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107 lines
3.7 KiB
107 lines
3.7 KiB
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 ALPHA, CLASSES_SEG, COLORS, MASK_COLORS |
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from models.utils import blob, letterbox, path_to_list, seg_postprocess |
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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, ratio, dwdh = letterbox(bgr, (W, H)) |
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dw, dh = int(dwdh[0]), int(dwdh[1]) |
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rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) |
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tensor, seg_img = blob(rgb, return_seg=True) |
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dwdh = np.array(dwdh * 2, dtype=np.float32) |
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tensor = np.ascontiguousarray(tensor) |
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# inference |
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data = Engine(tensor) |
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seg_img = seg_img[dh:H - dh, dw:W - dw, [2, 1, 0]] |
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bboxes, scores, labels, masks = seg_postprocess( |
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data, bgr.shape[:2], args.conf_thres, args.iou_thres) |
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if bboxes.size == 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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masks = masks[:, dh:H - dh, dw:W - dw, :] |
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mask_colors = MASK_COLORS[labels % len(MASK_COLORS)] |
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mask_colors = mask_colors.reshape(-1, 1, 1, 3) * ALPHA |
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mask_colors = masks @ mask_colors |
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inv_alph_masks = (1 - masks * 0.5).cumprod(0) |
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mcs = (mask_colors * inv_alph_masks).sum(0) * 2 |
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seg_img = (seg_img * inv_alph_masks[-1] + mcs) * 255 |
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draw = cv2.resize(seg_img.astype(np.uint8), draw.shape[:2][::-1]) |
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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().astype(np.int32).tolist() |
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cls_id = int(label) |
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cls = CLASSES_SEG[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(): |
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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('--conf-thres', |
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type=float, |
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default=0.25, |
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help='Confidence threshold') |
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parser.add_argument('--iou-thres', |
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type=float, |
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default=0.65, |
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help='Confidence threshold') |
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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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