaQRFeWOaN7d8io2lI70ATgLnlRTlaPPSqm9j3qRZGA60AXMoR0FN2pj7tVgxPU/lTt+OATQBNxnAGKGQNgGocs1O2k96BDygUcOBUcnmKnDBj7Uhiz/Ead5Z4APFAyqZHHUGovNYnJWtMRKy4PFN+zITgkUgKQkyPengb196s/YIj/EaQ2ewZVuaYFNoC3BqM2wz71oeTJtyDmgREdVyaAM77OwPDGnCCXORk1o+TnoMUoDDjH6UgKCLchsGPC+uakZGHVSc1cG4nhc1JtBHK4pgZ6r3IAqVY1boBVhoVJ6UCNVPGaBETREjjAoFsSOTUpB680HPqRTGM+zgcZFI6hRgc/SniEMc7jQ4SHls4pAQkuB8q5puXPUEVOk8LcBh+NPZwOgzQBWVBycE0pWP+7TjMO6YpPOTHIoAhMYJ4oEXOCMVMsiFu9PaVewzSAgFoTyGp5hK+hp4lT1xTw4/vCgCsUwORikCpjkirJUN1phtwc54FMDlvNGDR5yjn+tV9rD+DNR5xwVArjZqTNdrjAH5U37ZJj5Y2PtiovPRRgMufak+0gdGJpAWVeWQcpjPY0g0+3kO54Qx96bDLvPera9D6UxjEtLeP7sSD8Kk8lVUAKB+FP4pQfyoAy7qMncVU1Q8t15I4+tdBMAUIFc1qUcrLhVY4PakBaWWKNgWbp23Vcj1e0jBDN27DNc5HZ3MjZ8p/yq0mkXjLuMe30yaYGw3iG0CnakjfhinwaxHdD5I2X6msmPQpmB3SLz6VetNK+zDBm3H6UwKGtKWRs8fSsaxGOQfwra1hWCMOo9axbJhnFc1Q1gaypxyKftx0FNUnGeKeoLewrlkWNCZOTxT9qgGnbOOopfL4PeoAaRjHGaZubP3RUu0A9TQVIPAoAZv4HHNOVgfrSj3oACmlZgLx2OKNwxijaD3pm35s01cCQkYwBTFzj2ozjnHNN3EngcUAPzj3pMgde9N3MfSkbJXnvRYCTzF9RThKMfeqmYG7N+dILdzzuq7AaIcEZLCgMvqKqRoRwWyKl4HWpsBP5mPpTfN796j3DbijAxjNOwXJQ/PSlDc54qLFNI/2qLAWC2TzSH06VBkjkmgyHHSiwEhZSRg80hVSeWpB0ycUFFOMiqsAo2Dr1pxdQPu0zYoGRmkoEP8ANYj7tZ+pXO2Eg1b79KztWP7kjNaRJkeea9c77s57Vhu6ufStPVxm5YdTWUEPeuuOxiIetJjmnEYFNpjFxmlVfTmjBGKeox2oAcF4x0zUiJ3oVc1JwPagBr8VDI/rT3bPeqz/AHutAhHPJoUHFMzUsYzjtTSuMnhjz1XJrUhj2qM+lV7ZPQVZmbauQa6YqxnJjJ5APl9OlZ075PWnSykk5PNVWbOaznIcUJnPSlUc1EM5xVmFCZAKzRZr6RamWVcLxXoOnwGGEfLzXO+HrI8Mc812UcWxQMVnUlbQtIQMR1/KgvkdDmpNnrQV2iuZlGHqT7geMVyup3Yt4yFI3Vv65chFYKe9cJfzGWQ5Oa3gjJ7mfPK0rlm71EKkIPJpuK0AaRxRilPTg1Hk+tADx15oI9KQZPelzxQAo460vFNzxRmmBZhbDcV0FhJ8oya5hG+brWxp84GAT+Na05WJkjoTkYIOaOGpqyAxhhUZYhjzXZF6GD3Laxqw+tXbGA+ZnGcVStcuwBzXQ2sflpnvWdWajG5cEW41Kpz0pcsDjNKG4wTQWFeVe7OpC4IHNHJ6Ck3qetOEi4xnBpMBACTyKcQSOnWmFhnhqcmSeW4pIBpU9MUoXIwakfHQdKNnoaYETKQvHSoyD+NTmPPWgRANnvTuBCFal+f2qfAHam4GaVwGKSvJpd3vRjj/ABpQnr0pgOVwRwSTQTinBAKcFGemaAGDJFIc57cU5jjopFNAG7qaLABOe9GcDnmlKZ6U0qdvJoAQE5OVoKhx0xS7NwAzS7MdTQBF5A75o8lQamG0dScUbh2pgMVAOKk8kEZximhuScZFGHPIJ+lAAIQT0p/ljPFMG44ByKkAJHWgBG4XgZpEbn5x+FPCnOM8UhTB5YYpgOEgzx1p+8E9aZtXsaTj0zQA7zEUdahluwn3VZvpQUG6o2cKcYzVIVyWO53AZQj61MGHXOKgRiT93Ap+8d6AuSeZjgDIpWbjOKYGUDgdaVZM8beKaEM3pkljxUi7NuVoMYPUDFO2DoOlMBikgkcc0u3J6CgR8/KeKeU49TQA3ZjjqaQpigB88A4qQREjk0wGKhNOEYBBOanjRUOO9O8sMT1FAEXlrnjmmmL1/lVhY9tGTnGKEIreWp9qYI13datkKepppVewqgIPJ4yDUixnA704Kem3FSCPFACKpzz2qT8qYqsW6kD3p/lt0zVWELwKVWAIzSbMetJlgegpgWA4IpxkUL3zVXzyOMc0n2opnKUxFsOT04pwXf8AeJ+lVVu0P8JqZLkZxsOKEBYEC5B/SplhGc4psbxnkcGpwwzyapAHl4HSnKpBpysnTNSAx9zVCGeXnrUqgoOM0qkevHvTy5A+Xk0xBgkZINM25PCH8qVZJCclRirUch4+WgCqVyMGM1EYBj/VGtdOT0FSYUDkU7CuYbW6sPu4NVpLXAyAeK6Qwx8k45qF4Y8dqVhXMBUyMFcfUUhgX+7Wy0CE5xUbRAfwUWKuYht8/dJFAgkX+I1qshHQAVGyDqaVhFJI8H5nOal3qnRiakAjLcipDDGeQKYFYzSY4OKBLIOvNTNEmeeKb5cf97FAw84ngqKcrgcdKikBUfKwP1qEkjkilcC8rxmnBl7YrP3n0qVAW5PFFwLvGARTlJHHNQxHjBOBV2JIyAciqENjHqalxkcCpViiNPW2T1NMRTMeDnANTowAxsqytsuOtK8CqBtPNIClLG7+uPQVJEVC4K4qcxuB94Y9Kbg5+7TEOyuMAYpBx2zSZ74pPNxkdKAH4QnpzTHizytRtNz2FN+08YoAUxK3+sAqCS0gk4Ax9Kl80EY201plUdKQFU2Ow/KTSGPyxzmrKy7jkU5yGGGxQBXScD5QTUqzqeCtRyW4YZQ81XyycMeaBl0bGPysRViInHJzWdG471Yjcno2KALhjZzwQKTyWXuSaWKUAcmrKyKe4piKxiZhjNNFoV5BJNXC69cZpvnr06UgKotz34pPIwehNWtxPIxSbyOopgVDb7uNxFOWz9JKs5DdMUElR0GaAKzQFOOopRGoXkYqwJAfvCgxRuc5z+NAFfylzkUbMn1NTFUXvSKw3HmgRGYT3FNaMD+E1Y+bqCMUZU8Ec0DKbKB3xUZ35+WtAwB+lMMDr0HFAFLDkY21EYZD0FXzEfTFKqkdz+NAGf5UideaerDPzDFaBwRgioZLZW5wSaAK/mRDq4FPBH8JzSi2AGTGM0nl88AikA8EgcrmpFII6VEFkB4OaftOOetMCYbOCRS5jHcCodhHrSNnspoAnG08U1lQNzmoRkdVNSKEJ5yDQIdlFGdxoMqYxmmlRng0jRAc0DHCcKODzSrdOf4QahMQY+lKICD1OKBE5lOPugGoy8hPGPxpnC980okOelAXJVJxz1pDvPPGaaHJHQU5HY/wigBP3g7imNLIvGBUxznkUm0MeaAIDM1MaZs9Kti2QnninGKJByaBlA3DZGBx3p/m7+DUzxxnpxURVV6CgBPKQnJUZp44GMYqIyAdqejge9AClQehzUZXHYVZVh6U4mPHzYFAFJUwecCpcegzUnkxvzup4iAHBNAFTy13EtmhVAPH61b8nI65pGjUDlenpQIjFO5z1oVV75FPKr1BzSGcH/alouf3q56cVn3ep2+Gw5z6AVix2dwW/wBUxyeOKnOk3cg4VAPdq4zYd9sQE7CW+tOjvjJJsxxRFo0yA75FH61NHpiI+4y/pQI1bNdyjk1ooGI4NZtsRGeHzWikg29aYyQIR/GaVU+ppnmoODxTHvrZPvSAUAWNoAGRxS7FPUCqP9pwdN2aG1OPHFAFpo0AO0YrF1G5niUlZCuD2q6upqxw21R7mmvdac4+eZDn2zQIwTcXMmMySNn0NXdOSUH5t/8AwLNXTf2EIymW9lWnQanBM+EhbPvQMp6oD5LA9cVgWQAbGM8102qKHjJAwcVzlooWZuuc1hUNYGwiLgU9sDgVEo+UUuOnNcktzQd5goEmc4qMoMnFBQDkcUhE28bc0zzCvXpTAecdqMg9qVgHmXOKaHzxnikOMD+VAAGadgHbjnjpQWPQU0OB1pC/pTsJsCzim7yOc0u/K5IpARjkUC1AMTTwrYzmmduOtKS9BQ4blPNKGyM0xd5PPSneURyTinYB6tT+vfFCqKdhD0FSAwDnrTh0p2Rt47VHupgOGM0uO4qMdetSZAAFACPzx1pAPSnFMmk5BxRYBSSO1NG5jmnGPPejyz07VSYrCFCR1pOQT8wxSsoz1pDGB70IBpOcYFZGrZZWGOPWttdvPWsnVivlmtIkSPOtSXbOwPJFZjMOa0dUlxcvx3rJDbia6YmQ1jzSLknNKw46UJ9MVQyQDsakRT1pEXmpN23gUCFGBTJGAHIpjS4zULPnvxQMGf0qFmAJ55pc00rk0AIGqzbrvcVAqZNX7SPBzVxEy/EPKjz61VnlBJFOmlwuM1RlfLZya1ciUrjXfmo85pCcmlx6Vi3cseF+atXTrPzpVHpWfAhyOM812Xh+wLEP1p7IaOl0e1EESnb2rX3qODnNQxRmNAO1S5zXHN3ZohG24zmoLl9sRw1PdAe/FUr7csJwv40oiZyOsTbnYE8Vy1wBu681vag5eRgfWsScc4ArojsZlIimt09Kew5ph6VQDGPYU0DNONGzjOcUDADNLgEc0YFKMZoENxQeO1O70HrTAbjHtVq1m2uAar4oX5TTjuB19nOJIscdKlLKGBxWJpdxjjOPrWyh3uoGK7KbujGSNbTIt5DEcVvIvvkVR09AkfArURQFyawxWxdMTYOvSkKADmpOCOKTYS2TXAbkYjHXBxTNmTwMVdVAaQphuMVQFMo44qRFIFWcYPQGjtUgQhSvXpThnqKR2bPHSlDHbkigA3kHmguaN3POKU89s0WAY0oBxjNIs65yVpwjBJwKXyQevFMAEqEcCnBlI7imeUAfanbDQA4ADkmnBwKaAx7CjYTwOKVwHFwRxUQJJpdj5oMTcU7gODZ4yKUle5qPyj0NIYmGKLgKc546Uxs55GaXyz680BGGMmi4Aue1KCQcFetLtwuaaJGDcrii4DwSDjFSAnANRF2xxUXmlWy0n4YoAt5zxSHAJAzmqcl3gcPn2xUC3si8lc1VguagyDknilYRkckVmNfSNwEoV3Y5ZTTsK5fLRqODTQGYcdPWoUDDnFSgOegOKQDynq+aPlXqMmkEbHrShGFO4MCc9OlAgzyDzTjEw4pwjkAzRcY3advXmkBI6ipQhABJ5oZAeTTTE0M3Hpmn7dw60vkAjO6nCIqB6UwsMC8nDE1IuAOtKQp4AxTdgBOKYmKCR0p2QTQFwuc0uBt68+tAC554NLuwPvU3auBlulAMfamhD/M4z2ppm3HC0pCNx6UvlopztpgAY54AoMmDgilAH408KMc80AIrHOQKeGk7U5doGB1o3IOM4qkgF5PUAU8YHGKasq5xj8acHGeBVCF+godMjGOaCwPQYpwUMM5IoEReSRxszT1t1x80dSpEV53ZqTjux+lMCAwxjJ2ilVFOPkp4eIkgkg+9ThVwOT+VNAQ7QOe9SCMsOKsIsYAJA/GpB5YP3hTAqLBMTwoI+tTJbSk8qR+NWQIz9xufSpVilAyCPzqhFXyGBwRUwRQMbsGpxbswyxPPpT4tOQHcSaYivg7gAQRUwD96nNoD0zxTTER1zQBGDKDwRigSEnkn86lWJm4xxSmFVHTmgTFBG37xzTWc9MigRqR1INMeBs8FjTuFgZ2HQg/jULtIf4eal8vaPmXigmMfw0AVWaRRyKgY3DDire9d/wB2piynqBSAyPKnJ5ekMc+eHzWk23/ZphkUcBc+9AFA28xGd3NM+y3GeGFaLMnfr6VF5yBiO9KwyBLds5c1JtXOD2qcFX69fSneQmcimJsrARDtUm1SMLxU3lKv8OacI0J+7inYLlfyyO4NKJGTqpxVjy1FPVA3BFFguQJdMKsLcnAOTTDbRZ4BzSrb84DHFAi2lyGUckVOrFl61SEckZ4waXz3TqKYF8DAyaQk44FVo7uQ9cfSp1uA3UCgQxo+eSRSGMbeean3qeOKQ7PagCi8XtxTBF6ZrRKIRSBFHcUAUNpHGKYwzxir7BQeopQoPVRQBmYZTRtyeSa1DCh/hoFqg7GgChGAO9NmQPzxWibVKhkgUcAUAZgBXggYqZJBjpTzCMnJIpBbAjhqAHrID0NTDBxg81AYgB60za46ZFAF4Bj94/lQWQcEH61VjduhZiac+WGMnmgCfzIv7/4Zpu9ScCQH8awLjwvp91M006zMzekhFSWPhXSrO4W4iinV16ZkJpAbTpIOR09acnA5zn3qYGPaFGailQ9RmmA/bngml2xrn5arqSAc7vrQEXPLMaAJ0kjOQAKdnccjApgEIGApFSKyDgUAMKuehpvK9asHGMioH+Y85oAasxBwOnrUwn5xmoQgJ9venLDz96gCTdSh1ajyDjIamncnG3NAD/3Y6mlwvUGomLcfJQrEnnigBzsyn7vFMKl/apdjN1anCMk8ZoEVypHamHdu61dMLEelNMGOpzSGVNzdN1OTcDknNSvAexFNEeOopiFLj0JppZT/AA1JtwOBzUe8g4ZaBiMBjihS3epVKPjINSGNMDAouIrc7s44oJI71O0JPRqaIX7jIouBAAGxmnbVHTmpTER/CaYYj15FFxkRJBxSl8GnGMepNOEaqMnFADFlZu1OO488UZTPINB2N90GgCJi+cBqY3m4+Umpcc8DNPPyj5hQFyuiMTy3NSGNiOvFSKFb1FKY1xwxoArmFj05qNoHz0NWgpQ53U/zUxgtQBVjhl7nilNsWPOatblI+Wnblx1pAV/L2D/ClRiD0NT4T1oyooAYJUXqSKUOrUhjRmyc07ygOQeKYg3IDRvQ0nksRmm+V6nFAzzVryE8KaFxKc+ZxXNm4YHgnnpS7pZFKhmye1cZsdOyoo+eZRn/AGqiE1ojlfOUn2rnlguG2jaxwPSrCWFyWB2EfWkB0kQhYgoc1cRQDxis+yidEAZTxWkmfSmBTvZzFkBRXPy3rs5GBXUTWyTn5gTVVdEttx+Ukn1pAc4J33nnAPYUh82TgbmrrU0m2RgxjXj2qfZaQAbmiQe7Ci47HIRW88vAR8HuRViPS7tjtWP8TXQyaxpMH372AAejZqrJ4t0eH7sryY/upS5kHKyrHodzIoJ2r+tXrPQJIG3tLk+mKqnxrCx22mn3ExPTj/CnjXNfuP8Aj10UqvrJn+tJ1EhqLLep25WAjGeK5SIbJ3A9a3ntvFN8uJUgiHuw/pUKeHL23BeYxE9SVbNYylzFpWIlfCAUhYE9aJk8oYJJqIsMc8Vg4lXJAwOexo3DjPeoPMA5OaTz1HUHrS5R3LBxnim521EZlOcUzzW6KMinYTZZByetIwOagEp7cGlWYscGiwrkh+UdaTPvSGQGlLEduKYDtwHFI3PU8U0jdjmmkEEktRygSKfanbjmolkwRxkU/wA1WOD0pOIyQMTxQCepqLzlHC9aT7RyRgGiwXLSygcZpdy9d1UzOgXkCgTJjmlYVy6HXsetOKgj1+lURcqvQZpftMjdEI96fKBZb5Rk5/CmiY5+VGNQGeTuMfhSiSUjHShRHctCTj5lIppuYlOCRVUiRuCSaa0DsM4qrIGy79sTqDmnLdI3PQfWswwSLxtNOSFyMFeKOVE3Zfa5i67qDeRKBjk1ClsgXLflT1SLd93NFhi/bFJ+5msrVpGkQkJjitkRKBwtZOrAiJu1VHclnnGpQsZnJHU8ms0QkDJrY1CQCZlJ71nHk11R2MyIxjGBSrGO9POAelG4BTTEIRtyR0qJ2BPHWld/yqF27jpQMHYntUZzSkmheetADTnNKKXAzmjFAyW3j3sKvH90uBVW3+TrT5pxjgk1qrJEsjlfIPNVjSs24+1Nxk1DdwQDOamjQ59qYqZar9vBuIAFOKuMnsYfnUkZBNehaIsSxrxjiuNt0VBljjFdRpLhlUg9KqrG0Aizp8r60hANQqcDOeKUSHpnivOZqPUgdBmszVpWEJ+XAxWh5h7EVkazIRCx4PFXHclnD3rEytk45rNmbgird0dzEn1qi/NdCIRWJ71GepNSuOcCoieeKYCEZpu0jvTjikBoGO6DBoHFISTRk9aAHA4peMUwHNO7UwDHFGKTJJ9qXvTQi5ZNtfNdRpi+a6scVyducN1rrtEIYKSea6qRnPY6aJtuFArQj3FRuFZ9v/rea1FGR14rHF7oqlsKOOcUAjPWnBlHemlh1xmuHU2FDj0NOChuRxTAVIyFNM8z2IpWAm+57mmMVPIpm89waGYAcDmnYBfmz2xQTjqKarZHIINKcYzmhIBoxnceBT/MXtUTYBzuGab54HFVYVycSZGaeJMiqxmAGaQXIHXpRyhcslsdAKY0mO9Q+YHPBqN4/wDa/M0coXLaygKcHmg3GevFUkG3Iyc/WnnYBzkmiwE/msenX1pwYk8tVZZF6U5HBPNDQXLJJx1oDN2GagZ2IwM4pitIGODQojJxM3I2c+tKGLDLDFNWQEfMRn0oPP8AFTsId56g4prHzD8opoKq2WxUqzKB1FFhjP3g4CjFIbZn5bOKeZjkbelP8z/aNFhEH2ZFHzYxURhQZIarZUkZWmiMtzjimhlIjB46VJHKy9elLKZA2Fj4HtUYWRs/J+FVuSWxIGHA5p67u/FVVjZccGp4/NJxj8TQ0BOqd804JzTNjjr1pwDjgVFmO447hyBmnI7d8fSgA4560YboKaGLuzxijGeDSBG7sMU4gkDmmK43JFG9yMdaUrz7U8KADjmmBF83fj60qvnAp2PpTtnoMUxBt3UgibPX8KUcAYzxTt2RzmmAeWe9KIc8cCm5Pc0qtmmINhXuKftwOTxSc56ilxk8n8KYD1ZQvSlzmo87cgnmo/PxwOaYE469aTaWbpUImJP3c083TKMFcntTTEWFTA6UoIHU1XimeQ88VIAd2Dk1QiQNz8v61IJcDkU0Q5qRbdT1oAYJSBnt6VOkqv1WjyVAIAFIibewFAEphSTBYY9Ktx4CjHNVEiZumalWN0HU00BbB3dUGKGWPP8Aqx+VRxh89asqzelUISOJeqpirMaspBPSmJIQelWFYMMHpTQiRZ1RfX8KkW5BXIUCqu0FutIIWJznj60wJ3mBb0P1pVKscnP4VHtVTyMmnfKzAZZB7UCLO5ccLimOIz35pRDEPukk+5qRoAFyTx7UwKMkeT8hJNJ+9TluB9auiMH7pIprW+9fmNICg82eOBSDDLluTVmWwUjhhVMQyxvtDZAoAaYR6kUeW2O1OBYH5hTwyjoOaYFfYep7UghLc5OKnKljnODQDsGGYUhkJjRxtYfjSi2jTooqTcB0ApzFCOWwaAGCPA4H5UoB9KA2Dwwp3mbjximIQRd804R571KFBHJxQEB430IGRbB0BNOVJFX5QanWPA55qVUHegVzPJIPzg5qVHRR90irpjRj90Gozao7Y2kUARfaIv4mpGEUvRhVgaeobOcj3pWsx2H5UAU2sgOVlP0zSGCUAY5qeS2dACMj61E8VxjiTFACBShyc5qdZExyRmqxWQctuNPRTjPHNAFgsKTIPam7D1IApkrqq8daYhxwDT8kcg4qkJXz14p5csMCgCwJ2BxuqRLgnqc1UAb+7SlmxgcUAXTPlelRMxwCDVYSPjBFPBJHSgBHye1R9+M1OMd+aeFQD5V5oArKZD0BNTLHLwSvFK0jqPu8e1I0hdcbyvtSGPGM4xUqGIcHFZr53Y8w00s4GA/NAGuDDnkChlRxw2MVj+dKPvYqaK/GQu00xGiIFOOSRStEV6A1DHeoeMGphMG/iFACeUzDlTTlTZ25qTzewIp4dRjJFICAlG7EH6VF5YZvvVbc55AFNYDjigCt5OP4j+dKsDH+I1MwULknpUQuADgGmAphx1Ymo2cjhRU3nE9sCnhQR0oArebJgcGpBOOhGKec5x2pBEjHJBpCAOCBTiFbtTvs8Y6E04RKvQ0ANAAp4b8qQpjGKkTYeDSATcWGKRtwHSpgq54p+z0NLmGUV8zd86cU/wCU9qs4x1FROw7Ypp3ER7PamtEM5p+cjmlDDtmmBAQFPApUcZxg1YCDrS7Uzz1pXGVmwG700M+7vV0xoeM0C3XHUmlzICNXGMZpGdOhIpxt0B6tmq00Azwaa1ASVFPIIqMIue/4UpjyMHrTwronAqgDygehI+tIV2dADQjn+IUrZ6g/hSAYzY6pRuVl+bP0pwBPUcU7yhjJNMRF3+UcVKqZGCppDHk8HipcMi8HJpAMMa4+6ageNM8Kc1Y3SGkPPU4oGVQD3GKXkduKsgJnBYU8MnYA0AVFORwKcDtPepiwzkKKjcknOP0oAN4NNJLYAakLgc4pyzAc7c/hTEOG8fxigk9xmgurc4IpOD3oGeeLpFqpysYBHtVgWUSEEoAfUiuT83xXdqAkUiD3AT+dIdG8Q3PFxepGD1zLn+VeY60V1OnkOtP2aHJaSJfXJFVJdZ0yHlrmPAODjmuej8JFjm51Rie4RSf1q3D4Y0iIDzDPOfdsCs3iEPkLMnjDSoidpkfH91aqP46gJxDYyPz3OKux6dpEH+r01N2erEt/OrCukZ/cwwp/uoBUPEXZXIjJHiPXbkYtdJfB77CaXd4tuhxstlHXdgVr/apv+ehH0NRvMx+8xNS68ug+VGaPD+qz/Nea0ijuEYmhfCtipBm1G4lJ67RirzSL3IU+5p6/OdqsD9DSdWZVkQRaLoUDA/Y3n/66Of6VejOn22BbaVboR3YbqX7Mdu4kD8agkCqcZ/Wo5mwLw1W5Ufu9qD0VBUT307AhpGPOcZqorJnBbj6VMrxA/dLVSTC5o2E+5uWOfetaQboTjmsqwZN3EQFbDXCpEc4HHervYlnI6mm2T7uOazGBJ4rW1i7EinDL17VjeYSOapO5Au046Um0nml35IHQU4kYoGMKnqBSA89KfznvSEAHOeKAuNJwelAPGaQntigA8gnrSsApOB7Ub+MUhXIwTSY96dguKGP0zTiRj71Vywz1NG9TQFyxkcc5pGB4xUIkAbrxUnntnAINAx23b2yaciBxkjFMLuy8kCj3zRYCYqAOFB+tC7TnKiq+TjmgFs9aLCuW1iRuRUgGz8KqozY6kH61IJBjLZpNDTJ87j1pPLLe1MWVd2AKlBRj3/OiwXG4ZOgH4mmmSX0AApZMKfeod8m7ABIpoTJGkYHOah+1SdMVMN/Xyvzpjbj1UCgCMXLg8KKDcyeuKkSPceQtSmKDo2KYisLuYfxVmaldM8bA9TWuYYjnHNYuqKiIT+lVHclnDXiO0zcVCF29e3Wr13KvmEis53ycA10LYgVyAcetQs2RjND5IyDzULZ45pgKzUzJJzmjBHWlXGaBi4PWjFKeOlNLCgBNpJpVGDSE+9JmmBKXwMVGzluelNJzQBRcAx6GnBaFHrUiDPTvQlcCWBSzAY71vWdquzJ6iqen2+SpI4NbY2KAAK6qUDKcipIgBwWArV0ebY2zOeaqPCjtkirFpGsUvBxTqx0sODOwjO9B2pQvrVK0lyo5NXlOV4/WvLlGzOhBtXHvWBrL/K3TFbjjIOa5jXJVXhacSZHM3RTnisxzyfStK4VnB4rOdSvatkQVmHNMIxUrKaYR1pjIvrSHOR0pWHNNGc9KQxelL+FKO1LgdKYhgpRwKcFxRimA3IpRQV5pcfhQBLC2GFdfoxKxqSR6iuPjQ7hXR6ZciIKCRXRTZMjtbR1LDNaHnKOAD+VYenyq/wAymtlQ3GelZYlO9xwE81Cec08FPwpyoD1AxSOVHGM/QVx3NR4liIxu/CoHj3NkE/nTgFxwMU4jvRcBANnVs/WjzYxjJprkf3Sag8pjyBg1QNlguGJIIwKhkdm9hTCHHam5PcU0ibi46fNSlRjOKYzYI70hz2NUImAG3jApGAPBNNTaeu7NDAhhimO4bVXpk/Sm7iT0p3I53VGWbPHSmA8EjrSs5K8Go13Nwc1Iq+tKwCL83WpVbb05p6LnjtUojGccAUmBFvJBGKTGf/11Z8sKO2aasQ6kc0rgVNpLcmnkY6sauBVPG0UpgRsDnFHMOxSG0nk5zUoUYAqc26Ic4pwG7gAAUcyCxEjBRhowRUiyWx48ts9+aa0RLe3enGPCjHX1xRzCAyquQseB9ah81t3HApWPHPJpy42YApjHib5emage4bnaMU1vNP3R+FRsrnqppiFMrjnNSR3Eij19KiEch7cVKu5B0GaYEy3Dd8A1OkrN1xiqm5+uAaPNcccCiwF4ye1Kp3dM1SEw7uM1Oko2gjGaLDJXYR9QT9KBIpqPfu6gZpDtFFgJvNQE56UnnAj5RUDKhPDCl4HORgU7CuSCUAjgnNTLLwDioBIPQUvmZHK0CJshuxpCGI45FIpB6ClUkHnpTAcYztHAoEeB0p4kXOOafv4x/OnYCAAkEL1oEbhvepwCTkYp+APenYCuEI6gmnBUPVasrgnpwKXbH3U07CIgqbfu1C9vuOVGKvbV6AfnTwU/u07AZyq8fG38qsx/MBxVjbz90Yo8nuGxQAisV4NSpID2po245Ip6sMjmmImQZ7VJ5aN15qNWVuxqVSo6DFMByw59AKljjGMChWHc0qud3XiqQhwhKnJPFTLgDgVAZiWx2qdJVHvTAlBAHIpu4k8KTUgljI5UUeZChyDTEOQK/BBB9qlEGOQ+KiE+WyCAKa0w3Y3UCLAQEY3A0j4QdM+9RpNGo+9TxJE59aYWAM38ODUirKRzgfjTDhRwKaRkZDYPtQBMC6n5mXFDSqG+9+FQsSFxUZ5PJFAWLRlBHSoX5PYGod4B61FK7fwn9aLhYldR7Fqikyo9Ki3SDkuPekebapzzSGhDOgHJNNLo46ZqMXCf3RSG4UnjipuUSbe4JpG3seppI5lzzhqkLgngUxEJ3LxmkWQocgnipSEDZYH8qVXhPX9RQJipd+YcHrUgcbs4OaRDbKeGFSr5IYMGH50wsSrJIwGFNSeZJjG2mRzqD1UVYWeNxjIJp3FYSHcT0zVpQ3ZahQ7TxViNvWgQ5BxlqetAw1PCqvJpNgMOc9sUhiD9VFK8iKc4pouVHWlqANZoRxUBtNpwuTVk3Sngc09JFJ5NGoFJrVsfeqMWxzyK1SoA9qiZQeABihSAoG0Qnjg1BJaOpytXnynpURuGJwEBqgK4icc7smnhQw+bGacZiBwuDQdrDLUxDFiyxwAaeIB6Gnq0YB2jNOEiY5yKAGhQDgCnBc/wZp6eWWyOal3EYwKQFdo+MbKjaNP7tXmYkfdpmf8AZouBnSJCo5U5qsY0zurXZFfqgqGSDI+VAKAMwx5PSnizPBUA1eEC7cHr608RYX5STQBSFow/hH4Un2Rs5G4GtNAQPumpFBPUYoAyVtnGfmYU6OGTJ/e8e9a2FxzUMnlDmi4FTa4OfNzin5kI5bNSEw445+lRHBf5QcUwGO7ZwVJFPjKd1pzRscbRxUot2K88UCGFkPbmnq7EYA4qJoGB4zS+XKB3pDF3MTwBTw+KZhhyQTT15HI4oEJ5yZwWpSwIypNJ5SkdKbsIB5xQMBJg/ep4m55xUKtk7cA1IVQDB6+1AiyrsRwRUgdx1xUETR4wODUhXI4NS0Mk3kjtUbYf+GgJt6kk0o60gE8kY607gDin4GOaaUyeuKLgNBwaZK6gY2nNKx2H1qMyxs2DmmgEjG5uSauAMFwtV8LjjFNE7qcBTihq4D38zPrUbq5HNSiTIyTj61IrAjFGwFFoiBkZBqE+ZnBYitIr70wIGblQfencCjGeccmieWOAbpmKj6VdaBR8w5qFmEh2NGCPpTuBHC8UqBkfINOELFvlOaVoMYIXA9BUkbMByAKAG+TIfagJIp65FSiYk4I/Km7HL53HFICHL7uelG7B6HNWGT1pAij60xEO1WPSpVjAxxQUyeDTwrAdaQxCoxwKZtzwBipQGweKQl/7lFwIjblu9H2cj0xUyyYOCpFP3A9qVwKmwg9M0GJiOlWcgHOKcHTHSi4HlD3DHlmJ/GoXuEXqa8xufGeqykhZI4h/sL/jTrfW9QuWUzXDufyrxfYvqdvMejtdKmDkDPqarSanDGDmVR9K48TSH5mZs+5pwbjr17UeyFzHTSa5boBy7H2FQtr4/hhP/AjXPhwxxxT8880+RBzM07jW7pkIiWNT64zXO3+r65u+e72L2CcVJdB2VtrFeO1c7cI3mHdISfrVKKFdkzXt3M/725mck93Ndr4duJPJUFyT65rhYLeQkFa6Gwup7VQQhOPSlI0TPQyxKjJJph9O1cr/AGzfeWRHGAfVhUf9parICPMCD/ZFSoiZ1+07c9qcXSNdzyIoHqRXGD7dKf3t1I3tuxU0dqo++Sx9Sc1aRJ2lprFlbMN0u7PZea211C0uYhgHaR1Irz2Bdpwo5rq9PBNqpIFXyoCPUILDeSJW3jooHFYsmAcp0rQ1JdsgPc1mkcnmpsSGflxSBz070jbcfSmBh0p2HcmMpH4UgfP3sY9qYWz2pQuO1IY847GgcEZoB4zimncc460CFOMZNRsSTwOKOe4peTTuKxHjIzjmn7eOR1p6g56ZNO2tjpwKB2ISq55A5p/lIo4p4UdTijaoHLUAR5HSkIwuKlwvUUYYg4HFFwIxnAGDS7WXqaUlsYppBxzmmICxXOetG44oK5XpQoOelIBVJx1p6yMOlR5GaMjpmmkBOLj+9ipRdIvUZqiScnJqPzMcYyaLBc1GuVcDj86buyORWcHJ5Ip6yYH3qOUCw0wRsU1nU9v1qIsG4NIUJ6/nRYBxkYEjIFYWrTYjYk5rYcAcZrA1dhsYnNVFEyZytxMTKeOKrNkk5qSX75+tRE810IgTGKbjrkUpbsKb9TQMQ+lNK8daeSOtJkUAMOfxppJ704nvTc0AAb65o68GlC5PSlxQADj3p+Oc0gXFKTjigAzxwKsWihpBntVbNPikCPmrjuJnUwjZEMAdKchY5zWTbXwCgE1cS7A5BrrjJWMZRZrIenApdyqwODVOG7B4BzVxJN61bsxK6NvT50wACelahlCj5RXPWUgQjJrchlWRa8yvBqR0wd0SSMWjORXJ6tgydK6uWQBCK5LVQPMyTxWcAkZbruHtVWSFRkgZqWW5jj43VWe8Q9W4rUkpyxHr0quyHnPWrj3EZ6VA0qYzkUxlRgQfrTP4uTTpWDHg1GOvWgZJxnmngCoiaVWoQiZVzTggxUW/FNMjdjVWAmbjpSdfxqMEt6/WpER26CqSEPXCipY7kxsD0pIrOR2xzWpa6IJMF+taRixNofp2tPbtxnFdxpOotexhm4+tcxFoUYUYPaul0qyS2jUZ6e9FZPl1CDTZs7gBzikbLLhePemgD2p23bwvSuBmxGqNk5bNLg9+Kk4GfWmt908VIDSE28mmAAik+bnPFG7aOBmqsAxy59qYItw+ZqVmYnmoy752jFWiRGjVehzQHZfuilQsSQ/FTLGueoNO4iNQW5Jp6gBhkZNOdDjjGKibH97mjcY5wG9KjC4PNKo5PNQyMVYbWBpgWFUAccmmtu6dqYkjsOBQS/8AEcZoGW7dWYAHIHrV5YUHQ8+9VIGzHtFTg7eSaiQIseSm0nqajCdgMCmpcL0P6U8TIx4zio1GIIlDZyadkKaa/wA33TTSsnTAqgJN6sOaX5OwqDYwXDGjhe5osFyXCk9aTcB8ueKYrrnkfjTvlJoABEr54/Gk8tVOAM0uCORTdz+lMBSCOgx71HlgcnpT2PcikLrjmqQhj/N25pjL6gVIcNjGKa5AHJFUhETFugppbcDlRT2cNwDg1EQeg/OqAZsA6L+dSDzOigA+tJk4OelSo4AoERtHKwyZD+FN8l2HJJ/GrIYE9KeG46UhlVYSpHJx9afyR61MQpOetPA45HFMCFS2RwKlHI5obGeBSgMT04oESIQFwCTTtrZzSLGcdRT1Vt3Q4pgOT1xUm5M9Pmpu080qxO3QA1SAm+Ugcn8KfGi5B3E1Cqsn3qcsgHU/hTEWcKOhqVG4HSoFbAyRn8KUOzEDGKYFnyw/PSopEMY+U5NBJz96npgjBoEVDdvH94YrN1DxDFbKwBLNjoK3JbVJBk5NVjpcT/eRT/vLSA5JPFd5LkW9kCw7sSauWniW6+0hdRgEUfZ4wcCukFksIxHGgB9FAqCWzEoKNDuU9QRSsx3L9nPBdw+ZBMrKe+an81Yx8z7voKyrTTUslIt4/LDdRmrcdtIxPzYzVoRoxSK4yOaceT1qGGAou0HJqZYnz1BqhXJIxgZA/OpVBzmoxvAwRQzMvSi4yQkjtzTQ5B6Go/NJ4Dc0vzEckGncRMGQnrk/WlEeTzk/jUUT4b7oq1GrOcgYFMQ4QoB8xwacBGPu8mnNEOCeaikHZRigCVp9oC4zTd3OcFag3Mn8GR61NGglGeRQKw1nYcZzSqhcc4AqR4di/eqEErwcmgCR4lCYB/OqrQA9CfzqQyjOGU/jTxIhOAKBlExHOMtTXi4wc1oMQT8oqBwC3r7UmNGbJEw6dKaVKjkitJkj28r+VV2iVh0NTYLkUZQDuT6YqdXUHng9qhMbIOnFNwRznNMC35p6ZGKcMOcD+VV0IPep0IJ5b9adxCmJR1wfwpREMZ2jFP8ANToADQ0i568elAEJhTOdjU0oitkFgatq6sccYp+xM5OPrTAgWR8fLMRVm284nJmDUn2eFxgGhLdYTlCc/WgC2WuBwrL+NPE9yBghTVIM+7k4qdZihHG76UCCSS7/AOeQx9anijdky4xQZCRnBp0bN3yKYmH2csfvEU9IRGc5JqTL9qeCehFIAyZB1phWQDripBIF4IpDICeDSArvGz9TSiBAOpzVgFe/NI54zwKdxFdoT/Cfzqs0eWI5+tWxK2cEijbuOcDNMZQaKZfusMUimcH5lDD2q84LDHFRxxjPJNAEQlZRgKcmnfa2TgRljVrgdcUiYOSAMUAV/tjjqh+lSpdBwOMU4jJ4H409bZSuRjNIBysjH7w+lSEjHQVBtEXpmgzD+7kUWESfKTyBTSvfGBTfNUDIUCnLMGAJAxQMiNzsOMGn/aoyPmzUojjcEnqaZ5ChscEUaAN80MPkH4mmM2VwefwqfykAxil2KeMUXEVFjG0nFAjAHDVoiMbelReTk9AKXMBURHz97inkzD3FWFt2LcjihoiONpo5kBBHMf46nE6Y6U3yQOtMdF+lPRgTeYjDoKjJRmpirgYBFOVQB8zCi1gJBtxxUUkW454FSrgdxR5St945pXArm3jRdx5PtUR2Z71ZlUKOBxUUcO45JNVcCMOoIxx9anFwqjk1HIpHCgE0qRrj94ozQIl80NghhinBwCN2KjWKP0xTXiTPBP40gJWOfu9aQuy/eyar/Or5Vc1HJcvnBAppDJ/tIY7ehpAwP8YqqWZcHAJpd6AguQtOwF5EXrkGo5CQ2SQB7VCkiN908Ukuwjk0rAWxsZASaFcL3GKoxyRxnDPkVL9phA+Q5osBeEg6mo5bxU+UCqnmlu9IdxP3FIpcoF2OTfHnjNOUgHkCqiR5GRkVYROMUWAmKq3Q0hjWk8rPSoZVcHrge1JASOUXrimB1z8pqu6MemT9aaI9uG3GqEWmlA60wyK3c0zazjngUBSp6D60DJF2gA5P40jSNnC4pCCOeo9KQyBekRJoAkSSQdqebg461CkjN/yzIp5j3HvSsA7zi3pml3sDgilC7BgYpct3WkA44xQApHamuy9MGmKVX5jRYD42nb560tPkAK/41hASOchXbnrg1pWsNzxtikP4V57R0o6RJ1PHpUqsrDIasqGG5OMpj2NX47WYqPmA+lZsehYjIHfPNS7wCOariMxfedvwqVIVkYZzn60coXGXDrsIXBNc/dZ8zleK6dggUrtAHrWFf+WcjvnsaTVgTJLAggDbmuhtolKAYwcVzFjuMgC5611tmpYZPpUNFtkoiVR2xQAo5xUhQjnHFMKEZwMe1MkQKCalVPXrVZpCh71PHJuAODmncdi1CqhutdLp7R/Z8bsYrlQ5BrQsJWYEB+tHMVYu6kQ7DFZjR5+laEinb96oG2qMZ/GlcloqiM56U4JgEYAzU2xT0alEQHJ6elO4rFdUAbuaNhzwOtTnap4PNJk9Rz+FK47DVjbGDTxF64pRvPPQUMTnk0rjBYc+lP8ALRB8yjPrSBscA4pXyR2NK4EeYxntTDtbHJp3lbjzxUoQL1FVcViERR/3jj3pwiQdDzSlA2aQg9hQh2Aj0I/KkJ9WxUmAQMjmmlFzzTuKxHwOuDmpA0YHQA08Rg87aRoVJzjFK4WIiVzSnBHSl8kcnrRs2imJld4X6qRn3qA21xkkyD8KtkHPGabyOtUhFQxuOrU7AA9akdgTTOvSi4AoyMUeXk80YIoy2aaAXb15pMkcHIp2D9aU9MUwImUsCa5/WQQhzXSscqa53WhmM884qkRI412/eGoSCTxUrr85J4pucVqiSLHHTmjHNPbrURzkimMdtABye9NOOlGexpM8dKAGkZ70oAApVIzzTvloAMcUvbmkLYFN3ZPNADt2KaQaciEndUwiz1qkgIOgpvORV5bdTycVKlopOKpRFexnKWUVItxIO5rS+wqBnGaY9ngYAq+VoV0Qw3TrjBrVtb8kgbqzPs2Oe1TxQlSCM1cWyXY6e1lRmyc81u26qyffPtXJ2pZcZOa3bZ8oAWIqKsbocWarpHtOZP1rjvEc6wjCMc9zXVLAHQnP51xfieFkfBHArkS1NGcy0zO2S1IXqIjGaTk4FaCHl/c0hbjrTStG2gAJHejdS445phBoGO3jOKfG6g81BtyacBTEaINsw5P5UoNsB3NZoyKcMkVXMBo+fbjomali1KGJh+4U49aysE9Kds9aOYLHTW2tWkhx9nCH1rUjv4FTJwAK4u3TdKoHWult7RVh7Emt4SZnJG7ZzG7YFVIQdzXRRJEItrEE+xrndMsiOWcitoRJGmVJY/WsK8m9DSCSLRlSIfKpNKLkMMgHPpVJZHLY8sgVYRQO5zXLYsl+1AdVNElyFUHDflSeWpJJzUbo7fdHFOwxv2hm6LgeppBOqnBNPELleRVd48HkZqlYkmZ1k6U5VVRxgmq2G6KpqRI2cZJIpgT8E8UgUnkEVGITu68VME2j7wpaBYjLSDPpUDqeSetXGKdyKrybSflzTQEGTjAGaEQ55WnDpgU7ccYGadwRIqxqOTg09RBjk5qA57ik2k/dFAy4ssSAhf0qMtvJ5NV8Op6ClKuw4JFKwXLUUiqe1WRKpwMis1Y3XOTU0ciq3Oc/SpaGXGfAyvWmiSQ4IqNmGODTVm5waQFgFyfmP41ISAOuarElj7U7cVOBgigRN1BNOCg+9V/NIPbFSCQEDDc+1OwyX7vemsSxzQCG60hH5UJAJk9CRTMnNPwuc+1KD6CquSNJJHSo3TJOetTnOM1GRuoTHYqnYDgA5qZcFeF/Ol8o5poTa3U1Qh+3j7uKQR46VIGVcZ5p33jxwKEwsRbthoMtKYzmm+X/ALQqgHq6KPenCfdxgYqAxnHDDNIrMvvQIuJtI5qcKDVVbgAYKGpo1WQZJZRQBKIgKkRQemc0xYwDw2alDqO/6UwHbQozQrgj5TgU3zVI24Le+KkXbj5V59KaQhrDIAFL5JCZ7/SpFjfO5sAVMpHZvzqgIUZj8rAjHc07DA/LU4AP3iDUbb1OQOKAEwScsMCpowpPTmoVnJfDKanTJ6cZ9aYidiFXP8qbFKkhK4IPuKQKSeDSNE5OKAJGCg4DCjaByDmoltyp5BA9aeY88LmgBwyfT8akRQD1GfaofIxzk09FYHimBYGO/FSoVA4OPrUKqxHU04QO/XpTQmSkjn5h+FM2+YMGpEhC/LtqURqOvFVYCuLVRyOakVT7CpMIpwHH0p6ox6LketFgGCPZztBpwZuyn8KAJFblTj6VZhOevFMRWMm9sbiD6GpVQYyQTV1Le2Y5fBaphaQqhOfwoApCNNnK800KF5AOKmCYlwAcVa3QrwU3n1oAzc7jgMRn1qM20indgn3FavkBxub5R2AqKRMggM20elAGY6lge1RJHID938atNaO0mVY1II5gMBcj6UAZ5lZWxjNNaZg3KCr/AJIJJZMGq80AByyGhgVvPHXaRQbhT0FOEO5vlzg0GIITmkMYsiMPmakZUx8rUphU9SBTVtVLZ38e1IRGIlY53lTSGHYxw5NS+SAxxzSrbsGzkD8aLDIthxjODQkEuRk8etPa3cnIahY2AwGOaYmPELhuGGKmEbAff4qBhIQF3VNFG6D72R7mmA+MPnAzVkW7lckmokJU571KJ37Pg0CY4WZYctQLEqeJWFLHcTKOzVOl0DwwGaAGGSeLhFV8etJ9rvnbCQoPrUpk3DjApUc5wD+VMBUlumx5gA+lWRMeAcZqJpscHmoixLfdz7ikIsSF+2CKjMjKPug0bsLjmoTNIGOYsrTAlM74zjFRtOzfxj6UjYlXBXH40zYie1AB5jscBRQ08icYpTgciomlJbGz8aAHLNIrEtSmRjzzSLl+cU8DjjimIdHOV7D8aseeGXpiqygg9AakaUqOUz+FICcSkD7hNTpNkY2Y/GqBuwRgQtmoGmDN0YGk1cZrgBuuBTDAp/irODO5AXIxUyh2P3sY96LCHTW/puP0qLZLjEec/SrkRcY4z9asICeSMUm7AZ6/aFHzgYqwH3LgHBqw8QPUimmAbeMUuZAQeXg5ZzUo2gYDimGGX+8MUiRZOM80wJ13djmn/MpzwagEcqt8rjHvUw3D7xFQwASknoKbJcKo5oKAtzTGhU84zTSQxPODdBSDazfNjNAiPrgfSniA46inohEbKc8YxVdwWPIzVhlZcgCm8qOVqkBXIKjimCZkI5qZ2H900wqJPQCmBKsxYcjNKbhV7HNIsSIvHJpjJu6igRPHLG5z3qQxqT1qokeDxUpU4yDz70mgJjGqjOfypwRCMkVCjYxuNPOW6NU2YxHRDwOPpWfLbsshO7itMnaMcVDJE5X5cZppgZnlTOeGGKY0RDZLbvrViTzY2wF/GoJJCvVcnvVgSqyonzoAKeGjZcgLj3qrFHJOct93tVgJFHxxQA1kty3IH4U5bGNmzG/B7Cq8yKx4H5GpLcyxYAOF9zQBYNoUx+8OKU4QDD5+tSllkACvg0425KfMM0rgUzI5b5ZQKWOdozkvmpzCgHCj8qjVY2+UqR+FAE0d8rHGasB4yAS4qisaoSB096eGCn5hn6UrAXch/uikEeTzioVmTpyKerI7Yy1LVASNtUc1EWQ/SpvJH1FI0Kkd/wAKSYEW9egoyp7U8RKmeDmoHmCtjafrVIAJZTwMCniT2qMSqST2qVJV9KAHB1x05pwkA60HDDjg1EVYNhuRSAc849M1GFEv3sgVKqoO3NOBUUXSEfM0TQdggz6CmSvGuRvQY7ZrmFaQjl2I9M0oA9c+tcPKdDZutdRZ5lXjuOajGqiLlQWGe1ZAI6CnDOPY0cgtTSm1ZpV+WHb7k1VOoXPGJAoHcCoAD0xRtPQ00kFyb7TPN96ZiPrUDctzUijaPu1G/PQVMkVFl/TZVimGcfiK6y2lBT5ce9cbZQvNKoXHWuz03T5doDEYHU1gzWxaX5h604pkcLVxII4wMgcdak2jHyrUthYz/sxPValS2XHSrgQA5J59BS7QvQVLZVir9nXHQcVZsogu7j8qYW4PFW7Jcg84NJALMoWPkcCqcnlsOat3kZMXU1nCM9ieKuxIh2o2VPNO80k85pBDk+9PEfrRoA1XO4cCpg4xzTVjyRxUgTA+7SYCAZ9RTwo6mkyQcbad25pARlQDkCkMhHTGalY5HaojgZ4zTQDQxPXrT+e1NU+vFSB1x1FUxDehxS7iB1GKXap5BzTCrjjHFKwXDfu70EgD1pQvtTtuM5oC4nmAeopjNnnJocKxxmmMiqM5xVJA2L5m0delRPOS3BprEdjTT69adhXF81s8GlLMwphPGMU5eRTENUc8ipMjoMCmk7RgDmmE56g0WAlLKB71GWPUgCjcNp47U0EkgGmA7OeBil2k9qaDk1KCcdce9AED568/hXPay+EbjFdOcnIzgVg6vCCrA5PFUmSzhpQQ5zUROKuTpgkYqqxA7VsiBhfH1qPJ605jzTT1pjENLkmk6UFvb8aAGHOaQsSaCcGk70ALnnrTgec0wjikyfwoAsrKBTjN2zVXBpcHHFNMLFpbgjoanjv8HHrWeAaAo7ZqlMTRvQ3IYAVb3KU+tc/CzK3Ga1IZgF5JJrojK6IcSc4zwM1Kox2qusgLZxVmMkjGeKtIhsmDiMD1q9p90SxHU9qx5mwKuaWy+aDmoqbFQOrRZ3QHkCue13TpHVmPIxXUwzxrCuW7Vk61dI1uyIRXA3qbM80kTa5GOaixVy7XEzHpk1WxVoQ0jmjb6U7BNGOKYDVFNYdeDUgFJQMiHstOx271IFoIxQIZs7GnhB7U3eB7mml2OaBknC9KBljgc1EAzGr1qgVgTiqSuJli0sXcgnit+xt385eDtqpa3ES4zg1u2zI20g8dsV0wSsZSNmC3CxDHWpEQiktwTH8oqbDAdMe9Y4hO9zSIAHuKBKq8d6Tcp4OaQba5CyUS9804T+3AqLA9OacIx1JoGSNKNuN+KiLRA8tk0ojTuaaYo8+nvQMk3Kw44pAAD1pEjVW+9xTyB1HWgQ7cByaqyzDd6inSGTn0pioOrnimgEXLHNSBdpyeKbvCk7RSC4AOCpNUBMCPSg9OQAKYJSRmm5LZyaVhkgKgckUgb+7+lRiPccVOiIg460AMVWJyQM04/ex3pzSheOtN8xGHWgBwy3GaUKcE4zSptKkjFOOMdakCFjzzxQPl6U/agGaaTHznFADg3Gc/hSjcW7YpoMZxinAAnHNOwDgAeopcKvNJtx0NN3HdgjijUCQTYOM1JuBFQAKccVIAV7UwHjmnbgOAaiLMB2phLHpxTEWCxxSrgioFV8ffyaaTIpAPI9qdgLDEKOtRecBzxRyV6U0Rk9hTFca0mT1pqyODgGphGPSpEhUfw0ARJuf7zUG2Y8g4qwVCc9PamCcdOcUARi3Yck0u3yxuJBpTFJIcq3FNezmYZDU7iJ4pI3PPBq2sZK8YNZaW0xfhhx7VoxtIi4xuI9KYE6RuoGQKfhTgMBUC+axywZatQpk5LE/UU0IdHGjHHb2qZLVVBbeMZqQqAOCBTF+9zl/p0poBSykYVh+FQETN8iRc+uavKpYfJCqn1NSRxMAd2PwqgM3yLrbzgH60xLe5JIZyB6mtZbYls7uPSlNnubG5ufemIz1Ag4LbjUy/PyT+VaSWcca8gH8KZIkYXhOPpQgKPmiM4K/jVlX3Dgc03G4khaeBgdxTAkCsRljmnrET0qud+OZcU2K0lB3Gd2+jUDLBhOeTijy0z8rc0/Y4XHWmlSo6gGmImQbRzj86erei/rWe4uX+WPYvu1LHZTgAyXeTn+FKdwNMFcfMefapQwIwqZ+optuY9u0gk/3jU6EL0IOO1UIqtCpYFowPenBSD8spHtVl5SRjZzURj3Dng0CIWmYNjPPvS/a1YbSBkelRyDGQKYluxbIoGWUkG0lFG73NQNfajvwtpuUdw9KyyAEAc02NbvOAVApDJTqF5FhpLYgDqpNPXWEZsum0fQ8VXkt5WcF8n/dNPFsQQV4HvQFy61zAUDLK/PbpUJvZFO0NwfUVXVJFl3MA6jsamZtxxsX/AHaBaD0v50cKSm31A5qVr0PjOM1TkXcvzAp9BVZ0kz+7kOfcUAbUZdxlsAVDOFJ2ngfWszdeKOWWkM8wOJCKYrF944wvBwfY1AYlYcPmqvnlcnmlW9iQElsH/aoCw9ojuxuFPCBRjjNQLfwM/Drn2NSJdxt1X6UXHYeqb854pGt8HIoS6VmwAfxqQyrgg8GgViHZk47etBUrgUpkUHGaMryc5oAQovrUYLbsKOKjYF3ODgCnq38LGkMkJdmGWpWJxxk0mw44Ofemssgb5ScUxAZJRgYIFO89kHvTlJkIXv61P5XyEYBagRGtwWwCf0p4umj4VMg96jjR+Q/FP2OR8oJFAxRcsMk9T61Ml1IBkKDVJopzwFIH0pVt5U6ZzQBpJeBmwU5qX7QrcFcfSs1Y5FOTU8ZYnAXmmIsZXPy5/GmvIRxgZp4QkdMGlEQzhs5oERxzbRyvPrThNuGNv41YEAIxt4prQR7sZAoAh34PK8VMDvXoBTJYWI/dyDI9RT4Ubb87An2oAekKmnPEq9iTTAzF8AcVaVM/Wk2BW3Kq/dxTCI3wSp/KrTxbj2zUTW7Hv+VFxEYCDuPzp/3VyMGopLFh70C2lC4Umi4xjztnAyMUn2yQEAtgUjQsT+8zVeeE4GGOPpTAvJcEn72fxqR5plXIxgVnpDIYwf6VIolYbTRZCJor+R2wenrmrsVwjHGRmsnYxbBXbipFYx8gDNJxGbAK+maa2TgAc1Sh1AAbWTkd6uw3Al6DAqGmgGlJPQU5A/WrGQaXAFTzDsV2kKjlc0Byw4BFTHHek2qOaLisRBXPXGKY8Y681ZLKO4qN3UDrTTYWKbxkjhqjSKQPyRirLMD6VC7qTgEVohAY2LcYpwJQYbBqMrtGQ5/Cmjc38WR70wJDOo4ApvnpnBxmojszjBpfKQHkj86AHlwBnOR6UByRlRioniTnYSKiCSg4BOKALUcxDc802WbJxk4qsEZGyxOfSnb9pGQM+9FgJecZjyc+ppjw8ZY/N6CnB+MgVMjKRlutAFNSU4CmpvIiKltvJ9atYwN3FMkj8xODmgDMNsPM4P60stvKU4f8hU8kYU4INMLvH937vvQMZbwzwDJl59CK0ILrHElVkkbbkrkepqVXDjAXP0oaEXxslXjioHiAyMcVDyh43CponZhzyKm1gK0igttVTUiW7BcsRVg+WfqaQp78U7gVX3A47e1NLkYIzVwoCKrPF2VhmmIclxIOcZAqVL4FtrLg1WWKUc5FROJCfmXA9aTSGapmX2qJmSQ7TVFN0XLHI96ljuYurHmlawD3SKIHAOaSNWLAj9aXPnHKjcKkERA9KYE4Q4B4zTwuewNUjNNGeRkfWlWZwcg/hU2YF1UUdRS7E9KqC4lbsKcZZAueM0uVjPjvOO/4UZAORTMEfWnBT6da5jUcZOelSJIAPWoxEzZG2npEc9qXMgH7zjigMS30oC4FAyPzqedDSJPMyMio3PoakSCSQ4RGY+wqxDoWp3PEVjcNn/YrOVRMpRZHYSMl0mDjnmvRNMZTGOvSuRtPCmrxuGltHT6mums0ubZAkkR3isXqa3NggA4ApASDzxVffdSkYXYMdaUWs7tl5CaQXJmkVepFV5r1FPy8kelWF09upBNPFgE5bCj1NCQmyktxJIQFTANXbWRYcmSTBNV577TbYFZbyMEdl5/lWXNrtixH2cSyn/dwKfKLmOhmuo5EKIxP4VRBAbrWdb6g8ynK7ParJfPfmq5QuWwc9qXBANVllI681LvJPalawiTJ9eKUHnqaZuyKeWAABPNJjuOJyOQaZjHOTQWXHXkdqO2OpoSAZnPfik57HpUpC45NNOwD3p7CIGLfhSZJHFS7VJ46d6Xy1wcHFO4EYbA64qVJBjBNMwFz3pvBNFwJvOAOF5NP3Fh1qFWHp+lLvbnC0gBonJyDUZgkIyTnnpUnnMOOKXzSe/NFwK/lOO3FIEcduKtK3GCM07gdqq4rFdYGbpgGnG32Hk5FSNNiowzPwc0hi/Kh6U1wWXpimSYzjNRh39aaAcUIFKEUgcU0M2etSID+NAiMxEc1IiqR8xpzEGot3ONtMCzhAOBmsnV4wUPar4YnjPFZ2qyARn5+gyeKaWomcJfLsuCO1Z0hwTir97Jvmes6UHPWuiOxmRF6Qt6GmnINNBNMY/OeaQk0mc9aAeOtACGgDvS9gaXNAAEyKNmMUqnBp+cigBuMU8KAKQdeKUHnmmAYGelLtzxRikLEdqaQEynBxVmIn1zWcZGqaCU7+9aQdiWbUIBUZ7VPjaMkAVUhkVVBdsVIJoZ22+b+AroUtDJoQuJH2jpmtfT7VVOSOtMtbOIdAPXNa8MSIAc1nUmktTSKJQdqdeKwdXnkXOBxXQgIeTk1majChjNcLd2W0cRcyEk7hVYt+Vad3b4Y46VnOnPFWgTEDDGaQHnNIVPpQMkUxjyR+FGVFNpcAZpABOB6Co2PvTiOO9NwSaYCY5pwj9aVQBS5OTQIcML9alQ7qjVQW5HFWfOtoUzt+b600wLloGPzAqAPWtKHVEtRt4YiuXlvNx+TIqDznzncTVqdhctzurfxJMZBtK49DXQWOoG7i+d1BPYV5XDcOjgg11GnaioixgBvUU+bmGlY7cJsOd3WlEyh8N+dc/baowbLOD9atJqsLsAzBea55QaZRtCWPPenN8w+U1SjcOMhsrUxuCvAAxU2YyQrjqxpDt7nmqxunLYUCnqzsPmPFFgJg0YFKSMcVC5HQHmo95XpSsMsFsDk1E5Le1IWLjjrQoJGWPFUiRuSeN2KGDHoCRSkL3604NxwaoYkchTgoTUol3HiI0wEjqaaXbs3HpQBZjI7g0uQ5qurEdTzTw59DSsFyXylJ+agxRHotR5bFJvPeiwXHMi9F4pwiBAyT+dM3DrSq2e9MB/lg+tKIgo5xTd2OlAJPWgCUBcc9aQEDoaYVc9OPWkELn+L8KTBEoY46imFSW9aEQgkmpC+BjFCBjOVpwd/wpV2kHjpTlJPUUBcZkt68UGTb3yaeQDwcimNCpIPSgBHnPHy0nnMRjHIqUIo+9T9qDoBTuBEjOyjsKnhjbOS/Wo+d3HSnMNw+9imKxbCLjgjNPAG7FVIyU4zmplfLdaLASNGMg4zTliQkHYM+9N3BepxTgydcmgCTy+mMVIoGOQMd6ryLKy/IarPFcBSctmmBoOg2nyziofJkA3NIAKqI10q4IJqeO7ljOJFB+tNIRPGVfCmTn6VdjR40BZwV+lRw3Fu8fzFQ1QSJG0hb7Yyr6DmqCxfS9tc4dwD71cRkdNygEdq5txaFc+Y7mrEOpyRptW3dlpiNKW5uUb92iEfWpbW5upCRNAqqO4NZYmMrbljdWqaIXzNnz8L6YpgbAuVDbR/KnC5gDgPKufc1R82SNQXyfcLUizRyJza+YfdcUwNIXEIAIlTH+8KZJcxsMBgfpWVLpNlcHe8TIfQNT0srK2X9xwfUtmgC+ZYsYJA/CoWdCeCTUKMATk7j6UeYzH5YTQIc4UnjpQkh6A4FOSLdzJgUjhFyARxQBMkx6ZNSBsD56oG7WJcucCkjv4pmKiQE+hp3A01dQuRzUisuNx4+tZ+/wBWAFAf/ayKLgam+Pb8rUxt3UNis7OTnNTo78Y4X1qrisaSltgHNPGQOf1qotwAANwPFNkuJ8fuFVm9WNMLF4KgXcVpgQNnghajtJ71xtuDEo7bKtq/8LMpHtQBXEDEHawIqaPAXayjPqaCWzhen1pBIIgS0DE+xpgXI1tkUZZN57CntaxOuec/WslwkzblRlPqeauwXMarsZ1DemeaQiX7IEP3h9DR9mUMGCc+tQOu/LK7e3NQpLJ5mN5wvvTCxPNb+Y3v9arG1mGdpH41ZM7npzURuCGx0oAgG9eDED70pSPOXX8DT3vFGVVSxqpLc548vntQBP5UWQdo4qOaKKQcohFQJK5zvOB7io5JGU5VxikAhsrctxCin1HFRtph3AxzMg9M0efuU4bJqJrgR4JzmgZIbW8ibdHMrem4Uga9wTKkbH/ZppuBIw+fH1NSB2YfK+6kA1ZHI+dSlWInTHOSfSqzucAEZqSFSVPAx70ASkruJ4/Ok2pjPemsYhxgGm/uG6LtP1oEWYpOCCRU0Y3AhX/SqKIoOVZh9KdJLdKv7tlYf7Qp3GWjHLEPu7ifSpUlBTB+UjqKoJe3W0KIh74ani4QjlHDd807isXo2BPAyamjGCeMfSsr7esD8kj2xV2PUoiAS4yewoEXlXdypP404ROTyOKrpfqynAIIojvJWf8A2RQBaaCIL81GyKMBsVC90pH3M/Sk+0KibiOPQ0wJ1mQ/dbn3qQEbctyaoxXYckpGB7mlkuHz8uaQWLrTBRzwPSm5R+QM1TM78blFJ9pCkcfhTEXSuME4xSeaq5zUBnBAJ4pBPF6FqBFhZAxyBVqIY55qjHcxDgjBqcXg2/IOKTGWshjnmnjJPGKpC/iBwW5qcXsZHoahpgWGTceajeJlUlTSrKH5DAUhlxwQT70tQIAjsDk80ghAO6Q5xUrKWbIOBQdg6nJq7gQMGkOBkD2qI27g4DfnVpHOeeBSnHUtxTuBVeByPu/jmomgYdxirvnqPlHzVG0Rk5HB9qLgRLb4TIGT70QSPG5Dpge1OCTI3LZHpQ8rK3zx5+lAFpJyTx0qUzgDnFV7eSN/4TmrZVSv3azdrgiubobsY4ofdKvB21G4UMRnHtTlZdu3NVYRWeVY25y5oeQuvGVppwkhOc0/zlPIIH1qrDInL4xnP1pEdSMEU6R1cEHmqzxDqu4fSmInMjYODgUqyIwwGy9UikmM8kVGJXRvlXB96YGgX2nlgKMAnOQRWafPlbpUigRKRJuJ+tICw08u7CLhR3p+9iQxYE1XjnjX0x6GrAmi/uoKYC7nc/cP1pQA33lPHrQ9/wCWOAp+nWqc1yZuFyB3JpAXjs4+cAe5o8wA8KD9Kz0gRuS7Y9zVmFfKIIO5aYFlZhJwytmpUHy8NiqUl2uQVUg0q6gGIQAbqQFzYMZYnNItuc/Ngg0B42UBsk0/eI1BJ49KQB9n2j5VGPShYyrZ24/CnJcgg5OKX7VEfl8wFvTPNLULD1J6FaicYfIP4U4ylhjoPWo1GTjP400AxhtO5s0qTDPXNOeLHQ/rUZTcNucH2pgSeaHJC5pPLJ5KCmR27I3y/rVmMOp+b86TYCJGM8mnGHJ5xUhAJ4/lTdzZxj86m4ELQqeGAIqH7NEhyFxV0o2NxGajf5hgrimmBWEiBvlbFSLOH4ZgfekMMa8tTFjhd8bDj1FMCU227kSU3y9hwcmomVUk4dgPc1YE6oASQ1ADSjhcqcD3pE8xu+frVlbqJu1L+7c/K34VN2B8g+WgyAucVYisrmb/AFVvI/8Auqa9XhsNEtf9RYIzZ6suauLd7FHlRRxj0CivHdZtbHZyo8utfCWt3RUpZOinvJxity1+HGoOM3NxDCPY5rsnupnBUueaiYv3dqjnkx2SMSLwBpcP/H1evKR/CnAq7FoOh2R3x2PnH1c5q2MA5JH41HNfWkK/NcIPYHNHK3uA9bmKAkQWMMYHOQvNKdTkJ7j6VkXGtWoP7sSSH1xiqLa1IDiKFRkdSc0KFthNnQNcSS5OW9eaEMYO6WRE9SxrlpNRvJBjzGHsvFUn3yZ3Fifc5rSMRXO0fVdNtzhpw5HZBmqk3iqNVIt7MsfVziuXUkYyalEigH9arlFc05vEGpTEhGihB/uLyKzpXubh/wB9cTSD0LcU5ZEJyENP3gjPSi1gIfsypyEGT14pRGQen5VId31py4xzVAPSNhwKsI2COeagDY5zUgkXtQBaDA9TUqnjAPFUg35VYR+RgipsMnD46U7zAeTio1UEE5wKeIsjpQAbs9OaXcwPNSKpGAFp/lu3XFS5WGRKAepqQRjqad5LYGSKDkd6XNcB6oAOwp2EUcYqI7j34oVDnk8UhkhKkYIqNxGvsacw496gcE96aQrj/MQdBnNOLlgQFAqqOnWn+ZtPUmqsIf5e480/ytuTUP2hvTihbrLcimkBJuZeMdKa0maUSA8mg7WHYGgCJm4/pTd7lsA9Kd5WeQaafkyM0AKUB+8Tn1pjIB/FSFznFJuJ+tOwrj1ZUHygk0eYevt0qLcc5FOBOT6UWGSIGPPapPJBxk01ZMdqek6dxSBB5QXoeKzdUVGhI4zitYyxsKrXCxvC3yg8U1ITR5lqSkXGQMdqoOpNdJr1uBlgACK5xjjNdMdjIrlKbtxUhJPbFNIJ5zmqGREEd6AKftz1pSKBkRyBSFuakK8jikZQaBDA3fNOD+9N2YpNlAEnmH2pwk9TUJU5wKQKe9AF+OSInDEVMGtjyTWUFYHrTtjmqUhGm0tqo9TUL3UYOFAFUvKc44NSLAO/WhyCxIZnkJ5rU0izeeUVmqETHNdDpM8G3h9rZqotsHodbY2SpEAVHTvVmS2QLnIzWZBM+R+9ytaEc6bRnms6lKd7jjJDkXZxtzWDq5dd3y4rohMvXFY+ruJIz8tY2a3K3ONuJW+YVns/JyKt3xO48YqgwNaokXd19aVcMajHTGabk9KBk5UAnmnKy9MA1XZsjmkDYzigRKxAPWkLA/dqLqc09RmgYDk1LlVGTUbsqVWZixPNAE8lwD05qAks2SeKb0pwH5UwHDFHek9KUUgJYyOpq3FclOFFUBx9KkVtuOaaYGol1J/fIqVZwTliT9TWSJCe9SLIRTuB0MOqTou1HIFWo9YuOB5hOPWuaWfB6/hUn2nr81LQZ1cWtso+dQT2NXI9bQgZBX8a4pboY+8c0/7YFHBosB3MepxMQN4/GplnEnORiuFhuWkcZzXS6fv2Aljj0NPkC5s+Zxx0o87HvUAOe9BbPoKmwXLPnLj1oEueQOKrgnHalDjHagCfLMSO1KpwOetQhmBoaQg8CmBYGTmnb2HGc1XjZifmqbA9aQCiU5xijfk0qpnnpQRnikA7J9BinKmBn9Kh2HOd1Sx9MZ5pgSD607g9abt78U4YyCaQxVYZwKeCQevWoy6KelN++TjpSFcmLcdRUfmjPQ0zhRzSrhutMLkiOMZp4lGcZqNSvSgN2AoAsK4bqKXg9qiHHanAgnGaQxxAI+lDfKMgUpUkYBxUXkP1D59qaEDSnptpPNAIyaXynPWj7IzA5FMBfNQ5wwFTI+FySD9KqG2b049qdErK2D07UxF5Tu701y+QFIpiuqEktz6U5JkJ/lSGW4mcR8jmpEdv4hxUALMAAcU/LDjcKdhEshZhiPH41SktmY/60E96sKSD1zTfsqsxcs34VSERx2JJy7ce1TfZ7c/LvPHbNTxlY0wW/Op0jhZc4UmmBBFJsG1LdCP7x61ejJIG5QDVb7CCdwdh7CrcMKoOSSfemA9UGfu/lUuFUZx+lIziNcjGarG6ulOViDA+9MReXJGTwKDJtOAwqBbmRwN6BfWoppvmwvIpgWSxAyzU0BJBjABrPlnfGBwaLcz7suRikM0ltEB3AHP1pzzLaDcx4/OqbTsx2q7Z9qkXawG8k/UZp3EO/tO2kbOWJ/3arSamjPtjgkY9xir6mJR9wH2xSgqxyIwg9cUAVA1vOv763bmnrpluDvjQAmra7V96lxMyZijzmnYCl5ao21yPxqTamPlA49KqXkd6r5MDNn0pkNrqUvKQsBTsBbJweFIx61Ikhzt6D0qFILiNd0+d3oKkVncY8rH4UAWFZM8jirEcqonyKDWQ7TK+3G32q1CJQOGz9KANSOZT9+MZHfNK7QNnkg+xqg0cxXls0sRIBUge9FwLYlXd984FTiQuODmqKIytluF96sxJvyVfGPancCyIXOPmIz2qN7RN4EkK8/xZpYSScMSxHpVos2QDx70xXITGttDtjUY+tVUjaQlskEelaahJsJ1x60/7IoU4UZ7YoE2ZckdyE3CYAD1pqM+zcVDH1pboThtrKQv0qvHJ5Zw5JFMB3nruJZGz6YollCj5I+fcU9sbc8iogS2QzA+lJsaKc8smM4xUJmYjDAEVb8kFiS3HpTDaxy8eZz7VIFQICcoce2KQq3RhVhbVYnwWJP1qbZnhRkepoGQxxRBPmXNIW2n5E+UVdRFCYZMVAwWRyF+UUySFZC7YKcCpCVZcM4A9KkSMpw2CD3AprW43fLQMiVVY/eBA9DUDxMZMgn86tSWgKjse+BUqW0Sxg7gWx0oEVNrheCafGrtwzYFSFCvXNKFJIPJoGPBSMYXr6mhVKnKnGe9PFrJKAVQe9RkOh2nn2pgWY2Z1KyIHX1xSRxW6uCY1BHQYpbcnHOAKvQxwMMkru96YmMXy5ASFC+2KVYwRgDB9asqsMXzFhk9s1Koi++y8+1AimEdQQQMVWlGCCT+FapKyj5Rj8aiFmhYsz5PpTAprICAB+gqZee36VKID5mAuBU6xgHB/lQJlJvLLcnmmfZCzEjIFXhCm/PBHrT/ljOetFxFEWfy/MSKT7LjIBY1qqVlAwBSPAccAClzDMn7I+fmBqQ27xpxyKulGB7k+gp6J/fU/jRcDGDOpPyfjS4ZzuLdPSt3ZE42gDP0qCS1dDlFH5UKSAzBMy4KA8etTC7l4ywx9KW4jYAlhzVQwzN2wPTNMDUS+Tbh2AqpPdIGJikOTVN0aPmQ9O1KjKwG1BRYLmhAZpEyXAHvVsH5MZrNUKBy+DSblJ/1pNAi5sdckLx65pqXRVsMPyqvvJ+US5FIY5G6EHFAGh5u4ZAx9aXLEDsKpqk8g2kj8qVnkh4cnHtQMvxsnbrU+7A9TWbFdKo+ZgPrU6XUfUMDUNASSvjqnNRAf7OPekF2juckgUrSBh+7YD600A1lRhjGD61Sk2I3UZqxuOSGX8aiaASP978KoByThOn8qjnvAh5z+Aq1FZqfoPU1FcW/z8jP4UXAqm+Ei/LGR71UZ8tk5rUW13rlhxUL6eMZBoAomVl5BpWlklXBAP41OdPYZORTRp7H7zHb7UAQlW248kEetQFTu+4U/GtH7MVG1GP41G1kS2fMGe4JoAqOr7RhwRS73QAlc+4qeW0BxiVQcdjQLRjGAod/dVoAiIZ2BB/WpQrjoTUIsrlZMqCvu1SvHdAcuf+Aii4idPLKnccN70Hy0Xnb+FJbwW8hxMh3+rGrYsFhXcuCPSi4FI6jKn+ptpJD9MUsM2p3MoLxRQRnsxya0knRVwUwKXz48ZVKBkT2YkUGWZyR/d4FSxWkCAbFGfXvSl0lTbhgfWoPKkj53sy/WgC2VHRuRQoCnqKp794wpIPqaQoT96Q/gaBGgQWHGKRYznO3AqvCxAwGJPqanDhG/eEmkwHtHJjKcH3qMTSxnEiHHqKtCTcvy5ppk7YBqLjGpcxtwG5p+VfjGaaI4TztGab5exsoevakBOGC8A/hSb0z8wwaiKZbJfmmSbTxvB9qLATgRvkcUeSi8iqqO69FBqwsgK5ZcfSm00Agt1c5K017Jf4cVKtzGBzxQL2Fm27uaV5DSRW+yY4x+NNMTI+4HFaQwRSMisMEUc4+U8MbULePlpkH41Xk120Q5G5x7CudaNV+tMJQcCvL5To5jbk8QPn91CAPVjUEmtX0oO1wo/wBkVlbgce1AkZcdqFEVy3JPPJkvIzHvk1Dkdzio9zEdeKTO44phccxQHO6kEiITUbLk46U6OEY+bJNMBysG6ZqWMDHSmqqqRj86cH4xigBzKoFQNgH7woMjMQMc0xozjP500Jk0cgX3p5lyenFQiEjsad5LcAg4qrIVyXfn2p24Ypgj+Uc/rUyxgjHfFGw7jdxAxmpY296YIuPepEAHUcUmxkoUkjnipFUKTUQZR/CRUyyjHGKkCdCAOelSA4Yd6qiXAqVZx2qbFXLiS44xTxIareZuHBxSruPGalxGiyz56k/SkLREYxzUR3EYpCmCKEkDJT1pC5zjHWkz2peccYosAhYg9DSfe601nxnmmAjuc1VhCnavFRMwDe1SE5ppViScUxCFwe9IMDnqaXb3wKUp2JAzQIDJjik38UgjAHBzRlR2pjHGTAGF5qBi7t6Gpgd3rSc5PHShCGhCOSck07kHpTgMjJNIRzyeKGA3gEgCm7T3JqUMg7ZpSwPbFAEOScjkClVualO0daYQueAaAFJypNRkFsDnFOJIpS3I2gCnYGznNbtSyOR+VcVIuyQg16ZqERmgPI/KuE1O3MchOBitoSM3uZpxt5FR9DTZGweDQvzDrWgCZwDSbuOetSbaNnegBvy45pVVQcimlfSgAnp1oAUqM0nlil2kHnrS7ip5xQAgjB9aURrikMhzxTGkwKAJdiAcijeoxiqrTt2qMszdqALbzgd6hM/51GsRPU1J5Y7CgBDIxHFSwXDRMDSLCzdBkVbjsHPJBq4pibRpW2qNGn7xiB3qRvEbK22LJ/2qZb6epA3AmtKLR42H3Bn6V0JOxF0T6brLXa/MNpXqDRqmpxwJzliewqWLTTArEAdK5zXMiUcnpjFYVYFRkZ91eecxOzHNVGlBHIpGb1qM81itCx3mJ70m5fWmbTn0owM1QWJN6Y4NNLr70zaSOlJj3pBYcZcdBSicgYAxUeKNvFAAWZjyc0CjGCPSlz+VAC4pfamhgDRnB4oGOHFL+NJnApc0AL2pQcjmmdeBThQIeDTi3FRZozQBJvJOaUOcVHnmlB5FAx4Y05XqMKSeKmRAOW60wLltKQRW7aX8o2oFwnc1z0cmD8qgVoW8mQNzE/SrUhWOwSTfFlRzioizE89BWdY3YK7WY+1XTlsHd+FKQIlDNg4pRIRTAhIyD+tOAwKgZIJyCOeKk+0ZNV8H2FCg5oA0EKuOuKnUKR14FZyN6EVMJdvApDNAYC+opDtHNVRL1z0p3nA8Ciwix8o96Aw5I4xVUSEdAamWQEZ5phclDHFHmc9Rmq8kjY64FRb2z7etAy2SWOByaepK8VVSXB6ZNPaU0WEWeMcnP1pKgEhzTvM4waAJwM96VVYHrmoRcBOp+lOS6XvnmkBOCQRkVKOO/NV/OBxil8zPIpDsTYYngkU7rUJkOMYxSeZg5PSnYRY3HPWpFc5qi8+3kUiXRB5pgagcY6Dmo3KEfdGaqC4yPWplk3L92nYBjAk5Apn7zn5cYqXfntikMqngmgRIkrqvSniYsehqJWz3qTcOOn4UwJ429wKlWfnG5SPaqu0P2IBpv2UZBDkGgC8Vjc8kfnU0cCD7r4/Gs17a4XlTuFPi+0KfnVh9KpXEbEERhYt5rMPSrHmg85rNjmcJ/eNSqZc8RmqQF7ejfeUE0bl7DiqplKjDZzTVmXPJAH1piLasGYgjAqOVEyACc1WkulDfI31pwmDAZegCQQszfNtxUmwkYC00SIF65qeFlbk4H40gGxwKvJX5vrUqlge2KcAewAX3NMPBJ6j0FMB5ljHDA59qdtjlHLOR6ZqJLgqDi1JPrUyyO45j2+2KdgHC1jXDBm+masRXQi4J49M1FtI7k5qZYowvKfjTAmWeOQ9RTnkCrtDEH/ZNVGhRTkMF+lRNbNK+Y3Zj6CgC9HGuCzkn6mhnGcDFVI4rlDtlib2qVraVhxGadhXB40zjALHvUL2ew7xKwb/ZNK1lcj5wRwelOAlPVTkUAKCwj+Zj+NOS5XZgKCfWmtFNID+7x+NZ1z5sBxtOT2oGaJnWRtrOOO1SGZYUx2PcViuLhYw/2csfc4qaB3PzNGw9iaBGiNQWM4Eik9gBUhu5pxksBjsBVHz4lJ3wqW7E1B9oug5EIRB60Aav2iVGALnHoKmj1GRGwc8etUI/PkALBWb1qVFnJP7sECmBpRX/AJxIZCSfeq9wFRgcYJquJHQ5Ee0+xqN5pDJks30NICd+U+aTPsKYsaqMkkfU0gJcZ3AVEQCMls/SgBrwhmyrcfWo1hBY8v8A8Bqfyztyq4+tOjZBx5nPegCm6mJv3eW+tODyYOQVPtVpmiUElz+FRb0PzAEigAjedl+Zdw96kIBG0gLULEHlVIH1pVkQsAQSaEDJRJ5Q2jrSorOdzSU9ihUDaMUEBe3FMBd7BtpIx604wKFLhhURKyAKAR7mlkVlUICcUAAyepyKkVcjgHAqoIwsoLTMfbpV0TEgLGRxQK4FWY7csv0pyQR8ktk1IpOMsRn0FIHSQ7QwX2oAdDAh7HHtTnjRWAXH50qoqoQGaoUg3SZK5H1piJvIQDdIATU8DhV2lOOwppABGBj2pQ2XBYc+1AFiNVBwVx9asR7FbG3j1qrJMQoKnOOxp32sPF8sXzfWkwLLsHOAQtJtAGCc1CkkIH7wYapSYmwVYY9M0hC4TGDilKoFwRkVGX5wq5pCxzgjFOwDshfuR01o2ds7ttPB2cbc1IE8wcrilcCuGKNjdk08eYQSX49KcYEQ9KasWehNO6AWMMxyFqfaRzuyaiUGPjNH3jzzUsBsqBgSy5qusS8kqQKu7gP4TimM0JHc+1NMCp9limHzKPxpUsIuijH0p8sfmL8pZcVRNpOZQUmfNUBfNlCiHPOfWqjWiJlt2BUTfaYXAZmPuaVW3grIc57U0IakI3ZVuKeyyk/IePenwQiEcKMUTPMF4jFAxAswXiX5vaq0hmDfMST9aESZ3+ZSuaSSOKF+Xct780AirMs0pwRjHqaVHnjXaygAd81N9oA+8uR2zU6XFokZaVRj3NAEKvGEzuLH61YtrkKeYyR6monudNm+5DIfdQactxbQp9yZ/QBOKANYSRvHyo+lUZnkhJ2IQPU1WOpBcKluc+rHFNe4vJ2+Z0jT0AzSQF23lkc56nvVtb23Z/KLruHqayJLLzoflu5Ub/Y4zTYdEtNu6fzJZPUtQ1cDXku442wWTH1qB72BvlXLH/ZBqlHYW0UpZQPoTmte2dFUKvQelLYRRN2UXi2c/Ximh72b5hHHGnp1NaUio54ANAjIXGMUXAoiCVlwZOfpVc6UrSb5S7t9a1okIJLH6VIwYdCKOYZQjghhIYRLn1xVtZVK5B/CoZWCn5wPwpFniAzkCna4FhWjfJemSRQ4+U1EJYSc7sg0x5Ig/wAp6UragMmt42APOfUUxUkUffJX0NOe53NjAAp4nRR8yZ9xVCKrI7uPnwPerCRjoXX8KtRNBIO30ND28I5AApXGQ+XnhTmlWM52sxpxZFHyEZFAnB4JFAh32eNV3DJzSCGN8djTx93rUbgKOpB9qAJBBGhzuyaCY2OCtQq79wDSgMOQtKwFuMJjijy1zmqT7j93g05DLjk5x70uUC0VT+Fhmoyr7sb/AMBVR5MtwmTTvMfGeVp2AtsjDGD+dV5AynOAfpTWHmJzLz9aga5dAUCg+4ppATgsR12mkkLhTl+KhV5lUnZn61QmN/cybYtq/jTA0FmVFO7Jp63FvkEAZ96oLpF7jMtyuT2Wp4dKYN84J96V0BpLexbMFsH2pq3jh8DJXPWobfTUjkJGQfc5q+lvs96h8o7s+Y3kZh3JpvJB9aeUHr+VNPoa87Q6BV7DpxQ3qTmmANyM81KqnbzzRoA1DkkfpUioSc04INp4oUY6CpYCiMDr1pwXA60/cT2oCgnilYYzaCtL8oAGKfgHANO2A80ARjHaOghz0jxUqjB45pd5BHGKYEID554p53eopxIPGKQqdvpigBAOcgZNL5m09KZluzcUjbvXNUkBKGycipl5FVUfpxirImwOF4oegD9o7mlCL2zUTTEnpxQsh3e1ICwFUDNKQB92og4OAcipd3HNSNDlznipQxXknmoFYt9KXGP/AK9LcZP9obP3qVZT3NQJIAen0qQMMZ4oshE3mZOc0nmnPtURzj60uOKYXGsxHPNIsgxzUuB+FM2+gz9aegDg3HFLvPTsaYQduRxTeSMCgCYgEZ71Gc9yeKapx9akGT1GKBCqQVowCOaXaPXpRvHQjkUDQ7PHIppODx0oLAnjpTSpHQdaLCYoZc8k0gKEnJ/OkEbHtxSmM+lO4AzAAYppkOfagrgYzQEJ46UACnik3nJ9qXyCp5OaVEx1oVhCLlhigowp5kKdFFRPIW6mncYyUZUg965rVbEOrc/SujJHQVS1CNfILdDVRJaPP5rUByCeRUIQL0q9qHyzmquATW6IE4xnFMPPepMrzTQBQAiqM0qjkmhmC1G0uKBkjtgVWZ//AK1I8maizmgY4vimcsfrTgtOC5NAhirUipz604L61IAAABQBPDCjjtmra2CEhj+lUFcr0NWEvWCgVcWhNGlBZRoenFW9qAckY9qylupHA549KsQyeZgN1rVSRFmacUgzgDp3NXYpXK9SKx0ZlfI6VsWkqsASMHvWqkiWh28tGxLN9K5HWyolyMj1zXcZh29hn1rndb0uOcB45FOOwNRUs0OJyGQetN78U6ZDFIw9Ki3HtXI0bIfz3pOlMLE0bjQMUsaTJzSZoJ5oELn8KXNR5NLu5pgSDmjbnPNICKcD60hjAMUYxUgPtQVzQAzORS5o8sgcUmxvSgBR7Uu6mgHpijBoAdnmlBpoDcHFOCt6GgABpwGaXYafwBjvQIVcCnBsdOaZ0pC3NAyZW55NW4p9nTjNZ3mHsMVLGcnk00BsWs7NKACevNdFDC7oCTXMWIZpBt9a62DKRqDycU2IQQuB/wDXo+cVNvznPelJx2pWAhzjkqTSmb0HFSH5ugpQnHTNMCHzPSnJMx4J4qdIhnJxmnNEvXaKQ7kG/P8AFwKesm3vmnCJScFeKf5K4x/KiwCpPu6HFWFk4qt5QGAARSiMqRhzz60CLO4E800jjimYwPU0AN60AOWnAc0zBHI6U9RuFMCVSoPNPYo3So8YpdnftSHcGVOuMmmbA3tT8KD3FOwMcUWC4CP5fv09MoOCKjdFHINRKTSsBbJzzmnAqw5FVgzFcU5SwHSnYRI6io+Owpd7cfKfenBOckGgLDVJ5ABqdS4GeQKaAFJI5oNwBxjimMVnc8A4pu9u/PvSbsjrT1UMvXNMQ5ZSelThyB0qGFNrGrHmpjpmhAPjmkZuD0qfe+ctioF5A/pTyBwqnmrsIsrI2MmpVm7kjFVlyigE5NPAz+NAiZZgzD5fyqyZwBw/zVSBVWxk1KrREc9aYEEt5N5mCePpTw8RHzcn1FJMEx90moo3CtynFAywI1P3WxUUm4cDJIq3bfZXY9WNTSCEHCxE+9ILFO1dmzuLVMD+9yT+dWE2qpwmPeq0yqfvcUWAulm2ZByKYZmTkggetV42MQxywqSa5SSIrsbdTESpqoHAbip49RyQcfnXOPDcvJkLge1XIYbhlAyBiqTEbw1Bs8KcetTJerKQGOAKxVjlXHzZ9jVpIyVG4qKYGw00JX7vH0qKMxqxdZCo9jVFJkzt8zOPWhtkgPOAKYGpFdsWyrbgP71TG9nPCqCKx4JQvygcetWRcsowqmmI0kuWIAfApVuE8z5tuKpJdA4EiH8BT5PLccYFAy/JcqF/dlc1VZfM5cKSarsqKuNwyaaIG7N16HNAXLBt4/p9ajazyPkOfwp6W0y/PuLD0qdZmXG5QPpSAzfszBiQCWHqKja2kkfD4X3rejkj4bjPtUUixFtzkgemKYjHa1ljA2XAJ9AakilnhI3Nkd8VfYW/A+XB71FOFyFjIJ9qQEclyMbsMajM27DAHmmTRyLnGPpRGHKlWwKB3ElkiK8F8+4qNWK8np2qYwFVyME+9M5HDID9DQIV5nK9ePSmpcqnVPyFSbUCc8E1WxJ5mFXK+ppAKZo3c5UipQBjKHj/AGqQhiceVx3pGSfqqUDuKJZAOQNvamFWk4DhQT1FV2Zw5Vg2aEyWwAV980rjLv2d4cHzjjtxTHeZ8hSxx3PFVpLl4/lEm/61JHO0ibicD3NMTFj+0l/mJOO1WJWIAJDA/WqjXvltuJIHrSvqLBQyAP8AWncRf3wtED5nPcEUsIjYErnPrmqaXi3IAdcfSnGRE+VGx9KYFwRyIGO/ikiT+LOaqiZlUr5mc+vamrM6A7QTnrSCxoo8zOQjcVL++Ukkc1n/AGiXAcLtxTkvJSwLSEj0ouOxopvb5mJ4qRZUJ+YMMd6qNekx4jU7vUioPOfOHP5U7isawdSSMqfTNMywkycewWqO/gc4FTrMViCqBz3zQFi27MR7VF84IKnFVfPlQjI49c1aEwK5JFAFi3385f8ACrg2n72azY54VP3ufSrMd2rA4ViB3xQItqQGzkYqQyZPyEEVTEyn7vTvUiXKIMLGT+NS0BM0rH7ygYphlC9Dk+gpGd5OURR9aUxHbncgP0oAZ9o+bDA4qVZoyMKCPwqEW0hJPnj6AUCzcMWE7k+lGgD5ZSg4XJNRB+2VyfU04wopyS7H60ohtV+YxKGPUmncBryCJctIo/Gqz3Cx/N5oyatFYm/5YIQPWmvGkg2pGB9aAKqziTksW9gKfvzjFvJn/dxT/JSIZDYPtUpSaZAFk/GmIhBmx/qtn+8aQfas582ED3GalKOi4k5xUbuSmcbcfjQAskFzOQTcgf7q0xtL8wfPPIfcYFOjucDBJx7ilXVrfzPLJy30oYGdJ4YsnkMsiySv7yGlTSdiERWoXHTcc1rrMrnKrn6U8SMCAMA0tQMRIdTXHzIqA/dAqcLOzDzB8vetKSZw4UgYPtUckYkPCmncCpPAhUEIc/SoJYniQMqt9cVpxoqHlGJ96trKAMMMUnLsM5uIsH3O+T6VpwzgjAXNXpbe2uByq59cc1Alu0LkIAUoUriZSu7qOI4MDOf9laig1CMf8sXX6itGX7xXy8mqzW7FicKB6GqAaLtHcbGKmrYvNg6FvwqqiR7vmVT9KlaeBBtPy/WlYB5vosE459KiGpRZwKB5LHJUEetLiDP3F/AUWAc9wkkZKsCfSq0XlEnzRU3kRZ3otQz4dCAnPqKYEywwM2UzilaOJfvSACmWqKqhWR8nvU80RRchAR70AVWEO75WDCnKqtkA4/CoSIyDltp9SKuWgjZcZB96AFhZUByuakLowwIzn3p3lN/DtAqWOIBeSCe9S2gMp7xYJCHUClivIpX4UH8atXOnxXI4A+tVodGihOd/PancBfOYyYVcD9KnErgY2qT9aG08sn7uU578VWbTZBxvbFF0BchkBzuIDUSTsoKqN30qGGyWEcncfc1bWMMPur+FGgioHmLZ2VaVwANy4p/lBedxFORQx5xSbAjKRyDI4+lIsUY4ZCasiMY9KQREHrxU8wECwxqcqgx707yw44QGpjGO5p6LtFLmHYpNE5HCimCNkGcDIrSxUMmF525FNSBoph33ZOKnEzE9aiaPcenFMNqSDtbDexqtBFwTAHkVKsgbpVGJJ1XD8ml3sh5IzUuKHc+c1iHOe3SgxKG4HNTBc0pC9xXlnSQEAN049aXIIwcYqVlBHSmMNo4HWmIbjnGTikGQ2KCT+NNySM4oQEobA6Unm46Dk00KzE9qcsOCeafMMcHPQU7cCM9KVUwaQr1wam4BnHOetHmelJ5gPBFNB6nGRTAl4PJNBZeOaYI2PI70v2ZyoJamApZfqKY+DxnmpRCBg0jIAff1p3Ahxt5qRG49qcIweppRF1I6UuYBCA3Xinpgn3FAjYDnn609Y2PUACncB3QUZzinLFjPPHrTgvJwMmpAVCRnigEnIzTwMLzxSZU8UWC4LGCBwaUALzTwOadt3DpSsMZu46UeZ9KChzwKbsosA/cSc04AH+KmJxxin7QB0pgLt7UbAORSqB3p2FPWkKxHhTnIpCQKkK8cUwpwaAAHPWjysmjAC9PrSrIFNAxBHjHJxT0Rf73NRs3PemgkEkUxFvbxnOMUEZFVfPI78UonPpmk0wJCnem9O+TSeZznpS71J7ZoAC+OQKASeCuDSNIATzTDNnjrTAV89MdKiZehqUyHH3ahkYHkk0IBQApzmq90m6I5x7VMjL/dply5MRAAqkxM4jVUxMcDOKx3fB61uazHIpY9q50qxbmt4kD1bJ5p5PApsaHOaSUkDANUBHI3HWoSxJ4qQqTzThFxQBAFY08R5GanEeMGlC4yCeKAIxHx707ZilJx0oGecnigBDwetJk0pxjNGMA+tAABz1604rtNNU5PNP3AdaLgPiY5xV2NkUZZwDWYbtFGAKh88yk/NincLGvPqKRg7W5qt/bdzkiNmH0rO+Xd6/Wpkdc4wKfOwsWILy5mk/eyu3Oeta5lkeHaFP51jIEzkfnV+K6KLjqKq90BQnsmLE5qo9sy9uK25HD85AzUJjDE4rJgYpjKim7D61qyWpOMdKga2JpXGUdpzSbTVswbTUZiI7UXArlTxS7DipduOtJgUwI9pp3I5p+MUEZ+lADQ5zTg2e9NKUm0g5oGSc9jSZOaZkjHal3HrQA7NLnimA0uaAHhqdvOKi3YoLmgRKDnqaM1HkUFqBjyxpobmkzxSqpboOtADuTVqC3ZznGBSQQ854zWpDESvSqSFcltR5I+WteC4bAyTWfHCwxkDFXFHGBTYXL6TjAyamVlPJNZyqwAPNSrJg0gNEMpU8gUwBiT8wqsJMinq+WpAWhJtHrQJS5x3qJWGKeHxnAoGTjntTh1zzUKygUomDHAzigCbnqaM+tN3cYpeO560XEIGyelPAOc0DApM80XGPDAc0FuOBSbQeRUgUEc0CGqT6U8OVHNIFxSHigByyjPTNOLZOelRjHNKIwTnNADs575pQRRt4wKjIIoAeX54496kXDEZORVfoOacGPYGgC8GA4FKSPaqkZZiDipk5PNIBxINM8tWz2qwoAPzAU4Imc0wK4tix+U/nT1iKnBJH0qbpwDRk4yaYDfL3dzipFiwmCaaDu6Eig4UctTAQAhvv08uFYc5qszhm4f8qaytnOc07sRddl4zJjNAl4G181UTLZBpdpB9qdxF8Pv74+hpTOU7bsVTDADAbFOWXa2WGRTuMtreMf+WY/Go1nLuRtxmozIh6DFCZ3bh0pAaEPyfd496J7grj5ySKRJYyoyDmkKRt0zQBImqlBgxj6017tJCGPJqJ4YSOW59ajVI8YDDHvTuBaiuX6bAR71IYvtDYUhDVZJY4m4NW0liflThhTuItWtlLH1mBX3FOcKjnDj8Kq+a204yahErE4OM+uaq4Fh7gbuppslyNuOtNVGJG4ce1OdQ3AQClcAimi+XeDj27VI7xScJJJj/aqEQODz0q7HYLIm5pMnsKLgPtPJ3KTJ81XpJ4kwd4OO9UPszj5IFBb3NRvHNEP30aj1NPUC2btXf/WHHtTxLG7bVyT7VmqYADz9TVq2nto1JSTc3oVpiLavtb72B6MKne9CptQD3OKzJbwv12gU6O8QDHklh6npRcZpW8gkU5kOfTNDsMlVYn3NZy3yqSFHU1eE8hi3JCW46kUCLEbIQMk/XNWQwwMZb6msuITSMS4K+wq7GX2YByR2piJcIGyyDFMLp5uVAC/SozayyZYSIPbdUL+ZD3H50mx2HzO8kmVC7fYVCwmAyAPpTo5SAWDAfjmmvcSM3yyZ9sUrjsMM742mIgjvQ7EJlY+aZI0jHBc+uBTDO+Nin8cUrgPhKTH5wytU6ExgqVBHrVNJJcnaQSOuakju5EYh8GqJZIbt0baMY+lNkn3nBfr2FLu3ksirzUR8tWO5TuPpSGhwI5Utmq7mNSdiHP1qeREdBg81Gqwj5TmgZFsaZNrAD371XYNbEjO4dh1qx5sayELk/hVgK0qgLGSfpQBmgyTcFSR6Yp8sdxCo2WrkHsa1okdWA24b1NOnEwIJYZ7DNAFZERYA+0o/901Hv3EblFTkF0PmEZ7AUyG0aR8jIUUCYko8vDRqT60hd2wFUBj61oqsSptwKYY4wdysMmmBQZpUGxlzn3poWdxtXG39at/vC2fLyPXFTRxoASVK0WApiC6UDdI5A7Z4qYISvzFs/WrAhd8EFiKWRVjIyQaaBjEhZV5YsPSpVs0OHKtx70+CXDY2Eg1Zf5lyGx7UxEO2IY+T86tokZjymGJ7Cq+2TGdmRViFztx5WMelAXGqvkg/u8E1ILkJjP5VMjJj5iSfSo5kQjO3FMQG6gYYWPJNIqBhkNtNVVDMxG2nYlibO3igC2s0ijapZiPan/aWZQM4aqi3PzcnFTo6vkowagROJAnLg81KLnaMpyPTNVQ+0EFWJ9qPLkdCVTaaVhkr6go4dQvvTllhlAO4Gs7bOrneisPepVbghogPpRYDQMTFd0eGpyrLxlAPpWbmRTlGwPQVZhuLkHlMr7mlqFh80GWz+dOiXbjZJgdwTT/PLt/q+KkijSQ7jHik3oIZLGsi/MSw9jVS4syVzAWU+m6tZUVeAKY8YbIXAPrSUh2MmGXYNsyEMOpzTZI7aSUmNAT3rQa0ZzztP1pBp6ocrgGq5kBVQxxjA609Zog/L4qb7NsbJGapyaeZ5iVz7U7oRaMidSwxU0VwpXt9azU067RsZyB+tTraytwwAA96TswLRukLbWXB9qgkaJ3xuIPoDTDalekhHtSpDBEwLJub1Y0WSAkSA7hhjn606UzQc8FamE8SqNuM0jiaXHCham+oFJnZvnUjNQG5wSDw1WHg+c8Ee+ahmiUj7341oBn3k18GQ2qRupPzbuKm3LIiiVMP3qeO3J6OD7VOluw5Me7HagChIjqPkb8KrK8qy/OSv9a1ZIyxO2MgfTpUH2XfwHw3uKAHLcuowWyDUJn+bGCR7VWurS9jI8qZXHfcMVSjuNQifA8v3wKANmK8cSY52+hFW/tMm0nYSKp20iMo8xcMa0FZFAxkmgCmZo7kFJ4CR7cU6NLeMYiRlA96tNCHbdyBUZBDbVUH60ALGecb9v1NThY1PzTDnpzTUhwQzqD7U9kt2OSmD61LEG8RnAcEelN3FiCRj3zT1VAw44qUxxsBheveldICAzbTgMKcWLj5mGPUVK1qoXAUfWkFrsUbQCfejmQ7DUMQ43VMqpjg1A9qWO5sL7ClCYGAPxpPUCwIwM4OaQL2pmWAAB5pROqnaxyfWlqA4bx2yKfx0PBpUYOMg04rmpuUkNxml+6OlOAxSUh2InlYDKxlqaHLgExsPrU+KWncLEQXcPu4pwjUdBg07Ipc0rgkhnlj1zTWgRhgipc0maLsGkfNJf5fl5pu4nHH40gGBkUpyeelefY2AtgZJ/Ck3En5elIeSeKBkAnPWhAIVyeTzSgACmuBkcmmlgDjrTAsbxtxgZo3gYqDdgDK0ofIJo5QHvIxPy00Alhvam5z9aNrAjihRE2SqAM8c+9PVtvGBTFjc4wCSeanW0fOTxTBAJPwFHmEirCWyg/N0p/lIAdoqSrlYlie/wBaBExOKtKnFOCkHpzTEVxBg5qZFHcU/wDi6U8IeDikAzaOmKQr7cVMQNme9ROAOe3rQNAFA9hTgoFQk88E07OcgcY6UAPIweMmkx39e9IuehqQAUAIMjipBx3pMc0ucUguAA5yetL04xSZI6dDSfNjk8CmA04B6Um5ieO1BYnsaeACMmgBEds5A5p53HnA/GkAAHSl3Ac0DBQfSnhe/rTGY49KZ5uBg9qQriyKc8Y+lMxuHNIXJxUeSTgZpoRJ3oxnsMUiqVXJ69qUc4BpgRuCccCkzipinYCo2UjgYouAzJb6Gl2noO9HC8HtSF8GmA0g5wetKrgcDrSMSenemgEHNMRcUsy8gfhUUoXFMSUqOtKzqeeKkYwAY60yQZU/pSkhjxSOvy9TTQjmdXiLFgT1rnnVQx4FdLqq8tXMytgnjAreJAmFCnFQSMvWhm5PNM24+lWAm/2wKcG7k0gGT0FKqjPrQAvmEjCikOTS8KTR17UgEHtSke9PVCecUpQgZY4oAhxx3oC/3uKJJ0j4GCaqPM7njigCd5kQfKcmqzSs59qbj1PNKMZoGNIOacOelL1NIM5oAcB3p65pgHHXmlLdqALCt0NPMrHgDHvVZWOaeCTTuBZXHdianRwnHaqa4zU6nHBNAi2rg8UpQc81XDccc1Oj8c0WQEDx46jmomi44q8ArnpSNCpBx1qXEZltHk9KjKYrQeAr3quyAmgZVxR9Km8rmjy8dqQEJXHSgD1FSFaCtMCMryOKNo9Kk5NG2ncCPy+9BiqbBxmlKmkBAYT2NJ5JzVpUJqQRn0piKXknNKLdiRWiIhUscIOOKAM5bVu4qxFak45FaKxDnipFt8kYqhEEMBHVavwrjgrgU9E28kc1J168CmMBx0p6sw5zzSBc554py8dcUWEP804w1IGyacMHgUZweBSAfv7YqQNUGd34U8HA4oGWQ3FKGJqLOMc05T37UgJck04OVIxzUfXHvTwwzkdqLATLKaeCT3qJSWHSpVbAzimA87gKUMM896buJGab+OTSYFjjbwcU5WA/iquCccml9QKQFgyccGlBz161WVip5p4kJPC0wJiAeKTGDjdTd7be1Ir5yMc0gHGVl4BzS+YzdqFKc5p+Rj2oATaT0HPrTtpHvUZLdiAPWnhsDAPNMB2SvTilEpBphB9c0wgjvQBZE5709Z+DnAqmue5NPHP4UxE4ugW4BNKZ2I4wKhCjIOKeVX1/CgCSK4OcFufpUpKsDuPFVCFQ570u/cCBTAn2oOmM+1PXA71XVjnmpA4xgjmi4EoYA/1pBIoPTNNB44/WkyAMnFFxj2UOcheacUkAHFRGSTPy4xUiSEjBzmncQoQmnDevSm5bPJAqYR5HP480wGq57tj2o8zd/Eac0SY+UnNRPbttyW2imAu9RnLEinq6kZSqYiBbBZvripGhCH5Wb64pAWDMFNWEmYrkEfSqHlquDhvxqeOT+6p980XAvxXJcbWAH0ppDb+D9KqJcIj5ZNx9Aaf9oaR+m1R2qhFxJJkI6t7VOlzyNyY+gqslzsXh196njuEHzKAST3FNATrKpkDAt7A1oRPIwBG3Hpiq6xNIgcoFqSKRuigYpiZNIJMbgcH2qOaOeSIHdg+9W44XnGVTIHXmiRGQBQCCKqwjHEMyjkD8BQlldP8APGvHqa10MLDEmV+lItyqZSJ8r7rQBnNaGMZk+dvaljaOMYaNgtaRMMg+Zxu9BVe4WKNc5JNAEa7ZcFYgFHer0dyY4yq7z7EVQhyT8rge3Spi88R+RwSe1IC/DfBVwUUE9cinSyrKuEIB9RUD3M626q0MRbuxXmmBWKhi4jPoBVDHkNEeCpJqB5VfKspJHpTCl3PJiFN/ueKa0NxbEfaF8vP40gGNcrENqrg1ELqJWyZsHuKtM9so3ZVj3prmzYbhEhLdeKljsQ+cGfcrgD3NJJcBSMIST3FD2cEjblBA9BU62ke375UDpU2AhiA5YFyT2NAIXOcqR61MiR7tvmFiKc8cG7JP4GgCjcNkDbMEHr0qVnJRNl0r+4GadJDZFT5vI9KrGO1Qfu5QnsTTAuqWYYJUt69KkQMoOQGqiL5IyqgCQ+1TLq8Trs2xq390HmgC4kCtkttU00R4kwkhB9qihuo5WxypHrUyJHNJgO2fQUxFtQFA+Us3uajmRfMBk79ielMEUlvLksAvvVhVibnduP8Au1QGLqutRaUAPsc85P8AzyXNO0jVX1IFo7SaJfSUbTWuDHE/IwfpUoeMAyMCW9xSsO5RbzGYLgL64qTeAQpTcKlDPMSyhQPcVZt0c/N8o/CmS2VI590nlxRnP1q0LjZlJYgcVOkqxPwufXAqrcyiR8oD70DuRPdsSQigLTAY8fvAcmiRAY/ljO760wRuFyRj60AWllRIsxg596jSaSRuuPwqt5pd9uePYVOsscY5JH1pgW/tLQ4HJz6CpFvmz0/Cqcd1E3CjI9asjayYPyj1NAif7Vlhkj8qGkY8hs1V8oMcKwI9qGtwMDexPpmgCyX+UAgbvY0u/IwRmqgUx525Le9OWdox88Rz60AW9ikDhc0wQFHyo4+tRCdJF6YJqaFgeGbH1piFKyt93AA75py3CQjDu2fSoJphbks2D9KpfaIpW5pDNnzY7gjbKv0zUjSQKMNjjuKx0XecKo9jT2hnI6D8KBFqW4tD/q5Rn0qcOqxD97n9azFs3YgyoFPsasiJIwAzMBQBPLd7BkfpVuzvBMNpbB+mKomONseU2W9CKsRRmM7jwfpQ0I0mDMMD880wxsuMOc+9RLeANtAP4VOJUY5zz6VnZoBxZ1AGM+9J8/VeaeH3j5TTgT3GKkoaMnhxinAKDx1prSFR0J+lRsygbwDmiwEsihhg5/CoUj8snuD604Tgr15prszryOnpTVxDz5ajkKKrsm8ndIpX2oclkIYHFVBAIW3LkqfXtVpCLUUagEHBGeKkDkNtIwvY5qGIkNuGdtWdokHQfShgKIUY5bn8aHgjxwAPwpFg44JHtTlRg3LcVFx2IEghVtzDBqfGB8pGKk2n6/WgRjuBRcLFYYJIIpRboedoNWPKX0pfLGMUcwcpXNorjkCqcujqSWVvwxWgqur8Hj0NTCjmaHymRHYhSM4X61bFqNuAfxq2yK3UA0oGBxRzsOUoywHbjBNRxIobkMD71pYpCgPUUc4cpV2rHyATSgIwJZRVjywKYYjnjFHMLlZCIF6g8VJvSPCkgH3qYDimuiHllBx6ilcfKAYcUpORxTD5YHYU0OCcKc0WAlHPUUhSlG6nDNIdrkewYxTPKXfzU+KCKLhyjFQKeKeDTHTJyGINKM4/rQGw+ioiGXoSfrTVmOfmGKLD5ifFJimeZnoM04E+lILi4oxS0UDExRilooA+Yxhuc4FJuK+hzQFwMelNCv6Zx6VxGgoYHnvS9Tz1oWKRjwvSp47Zz1wBRoSVxg/hSBN2cKTV9LQL1GanSFV6LRdBYzIrd2zwcelWEsSwGTitDgNwOlKc8YFK9xlMWaqeamESDHy81MVJ5NNC45HXtQMBjHA4pQSxAo5HbFJuANADyOeTTtoyPSkyvX1pPM+bAGKTCxIqrzSnCnp9KhMnYdfSk831NKwE24Cm7yelRmRe3NLnIBFOwx5JpjYJx0o3Z+tNyxPSgA2YGKeAE59aQA4NPUcYoAaWB4xTd5APFTYxjgGoyVycilcBA+aerDHPSo8+lLgUAPL4Gaj8zgHsaCcHGDSdR04qhEgdTxyaA+CfemAe/Wl347A5pDQ8kkdMU1gxGKd5wxRuzyKAGn0JoEZOOaQAkn0pSSOO1ILDhHgdetKEAz0FRmTaQM80bjmi4DypJ9KQ5XODmozK2eacZBtwaADeQO9Rs4zkg04sMdM00kE9KdxDGbNNJPenHjnNJlvwouMA20jNPyp5xUTLxzULuy5ovcRY+UsTTGYdhxUCyHjrTicj71AEglApxJk7cVEAqjlqbJcLGMUAZerRAAnIx6Vx9y3zsAe9dJrF1vyB09K5CcnzD1reDIY4Ixp+zI96q+Y46GnebKe4rS4WJ9tOCenNVDNKO4ppnl7Nii4rGlsGMmmNJEoGSKzmd26uT+NJ1GaAsXJb8D5Y1/Oqjyyy8MxpmCTk1IBQMYF7d6MU+mk0XAQnAoH60dBSdaADpRnFLjmlOKAGjG6lGaMcUtACqaerdfWoec0/cRQBYXjnNSBhVTd6Uu89e9AFzfjvSiUjHNUyxznNAY/nTuFjSWfIqdH3YHespXI5q5A/AyaYF0xeawXbwe9NmsfLUk8GiO7lhPyIGHvWjDDcahFlkCrTauhXMLZijy8nNaVxZCBsbsmoTEABjrWTVirlExZB4wKQQ57VoeWAeRS+ScDigDO8jHFHkHtWj5HPIpBGM9KaAoiHBpREeavGIUeXjgCmIrLEVOMVIsfYCrCx5PNOCEniqsBCI+amRMHOKkWMDHrUoQnp09aaAYiYzn8alVAORQBjvmnjpTEAU04AdDR+NAzu4oAUxA9+lKFHbkilIOKcq5XA4NFgAAheOKVVzzmlC+pp+4dqQxpUDB604bV/hpQe5FSKc9qQDQAetO3AfKBSHqaeBkdKAEw3UU9RzSAAjA7UHjFMCdWxxSh3piEYGacWGetIB24nr3pwPNRhvU0/rjBoAk3DoaA2DUeG7ilG4nnpQIlD5PAp6EjOOKYoHQcGpVXIzmkMGZ8dBSBfU804qT3pp4PzHNIBeKUSKeC2BUTMG4C1Ii5H3cUAKFGcA5p6hs44FR/Pk4xinrIFAycmgZKCQcdaU4zz3pm7oRxmk79c+9MRLkBeO9JkA0wkAUn4UCJNzY4NB+bGTio95HalLZ45pgS7Py9aXgGoxjH3jTtwHvQBIqjtxTgoHOaiD/L9aXkjGKAHs4Iwp6UoCnjvVcll4wadnbyfyoAscjpgVJGNw75qqJ2UZAH41NHdAcsuKaYWJNjbs9RSvn+8aaLuMjjinJKr5yPxqrgXLVQVyxORUzqo5I3AVSEwT5VanfbBtwTmi4BMdxyg2gVGFZ+Cc4p+9JehJ9hSokjN/CoHrQgBHjiI80HFMeaNn/dZK0y4hy/zsWPbFQmzkQh/NA9BTAlaXBI24z607K7R83JoWMSR/MxJoS2JbkHilZgTpaSlQyDd7Vtx2ii3DOmB1xWbDKUwqggjv61cW5lI2kgr+tUhF2IxFMZOPQml+6vyYUepNUMyznMKAAdTmlAlLDzU3heoNO4WNi2u3hjOJlAqKLUWluCrH5fU1SEMVwygZUj+EVYz5WEKqT7U7iND7VZxngMW75FU7qR3OVi47EVAVfzAS2R/dqy9yiKAykmgCpbtKsmCMk06fk/vW20551Cl8fjUCywli7sT7GgCaP7P/Cxz6mlUhpgFm59AKjiAnJMQGPSrlsrIxUxKT7UxMsySpIgUsqkd6je2kSPeZ8gngE4qpdJ8xLDaPQGqyoZjgFmx0BqgJ2lnDHDsuP7ppXvBcDY8zBh13c02CDYx80lQaJGtYydmWPqRUhcaYUZcebgevrSLDhwIdz49ahz5jfKGq4twY0CyLzQNFhMI4DnFSvtPCnJrNki8xxI0jj0Wr8EhKbGUEjvSGN+zBcEjJNT+UAuSBULzuh+YZHtTZZ/Nj+TIPrTFYe0sanDAH60yRbd+PLR/TiqKIvmbpCSB71YE1uP9VG2R1yaLATG0t5BgxohI7Dmohp9vE/ywozH+Ijmoow8kpKnGDxmrweQgBlH1FFhEUUDB+YgCfSrSwlVzjH4UzzCoGODQ87hchi1AxhlkkfA+6PWrUZmVMK4H4VHbSNKnIHB9KsqrjJdRt9qYiCK2uLmQ/MMDuasCAAFXkZwOu2kaNuTG+Ce1QLbXMbH5jg0DLkot1QCFnB96hVsHGWNOh8sI3mdaIEQsWUHb70ARyXGcgcEdqbEzt0YA1eVbQqxCnd/eqsYsMWSNj70CJEkABDJ83rULxq2eDTtxY4KFSO5pqkyMRkDHegaIXjjjHB5qIhWXOC34VcMIzh8MewzTkjjPDcH2oBlFLNmXcpxUxhlKD95096tH5V2ryPWoCjbTht2aBEAaWI5ZWPpV2Niyhi5B9AKhSSRv3ewZ+tPdvs4BwWY9hQJg00iPwC/pmpZJd0YODnuKq7jkO24ZqdDH1LHHrQMfHJA3TOarXTFpAIXAYd6bdSMjZQLt9R1qkZgG3EkNRcC00dxnLSbh3pFMmPlUH3qBruToJAQamhkjxuPDe9AEiyyvgJhfxqwgulYcZz1OarRITKJFcEg/dNaIujtwSMn2piGKkg5f+dPm8xlG3Bx6mlEEsoDKfrUwBUBfL5oAigndGA2jI6mtAXIdhn5jVcqdhBiA96hQqh4bJ9KANMKjMDtC1Oq5YADA9ay1eVugYD1HNPf7RtyJSR6dKGhGtjaOCKjcOMszYHpWWguolLQyDd3381ctJ5WB+0FWPtUcrQXGXF2I1wWINRR3yFN0hCr7nrVmaNJCR5QIPeqF1b2xUR7AGqlYZcGoQLHkFSPrTW1myC/MwU+9Zoh2rt2hjjjimxaO18G8xGVaTSGX11C1lkBW5QZP3QetaGFkAJbArPtNCtYxzD8w4BPWl/sOSORngu3BPZugpNomxoPLFGMbsVEGSR8q3T3qomm3qtmSdZKtx2jRrkqC2KNBlhZCvHGKkDZGSKo/Z5HO4kr7U5d8Z+9mk0guy8rBulOqusoUcgiplYMMgg1LVikxelOpoZScA806pKE4zRilooCwUUUUhhRRRQAUUmaXNADWBJ4OKpXIuQDsYEemKv0mKpOwmjGBugNph+tTQHy2xtI9ea09tRGEbsir57kOLFQ8d6lFMVdo606oZSFoqMqwPDcU8dKQxGBIwKZ9wcn86lqOSJJBhhkU0xNEZm54AoZtq5Kk05LeOM/KuKV1JHFO6JaYxZR6YqTzFxwQagWGTPzNx7Uht9vIds07IWpN53tS+atQksFyBz61Gu92IKkH1o5UHMy5uA70oYHoagSJgfm5p5iHVeDSdhps+d/IUEjGakWJFHA61LjPajyyOteabjUQdulPwAfpTgvAHSkHWgAAyeKXqOlKWwcAcUoYA5NPoAgGFBoGTz0p24HPFJgkcUkwFYgLURPFSGPjrQExTYEfzE9RilZAe9PUA9sGhgo9qQFcjA4JpT2FSEqQR0zTNp3YBqhgRkZphDGn8jrS7geh/SgQ0Jgjg04Jt/pSg5zng07GBkmkA3ad1ICwzipMcDjJoOR14NIBnzY45FOG489KVfQ5p2QKTHYZgnFATrkU8HB6ZoJJouFhm3jpz6UEj6GkPck0hdRwPzppiEY9qbvHIpWJP41H5fencdh+/qaFbLUmOBTsbcZouFhcDnI5zTlDH0xTd4o8xB0BzSAk3YOKYcnOKFIPU4NAxjOaQxuwdTzSEnPoKerAn3FJIwIxihMkjZvSmlsmm5Ge9AI61QDwSSOtOIBJz3qMzhTweab53vSGSkKpHJNPMgwCMZ9Kq+cvIpjTjB7AUgJZZGJPAxVd3JHNRyTE9DkVC0pz0NVYCUEHrzTXl29/wqLLEdcCosrnnJp2Fcka6DdASagkmkbkDGe1OZ1A4qPzlB5FAMyNRWTktmuekPzHNdLqUm5SR0rmZm+c1rEgYTg0hYkcUm7NIx44rQABNNJFIWNBI9KBjcnOKcpGeaTaTzSHIFMRLuAoJqMHmn9cUAIenFJnincUH0oAbgYpfwoxSmgBO1HtR25paAuJ2xS45NLSZoAXHFJil3UE0hhik6HmjPFKBnFMQZozTwoA5pQAOgpAIAeKnhV1/iwKjySemKeMcZammBrWzxIwLksewrftrtpUA2iOP0rk4pApGBzWxZTKmGlYsR/CK0ixMvXkat9yM+5rM8sqSOta090Jox/CKodzg1MhIYq+tPCgL60oXnNKcKMVmMZt55FHlgHpUmOPSl2gkc1SQyExAHpSiMk9MipSvTHSnhcAYpiIwg9KPLHUCptuOaTvTAbtUDgc0jHauMVJ0Bx3qMk7sUwGjPcUuScDFOJyKTjGB1ouAqKT7U8Dmmhwo9SKcH46YouA8E04DHNRbiKXJPJouBIc56UoXimqCwzUgXI5pAIAfUYqTaSODihIwCSTwKcTkYFADAkgPB4qVS3QimCTkCpByaBjgyjv+FA2saaSQM7c1E02CPlIFAFggdjzTgMj3qqlwCeQc1Osqs1AEq1ICKiyD3FKGxSAmB3VIgAycVApOMinB36jFAIn3L2HNCuPXFQh2H3qQtkmkBaDrnAPNJgE/eqBCoHPWgsCTimBMVx90ikZ5B/FUYHTBNLz13cUASrITweaAwOeKYCBzRgk9qBk284pPMA7VGoJNKyE45pCJtwbnpSr81QgEEAmpBkEc0DsOO5cdCKUgk5FHXqaem0L15p3Aj2MakVOmaA2G6Gl8wEkFTQSSKop/lgEYFRg8ZFOUtjrTAcQqnNNYRtkEYJ700qST81JjI5PNIdiNo1UkA5qL5v4mwKnYbW7moyof+GmAisq08TjoM5pDbEc/pSCIsR1A+lICXzmHoaeDvXkc0scXl5J5FOTyZGxuKmmImt4wq5LgU9mUAnuKGt1CblkyfSqUm7uDTAvRSK3LnGKlJ7oQV9xVGMxyDA3ZFWo7ecgYIUe5ppgXbeMM3PT2FFypRxjiqZM8AALZ+lALynLyFRVXAc6Tv8AcyfpViFZ4V2ygexzT4nCLy+AKbcSF/mHQdKBEqyGFeJVxUovwI9v3j61ngeaNxOMVPFAApZZM/hQBesgJdx3+WwPQ96kuElU4iYdevrWdDclHIX52960Y7mcgB4B/vVVxFOZ7pSDkfWp0u5BGMcnvkU6aTy2LNt59aqEmbKxIT/tdqAuXWVXg81mUE9ADUduqyZWQgLVdbQxqSNzHvTDGxOV3D1pjNPYID+6G0HqR3p/2wQjBYAnuDVFAzEZyygetR3Cxn7j4b060AXWuVEm6Q7wamkurQRKY2ZW9hXPlZm6PnHY0+OSRflMZkOeoPSldhZGylx55wRn3qRrSN+WYg1VgVUQNht/tUyNI4z8zZ7YqibCRxETBUbvV1bHc26RjjsKiVJVOQrg/SrBZyADk+1IYS26ogYCkUZGVBH1qVA24Z/KrDIpTIwc+lAymkhc7dpxQ0Y3Hj8qmWIqTkYHpQAegHB9TQBB5aJycY7iqzTo0pWOPHvitJIdrcjOe9NMMXmbuPfFMRn7RnLDFSKZsbxwB0HWrT26g7kP1qRLQkFgQfamSVluEkwGPzHqKtvAohBXH51VSCOK4yWAz2IqxKV2EkhR2waB3JLWNSMAhTVhYGVwSRtHvVCxKhiQGY/7VaBzKpBGD2xQIsQvA5ZlKkr1AqC5uvm2hRg+9Nito41JCvv9QaAkbEgrlh6imUIfLZcEYanIqRoc8E0sMqJkFBn3qB2R2PzEH0pBcaEZiwUA/jUkAkX5Xcj2BqAF45Bubb71LJG+NwkyPagSJZQpHzSDHvUKNgkJhqrzSpwGZ8fSpIFRz8jZxQMcyMzgsvHfFE0eFHljafrUjAAgZY1EwzJ8rDPpQBWZHxjzDnvxSxPIgOBupLmaa2PIDZ9KZHqSqnMWWNAA80nU/Lk9BThdNEuHYke9VpJoLhtxDhh+VQtFu5LYXtzmgRoi6hlB3t+VQTXBRCYjwPWqDKAcE8diKkWWOBNsiyuD0AGaBjTqkg6sKikvZ3HRMH0WpTDBcnpsPpjFKLNo22jLpSFchgbEg39D3Fae8ykBYwcVAlq+TsAHtjpTXtLlJA/JyeQKaA0Y3RFw6lWHQg0I8bSgA8k9SahSFmUHaR75zTmRekjY+lMRrKHjTiYY9Aai8veTvdvY7s1BHBlBs5U981pRRBFGME+hpiKeZIxwzEe5qaGTcckAfWrDop+8p/Co1Pz/ACxnHoRQMseeVTgjHtTRdggBRuPeq81vKwPyqufeqiKlrJ+9Lc+hoEbP2sAAED6VLHLFuyazEaKUbkkwfelR4hLtZtze1IZtCUNjyzxUqhWPzKCfpWfAETkA/hU4IwShP0qWhFvyIyScdaeiBM4NVIp2J2uCv1NXFAxxWbKQpbBxjNLye1GKdikVYjwx9RikL4O0cmpeKYVDZ9fWgLCFgF+bikKK4zUBs+f9Y5/Gq00F6GAgYEepNNCZaZdxK7hj2pBiBdoxk1BBBeJ94rnuauJGQcuAx9abYrDPNZIywhYt6VJFP5nBVlPoaXjdwT9KcTtYcdakpD6KTcB1oBB6UrDFoopaQxKKWigY0jNJtx3p2KKdybCCgnnpS0UXCwmaKMUUAFIQe1LRTEJzQTSilpDCiiikMKKTBz1paAEIpmzB6k1JQRmmmJoZsGc0uBRtOOtOHFO4rCYpaKKRVj5/Ucc0uaQknoKbkhgPWuCxYnck0pHHPNDZIowwGKYgIJGRQoY8Ac08ZAGTSZHryKm4xQ3HNKSMcVGTuPXGKYSRx1pXAl3cetNLE+1NzxwKTcT17VQDgxGaQZbrS5AHvTSx4wcUgHFeaavANLxjk4oG3JoAOTRs7inB1x0oyBSuAqqqjkZxTsqenWmk5HFJ0PSgdh+SDgdaQrkZPWmg5J96CwC9f/rUILiEYOc0vX61Ez4PvTdzZpjJ9w7mmNLngA0zDN1PNKBjqKkkQ7jzQAPpQTz8o5pD0NMaJflPvilwqjmoM470pkUHkc0WGSMy8AdaaTnqRTGcZppI7UCHZGeTRuGajI5xkUoIHvRqBJnjgUm/bTC7Nx0FGAMc80XHcesmT70jHHOTio94XmmNcADgigkUlu1NJYDpUXmk00zHGM0wHu5XrgVGXUD71Rs+Qc8/Woy2MAU0gJGfg/pUW4nJLcelNIYmlRkBxjmmA/PoeKYRxTtwU47UwsfakAjBgMDP0qMk854pWmyeD+VQOWYZ5xRcBZH5xURYk9aTDZxjNJjaemKYEF1HvjNc3dxEPXTSAkH37VnTQhs/Liqi7CZzxGKTnPWr81pgmqbxbOK3TRIzoKTIpXzTCjdaoCQMKUqDUSgipU7ZpANKEHNISRU4FNZMjApXAiDU4GkaI9KaQVpjJM5o61Fk0ZPSmIk3dqA3WmDg80Z9KQEmaM5JpnNO6d6ADNGTilAJ6CpBH60hjUGc08KAPelHy0EknigQn1oBNJ096TPvikFx3OeTxTwcHpmod/bFKHyaYy4jVbgmCcd/WsxWxVlGGKpCNWOQt1PFTjjk1Rt2BHJrQTBXOc0xEkYDDrTigHAwaRVOcjpUwxxgUhkapkc03Z6etWCOB6U3ac0CEWMAdeaXaPSgBiQM4p+wjHOaBjcL3pjAcmpCg79aaU+hpiGD5hzk1GV+bpmrAHP1pNvPSkBEyfL0pFQk1N5fqaeFwtMCuYsnk1IEx06VJt9hTtmaBEQXNPA49qftHXHSgR5pDEUgHGcilyD06+lKsbZORShMNxTCwqjIpy4+lC8A5pNu76UgDauRyKTJH3TS7MHFKEweaB2EDEdeakBUil2cU9YwOtAiNUjPapFjTrilCg9hT1BHFMYzy1FL5a5zk/SpDQMA/WkIZgqMg4py7qXI7dKfk4oGL97qKAozzxSA/LzR15HSgB4RSacYUxTQ2wDA5p27P3hSAQR/N7UFDyB0p4II4pN9MCIx7lxuINCAp/FmpCOPrTVTkk0AOEg2+9KJOMY5oQovbmnKqOSeaAEHzEYpxTAzk0pwnIoDZyfyoC4igj1xUgb1FM+bFNMhJxihAWwwI4p4IAA4qirHOM4qQFgOtMRbJJHYUowf4hmqhdj3yKBxyKALLN2604RDbknGarbu/NSpIH+UsRTAkConXn60u9d3GKjbywcFqZtQnJJHvRYC0FDkEHpT/mIORxVRTg/LmnFpX5GQPrSAl+QNyTSMiHotIsRkXOcVOloNuc/jmqsIYkJQ72YkUx5JmkAABX6VeVQgC9atIERCSoNFgM9WUphkANOW5KAKSTzVp44plyeDUIgQ/dXd9KLBcUyI/Kkg+9RMwbhiMUrxYJwCDUSI3OefrTGWVIKqof2rSj0tREJGl3Z5x6VQgI6GME/WtCG+KKynauB0IpoRG1tGcqo2570Jaxo2Fk69QTTDeRyZD5+opoMZ/wBXuB96oDQWKGKPOVyPwqNruZkKwRBm9QeBUMcqkgSAA9PmNSu0anhj/uqKLgMRd5xeuzseyjgVopbRRJlQcelU4LhI5QXB8v1xzTbjVRO+233rGO+KoVi/kZw5xGO1JMyPFtgyF9Mdaznu32bWUfUmpbaR26t8o7DtRoIkSPaDtRvekFnC3zHj1q5b3KtJs2Z9zTZo2MoAUc+hpgjPe1i8zMWffd0p8bPCxVUAQ9SF4q+9rjAJA+tN2hh5R4HsamwyNJFU9Bg9wasQkFjsJ+tZstrKk2LdyfbNaVlFecCVox9KAuXVjcL80uR6ZpjukZ4GTU4dAuCvI71CHR3IOM9gaAIhukb5gCPanFdvQFfxpzObfJCAg+lCTCdsMv50DuRl3LDkt7UryOMLtx71Z8ogZG0Y7VVLtI5wMAUxFqNTMhUuFpvkEPhZAxFVijddxFJl4kLrnI7ZoAsyK/RuPpSrKIkwn3j3NV4JXlGZs5PYHirDRIRn096YmKlsoHnM5d/U1DJIkr7XQYB6kVKkwjGN2aV5E4LDOfagEiSDC8InHrUglmXOBHiq5KMMqcD60lvMjblj6jqTQBYSco+Tk57CrCzxucKvzehFZ+3D7vMYHvUqTYYNljjuaBk0sBzuK49qqOjRvlaszPJLjG7PsarPbT7gwky3oaAHyFinzfNVQXUhkEW1l/CnzOFXM2Sw/u1HFcQshIfb9aQy8ACuGVWP0qAxyAkomB7UxBLKcQspHqDVxYpQmGzuP5UxNmfJ50f3WP51GrSDLMpzWgYp4n3GJG9+tKfMfGEH0xQSZEjuzb5NxX0NNW4USqMcHtitwQPJ8kiLx0qqdOJkJEY+XuKA1FVC8ZHlqBjuKhWxX7xBP0qV3deHYgjoKls/Odsyuip25oGU2Fsjjej4B5x3q280cqj7Om1R61ZmhicZXy2A65qmJ44W+ZAT2AGKYhftqMds0KN+FV5ryLzCFART3xirscK3Tb+Ex2xU728LKVaPGO5FIDPguiD+7BI/vYq6kUzfMeQaksTGXMeMj1HSrMl4bNwvlxEHuaYWK0dm0hOGA9gKa2nkPh0LVrW8glj3KignuDS7BC2+SZyT2xmi4jOWIxgKV2+nFTfZXYhw3P5VckkjVg4zj3FNF5E77QyfjQOxVMgA2Ijb+5xU6EKOUYt3qeMoWOWA9MdKfGmWJ3bh2NAalJgsnBV81BNAijLqMHsav3DyIcooYioINTmdis9lkA4BXmhsCmLKI/OkWz6HrSx2r7tyqd1bIdWwW2qD2NSrCuCUwSe9LmsIoRTz/ceIgj0FWELnpEatxwleSRmnhFD5B5qXILESoGAJUg+4qZs7Pl60+jiobuWkV1kcHDAk1YFGKXFK5ViMqC+dv40rMEGTSsG/hIFGMjB5oAar7+xH1p4FJilFAhaMUUVJQm0CjFLSmncLDSoPUZpFRV6DFOpRRcLCUUtIaQBmlpBRQAtFJS0DCiiigBKMZpaKBDcUtLRQFhKKWigLCUUtFAxKKWkoEFFFFABS4pKKAFoopKBnz6XIzxxTMnIoYEjPOKVIz17GvPuUJuJ5HanF2A4FSeWFPNISMYyKLgRkkg5py8/X1p6qOlPVeOlJMCIqM4xUiqgOT1oYMeg4qI5zzxmnuBKV9KZtHJpV+7yacAGHXpSHYhbA55pNvQnpUxjBPpSiPjBPAptgQADPIpDjoalZQPwppxjjmhMBAQMgCgt3FKgyeRRs568ChisMYt270HdUmcLyKY3IAFTcYgH+1jFIRngHNJk8YHFNLEHincGgZcDmkAGetJvHJNIGHUUNiJAx/hpC57nim8/nUbA+vFCAkMoBpvmZ6cCmbR1FJgc89KegEgYZ6c0wk7qQkHB9KAOnelcBNxFLu6inYwelRuR7UXuAEjOTS7x0HNV2kx16Cmmc54qkhlkPzz0prPxnNV/M460wyKepoAmMmc84puVxio9wxgnmjPPWhoQ8nj0qJtx6VINtIx96AI8Y6ikBxzil3epzTSevIFO4AXXGAM1GTuOKGBxxSKdvOaTYCODnk8VGzgDA6+tPJLZJqJmAJ+XntQgAHB6Yo3nOKazkLkYpnmZGeBTAczEVE7nOcGndeM01/qPpTsBCzNk81Vck9zVpsc+9V3yOnSmhFKZc5PaqEqVqOuckVVkj4zirTEzPZeKYRjAq08fNRtHWiYiAj2pOh+lSbcimEY7U7jQ8NwKXdmoip+lAJBosDJCT1o3dsUwPxzShwRzRYBw2kH5aTah5xSZHrTfx4oAk2p6UvloScU3GehpMc8mgRKAgGKDsNR+2aUEZ5oCw7I7ClzimbiKOvegY7NBOKZkD600seT2oEKWPNJupue9ApjD6nJpy+tNLDsacuW6UASA8irUKHIJ6VFFDnqDWjHHhM4ppCHxrjAx+NWY2wcc4qv5ig8inGTAyPyqtBF9JM4AqYPz6VjC7ZTjbUcl3Mx+VsCpbGjfEoQHvThIx5Arn45blmwCa17bcgG5t2R+VIGXNpz9aUEYPFN8wYIoL5XgYpiFJJHA5o2sT05pd2FwaUNgdOaB2Arg8UgG480/rSDg0CFwBz6Un3ugpwXJx0oCHNFwGkdOacPlNKACTSsAKAF7UoIx/SmZxinE7h0oAdvUUEE89BQE4p5B28/lRcYhAAwDTQByR+VPwcUnTrQKwDBAzSj07Umcil3ErjFADxwODTgRxmoS+D7U4AnnNDGP3YHXijJK9ab1HNPTA5xQIcqEg5PNG3HXvT15Gc05Qcc80hjFUjmlzjNPGOeKTt0piG9Pxpy9KbnGcUZ44NAxecdwaUOQOeaQMc+1KTuGRigBwfHbilGD0PWohwKUNjOc0XAeeOtIH9qCcrTf0oGSBgegp4cAHFRBfQgUZ49xQIkDbyOKkXK9DxUAc9hUnJouBLnJwTTRjoMZ9abtbOeKVAWPA5pgP8vjqKYx2H1+lOKgcE0M6AetADBJuPPFT5AUcZNVsHrjipA+OBjFAiXkUjK2OBz601ZsEA9KnXL/dFAFcMwOStPEuTjFTBD0YDNO8hcdDmmFxsTA9c5PYVNFGScZwKZFGqNhgavJ5ajletCQFcRlG6k1NG/GCaSYbeQRzUIfAy3ftVXEWftGDjr70jTMw7n61GrRuuWG3FNbceU5AouMsoNy4Y5NSLFJ/Cdo+tVUm2/w81OspC5HPtTEDFlb5jk+tDEFcjGfamK0kuSyZFTCJMEsSDQBX3lOv3qmhcSMNzgVXkyW6HFRs4Xgdam4GjIVxhDuPsKWAbep5rMR5M5U1fhnZF+YA/SqTAmlgm2l1/D2pymbywxYMR6Uizh0zzj0zU8F9BHGyxxfOe5p6ARC4SP8A1gyT2oa4eYYCgL6jimuqznLYyfSoTA6naDn6UxFhow6jDMfbFTxskUeHdYye1VFeWDDlTgUx7h7ls7Av0FIZfgmjgl3l2kHXgU661VSw8ldpHqKzfnRSq5JbnHSq7RTs3zK2P9kZouFjUTUZZmw3z/jirsdyGXBISudEuw7RwasWjs0mCd3sDTTJNfzSHyr59xUiXcsbgkZFUJI3ODkrjsKu2rwKQZAXPuaYzSNz50QJjx+lPgjgxwrBj3NTxxQ3EIAUD6VG1vsbGTx70AO+zFmG1d3uakSEDoDkelSRL5OGDjPuanD5IyR+FNCZnyFkzx+dRxSByQcD8K0poEmwQwH1rNmtGSXhsgelAxkxCjg5Jqstxk+Xhw3c46U8M63GCpI+lWWiJbftC0gGKoiXLZY+1PEytkKPzprKT1pwjxxgCgLCKCzcr+VSNEwXhSfxpTEQc4/WmhwPlG5j7GgYgs5ZR87YX0FTQ232YEoPxNIrtgBWI+tOXPO5yT6U0JocGEoxuy3pQUlUY2g/SmJ5aEkkg+wpDdq5KLOgx780xCsZVIbzMH0NAuPnBeUKfaoDOOmd2e5pXhjK5LA+1K4El3LHcIU3kH1ArJkspyDhgyVcId3CIgAHelaB0zuP5GgChaW8tpIWhibJ67WrUtr25yRNCT6bjUMUxhbGRjuTU/2mOY4Rdx9apCLaXLnG7C+1KWG7IkKn6UQW+5cuR+NNuAh4SQDHWgZKtzubbvHuaQuSSqy+1Uvs8fLq+4+1VGS4ZiythR7UBc0Vhk87LDeKe8e59oUAd+azYrmYMVfLfjipFnG/BH5mgEaiSWirsUkOO2etSLIjjY8YI9WrNuGOxSAm09CpqkHZJg5Lkd8nNIZvtJBAyhQAfbmhruFl2SE81z9zqUYP7vcPU4quLmIMJBKzk/3jmi4jbDyLcYgO5farpSK4XY8ihj6msmHUJFh+Tyxn+IDmmLJsO9xvJ96Lgatus+nSDy54niz0JzitSG/a6yix845YVz721xdx/wCjRngZ4NMSS9tVMRbBHU9KAN6WJyCGlUkc4LCs0Wtzcy5iQhR1IaqywibkxmSQ87s1ftrxrEFVUA9wxzQAxRL5gVZmYjgg1oW4vgpEcasD3JxVZrvzm3MoXPoKtxXUoULCQSPUUMTGJDqCkmdcjP8AAasLHyDv2+oJq3HISoMrYPpRLCjKWGDSuwKs9ulwBkDcOhWoI7S7t5wyMPL9KuRCNiflA+hq0pOMZ6UmwIGadyApwO9TLuXGcZpj53jH51IIweTSYIVjkY5FIp28eYPxqQjjFVms1d9zMT7VBV2SNdwo4RpFDHoM1OGB6GqTaVas24p83rmrEcCxDCjge9Gg1clJqrJdtE2DbyEeqjNSsPNG35l96asLhuZCy+lCsDCG5SXkbl9m4qcHNRNCjDpTkVl4JyKHYESYzRiilqSgooooGFFFFABRRRQAUlLSUCCilo4oASiloxQAlFFFABTdwzjB/KnUUAFLSUtABRRRQMKSlooASilpMUCCjNFGKYgooozQM8A3DGAOe9NBPQcGn9OlRs+0ngV5xZJsLcZJpDDzwDxTVlbrUyyFh0pAMxjPpSpnNKAckmkDYoAf0zxxSFQabuOKYZCec0gHso4FJuxkUzcd2M0meetMBxc54ppcjqajLsDn0qMtuNOw7kpejBx1qMbh3pyjjJNKwDx0pwJ2gUzOO3FICTyAamzGSYz1akJA6YphOBz1qPzCSaYhzNz1qNmODzSbiRznFNNUkAEAjkU9enTkVGWA6mmmQjIBpMCbOTjP0qNuCTmojJgjnmkMm4UJCJjtAGTxUZkUKSRioW+bjPSkA7HrTsBIJuBjpQZMHIzUewDnP1oBXJpAP83cmM0xmJFKFUUhTnOcGgBjDPJph46U5hg8nmmNnGBVIBmTu65p20E5A5pgypPBoDnFMY/aSaCAO/1pozxyKaZdvvSuIkEnOOaYZKZ5oJ5HJpryKD70AOLflSZB6nmkEpPQZ/Cgnd1wKQEiFe5P0prlRTPMVc4yahaTfnAp2AVic9cCmdjyaN/yjLCkLYGc00AhUkZyQKaVAORml8zIwOKPmPPGKYEeCDTSQDzT2I/iP5VCwGMimAMy5wMmomOeOlP2lh6U1lwOetAER2nPNV3UE1ZK8Go2C46c1SEykycVA65PH5VedQB71XbiqTFYqFD9Kbt9amfIqNlzjiqQEZGelIVp5G0U0kgdKoRHszk00qRUwBzSetFwIOlLmn7aNgNO4hmSOaA+akMeTTfLxQMAxpA1Hl+9BjoAN9IX96PLzTvK4oAbu75pN3oKkERp/k5oAr5NOVC3+FW0t8irEduox0zTFcopbnPIq3HBtXOKtrBzzip1iHT0phciiQDG4YNXY0Ug+mKi2nHSnpJtHtQBWngO47c5qsYbhD0OK0i+88qM+tIxz0pWAowsx4Kc+tXoo14ygzQoINTBTnIbmnYLjwi5+6B9KlUYHXFRKeeetPz2oAlB5qTcMe9Vwwx9KPPAPSkBZ3ZxSr96q6zqe9TLKM4zzSGTqAaULzUXJ6UvmBccUCJxwOaXrUYk3DAxxTxQAoBz2xS4z060DCjnkU8YxkUDGhe5FPUDOaF55pQRnmmIGK5wOtJk5HNO6n60oUHk0hjQMnPal296eFGRTtuOaAGKgI6U7aR2yKf1NOAwKGxEflBvvLyKTYM4I4xUvLdeKTpycUARtH8vFJtxU+MjimbB3NFxiL6inYNOAXgCkYE96AF6DmjJPGKaCQMGk3HP9aYDgny9KYYznNTbu1NJyfSkIj28YzQFxwBUpUdKFAH0oGRAMRyKXZg1KxABApucimAZGMCmkgYJ5pRHnvUTxseAeM0gJdyt7U5SlViJM+tAZ+crRcC2WXsRSeaCOtZ8jzLxGBn3NMS4uejBPzouI0hI3JNNa8CmqZlnZcblpirIx3Eg0rlGpHP5oA2n6mn4X05qrbyso5GasmQn+GmgAnjgigIT16etHPcDFP3DGCKZI0hQP60+I7TkswoUqwHFTLC7DPAFFwE+0ICMdfU1KLjK8dTUJjUNljx6CplRCuFU/jTuA3MgOWG78ak/eOBhSKYTtfjrQXcfNuNUAjmZeCPxpm5933uake4yNpGTShFK7ttIBBMVwCAfrVoTmRduwAe1VAqPyw5prsM4zgCmkBoqgIyMUqEKxyfyqgjs4GFbAqYlwAVB+tUItmbsFIpQZCOFbNQxEtjduJz1qwHKH72DRYCNmkB6H8adH9nIBZSGpruJDgsd1NCSqvI+WiwCXDopxGDREjunWlQJIdoHze9Wlgfoh5xRYCBIipwG5PapkUq+WHFVXuvs0uwgFvSp0nMpB6A0wLLeW6AIMN60kAML/M2R3FOjt/MGUbHrSfZtrEFjTFYuzzWssewKxb0FZ5ieFt3lFR2yaeI2hOVIzT5XlliHmPxjoaAKr3HPPX2NOiuZF+XecH+E1Wa3BfcW2jPOBUy/ZolzmR29TxSC5Y+zQEF5eKiaMRrvhIUeveokm3SAsm5B2JqxcTwsoCpt9s5oGMt7xhJtlfjPetRI7KYbhOAfasiNEY5IHHtVxGt9m3Zj0OaaEbljHJ5eI7jevtVuMNG7bjuJrGtrh7RcCVT6DPIqyLq5lUkEUwLsqspLEhh6VLDIpXIGKyxfzx8SAfUVOlxMUyACD60JhY1fNVxt4pjGJVIDjd6VnpK4zujzmhIiXMm5R7U2wsTxSp5n7wNgdwKlkmTogP41V3ZJ3Ag+1SIGc8jAFJMB21XPzPz7VXud5YCPJqxIsf8Ae59qr5YPheRQxjFkkA2lmzU0byKPlWl5KgtxSfOD8ppCHiTPAGD3zTWEpb5WFKJCPvDcaildiOFP0FMCVoSyfPId1U0gCElY8n+8aswO7LymDRL5ZPzvg+gpiIVhVfmdjn2NKJAz4BOR0zTXmRTgY/KlhaJZQzqp9KARca6ZIRuiAOOSoql9tV5AnlSNn06VJPetJ8iouwdcUJcKV242n+9RYdyYQqzKrBUB7sanNstou6OSJs+hrCvII926SRpO4yajtViL75VcKv3cHii4WNV55WbAcZ+tSiWXbhgu3ucVVjniLfJgg96nknjRMZBB7Zp3ESxXCrkBPxFNa72thACT61SN2I/9UcH0NTGZZsFhj1piFlLSfNsUse+aruMpiRgD2xT5YSqlhIMelUiSZA2OlTcZA1pOzEKx25yMGtG1hnSPyhJn1LCnLKVj3qyce/NENwWBkZyPwoESCyK/e6evWmtBGpyIwSOpxV61YS9WP41anT5MoMds1VgM7fcNGEWCNU7uBzSNaOgDoxY+4p81theXbJ/uninEXSQBTJ8noR0osO5Xe4u7Y/uJGjY9SvXFWoX8xczhsnqSetRM6ogLRkk980gvI4OrgL1waVhF9YFZf3bMvoBSC0dpNvkkn1LVDDqcgQmFA2emasw6nPJGM24Z++DT0AsfZZSm35QB6npU8QNuoDOM9ioqMG5ZQy7VPoatW4eRcz7SR6dKYDC9xKfmIMY9qni808Y+TtT5HZYzsVSPc1ntcTDuD+OKQWNNWwfuAH2FSJcHdtxz71DBcq0aguM9+aS4RVUsPmPpmlYRYlmypCqSaq/a5UO0c+1UfOk3YdyoP92ryQjZvV8mjlQySGdzJiQjHpV9GU9DWSYg7ZH3qtRg7QHYilKIXNDcPWjNZzM0b5jKn6mphPIwGQPfFRyFcxaLqDgkUyUnZ8jAH1NQSMGAAAJqm88qNs3cUKIuYfJd3cL4dVK+oFTR3TPyQRTYg5POGHvTpFwCSp/CqshXZYScE4JqYHNZcbiNi4zu9DVmO7LnG3mplEakXKWoxk9eDT6ho0TFpKWikMSiiigQtGKSigAooooAWikpaBiUUUUCG5bd2xS7hTWC4NV5LTzMFJpEYdCDTEWs0m5t2CvHrVeOKdExJKJT9MVKjknBQg07AS0UlJ0pWC4+im7h60uaLDuLRSUUhhRRS0AJQRmlpKBHz3JKxHFRNk/40/PqMn6UvbOK84sam7GMZqdC+cY/KmqQeOlO3jsaYDySBUeWzwORQZiBxTPMY9BSAUlxyx4prEU0sSetOA3HrSQCE9xSgkjHepFCgetBG0/LTuOxEAx49aGj20pkx3FRmUdKdxAVA9aAVHfiml+MVG5GcdvWgCYzKRSGQjoeKgGOnpRnAPPFAEzMepNRs4AGPWoS2TT8cdKVhgZD1x+FN3HHvStjpUZbBwppiH7gPfPrTCxbp0phUt1PTtRyOaB3GNkjJphlboBj3qVgW7U3C9GxmncQLk855p45x60wMF6UFzUgOYheKQMOuKAwwcdaaWAFOwD93PXFNL5PtTA+DTGlYcZwO9FhkpIzkVG7DIweKTeB3ph+Y8DAosAGTd0HFRMTgdc1MB+FMOzrnNMCEuwIppZh1waefmbOOKTbx9O1Aho780bRmpAmSOgoMYDfeGfakMYHK8UzcT1pzEAVEZOMYqrCHFtpxTGcc54oBJ47UMgxzQBGzAcAYpFDEZOadgClVqYDQQMcUrEHp0p2BxxUcgGeKAE2g5pp6cU8YbHNBTk80wIxkD5uKQkD3pzLx61Gc9aAI371Cxqf1Hao2GTTQis+fSoiverTDnk9KhIwT6VdhFZl46VGc1YKkk0zbkjtTArsvHNN21MRg461Gwp3ERkZ4o207GKQgkccUwsNKilwBS9DSBeaAAimke9O25p3lmmBFgml2npUyJgcinbMU7ARBM8Y5pwjOamC+1ShBiiwiFYeo705Y1A5HNWBHgdKeI/pTAjVRgYHNTRr7U9YgPrUqhBwaYCKOOlPC9xRkYIxRnA64xQIGwTwOlNA56UoHJ70o4bimA3GTSlCop2eQaXk0AKF+WnA471Hg5wDgUdOtICU8HHWkc4AxTCCcc0MQB1zQMQPjGTzT9u41H5fIIqdOB6n0oAQQHOc1KkYVsk0ik9xTjz9KQFlWXIxSn5h0qBP0qYAY60DHKgUcCpV7VEPY08HoBQIlHXnpT91Rj3pQe1AD8nNKFOaFXPNPx0oAApIODSgYNPAFG3FK4CAGpFB7igZA6c05QcjIpNjECscjGKVVPrSu4XjvTQdxHNIBDn8KQAkdKlIBIA70/O0dKYEYBC88UqqpB5qN2ySDRnA4pgOIAzihT14oT7vPenqFPGaAI8ZNSCMbfejG08HNIW7dqAF29KOM5NCqAOuaXcoPWhiF2gr1xTCNrd6BJk4owM9eRQMa3UYpwHFHU5zxSHA70wF/GkI+Xg0mD25oGe/FIGIetN2qx+tLsycn9KOFNMCEwRgk45+tJ5KjkJxU2ASCBRntRYCMKo7Upx6U/H0puDmiwiPzNnfFWY5xjPWoGiLDrTNjJxg4qRlwyb+hxSjJ68iq8bdjUwbIHamBMoz060o3qcH+dCnA4NK5yP61VhBuKjO4CgTyL05FNBB5I6U9W4OMCkA5bg5G8AUrSktk9Kj3jOCmaXcAPu0wJF2twOvapgqxqNzn6VVVyG9DUwdScupIpgK7ALlRxQBvHFOaaEIFC9u9IvK5QCgByXEifKOgqRZGkzl8e1VmZgcYpwgBYMZCP8AZpgW4mCMQCcfWpHJPK8D3qHCoo24BHvTDK546gU0ImQgHO4fhUvmg8b+Pc1niTLcCpSpPIGKYE24byM89sUrrMpB3t+BqG3QCXLsQvtWoAshURrxjqaAMZInefeQW56mr8TmM9ABU0trLGQQvH1pFhCfNJjntQAx7vkYLCp4rlcDPJ+tV3ZS+1APqKjS3n83G3j1JpiuaSyEuHTg9+9WRJG5BZSx+lQwRRxxcHL+xqQtuHLbfwqkIkleLaRsA/Cq62yOpIBY1PiHZhkZm7HNV2IQ5yUH1oYDHjaBSQinNNhSOQbnUA9hTpHPGZFK5qvMDn93lR6+tSUXUWBeQ2D6EVNGltIpEi8+oqghJj+ZwPekiKlsFmC+tAiSdUWTEW8j1NX7MzMufO+UDp3qvIcRBYzn606KYwgGXGKdwZfCQycvIQR6ikk28CNtwHemPOJ1yiiqxDxtuyaBXL8N/ERsbcSKnFxbg/KrZPrWchRjucL/ACNPEjOTtHA70FGosqsnC7mpixyOTlio9BVSFm7Pt9quRzMykeZj8KaEOS3Uv8zkEfrU3lCM8c1HGVAz5h/KmPGwO9XOPQ0xEiMJX2bsVM9qu3McnPeoEIMZYxgEd6ak5jDFBk+lA2QyROrgBiaekTS/KG2kelTQ3seSXiyO9SG9szyg2H0NFhFQQPC/zvkUoRQ5YjJ96rX8Ul04yCUHTmqj3vljydnzLxxTA1QqNyQpFMfYc/IMCsX7ZLj7pU+oppuJD958CpuBdkdkcAZx7VDNcOzYaQKtRRTycqqsR7iq0sMtw52xtx37U7jJ5LiTeCu0r3z3qcJ9oj4G36Hiq0UZRD5qgjtip/I3x/K+w+makNyMSSQtsVufSpACPmbBamQxyxOd21/QmlmuR904DD0pjEZ1/jBz7Gp4F+YNGzk/3aqQyjdlhyamdpBzHIE+nFBNyaWaZ22yDAHaohbyI/mAMwqJCc5Yknuc1opOnk7N+WosO9x8EcMh+ZcVaeyg2fuuv1qorBxx8uO5pr7mbiQAjuDRcC9amWMlc7gPUVaLTiI+YygdqyRfyWyAKrPj+LFZ1xPc3Mm97lh6LnAqrisjeZpDgrIG+nanxSSOxUsp965qyuriGcrHEZB3JPFbMV0ZARIux/UUXAfLE5nyHAx0Ipz28Vy4Ezhsd6h2tn5nJU+gpsU0STeXsfmmIfJYQKC8d20br/CGxVW3vr2ynygSYe561ZuNHS43SGVlJHAqo1kIQAGO/wBe1IZ0VnrjMAstt859G6Vf3xIPOcMO+B0rldPMVnM8lwsjsehU9K1Re3F2mYosRA4Oe9NCNyJluE3xbiPQ09rbB5TINY5naJFKgox7Kat21zc7wfPJ9mGaYhzWLedkK2z68U/bIikAkjtST3l2W4hDr7Niqkk8bSBWeSJs9KQyeI4J82HJJ60Tm9jjzAilPQnpUuHMatDPGwHUE804rfPHujjjI/3utAFa1nvBIPtIXHsK2sCVQASRWI8tyG2SQPz0IFXbGJkjLEvk+tDEWpLcqRtBxTCDHIMyfhisy71u/t7gRG2Vo/73OcVM8P8AasQkRmi980l5gaLOwYYU49ac0YkwRUNrFJawKjyFyONxq2Nxxgrz6GhsCpPdC1jBETMc4+WoP7Zi85Y3SQFvXtRq13NZwF0KkDk8VlL4hW4g2zRRnI645oGdKYxKAyMPXrUscW3BYc+1czBrUEAysDJzzg5ret9St5QhFxH83YtzUu4rF/PalXIz82aYXXsQT7Gmqg37lY575rOxaJ80mQaTqKBxwKVirjqKQZpaBhRRRSAKKKKAFooozQMKSlzSUCInDr91QRTY5CxwUK1PSYqrisIQaCOKdSEUrhYaQR0pDuJ4PFO28UuKdxWGEbuhoVSD1p+KWi4WEFLSE4pN1Kw7jqWmgilzSHcWikzSbh60Bc+fARyaZI46LTGPv0puRnOK86xYoJzz1pwUdzzQuM5NDOACAv50wBpABxxSbiTwcU1uQOOtNJIFIBckMacZcY7UxRnOafgAdOfekwDzOOB+NMMhXgUhZRmmF93SmhilyCQetMMhGBSckHikIz3qhDt5Pen7jnBxioMsOacMnHFJgSsRziog2T3xT8NzTCpBFIB2QPam+Zg5Oaa3TGcUADvTC4NJ6UgyTwaUheuKazDoo+tFgJD068VHnnaOgpnmdsUnmYoSAexBOBUbDBzTfMJHWkLA9TmmAuQOnNNJyeuKQld3HagDIzQA7J5wOajZmBGRTiSOM/lTMk8H86QCeYcZIxSbmJz2NKdwPTiggnoMmncBFfb/AA5H8qHlIHFBBJwRSFONuKGx3IWkfGM8GkV+cYzUhQL3BqLp0FNCJlYZ+lIz57c1EW54FHmE8CiwD2Yd6j3MvQcUHk5pSSV9qAGZJ5IpChPJ707p16UhYqenHrQA3BWkLEnpQWOeDmgZp2AawBJ54qN3IbjipiTjB61GUJ4IpiGg98nNKTu6mlVcE8c0BR1Pei4DQvTFOIIPWnhVGKY+efei4Ee454pjMQemaeMd6R+cHH5UICLJz2phOKf5eM8im7aoCNiAeajcg9uamZdoqByegpgMb6c1EQfSpCG9TQFPck0xEBWkZPSp2AC8GotpJzVIRGUwabjGKnKlaYVO7k0wIdvOQKUpnB6VYEeego8k96oRDsp4TvUojXvTxGCeOlFhkIUDtmnBc84qUR47U4Lg9KYhix5PSnhO2OKmVOMmnAE0wIgnepQBx60uM04LigBMjoBQVyOaeAoUetJjJOaAI8cdTTguTTto9cU5QO1AhFAAxQeKXGWoI54oAaDzmlOeuOlKRgcU4ZAp3CxHg9elOJHtT9u4DNNZfm6ZFK4DfvD0FHl5Ip/OcdKdyOlAwVfapB16c0xRznJNSqABuzQAu3nkdaVeOKU9j1NApAPHHWlB656UijgnNLjLCgBytmpl2ge4qHGBwMCnICOvNFgJic/1qQAf/XpiYI9xUqjI5pMByk4pygE80gHpT+O/FK4Cr14p4xmmY5GOlSAYAobHYd07UnI74pN5zTi6hcnikAbV6nrSqg69BVeS7iQ4UjPvVGfUWH8YA9qLjsahIB+U0gPOc1iDUHPc1btbl3wAMn3qeYLF9snjGaeqYH9KYC3fj2pRnrzV3EP6DpUZ3FuBxQSckZ6UofFIQoOPc0pUn60wPjPYUnm56UxkjLtHXimFcd6duLLnHFHLc4FAWGheRinEfjSgCkK88NQFxDn3xRuGTxThTZGCnJ6UBcXcDRkd6arq/PancUBcXJxgUYDA+9REnPWgNxyaoQ8ggYBpojO7NO3A8805TnPFADQjE0vlnPWpM4xQSD7CmAwAgU7YSPej02mk3kHg80AIyEUInY1IH3DmnRbB1oAaIyT6U/YVAG7JpzMDz0oRxzgZoEOADYBNONuM8HiomfJ9KVJNo60wJPKIHBqLy3J46U/ziT1FO34+82aEMgKlT0yalG7GWP4UpKswC9KeYQuAxNMCDbvbCgk1OIpFXGCPapo9qdsVI8iDjkmlYCp5bkkDOakSVozsfaM9zU6bGA+bBqObygPmHI9aYDdm5gQWP0qxFGG6kDHUGq0dwDwDgD0p0cyRscgH3poRM7RpJ8uD61KtwuB8m/8ACo42SduV/HFOmT7PHlHBz2FMBRJHK3ETKPQVeQFVBjGR71TtJgYyGUfWlM+xgqtgUkBPNOwU5Bz9apC4DcNnNOkdXbDsTj0pyxow3Yx7mqAftwmVFPti+77rMKas6fdAJx605rvy0wvFICd5FTOwNnvTo3md+Izj1Jqks5c7uT9eKmF2wGUxjvTuBca4BG1iAR6VXnw4681F57TODlRj2qZnAT1agCAQu2Gxj0zSs2CA/UUn2l0XGeP1ppeOXq/zUADlsjjINTmNwgbII9BUYYnrkge1XraKHyiSxGfWgCqkiFvmYqBVlbZJSHDZ9mqN9qk7QGHqe1PhucZCsrGmTY0oWiiUB8A9sCmzSR5yxPsBVeFtx3yMv0olfa24x7lNMEhjguchDiiKTyTksTn+GqsmoOrbRwPSiKcM5eToegApFGxHKsowsePUmnMyxYCk59arxS/J+7J57GpYw/mfOR7DNUiWXbYlxljkH2qR4mb7p4HY1XeQbgpOPZaUyYTauR9aBoJbl1wmML3AqwjQpASMlvQ1RWZImOz52PY06V2UiR1Kk9ADQDLEEyvlHiYHPHFJJb87lTI96khmlZQ2O3pzUi3ETfLI2CaYBA/mjYvyn0NVr22hiyWUAnnOKfJFGr71LKexplwZGXDSBs/rTEY1zeRq21csB6CqDvuO5c49607yExjf5QA9qzMGYkEbV9RUMdi3b3OFJfKr2p4vVU7T9w96zxC6DakpYDrmowkgfcULD1FIDXI3ITEQwqqWaM5Kk+1LAjMBgkEdqla3lIO1QSe+aAHPdZiDADP0qmGM7nfgGnzx3ETAS4x6CojcbTjYB70AWVtpNvysCPY1PEmFw4JI9qqRz45AOatxyiQgHIzVolk6xLkkr24FRsm8kZ2nPQVYVHK4DZ9qVYvJJcMAfemBCr+RGQ7Ej6c0CeJTjrn1p5mV2zJzjvio3g84gpjZ6mpaGODqW2q3y+lTtaRNtOf0pkdkoTMeCw9+lWN5jADHPrmgB7RxRRjai89RTJJ/KAxGAPrUF1PE0igydDzzT5lEyDEi7fWiwF+2mM6nKhU+lS+XDuADAk9wax4rnyVKM4YD0NXbZba4Vt6BSf4s0XDUvtYORuJ+U9OanhtEgUF03Z9RmiyKQqEByvYk1daRmXbuTn3poVmZstvbyFsrweMgYqt9ljgB8ub5e6g0ajd+RujRlJI5IrGR2ClopH688cUXKsb0TxCM/MvHcmmiclv3RJJ9Kykt1ul3M53Dv0qKLz4ZdsbH65pks6OK5CKAwbf6HpVt7qOVQpCbh/ernPOuFBIm+b6Uv2u5dAZUBPTIFMRvLb2s2UMeHPdDiq58NzRsZbbUJIv9liTVS1uPsuHZjlucVqxalDKufOTPcZ5pDJLeK9jiCySeaR/Fmpt79yV+hqpb6jFLMQ2No6c9atvcxSIQI8j2NMVjPvLIzksty6sfXkU6wtLi3GJDuHqveoZdQso38u5iliPYqa0bG+guUxaz7wOueopAzSTdJEAAPoafGoj4Kc+wpiCQLlSDTXvHgXMkbOB3UVDBDrm1ju0KuuPeuXuPDLpcF4XIHYY4rf8A7fsg21vMU+pXippL6JoC8cqD05oVx2OJu7GdCUC7selRQQOIyhVQR0J61tsrXFwyM+1mP3geKlhsBbEl2EvfOK0JK9hqFxbDa8e5h3zXQWOpLdr88bxt6N3rJBh35KAH1rQjtDIEmjnXjsRUyQJmuOelPFV4p14DMCfarANYtGkRaKbuA70ZzSsVcdkUUwnDADrThRYLi0UUjEgcDNIYtFMDEjOKcDn2p2FcWimknNNJYHjpRYLklFQmZV6nmnhgadhXHHNJuwcUtNLgdQfyoAfRSA+lLSGFFFFIYUhXIxS0UXCwwRgdzQNw6kEU84poAFMQgBB5ajbz1pskgVc4JA9KI50dcg49qYj55JCmmlwRjtQcHqeT6UgUHtXm6Go4PkcUuD1PU0Kp9KUqR3ouAnFKE4yehprZFG/IxmkAY298mkKMRnNKAc5px4XrQhlfyjyetLtwR7U4y44H50xiSR1piAso5xzTGkPpRgnqaac9BSAbu9eKeJMAE1Gy+tOAGOTz2NMB5kb8KYZGI600nPGTTTgDk0AG/wBaQt2FKFVuhp+0DnFAEZzjv7UZIwMVNt4qM4GRQBGxOcAcVG24+wqUk9O9N2M2Nop3AiCMelL5RHJP4VKsbhuTilYZPNTcCNFVecc0uSTwKGK4prP0IGKNwuIcYycmmbsAkDilLDGAfrSDHHHFNAOWRm7Cg564pC+w4AqGSZieAcUhj2Yk8UYPr9ajDtznr2oM3b8/emIH4qEg+tTH5hzTSFximgI9p28nijZnHb3pQOevBoIwMAk+1UAqqAeTmncHIqIE8gA0jFu9FguSfKp5pjsCvSkUbs07FOwERbHIpy5br09acFB/woYleMUNAMzgEYpVzxmm5IPNL1wc0WFckwHzjAx+tMIx0x0oPXilGCcE0WYXGYHYUjDceBUp74qM5HanYLkZQDtUbkDgCpySTyKaEoSFcr7c9BSbcD3q0VA4qIqA2AOaoCA9elRlTnpxVr8OaRozg8UAU2IHAHPrUDFicdBV8w8H9artBzk/lVWFcq7WBz2p4Q54zVlYjvwRUqxDHA5phcpCJixzxThAF61aZCe1N2bvamIiC44ApCnFSlQD1NLt5piINq5p