Reference for ultralytics/models/sam/predict.py

class Predictor(BasePredictor):
    """Predictor class for SAM, enabling real-time image segmentation with promptable capabilities.

    This class extends BasePredictor and implements the Segment Anything Model (SAM) for advanced image segmentation
    tasks. It supports various input prompts like points, bounding boxes, and masks for fine-grained control over
    segmentation results.

    Attributes:
        args (SimpleNamespace): Configuration arguments for the predictor.
        model (torch.nn.Module): The loaded SAM model.
        device (torch.device): The device (CPU or GPU) on which the model is loaded.
        im (torch.Tensor): The preprocessed input image.
        features (torch.Tensor): Extracted image features.
        prompts (dict[str, Any]): Dictionary to store various types of prompts (e.g., bboxes, points, masks).
        segment_all (bool): Flag to indicate if full image segmentation should be performed.
        mean (torch.Tensor): Mean values for image normalization.
        std (torch.Tensor): Standard deviation values for image normalization.

    Methods:
        preprocess: Prepare input images for model inference.
        pre_transform: Perform initial transformations on the input image.
        inference: Perform segmentation inference based on input prompts.
        prompt_inference: Internal function for prompt-based segmentation inference.
        generate: Generate segmentation masks for an entire image.
        setup_model: Initialize the SAM model for inference.
        get_model: Build and return a SAM model.
        postprocess: Post-process model outputs to generate final results.
        setup_source: Set up the data source for inference.
        set_image: Set and preprocess a single image for inference.
        get_im_features: Extract image features using the SAM image encoder.
        set_prompts: Set prompts for subsequent inference.
        reset_image: Reset the current image and its features.
        remove_small_regions: Remove small disconnected regions and holes from masks.

    Examples:
        >>> predictor = Predictor()
        >>> predictor.setup_model(model_path="sam_model.pt")
        >>> predictor.set_image("image.jpg")
        >>> bboxes = [[100, 100, 200, 200]]
        >>> results = predictor(bboxes=bboxes)
    """

    stride = 16

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

        Sets up the Predictor object for SAM (Segment Anything Model) and applies any configuration overrides or
        callbacks provided. Initializes task-specific settings for SAM, such as retina_masks being set to True for
        optimal results.

        Args:
            cfg (dict): Configuration dictionary containing default settings.
            overrides (dict | None): Dictionary of values to override default configuration.
            _callbacks (dict | None): Dictionary of callback functions to customize behavior.
        """
        if overrides is None:
            overrides = {}
        overrides.update(dict(task="segment", mode="predict", batch=1))
        super().__init__(cfg, overrides, _callbacks)
        self.args.retina_masks = True
        self.im = None
        self.features = None
        self.prompts = {}
        self.segment_all = False

def _inference_features(
    self,
    features,
    bboxes=None,
    points=None,
    labels=None,
    masks=None,
    multimask_output=False,
):
    """Perform inference on image features using the SAM model.

    Args:
        features (torch.Tensor): Extracted image features with shape (B, C, H, W) from the SAM model image encoder.
        bboxes (np.ndarray | list[list[float]] | None): Bounding boxes in XYXY format with shape (N, 4).
        points (np.ndarray | list[list[float]] | None): Object location points with shape (N, 2), in pixels.
        labels (np.ndarray | list[int] | None): Point prompt labels with shape (N,). 1 = foreground, 0 = background.
        masks (list[np.ndarray] | np.ndarray | None): Masks for the objects, where each mask is a 2D array.
        multimask_output (bool): Flag to return multiple masks for ambiguous prompts.

    Returns:
        pred_masks (torch.Tensor): Output masks with shape (C, H, W), where C is the number of generated masks.
        pred_scores (torch.Tensor): Quality scores for each mask, with length C.
    """
    points = (points, labels) if points is not None else None
    # Embed prompts
    sparse_embeddings, dense_embeddings = self.model.prompt_encoder(points=points, boxes=bboxes, masks=masks)

    # Predict masks
    pred_masks, pred_scores = self.model.mask_decoder(
        image_embeddings=features,
        image_pe=self.model.prompt_encoder.get_dense_pe(),
        sparse_prompt_embeddings=sparse_embeddings,
        dense_prompt_embeddings=dense_embeddings,
        multimask_output=multimask_output,
    )

    # (N, d, H, W) --> (N*d, H, W), (N, d) --> (N*d, )
    # `d` could be 1 or 3 depends on `multimask_output`.
    return pred_masks.flatten(0, 1), pred_scores.flatten(0, 1)

def _prepare_prompts(self, dst_shape, src_shape, bboxes=None, points=None, labels=None, masks=None):
    """Prepare and transform the input prompts for processing based on the destination shape.

