feat: add pose estimation video

+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 motion Video')
+    parser.add_argument('--video', type=str, default='')
+    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('--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), trt_bool=str2bool(args.tensorrt))
+    else:
+        e = TfPoseEstimator(get_graph_path(args.model), target_size=(
+            432, 368), trt_bool=str2bool(args.tensorrt))
+    logger.debug('cam read+')
+    cam = cv2.VideoCapture(args.video)
+    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()