6qO1SGPoRTlUDqapDIioX3pwUntipQgAzTsUAMRcnFLjbnApTgd6Q+namAbc0oGOKYAc55p2QOtIQp9AKOmD2pc5FKFB4zTuMZ1OeKfye1GwH6UcKcZoEN+mTS808AHgUFR60ANGWxSqg7ninouO1BBPegYmdg65o4K5NJsIPXOelOIwKQhhBJyOlKo6Zo3eh49KAyntTAeVOBzTwPl9aYBUiHGckUhiqv5U4dMUo6j0pcjJoAQA4welPCgYoGOh+tPA7CgYoGe9SBNueKUAYxTjge9IQi4B54zUi+lM4z9KUNjigCYHjGaUdetMDhuBxTipFJgSbhkcVJkkc96hXINSA5HPUUMYHheOc1BK7bOV/OrAzjA/OgkLjIzSGc7cLLJIcAj0qNLWVs7ua6B1jfnbzTVQDooFJq4rmZBp8rH7vHvWxaQC3HzY/AUoYEc04sp7mjlC5IX3ngBR6mm55wDmkXb9frSllxkU7AwJC0ZXHFRnBPPWnBRwc0CE6jpS7OenFLk5wKd04zQMAuMZpeucUmevGaQE464oBi9MfrSMewpoI6frRz2BNMQpPrSYUgZzTsqoyetNzknAoAGPHTFNGSfanEA9TzRg46jFADQoJ6fjT1jJ6c00FR3o8wL0JzTHYAhBpxye+KaJc4x1p6/Ny3Wi4hACTyaU8duadgrSM3fPNMBv507GMAU0N2zSsxLjIyPagB3OM0Nx3phbB+8RTgCV/qaYCg9OcipVkUHBGKjC4PAzQWHQ9aBkjhD0P4VGxIweabux0pmXLEjpTETB8HBqRSC3Wq/zED1qaPAALDHrRcCxvKjIxSecwIzmonkXbhc/Wm5JwCSadwLBmLnqBT1lC8nmo0SNVyTyfWmSPsPyn8aLiLJmxygpu4SN85FRRsXXcWpjHLcE5pXGWJPKTAH6UwyjHypn600RkDLGnRqhbLEgH0ouBMks4jGAF+lI0juPmb86nWRUXav8qgmV3ySMD1piFSWOP7xJPoKUSeZJuVMfU1VVGzlSOKUFw3IouBeHOdxwRTWc7hh8/Wq+88BTg980/a+PvYouBOJVXljzTWmMnKgYqsw5ywJ96kTcOhwKAHbi3Xdj6YqSM8EJkVIJGbhuRQ6fJkMVFMCTZJIo2jP0ojUE7H3Kfc0yMIwGJM/7ppHI3BkyT79aYEkgjj4G5vXFTxSWwAEcJL9yaqmeQDiMH1NWIEjI3biG64oAa8h34MZHualMojixuOPakkiaTlWBPpSlDsxKCMUwES4PlkYOD3IqJh5fz/d+lQzzOpKK3y0xZAV+YlvxoAvRXIl43c1eiUy/KX49MVjxOpkzyB6Crkd4sT4QkZ9aAJ7i1SHDKMn2pqMDztFSnzJlyWwp5zTVMcYAUbmoCxIbeWQBowee4qVba4C9SW/vVZtb9/LCtHGg96sM4dMqwPsKaEZaLJE+WZi3c1ZinkZsYUe5p5RXGQuD7GpEtQV3b2zVEjBbyglg6knpSG2myZGVmpD9ojz84A7Gpob5lQqXbPckUDRXmvrmJSkIOfpmkiubkRhnwXPotWo90zlu3qaRreQOT/D3xQGpELuSRcSuQe3FMzO+CDlR68VYaa3JxuXI65qqbs3EoijRSmcFhRcZJI7OirM2R6A1Xa2WYfutsa+5rUFlb4GRl/8Ae6VXvLRQNqA/gKQrmabYQr+8K/VTmq5uRA2Y1LL7ir/2aNEBmjYg9EBqM2zSyAeSYVPQMaAuVo7qSRiTEw9OKspbu67sOhPc9KsR2DSv5fnMQOwFWRYXCtte4+QfwgUWC5mzeYkJUEPx1xWJK7qxyxHtXUTQTj5F2IDxnrWTNpM6S7tqyDvnikxplGOcrzkk083jE9/wpZ0lHypGMeiioI4bpm/eWxQdjU3YzQt7+QkJHu981eJuX/iH1FYyF7cghWU+uKvwmeb5uQuPSrTJZo2sKISZsMfrVg71lzGqlPSslWm3YY8VfgaXbuc7U7ZqyS0Ytx3k4YfwikbfK2FXAHr3qLZlt3mjaewNWllUptUq2OoFJoopta2+8tKg3moLlvk2LtC+vSpbiORuY9yH1zms9vtcTESr5mO+KVwI88FSpai3XUBKJLZN2D0c8VZSeKQFDEVPcgVDI7Rn93MxH901LGbsLfalH2qLDD+FCcCkvNPhEDNG8hfHALVStb6d4tpdVHQEirVpbXdyzB51x1Vh0phcxBa3bEQiUqcZwTmtSKGZY1jdtrAdQKt7JY32tsYjgsBRK6sDvZY8dyetNCeo+3hZF2lQxNI0Xl5JG0+9Qf2gq8K6ORyBmq8120/JYYPYGquKxowoJkbDhsdcVBLGzKY45CjetYrTXMEmULBfUGrEN3clwUbzAOuRRcQ5re8icl5A4HTntUkGnXU7F4gm4dfmqKa9mDnfHiqxeUN5iyMG7bSake5tWqGOUFnG4dRWs15HGq7Hy3cAVyMeuTwfujbhz1DEc1NBqss8i+Yip9BTuI2dWtHurVp4FJkAzzXIJqGoW0/z5jKn+Hj867aCdwAWQlO5rL123sZjHJErb2+8PWhjIbfxrf7gjKjY6FeDW3a+NpZAVnsD5fTzENcrFYwRTArCxb3robWDz4Qj7AP7tSF0WI72wv2YW8jZPVX4qpJK1jKwfLRHsO1WbvQLSS2Bhi8uX+8hqtDDPbr5Rt5XPTLqatMQ/IlxJbMWHcdxTItT1GKQwKHC543LmrcaNv2AiCQ9M9K2INNYRqzzxlj6U7iMz7bqkahJrVJEPR9tQG+kVyXl2g9VrZvra8t4d8SrIg+8CccVh3M0Eo+eEIfXNFwNO2u0iXdbFjIex6VrQ6myRj7Q6qfWsOy0WSaNJortBGegByRVy802WK33EiYDqQaTsxms2p2yAOXUg+hp8GpWs0myOVc+h4rlUCvwEww96YVaJuSN5PXtS5EHMd18pIPendq5qzubuFAN/mg8c9q3YZmdAWxms3FoakTnOOKbvO4AqfrQXFNWXJwRgVNi7ktFJmqp1C2+1G28wCUDoeKLAWiM0Y4qjc6rb2kixsSzN2HOKuRSrKgZSMGhpoBTGpOSopegp2aSgBnmAnHendaQqDSigQAj1pcn8KTA9KMigY6jNVLxrkp/ohTeOz9KitZb93IuookH+wc0WDmNCg1CH2ty3HvT9wYcGiwXGSxu4G2Qrj0FSLwME5NLScE5oACKqyQYO4kA+oq3SEBhg0J2E1c+djkNkDNKH+UnHNODdz+NNJOeBXlI3HFiy8DFMO4/WkHJ5qVBkc1QiMocZOTUZG1unNWHl2qRVSR/m4600ArN160gfB5qPcTjJp2AT9aAHFufY0nIPA4o24XrmmucDFADhgYJpTtHPamCTjB60uSw5pNANIBpBGdlOAA6nAoEntQAx0xg1EU5zU+Cx+nem45Oe1O4EYO09OKep3H0pGZe/FMHqO9LcCfr3qIsP/r0xmPTFREseM00gJSyjpSmbHQdqaqEilEZA60Meg0yO/TijJzl80rKB3qMn3JpWEKxyaYY93Q0bqUgnp3qkgGBB1xUgAxwcCm7TkdaCCB7U7IBrYB9aZv9FFJIfSoyOM55oSFccfelEe4cUxQWOCPrT8lfu9KdguO27GORn0qFwSMA4qTc7til25yaAI0jJXJOBT9qqvAyaaxI6GlGWoAMAHpTcZBp+PXg0Y49qYiHBBxik5xjrU4XNPEQHOMU0xlcAnjFKwPSrHlYPIpGXA6U7iKjL3xxRjb2/Cp+DnPFMPXjrTAjxu6A0LH6damTPSniPaM4696AK4QryaMDvUxA+tRMDk44oENbBHTFNPHQVIBnlqeFFOwEHlg5NRnHGB061aOO55ppA6ADFICAIDg44pdobrUm3BoxVAQkAdVqNkJ7Va257U0r70AVgnTNPOB0FNcgNjmlGCOBTQDWXPQc0zbxyKmHpRtGeKsTIQlBXac1LgYo2560CISM+1KqAdqk280HG3rQAADHPNIyjFLx9KbuweKAGsvIx0pAvXHSpdu4fShuBgUwI9px1pNhLHHNPApQOfpQAxUIzSnJORTySOg4qM9eCaBgASDS4HpxScqM7uacBxk0AGMdqUcdqFGRmlyegoEPzxTScDgUgB3f404Y9aBjd5x057Uwkn86ezA9qjckdKAHIFJ681IFHHAqKMEnpVpUBGevrQBGyntgCnJGB1qby07nik2fXFAho5NKPTFKFPYcVIq+1IASMkZJp6YU+tBzkelOAAwcUDQ5VJ60/AB6UwP6jNGcnAPFIY/IJNB47U0Zz6Yp/vQSOUZHNSKSevAqAHb0p4YkZ7UrgS8+lO3Y6c0xXOMMtSA0XKI/M5wc0hdu9OIBPFN6dqBAGBpwwOTTOhqMs2cCmBPuz0HFIeKapb0pSMNz0p3GAzj2qReTz2oB7AUEYySaTYEny9qO3Apgx2yc1JnAxj8KkNBM/hQGHFN980q7e4pgLkY4pC2Rg07AxxQBwc0xDARxRnIPagoCc5oIwOlIaDaPWjOOhpoGMkmlHX2pgODcU3JPB5pxx2603IWgBjAE9CDShE7nmlJ703PHai4hwUg8cipeNvJ59qhVjmpNw7nmkMC3bmlCZ60Ar1J5pN2c4zVIAZSDQrbfSlzxyacI16luKYCBx2AzTgSepzQ2w9KZnBoEThsYx+VMIyclOtAbHPGKkxvXJbAoAiKH+E4poVs89amHAwM0gVifSmMbggcdaaWanuVXIJzSBQ3QcUCEDE1NDjJ3DNMWLBzTidp56e1ADmY59h7VFJycDvUxcMNqIafFEBywyaAIQsgXA496aCYzjdnPtVksmccZNIkCsc7hRYQQsx4IzU5KgAYyfSm8oQApwfSptuxdx6mmMrTFx3waqmSQnBJP41clY4B4NQAgNkKM0WARdyx5xinR7pGwx6d6V2LjH+RSxrjHy5osArDYwAPNTRlj1YDFIVy3b8am8lGGepoEJJLGFwyljUbMpUYOKcY4w2CGqRbVGORx9aYDFkbcOAxp7s0oAIOPSopEKH73PYU5BIPmPJouBMsHkpvSMbvrUDs7tjG0+1O85w3TFLuRuS+D6UXAYI3JwC2at7SkeZDz9MGoDJs+ZDzTDI0jHAYn1oQFxJ1C4C7XqN4pGO9i34mq32iSNhlQcdzU8l8JEGSFPtVAMZcHLYqPcu7sB7VGXWRskkihvLkwijbQBL54U/KAfpUsaFn3dM0sVsqIp3nd6VbjnSI/Mgb69qYE0LmJgHY7TxiryxRPzjFUFCXDbhhh7nFXY8RgZcADtmhAOMaAMDG5x6U+K4a2Uh4DtNON3tBwCfpQZQwxsyfU0ySzC8bsGSPr6mrL3CxJuYZ46CsnzpMFIyN3bHaollnBxIcn1NO4xtxqYN0pkRljB7VYmud8eYvu+mKqzRlhlip+oo3ypDwV20rjJ7V5Q+4/dHr0rQXUPMBhVR6Z6Cs2ylR4iATuY45q4bMJEf4mPvVEtgdNDDzGMfrgHNMRjG+2ONU+gpiyPB+7WQE/3cUrXJjYZjLH1BoAuosjMBwPcUtxvt1zDIzyf3T0rOjv7p5CsUUYHrk5qVbqWGTLpuI96AsWLPdM7G4Uq4PGRxSz2bSSB2lbaOmO1TJqkWwk27sR6HioBq8Jz8jj0FMLAN0bCO3PPcmpDeshEbJvY9lp0F9YSNtkzG59RVmK4tzKVi2j/aK8mgVhDGqJv8pg3uapXCSMhKt9cmtKUA9856VQuohANxOc9hRYdjLSUwSHcAw7jFWMGUhoSo46Gh3tgfvfhioI2T7SGUnbn6mlYRO8WUAkAL9qs27ARCNlIx6inFH6wr5h65Y4qxA4KFGXae5pDsUZLdXfK4Uepp62ckkf7tsj3q08CHgNuFOFzFB8quMelUKxkmJoM7wc1HHJ82FiJPfBq9fNJKC6KMY/Os+FLksQAVH0pgXokeU8BvpmrSwjOMc+hqnGbiJsKcH+dK148cw+dd3pRYRd2G3I+VSD1AFBsLS5O8oC3tSxXsXIlUZPpUltPvl2QryKTQFVtFiDZQlR6dqntYjbv5TZCnoVq/cOUixtAbHINZwuSDjapz6UIbNBY4CSJHCg9xzVLV/D/wBoiAgbIPWrcMkTIBsG6rX277Kg8wgR+nWnYVzkB4YntAXLFh6DnFQrYupLmN9o64ruopbe7iPkyKSR0FY97aPbqW3gKe2aXKO7OdgaN5NhRx/vVqh/siBoUVgfaq+2GJskb29qY0sjHYoCD60hFhrtbgAyIpwfSmSbSd0S8DsKgWEHk4yPSjzSGA2lfegaREFSSX5sA+4q3FAMgMBt9hTZrYuu7Balh+TGGKn0ahCZq20giYbOnoalkuI3fLbfyqi0+2LoD7io4pkm4YkEeneqAsiEy3G8KMeoHWrkLrby5ESt6giqaXCx8IXQHrk1fs0jlGSwbPQ5osIsRX0byjZCAe4B6VrqC6gEnHcVzhjFtebiQATya1Ga5jVXhkVkosMnvtI+2bdjqp9SKwru1udPuV23Qz6r0/KtGTV7iNh90jvQbm0uPvsmT1BoSYFe4e4vLcAylyB24zUMdjAyr58Bzn861I4oDgRZ29cDpVh3hAAbjFMRUN3ZWuyHZtB6bRxUpsVumEkdyAmOinNRXC2s+FEq7hS2kT27l0IKjqB3oYEyaR5ILIVk9iMVDtjVikkBA74Ga2UZ2QNEo57HioJFMsnKFXHftUJjsQwS20Q8pAQD3IqCaGWB/MSZ9hOcdaranHK2Ps8oEo9RxT9MvJjE0d5NCjKeCOM/nVCJGMlwMw3BST3p1i8lm7fb72Jwfu5NWoraIyCQlS3Yg1Xv7IzSBwikDvilo9Blq4dLuMfZ7naw53Ic1nXfh+HUlDPdSCb++vFQrbeQ4ljDA99talpcwOAhk2y/3T1oatsO5yE2iahpdz5jXTzRepGa0NLuLuGXzA52MeU6iuhub5IbhbeWMlXHDYyDSR6bEJBLCzRr/cHSi+moi7DOsqBun1qQtgcc1We2ZkKmVgPai2EiLsdi2O5FZ2Q7ss7+KBg9sUY4weaMZGKkorP9pjLFAJB2GcUkU0jDEsZRvSrGzHRyKVl3Lg81SYmQfInzMcj2qH7XHKCIyQR3qU26Kpw7fSqQhaF9wCsCeg61aSZLJ0uCeJMH61MGixnOB6VC6QuAWUg+1NZVwNoP1oshF9ZRgY6U4MrdDVSEFevPvVgbRjmoaKuSZ9aXI9aj3AH5sU1nAYY70rFJnz0CMc0pfJ47VXzgfzpCTn8K8xGxOWBOc0eYASN2Kr5J6UrJuHXFAExII5b6VGyckjmlVNuOc0/KjvigCDZjIGc08LjGacZQBwKYCTnPA9aQwaRQaaoDZ5owM0gXmqsIkIQdvxphG4nmmFgq9QTTfNOKVgJfL3AjNATJ61EJTn1oLnPBoAmZ1A/pUJcnkUvLduab069aQDR1yRzUnGciowfmoYO3A4p2AlKA8mmBRmoySgwSab52eKVmMn3hc8Uxn96byeSacMAcimIixkgmkHL4PSpGGelMb5R1qkA84zwKTheaapBPShivSi4rCtIO/NRFtx6Gngpj3p21c/WgZH5W/B7CpCgVccUrYHFR5Y49KYiPAB45pyjPtTguDx0oK4PA5oAGVai4xwae6HaMUwIeT2qkgDjilA4JA5FJtPBAyKeq9/WiwrjCpbNPWMAcmnr256UrEAYFAxnABwPxpcqTx2pG4GB3pAjE5Ix6U7BcczVG/PelwQetG0HNBNyPbyeaTZhc96k246jmnqO2BTQEY3Dp1pVUk5bJqZQAenNBxnmmBAy4z2pmwA9c5qVzzg1EMZzQA3jBFI2T9KUj5sYpdnPtTAjB4PFGRkU5kY/4UwjpSsA5sZpm4jtmkYEHjmhQx6UwuKrMecU2ViB0oJK/jUZy59KAuQFiTnpU0eDg5oMAJ5PFPUKg6U0FxCv8QFMKnNT4NKQfaqJK6xnOc/hTmO3A71I3AFRkfjQAzJJ56UdecdKkKDGaAvOBzTArsrZ6nFORTmptozz1pOBRcCMgAnmgtkU4qd3tQq0XGMAOOaCMDAqTtRjocUwI8Gl2E84p/QZFN57UAJtB64pCdvSjaS3NIQWOPSi4XF2n1oGSKcq4ODwaR325pXAQjI680zY2OTT1bd0FPoEV9jNxmpo7dudx4pW4PApVZgOelA0SxwjA9qmCbeKqF3Bx1HanrK/HykmkwZOQoIOaQnPC0kaM/wB+pxGqgUIERopUZPSpAcfjQ2TjAoww+tDAUnI6Uu4ngDilwQOcZpFGM9qLhcXnOKBgAUZ7gU7vjHFA7ioeuadnJ96ZuAOAOKUNmgQ4ADrT1X3+WowCaUZxzSsOxLuyMDpSBqaMg0pyORSSAUUHNCjOCacR8vWncVhB6EUu0A+tNB55pSSBRcdiQEDj0pdy1D9KcDQJkpbI6U3g0dRimk4PTFMB6jHNLnuTmmZJHFL2pAw+lJznPNGTxT1HY0XGhuTjrzQMnrSshXJBpRyBTAM7QcU0Ekc07PJB60nORxxTQAeO1P5xTSDgYpVJJ5pMBCuORTcnONtSMOKac0gE46U1l+lOAzSkDHrTEQ7vmxmnY3UBGJ6cVIEOeOKdhjB70/gcCj8KAcHpQIXIxj0o3nHrShM89BTl2rn+dAwSQKDkf/WoLBuc0jFD/wDWpEIHTp6UCE78DNPDMFwAaC+OgoZmK8UDEEj5wc4pTgg5JFALnr0pSBjoaYiNNoPTNThuMYxSRQktnFTmIbecUARqOOmTT/4fu00FkOBjFAlwfc0wGhmQ+g9qbIxAOCR71KoLt90ZpzR844zQBXTOR396kEuzoKc20d6cFDrjHNAD0umPLbRSPcJnqSageJl5IH0qPew/hzQBc81nXlaiD9sU2OWQYBU4qUAEcD5jTAZHuVskVIx3DIyKnWLzAFNSGEhQCox6ZoEUweR1x6mrKuVUYoK9ioB7UFSAeM/SmBYDnGcjFOaTjOAD7VUIZR2FEfmK2Rgk+tMAmU9ecmmpDJ1PP41O6O2CCCfQU8RlFycigCtJDL3P4UwIfxqwxYnhuKaSAOmaABcP8pJHsKJAUXC7qdFEclt2AakLcYYZ+lOwFdGGz5jke9RyhdueCe2KstbK/KjiojCsbFuD7UrARQxttyXxU0cJaUHG76VNDHDKhyefSnQ3AtJCpAZT0xQAs0LAj5uD6GliQDhgWJ75q0gNy28qFHvVe4cxy7VYFfYUwJIwyP8AKuB61oK0bp87qreprKWbY2NxGaUybjls4p3Ank85JSVnDL6DtU0d4+CpyfeqajzGOCRkU0h4277T3ouBqxqAC7MfwqzDvOSkYK+p61mW9w2CoBf8ala/kjwoDfQCmInngZpPmJU+1OS32OAF3j0NMF2yRB3GT246Usdy84OJgo78UWGTzSIf4FjxxhRimxSsc4kGfrUEtsZVwzMR2PSoY4vs7fvlzzwc0wsaYUMuSCD64qIuUBIGf97vUrXKCIKpyD/d5oiEXXBBPqKZJVNzz+6jVD3xVZDP524tg+hq9JEI2LE9akSFHwyJz3zQFylPc3aR7AVx6AU63mCKpljw3fjrV9rQSHLvtx3FVZIIyxRZCcdzTsK5K94J3G23UKvoKuWsAmcMw2g+lVILQI4+8Pc1rR5RgARimK42eAxj5Zm4rIluJXl2kGTnrmt24QuoA4U+lU00xVm3hiAT0oKMu4hXG512+2aZbx4IYOF+tbF5Y5UNuB9BiqEUU0ZbcoCjoaQFlJGLDnj271dRVPbNY5DM5ZpTj0HFSLeS4CR/Ivr1pWC5cuJERu/4VkTNJLPlAQg7mtMGSUbYAGPdjVWaAx8SNlj2FMTZZglRIgpOSOw5q3GoaPegIJ7GspEjjG9ZMH61It/NIfL/AIfyosIvSSmFSWjLD2qnFBHczGT7vt3pWvPLUp3/ADqAOryBzuVgcjjFMZpG0i28lsD2oitvLcNG+QfQ1WS7leTYq7x3YmnXFxPtG1gvso5oA0Tbu43M/OO9ZrFrKUtOmVJ4296eLmRI92Wz70kV+POV8BiOfmHFAMv2rvJEWSIqD03DFRCTbJ+/jO3PU9KtnVjJEAdrH0A6VG5a6GCMj0oEWYZLUjNqMHvjjmql5bsxLPKWz2NTxWjRoSFC++aYsRYFmO4DuaBoxIotl1hyAh9TUeppHGvmBnAHoKmumBlYBRuHQ1Vlkl2gy5kUcYFJj0M+LU2hk4jyM9TVm4uzcodqlfxqSEWV2TG42v8A3CMVK1rLb/6qAyZ6BRmpEZsUmpORHEZDj+7V2RJ1jC3P3vfitWweaJAxhMT9xijU2huIizHL+1OwFKzC42vnaPQ1sw20PkbsDnpXImGUMCjPjPc1p28shADtIMdCDxTTEzTmSANiZiE/2akF3bQoDFMAF4GDzWZ5csjbSzlW7mqV1p09sd8eWU9cUw0Ok/t2wlIhmILHu3SpriWUR4tBvyOgbNcesKqolZifVSKv2V75GZIEfI7GgZpKk8jDzfkB6gmrqQwLjLBvc1TGrxXUPmeV5co+8rH+VSpcW12PkfEgHI9KYjTjuhbHcPu1YM8V8oZXRWPG0nmsGRpV6qWUdwaqtI5O8gqR0oA0rvS5LaQzk8HvmpdP1IpIq7vM5wUrGknklQFro5BzjNJBLJDMssZDMDz70Aeh293HInAKY7GmvcSedtWLcp/izWHaXJu2BD/8Bq99rFvnzz5WO5qOUBL+5a2lU7MBu56VQur6CWPbJEpfsRUl94isPs+xwJ1bjjoKwxJbmTMci7T2B6VaBo1bKe1tjvlEnsByBWyl/aXibYpD/I1zCs6jCAkVpW9zAkQyg8zHBxSsBoywDyzy2PUVmTabKSJbdsOO5rUtLzzBtdSrD171LJESxYN+FO4jIgvrqGQxXEXnHPBzjFLe6leWrJLFA+0HkZzVwhFYhthb1zzUi2+9QVfg/wAJ5oGQ23ii3uIv9WUl6bSe9WrfULmU4eFAp6MGqGSwt413rAu/uQKotqUFtMIpo3X0IHAqeVBc2pluQpeOQDvg1nLeztOCL5Qc48sqCDVtLhZ4wVmG0jkdaw9TgFo32iJww646YNCj3Hc6OOaSVfm4I64pxkbHAwPWua0rxDK7+Tcrg9FYdK6lQTECMEmk1YRES0jDPBpqxEMTj86JY3UbhkMPxzVWPVRnY689OuKPQC4gO7DcelT7cjBAqsJwxBK59xVkMhTOaTuIYYj/AAvimFCvJY5ojuFlYiM5I65GKmG1wVbGaQFdpQRgg5p25mT0pzxD+A8ipFGQASDTuM+b1cHsaX5jTQ2O1LvJPHFeVsdA5ULHJOKkVQAeajVmGTS5OKkZLxt6/hUW4FvWlK7icnFNKc/exTQDgvy04hRTBkHFIQelADyQKiP1pHB4GaaMdqaAidS0maCuDgZz6VKrZzninrjP9aYEALKeVp6kMeRgU5mwaj3jHAxUgWOCvA/GomwSeKi80jimGU9cU7APbI5FNaUjp370m5jzjFLsyTzRYLkRYk8k09OOxqUKo6Clyo5oYEZY+lGffrQzZPFIFJ4x0osIXIbgDFKiEg55pn3enWl81mIxTAa2VJAOKYVJHLc1J1+8OabgE470wG+WBjrmnA+xzUihVPWmmRRnjJoACDgUHaOCKjMrHsKNrEHmiwXF3gDjpTlckcimlPTpQQQOtVYVyQNk04FdvSogxFOB4x60WAMjaajJA5AyDTzx0GaTbxyKSAjLHPH5VIrdiOaQ8HFOXp04qhCFio6U0uwGDmplAIyfWpNi5+79aLjIUG7AxUqxZPJ4qRYx2NIcDipuOwwwg4xk0qxhBzRk9jTTnNUgEPtUeOTmpD8o5PPeow64zTJEYcdKjYEDmntMDwKYcsRzTAbkn2FLnilI4oCMetADQe/TPWjaAop5jGeTkUEdMdqYmiAjC89KYGwcgYqfbkjOKCAvXFAhgG4cilO0dhUUjknjjFRorFskmgZMAp5FJKQvSngYGBTWHIzzTQiAyMe3FSA4GMU7twKACTycU7gNIz2ppGB0qfAI96btzQmBFweOSabux9alwAfek25pjGlQfrQV460pBPbFIeTQIjK80/YMU0/L3yaaCznjIFIdxH+XqeKBucDaOKkCD6mngYHSncLkezHXmnhQRmj607NIBuwA5PSm7eOBUoG4c0oTnk/Si4WK5TJyc5pPLC8kce9W9uB0ppQsaYkVgMZwtKEJxk8VaEY4wKd5an/61DYymI23dO/WpUizjJqbaM4xgVJsG3jrSuIhEWTjtT1QKOnPrTsMvAo2tnnpQMcq4pMEmpAQq1EX+bHehBYcOKXhlzTDmnKDmmIiclWzz9KAxOPSpni5HGM00pjpj6UAKDnHWn5zTAMDmk/lQAu0saU9waXOBxSbx9KBiqSF4pSSBmkLADNICCM9KAFG7jJ4p6uFbPWmbsDrRx2oAnMm7jpQDzUIJCnHBpwYkcUWHck3c0vU88Go1PPNSZDLSEBIpOcdKQDnFOJ5xmi4AGY9af16Uw05WpgKBR2pM5/OkOPUUBYXjNKGI/8Ar0mckjGKCN3H60AO3EinAYpnOOKcBjjNIAA554FO+XB9KaMHPrSFSR6AUwFD/iPWlJzTQPWjIB4oGPUH1oX7xzzUecc4ow27g9fSiwEjsAOBzTAxI4HHrTwvHNGOOnFAAD7UbmOM01t2elMLenakA8DnrTt2DwtInPfFOIKnPWmIYXyMGm9TjvUxVSOnNM8tR9femAFdoxxQoI+lHl+/SlBFIYFyBwBQj5659hQBngGpABngigBVByCRTjjPtTQecDmkwcimhFkDaoJphc4yKYFZh7U3Bz3pgO2tIcHIpRARjLAYpQdw5JApAyhuFLUAP+ZTgc0+NfMOGO0etCS5HIAp2Qe9MB7wwqvynP1quXQMAGNSkggAnioZI1A60ALKyt0NIkQX5nH5U2NTjOM4qYliOnFACM5/hHFOU5Az1pBGWXg4pRFwRz9aYiRS2flOTQWkDYYc0xI5VJMdIxkZvmPNAEuGc/MOO1PG6Jsjj2qvuH3dxBqdHMS7id31p3AfiR+Qo+tLErE4xiohdEklgT9KdEWkc8lR7mncC8v+r7Cq7yBRhycUZVHAZ8596bMSxGI+PWgCNZFORz1psjuOgJpXCgDaOT69qljj2LuPzGkA5JCIsnB+tN3O/IXA9aG2sDkhWpouBt2/nVADTMvynj6UiLHu3M2T6UmZDgkAD3o2SSew+lADvMQnAX6YpWQjlztB6CoQh34HBHer0MUbxkyjNADI5yi437l9KUSbzkoceuKSWZFwkMajHc0q3TLxgH6UAKEXBYxtntULzMp5wo9DUz3TFcBTn9KieMTDccbqYFgXSGMfJtP86jM29tkhIWqhAR8ZJqSQqVySufrUsDTTyoUAViSfQVFO8mcI2BVSGUSLtZzx0qVmYn5WXincC9bbzgSYx71qwWnG5EX6msG3uHBAZh9a04tRAKqrdPyqkJmkFcgjAJFVJrWRyXYA47VdjuoXVdzKD9akLKXxkH6U7CMaRGUYRypPtSW7zrKBJhl9TWnJbxyOTuBA6ioAIPmRXGR3I6VQtWTMySDCDn1xUQdYyfvMfaoRciL5Q+8ihbhicgAZoCxKZ2mfGMKKkhiEZMmzLepqnJKVcbAN3fFWI5WkAG7mgCw0zPznp2oRgXzI2AO2ahmYFcI2G78VHGFPEkmfwoAvrqAkbywDtXvVhrxpItiDnHFZysYFxEqn61Mk7EYEfz0BcbGJzKfNXd6c1LvYth4wBSPEoBd3cN7VAZ2LhQzbPU0ASPaLgucYPSq/2GZhujcYJ6Zq2+9QBuJTHWoo0uA2636dwxoCw2FWtAQ+cnrzTx5Up+9mpnxONko2n1qA2JjYMkg9aAsK1rAqErkMfWoltd+FVCSf4uwqdJnD7ZSce4qxIwVMI5GaLgQiOCzU79rHuaar2rKxyRnuaoXErrKVdgB6moPMJO1e9O4jatGto2IIyO7VLcPbP/q0Le+azrYlQPMHFWA8O7acjNFwRDLcFRtGBnv1quCGJLSfgOKtS24Y5Rciq8bpHMUcAD1NIoswIWi3CTB9Ku26MoBEpU9+arRSQ7sL1qUoMmQvgUyR5kneXDXDGMdRWhDISoTcHjIxWZCWZc5G32q1DN5Rx2PrQO5FqNlg74wAvtWWmRJxyfSt+SCSaMsrDBrGntyHx/F64piImtYJiWkjO/1FQvIkCkRzTo3YBuKnF15HyscGqUqBpjMYy6H3qWCHwavdK7I90zA9AwHNOa6nKkgoAepI5rMuRsk85o/LTsM01b1X4yMemaQ7GpHFuOVnTcexq0iTIfm2n6NmsBpRu+Un8Klg1BkYJ5ZPuDQmFjohJMiBhHn1FLHrECnZKfLf0YVkpd6iiNLhCvYEVGt7a6hL5d6hhk6b0WquI3JYYrlQ4GQ3PA4qKK3GShwqD2qnbWU1rOBaXxkgP8DdK1/srlA7HnuBQIovYQsxw/HoKpMPsspO35fc4ro4rBAFfzNue1GqaZAYAwfLfSmMyIdUt3QRuSh7ehqdjFMnytk+grMuLYRx/MBmo7a8MJyQdvtQBpQW8ZY72UN6NVtLIKN3DHttqkIReASKRgjqatWcBRiiykdzQISbUXtp44ETYzHh8dKviSW6UJIHmY9zTI7R5ZvmQSAdD3q2uo21nF5brLGwPJKZpDMSfTZY5T+72r2FU3tN0y7XC88g8Vq3N46z+fHL5sXoRijyTqq+aoAA9aYFYrc2q5B3KB2OafaX0CMGmdtxPy5FTeQba2Kgl/U+lZ720Yj3rGznrnPApXA7WxuElwAg5HWrc5Kr0G0da88XVJE2oZXhA6EGtAX00cO6PVzMp/hIosB1r2trKu5kyfUVA5t45FUSyoew7VgWGt3cUm2V1ePsMVqvdQXoBYiM+9AGqrSBM+YD6Vi380skpVkjb60+WaG1QAXKnPYNmkgvrOWRUYhmPQ07CFtFwuCmw/7NS3DweU0bpuBHcdKnkjVyCnQehpv2qCF9hhP1x1oA59AY3JReAcjIrYtNVuzhXgwP7wp04jcebDEAe/FUE1EiYp5ePaiwHT28huY8nKn0NZ9/pKSMJhncPes46s0PygEGtCC+uXUF1A44zU2YyGNZIYvmUn3B5pkV59mcvJI49j0ovLpt6knI/wBmhbVZwrsxKnsaoRpw3kcwVo2XnrirKwjduwR9DVOytrWBtqrt+taowRxWcnYaRGQUUkcmmRu+SXQA+1WMVFKcH7pNSncdj5xwFP1pSoIFN3qvegS7icDpXmM3EY44xxS5I6Dk0/YWBPbvmo9oGSTUjF3E8MaBnAOM0gOByPxoLnOBjFMBxkbpjimmTkDtSOGYA5/CmhOc5piAnBIHNOBHYfWhQF7UocDgigY1fU00kZ6U4uADgVEwJHA/GhANLrngmmMxYVKsPPPWmMoBwDzTYiMZHbmnden5UYwMUAd84pgCg96UE8nHNAAx15p27HbAoABwOaawGaeBuApCvWkDEUZJNKVJGQelOX0FKcUXAZtJHIpoHPSnk88dKY7E/dpIBxI2461HkdRyabg5yaXGOgqxCMM9Cab5ZJHIp+WJ5FOAHXFAAEAPJFLtA6cikOMcUm8jtQNikA009OtB5GaFT5R35oJAMAOlLtducVIiAnPbtU4wSOKLlFfy3C9BmkGTwxqyy5HXAqFl98ii4DSgH1pNpH0p2z5euDSgqCMnincBFO04xS5bNBkAOFXim7wRg9aLisP3nGPSkL+9Mx8uaToM0WFclBI5FNZyMk81GzntTG5GSatIdxXck888cUw8kDtS4JFO2jrmgkiB54wKkX1xTcENkcg09QRTGGBjgU7Ixk00sQeaVRkHvQAhw3Qc00AkU8KOQKQjAPagGRkhV5pn315p564JpABkgUCIigFIDjoMVMygYpNgLA4oGNDnstKcd+9PYEDjr6VFhm4I/CgQFk6UKQOCaNgGe9KPbpTEAHekJ56U7H60hHGO9MBuCQeO/FIQSPx60uccdaTknrgUrjGOQDg80w5J9BUu3jtmkwcUwGqijHfNOxjOKUA9OlLjjmi4WI1PzHAqQDHanAAU5VxnJpXAjCgnPrTgo6CpNmV60qoAfXFA7DAoHWnHAp2z9f0pDtA5pCA4PJ6U1sdKXcScY4pwXNMBq9OlPGDTSSOAKcqg+1ACMmRkn8qdgdjRtOfalKgUWCwgHvmlOdvzde1JnHQUY70DGmNmJJNBjAI/Wpe2ab75ppiEA9s0vQ4HFN2knoacUGOcmmA4t6kZpAQe3SoDHzwcU5DjIzk1Ix7DJyaa2PoKeMY96jc5PTNMQufQZ96aQeppSSBTDuYelUA/v60DLdRzTQCAB3pRlTmgByqCfenEEDjkelIpGPeng8YoAbyBzThjgnig85o2570BYXseKVTkUmMA80cjgYpCJFweRSADnim/N26U8EY96Bi5O3OAKXoKjd+mDz6UKSQSadwuKR3BpoJyTimk+nNSKT1NIQqtinLjNJ36YpT2xQNIk4NITik6Dn8qUHHFA7CcjnGaXOfamkktSgHnmgTEJyc5wKcCpHHWkIyOtItAhQCTTwNo4pm45wKU7sYpjTHsxHNMLkdRRtzzninYGRkcUh3FXp0phHOcUO+DgDikBz3osK44Ak08AjqfypV27Oc5ppJySKAHZPrQW5pmG65GKkVh9aAEClmHGBTiEApxIJxmmsoxmgbEAweBgUbsZyBQcKOtM3DOCOKEIf5gBwBj2pNxY9eO9LtTPy00qD9TVDJlbam0Y+tIo3HJpgBA5pQQOh/WgRIMdOBUo3KuOKrl+aeZcY70ASGIscn8KacIc/zpglZuSxz71C8xZsHmi4y0SCOtJHk9VyKhSQscYxU4ynPWmIlUYOAMZpDJt4zmlUgcsc+1GFbsKAGC4bPAxTjIzLjdikZVB9qaZFU4HPtQBPGNiklyTULjc5yevcUeeoHzflSIyfeIOe1MQqw4IO786ldm24OCtQtNk7QvFIu5uj8UATweXu+cGrBjWRjjIFVF+XkHPvTxPtOA3PvRcC0kSx9CGI/OkWUFiG71TLyM/wB/n0FWIR5fLnd7U7jHuAT+7Gc/rTjbu6AM2B2AoEu/PyhfTFRB5RJwCRTEIISgxgk1KqRxjO0gn1qz1UFQTSiDzMCQY9hTAqH5uRziplO5RnCinvC8fCEbfSq7pI3PyqB1pgEmxTktz9ajaV0x8xxikdUIIAzjvTQrEgKB+JpAMMpLBmB/Cl81CQUJ+holtiFzvyfQVXSJg/PGPepuBoKVZcvyfTNHnMBtTaAKq7lTqT9TTldX6fjVXAfsaQnL1XkiWN+5qZpUAwo/Woi25stjHpmlcC3ahDguQ3oKuNboeeB9KzBsAGzP0zU0VwSmDwPrSAtJCrblRzgetNR0jc55+lPt5FCnbEwJ7560518xwqIFNUmBahYMODgH1qdZzHkqwYDrk1nhZEdd5zjsKuSJC0O52KD0FO4E9rqDSSMqIfcmnTyiPJYfgKzFuUWVQjMR0q7NcwYALAvjpmncCt50Uk2FVlX1FTSeXjhzn2qtDPE0pV1wO2KtebEgOIwB65oTBomhhIAIAY+tTIDHLnCg+maopeAuNoIFXAy7N7KTTJsWWcLyAOfSnmNGjy2B7VUgZpMEK3X0q1LGxQYBOetNMViCN/LycYX1Jq9DOqqWT5jVQQHad4O30pyOFGFwgHrTCxZlYOuZSwP6VQhYxyElNwzwanDiYcurY96d9nLdRj0waRVrE+fNwwP5nin5eJDt+ZvSqbyGHAz83amvfsgCn86BCm4uvNIZBj0HOKuQqjrvfdVeC6LDKv146VY/eEg8N60wK1+tyIibK385z2Jxis6SXUlhUSWTq4/2uK3nBJHz4A64qvdy8YycduKkZSji+0IC0Z3d8moHh2SZAwR61oQpJ1cYFQzjLnadx700SyCO3eZuJMHPrV5tOZUG6Q+9RwEBeY8Ee3WpRcuxKkkemaBDCJEUiMlffNZ9zaidctKQe5FapUNEzM/NZ3m+efJHyseM4oLRbt4oxbgFzkD7xOKkhjEoKfO+e/aoIrFo4xvkLn36VOlxJbDG0e2KESW47MWsXEpz1INSGHemXyynv6VGjSTHI2596syNOkG3ygT2OaoRNA4RQkJH41RvkmQ72kRR3461Cks8Lb1yW+nSrpAuIA9wwZhz0xRcZh3MMs674wDj25qiJpUO1jj2NdCdkiNuAVfUHrWO9ovmbgfk+tK4ivNmVNhUMPcVSOn72zGoHPIFXi7eYAT8gPGO9XpIkkRHRwG9qVguY6wNCGAgLdjxRE8io22EjnnIrfTUbmOIo5XjjO3rT47q2mj2ypsJ6kUWHcw4pZCDw232pWXc28o2e3HWpZreOORykrFScgCqVzcTKRhsfWgRJFNdWs4khjJAPKnoa1F8Rwuyx3EL22eCwOQKxhdGaIIxIPfFMkiVl+/uPoaLjOriljEqvbXHnBh93PBpb+7m2DfAyjse1cpDBJlfJYjB6A1sLe3MUQjuMyqBxnqKpMQk0iSp80vzHsay55UibbjJq5cGOZd8ShW75qo1isp3EkvSbGS2WqSRNtGNprsNIMTWpmdMhuhx0rjILFwxOVVR2PetyPWruO2W1XaIwMcAfzoTJOgtb4Q3BDDKnocVeGqRyMVAw3bjg1zNu7unzKD6c1HLM0MnyRnJ96oDqPJtrglbtY2J6LjFc/qemT6fK1xaBzbnqgrIu7iWaQByVPY7ulbWn64be18mZvPXGOeTQMbYzQahaMqybJV6qetRNDHbkoLpvcEdKq388YPn2UOH65PFVINU+0SBbkDzO7dqQ7GsLa2kAUmOUH1FMuNCVFM9v8gHJWmMqMECvhe5WtaH91CCdzRe5zQSc9h1w0iEjsRVpX80BI2bcavzyW7NzF8o7DvVaGUGRjBHgemKYDP7MmiIbYM5yAaWf90y74/KY9MVoo9w6f6vBHrUZ1eMnyLq2CsOjgZzQMNNupE3A7nB79qugyznacDHTPWq8d3BIpAVUxyMGkkV5kLJLz2wcUCNGKdwdhAJHqKQ2nmTh1RQO4IrNsb2W2ytwrOwPDYzmtNNYgkO1kK5OOeKBle+08s+/CjjpVaOJ4gCWZlPatVXgnYqG3H0aqV+8VoMFSM+nNAELRAHO/A7YNBuJzKFExKjsaxri8lLEW3zr6kc1Lbs8yqd2JfrSuBtrMwkXJPPXFasc4hIy+VPqa5iO/ntpwk8eV7OK2I54Hwzcg+9G4jdjmD8KwqKSR0bkcfWq6MshHlsAPapXDcZwRU2GfPiRrnmpQUQcLzUaq56flVhbRm5Jrx2dJGXJHoaRV7sKuJbhAM8mpCir94celK4GawzwO1R4BPSr0roORgVVyGbjp6VaAbwOOtNUEnJHFPBXp0NNeTjAxik0AuADzzUMh701pmJPpUW/d15oSAfnkdfeguec8AUmSTkGm/M3TqO9VcQ8MS3Wkz60Km3vzTjjAoAYCW7dKTBJwBT1Qnvwe1OCqM7sU7CI9pPAHFPKnFKWHApucn2oCwgzmlJ2ig8cjFQSThfvEA0JDHmbb90HPrShi/0rNlumOQrcVTlvWiP+sJ/Gm0I6DYB1bFNyq9Tk1zYvpmblzirtvK7kEKTSQG1lcY4pMBsjnFVIzKSMirKhscmmApwOBxTcMwyelP2AnnmjGeBSGR4UAClyT26U8JlulL8qnpT2EIEyv1pyJntxQGAfjpTt3J20DH7QvSmlhz6dqZkk8mg4ztosIGfccA0p4Gc0wqAOtGMjGcU7AwZgBTFUvxjGaeB+dLuK809guBhAA5o8vIJ9O9NVy56GnZY8dKBgRhf51GcYpz5IqFiTwAapEMdjrSYXGe1HJHIp+ABmmMZgEegpwUYzS5G6nNgikIYMClPX2oHoRRt9aLDVgOOtLnP0pGyV96TkLyKAHEj0qJmz/Sk3gkjHNMOQ3FOwCdHz+YoHXg0uPlzjmjac5oBoXr1xRnFLjA5pgxzk0CuO35JHSlCFqQdc0457UDEwBx2pMgcelBbFNIoJFLjHFNbnrS7RwcZoPvTGNxxwOaTbg81Jjv2pGG7NIZGeCaRT7ZpfLwOaeoCjpiqAAme2KPLyeMmnDPPHFP5GDjipYITy+PenJHmnAU4DHXigBpjC0nTOKeT+JpCpOfWgBuMnrmmlAfrUm0dSaYw5x2pAhOAKXnucUzBJ9KXbgZpoB6gE8jntQAQenem+ZjGKkBz1oAM01s0FgDimk/Nz0psByJkZNP9himbyuQAKaGNCAl9qQLzSL161ICM980xDDnGc8Um4Y605+ntUeOOaLgKqljmnbBjOOacnApTyehoAjKYHOc03JxgjFTNjpUJBzjvSGI2BzTSCTSgMSc0/HrTuTuM8snBzk0EEdRT8k4NLxxTuPYYF6U7GD7UHoaM5GaYCk0AU0nb2zTlzQwHlgBjGKb16UhXPU0BiMDFKwEinA560E7jTQxwcimqe9Kwh/HXGTTwBtwT1qMHB96eZBj1oGGwBqXGD0yaaGz+NBbB96aQD8+oxSgDOaQMCKXIxxmgBT7CjknNN3YHApqtkZ5oHcfuKntTs1ETTiwApCH4I57UY5zxTBIenalD5yAeKdxkmM9MUpQk+1RrnAzUmeKAGkADGaZtc85pwAPuacDzxinsIRRx8xFO2rjjtScE5PFKCBwBSAYWyMZ5FLgEAZ5pSoByO9IML9aLAKUOO9ORdo5pASQaUEmgBeAO9Cnr6Uo3DqOtJyfYe1MGH3jxS7BnjpSBe2eKXaVb2oQIeowKil45Uc08EFsHtT8p6c0AUSWz1NKuc5zVpiueg+lHGPu+9MCNTjqKkGCM54pAAxyVxTXfHANIYpBLZzxTNy5wFqPe3QGgsfemIsoq5ySaeZjwo6VXWVyMKM1Kik8sdooGOZ8Zwc+uKRWyAckYpQVyNoOPWpmi3jIGKBFch2J+amMxU7RVjYRxSDZ6DNAEIBPLU/eOg6047M+9JsXORTAFViTuFPCKvOaQSdihxTi5P3QBQA9WOMADFJ5Yc/Mfypitk4qUoVG7NADkQI2QpP1NW0OQP3dQBsKCQOamTDLncT7CmgJ93HKL9KRgrr/dPpVOeXyzxmnQ75OTj86q4izDJJCxzkj0qczlzx0qvJCGH3jn2NRiQRnYQfrRcCy/z8bsCoJIWxhJRVYykS4ycE1b8gAbwwH1p3Ag8hwMDn3oEbRnpzUn2lUJBfJpPOCnccn2oAcYRtBz19qrygRtx+dWBMHOGdQD6VVnVMkqT7ZpARsol5QnPvSrlYypA3U62RXOHIHtVsW8I55B9qQXMs57g0pVgvArQmCqMkE49qEiR13Nj6UrBczVnAGDnAqxHIGHygfU06WEM3yjj6UyO1Z5NpPHtTHcv296cEFQygY4q3DcAgkxkD1qlbwbGKgYqaXzIU4yF700xBdSQsNwdgfaliiV0VnmJz0FQoqO4DHHc5q6gggTCxfN1DGmA2S1UH5CCaWOEq2StOScD7xBz2FSNL5gxwvFMWoLb7T5nBPpUjrv+8qj3qoxKthJsk9qkjBHBJY/WmBYWNQODk1ZgZk5yG9jWcZniOATk9qYbnLjYPmPvRcDbNyYyM4Galt7jexDNjPTNZG8FPnPzU+KbC8KzMaaYzSkl2yBASxqGUbm2xk7j14qNBMRwoXPvUrPLCAMDPrTEytLYXJkWUzx7F5Cr1/GpobxlOGbpUXnSs5XAVT1NV5UbdlFO3vijYWpNdzENvyCKjin8w/cAJqKTbPHsUYIqNLd9v8ArNuPWi4rGkLkxx7Rgt7VNBfkgKODWGlwysQy5x3FTQ3ChtxBX3zSuOx0CmRU4DH6mpFVmj3Hn6isyC9QZLyNj2p0moEgCLO33pjLsoLqAu4L3x1NVp3VAFw6ntnvSR6hIJPlU7vcVGJ5HucyjJzxQIvQyo0fckDmneYJMDaCKiklRUAC/MabFuU7skCi4rF+OIn/AFYHPY1G2nkSE45PXjpTI5Zg2WG0dsVdSduBg/U0wKc12luwiVDI3fApFl87OYdo7c1JeeYzfu1G3uyjmltHgkXbIjFvUUANVHiIIQLnowPSpC8kDB5pCwPtUjXMcb+WFGfU0r3LYCkKT6kUgIL4SzxiSKXagGdpGCayWvpUVomJK9xWyIvNH7yU+2Kzr+yKvuU5X1oGQpd+Uig8rTTOLiQk8DsKqmxuWJk2nYOmD1pm+RuCm0CgRZZircYA9xVu2mjjfe4Htms0RSOR81SPHKrLlSw+tFxWNaW8tC43Dr7VEXtJ8iMnPTPpWdcQ5iBxhvftTLVmGUVwp7+9O4x7qscvzM3X0q88sDwKpC7h6rVeeB9u4vnPpTrdIwPnBGO5ouSEdpaM7FkwTySKqz2tv5v7uVsehFaBtw53R5/Cq8lnIXyxC0WHcqx6ZeNKTEVMfs2KBDKshVmYsOuDWvaiQDaqD0JqKRPIkZ3XOegFFguVYLcrOJHQsvcetWWliZjhAB6UxdQJIXy1Kn86hvCsw3IpU07APntWmiLwyKremajjhkMKpMQsg79jUEKvGpLg5FPEizqTI/ttpCL0AngXzU3FBxjqDTGF1ISzKyg9BVZLidF8tJ3VOyimC5vVhYI24joTyaVykLcNIrqskZHPfvVqWVbSNGNrsz3Ddazhqcl4PIu4uexAqlqE7wFUR2bHQNzilcbOofUrSS2XC/N3BXFc/ePHDL5kb7s88DpVWC9dsGQdD3rUhNnO4BcFz2p3uK9hunajHLLsmJjXOd1datwgswFdGBHFcvc2No0eEG1+5Bqol39j+UZYA4600I3Lq6AUqQc+gqKwvHs5d24bT2NUv7ZTcD5II7gmpRqWnyoBIjRufbincZ08GpeewAdVc9qzL+URykMoJJrI+Qy5RsqPQ81o2t3p8rLCykuePm9frRcRnzoDOp37VPoa0rQvAQ+4lcetFxY22/AkK/SpLSBYgVHzL2zTA0LacOSzVbuIILiMOG59BVB4h8uEwT6VOkYiiPD5oEI8hso9yJkjtnJqs2ozXkTJIij3A6VbiYSg5UcfnUSLCHYKBuPagCHTLS0LOJJ23n2pLmxFo7SBi4zUxiMbfLHz60s0oWA9ieoNFguZLTNdMFRipzjr0qcQ3EK4aUEHpilgXBYxoCT1GKmKyXA2bfLK9KBli0u5Yxhicr04q8utO8mxxgetYyLc29zh8MtWLyeBVGE2t60CPMIpAnOKkF6B1oorwzqAXoA4FMe4Ldc0UU7Ayq77gflJ9KiJKjnp60UVSENMvy9KgZ3Ix0ooqgGjOOeaX2xiiikCBQRwcn2qYAEcUUUIAKn1ppbA4yaKKYDgzZyKa7YPOaKKAGluc0BtoyaKKsRSurxvupxjrWVcPI3OSRRRTQisxlIxux7U6GyknOWbA9TRRSY0adrp8UZBY7iK0o4UA+XgUUVmNku3GAOKXsMmiimIM4/rQOuaKKpAx6sqdTTgN1FFDEhwiGcmlOxBmiioKI9wJ9PSmE/lRRTQCFhjIBzSKGPtRRVCHBSCeeaRgSKKKTBCKDupzNjIFFFUgZESCODQqkEZ70UUxDyABik27hjtRRTYkHcdqQuMYNFFSUN3c0oyTnPFFFUhCk9c/pTHbjbzxRRTEM7807HcUUUAMY7ulKOOO9FFABgnrShcc4oooBCk56CkIOOKKKAYhXJJz0pDg9R+NFFADcnsOKXbxRRQAbSByePSgKfoKKKBhsJ6U4oTRRRcQ4KScVIBjFFFJjQ7IBoZM0UUhjv4cY+hoYUUUCZEcj3ppxySetFFUBGWx05p4YkAYoooBCmM4yTTcEnr0oooQMToxpc4oopsBCdxBpMd6KKEIeF45NPH3hRRQwJMADGKQJu+lFFJDJ9qrjIFNc56DFFFIZHjHBpuPQUUVRLG7SB1phzzRRQAhPr0pQe2OaKKaEIcFqfgcCiimAFOc96QcHGKKKQB1bNJu55FFFCAUNnt+FNY4BooqgFXOakUbgKKKGMXP5ijaSc44oopAOQDPWpMqooooAY21gPSjbjOBmiikwG4Y844p23AGRxRRQIXaAuaVQoBPSiimMXJPbNIeOtFFMB+fl6cUbVPPNFFADlK8ikK5J2niiigBMH0OabjnnmiimBIqgCnhcUUUgQoBNNZcHriiigbFDjvzS7wflI/GiigBjHHI65pHf3yaKKZI0ScjigzMB0oooGAkOOKMZOQPrRRSATGSegpUI6cH1oopjHAYORwKdkk880UUyRGZuxx9KfFO4OG5H1oooGSbix4wKYQFOd2fwoooAI8FuFyferBjVPmPJoopgyLeCe+KV2AIKjGaKKQAgLHkEGnOpBBJzRRQA9fnxuNW0wo+XmiimBSuNPlnOTMRzwBUtpp8kWD5xJ9DRRQIuhmiHKg4qpLdGRsKtFFMZE29V+6M0uJCgzKcdxRRSYDY2Afld3uatMFkQH5c+lFFCEQiDYcs2PpSmMN93LUUUwHiHaBkgCpPMCr8p6UUUARea0pwemae7BFwpAoooAQTlsKEX8Ktxr5Q3gAGiimgGli53CTB9qnDPNhZBlB0NFFMBJIolIKqSQe/SnPfkJ5TQg4HGOtFFIDPdyX6MtSRzkDGM0UUXAcSOpH5GpkuzwqqAaKKpCY2W8zgbcN61CCwYbTnJoooGi9BGW45Le9TxrcRB87VHY0UUAEc75wWOfepzK0rAbWaiimIkK7xgcEVE7YUquVPeiikMoNuiyRng5zTDcNL1IAPWiigRIvyEbFzmhnTfygB9DRRQBHJLl8BdoqxbiU8npRRQMvyzRQwgO2w/qaLe5UsNoLD3ooqiSzIrlRsXg84FTB2jjAGC3oaKKBjR58jB5BgDtVpZXKqGAIHpRRTJFWV8lYxj61FBcMJWU4CDqRRRQBNN5TpvVdwPTIqrG83zeZwB93NFFAyo+o3EYcFQwPQ45qumpNIwjclfoM0UVIErSNFk7i6kdM1GtyCpBTOepHaiirRJUkkXzMK5FSJO4bG+iikwLKN5iHMik+/aoWW1K/fJfPYUUUDuWbWeGGJgZCWz0kqWRra7T5A3nD06UUUAW4I5FiAIwB71ObGYneAD9TRRVElddsTHfuU/7NOkja4iyrgn0IoopgUriwMCeYXXmqsUkRcrLJjHQ9qKKTKRG7yGVjE4eP3quQzMWVduO9FFK4MVrh1APfNWbe5E2VMBz6g0UVIiGTEb5iAznvWtClpcWjC4t1EmPv0UUAZdsyWd4ryxiaEH7hFXNQt9Jvz51orRyd1AxiiihCIbfTi0JxM7MPWqZtXDnI3YooqgFjEKNhkz/Snz28RXdGwbPaiimBXhhkL7FzmriRPCO2fWiikAonuI2y2WUnqRWtbXsMaByxB7gCiigC7JdiWEmCRd/amWV9dCQx3OE/2qKKoZba6tw/LFs9wKqPqMFvJym73oooET22rQSyEOSB2q3LJDND9wEevSiimBTtmEdyWibb6jGavTZI8xUOT6UUUhFd3CxEMjbvcVnyMWwCgKdzRRQUf//Z\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image \n",