    Args:
        dst_shape (tuple[int, int]): The target shape (height, width) for the prompts.
        src_shape (tuple[int, int]): The source shape (height, width) of the input image.
        bboxes (np.ndarray | list | None): Bounding boxes in XYXY format with shape (N, 4).
        points (np.ndarray | list | None): Points indicating object locations with shape (N, 2) or (N, num_points,
            2), in pixels.
        labels (np.ndarray | list | None): Point prompt labels with shape (N) or (N, num_points). 1 for foreground,
            0 for background.
        masks (list[np.ndarray] | np.ndarray | None): Masks for the objects, where each mask is a 2D array with
            shape (H, W).

    Returns:
        bboxes (torch.Tensor | None): Transformed bounding boxes.
        points (torch.Tensor | None): Transformed points.
        labels (torch.Tensor | None): Transformed labels.
        masks (torch.Tensor | None): Transformed masks.

    Raises:
        AssertionError: If the number of points don't match the number of labels, in case labels were passed.
    """
    r = 1.0 if self.segment_all else min(dst_shape[0] / src_shape[0], dst_shape[1] / src_shape[1])
    # Transform input prompts
    if points is not None:
        points = torch.as_tensor(points, dtype=self.torch_dtype, device=self.device)
        points = points[None] if points.ndim == 1 else points
        # Assuming labels are all positive if users don't pass labels.
        if labels is None:
            labels = np.ones(points.shape[:-1])
        labels = torch.as_tensor(labels, dtype=torch.int32, device=self.device)
        assert points.shape[-2] == labels.shape[-1], (
            f"Number of points {points.shape[-2]} should match number of labels {labels.shape[-1]}."
        )
        points *= r
        if points.ndim == 2:
            # (N, 2) --> (N, 1, 2), (N, ) --> (N, 1)
            points, labels = points[:, None, :], labels[:, None]
    if bboxes is not None:
        bboxes = torch.as_tensor(bboxes, dtype=self.torch_dtype, device=self.device)
        bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes
        bboxes *= r
    if masks is not None:
        masks = np.asarray(masks, dtype=np.uint8)
        masks = masks[None] if masks.ndim == 2 else masks
        letterbox = LetterBox(dst_shape, auto=False, center=False, padding_value=0, interpolation=cv2.INTER_NEAREST)
        masks = np.stack([letterbox(image=x).squeeze() for x in masks], axis=0)
        masks = torch.tensor(masks, dtype=self.torch_dtype, device=self.device)
    return bboxes, points, labels, masks

def generate(
    self,
    im,
    crop_n_layers=0,
    crop_overlap_ratio=512 / 1500,
    crop_downscale_factor=1,
    point_grids=None,
    points_stride=32,
    points_batch_size=64,
    conf_thres=0.88,
    stability_score_thresh=0.95,
    stability_score_offset=0.95,
    crop_nms_thresh=0.7,
):
    """Perform image segmentation using the Segment Anything Model (SAM).

    This method segments an entire image into constituent parts by leveraging SAM's advanced architecture and
    real-time performance capabilities. It can optionally work on image crops for finer segmentation.

    Args:
        im (torch.Tensor): Input tensor representing the preprocessed image with shape (N, C, H, W).
        crop_n_layers (int): Number of layers for additional mask predictions on image crops.
        crop_overlap_ratio (float): Overlap between crops, scaled down in subsequent layers.
        crop_downscale_factor (int): Scaling factor for sampled points-per-side in each layer.
        point_grids (list[np.ndarray] | None): Custom grids for point sampling normalized to [0,1].
        points_stride (int): Number of points to sample along each side of the image.
        points_batch_size (int): Batch size for the number of points processed simultaneously.
        conf_thres (float): Confidence threshold [0,1] for filtering based on mask quality prediction.
        stability_score_thresh (float): Stability threshold [0,1] for mask filtering based on stability.
        stability_score_offset (float): Offset value for calculating stability score.
        crop_nms_thresh (float): IoU cutoff for NMS to remove duplicate masks between crops.

    Returns:
        pred_masks (torch.Tensor): Segmented masks with shape (N, H, W).
        pred_scores (torch.Tensor): Confidence scores for each mask with shape (N,).
        pred_bboxes (torch.Tensor): Bounding boxes for each mask with shape (N, 4).

