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# Reference for `ultralytics/models/yolo/segment/predict.py`

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/segment/predict.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!

## `` ultralytics.models.yolo.segment.predict.SegmentationPredictor

    SegmentationPredictor(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Bases: `DetectionPredictor`

A class extending the DetectionPredictor class for prediction based on a segmentation model.

This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the prediction results.

Attributes:

Name | Type | Description
--- | --- | ---
`args` | `dict` | Configuration arguments for the predictor.
`model` | `Module` | The loaded YOLO segmentation model.
`batch` | `list` | Current batch of images being processed.

Methods:

Name | Description
--- | ---
`postprocess` | Apply non-max suppression and process segmentation detections.
`construct_results` | Construct a list of result objects from predictions.
`construct_result` | Construct a single result object from a prediction.

Examples:

    >>> from ultralytics.utils import ASSETS
    >>> from ultralytics.models.yolo.segment import SegmentationPredictor
    >>> args = dict(model="yolo11n-seg.pt", source=ASSETS)
    >>> predictor = SegmentationPredictor(overrides=args)
    >>> predictor.predict_cli()

This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the prediction results.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`cfg` | `dict` | Configuration for the predictor. | `DEFAULT_CFG`
`overrides` | `dict` | Configuration overrides that take precedence over cfg. | `None`
`_callbacks` | `list` | List of callback functions to be invoked during prediction. | `None`

Source code in `ultralytics/models/yolo/segment/predict.py`

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    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """
        Initialize the SegmentationPredictor with configuration, overrides, and callbacks.

        This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the
        prediction results.

        Args:
            cfg (dict): Configuration for the predictor.
            overrides (dict, optional): Configuration overrides that take precedence over cfg.
            _callbacks (list, optional): List of callback functions to be invoked during prediction.
        """
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = "segment"

### `` construct_result

    construct_result(pred, img, orig_img, img_path, proto)

Construct a single result object from the prediction.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`pred` | `Tensor` | The predicted bounding boxes, scores, and masks. | required
`img` | `Tensor` | The image after preprocessing. | required
`orig_img` | `ndarray` | The original image before preprocessing. | required
`img_path` | `str` | The path to the original image. | required
`proto` | `Tensor` | The prototype masks. | required

Returns:

Type | Description
--- | ---
`Results` | Result object containing the original image, image path, class names, bounding boxes, and masks.

Source code in `ultralytics/models/yolo/segment/predict.py`

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    def construct_result(self, pred, img, orig_img, img_path, proto):
        """
        Construct a single result object from the prediction.

        Args:
            pred (torch.Tensor): The predicted bounding boxes, scores, and masks.
            img (torch.Tensor): The image after preprocessing.
            orig_img (np.ndarray): The original image before preprocessing.
            img_path (str): The path to the original image.
            proto (torch.Tensor): The prototype masks.

        Returns:
            (Results): Result object containing the original image, image path, class names, bounding boxes, and masks.
        """
        if pred.shape[0] == 0:  # 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, pred[:, 6:], pred[:, :4], orig_img.shape[:2])  # HWC
        else:
            masks = ops.process_mask(proto, pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True)  # HWC
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
        if masks is not None:
            keep = masks.sum((-2, -1)) > 0  # only keep predictions with masks
            pred, masks = pred[keep], masks[keep]
        return Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], masks=masks)

### `` construct_results

    construct_results(preds, img, orig_imgs, protos)

Construct a list of result objects from the predictions.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`preds` | `list[Tensor]` | List of predicted bounding boxes, scores, and masks. | required
`img` | `Tensor` | The image after preprocessing. | required
`orig_imgs` | `list[ndarray]` | List of original images before preprocessing. | required
`protos` | `list[Tensor]` | List of prototype masks. | required

Returns:

Type | Description
--- | ---
`list[Results]` | List of result objects containing the original images, image paths, class names, bounding boxes, and masks.

Source code in `ultralytics/models/yolo/segment/predict.py`

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    def construct_results(self, preds, img, orig_imgs, protos):
        """
        Construct a list of result objects from the predictions.

        Args:
            preds (list[torch.Tensor]): List of predicted bounding boxes, scores, and masks.
            img (torch.Tensor): The image after preprocessing.
            orig_imgs (list[np.ndarray]): List of original images before preprocessing.
            protos (list[torch.Tensor]): List of prototype masks.

        Returns:
            (list[Results]): List of result objects containing the original images, image paths, class names,
                bounding boxes, and masks.
        """
        return [
            self.construct_result(pred, img, orig_img, img_path, proto)
            for pred, orig_img, img_path, proto in zip(preds, orig_imgs, self.batch[0], protos)
        ]

### `` postprocess

    postprocess(preds, img, orig_imgs)

Apply non-max suppression and process segmentation detections for each image in the input batch.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`preds` | `tuple` | Model predictions, containing bounding boxes, scores, classes, and mask coefficients. | required
`img` | `Tensor` | Input image tensor in model format, with shape (B, C, H, W). | required
`orig_imgs` | `list | Tensor | ndarray` | Original image or batch of images. | required

Returns:

Type | Description
--- | ---
`list` | List of Results objects containing the segmentation predictions for each image in the batch. Each Results object includes both bounding boxes and segmentation masks.

Examples:

    >>> predictor = SegmentationPredictor(overrides=dict(model="yolo11n-seg.pt"))
    >>> results = predictor.postprocess(preds, img, orig_img)

Source code in `ultralytics/models/yolo/segment/predict.py`

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    def postprocess(self, preds, img, orig_imgs):
        """
        Apply non-max suppression and process segmentation detections for each image in the input batch.

        Args:
            preds (tuple): Model predictions, containing bounding boxes, scores, classes, and mask coefficients.
            img (torch.Tensor): Input image tensor in model format, with shape (B, C, H, W).
            orig_imgs (list | torch.Tensor | np.ndarray): Original image or batch of images.

        Returns:
            (list): List of Results objects containing the segmentation predictions for each image in the batch.
                Each Results object includes both bounding boxes and segmentation masks.

        Examples:
            >>> predictor = SegmentationPredictor(overrides=dict(model="yolo11n-seg.pt"))
            >>> results = predictor.postprocess(preds, img, orig_img)
        """
        # Extract protos - tuple if PyTorch model or array if exported
        protos = preds[1][-1] if isinstance(preds[1], tuple) else preds[1]
        return super().postprocess(preds[0], img, orig_imgs, protos=protos)

📅 Created 1 year ago ✏️ Updated 1 year ago