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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 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, 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=1.0)
self.focal_length = focal_length
self.camera_center = [img_res[0] // 2, img_res[1] // 2]
self.faces = faces
def __call__(self, vertices, camera_translation, image):
material = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.2,
alphaMode='OPAQUE',
baseColorFactor=(0.8, 0.3, 0.3, 1.0))
camera_translation[0] *= -1.
mesh = trimesh.Trimesh(vertices, self.faces)
rot = trimesh.transformations.rotation_matrix(
np.radians(180), [1, 0, 0])
mesh.apply_transform(rot)
mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
scene = pyrender.Scene(bg_color = [1, 1, 1, 0.8], ambient_light=(0.4, 0.4, 0.4))
scene.add(mesh, 'mesh')
camera_pose = np.eye(4)
camera_pose[:3, 3] = camera_translation
camera = pyrender.IntrinsicsCamera(fx=self.focal_length, fy=self.focal_length,
cx=self.camera_center[0], cy=self.camera_center[1])
scene.add(camera, pose=camera_pose)
light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=300)
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)
color, rend_depth = self.renderer.render(scene, flags=pyrender.RenderFlags.RGBA)
color = color.astype(np.float32) / 255.0
valid_mask = (rend_depth > 0)[:,:,None]
output_img = (color[:, :, :3] * valid_mask +
(1 - valid_mask) * image)
return output_img
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.focal_length = 5000
def render(self, image, smpl_param, is_headroot=False):
pose = smpl_param['pred_pose']
if pose.size==72:
pose = pose.reshape(-1,3)
pose = RRR.from_rotvec(pose).as_matrix()
pose = pose.reshape(1,24,3,3)
pred_betas = torch.from_numpy(smpl_param['pred_shape'].reshape(1, 10).astype(np.float32)).to(self.device)
pred_rotmat = torch.from_numpy(pose.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)
vertices = smpl_output.vertices[0].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()
renderer = Renderer(focal_length=self.focal_length,
img_res=(image.shape[1], image.shape[0]), faces=self.smpl.faces)
renderImg = renderer(vertices, pred_camera_t.copy(), image / 255.0)
renderer.renderer.delete()
return renderImg
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