    Examples:
        >>> predictor = Predictor()
        >>> im = torch.rand(1, 3, 1024, 1024)  # Example input image
        >>> masks, scores, boxes = predictor.generate(im)
    """
    import torchvision  # scope for faster 'import ultralytics'

    self.segment_all = True
    ih, iw = im.shape[2:]
    crop_regions, layer_idxs = generate_crop_boxes((ih, iw), crop_n_layers, crop_overlap_ratio)
    if point_grids is None:
        point_grids = build_all_layer_point_grids(points_stride, crop_n_layers, crop_downscale_factor)
    pred_masks, pred_scores, pred_bboxes, region_areas = [], [], [], []
    for crop_region, layer_idx in zip(crop_regions, layer_idxs):
        x1, y1, x2, y2 = crop_region
        w, h = x2 - x1, y2 - y1
        area = torch.tensor(w * h, device=im.device)
        points_scale = np.array([[w, h]])  # w, h
        # Crop image and interpolate to input size
        crop_im = F.interpolate(im[..., y1:y2, x1:x2], (ih, iw), mode="bilinear", align_corners=False)
        # (num_points, 2)
        points_for_image = point_grids[layer_idx] * points_scale
        crop_masks, crop_scores, crop_bboxes = [], [], []
        for (points,) in batch_iterator(points_batch_size, points_for_image):
            pred_mask, pred_score = self.prompt_inference(crop_im, points=points, multimask_output=True)
            # Interpolate predicted masks to input size
            pred_mask = F.interpolate(pred_mask[None], (h, w), mode="bilinear", align_corners=False)[0]
            idx = pred_score > conf_thres
            pred_mask, pred_score = pred_mask[idx], pred_score[idx]

            stability_score = calculate_stability_score(
                pred_mask, self.model.mask_threshold, stability_score_offset
            )
            idx = stability_score > stability_score_thresh
            pred_mask, pred_score = pred_mask[idx], pred_score[idx]
            # Bool type is much more memory-efficient.
            pred_mask = pred_mask > self.model.mask_threshold
            # (N, 4)
            pred_bbox = batched_mask_to_box(pred_mask).float()
            keep_mask = ~is_box_near_crop_edge(pred_bbox, crop_region, [0, 0, iw, ih])
            if not torch.all(keep_mask):
                pred_bbox, pred_mask, pred_score = pred_bbox[keep_mask], pred_mask[keep_mask], pred_score[keep_mask]

            crop_masks.append(pred_mask)
            crop_bboxes.append(pred_bbox)
            crop_scores.append(pred_score)

        # Do nms within this crop
        crop_masks = torch.cat(crop_masks)
        crop_bboxes = torch.cat(crop_bboxes)
        crop_scores = torch.cat(crop_scores)
        keep = torchvision.ops.nms(crop_bboxes, crop_scores, self.args.iou)  # NMS
        crop_bboxes = uncrop_boxes_xyxy(crop_bboxes[keep], crop_region)
        crop_masks = uncrop_masks(crop_masks[keep], crop_region, ih, iw)
        crop_scores = crop_scores[keep]

        pred_masks.append(crop_masks)
        pred_bboxes.append(crop_bboxes)
        pred_scores.append(crop_scores)
        region_areas.append(area.expand(crop_masks.shape[0]))

    pred_masks = torch.cat(pred_masks)
    pred_bboxes = torch.cat(pred_bboxes)
    pred_scores = torch.cat(pred_scores)
    region_areas = torch.cat(region_areas)

    # Remove duplicate masks between crops
    if len(crop_regions) > 1:
        scores = 1 / region_areas
        keep = torchvision.ops.nms(pred_bboxes, scores, crop_nms_thresh)
        pred_masks, pred_bboxes, pred_scores = pred_masks[keep], pred_bboxes[keep], pred_scores[keep]

    return pred_masks, pred_scores, pred_bboxes

def get_im_features(self, im):
    """Extract image features using the SAM model's image encoder for subsequent mask prediction."""
    return self.model.image_encoder(im)

def get_model(self):
    """Retrieve or build the Segment Anything Model (SAM) for image segmentation tasks."""
    from .build import build_sam  # slow import

    return build_sam(self.args.model)

def inference(self, im, bboxes=None, points=None, labels=None, masks=None, multimask_output=False, *args, **kwargs):
    """Perform image segmentation inference based on the given input cues, using the currently loaded image.

    This method leverages SAM's (Segment Anything Model) architecture consisting of image encoder, prompt encoder,
    and mask decoder for real-time and promptable segmentation tasks.

    Args:
        im (torch.Tensor): The preprocessed input image in tensor format, with shape (N, C, H, W).
        bboxes (np.ndarray | list | None): Bounding boxes with shape (N, 4), in XYXY format.
        points (np.ndarray | list | None): Points indicating object locations with shape (N, 2), in pixels.
        labels (np.ndarray | list | None): Labels for point prompts, shape (N,). 1 = foreground, 0 = background.
        masks (np.ndarray | None): Low-resolution masks from previous predictions, shape (N, H, W). For SAM H=W=256.
        multimask_output (bool): Flag to return multiple masks. Helpful for ambiguous prompts.
        *args (Any): Additional positional arguments.
        **kwargs (Any): Additional keyword arguments.

