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Referencia para ultralytics/models/sam/predict.py

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ultralytics.models.sam.predict.Predictor

Bases: BasePredictor

Clase Predictor para el Modelo de Todo Segmento (SAM), que extiende a BasePredictor.

La clase proporciona una interfaz para la inferencia de modelos adaptada a las tareas de segmentación de imágenes. Con una arquitectura avanzada y capacidades de segmentación que se pueden activar, facilita la generación de máscaras flexibles y en tiempo real. en tiempo real. La clase es capaz de trabajar con varios tipos de indicaciones, como cuadros delimitadores puntos y máscaras de baja resolución.

Atributos:

Nombre Tipo Descripción
cfg dict

Diccionario de configuración que especifica los parámetros relacionados con el modelo y las tareas.

overrides dict

Diccionario que contiene valores que anulan la configuración por defecto.

_callbacks dict

Diccionario de funciones de llamada de retorno definidas por el usuario para aumentar el comportamiento.

args namespace

Espacio de nombres para contener argumentos de la línea de comandos u otras variables operativas.

im Tensor

Imagen de entrada preprocesada tensor.

features Tensor

Características de imagen extraídas utilizadas para la inferencia.

prompts dict

Colección de varios tipos de indicaciones, como cajas delimitadoras y puntos.

segment_all bool

Indicador para controlar si se segmentan todos los objetos de la imagen o sólo los especificados.

Código fuente en ultralytics/models/sam/predict.py
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class Predictor(BasePredictor):
    """
    Predictor class for the Segment Anything Model (SAM), extending BasePredictor.

    The class provides an interface for model inference tailored to image segmentation tasks.
    With advanced architecture and promptable segmentation capabilities, it facilitates flexible and real-time
    mask generation. The class is capable of working with various types of prompts such as bounding boxes,
    points, and low-resolution masks.

    Attributes:
        cfg (dict): Configuration dictionary specifying model and task-related parameters.
        overrides (dict): Dictionary containing values that override the default configuration.
        _callbacks (dict): Dictionary of user-defined callback functions to augment behavior.
        args (namespace): Namespace to hold command-line arguments or other operational variables.
        im (torch.Tensor): Preprocessed input image tensor.
        features (torch.Tensor): Extracted image features used for inference.
        prompts (dict): Collection of various prompt types, such as bounding boxes and points.
        segment_all (bool): Flag to control whether to segment all objects in the image or only specified ones.
    """

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

        The method sets up the Predictor object and applies any configuration overrides or callbacks provided. It
        initializes task-specific settings for SAM, such as retina_masks being set to True for optimal results.

        Args:
            cfg (dict): Configuration dictionary.
            overrides (dict, optional): Dictionary of values to override default configuration.
            _callbacks (dict, optional): Dictionary of callback functions to customize behavior.
        """
        if overrides is None:
            overrides = {}
        overrides.update(dict(task="segment", mode="predict", imgsz=1024))
        super().__init__(cfg, overrides, _callbacks)
        self.args.retina_masks = True
        self.im = None
        self.features = None
        self.prompts = {}
        self.segment_all = False

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

        The 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]): BCHW tensor format or list of HWC numpy arrays.

        Returns:
            (torch.Tensor): The preprocessed image tensor.
        """
        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)
        im = im.half() if self.model.fp16 else im.float()
        if not_tensor:
            im = (im - self.mean) / self.std
        return im

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

        The 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 images in HWC numpy array format.

        Returns:
            (List[np.ndarray]): List of transformed images.
        """
        assert len(im) == 1, "SAM model does not currently support batched inference"
        letterbox = LetterBox(self.args.imgsz, auto=False, center=False)
        return [letterbox(image=x) for x in im]

    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, optional): Bounding boxes with shape (N, 4), in XYXY format.
            points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels.
            labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background.
            masks (np.ndarray, optional): Low-resolution masks from previous predictions shape (N,H,W). For SAM H=W=256.
            multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts.

        Returns:
            (tuple): Contains the following three elements.
                - np.ndarray: The output masks in shape CxHxW, where C is the number of generated masks.
                - np.ndarray: An array of length C containing quality scores predicted by the model for each mask.
                - np.ndarray: Low-resolution logits of shape CxHxW for subsequent inference, where H=W=256.
        """
        # 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)

        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)

    def prompt_inference(self, im, bboxes=None, points=None, labels=None, masks=None, multimask_output=False):
        """
        Internal function for image segmentation inference based on cues like bounding boxes, points, and masks.
        Leverages SAM's specialized architecture for prompt-based, real-time segmentation.