"\n",
"results_dir = os.path.join(os.environ[\"LOCAL_PROJECT_DIR\"], \"local/training/tao/detectnet_v2/resnet18_palletjack/test_loco/images_annotated\")\n",
"# pil_img = Image(filename=os.path.join(os.getenv(\"LOCAL_PROJECT_DIR\"), 'detecnet_v2/july_resnet18_trials/new_pellet_distractors_10k/test_loco/images_annotated/1564562568.298206.jpg'))\n",
" \n",
"image_names = [\"1564562568.298206.jpg\", \"1564562628.517229.jpg\", \"1564562843.0618184.jpg\", \"593768,3659.jpg\", \"516447400,977.jpg\"] \n",
" \n",
"images = [Image(filename = os.path.join(results_dir, image_name)) for image_name in image_names]\n",
"\n",
"display(*images)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Next Steps <a class=\"anchor\" id=\"head-8\"></a>\n",
"\n",
"#### Generating Synthetic Data for your use case:\n",
"\n",
"* Make changes in the Domain Randomization under the Synthetic Data Generation script (`palletjack_sdg/standalone_palletjack_sdg.py`\n",
"* Add additional objects of interest in the scene (similar to how `palletjacks` are added, you can add `forklifts`, `ladders` etc.) to generate data\n",
"* Use [different](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#downloading-the-models) models for training with TAO (for object detection, you can use `YOLO`, `SSD`, `EfficientDet`) \n",
"* Replicator provides Semantic Segmentation, Instance Segmentation, Depth and various other ground truth annotations along with RGB. You can also write your own ground truth annotator (eg: Pose Estimation: Refer to [sample](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_replicator_offline_pose_estimation.html). These can be used for training a model of your own framework and choice\n",
"* Exploring the option of using `Synthetic + Real` data for training a network. Can be particularly useful for generating more data around particular corner cases\n",
"\n",
"\n",
"#### Deploying Trained Models:\n",
"\n",
"* After obtaining satisfactory results with the training process, you can further optimize your model for deployment with the help of Pruning and QAT.\n",
"* TAO models can directly be deployed on Jetson with Isaac ROS or Deepstream which ensures your end-to-end pipeline being optimized (data acquisition -> model inference -> results)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"vscode": {
"interpreter": {
"hash": "f23a2831654361cfd8b219e05b5055fdda3e37fe5c0b020e6226f740844c300a"
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/README.md | # Requirements
- Install [Isaac Sim](https://docs.omniverse.nvidia.com/isaacsim/latest/install_workstation.html)
- Training via TAO Toolkit Docker container (TAO setup instructions in `local_train` notebook)
## Synthetic Data Generation
- Provide the path of your Isaac Sim installation folder in the `generate_data.sh` script
- Make the script an executable after adding the Isaac Sim Path (`chmod +x generate_data.sh`)
- Run the script (`./generate_data.sh`)
- We will generate data for the `palletjack` class of objects with annotations in KITTI format
- This generated data can be used to train your own model (framework and architecture of your choice)
## Training with TAO Toolkit
- The data generated in the previus step can be directly fed to TAO for training
- The `local_train` notebook provides a walkthrough of the steps:
- Setting up TAO docker container
- Downloading pre-trained model, we will use the `DetectNet_v2` model with a `resnet_18` backbone
- Running TAO training with `spec` files provided
- Visualizing model performance on real world data
- Visualize model metric with Tensorboard
<img src="../images/tensorboard/tensorboard_resized_palletjack.png"/>
## Next steps
### Generating Synthetic Data for your use case
- Make changes in the Domain Randomization under the Synthetic Data Generation [script](../palletjack_sdg/standalone_palletjack_sdg.py)
- Add additional objects of interest in the scene (similar to how palletjacks are added, you can add forklifts, ladders etc.) to generate dataUse different models for training with TAO (for object detection, you can use YOLO, SSD, EfficientDet)
- Replicator provides Semantic Segmentation, Instance Segmentation, Depth and various other ground truth annotations along with RGB. You can also write your own ground truth annotator (eg: Pose Estimation: Refer to [sample](https://docs.omniverse.nvidia.com/isaacsim/latest/tutorial_replicator_offline_pose_estimation.html) These can be used for training a model of your own framework and choice)
- Exploring the option of using Synthetic + Real data for training a network. Can be particularly useful for generating more data around particular corner cases
### Deploying Trained Models
- The trained model can be pruned and optimized for inference with TAO
- This can then be deployed on a robot with NVIDIA Jetson |
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/generate_data.sh | #!/bin/bash
# This is the path where Isaac Sim is installed which contains the python.sh script
ISAAC_SIM_PATH="<ENTER_FULL_PATH_TO_ISAAC_SIM_HERE>"
## Go to location of the SDG script
cd ../palletjack_sdg
SCRIPT_PATH="${PWD}/standalone_palletjack_sdg.py"
OUTPUT_WAREHOUSE="${PWD}/palletjack_data/distractors_warehouse"
OUTPUT_ADDITIONAL="${PWD}/palletjack_data/distractors_additional"
OUTPUT_NO_DISTRACTORS="${PWD}/palletjack_data/no_distractors"
## Go to Isaac Sim location for running with ./python.sh
cd $ISAAC_SIM_PATH
echo "Starting Data Generation"
./python.sh $SCRIPT_PATH --height 544 --width 960 --num_frames 2000 --distractors warehouse --data_dir $OUTPUT_WAREHOUSE
./python.sh $SCRIPT_PATH --height 544 --width 960 --num_frames 2000 --distractors additional --data_dir $OUTPUT_ADDITIONAL
./python.sh $SCRIPT_PATH --height 544 --width 960 --num_frames 1000 --distractors None --data_dir $OUTPUT_NO_DISTRACTORS
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/training/tao/specs/training/resnet18_distractors.txt | random_seed: 42
dataset_config {
data_sources {
tfrecords_path: "/workspace/tao-experiments/local/training/tao/tfrecords/distractors_warehouse/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
}
data_sources {
tfrecords_path: "/workspace/tao-experiments/local/training/tao/tfrecords/distractors_additional/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
}
data_sources {
tfrecords_path: "/workspace/tao-experiments/local/training/tao/tfrecords/no_distractors/*"
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
}
image_extension: "png"
target_class_mapping {
key: "palletjack"
value: "palletjack"
}
validation_fold: 0
}
augmentation_config {
preprocessing {
output_image_width: 960
output_image_height: 544
min_bbox_width: 20.0
min_bbox_height: 20.0
output_image_channel: 3
}
spatial_augmentation {
hflip_probability: 0.5
zoom_min: 0.5
zoom_max: 1.5
translate_max_x: 8.0
translate_max_y: 8.0
}
color_augmentation {
hue_rotation_max: 25.0
saturation_shift_max: 0.20000000298
contrast_scale_max: 0.10000000149
contrast_center: 0.5
}
}
postprocessing_config {
target_class_config {
key: "palletjack"
value {
clustering_config {
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.00499999988824
dbscan_eps: 0.15000000596
dbscan_min_samples: 0.0500000007451
minimum_bounding_box_height: 20
}
}
}
}
model_config {
pretrained_model_file: "/workspace/tao-experiments/local/training/tao/pretrained_model/resnet18.hdf5"
num_layers: 18
use_batch_norm: true
objective_set {
bbox {
scale: 35.0
offset: 0.5
}
cov {
}
}
arch: "resnet"
}
evaluation_config {
validation_period_during_training: 10
first_validation_epoch: 5
minimum_detection_ground_truth_overlap {
key: "palletjack"
value: 0.5
}
evaluation_box_config {
key: "palletjack"
value {
minimum_height: 25
maximum_height: 9999
minimum_width: 25
maximum_width: 9999
}
}
average_precision_mode: INTEGRATE
}
cost_function_config {
target_classes {
name: "palletjack"
class_weight: 1.0
coverage_foreground_weight: 0.0500000007451
objectives {
name: "cov"
initial_weight: 1.0
weight_target: 1.0
}
objectives {
name: "bbox"
initial_weight: 10.0
weight_target: 1.0
}
}
enable_autoweighting: true
max_objective_weight: 0.999899983406
min_objective_weight: 9.99999974738e-05
}
training_config {
batch_size_per_gpu: 32
num_epochs: 100
learning_rate {
soft_start_annealing_schedule {
min_learning_rate: 5e-06
max_learning_rate: 5e-04
soft_start: 0.10000000149
annealing: 0.699999988079
}
}
regularizer {
type: L1
weight: 3.00000002618e-09
}
optimizer {
adam {
epsilon: 9.99999993923e-09
beta1: 0.899999976158
beta2: 0.999000012875
}
}
cost_scaling {
initial_exponent: 20.0
increment: 0.005
decrement: 1.0
}
visualizer{
enabled: true
num_images: 10
scalar_logging_frequency: 10
infrequent_logging_frequency: 5
target_class_config {
key: "palletjack"
value: {
coverage_threshold: 0.005
}
}
}
checkpoint_interval: 10
}
bbox_rasterizer_config {
target_class_config {
key: "palletjack"
value {
cov_center_x: 0.5
cov_center_y: 0.5
cov_radius_x: 1.0
cov_radius_y: 1.0
bbox_min_radius: 1.0
}
}
deadzone_radius: 0.400000154972
}
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/training/tao/specs/inference/new_inference_specs.txt | inferencer_config{
# defining target class names for the experiment.
# Note: This must be mentioned in order of the networks classes.
target_classes: "palletjack"
# Inference dimensions.
image_width: 960
image_height: 544
# Must match what the model was trained for.
image_channels: 3
batch_size: 32
gpu_index: 0
# model handler config
tlt_config{
model: "/workspace/tao-experiments/local/training/tao/detectnet_v2/resnet18_palletjack/weights/model.tlt"
}
}
bbox_handler_config{
kitti_dump: true
disable_overlay: false
overlay_linewidth: 2
classwise_bbox_handler_config{
key:"palletjack"
value: {
confidence_model: "aggregate_cov"
output_map: "palletjack"
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
coverage_threshold: 0.005
clustering_algorithm: DBSCAN
coverage_threshold: 0.005
dbscan_eps: 0.3
dbscan_min_samples: 0.05
dbscan_confidence_threshold: 0.9
minimum_bounding_box_height: 20
}
}
}
classwise_bbox_handler_config{
key:"default"
value: {
confidence_model: "aggregate_cov"
bbox_color{
R: 255
G: 0
B: 0
}
clustering_config{
clustering_algorithm: DBSCAN
dbscan_confidence_threshold: 0.9
coverage_threshold: 0.005
dbscan_eps: 0.3
dbscan_min_samples: 0.05
minimum_bounding_box_height: 20
}
}
}
}
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/training/tao/specs/tfrecords/distractors_warehouse.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_warehouse/Camera"
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/training/tao/specs/tfrecords/no_distractors.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/no_distractors/Camera"
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/local/training/tao/specs/tfrecords/distractors_additional.txt | kitti_config {
root_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
image_dir_name: "rgb"
label_dir_name: "object_detection"
image_extension: ".png"
partition_mode: "random"
num_partitions: 2
val_split: 10
num_shards: 10
}
image_directory_path: "/workspace/tao-experiments/palletjack_sdg/palletjack_data/distractors_additional/Camera"
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/palletjack_sdg/palletjack_datagen.sh | #!/bin/bash
# This is the path where Isaac Sim is installed which contains the python.sh script
ISAAC_SIM_PATH='/isaac-sim'
echo "Starting Data Generation"
cd $ISAAC_SIM_PATH
echo $PWD
./python.sh /isaac-sim/palletjack_sdg/standalone_palletjack_sdg.py --headless True --height 544 --width 960 --num_frames 2000 --distractors warehouse --data_dir /isaac-sim/palletjack_sdg/palletjack_data/distractors_warehouse
./python.sh /isaac-sim/palletjack_sdg/standalone_palletjack_sdg.py --headless True --height 544 --width 960 --num_frames 2000 --distractors additional --data_dir /isaac-sim/palletjack_sdg/palletjack_data/distractors_additional
./python.sh /isaac-sim/palletjack_sdg/standalone_palletjack_sdg.py --headless True --height 544 --width 960 --num_frames 1000 --distractors None --data_dir /isaac-sim/palletjack_sdg/palletjack_data/no_distractors
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/palletjack_with_tao/palletjack_sdg/standalone_palletjack_sdg.py | # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
#
# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: MIT
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
from omni.isaac.kit import SimulationApp
import os
import argparse
parser = argparse.ArgumentParser("Dataset generator")
parser.add_argument("--headless", type=bool, default=False, help="Launch script headless, default is False")
parser.add_argument("--height", type=int, default=544, help="Height of image")
parser.add_argument("--width", type=int, default=960, help="Width of image")
parser.add_argument("--num_frames", type=int, default=1000, help="Number of frames to record")
parser.add_argument("--distractors", type=str, default="warehouse",
help="Options are 'warehouse' (default), 'additional' or None")
parser.add_argument("--data_dir", type=str, default=os.getcwd() + "/_palletjack_data",
help="Location where data will be output")
args, unknown_args = parser.parse_known_args()
# This is the config used to launch simulation.
CONFIG = {"renderer": "RayTracedLighting", "headless": args.headless,
"width": args.width, "height": args.height, "num_frames": args.num_frames}
simulation_app = SimulationApp(launch_config=CONFIG)
## This is the path which has the background scene in which objects will be added.
ENV_URL = "/Isaac/Environments/Simple_Warehouse/warehouse.usd"
import carb
import omni
import omni.usd
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.stage import get_current_stage, open_stage
from pxr import Semantics
import omni.replicator.core as rep
from omni.isaac.core.utils.semantics import get_semantics
# Increase subframes if shadows/ghosting appears of moving objects
# See known issues: https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator.html#known-issues
rep.settings.carb_settings("/omni/replicator/RTSubframes", 4)
# This is the location of the palletjacks in the simready asset library
PALLETJACKS = ["http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Scale_A/PalletTruckScale_A01_PR_NVD_01.usd",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Heavy_Duty_A/HeavyDutyPalletTruck_A01_PR_NVD_01.usd",
"http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/DigitalTwin/Assets/Warehouse/Equipment/Pallet_Trucks/Low_Profile_A/LowProfilePalletTruck_A01_PR_NVD_01.usd"]
# The warehouse distractors which will be added to the scene and randomized
DISTRACTORS_WAREHOUSE = 2 * ["/Isaac/Environments/Simple_Warehouse/Props/S_TrafficCone.usd",
"/Isaac/Environments/Simple_Warehouse/Props/S_WetFloorSign.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_A_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_B_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BarelPlastic_C_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticB_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticD_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BottlePlasticE_01.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_BucketPlastic_B.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1262.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1268.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1482.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_1683.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxB_01_291.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxD_01_1454.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CardBoxD_01_1513.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_A_04.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_B_03.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_B_05.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_C_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_CratePlastic_E_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_PushcartA_02.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_RackPile_04.usd",
"/Isaac/Environments/Simple_Warehouse/Props/SM_RackPile_03.usd"]
## Additional distractors which can be added to the scene
DISTRACTORS_ADDITIONAL = ["/Isaac/Environments/Hospital/Props/Pharmacy_Low.usd",
"/Isaac/Environments/Hospital/Props/SM_BedSideTable_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_BooksSet_26.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleB.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleA.usd",
"/Isaac/Environments/Hospital/Props/SM_BottleC.usd",
"/Isaac/Environments/Hospital/Props/SM_Cart_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Chair_02a.usd",
"/Isaac/Environments/Hospital/Props/SM_Chair_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Computer_02b.usd",
"/Isaac/Environments/Hospital/Props/SM_Desk_04a.usd",
"/Isaac/Environments/Hospital/Props/SM_DisposalStand_02.usd",
"/Isaac/Environments/Hospital/Props/SM_FirstAidKit_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_GasCart_01c.usd",
"/Isaac/Environments/Hospital/Props/SM_Gurney_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_HospitalBed_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_MedicalBag_01a.usd",
"/Isaac/Environments/Hospital/Props/SM_Mirror.usd",
"/Isaac/Environments/Hospital/Props/SM_MopSet_01b.usd",
"/Isaac/Environments/Hospital/Props/SM_SideTable_02a.usd",
"/Isaac/Environments/Hospital/Props/SM_SupplyCabinet_01c.usd",
"/Isaac/Environments/Hospital/Props/SM_SupplyCart_01e.usd",
"/Isaac/Environments/Hospital/Props/SM_TrashCan.usd",
"/Isaac/Environments/Hospital/Props/SM_Washbasin.usd",
"/Isaac/Environments/Hospital/Props/SM_WheelChair_01a.usd",
"/Isaac/Environments/Office/Props/SM_WaterCooler.usd",
"/Isaac/Environments/Office/Props/SM_TV.usd",
"/Isaac/Environments/Office/Props/SM_TableC.usd",
"/Isaac/Environments/Office/Props/SM_Recliner.usd",
"/Isaac/Environments/Office/Props/SM_Personenleitsystem_Red1m.usd",
"/Isaac/Environments/Office/Props/SM_Lamp02_162.usd",
"/Isaac/Environments/Office/Props/SM_Lamp02.usd",
"/Isaac/Environments/Office/Props/SM_HandDryer.usd",
"/Isaac/Environments/Office/Props/SM_Extinguisher.usd"]
# The textures which will be randomized for the wall and floor
TEXTURES = ["/Isaac/Materials/Textures/Patterns/nv_asphalt_yellow_weathered.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_hexagonal_green_white.jpg",
"/Isaac/Materials/Textures/Patterns/nv_rubber_woven_charcoal.jpg",
"/Isaac/Materials/Textures/Patterns/nv_granite_tile.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_square_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_marble.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_reclaimed.jpg",
"/Isaac/Materials/Textures/Patterns/nv_concrete_aged_with_lines.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wooden_wall.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stone_painted_grey.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_shingles_brown.jpg",
"/Isaac/Materials/Textures/Patterns/nv_tile_hexagonal_various.jpg",
"/Isaac/Materials/Textures/Patterns/nv_carpet_abstract_pattern.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_siding_weathered_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_animalfur_pattern_greys.jpg",
"/Isaac/Materials/Textures/Patterns/nv_artificialgrass_green.jpg",
"/Isaac/Materials/Textures/Patterns/nv_bamboo_desktop.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_reclaimed.jpg",
"/Isaac/Materials/Textures/Patterns/nv_brick_red_stacked.jpg",
"/Isaac/Materials/Textures/Patterns/nv_fireplace_wall.jpg",
"/Isaac/Materials/Textures/Patterns/nv_fabric_square_grid.jpg",
"/Isaac/Materials/Textures/Patterns/nv_granite_tile.jpg",
"/Isaac/Materials/Textures/Patterns/nv_marble.jpg",
"/Isaac/Materials/Textures/Patterns/nv_gravel_grey_leaves.jpg",
"/Isaac/Materials/Textures/Patterns/nv_plastic_blue.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stone_red_hatch.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stucco_red_painted.jpg",
"/Isaac/Materials/Textures/Patterns/nv_rubber_woven_charcoal.jpg",
"/Isaac/Materials/Textures/Patterns/nv_stucco_smooth_blue.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wood_shingles_brown.jpg",
"/Isaac/Materials/Textures/Patterns/nv_wooden_wall.jpg"]
def update_semantics(stage, keep_semantics=[]):
""" Remove semantics from the stage except for keep_semantic classes"""
for prim in stage.Traverse():
if prim.HasAPI(Semantics.SemanticsAPI):
processed_instances = set()
for property in prim.GetProperties():
is_semantic = Semantics.SemanticsAPI.IsSemanticsAPIPath(property.GetPath())
if is_semantic:
instance_name = property.SplitName()[1]
if instance_name in processed_instances:
# Skip repeated instance, instances are iterated twice due to their two semantic properties (class, data)
continue
processed_instances.add(instance_name)
sem = Semantics.SemanticsAPI.Get(prim, instance_name)
type_attr = sem.GetSemanticTypeAttr()
data_attr = sem.GetSemanticDataAttr()
for semantic_class in keep_semantics:
# Check for our data classes needed for the model
if data_attr.Get() == semantic_class:
continue
else:
# remove semantics of all other prims
prim.RemoveProperty(type_attr.GetName())
prim.RemoveProperty(data_attr.GetName())
prim.RemoveAPI(Semantics.SemanticsAPI, instance_name)
# needed for loading textures correctly
def prefix_with_isaac_asset_server(relative_path):
assets_root_path = get_assets_root_path()
if assets_root_path is None:
raise Exception("Nucleus server not found, could not access Isaac Sim assets folder")
return assets_root_path + relative_path
def full_distractors_list(distractor_type="warehouse"):
"""Distractor type allowed are warehouse, additional or None. They load corresponding objects and add
them to the scene for DR"""
full_dist_list = []
if distractor_type == "warehouse":
for distractor in DISTRACTORS_WAREHOUSE:
full_dist_list.append(prefix_with_isaac_asset_server(distractor))
elif distractor_type == "additional":
for distractor in DISTRACTORS_ADDITIONAL:
full_dist_list.append(prefix_with_isaac_asset_server(distractor))
else:
print("No Distractors being added to the current scene for SDG")
return full_dist_list
def full_textures_list():
full_tex_list = []
for texture in TEXTURES:
full_tex_list.append(prefix_with_isaac_asset_server(texture))
return full_tex_list
def add_palletjacks():
rep_obj_list = [rep.create.from_usd(palletjack_path, semantics=[("class", "palletjack")], count=2) for palletjack_path in PALLETJACKS]
rep_palletjack_group = rep.create.group(rep_obj_list)
return rep_palletjack_group
def add_distractors(distractor_type="warehouse"):
full_distractors = full_distractors_list(distractor_type)
distractors = [rep.create.from_usd(distractor_path, count=1) for distractor_path in full_distractors]
distractor_group = rep.create.group(distractors)
return distractor_group
# This will handle replicator
def run_orchestrator():
rep.orchestrator.run()
# Wait until started
while not rep.orchestrator.get_is_started():
simulation_app.update()
# Wait until stopped
while rep.orchestrator.get_is_started():
simulation_app.update()
rep.BackendDispatch.wait_until_done()
rep.orchestrator.stop()
def main():
# Open the environment in a new stage
print(f"Loading Stage {ENV_URL}")
open_stage(prefix_with_isaac_asset_server(ENV_URL))
stage = get_current_stage()
# Run some app updates to make sure things are properly loaded
for i in range(100):
if i % 10 == 0:
print(f"App uppdate {i}..")
