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from models import TRTModule # isort:skip
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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 torch
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from config import CLASSES, COLORS
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from models.torch_utils import det_postprocess
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from models.utils import blob, letterbox, path_to_list
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def main(args: argparse.Namespace) -> None:
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device = torch.device(args.device)
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Engine = TRTModule(args.engine, device)
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H, W = Engine.inp_info[0].shape[-2:]
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# set desired output names order
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Engine.set_desired(['num_dets', 'bboxes', 'scores', 'labels'])
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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, ratio, dwdh = letterbox(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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dwdh = torch.asarray(dwdh * 2, dtype=torch.float32, device=device)
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tensor = torch.asarray(tensor, device=device)
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# inference
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data = Engine(tensor)
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bboxes, scores, labels = det_postprocess(data)
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if bboxes.numel() == 0:
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# if no bounding box
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print(f'{image}: no object!')
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continue
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bboxes -= dwdh
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bboxes /= ratio
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for (bbox, score, label) in zip(bboxes, scores, labels):
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bbox = bbox.round().int().tolist()
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cls_id = int(label)
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cls = CLASSES[cls_id]
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color = COLORS[cls]
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cv2.rectangle(draw, bbox[:2], bbox[2:], color, 2)
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cv2.putText(draw,
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f'{cls}:{score:.3f}', (bbox[0], bbox[1] - 2),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.75, [225, 255, 255],
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thickness=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() -> argparse.Namespace:
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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('--device',
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type=str,
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default='cuda:0',
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help='TensorRT infer device')
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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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