Spaces:
Build error
Build error
File size: 7,535 Bytes
4409449 8554568 4409449 8554568 4409449 8554568 4409449 8554568 4409449 8554568 b625c80 4409449 8554568 4409449 8554568 4409449 b625c80 8554568 b625c80 4409449 8554568 b625c80 8554568 b625c80 4409449 b625c80 8554568 4409449 8554568 4409449 8554568 b625c80 8554568 b625c80 8554568 b625c80 8554568 b625c80 8554568 4409449 8554568 4409449 8554568 4409449 8554568 b625c80 4409449 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 |
import os
os.environ['PYOPENGL_PLATFORM'] = 'egl'
import torch
import numpy as np
import cv2
import matplotlib.pyplot as plt
import glob
import pickle
import pyrender
import trimesh
from shapely import geometry
from smplx import SMPL as _SMPL
from smplx.utils import SMPLOutput as ModelOutput
from scipy.spatial.transform.rotation import Rotation as RRR
class SMPL(_SMPL):
""" Extension of the official SMPL implementation to support more joints """
def __init__(self, *args, **kwargs):
super(SMPL, self).__init__(*args, **kwargs)
# joints = [constants.JOINT_MAP[i] for i in constants.JOINT_NAMES]
# J_regressor_extra = np.load(config.JOINT_REGRESSOR_TRAIN_EXTRA)
# self.register_buffer('J_regressor_extra', torch.tensor(J_regressor_extra, dtype=torch.float32))
# self.joint_map = torch.tensor(joints, dtype=torch.long)
def forward(self, *args, **kwargs):
kwargs['get_skin'] = True
smpl_output = super(SMPL, self).forward(*args, **kwargs)
# extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices) #Additional 9 joints #Check doc/J_regressor_extra.png
# joints = torch.cat([smpl_output.joints, extra_joints], dim=1) #[N, 24 + 21, 3] + [N, 9, 3]
# joints = joints[:, self.joint_map, :]
joints = smpl_output.joints
output = ModelOutput(vertices=smpl_output.vertices,
global_orient=smpl_output.global_orient,
body_pose=smpl_output.body_pose,
joints=joints,
betas=smpl_output.betas,
full_pose=smpl_output.full_pose)
return output
class Renderer:
"""
Renderer used for visualizing the SMPL model
Code adapted from https://github.com/vchoutas/smplify-x
"""
def __init__(self,
vertices,
focal_length=5000,
img_res=(224, 224),
faces=None):
self.renderer = pyrender.OffscreenRenderer(viewport_width=img_res[0],
viewport_height=img_res[1],
point_size=2.0)
self.focal_length = focal_length
self.camera_center = [img_res[0] // 2, img_res[1] // 2]
self.faces = faces
if torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
self.rot = trimesh.transformations.rotation_matrix(
np.radians(180), [1, 0, 0])
minx, miny, minz = vertices.min(axis=(0, 1))
maxx, maxy, maxz = vertices.max(axis=(0, 1))
minx = minx - 0.5
maxx = maxx + 0.5
minz = minz - 0.5
maxz = maxz + 0.5
floor = geometry.Polygon([[minx, minz], [minx, maxz], [maxx, maxz],
[maxx, minz]])
self.floor = trimesh.creation.extrude_polygon(floor, 1e-5)
self.floor.visual.face_colors = [0, 0, 0, 0.2]
self.floor.apply_transform(self.rot)
self.floor_pose = np.array(
[[1, 0, 0, 0], [0, np.cos(np.pi / 2), -np.sin(np.pi / 2), miny],
[0, np.sin(np.pi / 2), np.cos(np.pi / 2), 0], [0, 0, 0, 1]])
c = -np.pi / 6
self.camera_pose = [[1, 0, 0, (minx + maxx) / 2],
[0, np.cos(c), -np.sin(c), 1.5],
[
0,
np.sin(c),
np.cos(c),
max(4, minz + (1.5 - miny) * 2, (maxx - minx))
], [0, 0, 0, 1]]
def __call__(self, vertices, camera_translation):
floor_render = pyrender.Mesh.from_trimesh(self.floor, smooth=False)
material = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.1,
alphaMode='OPAQUE',
baseColorFactor=(0.658, 0.214, 0.0114, 0.2))
mesh = trimesh.Trimesh(vertices, self.faces)
mesh.apply_transform(self.rot)
mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
camera = pyrender.PerspectiveCamera(yfov=(np.pi / 3.0), znear=0.5)
light = pyrender.DirectionalLight(color=[1, 1, 1], intensity=350)
spot_l = pyrender.SpotLight(color=np.ones(3),
intensity=300.0,
innerConeAngle=np.pi / 16,
outerConeAngle=np.pi / 6)
point_l = pyrender.PointLight(color=np.ones(3), intensity=300.0)
scene = pyrender.Scene(bg_color=(1., 1., 1., 0.8),
ambient_light=(0.4, 0.4, 0.4))
scene.add(floor_render, pose=self.floor_pose)
scene.add(mesh, 'mesh')
light_pose = np.eye(4)
light_pose[:3, 3] = np.array([0, -1, 1])
scene.add(light, pose=light_pose)
light_pose[:3, 3] = np.array([0, 1, 1])
scene.add(light, pose=light_pose)
light_pose[:3, 3] = np.array([1, 1, 2])
scene.add(light, pose=light_pose)
scene.add(camera, pose=self.camera_pose)
flags = pyrender.RenderFlags.RGBA | pyrender.RenderFlags.SHADOWS_DIRECTIONAL
color, rend_depth = self.renderer.render(scene, flags=flags)
return color
class SMPLRender():
def __init__(self, SMPL_MODEL_DIR):
if torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
self.smpl = SMPL(SMPL_MODEL_DIR, batch_size=1,
create_transl=False).to(self.device)
self.pred_camera_t = []
self.focal_length = 110
def init_renderer(self, res, smpl_param, is_headroot=False):
poses = smpl_param['pred_pose']
pred_rotmats = []
for pose in poses:
if pose.size == 72:
pose = pose.reshape(-1, 3)
pose = RRR.from_rotvec(pose).as_matrix()
pose = pose.reshape(1, 24, 3, 3)
pred_rotmats.append(
torch.from_numpy(pose.astype(np.float32)[None]).to(
self.device))
pred_rotmat = torch.cat(pred_rotmats, dim=0)
pred_betas = torch.from_numpy(smpl_param['pred_shape'].reshape(
1, 10).astype(np.float32)).to(self.device)
pred_camera_t = smpl_param['pred_root'].reshape(1,
3).astype(np.float32)
smpl_output = self.smpl(betas=pred_betas,
body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:, 0].unsqueeze(1),
pose2rot=False)
self.vertices = smpl_output.vertices.detach().cpu().numpy()
pred_camera_t = pred_camera_t[0]
if is_headroot:
pred_camera_t = pred_camera_t - smpl_output.joints[
0, 12].detach().cpu().numpy()
self.pred_camera_t.append(pred_camera_t)
self.renderer = Renderer(vertices=self.vertices,
focal_length=self.focal_length,
img_res=(res[1], res[0]),
faces=self.smpl.faces)
def render(self, index):
renderImg = self.renderer(self.vertices[index, ...],
self.pred_camera_t)
return renderImg
|