80 lines
3.4 KiB
Python
80 lines
3.4 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import torch
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import numpy as np
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import os
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from PIL import Image
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from ultralytics.yolo.engine.predictor import BasePredictor
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from ultralytics.yolo.engine.results import Results
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from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops
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class DetectionPredictor(BasePredictor):
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def postprocess(self, preds, img, orig_imgs):
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"""Postprocesses predictions and returns a list of Results objects."""
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preds = ops.non_max_suppression(preds,
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self.args.conf,
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self.args.iou,
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agnostic=self.args.agnostic_nms,
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max_det=self.args.max_det,
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classes=self.args.classes)
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results = []
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for i, pred in enumerate(preds):
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs
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if not isinstance(orig_imgs, torch.Tensor):
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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path = self.batch[0]
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img_path = path[i] if isinstance(path, list) else path
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results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))
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# print('results2222222', results)
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return results
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def boxesMov_output(self, path, img_MovBoxes):
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if len(img_MovBoxes) != 0:
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##保存判断为运动框中最后十帧所有运动框
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MovBox_save = self.save_dir / 'real_MovBox/'
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if not os.path.exists(MovBox_save):
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MovBox_save.mkdir(parents=True, exist_ok=True)
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# print('img_MovBoxes', img_MovBoxes)
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img_MovBoxes.sort(key=lambda x: x[0], reverse=True) ##按照ID降序
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index = np.unique(np.array(img_MovBoxes, dtype=object)[:, 0]) ##保留所有运动框的ID,升序排序
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# print('index', index)
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if len(index) > 10:
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real_MovBox = [box for box in img_MovBoxes if box[0] > index[-11]]
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else:
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real_MovBox = [box for box in img_MovBoxes]
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num = 0
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for mv_box in real_MovBox:
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num += 1
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# img_crop = str(MovBox_save) + '\\' + str(video_num) + '_'+ str(i) + '.jpg'
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# img_crop = str(MovBox_save) + '\\' + str(path).split('.mp4')[0].split('\\')[-1] + \
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# str(mv_box[0]) + '_' + str(num) + '.jpg'
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img_crop = str(MovBox_save) + '/' + str(path).split('.mp4')[0].split('\\')[-1] + '_' + str(
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mv_box[0]) + '_' + str(num) + '.jpg'
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Image.fromarray(mv_box[1]).save(img_crop, quality=95, subsampling=0)
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# print("99999999999999", real_MovBox)
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return real_MovBox
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else:
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return None
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def predict(cfg=DEFAULT_CFG, use_python=False):
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"""Runs YOLO model inference on input image(s)."""
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model = cfg.model or 'yolov8n.pt'
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source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \
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else 'https://ultralytics.com/images/bus.jpg'
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args = dict(model=model, source=source)
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if use_python:
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from ultralytics import YOLO
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YOLO(model)(**args)
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else:
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predictor = DetectionPredictor(overrides=args)
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predictor.predict_cli()
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if __name__ == '__main__':
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predict()
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