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import argparse
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from pathlib import Path
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import cv2
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import numpy as np
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from config import CLASSES_CLS
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from models.utils import blob, path_to_list
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def main(args: argparse.Namespace) -> None:
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if args.method == 'cudart':
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from models.cudart_api import TRTEngine
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elif args.method == 'pycuda':
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from models.pycuda_api import TRTEngine
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else:
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raise NotImplementedError
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Engine = TRTEngine(args.engine)
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H, W = Engine.inp_info[0].shape[-2:]
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images = path_to_list(args.imgs)
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save_path = Path(args.out_dir)
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if not args.show and not save_path.exists():
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save_path.mkdir(parents=True, exist_ok=True)
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for image in images:
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save_image = save_path / image.name
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bgr = cv2.imread(str(image))
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draw = bgr.copy()
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bgr = cv2.resize(bgr, (W, H))
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rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
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tensor = blob(rgb, return_seg=False)
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tensor = np.ascontiguousarray(tensor)
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# inference
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data = Engine(tensor)
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data = data[0]
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score = data.max().item()
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cls_id = data.argmax().item()
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cls = CLASSES_CLS[cls_id]
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text = f'{cls}:{score:.3f}'
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(_w, _h), _bl = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 1)
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_y1 = min(10, draw.shape[0])
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cv2.rectangle(draw, (10, _y1), (10 + _w, _y1 + _h + _bl), (0, 0, 255),
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-1)
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cv2.putText(draw, text, (10, _y1 + _h), cv2.FONT_HERSHEY_SIMPLEX, 0.75,
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(255, 255, 255), 2)
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if args.show:
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cv2.imshow('result', draw)
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cv2.waitKey(0)
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else:
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cv2.imwrite(str(save_image), draw)
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--engine', type=str, help='Engine file')
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parser.add_argument('--imgs', type=str, help='Images file')
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parser.add_argument('--show',
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action='store_true',
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help='Show the detection results')
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parser.add_argument('--out-dir',
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type=str,
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default='./output',
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help='Path to output file')
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parser.add_argument('--method',
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type=str,
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default='cudart',
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help='CUDART pipeline')
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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