9181ff62f5
Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
74 lines
3.3 KiB
Python
74 lines
3.3 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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from ultralytics.engine.results import Results
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from ultralytics.models.yolo.detect.predict import DetectionPredictor
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from ultralytics.utils import DEFAULT_CFG, ops
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class SegmentationPredictor(DetectionPredictor):
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"""
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A class extending the DetectionPredictor class for prediction based on a segmentation model.
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Example:
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```python
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from ultralytics.utils import ASSETS
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from ultralytics.models.yolo.segment import SegmentationPredictor
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args = dict(model="yolo11n-seg.pt", source=ASSETS)
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predictor = SegmentationPredictor(overrides=args)
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predictor.predict_cli()
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```
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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"""Initializes the SegmentationPredictor with the provided configuration, overrides, and callbacks."""
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super().__init__(cfg, overrides, _callbacks)
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self.args.task = "segment"
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def postprocess(self, preds, img, orig_imgs):
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"""Applies non-max suppression and processes detections for each image in an input batch."""
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# tuple if PyTorch model or array if exported
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protos = preds[1][-1] if isinstance(preds[1], tuple) else preds[1]
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return super().postprocess(preds[0], img, orig_imgs, protos=protos)
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def construct_results(self, preds, img, orig_imgs, protos):
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"""
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Constructs a list of result objects from the predictions.
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Args:
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preds (List[torch.Tensor]): List of predicted bounding boxes, scores, and masks.
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img (torch.Tensor): The image after preprocessing.
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orig_imgs (List[np.ndarray]): List of original images before preprocessing.
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protos (List[torch.Tensor]): List of prototype masks.
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Returns:
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(list): List of result objects containing the original images, image paths, class names, bounding boxes, and masks.
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"""
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return [
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self.construct_result(pred, img, orig_img, img_path, proto)
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for pred, orig_img, img_path, proto in zip(preds, orig_imgs, self.batch[0], protos)
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]
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def construct_result(self, pred, img, orig_img, img_path, proto):
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"""
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Constructs the result object from the prediction.
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Args:
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pred (np.ndarray): The predicted bounding boxes, scores, and masks.
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img (torch.Tensor): The image after preprocessing.
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orig_img (np.ndarray): The original image before preprocessing.
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img_path (str): The path to the original image.
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proto (torch.Tensor): The prototype masks.
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Returns:
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(Results): The result object containing the original image, image path, class names, bounding boxes, and masks.
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"""
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if not len(pred): # save empty boxes
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masks = None
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elif self.args.retina_masks:
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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masks = ops.process_mask_native(proto, pred[:, 6:], pred[:, :4], orig_img.shape[:2]) # HWC
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else:
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masks = ops.process_mask(proto, pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
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return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks)
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