triple-Mu
2 years ago
parent
64dd1005ff
commit
c2c62cf338
5 changed files with 128 additions and 5 deletions
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After Width: | Height: | Size: 476 KiB |
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After Width: | Height: | Size: 165 KiB |
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from models import TRTModule |
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from pathlib import Path |
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import cv2 |
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import argparse |
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import numpy as np |
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import torch |
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import random |
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random.seed(0) |
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SUFFIXS = ('.bmp', '.dng', '.jpeg', '.jpg', '.mpo', '.png', '.tif', '.tiff', '.webp', '.pfm') |
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CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', |
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'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', |
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'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', |
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'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', |
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'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', |
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'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', |
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'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', |
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'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', |
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'hair drier', 'toothbrush') |
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COLORS = {cls: [random.randint(0, 255) for _ in range(3)] for i, cls in enumerate(CLASSES)} |
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def letterbox(im, new_shape=(640, 640), color=(114, 114, 114)): |
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# Resize and pad image while meeting stride-multiple constraints |
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shape = im.shape[:2] # current shape [height, width] |
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if isinstance(new_shape, int): |
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new_shape = (new_shape, new_shape) |
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# Scale ratio (new / old) |
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) |
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# Compute padding |
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) |
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding |
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dw /= 2 # divide padding into 2 sides |
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dh /= 2 |
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if shape[::-1] != new_unpad: # resize |
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) |
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) |
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) |
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border |
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return im, np.array([r, r, r, r], dtype=np.float32), np.array([dw, dh, dw, dh], dtype=np.float32) |
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def blob(im): |
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im = im.transpose(2, 0, 1) |
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im = im[np.newaxis, ...] |
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im = np.ascontiguousarray(im).astype(np.float32) / 255 |
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return im |
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def main(args): |
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device = torch.device(args.device) |
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Engine = TRTModule(args.engine, device) |
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images_path = Path(args.imgs) |
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assert images_path.exists() |
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save_path = Path(args.out_dir) |
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if images_path.is_dir(): |
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images = [i.absolute() for i in images_path.iterdir() if i.suffix in SUFFIXS] |
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else: |
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assert images_path.suffix in SUFFIXS |
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images = [images_path.absolute()] |
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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) |
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rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB) |
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tensor = blob(rgb) |
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ratio = torch.asarray(ratio, dtype=torch.float32, device=device) |
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dwdh = torch.asarray(dwdh, dtype=torch.float32, device=device) |
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tensor = torch.asarray(tensor, device=device) |
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num_dets, bboxes, scores, labels = Engine(tensor) |
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bboxes = bboxes[0, :num_dets.item()] |
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scores = scores[0, :num_dets.item()] |
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labels = labels[0, :num_dets.item()] |
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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, f'{cls}:{score:.3f}', (bbox[0], bbox[1] - 2), |
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cv2.FONT_HERSHEY_SIMPLEX, 0.75, |
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[225, 255, 255], 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( |
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'--show', action='store_true', help='Show the detection results') |
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parser.add_argument( |
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'--out-dir', type=str, default='./output', help='Path to output file') |
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parser.add_argument( |
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'--device', type=str, default='cuda:0', 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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