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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 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=1.0)
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")
vertices = np.concatenate(vertices)
# Center the first root to the first frame
vertices -= vertices[[0], [0], :]
# Remove the floor
vertices[..., 2] -= vertices[..., 2].min()
data = vertices[..., [2, 0, 1]]
minx, miny, _ = data.min(axis=(0, 1))
maxx, maxy, _ = data.max(axis=(0, 1))
minz, maxz = -0.5, 0.5
minx = minx - 0.5
maxx = maxx + 0.5
miny = miny - 0.5
maxy = maxy + 0.5
polygon = geometry.Polygon([[minx, minz], [minx, maxz], [maxx, maxz],
[maxx, minz]])
self.polygon_mesh = trimesh.creation.extrude_polygon(polygon, 1e-5)
self.polygon_mesh.visual.face_colors = [0, 0, 0, 0.21]
self.rot = trimesh.transformations.rotation_matrix(
np.radians(180), [1, 0, 0])
# self.polygon_mesh.apply_transform(self.rot)
def __call__(self, vertices, camera_translation):
scene = pyrender.Scene(bg_color=(1., 1., 1., 0.8),
ambient_light=(0.4, 0.4, 0.4))
material = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.4,
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)
scene.add(mesh, 'mesh')
polygon_render = pyrender.Mesh.from_trimesh(self.polygon_mesh,
smooth=False)
c = np.pi / 2
scene.add(polygon_render)
camera_pose = np.eye(4)
camera_translation[0] *= -1.
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, 1, 1], 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)
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.vertices = []
self.pred_camera_t = []
self.focal_length = 5000
def fit(self, 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()
self.vertices.append(vertices[None])
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)
def init_renderer(self, res):
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][0],
self.pred_camera_t[index].copy())
return renderImg
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