def process_video(self, video_file_path=None):
import keyboard
from utils.demo_utils import OpenCVCapture, frames2video
capture = OpenCVCapture(video_file_path)
video_length = int(capture.cap.get(cv2.CAP_PROP_FRAME_COUNT))
results, result_frames = {}, []
for frame_id in range(video_length):
print('Processing video {}/{}'.format(frame_id, video_length))
frame = capture.read()
with torch.no_grad():
outputs = self.single_image_forward(frame)
vis_dict = {'image_org': outputs['meta_data']['image_org'].cpu()}
single_batch_results = self.reorganize_results(
outputs,
[os.path.basename(video_file_path) + '_' + str(frame_id)],
outputs['reorganize_idx'].cpu().numpy())
results.update(single_batch_results)
vis_eval_results = self.visualizer.visulize_result_onorg(
outputs['verts'],
outputs['verts_camed'],
vis_dict,
reorganize_idx=outputs['reorganize_idx'].cpu().numpy())
result_frames.append(vis_eval_results[0])
if self.save_dict_results:
print('Saving parameter results to {}'.format(
video_file_path.replace('.mp4', '_results.npz')))
np.savez(video_file_path.replace('.mp4', '_results.npz'),
results=results)
video_save_name = video_file_path.replace('.mp4', '_results.mp4')
print('Writing results to {}'.format(video_save_name))
frames2video(result_frames, video_save_name, fps=args.fps_save)
def process_video(self, video_file_path=None):
import keyboard
from utils.demo_utils import OpenCVCapture, frames2video
capture = OpenCVCapture(video_file_path)
video_length = int(capture.cap.get(cv2.CAP_PROP_FRAME_COUNT))
video_basename = get_video_bn(video_file_path)
print('Processing {}, saving to {}'.format(video_file_path,
self.output_dir))
os.makedirs(self.output_dir, exist_ok=True)
if not os.path.isdir(self.output_dir):
self.output_dir = video_file_path.replace(
os.path.basename(video_file_path), '')
results, result_frames = {}, []
for frame_id in range(video_length):
print('Processing video {}/{}'.format(frame_id, video_length))
frame = capture.read()
with torch.no_grad():
outputs = self.single_image_forward(frame)
vis_dict = {'image_org': outputs['meta_data']['image_org'].cpu()}
img_paths = [str(frame_id) for _ in range(1)]
single_batch_results = self.reorganize_results(
outputs, img_paths, outputs['reorganize_idx'].cpu().numpy())
results.update(single_batch_results)
vis_eval_results = self.visualizer.visulize_result_onorg(
outputs['verts'],
outputs['verts_camed'],
vis_dict,
reorganize_idx=outputs['reorganize_idx'].cpu().numpy())
result_frames.append(vis_eval_results[0])
outputs['meta_data']['imgpath'] = img_paths
if self.save_mesh:
save_meshes(outputs['reorganize_idx'].cpu().numpy(), outputs,
self.output_dir, self.smpl_faces)
if self.save_dict_results:
save_dict_path = os.path.join(self.output_dir,
video_basename + '_results.npz')
print('Saving parameter results to {}'.format(save_dict_path))
np.savez(save_dict_path, results=results)
if self.save_video_results:
video_save_name = os.path.join(self.output_dir,
video_basename + '_results.mp4')
print('Writing results to {}'.format(video_save_name))
frames2video(result_frames, video_save_name, fps=args.fps_save)
def process_video(self, video_file_path=None):
import keyboard
from utils.demo_utils import OpenCVCapture, frames2video
capture = OpenCVCapture(video_file_path)
video_length = int(capture.cap.get(cv2.CAP_PROP_FRAME_COUNT))
result_frames = []
for frame_id in range(video_length):
print('Processing video {}/{}'.format(frame_id, video_length))
frame = capture.read()
with torch.no_grad():
outputs = self.single_image_forward(frame[:,:,::-1])
vis_dict = {'image_org': outputs['input_image'].cpu()}
vis_eval_results = self.visualizer.visulize_result_onorg(outputs['verts'], outputs['verts_camed'], vis_dict, reorganize_idx=outputs['reorganize_idx'])
result_frames.append(vis_eval_results[0])
video_save_name = video_file_path.replace('.mp4', '_results.mp4')
print('Writing results to {}'.format(video_save_name))
frames2video(result_frames, video_save_name, fps=30)