    Returns:
        pred_masks (torch.Tensor): The output masks in shape (C, H, W), where C is the number of generated masks.
        pred_scores (torch.Tensor): An array of length C containing quality scores predicted by the model for each
            mask.

    Examples:
        >>> predictor = Predictor()
        >>> predictor.setup_model(model_path="sam_model.pt")
        >>> predictor.set_image("image.jpg")
        >>> results = predictor(bboxes=[[0, 0, 100, 100]])
    """
    # Override prompts if any stored in self.prompts
    bboxes = self.prompts.pop("bboxes", bboxes)
    points = self.prompts.pop("points", points)
    masks = self.prompts.pop("masks", masks)
    labels = self.prompts.pop("labels", labels)

    if all(i is None for i in [bboxes, points, masks]):
        return self.generate(im, *args, **kwargs)

    return self.prompt_inference(im, bboxes, points, labels, masks, multimask_output)

@smart_inference_mode()
def inference_features(
    self,
    features,
    src_shape,
    dst_shape=None,
    bboxes=None,
    points=None,
    labels=None,
    masks=None,
    multimask_output=False,
):
    """Perform prompts preprocessing and inference on provided image features using the SAM model.

    Args:
        features (torch.Tensor | dict[str, Any]): Extracted image features from the SAM/SAM2 model image encoder.
        src_shape (tuple[int, int]): The source shape (height, width) of the input image.
        dst_shape (tuple[int, int] | None): The target shape (height, width) for the prompts. If None, defaults to
            (imgsz, imgsz).
        bboxes (np.ndarray | list[list[float]] | None): Bounding boxes in xyxy format with shape (N, 4).
        points (np.ndarray | list[list[float]] | None): Points indicating object locations with shape (N, 2), in
            pixels.
        labels (np.ndarray | list[int] | None): Point prompt labels with shape (N, ).
        masks (list[np.ndarray] | np.ndarray | None): Masks for the objects, where each mask is a 2D array.
        multimask_output (bool): Flag to return multiple masks for ambiguous prompts.

    Returns:
        pred_masks (torch.Tensor): The output masks in shape (C, H, W), where C is the number of generated masks.
        pred_bboxes (torch.Tensor): Bounding boxes for each mask with shape (N, 6), where N is the number of boxes.
            Each box is in xyxy format with additional columns for score and class.

    Notes:
        - The input features is a torch.Tensor of shape (B, C, H, W) if performing on SAM, or a dict[str, Any] if performing on SAM2.
    """
    dst_shape = dst_shape or (self.args.imgsz, self.args.imgsz)
    prompts = self._prepare_prompts(dst_shape, src_shape, bboxes, points, labels, masks)
    pred_masks, pred_scores = self._inference_features(features, *prompts, multimask_output)
    if pred_masks.shape[0] == 0:
        pred_masks, pred_bboxes = None, torch.zeros((0, 6), device=pred_masks.device)
    else:
        pred_masks = ops.scale_masks(pred_masks[None].float(), src_shape, padding=False)[0]
        pred_masks = pred_masks > self.model.mask_threshold  # to bool
        pred_bboxes = batched_mask_to_box(pred_masks)
        # NOTE: SAM models do not return cls info. This `cls` here is just a placeholder for consistency.
        cls = torch.arange(pred_masks.shape[0], dtype=torch.int32, device=pred_masks.device)
        pred_bboxes = torch.cat([pred_bboxes, pred_scores[:, None], cls[:, None]], dim=-1)
    return pred_masks, pred_bboxes

def postprocess(self, preds, img, orig_imgs):
    """Post-process SAM's inference outputs to generate object detection masks and bounding boxes.

    This method scales masks and boxes to the original image size and applies a threshold to the mask
    predictions. It leverages SAM's advanced architecture for real-time, promptable segmentation tasks.

    Args:
        preds (tuple): The output from SAM model inference, containing:
            - pred_masks (torch.Tensor): Predicted masks with shape (N, 1, H, W).
            - pred_scores (torch.Tensor): Confidence scores for each mask with shape (N, 1).
            - pred_bboxes (torch.Tensor, optional): Predicted bounding boxes if segment_all is True.
        img (torch.Tensor): The processed input image tensor with shape (C, H, W).
        orig_imgs (list[np.ndarray] | torch.Tensor): The original, unprocessed images.

    Returns:
        (list[Results]): List of Results objects containing detection masks, bounding boxes, and other metadata for
            each processed image.

