退购1.1定位算法

ultralytics/yolo/v8/detect/predict.py

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