simulation_app.update()
textures = full_textures_list()
rep_palletjack_group = add_palletjacks()
rep_distractor_group = add_distractors(distractor_type=args.distractors)
# We only need labels for the palletjack objects
update_semantics(stage=stage, keep_semantics=["palletjack"])
# Create camera with Replicator API for gathering data
cam = rep.create.camera(clipping_range=(0.1, 1000000))
# trigger replicator pipeline
with rep.trigger.on_frame(num_frames=CONFIG["num_frames"]):
# Move the camera around in the scene, focus on the center of warehouse
with cam:
rep.modify.pose(position=rep.distribution.uniform((-9.2, -11.8, 0.4), (7.2, 15.8, 4)),
look_at=(0, 0, 0))
# Get the Palletjack body mesh and modify its color
with rep.get.prims(path_pattern="SteerAxles"):
rep.randomizer.color(colors=rep.distribution.uniform((0, 0, 0), (1, 1, 1)))
# Randomize the pose of all the added palletjacks
with rep_palletjack_group:
rep.modify.pose(position=rep.distribution.uniform((-6, -6, 0), (6, 12, 0)),
rotation=rep.distribution.uniform((0, 0, 0), (0, 0, 360)),
scale=rep.distribution.uniform((0.01, 0.01, 0.01), (0.01, 0.01, 0.01)))
# Modify the pose of all the distractors in the scene
with rep_distractor_group:
rep.modify.pose(position=rep.distribution.uniform((-6, -6, 0), (6, 12, 0)),
rotation=rep.distribution.uniform((0, 0, 0), (0, 0, 360)),
scale=rep.distribution.uniform(1, 1.5))
# Randomize the lighting of the scene
with rep.get.prims(path_pattern="RectLight"):
rep.modify.attribute("color", rep.distribution.uniform((0, 0, 0), (1, 1, 1)))
rep.modify.attribute("intensity", rep.distribution.normal(100000.0, 600000.0))
rep.modify.visibility(rep.distribution.choice([True, False, False, False, False, False, False]))
# select floor material
random_mat_floor = rep.create.material_omnipbr(diffuse_texture=rep.distribution.choice(textures),
roughness=rep.distribution.uniform(0, 1),
metallic=rep.distribution.choice([0, 1]),
emissive_texture=rep.distribution.choice(textures),
emissive_intensity=rep.distribution.uniform(0, 1000),)
with rep.get.prims(path_pattern="SM_Floor"):
rep.randomizer.materials(random_mat_floor)
# select random wall material
random_mat_wall = rep.create.material_omnipbr(diffuse_texture=rep.distribution.choice(textures),
roughness=rep.distribution.uniform(0, 1),
metallic=rep.distribution.choice([0, 1]),
emissive_texture=rep.distribution.choice(textures),
emissive_intensity=rep.distribution.uniform(0, 1000),)
with rep.get.prims(path_pattern="SM_Wall"):
rep.randomizer.materials(random_mat_wall)
# Set up the writer
writer = rep.WriterRegistry.get("KittiWriter")
# output directory of writer
output_directory = args.data_dir
print("Outputting data to ", output_directory)
# use writer for bounding boxes, rgb and segmentation
writer.initialize(output_dir=output_directory,
omit_semantic_type=True,)
# attach camera render products to wrieter so that data is outputted
RESOLUTION = (CONFIG["width"], CONFIG["height"])
render_product = rep.create.render_product(cam, RESOLUTION)
writer.attach(render_product)
# run rep pipeline
run_orchestrator()
simulation_app.update()
if __name__ == "__main__":
try:
main()
except Exception as e:
carb.log_error(f"Exception: {e}")
import traceback
traceback.print_exc()
finally:
simulation_app.close()
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/run.sh | #!/bin/bash
# start the app
docker-compose up
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/docker-compose.yml | version: '3'
services:
deployment:
build: deployment
# Access the lab with: http://127.0.0.1:8884/lab?token=nvsecuretoken2
command: jupyter lab -y --allow-root --no-browser --ip=0.0.0.0 --port=8884 --notebook-dir=/opt/project/ --NotebookApp.token='nvsecuretoken2' --NotebookApp.password='nvsecurepassword'
shm_size: '2gb'
volumes:
- './deployment/code/:/opt/project/'
- './models:/opt/models/'
ports:
- "8884:8884"
training:
build: training
# Access the lab with: http://127.0.0.1:8883/lab?token=nvsecuretoken1
command: jupyter lab -y --allow-root --no-browser --ip=0.0.0.0 --port=8883 --notebook-dir=/opt/project/ --NotebookApp.token='nvsecuretoken1' --NotebookApp.password='nvsecurepassword'
volumes:
- './training/code/:/opt/project/'
ports:
- "8883:8883"
data-generation:
build: data_generation
# Access the lab with: http://127.0.0.1:8882/lab?token=nvsecuretoken0s
command: jupyter lab -y --allow-root --no-browser --ip=0.0.0.0 --port=8882 --notebook-dir=/opt/project/ --NotebookApp.token='nvsecuretoken0' --NotebookApp.password='nvsecurepassword'
volumes:
- './data_generation/code/:/opt/project/'
ports:
- "8882:8882"
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/README.md | ## Getting started
### Install Dependencies
- [`docker-compose`](https://docs.docker.com/compose/install/)
- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
### Run the labs
```
bash run.sh
```
### Access the labs
Part 1: Generate synthetic data with Omnvierse - [http://127.0.0.1:8882/lab?token=nvsecuretoken0](http://127.0.0.1:8882/lab?token=nvsecuretoken0) \
Part 2: Training a model with synthetic data - [http://127.0.0.1:8883/lab?token=nvsecuretoken1](http://127.0.0.1:8883/lab?token=nvsecuretoken1) \
Part 3: Deploy model to Triton - [http://127.0.0.1:8884/lab?token=nvsecuretoken2](http://127.0.0.1:8884/lab?token=nvsecuretoken2)
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/requirements.txt | jupyterlab==3.4.5
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/visualize.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import os
import json
import hashlib
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from optparse import OptionParser
"""
Takes in the data from a specific label id and maps it to the proper color for the bounding box
"""
def data_to_colour(data):
if isinstance(data, str):
data = bytes(data, "utf-8")
else:
data = bytes(data)
m = hashlib.sha256()
m.update(data)
key = int(m.hexdigest()[:8], 16)
r = ((((key >> 0) & 0xFF) + 1) * 33) % 255
g = ((((key >> 8) & 0xFF) + 1) * 33) % 255
b = ((((key >> 16) & 0xFF) + 1) * 33) % 255
# illumination normalization to 128
inv_norm_i = 128 * (3.0 / (r + g + b))
return (
int(r * inv_norm_i) / 255,
int(g * inv_norm_i) / 255,
int(b * inv_norm_i) / 255,
)
"""
Takes in the path to the rgb image for the background, then it takes bounding box data, the labels and the place to store the visualization. It outputs a colorized bounding box.
"""
def colorize_bbox_2d(rgb_path, data, id_to_labels, file_path):
rgb_img = Image.open(rgb_path)
colors = [data_to_colour(bbox["semanticId"]) for bbox in data]
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(rgb_img)
for bbox_2d, color, index in zip(data, colors, range(len(data))):
labels = id_to_labels[str(index)]
rect = patches.Rectangle(
xy=(bbox_2d["x_min"], bbox_2d["y_min"]),
width=bbox_2d["x_max"] - bbox_2d["x_min"],
height=bbox_2d["y_max"] - bbox_2d["y_min"],
edgecolor=color,
linewidth=2,
label=labels,
fill=False,
)
ax.add_patch(rect)
plt.legend(loc="upper left")
plt.savefig(file_path)
"""
Parses command line options. Requires input directory, output directory, and number for image to use.
"""
def parse_input():
usage = "usage: visualize.py [options] arg1 arg2 arg3"
parser = OptionParser(usage)
parser.add_option(
"-d",
"--data_dir",
dest="data_dir",
help="Directory location for Omniverse synthetic data",
)
parser.add_option(
"-o", "--out_dir", dest="out_dir", help="Directory location for output image"
)
parser.add_option(
"-n", "--number", dest="number", help="Number of image to use for visualization"
)
(options, args) = parser.parse_args()
return options, args
def main():
options, args = parse_input()
out_dir = options.data_dir
rgb = "png/rgb_" + options.number + ".png"
rgb_path = os.path.join(out_dir, rgb)
bbox2d_tight_file_name = "npy/bounding_box_2d_tight_" + options.number + ".npy"
data = np.load(os.path.join(options.data_dir, bbox2d_tight_file_name))
# Check for labels
bbox2d_tight_labels_file_name = (
"json/bounding_box_2d_tight_labels_" + options.number + ".json"
)
with open(
os.path.join(options.data_dir, bbox2d_tight_labels_file_name), "r"
) as json_data:
bbox2d_tight_id_to_labels = json.load(json_data)
# colorize and save image
colorize_bbox_2d(
rgb_path,
data,
bbox2d_tight_id_to_labels,
os.path.join(options.out_dir, "bbox2d_tight.png"),
)
if __name__ == "__main__":
main()
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/export.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import os
import torch
import torchvision
from optparse import OptionParser
def parse_input():
usage = "usage: export.py [options] arg1 "
parser = OptionParser(usage)
parser.add_option(
"-d",
"--pytorch_dir",
dest="pytorch_dir",
help="Location of output PyTorch model",
)
parser.add_option(
"-o",
"--output_dir",
dest="output_dir",
help="Export and save ONNX model to this path",
)
(options, args) = parser.parse_args()
return options, args
def main():
torch.manual_seed(0)
options, args = parse_input()
model = torch.load(options.pytorch_dir)
model.eval()
OUTPUT_DIR = options.output_dir
os.makedirs(OUTPUT_DIR, exist_ok=True)
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
weights="DEFAULT", num_classes=91
)
model.eval()
dummy_input = torch.rand(1, 3, 1024, 1024)
torch.onnx.export(
model,
dummy_input,
os.path.join(OUTPUT_DIR, "model.onnx"),
opset_version=11,
input_names=["input"],
output_names=["output"],
)
if __name__ == "__main__":
main()
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/training/code/train.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
from PIL import Image
import os
import numpy as np
import torch
import torch.utils.data
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision import transforms as T
import json
import shutil
from optparse import OptionParser
from torch.utils.tensorboard import SummaryWriter
class FruitDataset(torch.utils.data.Dataset):
def __init__(self, root, transforms):
self.root = root
self.transforms = transforms
list_ = os.listdir(root)
for file_ in list_:
name, ext = os.path.splitext(file_)
ext = ext[1:]
if ext == "":
continue
if os.path.exists(root + "/" + ext):
shutil.move(root + "/" + file_, root + "/" + ext + "/" + file_)
else:
os.makedirs(root + "/" + ext)
shutil.move(root + "/" + file_, root + "/" + ext + "/" + file_)
self.imgs = list(sorted(os.listdir(os.path.join(root, "png"))))
self.label = list(sorted(os.listdir(os.path.join(root, "json"))))
self.box = list(sorted(os.listdir(os.path.join(root, "npy"))))
def __getitem__(self, idx):
img_path = os.path.join(self.root, "png", self.imgs[idx])
img = Image.open(img_path).convert("RGB")
label_path = os.path.join(self.root, "json", self.label[idx])
with open(os.path.join("root", label_path), "r") as json_data:
json_labels = json.load(json_data)
box_path = os.path.join(self.root, "npy", self.box[idx])
dat = np.load(str(box_path))
boxes = []
labels = []
for i in dat:
obj_val = i[0]
xmin = torch.as_tensor(np.min(i[1]), dtype=torch.float32)
xmax = torch.as_tensor(np.max(i[3]), dtype=torch.float32)
ymin = torch.as_tensor(np.min(i[2]), dtype=torch.float32)
ymax = torch.as_tensor(np.max(i[4]), dtype=torch.float32)
if (ymax > ymin) & (xmax > xmin):
boxes.append([xmin, ymin, xmax, ymax])
area = (xmax - xmin) * (ymax - ymin)
labels += [json_labels.get(str(obj_val)).get("class")]
label_dict = {}
static_labels = {
"apple": 0,
"avocado": 1,
"kiwi": 2,
"lime": 3,
"lychee": 4,
"pomegranate": 5,
"onion": 6,
"strawberry": 7,
"lemon": 8,
"orange": 9,
}
labels_out = []
for i in range(len(labels)):
label_dict[i] = labels[i]
for i in label_dict:
fruit = label_dict[i]
final_fruit_label = static_labels[fruit]
labels_out += [final_fruit_label]
target = {}
target["boxes"] = torch.as_tensor(boxes, dtype=torch.float32)
target["labels"] = torch.as_tensor(labels_out, dtype=torch.int64)
target["image_id"] = torch.tensor([idx])
target["area"] = area
if self.transforms is not None:
img = self.transforms(img)
return img, target
def __len__(self):
return len(self.imgs)
"""
Parses command line options. Requires input data directory, output torch file, and number epochs used to train.
"""
def parse_input():
usage = "usage: train.py [options] arg1 arg2 "
parser = OptionParser(usage)
parser.add_option(
"-d",
"--data_dir",
dest="data_dir",
help="Directory location for Omniverse synthetic data.",
)
parser.add_option(
"-o",
"--output_file",
dest="output_file",
help="Save torch model to this file and location (file ending in .pth)",
)
parser.add_option(
"-e",
"--epochs",
dest="epochs",
help="Give number of epochs to be used for training",
)
(options, args) = parser.parse_args()
return options, args
def get_transform(train):
transforms = []
transforms.append(T.PILToTensor())
transforms.append(T.ConvertImageDtype(torch.float))
return T.Compose(transforms)
def collate_fn(batch):
return tuple(zip(*batch))
def create_model(num_classes):
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights="DEFAULT")
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
return model
def main():
writer = SummaryWriter()
options, args = parse_input()
dataset = FruitDataset(options.data_dir, get_transform(train=True))
train_size = int(len(dataset) * 0.7)
valid_size = int(len(dataset) * 0.2)
test_size = len(dataset) - valid_size - train_size
train, valid, test = torch.utils.data.random_split(
dataset, [train_size, valid_size, test_size]
)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=16, shuffle=True, num_workers=4, collate_fn=collate_fn
)
validloader = torch.utils.data.DataLoader(
valid, batch_size=16, shuffle=True, num_workers=4, collate_fn=collate_fn
)
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
num_classes = 10
num_epochs = int(options.epochs)
model = create_model(num_classes)
model.to(device)
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.001)
len_dataloader = len(data_loader)
model.train()
for epoch in range(num_epochs):
optimizer.zero_grad()
i = 0
for imgs, annotations in data_loader:
i += 1
imgs = list(img.to(device) for img in imgs)
annotations = [{k: v.to(device) for k, v in t.items()} for t in annotations]
loss_dict = model(imgs, annotations)
losses = sum(loss for loss in loss_dict.values())
writer.add_scalar("Loss/train", losses, epoch)
losses.backward()
optimizer.step()
print(f"Iteration: {i}/{len_dataloader}, Loss: {losses}")
writer.close()
torch.save(model, options.output_file)
if __name__ == "__main__":
main()
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/data_generation/requirements.txt | jupyterlab==3.2.9
opencv-python-headless==4.5.4.60
matplotlib==3.3.4
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/data_generation/code/generate_data_gui.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import datetime
now = datetime.datetime.now()
from functools import partial
import omni.replicator.core as rep
with rep.new_layer():
# Define paths for the character, the props, the environment and the surface where the assets will be scattered in.
CRATE = "omniverse://localhost/NVIDIA/Samples/Marbles/assets/standalone/SM_room_crate_3/SM_room_crate_3.usd"
SURFACE = (
"omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Basic/display_riser.usd"
)
ENVS = "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Interior/ZetCG_ExhibitionHall.usd"
FRUIT_PROPS = {
"apple": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Apple.usd",
"avocado": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Avocado01.usd",
"kiwi": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Kiwi01.usd",
"lime": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Lime01.usd",
"lychee": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Lychee01.usd",
"pomegranate": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Pomegranate01.usd",
"onion": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Vegetables/RedOnion.usd",
"strawberry": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Berries/strawberry.usd",
"lemon": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Decor/Tchotchkes/Lemon_01.usd",
"orange": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Decor/Tchotchkes/Orange_01.usd",
}
# Define randomizer function for Base assets. This randomization includes placement and rotation of the assets on the surface.
def random_props(file_name, class_name, max_number=1, one_in_n_chance=3):
instances = rep.randomizer.instantiate(
file_name, size=max_number, mode="scene_instance"
)
print(file_name)
with instances:
rep.modify.semantics([("class", class_name)])
rep.modify.pose(
position=rep.distribution.uniform((-8, 5, -25), (8, 30, 25)),
rotation=rep.distribution.uniform((-180, -180, -180), (180, 180, 180)),
scale=rep.distribution.uniform((0.8), (1.2)),
)
rep.modify.visibility(
rep.distribution.choice([True], [False] * (one_in_n_chance))
)
return instances.node
# Define randomizer function for sphere lights.
def sphere_lights(num):
lights = rep.create.light(
light_type="Sphere",
temperature=rep.distribution.normal(6500, 500),
intensity=rep.distribution.normal(30000, 5000),
position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)),
scale=rep.distribution.uniform(50, 100),
count=num,
)
return lights.node
rep.randomizer.register(random_props)
# Setup the static elements
env = rep.create.from_usd(ENVS)
surface = rep.create.from_usd(SURFACE)
with surface:
rep.physics.collider()
crate = rep.create.from_usd(CRATE)
with crate:
rep.physics.collider("none")
rep.physics.mass(mass=10000)
rep.modify.pose(position=(0, 20, 0), rotation=(0, 0, 90))
# Setup camera and attach it to render product
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
rep.randomizer.register(sphere_lights)
# trigger on frame for an interval
with rep.trigger.on_frame(num_frames=100):
for n, f in FRUIT_PROPS.items():
random_props(f, n)
rep.randomizer.sphere_lights(5)
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-3, 114, -17), (-1, 116, -15)),
look_at=(0, 20, 0),
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
now = now.strftime("%Y-%m-%d")
output_dir = "fruit_data_" + now
writer.initialize(output_dir=output_dir, rgb=True, bounding_box_2d_tight=True)
writer.attach([render_product])
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/data_generation/code/generate_data_headless.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import datetime
now = datetime.datetime.now()
from functools import partial
import omni.replicator.core as rep
with rep.new_layer():
# Define paths for the character, the props, the environment and the surface where the assets will be scattered in.
CRATE = "omniverse://localhost/NVIDIA/Samples/Marbles/assets/standalone/SM_room_crate_3/SM_room_crate_3.usd"
SURFACE = (
"omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Basic/display_riser.usd"
)
ENVS = "omniverse://localhost/NVIDIA/Assets/Scenes/Templates/Interior/ZetCG_ExhibitionHall.usd"
FRUIT_PROPS = {
"apple": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Apple.usd",
"avocado": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Avocado01.usd",
"kiwi": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Kiwi01.usd",
"lime": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Lime01.usd",
"lychee": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Lychee01.usd",
"pomegranate": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Fruit/Pomegranate01.usd",
"onion": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Vegetables/RedOnion.usd",
"strawberry": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Food/Berries/strawberry.usd",
"lemon": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Decor/Tchotchkes/Lemon_01.usd",
"orange": "omniverse://localhost/NVIDIA/Assets/ArchVis/Residential/Decor/Tchotchkes/Orange_01.usd",
}
# Define randomizer function for Base assets. This randomization includes placement and rotation of the assets on the surface.
def random_props(file_name, class_name, max_number=1, one_in_n_chance=3):
instances = rep.randomizer.instantiate(
file_name, size=max_number, mode="scene_instance"
)
print(file_name)
with instances:
rep.modify.semantics([("class", class_name)])
rep.modify.pose(
position=rep.distribution.uniform((-8, 5, -25), (8, 30, 25)),
rotation=rep.distribution.uniform((-180, -180, -180), (180, 180, 180)),
scale=rep.distribution.uniform((0.8), (1.2)),
)
rep.modify.visibility(
rep.distribution.choice([True], [False] * (one_in_n_chance))
)
return instances.node
# Define randomizer function for sphere lights.
def sphere_lights(num):
lights = rep.create.light(
light_type="Sphere",
temperature=rep.distribution.normal(6500, 500),
intensity=rep.distribution.normal(30000, 5000),
position=rep.distribution.uniform((-300, -300, -300), (300, 300, 300)),
scale=rep.distribution.uniform(50, 100),
count=num,
)
return lights.node
rep.randomizer.register(random_props)
# Setup the static elements
env = rep.create.from_usd(ENVS)
surface = rep.create.from_usd(SURFACE)
with surface:
rep.physics.collider()
crate = rep.create.from_usd(CRATE)
with crate:
rep.physics.collider("none")
rep.physics.mass(mass=10000)
rep.modify.pose(position=(0, 20, 0), rotation=(0, 0, 90))
# Setup camera and attach it to render product
camera = rep.create.camera()
render_product = rep.create.render_product(camera, resolution=(1024, 1024))
rep.randomizer.register(sphere_lights)
# trigger on frame for an interval
with rep.trigger.on_frame(num_frames=100):
for n, f in FRUIT_PROPS.items():
random_props(f, n)
rep.randomizer.sphere_lights(5)
with camera:
rep.modify.pose(
position=rep.distribution.uniform((-3, 114, -17), (-1, 116, -15)),
look_at=(0, 20, 0),
)
# Initialize and attach writer
writer = rep.WriterRegistry.get("BasicWriter")
now = now.strftime("%Y-%m-%d")
output_dir = "fruit_data_" + now
writer.initialize(output_dir=output_dir, rgb=True, bounding_box_2d_tight=True)
writer.attach([render_product])
# Run Replicator script headlessly
rep.orchestrator.run()
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/deployment/requirements.txt | tritonclient[all]==2.23.0
jupyterlab==3.4.5
opencv-python-headless==4.5.4.60
matplotlib==3.5.3
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/deployment/code/deploy.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES.
# SPDX-License-Identifier: BSD-3-Clause
#
# Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import tritonclient.grpc as grpcclient
from optparse import OptionParser
# load image data
import cv2
import numpy as np
from matplotlib import pyplot as plt
import subprocess
def install(name):
subprocess.call(["pip", "install", name])
"""
Parses command line options. Requires input sample png
"""
def parse_input():
usage = "usage: deploy.py [options] arg1 "
parser = OptionParser(usage)
parser.add_option(
"-p", "--png", dest="png", help="Directory location for single sample image."
)
(options, args) = parser.parse_args()
return options, args
def main():
options, args = parse_input()
target_width, target_height = 1024, 1024
# add path to test image
image_sample = options.png
image_bgr = cv2.imread(image_sample)
image_bgr
image_bgr = cv2.resize(image_bgr, (target_width, target_height))
image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
image = np.float32(image_rgb)
# preprocessing
image = image / 255
image = np.moveaxis(image, -1, 0) # HWC to CHW
image = image[np.newaxis, :] # add batch dimension
image = np.float32(image)
plt.imshow(image_rgb)
inference_server_url = "0.0.0.0:9001"
triton_client = grpcclient.InferenceServerClient(url=inference_server_url)
# find out info about model
model_name = "fasterrcnn_resnet50"
triton_client.get_model_config(model_name)
# create input
input_name = "input"
inputs = [grpcclient.InferInput(input_name, image.shape, "FP32")]
inputs[0].set_data_from_numpy(image)
output_name = "output"
outputs = [grpcclient.InferRequestedOutput("output")]
results = triton_client.infer(model_name, inputs, outputs=outputs)
output = results.as_numpy("output")
# annotate
annotated_image = image_bgr.copy()
if output.size > 0: # ensure something is found
for box in output:
box_top_left = int(box[0]), int(box[1])
box_bottom_right = int(box[2]), int(box[3])
text_origin = int(box[0]), int(box[3])
border_color = (50, 0, 100)
text_color = (255, 255, 255)
font_scale = 0.9
thickness = 1
# bounding box
cv2.rectangle(
annotated_image,
box_top_left,
box_bottom_right,
border_color,
thickness=5,
lineType=cv2.LINE_8,
)
plt.imshow(cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB))
if __name__ == "__main__":
main()
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/docs/part_2.md | # Part 2: Training a model with synthetic data
## Setup training
To use the training script you can see required parameters by running
-`python train.py --help`
- Example command:
- `python train.py -d /home/omni.replicator_out/fruit_data_$DATE/ -o /home/model.pth -e 10`
## Visualize training
We have included a visdualization script to run after your first training. This will show how Omniverse generates the labeled data. To see required parameters
- `python visualize.py --help`
- Example command:
- `python visualize.py -d /home/$USER/omni.replicator_out/fruit_data_$DATE -o /home/$USER -n 0`
## Export model
- To use the export script you can see required parameters by running
- `python export.py --help`
- Example command, make sure to dave to the `models/fasterrcnn_resnet50/1`
- `python export.py -d /home/out.pth -o /home/models/fasterrcnn_resnet50/1`
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/docs/part_3.md | # Part 3: Deploy model to Triton
## Start triton server
When we start the server we want our model to be properly located in the `/models/fasterrcnn_resnet50/1` folder.
`sudo docker run --gpus=1 --rm -p9000:8000 -p9001:8001 -p9002:8002 -v /home/$USER/sdg_workflow/models/:/models nvcr.io/nvidia/tritonserver:23.01-py3 tritonserver --model-repository=/models`
Once started, you should see:
```
+---------------------+---------+--------+
| Model | Version | Status |
+---------------------+---------+--------+
| fasterrcnn_resnet50 | 1 | READY |
+---------------------+---------+--------+
```
## Start triton client
In another terminal window, with your server running start your client
- `sudo docker run -it --rm --net=host -v /home/zoe/Desktop/sdg_workflow:/workspace nvcr.io/nvidia/tritonserver:23.01-py3-sdk`
- To use the deploy script you can see required parameters by running
- `python deploy.py --help`
- Example command:
- ` python deploy.py -p /workspace/rgb_0.png`
|
NVIDIA-Omniverse/synthetic-data-examples/end-to-end-workflows/object_detection_fruit/docs/part_1.md | # Part 1: Generate synthetic data with Omnvierse
## Install Dependencies
In this section you can generate your synthetic data using the Omniverse GUI or as a headless version in your local terminal. Either option requires an Omniverse install.
- [Install Omniverse Launcher](https://docs.omniverse.nvidia.com/prod_install-guide/prod_install-guide/overview.html#omniverse-install-guide)
## Omniverse Launcher & Code
- [Install Omniverse Code](https://docs.omniverse.nvidia.com/prod_workflows/prod_workflows/extensions/environment_configuration.html#step-2-install-omniverse-code) from the `Exchange` tab within Omniverse Launcher
## Generate data in Omniverse GUI
Copy the contents of the generate_data.py script into the Script Editor tab in the bottom section of the Code window. Press the RUn1 button or ctrl + Enter on your keyboard to load the scene in the Viewport. From there you can preview a single scene in the Replciator tab at the top by clicking Preview or run the full script by clicking Run. If you make no changes to this script it will generate 100 frames.
- From inside the Code GUI using the [script editor](https://docs.omniverse.nvidia.com/app_code/prod_extensions/ext_script-editor.html)
- If using Linux, copy code from `generate_data_gui.py` into the Script Editor window
-Execute code by clicking the `Run` button or pressing `ctrl+Enter`
- To preview what the scene will look like click Replicator then `Preview` in the top bar of your Omniverse Code window
- When you are ready to generate all your data go ahead and click `Replicator` and then `Run`, this will generate the designated number of frames and drop the RGB, bounding box data, and labels into the desired folder
## Generate data headlessly
Follow the documentation guidelines to launch a terminal in the correct folder location. The correct script to pass to your --/omni/replicator.scrip is generate_data_headless.py. This will generate and save the synthetic data in the same way as before, without utilizing the Omniverse GUI.
- [How to run](https://docs.omniverse.nvidia.com/prod_extensions/prod_extensions/ext_replicator/headless_example.html)
- Script location: `/FruitBasketOVEReplicatorDemo/data_generation/code/generate_data_headless.py`
- We need to locate `omni.code.replicator.sh`
To find look for where Omniverse ode is locally installed
- Run (script dictates where the output data is stored):
`./omni.code.replicator.sh --no-window --/omni/replicator/script= “/FruitBasketOVEReplicatorDemo/data_generation/code/generate_data_headless.py”`
|
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/build_trt_fp32.sh | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
ONNX_PATH=${1:-'pallet_model_v1.onnx'}
OUTPUT_PATH=${2:-'pallet_model_v1.engine'}
/usr/src/tensorrt/bin/trtexec \
--onnx=$ONNX_PATH \
--minShapes=input:1x3x192x192 \
--maxShapes=input:1x3x1536x1536 \
--optShapes=input:1x3x256x256 \
--saveEngine=$OUTPUT_PATH |
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/predict.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import utils
import cv2
import torch
if __name__ == "__main__":
# Parse command line arguments
parser = argparse.ArgumentParser()
parser.add_argument(
"engine",
type=str,
help="The file path of the TensorRT engine."
)
parser.add_argument(
"image",
type=str,
help="The file path of the image provided as input for inference."
)
parser.add_argument(
"--output",
type=str,
default=None,
help="The path to output the inference visualization."
)
parser.add_argument(
"--inference-size",
type=str,
default="512x512",
help="The height and width that the image is resized to for inference."
" Denoted as (height)x(width)."
)
parser.add_argument(
"--peak-window",
type=str,
default="7x7",
help="The size of the window used when finding local peaks. Denoted as "
" (window_height)x(window_width)."
)
parser.add_argument(
'--peak-threshold',
type=float,
default=0.5,
help="The heatmap threshold to use when finding peaks. Values must be "
" larger than this value to be considered peaks."
)
parser.add_argument(
'--line-thickness',
type=int,
default=1,
help="The line thickness for drawn boxes"
)
args = parser.parse_args()
# Parse inference height, width from arguments
inference_size = tuple(int(x) for x in args.inference_size.split('x'))
peak_window = tuple(int(x) for x in args.peak_window.split('x'))
if args.output is None:
output_path = '.'.join(args.image.split('.')[:-1]) + "_output.jpg"
else:
output_path = args.output
# Create offset grid
offset_grid = utils.make_offset_grid(inference_size).to("cuda")
# Load model
model = utils.load_trt_engine_wrapper(
args.engine,
input_names=["input"],
output_names=["heatmap", "vectormap"]
)
# Load image
image = cv2.imread(args.image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Pad and resize image (aspect ratio preserving resize)
image, _, _ = utils.pad_resize(image, inference_size)
with torch.no_grad():
# Format image for inference
x = utils.format_bgr8_image(image)
x = x.to("cuda")
# Execute model
heatmap, vectormap = model(x)
# Scale and offset vectormap
keypointmap = utils.vectormap_to_keypointmap(
offset_grid,
vectormap
)
# Find local peaks
peak_mask = utils.find_heatmap_peak_mask(
heatmap,
peak_window,
args.peak_threshold
)
# Extract keypoints at local peak
keypoints = keypointmap[0][peak_mask[0, 0]]
# Draw
vis_image = utils.draw_box(
image,
keypoints,
color=(118, 186, 0),
thickness=args.line_thickness
)
vis_image = cv2.cvtColor(vis_image, cv2.COLOR_RGB2BGR)
cv2.imwrite(output_path, vis_image)
|
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/build_trt_fp16.sh | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
ONNX_PATH=${1:-'pallet_model_v1.onnx'}
OUTPUT_PATH=${2:-'pallet_model_v1.engine'}
/usr/src/tensorrt/bin/trtexec \
--onnx=$ONNX_PATH \
--minShapes=input:1x3x192x192 \
--maxShapes=input:1x3x1536x1536 \
--optShapes=input:1x3x256x256 \
--saveEngine=$OUTPUT_PATH \
--fp16 |
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/utils.py | # SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn.functional as F
import numpy as np
import cv2
import einops
import tensorrt as trt
import torch2trt
from typing import Sequence
BOX_EDGES = [
[0, 1],
[1, 5],
[5, 4],
[4, 0],
[2, 3],
[3, 7],
[7, 6],
[6, 2],
[0, 2],
[1, 3],
[4, 6],
[5, 7]
]
def make_offset_grid(
size,
stride=(1, 1)
):
grid = torch.stack(
torch.meshgrid(
stride[0] * (torch.arange(size[0]) + 0.5),
stride[1] * (torch.arange(size[1]) + 0.5)
),
dim=-1
)
return grid
def vectormap_to_keypointmap(
offset_grid,
vector_map,
vector_scale: float = 1./256.
):
vector_map = vector_map / vector_scale
keypoint_map = einops.rearrange(vector_map, "b (k d) h w -> b h w k d", d=2)
keypoint_map = keypoint_map + offset_grid[:, :, None, :]
# yx -> xy
keypoint_map = keypoint_map[..., [1, 0]]
return keypoint_map
def find_heatmap_peak_mask(heatmap, window=3, threshold=0.5):
all_indices = torch.arange(
heatmap.numel(),
device=heatmap.device
)
all_indices = all_indices.reshape(heatmap.shape)
if isinstance(window, int):
window = (window, window)
values, max_indices = F.max_pool2d_with_indices(
heatmap,
kernel_size=window,
stride=1,
padding=(window[0] // 2, window[1] // 2)
)
is_above_threshold = heatmap >= threshold
is_max = max_indices == all_indices
is_peak = is_above_threshold & is_max
return is_peak
def draw_box(image_bgr, keypoints, color=(118, 186, 0), thickness=1):
num_objects = int(keypoints.shape[0])
for i in range(num_objects):
keypoints_i = keypoints[i]
kps_i = [(int(x), int(y)) for x, y in keypoints_i]
edges = BOX_EDGES
for e in edges:
cv2.line(
image_bgr,
kps_i[e[0]],
kps_i[e[1]],
(118, 186, 0),
thickness=thickness
)
return image_bgr
def pad_resize(image, output_shape):
ar_i = image.shape[1] / image.shape[0]
ar_o = output_shape[1] / output_shape[0]
# resize
if ar_i > ar_o:
w_i = output_shape[1]
h_i = min(int(w_i / ar_i), output_shape[0])
else:
h_i = output_shape[0]
w_i = min(int(h_i * ar_i), output_shape[1])
# paste
pad_left = (output_shape[1] - w_i) // 2
pad_top = (output_shape[0] - h_i) // 2
image_resize = cv2.resize(image, (w_i, h_i))
out = np.zeros_like(
image,
shape=(output_shape[0], output_shape[1], image.shape[2])
)
out[pad_top:pad_top + h_i, pad_left:pad_left + w_i] = image_resize
pad = (pad_top, pad_left)
scale = (image.shape[0] / h_i, image.shape[1] / w_i)
return out, pad, scale
def load_trt_engine(path: str):
with trt.Logger() as logger, trt.Runtime(logger) as runtime:
with open(path, 'rb') as f:
engine_bytes = f.read()
engine = runtime.deserialize_cuda_engine(engine_bytes)
return engine
def load_trt_engine_wrapper(
path: str,
input_names: Sequence,
output_names: Sequence
):
engine = load_trt_engine(path)
wrapper = torch2trt.TRTModule(
engine=engine,
input_names=input_names,
output_names=output_names
)
return wrapper
def format_bgr8_image(image, device="cuda"):
x = torch.from_numpy(image)
x = x.permute(2, 0, 1)[None, ...]
x = (x / 255 - 0.45) / 0.25
return x |
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/build_trt_int8.sh | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
ONNX_PATH=${1:-'pallet_model_v1.onnx'}
OUTPUT_PATH=${2:-'pallet_model_v1.engine'}
/usr/src/tensorrt/bin/trtexec \
--onnx=$ONNX_PATH \
--minShapes=input:1x3x192x192 \
--maxShapes=input:1x3x1536x1536 \
--optShapes=input:1x3x256x256 \
--saveEngine=$OUTPUT_PATH \
--int8 |
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/README.md | # SDG Pallet Model
<img src="images/test_image_1_output.jpg" height="256"/>
This repository contains code for performing optimized TensorRT inference with a pre-trained
pallet detection model that was trained using synthetic data with [NVIDIA Omniverse Replicator](https://developer.nvidia.com/omniverse/replicator).
The model takes as input a monocular RGB image, and outputs the pallet box estimates. The box esimates
are defined for each pallet side face. So a single pallet may have multiple box
estimates.
If you have any questions, please feel free to reach out by opening an issue!
## Instructions
### Step 1 - Install dependencies
Assumes you've already set up your system with OpenCV, PyTorch and numpy.
Install einops for some utility functions.
```bash
pip3 install einops
```
Install [torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt). This is used
for the ``TRTModule`` class which simplifies engine inference.
```bash
git clone https://github.com/NVIDIA-AI-IOT/torch2trt
cd torch2trt
python3 setup.py develop
```
### Step 2 - Download the ONNX model
Download the pallet model ONNX file.
| Model | Notes | Links |
|-------|-------|-------|
| pallet_model_v1_all | Trained for wood and other pallets (metal, plastic). | [onnx](https://drive.google.com/file/d/1Vsl7s5YhBFxkTkd3UYYgPWFCLNRm_O_Q/view?usp=share_link) |
| pallet_model_v1_wood | Trained only for wood pallets. | [onnx](https://drive.google.com/file/d/1Fd1gS7NYkWHPhUn7iZLK43hLQ1qDkuvb/view?usp=share_link) |
### Step 3 - Build the TensorRT engine
#### Option 1 (*recommended*) - Build the FP16 engine
To build the FP16 engine, call the following:
```bash
./build_trt_fp16.sh <onnx_path> <engine_output_path>
```
#### Option 2 - Build the INT8 engine
> The INT8 model instructions do not yet include calibration. Please only use
> this model for throughput profiling. The accuracy is likely to vary from
> FP32/FP16 models. However, once calibration is included, this may become
> the recommended option given the improved throughput results.
To build the INT8 engine, call the following:
```bash
./build_trt_int8.sh <onnx_path> <engine_output_path>
```
We hope to provide instructions for using the Deep Learning Accelerator (DLA)
on Jetson AGX Orin, and INT8 calibration soon.
### Step 3 - Profile the engine
To profile the engine with the ``trtexec`` tool, call the following:
```bash
./profile_engine.sh <engine_path>
```
Here are the results for a model inference at 256x256 resolution,
profiled on Jetson AGX Orin.
<a id="throughput_results"/>
| Precision | Throughput (FPS) |
|-----------|------------------|
| FP16 | 465 |
| INT8 | 710 |
Notes:
- Called ``jetson_clocks`` before running
- Using MAXN power mode by calling ``sudo nvpmodel -m0``
- Batch size 1
- ``--useSpinWait`` flag enabled to stabilize timings
- ``--useCudaGraph`` flag enabled to use CUDA graph optimizations. Cuda graph
isn't yet used in the predict function.
### Step 4 - Run inference on an example image.
```bash
python3 predict.py <engine_path> <image_path> --output=<output_path>
```
For more options
```
python3 predict.py --help
```
### Next Steps
Try modifying the predict.py code to visualize inference on a live camera feed.
|
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/LICENSE.md | SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: Apache-2.0
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
|
NVIDIA-Omniverse/synthetic-data-examples/training_examples/sdg_pallet_model/profile_engine.sh | #!/bin/bash
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
ENGINE_PATH=${1:-'pallet_model.engine'}
SHAPE="1x3x256x256"
/usr/src/tensorrt/bin/trtexec \
--loadEngine=$ENGINE_PATH \
--shapes=input:$SHAPE \
--useSpinWait \
--useCudaGraph |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/build_foxy.sh | #!/bin/bash
set -e
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
# MY_DIR="$(realpath -s "$SCRIPT_DIR"/build_ws/foxy)"
docker build . --network=host -f ubuntu_20_foxy_minimal.dockerfile -t isaac_sim_ros:ubuntu_20_foxy
rm -rf build_ws/foxy
mkdir -p build_ws/foxy
pushd build_ws/foxy
docker cp $(docker create --rm isaac_sim_ros:ubuntu_20_foxy):/workspace/foxy_ws foxy_ws
docker cp $(docker create --rm isaac_sim_ros:ubuntu_20_foxy):/workspace/build_ws isaac_sim_ros_ws
popd |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/build_humble.sh | #!/bin/bash
set -e
SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
docker build . --network=host -f ubuntu_20_humble_minimal.dockerfile -t isaac_sim_ros:ubuntu_20_humble
rm -rf build_ws/humble
mkdir -p build_ws/humble
pushd build_ws/humble
docker cp $(docker create --rm isaac_sim_ros:ubuntu_20_humble):/workspace/humble_ws humble_ws
docker cp $(docker create --rm isaac_sim_ros:ubuntu_20_humble):/workspace/build_ws isaac_sim_ros_ws
popd |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/README.md | # Isaac Sim ROS & ROS2 Workspaces
This repository contains three workspaces: `noetic_ws` (ROS Noetic), `foxy_ws` (ROS2 Foxy) and `humble_ws` (ROS2 Humble).