    Examples:
        >>> predictor = Predictor()
        >>> preds = predictor.inference(img)
        >>> results = predictor.postprocess(preds, img, orig_imgs)
    """
    # (N, 1, H, W), (N, 1)
    pred_masks, pred_scores = preds[:2]
    pred_bboxes = preds[2] if self.segment_all else None
    names = dict(enumerate(str(i) for i in range(pred_masks.shape[0])))

    if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
        orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)[..., ::-1]

    results = []
    for masks, orig_img, img_path in zip([pred_masks], orig_imgs, self.batch[0]):
        if masks.shape[0] == 0:
            masks, pred_bboxes = None, torch.zeros((0, 6), device=pred_masks.device)
        else:
            masks = ops.scale_masks(masks[None].float(), orig_img.shape[:2], padding=False)[0]
            masks = masks > self.model.mask_threshold  # to bool
            if pred_bboxes is not None:
                pred_bboxes = ops.scale_boxes(img.shape[2:], pred_bboxes.float(), orig_img.shape, padding=False)
            else:
                pred_bboxes = batched_mask_to_box(masks)
            # NOTE: SAM models do not return cls info. This `cls` here is just a placeholder for consistency.
            cls = torch.arange(pred_masks.shape[0], dtype=torch.int32, device=pred_masks.device)
            idx = pred_scores > self.args.conf
            pred_bboxes = torch.cat([pred_bboxes, pred_scores[:, None], cls[:, None]], dim=-1)[idx]
            masks = masks[idx]
        results.append(Results(orig_img, path=img_path, names=names, masks=masks, boxes=pred_bboxes))
    # Reset segment-all mode.
    self.segment_all = False
    return results

def pre_transform(self, im):
    """Perform initial transformations on the input image for preprocessing.

    This method applies transformations such as resizing to prepare the image for further preprocessing. Currently,
    batched inference is not supported; hence the list length should be 1.

    Args:
        im (list[np.ndarray]): List containing a single image in HWC numpy array format.

    Returns:
        (list[np.ndarray]): List containing the transformed image.

    Raises:
        AssertionError: If the input list contains more than one image.

    Examples:
        >>> predictor = Predictor()
        >>> image = np.random.rand(480, 640, 3)  # Single HWC image
        >>> transformed = predictor.pre_transform([image])
        >>> print(len(transformed))
        1
    """
    assert len(im) == 1, "SAM model does not currently support batched inference"
    letterbox = LetterBox(self.imgsz, auto=False, center=False)
    return [letterbox(image=x) for x in im]

def preprocess(self, im):
    """Preprocess the input image for model inference.

    This method prepares the input image by applying transformations and normalization. It supports both
    torch.Tensor and list of np.ndarray as input formats.

    Args:
        im (torch.Tensor | list[np.ndarray]): Input image(s) in BCHW tensor format or list of HWC numpy arrays.

    Returns:
        (torch.Tensor): The preprocessed image tensor, normalized and converted to the appropriate dtype.

    Examples:
        >>> predictor = Predictor()
        >>> image = torch.rand(1, 3, 640, 640)
        >>> preprocessed_image = predictor.preprocess(image)
    """
    if self.im is not None:
        return self.im
    not_tensor = not isinstance(im, torch.Tensor)
    if not_tensor:
        im = np.stack(self.pre_transform(im))
        im = im[..., ::-1].transpose((0, 3, 1, 2))
        im = np.ascontiguousarray(im)
        im = torch.from_numpy(im)

    im = im.to(self.device)
    if not_tensor:
        im = (im - self.mean) / self.std
    im = im.half() if self.model.fp16 else im.float()
    return im

def prompt_inference(self, im, bboxes=None, points=None, labels=None, masks=None, multimask_output=False):
    """Perform image segmentation inference based on input cues using SAM's specialized architecture.

    This internal function leverages the Segment Anything Model (SAM) for prompt-based, real-time segmentation. It
    processes various input prompts such as bounding boxes, points, and masks to generate segmentation masks.

    Args:
        im (torch.Tensor): Preprocessed input image tensor with shape (N, C, H, W).
        bboxes (np.ndarray | list | None): Bounding boxes in XYXY format with shape (N, 4).
        points (np.ndarray | list | None): Points indicating object locations with shape (N, 2) or (N, num_points,
            2), in pixels.
        labels (np.ndarray | list | None): Point prompt labels with shape (N) or (N, num_points). 1 for foreground,
            0 for background.
        masks (np.ndarray | None): Low-res masks from previous predictions with shape (N, H, W). For SAM, H=W=256.
        multimask_output (bool): Flag to return multiple masks for ambiguous prompts.

    Returns:
        pred_masks (torch.Tensor): Output masks with shape (C, H, W), where C is the number of generated masks.
        pred_scores (torch.Tensor): Quality scores predicted by the model for each mask, with length C.

    Examples:
        >>> predictor = Predictor()
        >>> im = torch.rand(1, 3, 1024, 1024)
        >>> bboxes = [[100, 100, 200, 200]]
        >>> masks, scores, logits = predictor.prompt_inference(im, bboxes=bboxes)
    """
    features = self.get_im_features(im) if self.features is None else self.features

    prompts = self._prepare_prompts(im.shape[2:], self.batch[1][0].shape[:2], bboxes, points, labels, masks)
    return self._inference_features(features, *prompts, multimask_output)

@staticmethod
def remove_small_regions(masks, min_area=0, nms_thresh=0.7):
    """Remove small disconnected regions and holes from segmentation masks.