        Args:
            im (torch.Tensor): The preprocessed input image in tensor format, with shape (N, C, H, W).
            bboxes (np.ndarray | List, optional): Bounding boxes with shape (N, 4), in XYXY format.
            points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels.
            labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background.
            masks (np.ndarray, optional): Low-resolution masks from previous predictions shape (N,H,W). For SAM H=W=256.
            multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts.

        Returns:
            (tuple): Contains the following three elements.
                - np.ndarray: The output masks in shape CxHxW, where C is the number of generated masks.
                - np.ndarray: An array of length C containing quality scores predicted by the model for each mask.
                - np.ndarray: Low-resolution logits of shape CxHxW for subsequent inference, where H=W=256.
        """
        features = self.model.image_encoder(im) if self.features is None else self.features

        src_shape, dst_shape = self.batch[1][0].shape[:2], im.shape[2:]
        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=torch.float32, 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[0])
            labels = torch.as_tensor(labels, dtype=torch.int32, device=self.device)
            points *= r
            # (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=torch.float32, device=self.device)
            bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes
            bboxes *= r
        if masks is not None:
            masks = torch.as_tensor(masks, dtype=torch.float32, device=self.device).unsqueeze(1)

        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 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 function 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 dimensions (N, C, H, W).
            crop_n_layers (int): Specifies the number of layers for additional mask predictions on image crops.
                                 Each layer produces 2**i_layer number of image crops.
            crop_overlap_ratio (float): Determines the overlap between crops. Scaled down in subsequent layers.
            crop_downscale_factor (int): Scaling factor for the number of sampled points-per-side in each layer.
            point_grids (list[np.ndarray], optional): Custom grids for point sampling normalized to [0,1].
                                                      Used in the nth crop layer.
            points_stride (int, optional): Number of points to sample along each side of the image.
                                           Exclusive with 'point_grids'.
            points_batch_size (int): Batch size for the number of points processed simultaneously.
            conf_thres (float): Confidence threshold [0,1] for filtering based on the model's mask quality prediction.
            stability_score_thresh (float): Stability threshold [0,1] for mask filtering based on mask 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:
            (tuple): A tuple containing segmented masks, confidence scores, and bounding boxes.
        """
        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(len(crop_masks)))

        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 setup_model(self, model, verbose=True):
        """
        Initializes 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): A pre-trained SAM model. If None, a model will be built based on configuration.
            verbose (bool): If True, prints selected device information.

        Attributes:
            model (torch.nn.Module): The SAM model allocated to the chosen device for inference.
            device (torch.device): The device to which the model and tensors are allocated.
            mean (torch.Tensor): The mean values for image normalization.
            std (torch.Tensor): The standard deviation values for image normalization.
        """
        device = select_device(self.args.device, verbose=verbose)
        if model is None:
            model = build_sam(self.args.model)
        model.eval()
        self.model = model.to(device)
        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 = False
        self.done_warmup = True

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

        The method scales masks and boxes to the original image size and applies a threshold to the mask predictions.
        The SAM model uses advanced architecture and promptable segmentation tasks to achieve real-time performance.

        Args:
            preds (tuple): The output from SAM model inference, containing masks, scores, and optional bounding boxes.
            img (torch.Tensor): The processed input image tensor.
            orig_imgs (list | torch.Tensor): The original, unprocessed images.

        Returns:
            (list): List of Results objects containing detection masks, bounding boxes, and other metadata.
        """
        # (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(len(pred_masks))))

        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, masks in enumerate([pred_masks]):
            orig_img = orig_imgs[i]
            if pred_bboxes is not None:
                pred_bboxes = ops.scale_boxes(img.shape[2:], pred_bboxes.float(), orig_img.shape, padding=False)
                cls = torch.arange(len(pred_masks), dtype=torch.int32, device=pred_masks.device)
                pred_bboxes = torch.cat([pred_bboxes, pred_scores[:, None], cls[:, None]], dim=-1)

            masks = ops.scale_masks(masks[None].float(), orig_img.shape[:2], padding=False)[0]
            masks = masks > self.model.mask_threshold  # to bool
            img_path = self.batch[0][i]
            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 setup_source(self, source):
        """
        Sets up the data source for inference.

        This method configures the data source from which images will be fetched for inference. The source could be a
        directory, a video file, or other types of image data sources.

        Args:
            source (str | Path): The path to the image data source for inference.
        """
        if source is not None:
            super().setup_source(source)

    def set_image(self, image):
        """
        Preprocesses and sets a single image for inference.

