98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
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"""trt_yolo_cv.py
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This script could be used to make object detection video with
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TensorRT optimized YOLO engine.
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"cv" means "create video"
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made by BigJoon (ref. jkjung-avt)
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"""
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import os
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import argparse
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import cv2
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import pycuda.autoinit # This is needed for initializing CUDA driver
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from utils.yolo_classes import get_cls_dict
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from utils.visualization import BBoxVisualization
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from utils.yolo_with_plugins import TrtYOLO
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def parse_args():
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"""Parse input arguments."""
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desc = ('Run the TensorRT optimized object detecion model on an input '
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'video and save BBoxed overlaid output as another video.')
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parser = argparse.ArgumentParser(description=desc)
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parser.add_argument(
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'-v', '--video', type=str, required=True,
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help='input video file name')
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parser.add_argument(
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'-o', '--output', type=str, required=True,
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help='output video file name')
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parser.add_argument(
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'-c', '--category_num', type=int, default=80,
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help='number of object categories [80]')
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parser.add_argument(
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'-m', '--model', type=str, required=True,
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help=('[yolov3-tiny|yolov3|yolov3-spp|yolov4-tiny|yolov4|'
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'yolov4-csp|yolov4x-mish|yolov4-p5]-[{dimension}], where '
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'{dimension} could be either a single number (e.g. '
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'288, 416, 608) or 2 numbers, WxH (e.g. 416x256)'))
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parser.add_argument(
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'-l', '--letter_box', action='store_true',
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help='inference with letterboxed image [False]')
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args = parser.parse_args()
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return args
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def loop_and_detect(cap, trt_yolo, conf_th, vis, writer):
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"""Continuously capture images from camera and do object detection.
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# Arguments
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cap: the camera instance (video source).
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trt_yolo: the TRT YOLO object detector instance.
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conf_th: confidence/score threshold for object detection.
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vis: for visualization.
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writer: the VideoWriter object for the output video.
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"""
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while True:
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ret, frame = cap.read()
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if frame is None: break
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boxes, confs, clss = trt_yolo.detect(frame, conf_th)
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frame = vis.draw_bboxes(frame, boxes, confs, clss)
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writer.write(frame)
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print('.', end='', flush=True)
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print('\nDone.')
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def main():
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args = parse_args()
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if args.category_num <= 0:
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raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
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if not os.path.isfile('yolo/%s.trt' % args.model):
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raise SystemExit('ERROR: file (yolo/%s.trt) not found!' % args.model)
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cap = cv2.VideoCapture(args.video)
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if not cap.isOpened():
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raise SystemExit('ERROR: failed to open the input video file!')
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frame_width, frame_height = int(cap.get(3)), int(cap.get(4))
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writer = cv2.VideoWriter(
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args.output,
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cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width, frame_height))
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cls_dict = get_cls_dict(args.category_num)
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vis = BBoxVisualization(cls_dict)
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trt_yolo = TrtYOLO(args.model, args.category_num, args.letter_box)
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loop_and_detect(cap, trt_yolo, conf_th=0.3, vis=vis, writer=writer)
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writer.release()
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cap.release()
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if __name__ == '__main__':
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main()
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