    This function performs post-processing on segmentation masks generated by the Segment Anything Model (SAM). It
    removes small disconnected regions and holes from the input masks, and then performs Non-Maximum Suppression
    (NMS) to eliminate any newly created duplicate boxes.

    Args:
        masks (torch.Tensor): Segmentation masks to be processed, with shape (N, H, W) where N is the number of
            masks, H is height, and W is width.
        min_area (int): Minimum area threshold for removing disconnected regions and holes. Regions smaller than
            this will be removed.
        nms_thresh (float): IoU threshold for the NMS algorithm to remove duplicate boxes.

    Returns:
        new_masks (torch.Tensor): Processed masks with small regions removed, shape (N, H, W).
        keep (list[int]): Indices of remaining masks after NMS, for filtering corresponding boxes.

    Examples:
        >>> masks = torch.rand(5, 640, 640) > 0.5  # 5 random binary masks
        >>> new_masks, keep = remove_small_regions(masks, min_area=100, nms_thresh=0.7)
        >>> print(f"Original masks: {masks.shape}, Processed masks: {new_masks.shape}")
        >>> print(f"Indices of kept masks: {keep}")
    """
    import torchvision  # scope for faster 'import ultralytics'

    if masks.shape[0] == 0:
        return masks

    # Filter small disconnected regions and holes
    new_masks = []
    scores = []
    for mask in masks:
        mask = mask.cpu().numpy().astype(np.uint8)
        mask, changed = remove_small_regions(mask, min_area, mode="holes")
        unchanged = not changed
        mask, changed = remove_small_regions(mask, min_area, mode="islands")
        unchanged = unchanged and not changed

        new_masks.append(torch.as_tensor(mask).unsqueeze(0))
        # Give score=0 to changed masks and 1 to unchanged masks so NMS prefers masks not needing postprocessing
        scores.append(float(unchanged))

    # Recalculate boxes and remove any new duplicates
    new_masks = torch.cat(new_masks, dim=0)
    boxes = batched_mask_to_box(new_masks)
    keep = torchvision.ops.nms(boxes.float(), torch.as_tensor(scores), nms_thresh)

    return new_masks[keep].to(device=masks.device, dtype=masks.dtype), keep

def reset_image(self):
    """Reset the current image and its features, clearing them for subsequent inference."""
    self.im = None
    self.features = None

def set_image(self, image):
    """Preprocess and set a single image for inference.

    This method prepares the model for inference on a single image by setting up the model if not already
    initialized, configuring the data source, and preprocessing the image for feature extraction. It ensures that
    only one image is set at a time and extracts image features for subsequent use.

    Args:
        image (str | np.ndarray): Path to the image file as a string, or a numpy array representing an image read by
            cv2.

    Raises:
        AssertionError: If more than one image is attempted to be set.

    Examples:
        >>> predictor = Predictor()
        >>> predictor.set_image("path/to/image.jpg")
        >>> predictor.set_image(cv2.imread("path/to/image.jpg"))

    Notes:
        - This method should be called before performing inference on a new image.
        - The extracted features are stored in the `self.features` attribute for later use.
    """
    if self.model is None:
        self.setup_model()
    self.setup_source(image)
    assert len(self.dataset) == 1, "`set_image` only supports setting one image!"
    for batch in self.dataset:
        im = self.preprocess(batch[1])
        self.features = self.get_im_features(im)
        break

def set_prompts(self, prompts):
    """Set prompts for subsequent inference operations."""
    self.prompts = prompts

def setup_model(self, model=None, verbose=True):
    """Initialize the Segment Anything Model (SAM) for inference.

    This method sets up the SAM model by allocating it to the appropriate device and initializing the necessary
    parameters for image normalization and other Ultralytics compatibility settings.

    Args:
        model (torch.nn.Module | None): A pretrained SAM model. If None, a new model is built based on config.
        verbose (bool): If True, prints selected device information.

