initial project version!

ultralytics/models/yolo/detect/predict.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import ops
+
+
+class DetectionPredictor(BasePredictor):
+    """
+    A class extending the BasePredictor class for prediction based on a detection model.
+
+    Example:
+        ```python
+        from ultralytics.utils import ASSETS
+        from ultralytics.models.yolo.detect import DetectionPredictor
+
+        args = dict(model='yolov8n.pt', source=ASSETS)
+        predictor = DetectionPredictor(overrides=args)
+        predictor.predict_cli()
+        ```
+    """
+
+    def postprocess(self, preds, img, orig_imgs):
+        """Post-processes 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)
+
+        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)
+            img_path = self.batch[0][i]
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
+        return results