[Click here for usage and installation instructions with Isaac Sim](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_ros.html)
When cloning this repository, all three workspaces are downloaded. Depending on which ROS distro you are using, follow the [setup instructions](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_ros.html#setting-up-workspaces) for building your specific workspace. |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/fastdds.xml | <?xml version="1.0" encoding="UTF-8" ?>
<license>Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.</license>
<profiles xmlns="http://www.eprosima.com/XMLSchemas/fastRTPS_Profiles" >
<transport_descriptors>
<transport_descriptor>
<transport_id>UdpTransport</transport_id>
<type>UDPv4</type>
</transport_descriptor>
</transport_descriptors>
<participant profile_name="udp_transport_profile" is_default_profile="true">
<rtps>
<userTransports>
<transport_id>UdpTransport</transport_id>
</userTransports>
<useBuiltinTransports>false</useBuiltinTransports>
</rtps>
</participant>
</profiles> |
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/CMakeLists.txt | cmake_minimum_required(VERSION 3.5)
project(isaac_tutorials)
find_package(ament_cmake REQUIRED)
find_package(rclpy REQUIRED)
install(DIRECTORY
rviz2
scripts
DESTINATION share/${PROJECT_NAME})
# Install Python executables
install(PROGRAMS
scripts/ros2_publisher.py
DESTINATION lib/${PROJECT_NAME}
)
ament_package()
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/package.xml | <?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>isaac_tutorials</name>
<version>0.1.0</version>
<description>
The isaac_tutorials package
</description>
<maintainer email="[email protected]">isaac sim</maintainer>
<license>Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.</license>
<url type="Documentation">https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html</url>
<url type="Forums">https://forums.developer.nvidia.com/c/agx-autonomous-machines/isaac/simulation</url>
<buildtool_depend>ament_cmake</buildtool_depend>
<build_depend>launch</build_depend>
<build_depend>launch_ros</build_depend>
<build_depend>joint_state_publisher</build_depend>
<build_depend>rviz2</build_depend>
<build_depend>turtlebot3</build_depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/scripts/ros2_publisher.py | #!/usr/bin/env python3
# Copyright (c) 2020-2022, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
import rclpy
from rclpy.node import Node
from sensor_msgs.msg import JointState
import numpy as np
import time
class TestROS2Bridge(Node):
def __init__(self):
super().__init__("test_ros2bridge")
# Create the publisher. This publisher will publish a JointState message to the /joint_command topic.
self.publisher_ = self.create_publisher(JointState, "joint_command", 10)
# Create a JointState message
self.joint_state = JointState()
self.joint_state.name = [
"panda_joint1",
"panda_joint2",
"panda_joint3",
"panda_joint4",
"panda_joint5",
"panda_joint6",
"panda_joint7",
"panda_finger_joint1",
"panda_finger_joint2",
]
num_joints = len(self.joint_state.name)
# make sure kit's editor is playing for receiving messages
self.joint_state.position = np.array([0.0] * num_joints, dtype=np.float64).tolist()
self.default_joints = [0.0, -1.16, -0.0, -2.3, -0.0, 1.6, 1.1, 0.4, 0.4]
# limiting the movements to a smaller range (this is not the range of the robot, just the range of the movement
self.max_joints = np.array(self.default_joints) + 0.5
self.min_joints = np.array(self.default_joints) - 0.5
# position control the robot to wiggle around each joint
self.time_start = time.time()
timer_period = 0.05 # seconds
self.timer = self.create_timer(timer_period, self.timer_callback)
def timer_callback(self):
self.joint_state.header.stamp = self.get_clock().now().to_msg()
joint_position = (
np.sin(time.time() - self.time_start) * (self.max_joints - self.min_joints) * 0.5 + self.default_joints
)
self.joint_state.position = joint_position.tolist()
# Publish the message to the topic
self.publisher_.publish(self.joint_state)
def main(args=None):
rclpy.init(args=args)
ros2_publisher = TestROS2Bridge()
rclpy.spin(ros2_publisher)
# Destroy the node explicitly
ros2_publisher.destroy_node()
rclpy.shutdown()
if __name__ == "__main__":
main()
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/rviz2/rtx_lidar.rviz | Panels:
- Class: rviz_common/Displays
Help Height: 138
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Status1
- /PointCloud21
- /LaserScan1
Splitter Ratio: 0.5
Tree Height: 938
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /2D Goal Pose1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz_default_plugins/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: PointCloud2
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /point_cloud
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/LaserScan
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 28
Min Color: 0; 0; 0
Min Intensity: 0
Name: LaserScan
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.03999999910593033
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /scan
Use Fixed Frame: true
Use rainbow: true
Value: true
Enabled: true
Global Options:
Background Color: 48; 48; 48
Fixed Frame: sim_lidar
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/Interact
Hide Inactive Objects: true
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /initialpose
- Class: rviz_default_plugins/SetGoal
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /goal_pose
- Class: rviz_default_plugins/PublishPoint
Single click: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /clicked_point
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Class: rviz_default_plugins/Orbit
Distance: 33.790897369384766
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Focal Point:
X: 4.0773420333862305
Y: 8.741633415222168
Z: 1.6276212930679321
Focal Shape Fixed Size: true
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 0.4153982698917389
Target Frame: <Fixed Frame>
Value: Orbit (rviz)
Yaw: 3.3785688877105713
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 1342
Hide Left Dock: false
Hide Right Dock: false
QMainWindow State: 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
Selection:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: false
Width: 2578
X: 1999
Y: 339
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/rviz2/turtle_stereo.rviz | Panels:
- Class: rviz_common/Displays
Help Height: 138
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Camera1
- /Camera2
Splitter Ratio: 0.5
Tree Height: 1120
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /2D Goal Pose1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz_default_plugins/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Class: rviz_default_plugins/Camera
Enabled: true
Image Rendering: background and overlay
Name: Camera
Overlay Alpha: 0.5
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /rgb
Value: true
Visibility:
Camera: true
Grid: true
Value: true
Zoom Factor: 1
- Class: rviz_default_plugins/Camera
Enabled: true
Image Rendering: background and overlay
Name: Camera
Overlay Alpha: 0.5
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /rgb_2
Value: true
Visibility:
Camera: true
Grid: true
Value: true
Zoom Factor: 1
Enabled: true
Global Options:
Background Color: 48; 48; 48
Fixed Frame: sim_camera
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/Interact
Hide Inactive Objects: true
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /initialpose
- Class: rviz_default_plugins/SetGoal
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /goal_pose
- Class: rviz_default_plugins/PublishPoint
Single click: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /clicked_point
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Class: rviz_default_plugins/Orbit
Distance: 10
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Focal Point:
X: -7.855632781982422
Y: -8.80923843383789
Z: 11.783849716186523
Focal Shape Fixed Size: true
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 0.785398006439209
Target Frame: <Fixed Frame>
Value: Orbit (rviz)
Yaw: 0.785398006439209
Saved: ~
Window Geometry:
Camera:
collapsed: false
Displays:
collapsed: false
Height: 1658
Hide Left Dock: false
Hide Right Dock: true
QMainWindow State: 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
Selection:
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Tool Properties:
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Views:
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NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/rviz2/camera_lidar.rviz | Panels:
- Class: rviz_common/Displays
Help Height: 0
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Status1
- /Camera1 - RGB1
- /TF1
Splitter Ratio: 0.5
Tree Height: 871
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /2D Goal Pose1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz_default_plugins/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Class: rviz_default_plugins/Image
Enabled: false
Max Value: 1
Median window: 5
Min Value: 0
Name: Camera1 - Depth
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /camera_1/depth/image_rect_raw
Value: false
- Class: rviz_default_plugins/Image
Enabled: true
Max Value: 1
Median window: 5
Min Value: 0
Name: Camera2 - Depth
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /camera_2/depth/image_rect_raw
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/LaserScan
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 34
Min Color: 0; 0; 0
Min Intensity: 1
Name: LaserScan
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.05000000074505806
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
Filter size: 10
History Policy: Keep Last
Reliability Policy: Reliable
Value: /laser_scan
Use Fixed Frame: true
Use rainbow: true
Value: true
- Class: rviz_default_plugins/Image
Enabled: true
Max Value: 1
Median window: 5
Min Value: 0
Name: Camera2 - RGB
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /camera_2/rgb/image_raw
Value: true
- Class: rviz_default_plugins/Image
Enabled: false
Max Value: 1
Median window: 5
Min Value: 0
Name: Camera1 - RGB
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /camera_1/rgb/image_raw
Value: false
- Class: rviz_default_plugins/TF
Enabled: true
Frame Timeout: 15
Frames:
All Enabled: true
Camera_1:
Value: true
Camera_2:
Value: true
base_footprint:
Value: true
base_link:
Value: true
base_scan:
Value: true
caster_back_link:
Value: true
imu_link:
Value: true
odom:
Value: true
wheel_left_link:
Value: true
wheel_right_link:
Value: true
world:
Value: true
Marker Scale: 1
Name: TF
Show Arrows: true
Show Axes: true
Show Names: false
Tree:
world:
Camera_1:
{}
Camera_2:
{}
base_link:
base_footprint:
{}
base_scan:
{}
caster_back_link:
{}
imu_link:
{}
wheel_left_link:
{}
wheel_right_link:
{}
Update Interval: 0
Value: true
Enabled: true
Global Options:
Background Color: 48; 48; 48
Fixed Frame: world
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/Interact
Hide Inactive Objects: true
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Covariance x: 0.25
Covariance y: 0.25
Covariance yaw: 0.06853891909122467
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /initialpose
- Class: rviz_default_plugins/SetGoal
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /goal_pose
- Class: rviz_default_plugins/PublishPoint
Single click: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /clicked_point
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Class: rviz_default_plugins/Orbit
Distance: 8.917070388793945
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Focal Point:
X: -1.0011783838272095
Y: 1.8463866710662842
Z: 0.4111546277999878
Focal Shape Fixed Size: true
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 0.329797625541687
Target Frame: <Fixed Frame>
Value: Orbit (rviz)
Yaw: 0.25040191411972046
Saved: ~
Window Geometry:
Camera1 - Depth:
collapsed: false
Camera1 - RGB:
collapsed: false
Camera2 - Depth:
collapsed: false
Camera2 - RGB:
collapsed: false
Displays:
collapsed: false
Height: 1022
Hide Left Dock: false
Hide Right Dock: false
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Selection:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: false
Width: 2527
X: 1351
Y: 96
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/rviz2/carter_stereo.rviz | Panels:
- Class: rviz_common/Displays
Help Height: 138
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Status1
- /TF1
- /Odometry1/Topic1
Splitter Ratio: 0.5
Tree Height: 469
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /2D Goal Pose1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz_default_plugins/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Class: rviz_default_plugins/TF
Enabled: true
Frame Timeout: 15
Frames:
All Enabled: true
base_link:
Value: true
chassis_link:
Value: true
front_2d_lidar:
Value: true
front_3d_lidar:
Value: true
front_stereo_camera:imu:
Value: true
front_stereo_camera:left_rgb:
Value: true
front_stereo_camera:right_rgb:
Value: true
left_stereo_camera:imu:
Value: true
left_stereo_camera:left_rgb:
Value: true
left_stereo_camera:right_rgb:
Value: true
odom:
Value: true
rear_2d_lidar:
Value: true
rear_stereo_camera:imu:
Value: true
rear_stereo_camera:left_rgb:
Value: true
rear_stereo_camera:right_rgb:
Value: true
right_stereo_camera:imu:
Value: true
right_stereo_camera:left_rgb:
Value: true
right_stereo_camera:right_rgb:
Value: true
Marker Scale: 1
Name: TF
Show Arrows: true
Show Axes: true
Show Names: false
Tree:
odom:
base_link:
chassis_link:
{}
front_2d_lidar:
{}
front_3d_lidar:
{}
front_stereo_camera:imu:
{}
front_stereo_camera:left_rgb:
{}
front_stereo_camera:right_rgb:
{}
left_stereo_camera:imu:
{}
left_stereo_camera:left_rgb:
{}
left_stereo_camera:right_rgb:
{}
rear_2d_lidar:
{}
rear_stereo_camera:imu:
{}
rear_stereo_camera:left_rgb:
{}
rear_stereo_camera:right_rgb:
{}
right_stereo_camera:imu:
{}
right_stereo_camera:left_rgb:
{}
right_stereo_camera:right_rgb:
{}
Update Interval: 0
Value: true
- Angle Tolerance: 0.10000000149011612
Class: rviz_default_plugins/Odometry
Covariance:
Orientation:
Alpha: 0.5
Color: 255; 255; 127
Color Style: Unique
Frame: Local
Offset: 1
Scale: 1
Value: true
Position:
Alpha: 0.30000001192092896
Color: 204; 51; 204
Scale: 1
Value: true
Value: true
Enabled: true
Keep: 100
Name: Odometry
Position Tolerance: 0.10000000149011612
Shape:
Alpha: 1
Axes Length: 1
Axes Radius: 0.10000000149011612
Color: 255; 25; 0
Head Length: 0.30000001192092896
Head Radius: 0.10000000149011612
Shaft Length: 1
Shaft Radius: 0.05000000074505806
Value: Arrow
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: odom
Value: true
- Class: rviz_default_plugins/Image
Enabled: true
Max Value: 1
Median window: 5
Min Value: 0
Name: Left Camera - RGB
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /front_stereo_camera/left_rgb/image_raw
Value: true
- Class: rviz_default_plugins/Image
Enabled: true
Max Value: 1
Median window: 5
Min Value: 0
Name: Right Camera - RGB
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /front_stereo_camera/right_rgb/image_raw
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: PointCloud2
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /front_3d_lidar/point_cloud
Use Fixed Frame: true
Use rainbow: true
Value: true
Enabled: true
Global Options:
Background Color: 48; 48; 48
Fixed Frame: odom
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/Interact
Hide Inactive Objects: true
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /initialpose
- Class: rviz_default_plugins/SetGoal
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /goal_pose
- Class: rviz_default_plugins/PublishPoint
Single click: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /clicked_point
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Class: rviz_default_plugins/Orbit
Distance: 7.74399995803833
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Focal Point:
X: 0.08670726418495178
Y: -0.48873579502105713
Z: 2.1064507961273193
Focal Shape Fixed Size: true
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 0.8903975486755371
Target Frame: <Fixed Frame>
Value: Orbit (rviz)
Yaw: 1.9503958225250244
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 752
Hide Left Dock: false
Hide Right Dock: true
Left Camera - RGB:
collapsed: false
QMainWindow State: 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
Right Camera - RGB:
collapsed: false
Selection:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: true
Width: 1387
X: 344
Y: 122
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_tutorials/rviz2/camera_manual.rviz | Panels:
- Class: rviz_common/Displays
Help Height: 138
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /Status1
Splitter Ratio: 0.5
Tree Height: 1286
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /2D Goal Pose1
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz_default_plugins/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Class: rviz_default_plugins/Camera
Enabled: true
Image Rendering: background and overlay
Name: Depth
Overlay Alpha: 0.5
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /depth
Value: true
Visibility:
Grid: true
RGB: true
Value: true
Zoom Factor: 1
- Class: rviz_default_plugins/Camera
Enabled: true
Image Rendering: background and overlay
Name: RGB
Overlay Alpha: 0.5
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /rgb
Value: true
Visibility:
Depth: true
Grid: true
Value: true
Zoom Factor: 1
Enabled: true
Global Options:
Background Color: 48; 48; 48
Fixed Frame: sim_camera
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/Interact
Hide Inactive Objects: true
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /initialpose
- Class: rviz_default_plugins/SetGoal
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /goal_pose
- Class: rviz_default_plugins/PublishPoint
Single click: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /clicked_point
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Class: rviz_default_plugins/Orbit
Distance: 10
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Focal Point:
X: 0
Y: 0
Z: 0
Focal Shape Fixed Size: true
Focal Shape Size: 0.05000000074505806
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Pitch: 0.785398006439209
Target Frame: <Fixed Frame>
Value: Orbit (rviz)
Yaw: 0.785398006439209
Saved: ~
Window Geometry:
Depth:
collapsed: false
Displays:
collapsed: false
Height: 1690
Hide Left Dock: false
Hide Right Dock: false
QMainWindow State: 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
RGB:
collapsed: false
Selection:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: false
Width: 2588
X: 3535
Y: 1306
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_ros2_messages/CMakeLists.txt | cmake_minimum_required(VERSION 3.5)
project(isaac_ros2_messages)
# Default to C99
if(NOT CMAKE_C_STANDARD)
set(CMAKE_C_STANDARD 99)
endif()
# Default to C++14
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 14)
endif()
if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
add_compile_options(-Wall -Wextra -Wpedantic)
endif()
# find dependencies
find_package(ament_cmake REQUIRED)
# uncomment the following section in order to fill in
# further dependencies manually.
# find_package(<dependency> REQUIRED)
if(BUILD_TESTING)
find_package(ament_lint_auto REQUIRED)
# the following line skips the linter which checks for copyrights
# uncomment the line when a copyright and license is not present in all source files
#set(ament_cmake_copyright_FOUND TRUE)
# the following line skips cpplint (only works in a git repo)
# uncomment the line when this package is not in a git repo
#set(ament_cmake_cpplint_FOUND TRUE)
ament_lint_auto_find_test_dependencies()
endif()
find_package(builtin_interfaces REQUIRED)
find_package(rosidl_default_generators REQUIRED)
IF (WIN32)
find_package(FastRTPS REQUIRED)
ELSE()
find_package(fastrtps REQUIRED)
ENDIF()
find_package(std_msgs REQUIRED)
find_package(geometry_msgs REQUIRED)
rosidl_generate_interfaces(${PROJECT_NAME}
"srv/IsaacPose.srv"
DEPENDENCIES builtin_interfaces std_msgs geometry_msgs
)
ament_package()
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/isaac_ros2_messages/package.xml | <?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>isaac_ros2_messages</name>
<version>0.1.0</version>
<description>ROS2 messages for isaac ros2 bridge</description>
<maintainer email="[email protected]">isaac sim</maintainer>
<license>Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.</license>
<url type="Documentation">https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html</url>
<url type="Forums">https://forums.developer.nvidia.com/c/agx-autonomous-machines/isaac/simulation</url>
<buildtool_depend>ament_cmake</buildtool_depend>
<test_depend>ament_lint_auto</test_depend>
<test_depend>ament_lint_common</test_depend>
<build_depend>rosidl_default_generators</build_depend>
<build_depend>std_msgs</build_depend>
<build_depend>geometry_msgs</build_depend>
<exec_depend>builtin_interfaces</exec_depend>
<exec_depend>rosidl_default_runtime</exec_depend>
<exec_depend>std_msgs</exec_depend>
<exec_depend>geometry_msgs</exec_depend>
<member_of_group>rosidl_interface_packages</member_of_group>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/custom_message/CMakeLists.txt | cmake_minimum_required(VERSION 3.5)
project(custom_message)
# Default to C99
if(NOT CMAKE_C_STANDARD)
set(CMAKE_C_STANDARD 99)
endif()
# Default to C++14
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 14)
endif()
if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
add_compile_options(-Wall -Wextra -Wpedantic)
endif()
# find dependencies
find_package(ament_cmake REQUIRED)
find_package(std_msgs REQUIRED)
find_package(rosidl_default_generators REQUIRED)
rosidl_generate_interfaces(${PROJECT_NAME}
"msg/SampleMsg.msg"
DEPENDENCIES std_msgs
)
# uncomment the following section in order to fill in
# further dependencies manually.
# find_package(<dependency> REQUIRED)
if(BUILD_TESTING)
find_package(ament_lint_auto REQUIRED)
# the following line skips the linter which checks for copyrights
# uncomment the line when a copyright and license is not present in all source files
#set(ament_cmake_copyright_FOUND TRUE)
# the following line skips cpplint (only works in a git repo)
# uncomment the line when this package is not in a git repo
#set(ament_cmake_cpplint_FOUND TRUE)
ament_lint_auto_find_test_dependencies()
endif()
ament_package()
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/custom_message/package.xml | <?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>custom_message</name>
<version>0.0.0</version>
<description>Custom Message Sample Package</description>
<maintainer email="[email protected]">isaac sim</maintainer>
<license>TODO: License declaration</license>
<buildtool_depend>ament_cmake</buildtool_depend>
<depend>std_msgs</depend>
<buildtool_depend>rosidl_default_generators</buildtool_depend>
<exec_depend>rosidl_default_runtime</exec_depend>
<member_of_group>rosidl_interface_packages</member_of_group>
<test_depend>ament_lint_auto</test_depend>
<test_depend>ament_lint_common</test_depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/CMakeLists.txt | cmake_minimum_required(VERSION 3.5)
project(carter_navigation)
find_package(ament_cmake REQUIRED)
install(DIRECTORY
launch
params
maps
rviz2
DESTINATION share/${PROJECT_NAME})
ament_package()
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/package.xml | <?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>carter_navigation</name>
<version>0.1.0</version>
<description>
The carter_navigation package
</description>
<maintainer email="[email protected]">isaac sim</maintainer>
<license>Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, disclosure or
distribution of this software and related documentation without an express
license agreement from NVIDIA CORPORATION is strictly prohibited.</license>
<url type="Documentation">https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html</url>
<url type="Forums">https://forums.developer.nvidia.com/c/agx-autonomous-machines/isaac/simulation</url>
<buildtool_depend>ament_cmake</buildtool_depend>
<build_depend>launch</build_depend>
<build_depend>launch_ros</build_depend>
<build_depend>joint_state_publisher</build_depend>
<build_depend>rviz2</build_depend>
<build_depend>nav2_amcl</build_depend>
<build_depend>nav2_bringup</build_depend>
<build_depend>nav2_bt_navigator</build_depend>
<build_depend>nav2_costmap_2d</build_depend>
<build_depend>nav2_core</build_depend>
<build_depend>nav2_dwb_controller</build_depend>
<build_depend>nav2_lifecycle_manager</build_depend>
<build_depend>nav2_map_server</build_depend>
<build_depend>nav2_recoveries</build_depend>
<build_depend>nav2_planner</build_depend>
<build_depend>nav2_msgs</build_depend>
<build_depend>nav2_navfn_planner</build_depend>
<build_depend>nav2_rviz_plugins</build_depend>
<build_depend>nav2_behavior_tree</build_depend>
<build_depend>nav2_util</build_depend>
<build_depend>nav2_voxel_grid</build_depend>
<build_depend>nav2_controller</build_depend>
<build_depend>nav2_waypoint_follower</build_depend>
<build_depend>pointcloud_to_laserscan</build_depend>
<build_depend>navigation2</build_depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/carter_navigation.launch.py | ## Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
## NVIDIA CORPORATION and its licensors retain all intellectual property
## and proprietary rights in and to this software, related documentation
## and any modifications thereto. Any use, reproduction, disclosure or
## distribution of this software and related documentation without an express
## license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch.actions import IncludeLaunchDescription
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration
from launch_ros.actions import Node
def generate_launch_description():
use_sim_time = LaunchConfiguration("use_sim_time", default="True")
map_dir = LaunchConfiguration(
"map",
default=os.path.join(
get_package_share_directory("carter_navigation"), "maps", "carter_warehouse_navigation.yaml"
),
)
param_dir = LaunchConfiguration(
"params_file",
default=os.path.join(
get_package_share_directory("carter_navigation"), "params", "carter_navigation_params.yaml"
),
)
nav2_bringup_launch_dir = os.path.join(get_package_share_directory("nav2_bringup"), "launch")
rviz_config_dir = os.path.join(get_package_share_directory("carter_navigation"), "rviz2", "carter_navigation.rviz")
return LaunchDescription(
[
DeclareLaunchArgument("map", default_value=map_dir, description="Full path to map file to load"),
DeclareLaunchArgument(
"params_file", default_value=param_dir, description="Full path to param file to load"
),
DeclareLaunchArgument(
"use_sim_time", default_value="true", description="Use simulation (Omniverse Isaac Sim) clock if true"
),
IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(nav2_bringup_launch_dir, "rviz_launch.py")),
launch_arguments={"namespace": "", "use_namespace": "False", "rviz_config": rviz_config_dir}.items(),
),
IncludeLaunchDescription(
PythonLaunchDescriptionSource([nav2_bringup_launch_dir, "/bringup_launch.py"]),
launch_arguments={"map": map_dir, "use_sim_time": use_sim_time, "params_file": param_dir}.items(),
),
Node(
package='pointcloud_to_laserscan', executable='pointcloud_to_laserscan_node',
remappings=[('cloud_in', ['/front_3d_lidar/point_cloud']),
('scan', ['/scan'])],
parameters=[{
'target_frame': 'front_3d_lidar',
'transform_tolerance': 0.01,
'min_height': -0.4,
'max_height': 1.5,
'angle_min': -1.5708, # -M_PI/2
'angle_max': 1.5708, # M_PI/2
'angle_increment': 0.0087, # M_PI/360.0
'scan_time': 0.3333,
'range_min': 0.05,
'range_max': 100.0,
'use_inf': True,
'inf_epsilon': 1.0,
# 'concurrency_level': 1,
}],
name='pointcloud_to_laserscan'
)
]
)
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/carter_navigation_individual.launch.py | ## Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
## NVIDIA CORPORATION and its licensors retain all intellectual property
## and proprietary rights in and to this software, related documentation
## and any modifications thereto. Any use, reproduction, disclosure or
## distribution of this software and related documentation without an express
## license agreement from NVIDIA CORPORATION is strictly prohibited.
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, ExecuteProcess, IncludeLaunchDescription
from launch.conditions import IfCondition
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration, PythonExpression, TextSubstitution
from launch_ros.actions import Node
def generate_launch_description():
# Get the launch directory
nav2_launch_dir = os.path.join(get_package_share_directory("nav2_bringup"), "launch")
# Create the launch configuration variables
slam = LaunchConfiguration("slam")
namespace = LaunchConfiguration("namespace")
use_namespace = LaunchConfiguration("use_namespace")
map_yaml_file = LaunchConfiguration("map")
use_sim_time = LaunchConfiguration("use_sim_time")
params_file = LaunchConfiguration("params_file")
default_bt_xml_filename = LaunchConfiguration("default_bt_xml_filename")
autostart = LaunchConfiguration("autostart")
# Declare the launch arguments
declare_namespace_cmd = DeclareLaunchArgument("namespace", default_value="", description="Top-level namespace")
declare_use_namespace_cmd = DeclareLaunchArgument(
"use_namespace", default_value="false", description="Whether to apply a namespace to the navigation stack"
)
declare_slam_cmd = DeclareLaunchArgument("slam", default_value="False", description="Whether run a SLAM")
declare_map_yaml_cmd = DeclareLaunchArgument(
"map",
default_value=os.path.join(nav2_launch_dir, "maps", "carter_warehouse_navigation.yaml"),
description="Full path to map file to load",
)
declare_use_sim_time_cmd = DeclareLaunchArgument(
"use_sim_time", default_value="True", description="Use simulation (Isaac Sim) clock if true"
)
declare_params_file_cmd = DeclareLaunchArgument(
"params_file",
default_value=os.path.join(nav2_launch_dir, "params", "nav2_params.yaml"),
description="Full path to the ROS2 parameters file to use for all launched nodes",
)
declare_bt_xml_cmd = DeclareLaunchArgument(
"default_bt_xml_filename",
default_value=os.path.join(
get_package_share_directory("nav2_bt_navigator"), "behavior_trees", "navigate_w_replanning_and_recovery.xml"
),
description="Full path to the behavior tree xml file to use",
)
declare_autostart_cmd = DeclareLaunchArgument(
"autostart", default_value="true", description="Automatically startup the nav2 stack"
)
bringup_cmd = IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(nav2_launch_dir, "bringup_launch.py")),
launch_arguments={
"namespace": namespace,
"use_namespace": use_namespace,
"slam": slam,
"map": map_yaml_file,
"use_sim_time": use_sim_time,
"params_file": params_file,
"default_bt_xml_filename": default_bt_xml_filename,
"autostart": autostart,
}.items(),
)
# Create the launch description and populate
ld = LaunchDescription()
# Declare the launch options
ld.add_action(declare_namespace_cmd)
ld.add_action(declare_use_namespace_cmd)
ld.add_action(declare_slam_cmd)
ld.add_action(declare_map_yaml_cmd)
ld.add_action(declare_use_sim_time_cmd)
ld.add_action(declare_params_file_cmd)
ld.add_action(declare_bt_xml_cmd)
ld.add_action(declare_autostart_cmd)
ld.add_action(bringup_cmd)
return ld
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/multiple_robot_carter_navigation_hospital.launch.py | ## Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
## NVIDIA CORPORATION and its licensors retain all intellectual property
## and proprietary rights in and to this software, related documentation
## and any modifications thereto. Any use, reproduction, disclosure or
## distribution of this software and related documentation without an express
## license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
Example for spawing multiple robots in Gazebo.
This is an example on how to create a launch file for spawning multiple robots into Gazebo
and launch multiple instances of the navigation stack, each controlling one robot.
The robots co-exist on a shared environment and are controlled by independent nav stacks
"""
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, ExecuteProcess, GroupAction, IncludeLaunchDescription, LogInfo
from launch.conditions import IfCondition
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration, TextSubstitution
from launch_ros.actions import Node
def generate_launch_description():
# Get the launch and rviz directories
carter_nav2_bringup_dir = get_package_share_directory("carter_navigation")
nav2_bringup_dir = get_package_share_directory("nav2_bringup")
nav2_bringup_launch_dir = os.path.join(nav2_bringup_dir, "launch")
rviz_config_dir = os.path.join(carter_nav2_bringup_dir, "rviz2", "carter_navigation_namespaced.rviz")
# Names and poses of the robots
robots = [{"name": "carter1"}, {"name": "carter2"}, {"name": "carter3"}]
# Common settings
ENV_MAP_FILE = "carter_hospital_navigation.yaml"
use_sim_time = LaunchConfiguration("use_sim_time", default="True")
map_yaml_file = LaunchConfiguration("map")
default_bt_xml_filename = LaunchConfiguration("default_bt_xml_filename")
autostart = LaunchConfiguration("autostart")
rviz_config_file = LaunchConfiguration("rviz_config")
use_rviz = LaunchConfiguration("use_rviz")
log_settings = LaunchConfiguration("log_settings", default="true")
# Declare the launch arguments
declare_map_yaml_cmd = DeclareLaunchArgument(
"map",
default_value=os.path.join(carter_nav2_bringup_dir, "maps", ENV_MAP_FILE),
description="Full path to map file to load",
)
declare_robot1_params_file_cmd = DeclareLaunchArgument(
"carter1_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_1.yaml"
),
description="Full path to the ROS2 parameters file to use for robot1 launched nodes",
)
declare_robot2_params_file_cmd = DeclareLaunchArgument(
"carter2_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_2.yaml"
),
description="Full path to the ROS2 parameters file to use for robot2 launched nodes",
)
declare_robot3_params_file_cmd = DeclareLaunchArgument(
"carter3_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "hospital", "multi_robot_carter_navigation_params_3.yaml"
),
description="Full path to the ROS2 parameters file to use for robot3 launched nodes",
)
declare_bt_xml_cmd = DeclareLaunchArgument(
"default_bt_xml_filename",
default_value=os.path.join(
get_package_share_directory("nav2_bt_navigator"), "behavior_trees", "navigate_w_replanning_and_recovery.xml"
),
description="Full path to the behavior tree xml file to use",
)
declare_autostart_cmd = DeclareLaunchArgument(
"autostart", default_value="True", description="Automatically startup the stacks"
)
declare_rviz_config_file_cmd = DeclareLaunchArgument(
"rviz_config", default_value=rviz_config_dir, description="Full path to the RVIZ config file to use."
)
declare_use_rviz_cmd = DeclareLaunchArgument("use_rviz", default_value="True", description="Whether to start RVIZ")
# Define commands for launching the navigation instances
nav_instances_cmds = []
for robot in robots:
params_file = LaunchConfiguration(robot["name"] + "_params_file")
group = GroupAction(
[
IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(nav2_bringup_launch_dir, "rviz_launch.py")),
condition=IfCondition(use_rviz),
launch_arguments={
"namespace": TextSubstitution(text=robot["name"]),
"use_namespace": "True",
"rviz_config": rviz_config_file,
}.items(),
),
IncludeLaunchDescription(
PythonLaunchDescriptionSource(
os.path.join(carter_nav2_bringup_dir, "launch", "carter_navigation_individual.launch.py")
),
launch_arguments={
"namespace": robot["name"],
"use_namespace": "True",
"map": map_yaml_file,
"use_sim_time": use_sim_time,
"params_file": params_file,
"default_bt_xml_filename": default_bt_xml_filename,
"autostart": autostart,
"use_rviz": "False",
"use_simulator": "False",
"headless": "False",
}.items(),
),
Node(
package='pointcloud_to_laserscan', executable='pointcloud_to_laserscan_node',
remappings=[('cloud_in', ['front_3d_lidar/point_cloud']),
('scan', ['scan'])],
parameters=[{
'target_frame': 'front_3d_lidar',
'transform_tolerance': 0.01,
'min_height': -0.4,
'max_height': 1.5,
'angle_min': -1.5708, # -M_PI/2
'angle_max': 1.5708, # M_PI/2
'angle_increment': 0.0087, # M_PI/360.0
'scan_time': 0.3333,
'range_min': 0.05,
'range_max': 100.0,
'use_inf': True,
'inf_epsilon': 1.0,
# 'concurrency_level': 1,
}],
name='pointcloud_to_laserscan',
namespace = robot["name"]
),
LogInfo(condition=IfCondition(log_settings), msg=["Launching ", robot["name"]]),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " map yaml: ", map_yaml_file]),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " params yaml: ", params_file]),
LogInfo(
condition=IfCondition(log_settings),
msg=[robot["name"], " behavior tree xml: ", default_bt_xml_filename],
),
LogInfo(
condition=IfCondition(log_settings), msg=[robot["name"], " rviz config file: ", rviz_config_file]
),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " autostart: ", autostart]),
]
)
nav_instances_cmds.append(group)
# Create the launch description and populate
ld = LaunchDescription()
# Declare the launch options
ld.add_action(declare_map_yaml_cmd)
ld.add_action(declare_robot1_params_file_cmd)
ld.add_action(declare_robot2_params_file_cmd)
ld.add_action(declare_robot3_params_file_cmd)
ld.add_action(declare_bt_xml_cmd)
ld.add_action(declare_use_rviz_cmd)
ld.add_action(declare_autostart_cmd)
ld.add_action(declare_rviz_config_file_cmd)
for simulation_instance_cmd in nav_instances_cmds:
ld.add_action(simulation_instance_cmd)
return ld
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/launch/multiple_robot_carter_navigation_office.launch.py | ## Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
## NVIDIA CORPORATION and its licensors retain all intellectual property
## and proprietary rights in and to this software, related documentation
## and any modifications thereto. Any use, reproduction, disclosure or
## distribution of this software and related documentation without an express
## license agreement from NVIDIA CORPORATION is strictly prohibited.
"""
Example for spawing multiple robots in Gazebo.
This is an example on how to create a launch file for spawning multiple robots into Gazebo
and launch multiple instances of the navigation stack, each controlling one robot.
The robots co-exist on a shared environment and are controlled by independent nav stacks
"""
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument, ExecuteProcess, GroupAction, IncludeLaunchDescription, LogInfo
from launch.conditions import IfCondition
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch.substitutions import LaunchConfiguration, TextSubstitution
from launch_ros.actions import Node
def generate_launch_description():
# Get the launch and rviz directories
carter_nav2_bringup_dir = get_package_share_directory("carter_navigation")
nav2_bringup_dir = get_package_share_directory("nav2_bringup")
nav2_bringup_launch_dir = os.path.join(nav2_bringup_dir, "launch")
rviz_config_dir = os.path.join(carter_nav2_bringup_dir, "rviz2", "carter_navigation_namespaced.rviz")
# Names and poses of the robots
robots = [{"name": "carter1"}, {"name": "carter2"}, {"name": "carter3"}]
# Common settings
ENV_MAP_FILE = "carter_office_navigation.yaml"
use_sim_time = LaunchConfiguration("use_sim_time", default="True")
map_yaml_file = LaunchConfiguration("map")
default_bt_xml_filename = LaunchConfiguration("default_bt_xml_filename")
autostart = LaunchConfiguration("autostart")
rviz_config_file = LaunchConfiguration("rviz_config")
use_rviz = LaunchConfiguration("use_rviz")
log_settings = LaunchConfiguration("log_settings", default="true")
# Declare the launch arguments
declare_map_yaml_cmd = DeclareLaunchArgument(
"map",
default_value=os.path.join(carter_nav2_bringup_dir, "maps", ENV_MAP_FILE),
description="Full path to map file to load",
)
declare_robot1_params_file_cmd = DeclareLaunchArgument(
"carter1_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "office", "multi_robot_carter_navigation_params_1.yaml"
),
description="Full path to the ROS2 parameters file to use for robot1 launched nodes",
)
declare_robot2_params_file_cmd = DeclareLaunchArgument(
"carter2_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "office", "multi_robot_carter_navigation_params_2.yaml"
),
description="Full path to the ROS2 parameters file to use for robot2 launched nodes",
)
declare_robot3_params_file_cmd = DeclareLaunchArgument(
"carter3_params_file",
default_value=os.path.join(
carter_nav2_bringup_dir, "params", "office", "multi_robot_carter_navigation_params_3.yaml"
),
description="Full path to the ROS2 parameters file to use for robot3 launched nodes",
)
declare_bt_xml_cmd = DeclareLaunchArgument(
"default_bt_xml_filename",
default_value=os.path.join(
get_package_share_directory("nav2_bt_navigator"), "behavior_trees", "navigate_w_replanning_and_recovery.xml"
),
description="Full path to the behavior tree xml file to use",
)
declare_autostart_cmd = DeclareLaunchArgument(
"autostart", default_value="True", description="Automatically startup the stacks"
)
declare_rviz_config_file_cmd = DeclareLaunchArgument(
"rviz_config", default_value=rviz_config_dir, description="Full path to the RVIZ config file to use."