    Examples:
        >>> predictor = Predictor()
        >>> predictor.setup_model(model=sam_model, verbose=True)
    """
    device = select_device(self.args.device, verbose=verbose)
    if model is None:
        model = self.get_model()
    model.eval()
    model = model.to(device)
    self.model = model.half() if self.args.half else model.float()
    self.device = device
    self.mean = torch.tensor([123.675, 116.28, 103.53]).view(-1, 1, 1).to(device)
    self.std = torch.tensor([58.395, 57.12, 57.375]).view(-1, 1, 1).to(device)

    # Ultralytics compatibility settings
    self.model.pt = False
    self.model.triton = False
    self.model.stride = 32
    self.model.fp16 = self.args.half
    self.done_warmup = True
    self.torch_dtype = torch.float16 if self.model.fp16 else torch.float32

def setup_source(self, source):
    """Set up the data source for SAM inference."""
    if source is None:  # handle the situation when set_imgsz in advance
        return
    super().setup_source(source, self.stride)
    assert isinstance(self.imgsz, (tuple, list)) and self.imgsz[0] == self.imgsz[1], (
        f"SAM models only support square image size, but got {self.imgsz}."
    )
    self.model.set_imgsz(self.imgsz)

class SAM2Predictor(Predictor):

def _inference_features(
    self,
    features,
    points=None,
    labels=None,
    masks=None,
    multimask_output=False,
    img_idx=-1,
):
    """Perform inference on image features using the SAM2 model.

    Args:
        features (torch.Tensor | dict[str, Any]): Extracted image features with shape (B, C, H, W) from the SAM2
        model image encoder, it could also be a dictionary including:
            - image_embed (torch.Tensor): Image embedding with shape (B, C, H, W).
            - high_res_feats (list[torch.Tensor]): List of high-resolution feature maps from the backbone, each with shape (B, C, H, W).
        points (np.ndarray | list[list[float]] | None): Object location points with shape (N, 2), in pixels.
        labels (np.ndarray | list[int] | None): Point prompt labels with shape (N,). 1 = foreground, 0 = background.
        masks (list[np.ndarray] | np.ndarray | None): Masks for the objects, where each mask is a 2D array.
        multimask_output (bool): Flag to return multiple masks for ambiguous prompts.
        img_idx (int): Index of the image in the batch to process.

    Returns:
        pred_masks (torch.Tensor): Output masks with shape (C, H, W), where C is the number of generated masks.
        pred_scores (torch.Tensor): Quality scores for each mask, with length C.
    """
    points = (points, labels) if points is not None else None
    sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder(
        points=points,
        boxes=None,
        masks=masks,
    )
    # Predict masks
    batched_mode = points is not None and points[0].shape[0] > 1  # multi object prediction
    high_res_features = None
    if isinstance(features, dict):
        high_res_features = [feat_level[img_idx].unsqueeze(0) for feat_level in features["high_res_feats"]]
        features = features["image_embed"][[img_idx]]
    pred_masks, pred_scores, _, _ = self.model.sam_mask_decoder(
        image_embeddings=features,
        image_pe=self.model.sam_prompt_encoder.get_dense_pe(),
        sparse_prompt_embeddings=sparse_embeddings,
        dense_prompt_embeddings=dense_embeddings,
        multimask_output=multimask_output,
        repeat_image=batched_mode,
        high_res_features=high_res_features,
    )
    # (N, d, H, W) --> (N*d, H, W), (N, d) --> (N*d, )
    # `d` could be 1 or 3 depends on `multimask_output`.
    return pred_masks.flatten(0, 1), pred_scores.flatten(0, 1)

def _prepare_prompts(self, dst_shape, src_shape, bboxes=None, points=None, labels=None, masks=None):
    """Prepare and transform the input prompts for processing based on the destination shape.

    Args:
        dst_shape (tuple[int, int]): The target shape (height, width) for the prompts.
        src_shape (tuple[int, int]): The source shape (height, width) of the input image.
        bboxes (np.ndarray | list | None): Bounding boxes in XYXY format with shape (N, 4).
        points (np.ndarray | list | None): Points indicating object locations with shape (N, 2) or (N, num_points,
            2), in pixels.
        labels (np.ndarray | list | None): Point prompt labels with shape (N,) or (N, num_points). 1 for foreground,
            0 for background.
        masks (list | np.ndarray | None): Masks for the objects, where each mask is a 2D array.

    Returns:
        points (torch.Tensor | None): Transformed points.
        labels (torch.Tensor | None): Transformed labels.
        masks (torch.Tensor | None): Transformed masks.

    Raises:
        AssertionError: If the number of points don't match the number of labels, in case labels were passed.
    """
    bboxes, points, labels, masks = super()._prepare_prompts(dst_shape, src_shape, bboxes, points, labels, masks)
    if bboxes is not None:
        bboxes = bboxes.view(-1, 2, 2)
        bbox_labels = torch.tensor([[2, 3]], dtype=torch.int32, device=bboxes.device).expand(bboxes.shape[0], -1)
        # NOTE: merge "boxes" and "points" into a single "points" input
        # (where boxes are added at the beginning) to model.sam_prompt_encoder
        if points is not None:
            points = torch.cat([bboxes, points], dim=1)
            labels = torch.cat([bbox_labels, labels], dim=1)
        else:
            points, labels = bboxes, bbox_labels
    return points, labels, masks

