def _create_networks(self):
# 0. create generator
self.generator = self._create_generator().cuda()
# 0. create bgnet
if self._opt.bg_model != 'ORIGINAL':
self.bgnet = self._create_bgnet().cuda()
else:
self.bgnet = self.generator.bg_model
# 2. create hmr
self.hmr = self._create_hmr().cuda()
# 3. create render
self.render = SMPLRenderer(image_size=self._opt.image_size,
tex_size=self._opt.tex_size,
has_front=self._opt.front_warp,
fill_back=False).cuda()
# 4. pre-processor
if self._opt.has_detector:
self.detector = PersonMaskRCNNDetector(ks=self._opt.bg_ks,
threshold=0.5,
to_gpu=True)
else:
self.detector = None
def _create_render(self):
render = SMPLRenderer(map_name=self._opt.map_name,
uv_map_path=self._opt.uv_mapping,
tex_size=self._opt.tex_size,
image_size=self._opt.image_size, fill_back=False,
anti_aliasing=True, background_color=(0, 0, 0), has_front=False)
return render
def visual(model, out_dir):
global visualizer
render = SMPLRenderer(image_size=IMG_SIZE).cuda()
texs = render.debug_textures().cuda()[None]
with h5py.File(osp.join(out_dir, 'smpl_infos.h5'), 'r') as reader:
cams_crop = reader['cam_crop']
poses = reader['pose']
shapes = reader['shape']
frame_ids = reader['f_id']
scan_image_paths = sorted(
glob.glob(osp.join(out_dir, 'cropped_frames', '*.png')))
for i in range(len(frame_ids) - 1):
assert frame_ids[i] < frame_ids[i + 1]
image_paths = [scan_image_paths[f_id] for f_id in frame_ids]
for i in tqdm(range(len(image_paths))):
im_path = image_paths[i]
image = cv2.imread(im_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = np.transpose(image, (2, 0, 1))
image = image.astype(np.float) / 255
image = torch.tensor(image).float()[None].cuda()
cams = torch.tensor(cams_crop[i]).float()[None].cuda()
pose = torch.tensor(poses[i]).float()[None].cuda()
shape = torch.tensor(shapes[i]).float()[None].cuda()
verts, _, _ = model.smpl(beta=shape, theta=pose, get_skin=True)
rd_imgs, _ = render.render(cams, verts, texs.clone())
sil = render.render_silhouettes(cams, verts)
masked_img = image * sil[:, None, :, :]
visualizer.vis_named_img('rd_imgs', rd_imgs, denormalize=False)
visualizer.vis_named_img('masked_img',
masked_img,
denormalize=False)
visualizer.vis_named_img('imgs', image, denormalize=False)
time.sleep(1)
def _create_render(self, faces):
render = SMPLRenderer(faces=faces, map_name=self._opt.map_name, uv_map_path=self._opt.uv_mapping,
tex_size=self._opt.tex_size, image_size=self._opt.image_size, fill_back=True,
anti_aliasing=True, background_color=(0, 0, 0), has_front_map=True).cuda()
return render
