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#-------------------------------------------------------------------------------
# Name: mst_utils.py
# Purpose: utilize functions for skeleton generation
# RigNet Copyright 2020 University of Massachusetts
# RigNet is made available under General Public License Version 3 (GPLv3), or under a Commercial License.
# Please see the LICENSE README.txt file in the main directory for more information and instruction on using and licensing RigNet.
#-------------------------------------------------------------------------------
import sys
import numpy as np
from .rig_parser import TreeNode
from .rig_parser import Skel
import torch
def inside_check(pts, vox):
"""
Check where points are inside or outside the mesh based on its voxelization.
:param pts: points to be checked
:param vox: voxelized mesh
:return: internal points, and index of them in the input array.
"""
vc = (pts - vox.translate) / vox.scale * vox.dims[0]
vc = np.round(vc).astype(int)
ind1 = np.logical_and(np.all(vc >= 0, axis=1), np.all(vc < 88, axis=1))
vc = np.clip(vc, 0, 87)
ind2 = vox.data[vc[:, 0], vc[:, 1], vc[:, 2]]
ind = np.logical_and(ind1, ind2)
pts = pts[ind]
return pts, np.argwhere(ind).squeeze()
def sample_on_bone(p_pos, ch_pos):
"""
sample points on a bone
:param p_pos: parent joint position
:param ch_pos: child joint position
:return: a array of samples on this bone.
"""
ray = ch_pos - p_pos
bone_length = np.sqrt(np.sum((p_pos - ch_pos) ** 2))
num_step = np.round(bone_length / 0.01)
i_step = np.arange(1, num_step + 1)
unit_step = ray / (num_step + 1e-30)
unit_step = np.repeat(unit_step[np.newaxis, :], num_step, axis=0)
res = p_pos + unit_step * i_step[:, np.newaxis]
return res
def minKey(key, mstSet, nV):
# Initilaize min value
min = sys.maxsize
for v in range(nV):
if key[v] < min and mstSet[v] == False:
min = key[v]
min_index = v
return min_index
def primMST_normal(graph, init_id, normal_matrix):
"""
Modified Prim's algorithm to generate a minimum spanning tree (MST).
:param graph: pairwise cost matrix
:param init_id: init node ID as root
:return: parent array, key array, init_id
"""
nV = graph.shape[0]
key = [sys.maxsize] * nV
parent = [None] * nV
mstSet = [False] * nV
key[init_id] = 0
parent[init_id] = -1
previous_normal = np.zeros((nV, 3))
while not all(mstSet):
u = minKey(key, mstSet, nV)
mstSet[u] = True
if parent[u] >= 0:
previous_normal[u] = normal_matrix[u, parent[u]]
updated_normal = np.dot(previous_normal[u], normal_matrix[u, :].T) #1*n
updated_normal[updated_normal<0]=0
# print('updated_normal',updated_normal.shape)
graph[u, :] = graph[u, :] +(1e8*updated_normal**2+1)
graph[:, u] = graph[:, u] +(1e8*updated_normal**2+1)
for v in range(nV):
if graph[u, v] > 0 and mstSet[v] is False and key[v] > graph[u, v]:
key[v] = graph[u, v]
parent[v] = u
return parent, key, init_id
def loadSkel_recur(p_node, parent_id, joint_name, joint_pos, parent):
"""
Converst prim algorithm result to our skel/info format recursively
:param p_node: Root node
:param parent_id: parent name of current step of recursion.
:param joint_name: list of joint names
:param joint_pos: joint positions
:param parent: parent index returned by prim alg.
:return: p_node (root) will be expanded to linked with all joints
"""
for i in range(len(parent)):
if parent[i] == parent_id:
if joint_name is not None:
ch_node = TreeNode(joint_name[i], tuple(joint_pos[i]))
else:
ch_node = TreeNode('joint_{}'.format(i), tuple(joint_pos[i]))
p_node.children.append(ch_node)
ch_node.parent = p_node
loadSkel_recur(ch_node, i, joint_name, joint_pos, parent)
def unique_rows(a):
"""
remove repeat rows from a numpy array
"""
a = np.ascontiguousarray(a)
unique_a = np.unique(a.view([('', a.dtype)]*a.shape[1]))
return unique_a.view(a.dtype).reshape((unique_a.shape[0], a.shape[1]))
def increase_cost_for_outside_bone(cost_matrix, joint_pos, vox):
"""
increase connectivity cost for bones outside the meshs
"""
for i in range(len(joint_pos)):
for j in range(i+1, len(joint_pos)):
bone_samples = sample_on_bone(joint_pos[i], joint_pos[j])
bone_samples_vox = (bone_samples - vox.translate) / vox.scale * vox.dims[0]
bone_samples_vox = np.round(bone_samples_vox).astype(int)
ind1 = np.logical_and(np.all(bone_samples_vox >= 0, axis=1), np.all(bone_samples_vox < vox.dims[0], axis=1))
bone_samples_vox = np.clip(bone_samples_vox, 0, vox.dims[0]-1)
ind2 = vox.data[bone_samples_vox[:, 0], bone_samples_vox[:, 1], bone_samples_vox[:, 2]]
in_flags = np.logical_and(ind1, ind2)
outside_bone_sample = np.sum(in_flags == False)
if outside_bone_sample > 1:
cost_matrix[i, j] = 2 * outside_bone_sample
cost_matrix[j, i] = 2 * outside_bone_sample
if np.abs(joint_pos[i, 0]) < 2e-2 and np.abs(joint_pos[j, 0]) < 2e-2:
cost_matrix[i, j] *= 0.5
cost_matrix[j, i] *= 0.5
return cost_matrix
def increase_cost_for_outside_bone_tensor(cost_matrix, joint_pos, vox,resolution=64):
"""
increase connectivity cost for bones outside the meshs
vox is a tensor with size(N,3), N is the number of voxels that inside the mesh, range (0,64)
"""
vox = torch.clamp(vox, 0, resolution-1).long()
for i in range(len(joint_pos)):
for j in range(i+1, len(joint_pos)):
bone_samples = sample_on_bone(joint_pos[i], joint_pos[j]) # return coordinates of points on the bone
bone_samples_vox = bone_samples * (resolution/2) + (resolution/2)
bone_samples_vox = np.round(bone_samples_vox).astype(int)
bone_samples_vox = np.clip(bone_samples_vox, 0, resolution-1)
vox_remap = torch.zeros((resolution,resolution,resolution))
vox_remap[vox[:,0],vox[:,1],vox[:,2]] = 1
vox_remap = vox_remap.numpy()
inside_index = vox_remap[bone_samples_vox[:,0],bone_samples_vox[:,1],bone_samples_vox[:,2]]
outside_bone_sample = np.sum(inside_index == 0)
# check the intersection of the bone with the mesh
if outside_bone_sample > 1:
cost_matrix[i, j] = 2 * outside_bone_sample
cost_matrix[j, i] = 2 * outside_bone_sample
if np.abs(joint_pos[i, 0]) < 2e-2 and np.abs(joint_pos[j, 0]) < 2e-2:
cost_matrix[i, j] *= 0.5
cost_matrix[j, i] *= 0.5
return cost_matrix
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