def get_im_features(self, im):
    """Extract image features from the SAM image encoder for subsequent processing."""
    backbone_out = self.model.forward_image(im)
    _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out)
    if self.model.directly_add_no_mem_embed:
        vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed
    feats = [
        feat.permute(1, 2, 0).view(1, -1, *feat_size) for feat, feat_size in zip(vision_feats, self._bb_feat_sizes)
    ]
    return {"image_embed": feats[-1], "high_res_feats": feats[:-1]}

def get_model(self):
    """Retrieve and initialize the Segment Anything Model 2 (SAM2) for image segmentation tasks."""
    from .build import build_sam  # slow import

    return build_sam(self.args.model)

def setup_source(self, source):
    """Set up the data source and image size for SAM2 inference."""
    super().setup_source(source)
    self._bb_feat_sizes = [[int(x / (self.stride * i)) for x in self.imgsz] for i in [1 / 4, 1 / 2, 1]]

class SAM2VideoPredictor(SAM2Predictor):
    """SAM2VideoPredictor to handle user interactions with videos and manage inference states.

    This class extends the functionality of SAM2Predictor to support video processing and maintains the state of
    inference operations. It includes configurations for managing non-overlapping masks, clearing memory for
    non-conditional inputs, and setting up callbacks for prediction events.

    Attributes:
        inference_state (dict): A dictionary to store the current state of inference operations.
        non_overlap_masks (bool): A flag indicating whether masks should be non-overlapping.
        clear_non_cond_mem_around_input (bool): A flag to control clearing non-conditional memory around inputs.
        clear_non_cond_mem_for_multi_obj (bool): A flag to control clearing non-conditional memory for multi-object
            scenarios.
        callbacks (dict): A dictionary of callbacks for various prediction lifecycle events.

    Methods:
        get_model: Retrieve and configure the model with binarization enabled.
        inference: Perform image segmentation inference based on the given input cues.
        postprocess: Post-process the predictions to apply non-overlapping constraints if required.
        add_new_prompts: Add new points or masks to a specific frame for a given object ID.
        propagate_in_video_preflight: Prepare inference_state and consolidate temporary outputs before tracking.
        init_state: Initialize an inference state for the predictor.
        get_im_features: Extract image features using SAM2's image encoder for subsequent segmentation tasks.

    Examples:
        >>> predictor = SAM2VideoPredictor(cfg=DEFAULT_CFG)
        >>> predictor.set_image("path/to/video_frame.jpg")
        >>> bboxes = [[100, 100, 200, 200]]
        >>> results = predictor(bboxes=bboxes)

    Notes:
        The `fill_hole_area` attribute is defined but not used in the current implementation.
    """

    # fill_hole_area = 8  # not used

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

        This constructor initializes the SAM2VideoPredictor with a given configuration, applies any specified overrides,
        and sets up the inference state along with certain flags that control the behavior of the predictor.

        Args:
            cfg (dict): Configuration dictionary containing default settings.
            overrides (dict | None): Dictionary of values to override default configuration.
            _callbacks (dict | None): Dictionary of callback functions to customize behavior.
        """
        super().__init__(cfg, overrides, _callbacks)
        self.inference_state = {}
        self.non_overlap_masks = True
        self.clear_non_cond_mem_around_input = False
        self.clear_non_cond_mem_for_multi_obj = False
        self.callbacks["on_predict_start"].append(self.init_state)

def _add_output_per_object(
    self, frame_idx, current_out, storage_key, inference_state: dict[str, Any] | None = None
):
    """Split a multi-object output into per-object output slices and add them into Output_Dict_Per_Obj.

    The resulting slices share the same tensor storage.

    Args:
        frame_idx (int): The index of the current frame.
        current_out (dict): The current output dictionary containing multi-object outputs.
        storage_key (str): The key used to store the output in the per-object output dictionary.
        inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's
            inference state.
    """
    inference_state = inference_state or self.inference_state
    maskmem_features = current_out["maskmem_features"]
    assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor)

    maskmem_pos_enc = current_out["maskmem_pos_enc"]
    assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list)

    for obj_idx, obj_output_dict in inference_state["output_dict_per_obj"].items():
        obj_slice = slice(obj_idx, obj_idx + 1)
        obj_out = {
            "maskmem_features": None,
            "maskmem_pos_enc": None,
            "pred_masks": current_out["pred_masks"][obj_slice],
            "obj_ptr": current_out["obj_ptr"][obj_slice],
        }
        if maskmem_features is not None:
            obj_out["maskmem_features"] = maskmem_features[obj_slice]
        if maskmem_pos_enc is not None:
            obj_out["maskmem_pos_enc"] = [x[obj_slice] for x in maskmem_pos_enc]
        obj_output_dict[storage_key][frame_idx] = obj_out