)
declare_use_rviz_cmd = DeclareLaunchArgument("use_rviz", default_value="True", description="Whether to start RVIZ")
# Define commands for launching the navigation instances
nav_instances_cmds = []
for robot in robots:
params_file = LaunchConfiguration(robot["name"] + "_params_file")
group = GroupAction(
[
IncludeLaunchDescription(
PythonLaunchDescriptionSource(os.path.join(nav2_bringup_launch_dir, "rviz_launch.py")),
condition=IfCondition(use_rviz),
launch_arguments={
"namespace": TextSubstitution(text=robot["name"]),
"use_namespace": "True",
"rviz_config": rviz_config_file,
}.items(),
),
IncludeLaunchDescription(
PythonLaunchDescriptionSource(
os.path.join(carter_nav2_bringup_dir, "launch", "carter_navigation_individual.launch.py")
),
launch_arguments={
"namespace": robot["name"],
"use_namespace": "True",
"map": map_yaml_file,
"use_sim_time": use_sim_time,
"params_file": params_file,
"default_bt_xml_filename": default_bt_xml_filename,
"autostart": autostart,
"use_rviz": "False",
"use_simulator": "False",
"headless": "False",
}.items(),
),
Node(
package='pointcloud_to_laserscan', executable='pointcloud_to_laserscan_node',
remappings=[('cloud_in', ['front_3d_lidar/point_cloud']),
('scan', ['scan'])],
parameters=[{
'target_frame': 'front_3d_lidar',
'transform_tolerance': 0.01,
'min_height': -0.4,
'max_height': 1.5,
'angle_min': -1.5708, # -M_PI/2
'angle_max': 1.5708, # M_PI/2
'angle_increment': 0.0087, # M_PI/360.0
'scan_time': 0.3333,
'range_min': 0.05,
'range_max': 100.0,
'use_inf': True,
'inf_epsilon': 1.0,
# 'concurrency_level': 1,
}],
name='pointcloud_to_laserscan',
namespace = robot["name"]
),
LogInfo(condition=IfCondition(log_settings), msg=["Launching ", robot["name"]]),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " map yaml: ", map_yaml_file]),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " params yaml: ", params_file]),
LogInfo(
condition=IfCondition(log_settings),
msg=[robot["name"], " behavior tree xml: ", default_bt_xml_filename],
),
LogInfo(
condition=IfCondition(log_settings), msg=[robot["name"], " rviz config file: ", rviz_config_file]
),
LogInfo(condition=IfCondition(log_settings), msg=[robot["name"], " autostart: ", autostart]),
]
)
nav_instances_cmds.append(group)
# Create the launch description and populate
ld = LaunchDescription()
# Declare the launch options
ld.add_action(declare_map_yaml_cmd)
ld.add_action(declare_robot1_params_file_cmd)
ld.add_action(declare_robot2_params_file_cmd)
ld.add_action(declare_robot3_params_file_cmd)
ld.add_action(declare_bt_xml_cmd)
ld.add_action(declare_use_rviz_cmd)
ld.add_action(declare_autostart_cmd)
ld.add_action(declare_rviz_config_file_cmd)
for simulation_instance_cmd in nav_instances_cmds:
ld.add_action(simulation_instance_cmd)
return ld
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/maps/carter_office_navigation.yaml | image: carter_office_navigation.png
resolution: 0.05
origin: [-29.975, -39.975, 0.0000]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.196
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/maps/carter_hospital_navigation.yaml | image: carter_hospital_navigation.png
resolution: 0.05
origin: [-49.625, -4.675, 0.0000]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.196
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/maps/carter_warehouse_navigation.yaml | image: carter_warehouse_navigation.png
resolution: 0.05
origin: [-11.975, -17.975, 0.0000]
negate: 0
occupied_thresh: 0.65
free_thresh: 0.196
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/rviz2/carter_navigation_namespaced.rviz | Panels:
- Class: rviz_common/Displays
Help Height: 195
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /RobotModel1/Status1
- /TF1/Frames1
- /TF1/Tree1
- /Global Planner1/Global Costmap1
Splitter Ratio: 0.5833333134651184
Tree Height: 464
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
- Class: nav2_rviz_plugins/Navigation 2
Name: Navigation 2
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz_default_plugins/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Alpha: 1
Class: rviz_default_plugins/RobotModel
Collision Enabled: false
Description File: ""
Description Source: Topic
Description Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/robot_description
Enabled: false
Links:
All Links Enabled: true
Expand Joint Details: false
Expand Link Details: false
Expand Tree: false
Link Tree Style: Links in Alphabetic Order
Name: RobotModel
TF Prefix: ""
Update Interval: 0
Value: true
Visual Enabled: true
- Class: rviz_default_plugins/TF
Enabled: true
Frame Timeout: 15
Frames:
All Enabled: false
Marker Scale: 1
Name: TF
Show Arrows: true
Show Axes: true
Show Names: false
Tree:
{}
Update Interval: 0
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/LaserScan
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 0
Min Color: 0; 0; 0
Min Intensity: 0
Name: LaserScan
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Best Effort
Value: <robot_namespace>/scan
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: ""
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: Bumper Hit
Position Transformer: ""
Selectable: true
Size (Pixels): 3
Size (m): 0.07999999821186066
Style: Spheres
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Best Effort
Value: <robot_namespace>/mobile_base/sensors/bumper_pointcloud
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Class: rviz_default_plugins/Map
Color Scheme: map
Draw Behind: true
Enabled: true
Name: Map
Topic:
Depth: 1
Durability Policy: Transient Local
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/map
Use Timestamp: false
Value: true
- Alpha: 1
Arrow Length: 0.019999999552965164
Axes Length: 0.30000001192092896
Axes Radius: 0.009999999776482582
Class: rviz_default_plugins/PoseArray
Color: 0; 180; 0
Enabled: true
Head Length: 0.07000000029802322
Head Radius: 0.029999999329447746
Name: Amcl Particle Swarm
Shaft Length: 0.23000000417232513
Shaft Radius: 0.009999999776482582
Shape: Arrow (Flat)
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Best Effort
Value: <robot_namespace>/particlecloud
Value: true
- Class: rviz_common/Group
Displays:
- Alpha: 0.30000001192092896
Class: rviz_default_plugins/Map
Color Scheme: costmap
Draw Behind: false
Enabled: true
Name: Global Costmap
Topic:
Depth: 1
Durability Policy: Transient Local
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/global_costmap/costmap
Use Timestamp: false
Value: true
- Alpha: 1
Buffer Length: 1
Class: rviz_default_plugins/Path
Color: 255; 0; 0
Enabled: true
Head Diameter: 0.019999999552965164
Head Length: 0.019999999552965164
Length: 0.30000001192092896
Line Style: Lines
Line Width: 0.029999999329447746
Name: Path
Offset:
X: 0
Y: 0
Z: 0
Pose Color: 255; 85; 255
Pose Style: Arrows
Radius: 0.029999999329447746
Shaft Diameter: 0.004999999888241291
Shaft Length: 0.019999999552965164
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/plan
Value: true
- Alpha: 1
Class: rviz_default_plugins/Polygon
Color: 25; 255; 0
Enabled: false
Name: Polygon
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/global_costmap/published_footprint
Value: false
Enabled: true
Name: Global Planner
- Class: rviz_common/Group
Displays:
- Alpha: 0.699999988079071
Class: rviz_default_plugins/Map
Color Scheme: costmap
Draw Behind: false
Enabled: true
Name: Local Costmap
Topic:
Depth: 1
Durability Policy: Transient Local
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/local_costmap/costmap
Use Timestamp: false
Value: true
- Alpha: 1
Buffer Length: 1
Class: rviz_default_plugins/Path
Color: 0; 12; 255
Enabled: true
Head Diameter: 0.30000001192092896
Head Length: 0.20000000298023224
Length: 0.30000001192092896
Line Style: Lines
Line Width: 0.029999999329447746
Name: Local Plan
Offset:
X: 0
Y: 0
Z: 0
Pose Color: 255; 85; 255
Pose Style: None
Radius: 0.029999999329447746
Shaft Diameter: 0.10000000149011612
Shaft Length: 0.10000000149011612
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/local_plan
Value: true
- Class: rviz_default_plugins/MarkerArray
Enabled: false
Name: Trajectories
Namespaces:
{}
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/marker
Value: false
- Alpha: 1
Class: rviz_default_plugins/Polygon
Color: 25; 255; 0
Enabled: true
Name: Polygon
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/local_costmap/published_footprint
Value: true
Enabled: true
Name: Controller
Enabled: true
Global Options:
Background Color: 48; 48; 48
Fixed Frame: map
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: <robot_namespace>/initialpose
- Class: nav2_rviz_plugins/GoalTool
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Angle: -1.5700000524520874
Class: rviz_default_plugins/TopDownOrtho
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Scale: 134.638427734375
Target Frame: <Fixed Frame>
Value: TopDownOrtho (rviz_default_plugins)
X: -0.032615214586257935
Y: -0.0801941454410553
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 914
Hide Left Dock: false
Hide Right Dock: true
Navigation 2:
collapsed: false
QMainWindow State: 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
Selection:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: true
Width: 1545
X: 186
Y: 117
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/rviz2/carter_navigation.rviz | Panels:
- Class: rviz_common/Displays
Help Height: 0
Name: Displays
Property Tree Widget:
Expanded:
- /Global Options1
- /RobotModel1
- /TF1/Frames1
- /TF1/Tree1
Splitter Ratio: 0.5833333134651184
Tree Height: 592
- Class: rviz_common/Selection
Name: Selection
- Class: rviz_common/Tool Properties
Expanded:
- /Publish Point1
Name: Tool Properties
Splitter Ratio: 0.5886790156364441
- Class: rviz_common/Views
Expanded:
- /Current View1
Name: Views
Splitter Ratio: 0.5
- Class: nav2_rviz_plugins/Navigation 2
Name: Navigation 2
Visualization Manager:
Class: ""
Displays:
- Alpha: 0.5
Cell Size: 1
Class: rviz_default_plugins/Grid
Color: 160; 160; 164
Enabled: true
Line Style:
Line Width: 0.029999999329447746
Value: Lines
Name: Grid
Normal Cell Count: 0
Offset:
X: 0
Y: 0
Z: 0
Plane: XY
Plane Cell Count: 10
Reference Frame: <Fixed Frame>
Value: true
- Alpha: 1
Class: rviz_default_plugins/RobotModel
Collision Enabled: false
Description File: ""
Description Source: File
Description Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /robot_description
Enabled: true
Links:
All Links Enabled: true
Expand Joint Details: false
Expand Link Details: false
Expand Tree: false
Link Tree Style: ""
Name: RobotModel
TF Prefix: ""
Update Interval: 0
Value: true
Visual Enabled: true
- Class: rviz_default_plugins/TF
Enabled: true
Frame Timeout: 15
Frames:
All Enabled: false
base_link:
Value: true
carter_camera_stereo_left:
Value: true
carter_camera_stereo_right:
Value: true
carter_lidar:
Value: true
chassis_link:
Value: true
com_offset:
Value: true
imu:
Value: true
left_wheel_link:
Value: true
odom:
Value: true
rear_pivot_link:
Value: true
rear_wheel_link:
Value: true
right_wheel_link:
Value: true
Marker Scale: 1
Name: TF
Show Arrows: true
Show Axes: true
Show Names: false
Tree:
{}
Update Interval: 0
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/LaserScan
Color: 255; 255; 255
Color Transformer: Intensity
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 0
Min Color: 0; 0; 0
Min Intensity: 0
Name: LaserScan
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Best Effort
Value: /scan
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: ""
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: Bumper Hit
Position Transformer: ""
Selectable: true
Size (Pixels): 3
Size (m): 0.07999999821186066
Style: Spheres
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Best Effort
Value: /mobile_base/sensors/bumper_pointcloud
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Class: rviz_default_plugins/Map
Color Scheme: map
Draw Behind: true
Enabled: true
Name: Map
Topic:
Depth: 1
Durability Policy: Transient Local
History Policy: Keep Last
Reliability Policy: Reliable
Value: /map
Update Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /map_updates
Use Timestamp: false
Value: true
- Alpha: 1
Arrow Length: 0.019999999552965164
Axes Length: 0.30000001192092896
Axes Radius: 0.009999999776482582
Class: rviz_default_plugins/PoseArray
Color: 0; 180; 0
Enabled: true
Head Length: 0.07000000029802322
Head Radius: 0.029999999329447746
Name: Amcl Particle Swarm
Shaft Length: 0.23000000417232513
Shaft Radius: 0.009999999776482582
Shape: Arrow (Flat)
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Best Effort
Value: /particlecloud
Value: true
- Class: rviz_common/Group
Displays:
- Alpha: 0.30000001192092896
Class: rviz_default_plugins/Map
Color Scheme: costmap
Draw Behind: false
Enabled: true
Name: Global Costmap
Topic:
Depth: 1
Durability Policy: Transient Local
History Policy: Keep Last
Reliability Policy: Reliable
Value: /global_costmap/costmap
Update Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /global_costmap/costmap_updates
Use Timestamp: false
Value: true
- Alpha: 0.30000001192092896
Class: rviz_default_plugins/Map
Color Scheme: costmap
Draw Behind: false
Enabled: true
Name: Downsampled Costmap
Topic:
Depth: 1
Durability Policy: Transient Local
History Policy: Keep Last
Reliability Policy: Reliable
Value: /downsampled_costmap
Update Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /downsampled_costmap_updates
Use Timestamp: false
Value: true
- Alpha: 1
Buffer Length: 1
Class: rviz_default_plugins/Path
Color: 255; 0; 0
Enabled: true
Head Diameter: 0.019999999552965164
Head Length: 0.019999999552965164
Length: 0.30000001192092896
Line Style: Lines
Line Width: 0.029999999329447746
Name: Path
Offset:
X: 0
Y: 0
Z: 0
Pose Color: 255; 85; 255
Pose Style: Arrows
Radius: 0.029999999329447746
Shaft Diameter: 0.004999999888241291
Shaft Length: 0.019999999552965164
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /plan
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud
Color: 125; 125; 125
Color Transformer: FlatColor
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: VoxelGrid
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.05000000074505806
Style: Boxes
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /global_costmap/voxel_marked_cloud
Use Fixed Frame: true
Use rainbow: true
Value: true
- Alpha: 1
Class: rviz_default_plugins/Polygon
Color: 25; 255; 0
Enabled: false
Name: Polygon
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /global_costmap/published_footprint
Value: false
Enabled: true
Name: Global Planner
- Class: rviz_common/Group
Displays:
- Alpha: 0.699999988079071
Class: rviz_default_plugins/Map
Color Scheme: costmap
Draw Behind: false
Enabled: true
Name: Local Costmap
Topic:
Depth: 1
Durability Policy: Transient Local
History Policy: Keep Last
Reliability Policy: Reliable
Value: /local_costmap/costmap
Update Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /local_costmap/costmap_updates
Use Timestamp: false
Value: true
- Alpha: 1
Buffer Length: 1
Class: rviz_default_plugins/Path
Color: 0; 12; 255
Enabled: true
Head Diameter: 0.30000001192092896
Head Length: 0.20000000298023224
Length: 0.30000001192092896
Line Style: Lines
Line Width: 0.029999999329447746
Name: Local Plan
Offset:
X: 0
Y: 0
Z: 0
Pose Color: 255; 85; 255
Pose Style: None
Radius: 0.029999999329447746
Shaft Diameter: 0.10000000149011612
Shaft Length: 0.10000000149011612
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /local_plan
Value: true
- Class: rviz_default_plugins/MarkerArray
Enabled: false
Name: Trajectories
Namespaces:
{}
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /marker
Value: false
- Alpha: 1
Class: rviz_default_plugins/Polygon
Color: 25; 255; 0
Enabled: true
Name: Polygon
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /local_costmap/published_footprint
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: VoxelGrid
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /local_costmap/voxel_marked_cloud
Use Fixed Frame: true
Use rainbow: true
Value: true
Enabled: true
Name: Controller
- Class: rviz_common/Group
Displays:
- Class: rviz_default_plugins/Image
Enabled: true
Max Value: 1
Median window: 5
Min Value: 0
Name: RealsenseCamera
Normalize Range: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /intel_realsense_r200_depth/image_raw
Value: true
- Alpha: 1
Autocompute Intensity Bounds: true
Autocompute Value Bounds:
Max Value: 10
Min Value: -10
Value: true
Axis: Z
Channel Name: intensity
Class: rviz_default_plugins/PointCloud2
Color: 255; 255; 255
Color Transformer: RGB8
Decay Time: 0
Enabled: true
Invert Rainbow: false
Max Color: 255; 255; 255
Max Intensity: 4096
Min Color: 0; 0; 0
Min Intensity: 0
Name: RealsenseDepthImage
Position Transformer: XYZ
Selectable: true
Size (Pixels): 3
Size (m): 0.009999999776482582
Style: Flat Squares
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /intel_realsense_r200_depth/points
Use Fixed Frame: true
Use rainbow: true
Value: true
Enabled: false
Name: Realsense
- Class: rviz_default_plugins/MarkerArray
Enabled: true
Name: MarkerArray
Namespaces:
{}
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /waypoints
Value: true
Enabled: true
Global Options:
Background Color: 48; 48; 48
Fixed Frame: map
Frame Rate: 30
Name: root
Tools:
- Class: rviz_default_plugins/MoveCamera
- Class: rviz_default_plugins/Select
- Class: rviz_default_plugins/FocusCamera
- Class: rviz_default_plugins/Measure
Line color: 128; 128; 0
- Class: rviz_default_plugins/SetInitialPose
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /initialpose
- Class: rviz_default_plugins/PublishPoint
Single click: true
Topic:
Depth: 5
Durability Policy: Volatile
History Policy: Keep Last
Reliability Policy: Reliable
Value: /clicked_point
- Class: nav2_rviz_plugins/GoalTool
Transformation:
Current:
Class: rviz_default_plugins/TF
Value: true
Views:
Current:
Angle: 1.5700000524520874
Class: rviz_default_plugins/TopDownOrtho
Enable Stereo Rendering:
Stereo Eye Separation: 0.05999999865889549
Stereo Focal Distance: 1
Swap Stereo Eyes: false
Value: false
Invert Z Axis: false
Name: Current View
Near Clip Distance: 0.009999999776482582
Scale: 21.35869598388672
Target Frame: <Fixed Frame>
Value: TopDownOrtho (rviz_default_plugins)
X: 0
Y: 1
Saved: ~
Window Geometry:
Displays:
collapsed: false
Height: 932
Hide Left Dock: false
Hide Right Dock: false
Navigation 2:
collapsed: false
QMainWindow State: 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
RealsenseCamera:
collapsed: false
Selection:
collapsed: false
Tool Properties:
collapsed: false
Views:
collapsed: false
Width: 1545
X: 301
Y: 54
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/params/carter_navigation_params.yaml | amcl:
ros__parameters:
use_sim_time: True
alpha1: 0.2
alpha2: 0.2
alpha3: 0.2
alpha4: 0.2
alpha5: 0.2
base_frame_id: "base_link"
beam_skip_distance: 0.5
beam_skip_error_threshold: 0.9
beam_skip_threshold: 0.3
do_beamskip: false
global_frame_id: "map"
lambda_short: 0.1
laser_likelihood_max_dist: 2.0
laser_max_range: 100.0
laser_min_range: 0.4
laser_model_type: "likelihood_field"
max_beams: 60
max_particles: 2000
min_particles: 500
odom_frame_id: "odom"
pf_err: 0.05
pf_z: 0.99
recovery_alpha_fast: 0.0
recovery_alpha_slow: 0.0
resample_interval: 1
robot_model_type: "differential"
save_pose_rate: 0.5
sigma_hit: 0.2
tf_broadcast: true
transform_tolerance: 1.0
update_min_a: 0.2
update_min_d: 0.25
z_hit: 0.5
z_max: 0.05
z_rand: 0.5
z_short: 0.05
set_initial_pose: True
initial_pose: {x: -6.0, y: -1.0, z: 0.0, yaw: 3.14159}
amcl_map_client:
ros__parameters:
use_sim_time: True
amcl_rclcpp_node:
ros__parameters:
use_sim_time: True
bt_navigator:
ros__parameters:
use_sim_time: True
global_frame: map
robot_base_frame: base_link
odom_topic: /odom
default_bt_xml_filename: "navigate_w_replanning_and_recovery.xml"
plugin_lib_names:
- nav2_compute_path_to_pose_action_bt_node
- nav2_follow_path_action_bt_node
- nav2_back_up_action_bt_node
- nav2_spin_action_bt_node
- nav2_wait_action_bt_node
- nav2_clear_costmap_service_bt_node
- nav2_is_stuck_condition_bt_node
- nav2_goal_reached_condition_bt_node
- nav2_goal_updated_condition_bt_node
- nav2_initial_pose_received_condition_bt_node
- nav2_reinitialize_global_localization_service_bt_node
- nav2_rate_controller_bt_node
- nav2_distance_controller_bt_node
- nav2_speed_controller_bt_node
- nav2_recovery_node_bt_node
- nav2_pipeline_sequence_bt_node
- nav2_round_robin_node_bt_node
- nav2_transform_available_condition_bt_node
- nav2_time_expired_condition_bt_node
- nav2_distance_traveled_condition_bt_node
bt_navigator_rclcpp_node:
ros__parameters:
use_sim_time: True
velocity_smoother:
ros__parameters:
smoothing_frequency: 20.0
scale_velocities: false
feedback: "OPEN_LOOP"
max_velocity: [1.8, 0.0, 1.2]
min_velocity: [-1.8, 0.0, -1.2]
# deadband_velocity: [0.0, 0.0, 0.0]
velocity_timeout: 1.0
# max_accel: [1.0, 0.0, 1.25]
# max_decel: [-1.0, 0.0, -1.25]
odom_topic: "odom"
odom_duration: 0.1
controller_server:
ros__parameters:
use_sim_time: True
controller_frequency: 20.0
min_x_velocity_threshold: 0.001
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
controller_plugins: ["FollowPath"]
# DWB parameters
FollowPath:
plugin: "dwb_core::DWBLocalPlanner"
debug_trajectory_details: True
min_vel_x: 0.0
min_vel_y: 0.0
max_vel_x: 1.8
max_vel_y: 0.0
max_vel_theta: 1.2
min_speed_xy: 0.0
max_speed_xy: 1.0
min_speed_theta: 0.0
acc_lim_x: 2.5
acc_lim_y: 0.0
acc_lim_theta: 3.2
decel_lim_x: -2.5
decel_lim_y: 0.0
decel_lim_theta: -3.2
vx_samples: 20
vy_samples: 5
vtheta_samples: 20
sim_time: 1.7
linear_granularity: 0.05
angular_granularity: 0.025
transform_tolerance: 0.2
xy_goal_tolerance: 0.25
trans_stopped_velocity: 0.25
short_circuit_trajectory_evaluation: True
stateful: True
critics: ["RotateToGoal", "Oscillation", "BaseObstacle", "GoalAlign", "PathAlign", "PathDist", "GoalDist"]
BaseObstacle.scale: 0.02
PathAlign.scale: 32.0
PathAlign.forward_point_distance: 0.1
GoalAlign.scale: 24.0
GoalAlign.forward_point_distance: 0.1
PathDist.scale: 32.0
GoalDist.scale: 24.0
RotateToGoal.scale: 32.0
RotateToGoal.slowing_factor: 5.0
RotateToGoal.lookahead_time: -1.0
controller_server_rclcpp_node:
ros__parameters:
use_sim_time: True
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
footprint_padding: 0.25
global_frame: odom
robot_base_frame: base_link
use_sim_time: True
rolling_window: true
width: 10
height: 10
resolution: 0.05
transform_tolerance: 0.3
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
plugins: ["hesai_voxel_layer", "front_rplidar_obstacle_layer", "back_rplidar_obstacle_layer", "inflation_layer"]
# plugins: ["hesai_voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
enabled: True
cost_scaling_factor: 0.3
inflation_radius: 1.0
hesai_voxel_layer:
plugin: "nav2_costmap_2d::VoxelLayer"
enabled: True
footprint_clearing_enabled: true
max_obstacle_height: 2.0
publish_voxel_map: False
origin_z: 0.0
z_voxels: 16
z_resolution: 0.2
unknown_threshold: 15
observation_sources: pointcloud
pointcloud: # no frame set, uses frame from message
topic: /front_3d_lidar/point_cloud
max_obstacle_height: 2.0
min_obstacle_height: 0.1
obstacle_max_range: 10.0
obstacle_min_range: 0.0
raytrace_max_range: 10.0
raytrace_min_range: 0.0
clearing: True
marking: True
data_type: "PointCloud2"
front_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /front_2d_lidar/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
back_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /back_2d_lidar/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
local_costmap_client:
ros__parameters:
use_sim_time: True
local_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
global_costmap:
global_costmap:
ros__parameters:
footprint_padding: 0.25
update_frequency: 1.0
publish_frequency: 1.0
global_frame: map
robot_base_frame: base_link
use_sim_time: True
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
# rolling_window: True
# width: 200
# height: 200
resolution: 0.05
# origin_x: -100.0
# origin_y: -100.0
plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
raytrace_max_range: 10.0
raytrace_min_range: 0.0
obstacle_max_range: 10.0
obstacle_min_range: 0.0
static_layer:
plugin: "nav2_costmap_2d::StaticLayer"
map_subscribe_transient_local: True
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 0.3
inflation_radius: 1.0
always_send_full_costmap: True
global_costmap_client:
ros__parameters:
use_sim_time: True
global_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
map_server:
ros__parameters:
use_sim_time: True
yaml_filename: "carter_warehouse_navigation.yaml"
map_saver:
ros__parameters:
use_sim_time: True
save_map_timeout: 5000
free_thresh_default: 0.25
occupied_thresh_default: 0.65
map_subscribe_transient_local: True
planner_server:
ros__parameters:
expected_planner_frequency: 10.0
use_sim_time: True
planner_plugins: ["GridBased"]
GridBased:
plugin: "nav2_navfn_planner/NavfnPlanner"
tolerance: 0.5
use_astar: false
allow_unknown: true
planner_server_rclcpp_node:
ros__parameters:
use_sim_time: True
recoveries_server:
ros__parameters:
costmap_topic: local_costmap/costmap_raw
footprint_topic: local_costmap/published_footprint
cycle_frequency: 5.0
recovery_plugins: ["spin", "backup", "wait"]
spin:
plugin: "nav2_recoveries/Spin"
backup:
plugin: "nav2_recoveries/BackUp"
wait:
plugin: "nav2_recoveries/Wait"
global_frame: odom
robot_base_frame: base_link
transform_timeout: 0.1
use_sim_time: True
simulate_ahead_time: 2.0
max_rotational_vel: 1.0
min_rotational_vel: 0.4
rotational_acc_lim: 3.2
robot_state_publisher:
ros__parameters:
use_sim_time: True
waypoint_follower:
ros__parameters:
use_sim_time: True
loop_rate: 20
stop_on_failure: false
waypoint_task_executor_plugin: "wait_at_waypoint"
wait_at_waypoint:
plugin: "nav2_waypoint_follower::WaitAtWaypoint"
enabled: True
waypoint_pause_duration: 200
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/params/hospital/multi_robot_carter_navigation_params_2.yaml | amcl:
ros__parameters:
use_sim_time: True
alpha1: 0.2
alpha2: 0.2
alpha3: 0.2
alpha4: 0.2
alpha5: 0.2
base_frame_id: "base_link"
beam_skip_distance: 0.5
beam_skip_error_threshold: 0.9
beam_skip_threshold: 0.3
do_beamskip: false
global_frame_id: "map"
lambda_short: 0.1
laser_likelihood_max_dist: 2.0
laser_max_range: 100.0
laser_min_range: 0.4
laser_model_type: "likelihood_field"
max_beams: 60
max_particles: 2000
min_particles: 500
odom_frame_id: "odom"
pf_err: 0.05
pf_z: 0.99
recovery_alpha_fast: 0.0
recovery_alpha_slow: 0.0
resample_interval: 3
robot_model_type: "differential"
save_pose_rate: 0.5
sigma_hit: 0.2
tf_broadcast: true
transform_tolerance: 1.0
update_min_a: 0.2
update_min_d: 0.25
z_hit: 0.5
z_max: 0.05
z_rand: 0.5
z_short: 0.05
set_initial_pose: True
initial_pose: {x: 4.0, y: -1.0, z: 0.0, yaw: 3.14159}
amcl_map_client:
ros__parameters:
use_sim_time: True
amcl_rclcpp_node:
ros__parameters:
use_sim_time: True
bt_navigator:
ros__parameters:
use_sim_time: True
enable_groot_monitoring: False
global_frame: map
robot_base_frame: base_link
odom_topic: odom
default_bt_xml_filename: "navigate_w_replanning_and_recovery.xml"
plugin_lib_names:
- nav2_compute_path_to_pose_action_bt_node
- nav2_follow_path_action_bt_node
- nav2_back_up_action_bt_node
- nav2_spin_action_bt_node
- nav2_wait_action_bt_node
- nav2_clear_costmap_service_bt_node
- nav2_is_stuck_condition_bt_node
- nav2_goal_reached_condition_bt_node
- nav2_goal_updated_condition_bt_node
- nav2_initial_pose_received_condition_bt_node
- nav2_reinitialize_global_localization_service_bt_node
- nav2_rate_controller_bt_node
- nav2_distance_controller_bt_node
- nav2_speed_controller_bt_node
- nav2_recovery_node_bt_node
- nav2_pipeline_sequence_bt_node
- nav2_round_robin_node_bt_node
- nav2_transform_available_condition_bt_node
- nav2_time_expired_condition_bt_node
- nav2_distance_traveled_condition_bt_node
bt_navigator_rclcpp_node:
ros__parameters:
use_sim_time: True
enable_groot_monitoring: False
velocity_smoother:
ros__parameters:
smoothing_frequency: 20.0
scale_velocities: false
feedback: "OPEN_LOOP"
max_velocity: [1.8, 0.0, 1.2]
min_velocity: [-1.8, 0.0, -1.2]
# deadband_velocity: [0.0, 0.0, 0.0]
velocity_timeout: 1.0
# max_accel: [1.0, 0.0, 1.25]
# max_decel: [-1.0, 0.0, -1.25]
odom_topic: "odom"
odom_duration: 0.1
controller_server:
ros__parameters:
use_sim_time: True
controller_frequency: 20.0
min_x_velocity_threshold: 0.001
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
controller_plugins: ["FollowPath"]
# DWB parameters
FollowPath:
plugin: "dwb_core::DWBLocalPlanner"
debug_trajectory_details: True
min_vel_x: 0.0
min_vel_y: 0.0
max_vel_x: 1.8
max_vel_y: 0.0
max_vel_theta: 1.2
min_speed_xy: 0.0
max_speed_xy: 1.0
min_speed_theta: 0.0
acc_lim_x: 2.5
acc_lim_y: 0.0
acc_lim_theta: 3.2
decel_lim_x: -2.5
decel_lim_y: 0.0
decel_lim_theta: -3.2
vx_samples: 20
vy_samples: 5
vtheta_samples: 20
sim_time: 1.7
linear_granularity: 0.05
angular_granularity: 0.025
transform_tolerance: 0.2
xy_goal_tolerance: 0.25
trans_stopped_velocity: 0.25
short_circuit_trajectory_evaluation: True
stateful: True
critics: ["RotateToGoal", "Oscillation", "BaseObstacle", "GoalAlign", "PathAlign", "PathDist", "GoalDist"]
BaseObstacle.scale: 0.02
PathAlign.scale: 32.0
PathAlign.forward_point_distance: 0.1
GoalAlign.scale: 24.0
GoalAlign.forward_point_distance: 0.1
PathDist.scale: 32.0
GoalDist.scale: 24.0
RotateToGoal.scale: 32.0
RotateToGoal.slowing_factor: 5.0
RotateToGoal.lookahead_time: -1.0
controller_server_rclcpp_node:
ros__parameters:
use_sim_time: True
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
footprint_padding: 0.25
global_frame: odom
robot_base_frame: base_link
use_sim_time: True
rolling_window: True
width: 7
height: 7
resolution: 0.05
transform_tolerance: 0.3
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
plugins: ["hesai_voxel_layer", "front_rplidar_obstacle_layer", "back_rplidar_obstacle_layer", "inflation_layer"]
# plugins: ["hesai_voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
enabled: True
cost_scaling_factor: 0.3
inflation_radius: 1.0
hesai_voxel_layer:
plugin: "nav2_costmap_2d::VoxelLayer"
enabled: True
footprint_clearing_enabled: true
max_obstacle_height: 2.0
publish_voxel_map: False
origin_z: 0.0
z_voxels: 16
z_resolution: 0.2
unknown_threshold: 15
observation_sources: pointcloud
pointcloud: # no frame set, uses frame from message
topic: /carter2/front_3d_lidar/point_cloud
max_obstacle_height: 2.0
min_obstacle_height: 0.1
obstacle_max_range: 10.0
obstacle_min_range: 0.0
raytrace_max_range: 10.0
raytrace_min_range: 0.0
clearing: True
marking: True
data_type: "PointCloud2"
front_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter2/front_2d_lidar/scan
max_obstacle_height: 2.0
raytrace_max_range: 25
clearing: True
marking: True
data_type: "LaserScan"
back_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter2/back_2d_lidar/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
local_costmap_client:
ros__parameters:
use_sim_time: True
local_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
global_costmap:
global_costmap:
ros__parameters:
footprint_padding: 0.25
update_frequency: 1.0
publish_frequency: 1.0
global_frame: map
robot_base_frame: base_link
use_sim_time: True
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
resolution: 0.05
plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter2/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
raytrace_max_range: 10.0
raytrace_min_range: 0.0
obstacle_max_range: 10.0
obstacle_min_range: 0.0
static_layer:
plugin: "nav2_costmap_2d::StaticLayer"
map_subscribe_transient_local: True
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 0.3
inflation_radius: 1.0
always_send_full_costmap: True
global_costmap_client:
ros__parameters:
use_sim_time: True
global_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
map_server:
ros__parameters:
use_sim_time: True
yaml_filename: "carter_hospital_navigation.yaml"
map_saver:
ros__parameters:
use_sim_time: True
save_map_timeout: 5000
free_thresh_default: 0.25
occupied_thresh_default: 0.65
map_subscribe_transient_local: True
planner_server:
ros__parameters:
expected_planner_frequency: 10.0
use_sim_time: True
planner_plugins: ["GridBased"]
GridBased:
plugin: "nav2_navfn_planner/NavfnPlanner"
tolerance: 0.5
use_astar: false
allow_unknown: true
planner_server_rclcpp_node:
ros__parameters:
use_sim_time: True
recoveries_server:
ros__parameters:
costmap_topic: local_costmap/costmap_raw
footprint_topic: local_costmap/published_footprint
cycle_frequency: 10.0
recovery_plugins: ["spin", "backup", "wait"]
spin:
plugin: "nav2_recoveries/Spin"
backup:
plugin: "nav2_recoveries/BackUp"
wait:
plugin: "nav2_recoveries/Wait"
global_frame: odom
robot_base_frame: base_link
transform_timeout: 0.2
use_sim_time: True
simulate_ahead_time: 2.0
max_rotational_vel: 1.0
min_rotational_vel: 0.4
rotational_acc_lim: 3.2
robot_state_publisher:
ros__parameters:
use_sim_time: True
waypoint_follower:
ros__parameters:
use_sim_time: True
loop_rate: 20
stop_on_failure: false
waypoint_task_executor_plugin: "wait_at_waypoint"
wait_at_waypoint:
plugin: "nav2_waypoint_follower::WaitAtWaypoint"
enabled: True
waypoint_pause_duration: 200
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/params/hospital/multi_robot_carter_navigation_params_1.yaml | amcl:
ros__parameters:
use_sim_time: True
alpha1: 0.2
alpha2: 0.2
alpha3: 0.2
alpha4: 0.2
alpha5: 0.2
base_frame_id: "base_link"
beam_skip_distance: 0.5
beam_skip_error_threshold: 0.9
beam_skip_threshold: 0.3
do_beamskip: false
global_frame_id: "map"
lambda_short: 0.1
laser_likelihood_max_dist: 2.0
laser_max_range: 100.0
laser_min_range: 0.4
laser_model_type: "likelihood_field"
max_beams: 60
max_particles: 2000
min_particles: 500
odom_frame_id: "odom"
pf_err: 0.05
pf_z: 0.99
recovery_alpha_fast: 0.0
recovery_alpha_slow: 0.0
resample_interval: 3
robot_model_type: "differential"
save_pose_rate: 0.5
sigma_hit: 0.2
tf_broadcast: true
transform_tolerance: 1.0
update_min_a: 0.2
update_min_d: 0.25
z_hit: 0.5
z_max: 0.05
z_rand: 0.5
z_short: 0.05
set_initial_pose: True
initial_pose: {x: 0.0, y: 0.0, z: 0.0, yaw: 3.14159}
amcl_map_client:
ros__parameters:
use_sim_time: True
amcl_rclcpp_node:
ros__parameters:
use_sim_time: True
bt_navigator:
ros__parameters:
use_sim_time: True
enable_groot_monitoring: False
global_frame: map
robot_base_frame: base_link
odom_topic: odom
default_bt_xml_filename: "navigate_w_replanning_and_recovery.xml"
plugin_lib_names:
- nav2_compute_path_to_pose_action_bt_node
- nav2_follow_path_action_bt_node
- nav2_back_up_action_bt_node
- nav2_spin_action_bt_node
- nav2_wait_action_bt_node
- nav2_clear_costmap_service_bt_node
- nav2_is_stuck_condition_bt_node
- nav2_goal_reached_condition_bt_node
- nav2_goal_updated_condition_bt_node
- nav2_initial_pose_received_condition_bt_node
- nav2_reinitialize_global_localization_service_bt_node
- nav2_rate_controller_bt_node
- nav2_distance_controller_bt_node
- nav2_speed_controller_bt_node
- nav2_recovery_node_bt_node
- nav2_pipeline_sequence_bt_node
- nav2_round_robin_node_bt_node
- nav2_transform_available_condition_bt_node
- nav2_time_expired_condition_bt_node
- nav2_distance_traveled_condition_bt_node
bt_navigator_rclcpp_node:
ros__parameters:
use_sim_time: True
enable_groot_monitoring: False
velocity_smoother:
ros__parameters:
smoothing_frequency: 20.0
scale_velocities: false
feedback: "OPEN_LOOP"
max_velocity: [1.8, 0.0, 1.2]
min_velocity: [-1.8, 0.0, -1.2]
# deadband_velocity: [0.0, 0.0, 0.0]
velocity_timeout: 1.0
# max_accel: [1.0, 0.0, 1.25]
# max_decel: [-1.0, 0.0, -1.25]
odom_topic: "odom"
odom_duration: 0.1
controller_server:
ros__parameters:
use_sim_time: True
controller_frequency: 20.0
min_x_velocity_threshold: 0.001
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
controller_plugins: ["FollowPath"]
# DWB parameters
FollowPath:
plugin: "dwb_core::DWBLocalPlanner"
debug_trajectory_details: True
min_vel_x: 0.0
min_vel_y: 0.0
max_vel_x: 1.8
max_vel_y: 0.0
max_vel_theta: 1.2
min_speed_xy: 0.0
max_speed_xy: 1.0
min_speed_theta: 0.0
acc_lim_x: 2.5
acc_lim_y: 0.0
acc_lim_theta: 3.2
decel_lim_x: -2.5
decel_lim_y: 0.0
decel_lim_theta: -3.2
vx_samples: 20
vy_samples: 5
vtheta_samples: 20
sim_time: 1.7
linear_granularity: 0.05
angular_granularity: 0.025
transform_tolerance: 0.2
xy_goal_tolerance: 0.25
trans_stopped_velocity: 0.25
short_circuit_trajectory_evaluation: True
stateful: True
critics: ["RotateToGoal", "Oscillation", "BaseObstacle", "GoalAlign", "PathAlign", "PathDist", "GoalDist"]
BaseObstacle.scale: 0.02
PathAlign.scale: 32.0
PathAlign.forward_point_distance: 0.1
GoalAlign.scale: 24.0
GoalAlign.forward_point_distance: 0.1
PathDist.scale: 32.0
GoalDist.scale: 24.0
RotateToGoal.scale: 32.0
RotateToGoal.slowing_factor: 5.0
RotateToGoal.lookahead_time: -1.0
controller_server_rclcpp_node:
ros__parameters:
use_sim_time: True
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
footprint_padding: 0.25
global_frame: odom
robot_base_frame: base_link
use_sim_time: True
rolling_window: True
width: 7
height: 7
resolution: 0.05
transform_tolerance: 0.3
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
plugins: ["hesai_voxel_layer", "front_rplidar_obstacle_layer", "back_rplidar_obstacle_layer", "inflation_layer"]
# plugins: ["hesai_voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
enabled: True
cost_scaling_factor: 0.3
inflation_radius: 1.0
hesai_voxel_layer:
plugin: "nav2_costmap_2d::VoxelLayer"
enabled: True
footprint_clearing_enabled: true
max_obstacle_height: 2.0
publish_voxel_map: False
origin_z: 0.0
z_voxels: 16
z_resolution: 0.2
unknown_threshold: 15
observation_sources: pointcloud
pointcloud: # no frame set, uses frame from message
topic: /carter1/front_3d_lidar/point_cloud
max_obstacle_height: 2.0
min_obstacle_height: 0.1
obstacle_max_range: 10.0
obstacle_min_range: 0.0
raytrace_max_range: 10.0
raytrace_min_range: 0.0
clearing: True
marking: True
data_type: "PointCloud2"
front_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter1/front_2d_lidar/scan
max_obstacle_height: 2.0
raytrace_max_range: 25
clearing: True
marking: True
data_type: "LaserScan"
back_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter1/back_2d_lidar/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
local_costmap_client:
ros__parameters:
use_sim_time: True
local_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
global_costmap:
global_costmap:
ros__parameters:
footprint_padding: 0.25
update_frequency: 1.0
publish_frequency: 1.0
global_frame: map
robot_base_frame: base_link
use_sim_time: True
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
resolution: 0.05
plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter1/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
raytrace_max_range: 10.0
raytrace_min_range: 0.0
obstacle_max_range: 10.0
obstacle_min_range: 0.0
static_layer:
plugin: "nav2_costmap_2d::StaticLayer"
map_subscribe_transient_local: True
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 0.3
inflation_radius: 1.0
always_send_full_costmap: True
global_costmap_client:
ros__parameters:
use_sim_time: True
global_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
map_server:
ros__parameters:
use_sim_time: True
yaml_filename: "carter_hospital_navigation.yaml"
map_saver:
ros__parameters:
use_sim_time: True
save_map_timeout: 5000
free_thresh_default: 0.25
occupied_thresh_default: 0.65
map_subscribe_transient_local: True
planner_server:
ros__parameters:
expected_planner_frequency: 10.0
use_sim_time: True
planner_plugins: ["GridBased"]
GridBased:
plugin: "nav2_navfn_planner/NavfnPlanner"
tolerance: 0.5
use_astar: false
allow_unknown: true
planner_server_rclcpp_node:
ros__parameters:
use_sim_time: True
recoveries_server:
ros__parameters:
costmap_topic: local_costmap/costmap_raw
footprint_topic: local_costmap/published_footprint
cycle_frequency: 10.0
recovery_plugins: ["spin", "backup", "wait"]
spin:
plugin: "nav2_recoveries/Spin"
backup:
plugin: "nav2_recoveries/BackUp"
wait:
plugin: "nav2_recoveries/Wait"
global_frame: odom
robot_base_frame: base_link
transform_timeout: 0.2
use_sim_time: True
simulate_ahead_time: 2.0
max_rotational_vel: 1.0
min_rotational_vel: 0.4
rotational_acc_lim: 3.2
robot_state_publisher:
ros__parameters:
use_sim_time: True
waypoint_follower:
ros__parameters:
use_sim_time: True
loop_rate: 20
stop_on_failure: false
waypoint_task_executor_plugin: "wait_at_waypoint"
wait_at_waypoint:
plugin: "nav2_waypoint_follower::WaitAtWaypoint"
enabled: True
waypoint_pause_duration: 200
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/params/hospital/multi_robot_carter_navigation_params_3.yaml | amcl:
ros__parameters:
use_sim_time: True
alpha1: 0.2
alpha2: 0.2
alpha3: 0.2
alpha4: 0.2
alpha5: 0.2
base_frame_id: "base_link"
beam_skip_distance: 0.5
beam_skip_error_threshold: 0.9
beam_skip_threshold: 0.3
do_beamskip: false
global_frame_id: "map"
lambda_short: 0.1
laser_likelihood_max_dist: 2.0
laser_max_range: 100.0
laser_min_range: 0.4
laser_model_type: "likelihood_field"
max_beams: 60
max_particles: 2000
min_particles: 500
odom_frame_id: "odom"
pf_err: 0.05
pf_z: 0.99
recovery_alpha_fast: 0.0
recovery_alpha_slow: 0.0
resample_interval: 3
robot_model_type: "differential"
save_pose_rate: 0.5
sigma_hit: 0.2
tf_broadcast: true
transform_tolerance: 1.0
update_min_a: 0.2
update_min_d: 0.25
z_hit: 0.5
z_max: 0.05
z_rand: 0.5
z_short: 0.05
set_initial_pose: True
initial_pose: {x: 7.0, y: 3.0, z: 0.0, yaw: 3.14159}
amcl_map_client:
ros__parameters:
use_sim_time: True
amcl_rclcpp_node:
ros__parameters:
use_sim_time: True
bt_navigator:
ros__parameters:
use_sim_time: True
enable_groot_monitoring: False
global_frame: map
robot_base_frame: base_link
odom_topic: odom
default_bt_xml_filename: "navigate_w_replanning_and_recovery.xml"
plugin_lib_names:
- nav2_compute_path_to_pose_action_bt_node
- nav2_follow_path_action_bt_node
- nav2_back_up_action_bt_node
- nav2_spin_action_bt_node
- nav2_wait_action_bt_node
- nav2_clear_costmap_service_bt_node
- nav2_is_stuck_condition_bt_node
- nav2_goal_reached_condition_bt_node
- nav2_goal_updated_condition_bt_node
- nav2_initial_pose_received_condition_bt_node
- nav2_reinitialize_global_localization_service_bt_node
- nav2_rate_controller_bt_node
- nav2_distance_controller_bt_node
- nav2_speed_controller_bt_node
- nav2_recovery_node_bt_node
- nav2_pipeline_sequence_bt_node
- nav2_round_robin_node_bt_node
- nav2_transform_available_condition_bt_node
- nav2_time_expired_condition_bt_node
- nav2_distance_traveled_condition_bt_node
bt_navigator_rclcpp_node:
ros__parameters:
use_sim_time: True
enable_groot_monitoring: False
velocity_smoother:
ros__parameters:
smoothing_frequency: 20.0
scale_velocities: false
feedback: "OPEN_LOOP"
max_velocity: [1.8, 0.0, 1.2]
min_velocity: [-1.8, 0.0, -1.2]
# deadband_velocity: [0.0, 0.0, 0.0]
velocity_timeout: 1.0
# max_accel: [1.0, 0.0, 1.25]
# max_decel: [-1.0, 0.0, -1.25]
odom_topic: "odom"
odom_duration: 0.1
controller_server:
ros__parameters:
use_sim_time: True
controller_frequency: 20.0
min_x_velocity_threshold: 0.001
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
controller_plugins: ["FollowPath"]
# DWB parameters
FollowPath:
plugin: "dwb_core::DWBLocalPlanner"
debug_trajectory_details: True
min_vel_x: 0.0
min_vel_y: 0.0
max_vel_x: 1.8
max_vel_y: 0.0
max_vel_theta: 1.2
min_speed_xy: 0.0
max_speed_xy: 1.0
min_speed_theta: 0.0
acc_lim_x: 2.5
acc_lim_y: 0.0
acc_lim_theta: 3.2
decel_lim_x: -2.5
decel_lim_y: 0.0
decel_lim_theta: -3.2
vx_samples: 20
vy_samples: 5
vtheta_samples: 20
sim_time: 1.7
linear_granularity: 0.05
angular_granularity: 0.025
transform_tolerance: 0.2
xy_goal_tolerance: 0.25
trans_stopped_velocity: 0.25
short_circuit_trajectory_evaluation: True
stateful: True
critics: ["RotateToGoal", "Oscillation", "BaseObstacle", "GoalAlign", "PathAlign", "PathDist", "GoalDist"]
BaseObstacle.scale: 0.02
PathAlign.scale: 32.0
PathAlign.forward_point_distance: 0.1
GoalAlign.scale: 24.0
GoalAlign.forward_point_distance: 0.1
PathDist.scale: 32.0
GoalDist.scale: 24.0
RotateToGoal.scale: 32.0
RotateToGoal.slowing_factor: 5.0
RotateToGoal.lookahead_time: -1.0
controller_server_rclcpp_node:
ros__parameters:
use_sim_time: True
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
footprint_padding: 0.25
global_frame: odom
robot_base_frame: base_link
use_sim_time: True
rolling_window: True
width: 7
height: 7
resolution: 0.05
transform_tolerance: 0.3
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
plugins: ["hesai_voxel_layer", "front_rplidar_obstacle_layer", "back_rplidar_obstacle_layer", "inflation_layer"]
# plugins: ["hesai_voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
enabled: True
cost_scaling_factor: 0.3
inflation_radius: 1.0
hesai_voxel_layer:
plugin: "nav2_costmap_2d::VoxelLayer"
enabled: True
footprint_clearing_enabled: true
max_obstacle_height: 2.0
publish_voxel_map: False
origin_z: 0.0
z_voxels: 16
z_resolution: 0.2
unknown_threshold: 15
observation_sources: pointcloud
pointcloud: # no frame set, uses frame from message
topic: /carter3/front_3d_lidar/point_cloud
max_obstacle_height: 2.0
min_obstacle_height: 0.1
obstacle_max_range: 10.0
obstacle_min_range: 0.0
raytrace_max_range: 10.0
raytrace_min_range: 0.0
clearing: True
marking: True
data_type: "PointCloud2"
front_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter3/front_2d_lidar/scan
max_obstacle_height: 2.0
raytrace_max_range: 25
clearing: True
marking: True
data_type: "LaserScan"
back_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter3/back_2d_lidar/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
local_costmap_client:
ros__parameters:
use_sim_time: True
local_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
global_costmap:
global_costmap:
ros__parameters:
footprint_padding: 0.25
update_frequency: 1.0
publish_frequency: 1.0
global_frame: map
robot_base_frame: base_link
use_sim_time: True
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
resolution: 0.05
plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter3/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
raytrace_max_range: 10.0
raytrace_min_range: 0.0
obstacle_max_range: 10.0
obstacle_min_range: 0.0
static_layer:
plugin: "nav2_costmap_2d::StaticLayer"
map_subscribe_transient_local: True
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 0.3
inflation_radius: 1.0
always_send_full_costmap: True
global_costmap_client:
ros__parameters:
use_sim_time: True
global_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
map_server:
ros__parameters:
use_sim_time: True
yaml_filename: "carter_hospital_navigation.yaml"
map_saver:
ros__parameters:
use_sim_time: True
save_map_timeout: 5000
free_thresh_default: 0.25
occupied_thresh_default: 0.65
map_subscribe_transient_local: True
planner_server:
ros__parameters:
expected_planner_frequency: 10.0
use_sim_time: True
planner_plugins: ["GridBased"]
GridBased:
plugin: "nav2_navfn_planner/NavfnPlanner"
tolerance: 0.5
use_astar: false
allow_unknown: true
planner_server_rclcpp_node:
ros__parameters:
use_sim_time: True
recoveries_server:
ros__parameters:
costmap_topic: local_costmap/costmap_raw
footprint_topic: local_costmap/published_footprint
cycle_frequency: 10.0
recovery_plugins: ["spin", "backup", "wait"]
spin:
plugin: "nav2_recoveries/Spin"
backup:
plugin: "nav2_recoveries/BackUp"
wait:
plugin: "nav2_recoveries/Wait"
global_frame: odom
robot_base_frame: base_link
transform_timeout: 0.2
use_sim_time: True
simulate_ahead_time: 2.0
max_rotational_vel: 1.0
min_rotational_vel: 0.4
rotational_acc_lim: 3.2
robot_state_publisher:
ros__parameters:
use_sim_time: True
waypoint_follower:
ros__parameters:
use_sim_time: True
loop_rate: 20
stop_on_failure: false
waypoint_task_executor_plugin: "wait_at_waypoint"
wait_at_waypoint:
plugin: "nav2_waypoint_follower::WaitAtWaypoint"
enabled: True
waypoint_pause_duration: 200
|
NVIDIA-Omniverse/IsaacSim-ros_workspaces/foxy_ws/src/navigation/carter_navigation/params/office/multi_robot_carter_navigation_params_2.yaml | amcl:
ros__parameters:
use_sim_time: True
alpha1: 0.2
alpha2: 0.2
alpha3: 0.2
alpha4: 0.2
alpha5: 0.2
base_frame_id: "base_link"
beam_skip_distance: 0.5
beam_skip_error_threshold: 0.9
beam_skip_threshold: 0.3
do_beamskip: false
global_frame_id: "map"
lambda_short: 0.1
laser_likelihood_max_dist: 2.0
laser_max_range: 100.0
laser_min_range: 0.4
laser_model_type: "likelihood_field"
max_beams: 60
max_particles: 2000
min_particles: 500
odom_frame_id: "odom"
pf_err: 0.05
pf_z: 0.99
recovery_alpha_fast: 0.0
recovery_alpha_slow: 0.0
resample_interval: 3
robot_model_type: "differential"
save_pose_rate: 0.5
sigma_hit: 0.2
tf_broadcast: true
transform_tolerance: 1.0
update_min_a: 0.2
update_min_d: 0.25
z_hit: 0.5
z_max: 0.05
z_rand: 0.5
z_short: 0.05
set_initial_pose: True
initial_pose: {x: 2.5, y: 0.0, z: 0.0, yaw: 3.14159}
amcl_map_client:
ros__parameters:
use_sim_time: True
amcl_rclcpp_node:
ros__parameters:
use_sim_time: True
bt_navigator:
ros__parameters:
use_sim_time: True
enable_groot_monitoring: False
global_frame: map
robot_base_frame: base_link
odom_topic: odom
default_bt_xml_filename: "navigate_w_replanning_and_recovery.xml"
plugin_lib_names:
- nav2_compute_path_to_pose_action_bt_node
- nav2_follow_path_action_bt_node
- nav2_back_up_action_bt_node
- nav2_spin_action_bt_node
- nav2_wait_action_bt_node
- nav2_clear_costmap_service_bt_node
- nav2_is_stuck_condition_bt_node
- nav2_goal_reached_condition_bt_node
- nav2_goal_updated_condition_bt_node
- nav2_initial_pose_received_condition_bt_node
- nav2_reinitialize_global_localization_service_bt_node
- nav2_rate_controller_bt_node
- nav2_distance_controller_bt_node
- nav2_speed_controller_bt_node
- nav2_recovery_node_bt_node
- nav2_pipeline_sequence_bt_node
- nav2_round_robin_node_bt_node
- nav2_transform_available_condition_bt_node
- nav2_time_expired_condition_bt_node
- nav2_distance_traveled_condition_bt_node
bt_navigator_rclcpp_node:
ros__parameters:
use_sim_time: True
enable_groot_monitoring: False
velocity_smoother:
ros__parameters:
smoothing_frequency: 20.0
scale_velocities: false
feedback: "OPEN_LOOP"
max_velocity: [1.8, 0.0, 1.2]
min_velocity: [-1.8, 0.0, -1.2]
# deadband_velocity: [0.0, 0.0, 0.0]
velocity_timeout: 1.0
# max_accel: [1.0, 0.0, 1.25]
# max_decel: [-1.0, 0.0, -1.25]
odom_topic: "odom"
odom_duration: 0.1
controller_server:
ros__parameters:
use_sim_time: True
controller_frequency: 20.0
min_x_velocity_threshold: 0.001
min_y_velocity_threshold: 0.5
min_theta_velocity_threshold: 0.001
controller_plugins: ["FollowPath"]
# DWB parameters
FollowPath:
plugin: "dwb_core::DWBLocalPlanner"
debug_trajectory_details: True
min_vel_x: 0.0
min_vel_y: 0.0
max_vel_x: 1.8
max_vel_y: 0.0
max_vel_theta: 1.2
min_speed_xy: 0.0
max_speed_xy: 1.0
min_speed_theta: 0.0
acc_lim_x: 2.5
acc_lim_y: 0.0
acc_lim_theta: 3.2
decel_lim_x: -2.5
decel_lim_y: 0.0
decel_lim_theta: -3.2
vx_samples: 20
vy_samples: 5
vtheta_samples: 20
sim_time: 1.7
linear_granularity: 0.05
angular_granularity: 0.025
transform_tolerance: 0.2
xy_goal_tolerance: 0.25
trans_stopped_velocity: 0.25
short_circuit_trajectory_evaluation: True
stateful: True
critics: ["RotateToGoal", "Oscillation", "BaseObstacle", "GoalAlign", "PathAlign", "PathDist", "GoalDist"]
BaseObstacle.scale: 0.02
PathAlign.scale: 32.0
PathAlign.forward_point_distance: 0.1
GoalAlign.scale: 24.0
GoalAlign.forward_point_distance: 0.1
PathDist.scale: 32.0
GoalDist.scale: 24.0
RotateToGoal.scale: 32.0
RotateToGoal.slowing_factor: 5.0
RotateToGoal.lookahead_time: -1.0
controller_server_rclcpp_node:
ros__parameters:
use_sim_time: True
local_costmap:
local_costmap:
ros__parameters:
update_frequency: 5.0
publish_frequency: 2.0
footprint_padding: 0.25
global_frame: odom
robot_base_frame: base_link
use_sim_time: True
rolling_window: True
width: 10
height: 10
resolution: 0.05
transform_tolerance: 0.3
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
plugins: ["hesai_voxel_layer", "front_rplidar_obstacle_layer", "back_rplidar_obstacle_layer", "inflation_layer"]
# plugins: ["hesai_voxel_layer", "inflation_layer"]
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
enabled: True
cost_scaling_factor: 0.3
inflation_radius: 1.0
hesai_voxel_layer:
plugin: "nav2_costmap_2d::VoxelLayer"
enabled: True
footprint_clearing_enabled: true
max_obstacle_height: 2.0
publish_voxel_map: False
origin_z: 0.0
z_voxels: 16
z_resolution: 0.2
unknown_threshold: 15
observation_sources: pointcloud
pointcloud: # no frame set, uses frame from message
topic: /carter2/front_3d_lidar/point_cloud
max_obstacle_height: 2.0
min_obstacle_height: 0.1
obstacle_max_range: 10.0
obstacle_min_range: 0.0
raytrace_max_range: 10.0
raytrace_min_range: 0.0
clearing: True
marking: True
data_type: "PointCloud2"
front_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter2/front_2d_lidar/scan
max_obstacle_height: 2.0
raytrace_max_range: 25
clearing: True
marking: True
data_type: "LaserScan"
back_rplidar_obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter2/back_2d_lidar/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
local_costmap_client:
ros__parameters:
use_sim_time: True
local_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
global_costmap:
global_costmap:
ros__parameters:
footprint_padding: 0.25
update_frequency: 1.0
publish_frequency: 1.0
global_frame: map
robot_base_frame: base_link
use_sim_time: True
footprint: "[ [0.14, 0.25], [0.14, -0.25], [-0.607, -0.25], [-0.607, 0.25] ]"
resolution: 0.05
plugins: ["static_layer", "obstacle_layer", "inflation_layer"]
obstacle_layer:
plugin: "nav2_costmap_2d::ObstacleLayer"
enabled: True
observation_sources: scan
scan:
topic: /carter2/scan
max_obstacle_height: 2.0
clearing: True
marking: True
data_type: "LaserScan"
raytrace_max_range: 10.0
raytrace_min_range: 0.0
obstacle_max_range: 10.0
obstacle_min_range: 0.0
static_layer:
plugin: "nav2_costmap_2d::StaticLayer"
map_subscribe_transient_local: True
inflation_layer:
plugin: "nav2_costmap_2d::InflationLayer"
cost_scaling_factor: 0.3
inflation_radius: 1.0
always_send_full_costmap: True
global_costmap_client:
ros__parameters:
use_sim_time: True
global_costmap_rclcpp_node:
ros__parameters:
use_sim_time: True
map_server:
ros__parameters:
use_sim_time: True
yaml_filename: "carter_hospital_navigation.yaml"
map_saver:
ros__parameters:
use_sim_time: True
save_map_timeout: 5000
free_thresh_default: 0.25
occupied_thresh_default: 0.65
map_subscribe_transient_local: True
planner_server:
ros__parameters:
expected_planner_frequency: 10.0
use_sim_time: True
planner_plugins: ["GridBased"]
GridBased:
plugin: "nav2_navfn_planner/NavfnPlanner"
tolerance: 0.5
use_astar: false
allow_unknown: true
planner_server_rclcpp_node:
ros__parameters:
use_sim_time: True
recoveries_server:
ros__parameters:
costmap_topic: local_costmap/costmap_raw
footprint_topic: local_costmap/published_footprint
cycle_frequency: 10.0
recovery_plugins: ["spin", "backup", "wait"]
spin:
plugin: "nav2_recoveries/Spin"
backup:
plugin: "nav2_recoveries/BackUp"
wait:
plugin: "nav2_recoveries/Wait"
global_frame: odom
robot_base_frame: base_link
transform_timeout: 0.2
use_sim_time: True
simulate_ahead_time: 2.0
max_rotational_vel: 1.0
min_rotational_vel: 0.4
rotational_acc_lim: 3.2
robot_state_publisher:
ros__parameters:
use_sim_time: True
waypoint_follower:
ros__parameters:
use_sim_time: True
loop_rate: 20
stop_on_failure: false
waypoint_task_executor_plugin: "wait_at_waypoint"
wait_at_waypoint:
plugin: "nav2_waypoint_follower::WaitAtWaypoint"
enabled: True
waypoint_pause_duration: 200
|
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