#!/usr/bin/python3 import time import cv2 from pathlib import Path import argparse import os from rtmo_gpu import RTMO_GPU_Batch, draw_skeleton, resize_to_fit_screen, draw_bbox if __name__ == "__main__": # Set up argument parsing parser = argparse.ArgumentParser(description='Process the path to a video file folder.') parser.add_argument('path', type=str, help='Path to the folder containing video files (required)') parser.add_argument('model_path', type=str, help='Path to a RTMO ONNX (or engine) model file (required)') parser.add_argument('--yolo_nas_pose', action='store_true', help='Use YOLO NAS Pose (flat format only) instead of RTMO Model') parser.add_argument('--batch_size', type=int, default=1, help='Path to a RTMO ONNX input batch size') # Parse the command-line arguments args = parser.parse_args() model = args.model_path # 'rtmo-s_8xb32-600e_body7-640x640.onnx' body = RTMO_GPU_Batch(model=model, is_yolo_nas_pose=args.yolo_nas_pose, batch_size=args.batch_size) for mp4_path in Path(args.path).glob('*'): # Now, use the best.url, which is the direct video link for streaming cap = cv2.VideoCapture(filename=os.path.abspath(mp4_path)) frame_idx = 0 s = time.time() while cap.isOpened(): success, frame = cap.read() frame_idx += 1 if not success: break frame_out, bboxes, bboxes_scores, keypoints, scores = body(frame) if keypoints is not None: if frame_idx % args.batch_size == 0 and frame_idx: current_time = time.time() det_time = current_time - s fps = round(args.batch_size / det_time, 1) print(f'det: {fps} FPS') s = current_time img_show = frame_out.copy() # if you want to use black background instead of original image, # img_show = np.zeros(img_show.shape, dtype=np.uint8) img_show = draw_skeleton(img_show, keypoints, scores, kpt_thr=0.3, line_width=2) img_show = draw_bbox(img_show, bboxes, bboxes_scores) img_show = resize_to_fit_screen(img_show, 720, 480) cv2.putText(img_show, f'{fps:.1f}', (10, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1, cv2.LINE_AA) cv2.imshow(f'{model}', img_show) cv2.waitKey(10)