pose_model.cuda()
pose_model.eval()
webcam = args.webcam
mode = args.mode
if not os.path.exists(args.outputpath):
os.mkdir(args.outputpath)
# Load input video
data_loader = WebcamLoader(webcam).start()
(fourcc, fps, frameSize) = data_loader.videoinfo()
# Load detection loader
print('Loading YOLO model..')
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
det_processor = DetectionProcessor(det_loader).start()
# Data writer
save_path = os.path.join(args.outputpath, 'AlphaPose_webcam' + webcam + '.avi')
writer = DataWriter(args.save_video, save_path,
cv2.VideoWriter_fourcc(*'XVID'), fps, frameSize).start()
runtime_profile = {'dt': [], 'pt': [], 'pn': []}
sys.stdout.flush()
batchSize = args.posebatch
def run():
ret = []
# for i in im_names_desc:
# try:
start_time = getTime()
with torch.no_grad():
if __name__ == "__main__":
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
webcam = args.webcam
mode = args.mode
if not os.path.exists(args.outputpath):
os.mkdir(args.outputpath)
# Load input video
data_loader = WebcamLoader(webcam).start()
(fourcc, fps, frameSize) = data_loader.videoinfo()
# Load detection loader
print('Loading YOLO model..')
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
det_processor = DetectionProcessor(det_loader).start()
# Load pose model
pose_dataset = Mscoco()
if args.fast_inference:
pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
else:
pose_model = InferenNet(4 * 1 + 1, pose_dataset)
pose_model.to(device)
pose_model.eval()
# Data writer
save_path = os.path.join(args.outputpath,
'AlphaPose_webcam' + webcam + '.avi')
writer = DataWriter(args.save_video, save_path,
cv2.VideoWriter_fourcc(*'XVID'), fps,
if __name__ == "__main__":
webcam = args.webcam
mode = args.mode
if not os.path.exists(args.outputpath):
os.mkdir(args.outputpath)
# Load input video
data_loader = WebcamLoader(webcam).start()
(fourcc,fps,frameSize) = data_loader.videoinfo()
# Load detection loader
print('Loading YOLO model..')
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
det_processor = DetectionProcessor(det_loader).start()
# Load pose model
pose_dataset = Mscoco()
if args.fast_inference:
pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
else:
pose_model = InferenNet(4 * 1 + 1, pose_dataset)
pose_model.cuda()
pose_model.eval()
# Data writer
save_path = os.path.join(args.outputpath, 'AlphaPose_webcam'+webcam+'.avi')
writer = DataWriter(args.save_video, save_path, cv2.VideoWriter_fourcc(*'XVID'), fps, frameSize).start()
runtime_profile = {