import argparse import logging import time import cv2 import numpy as np from tf_pose.estimator import TfPoseEstimator from tf_pose.networks import get_graph_path, model_wh logger = logging.getLogger('TfPoseEstimator-WebCam') logger.setLevel(logging.DEBUG) ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s') ch.setFormatter(formatter) logger.addHandler(ch) fps_time = 0 def str2bool(v): return v.lower() in ("yes", "true", "t", "1") if __name__ == '__main__': parser = argparse.ArgumentParser(description='tf-pose-estimation realtime webcam') # parser.add_argument('--camera', type=int, default=0) parser.add_argument('--camera', type=int, default=cv2.CAP_DSHOW + 0) parser.add_argument('--resize', type=str, default='0x0', help='if provided, resize images before they are processed. default=0x0, Recommends : 432x368 or 656x368 or 1312x736 ') parser.add_argument('--resize-out-ratio', type=float, default=4.0, help='if provided, resize heatmaps before they are post-processed. default=1.0') # parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin / mobilenet_v2_large / mobilenet_v2_small') parser.add_argument('--model', type=str, default='mobilenet_v2_small', help='cmu / mobilenet_thin / mobilenet_v2_large / mobilenet_v2_small') parser.add_argument('--show-process', type=bool, default=False, help='for debug purpose, if enabled, speed for inference is dropped.') parser.add_argument('--tensorrt', type=str, default="False", help='for tensorrt process.') args = parser.parse_args() logger.debug('initialization %s : %s' % (args.model, get_graph_path(args.model))) w, h = model_wh(args.resize) if w > 0 and h > 0: e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h)) else: e = TfPoseEstimator(get_graph_path(args.model), target_size=(432, 368)) logger.debug('cam read+') cam = cv2.VideoCapture(args.camera) # cam.set(cv2.CAP_PROP_FRAME_WIDTH, 1280) # cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 720) ret_val, image = cam.read() logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0])) while True: ret_val, image = cam.read() logger.debug('image process+') humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio) logger.debug('postprocess+') image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False) logger.debug('show+') cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) cv2.imshow('tf-pose-estimation result', image) fps_time = time.time() if cv2.waitKey(1) == 27: break logger.debug('finished+') cv2.destroyAllWindows()