更新 detacttracking

detecttracking/ultralytics/models/yolo/segment/predict.py

@@ -0,0 +1,53 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from ultralytics.engine.results import Results
+from ultralytics.models.yolo.detect.predict import DetectionPredictor
+from ultralytics.utils import DEFAULT_CFG, ops
+
+
+class SegmentationPredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on a segmentation model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.segment import SegmentationPredictor
+
+        args = dict(model='yolov8n-seg.pt', source=ASSETS)
+        predictor = SegmentationPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = 'segment'
+
+    def postprocess(self, preds, img, orig_imgs):
+        p = ops.non_max_suppression(preds[0],
+                                    self.args.conf,
+                                    self.args.iou,
+                                    agnostic=self.args.agnostic_nms,
+                                    max_det=self.args.max_det,
+                                    nc=len(self.model.names),
+                                    classes=self.args.classes)
+
+        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
+            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
+
+        results = []
+        proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
+        for i, pred in enumerate(p):
+            orig_img = orig_imgs[i]
+            img_path = self.batch[0][i]
+            if not len(pred):  # save empty boxes
+                masks = None
+            elif self.args.retina_masks:
+                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+                masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
+            else:
+                masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
+                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks))
+        return results