更新 detacttracking

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

@@ -0,0 +1,52 @@
+# 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, LOGGER, ops
+
+
+class PosePredictor(DetectionPredictor):
+    """
+    A class extending the DetectionPredictor class for prediction based on a pose model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.pose import PosePredictor
+
+        args = dict(model='yolov8n-pose.pt', source=ASSETS)
+        predictor = PosePredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = 'pose'
+        if isinstance(self.args.device, str) and self.args.device.lower() == 'mps':
+            LOGGER.warning("WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
+                           'See https://github.com/ultralytics/ultralytics/issues/4031.')
+
+    def postprocess(self, preds, img, orig_imgs):
+        """Return detection results for a given input image or list of images."""
+        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,
+                                        nc=len(self.model.names))
+
+        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
+            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
+
+        results = []
+        for i, pred in enumerate(preds):
+            orig_img = orig_imgs[i]
+            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
+            pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
+            pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
+            img_path = self.batch[0][i]
+            results.append(
+                Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts))
+        return results