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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
+
+import numpy as np
+import torch
+
+
+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 preprocess(self, im):
+        """
+        Prepares input image before inference.
+
+        Args:
+            im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list.
+        """
+        not_tensor = not isinstance(im, torch.Tensor)
+        if not_tensor:
+            im = np.stack(self.pre_transform(im))
+            im_rgb = im.copy()
+            # for i in range(im.shape[3] // 3):
+            #     start_idx = i * 3
+            #     channel_idx = list(range(start_idx, start_idx + 3))
+            #     im_rgb[..., channel_idx] = im[..., channel_idx][:, :, ::-1]  # BGR to RGB
+            im = im_rgb.transpose((0, 3, 1, 2))  # BHWC to BCHW
+            im = np.ascontiguousarray(im)  # contiguous
+            im = torch.from_numpy(im)
+
+        im = im.to(self.device)
+        im = im.half() if self.model.fp16 else im.float()  # uint8 to fp16/32
+        if not_tensor:
+            im /= 255  # 0 - 255 to 0.0 - 1.0
+        return im
+
+    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)
+            # pred = self.fliplr(pred, orig_img.shape[1])
+            img_path = self.batch[0][i]
+            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
+        return results
+    
+    def fliplr(self, pred, w):
+        """Reverses the order of the bounding boxes and segments horizontally."""
+        bboxes = pred.clone()
+        if True: # "xyxy"
+            x1 = bboxes[:, 0].clone()
+            x2 = bboxes[:, 2].clone()
+            bboxes[:, 0] = w - x2
+            bboxes[:, 2] = w - x1
+        else:
+            bboxes[:, 0] = w - bboxes[:, 0]
+        return bboxes