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| import math | |
| import sys | |
| from argparse import ArgumentParser | |
| from pathlib import Path | |
| import cv2 | |
| import onnxruntime | |
| from config import (CLASS_COLORS, CLASS_NAMES, ModelType, YOLOv5_ANCHORS, | |
| YOLOv7_ANCHORS) | |
| from cv2_nms import non_max_suppression | |
| from numpy_coder import Decoder | |
| from preprocess import Preprocess | |
| from tqdm import tqdm | |
| # Add __FILE__ to sys.path | |
| sys.path.append(str(Path(__file__).resolve().parents[0])) | |
| IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', | |
| '.tiff', '.webp') | |
| def path_to_list(path: str): | |
| path = Path(path) | |
| if path.is_file() and path.suffix in IMG_EXTENSIONS: | |
| res_list = [str(path.absolute())] | |
| elif path.is_dir(): | |
| res_list = [ | |
| str(p.absolute()) for p in path.iterdir() | |
| if p.suffix in IMG_EXTENSIONS | |
| ] | |
| else: | |
| raise RuntimeError | |
| return res_list | |
| def parse_args(): | |
| parser = ArgumentParser() | |
| parser.add_argument( | |
| 'img', help='Image path, include image file, dir and URL.') | |
| parser.add_argument('onnx', type=str, help='Onnx file') | |
| parser.add_argument('--type', type=str, help='Model type') | |
| parser.add_argument( | |
| '--img-size', | |
| nargs='+', | |
| type=int, | |
| default=[640, 640], | |
| help='Image size of height and width') | |
| parser.add_argument( | |
| '--out-dir', default='./output', type=str, help='Path to output file') | |
| parser.add_argument( | |
| '--show', action='store_true', help='Show the detection results') | |
| parser.add_argument( | |
| '--score-thr', type=float, default=0.3, help='Bbox score threshold') | |
| parser.add_argument( | |
| '--iou-thr', type=float, default=0.7, help='Bbox iou threshold') | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = parse_args() | |
| out_dir = Path(args.out_dir) | |
| model_type = ModelType(args.type.lower()) | |
| if not args.show: | |
| out_dir.mkdir(parents=True, exist_ok=True) | |
| files = path_to_list(args.img) | |
| session = onnxruntime.InferenceSession( | |
| args.onnx, providers=['CPUExecutionProvider']) | |
| preprocessor = Preprocess(model_type) | |
| decoder = Decoder(model_type, model_only=True) | |
| if model_type == ModelType.YOLOV5: | |
| anchors = YOLOv5_ANCHORS | |
| elif model_type == ModelType.YOLOV7: | |
| anchors = YOLOv7_ANCHORS | |
| else: | |
| anchors = None | |
| for file in tqdm(files): | |
| image = cv2.imread(file) | |
| image_h, image_w = image.shape[:2] | |
| img, (ratio_w, ratio_h) = preprocessor(image, args.img_size) | |
| features = session.run(None, {'images': img}) | |
| decoder_outputs = decoder( | |
| features, | |
| args.score_thr, | |
| num_labels=len(CLASS_NAMES), | |
| anchors=anchors) | |
| nmsd_boxes, nmsd_scores, nmsd_labels = non_max_suppression( | |
| *decoder_outputs, args.score_thr, args.iou_thr) | |
| for box, score, label in zip(nmsd_boxes, nmsd_scores, nmsd_labels): | |
| x0, y0, x1, y1 = box | |
| x0 = math.floor(min(max(x0 / ratio_w, 1), image_w - 1)) | |
| y0 = math.floor(min(max(y0 / ratio_h, 1), image_h - 1)) | |
| x1 = math.ceil(min(max(x1 / ratio_w, 1), image_w - 1)) | |
| y1 = math.ceil(min(max(y1 / ratio_h, 1), image_h - 1)) | |
| cv2.rectangle(image, (x0, y0), (x1, y1), CLASS_COLORS[label], 2) | |
| cv2.putText(image, f'{CLASS_NAMES[label]}: {score:.2f}', | |
| (x0, y0 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, | |
| (0, 255, 255), 2) | |
| if args.show: | |
| cv2.imshow('result', image) | |
| cv2.waitKey(0) | |
| else: | |
| cv2.imwrite(f'{out_dir / Path(file).name}', image) | |
| if __name__ == '__main__': | |
| main() | |