        This function sets up the model if not already initialized, configures the data source to the specified image,
        and preprocesses the image for feature extraction. Only one image can be set at a time.

        Args:
            image (str | np.ndarray): Image file path as a string, or a np.ndarray image read by cv2.

        Raises:
            AssertionError: If more than one image is set.
        """
        if self.model is None:
            model = build_sam(self.args.model)
            self.setup_model(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.model.image_encoder(im)
            self.im = im
            break

    def set_prompts(self, prompts):
        """Set prompts in advance."""
        self.prompts = prompts

    def reset_image(self):
        """Resets the image and its features to None."""
        self.im = None
        self.features = None

    @staticmethod
    def remove_small_regions(masks, min_area=0, nms_thresh=0.7):
        """
        Perform post-processing on segmentation masks generated by the Segment Anything Model (SAM). Specifically, this
        function 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): A tensor containing the masks to be processed. Shape should be (N, H, W), where N is
                                  the number of masks, H is height, and W is width.
            min_area (int): The minimum area below which disconnected regions and holes will be removed. Defaults to 0.
            nms_thresh (float): The IoU threshold for the NMS algorithm. Defaults to 0.7.

        Returns:
            (tuple([torch.Tensor, List[int]])):
                - new_masks (torch.Tensor): The processed masks with small regions removed. Shape is (N, H, W).
                - keep (List[int]): The indices of the remaining masks post-NMS, which can be used to filter the boxes.
        """
        import torchvision  # scope for faster 'import ultralytics'

        if len(masks) == 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

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

Inicializa el Pronosticador con la configuración, las anulaciones y las llamadas de retorno.

El método configura el objeto Pronosticador y aplica las anulaciones de configuración o las llamadas de retorno que se hayan proporcionado. En inicializa los ajustes específicos de la tarea para SAM, como que retina_masks se establezca en True para obtener resultados óptimos.

Parámetros:

Nombre Tipo Descripción Por defecto
cfg dict

Diccionario de configuración.

DEFAULT_CFG
overrides dict

Diccionario de valores para anular la configuración por defecto.

None
_callbacks dict

Diccionario de funciones de llamada de retorno para personalizar el comportamiento.

None
Código fuente en ultralytics/models/sam/predict.py
def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
    """
    Initialize the Predictor with configuration, overrides, and callbacks.

    The method sets up the Predictor object and applies any configuration overrides or callbacks provided. It
    initializes task-specific settings for SAM, such as retina_masks being set to True for optimal results.

    Args:
        cfg (dict): Configuration dictionary.
        overrides (dict, optional): Dictionary of values to override default configuration.
        _callbacks (dict, optional): Dictionary of callback functions to customize behavior.
    """
    if overrides is None:
        overrides = {}
    overrides.update(dict(task="segment", mode="predict", imgsz=1024))
    super().__init__(cfg, overrides, _callbacks)
    self.args.retina_masks = True
    self.im = None
    self.features = None
    self.prompts = {}
    self.segment_all = False

generate(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)

Realiza la segmentación de la imagen utilizando el modelo Segment Anything (SAM).

Esta función segmenta una imagen entera en partes constituyentes aprovechando la arquitectura avanzada de SAM y su rendimiento en tiempo real. Opcionalmente, puede trabajar con recortes de imagen para una segmentación más fina.

Parámetros:

Nombre Tipo Descripción Por defecto
im Tensor

Entrada tensor que representa la imagen preprocesada con dimensiones (N, C, H, W).

necesario
crop_n_layers int

Especifica el número de capas para las predicciones de máscara adicionales en los recortes de imagen. Cada capa produce 2**i_número de capas de recortes de imagen.

0
crop_overlap_ratio float

Determina el solapamiento entre cultivos. Se reduce en capas posteriores.

512 / 1500
crop_downscale_factor int

Factor de escala para el número de puntos muestreados por lado en cada capa.

1
point_grids list[ndarray]

Cuadrículas personalizadas para el muestreo de puntos normalizadas a [0,1]. Utilizadas en la enésima capa de cultivo.

None
points_stride int

Número de puntos a muestrear a lo largo de cada lado de la imagen. Exclusivo con 'rejilla_puntos'.

32
points_batch_size int

Tamaño del lote para el número de puntos procesados simultáneamente.

64
conf_thres float

Umbral de confianza [0,1] para el filtrado basado en la predicción de la calidad de la máscara del modelo.

0.88
stability_score_thresh float

Umbral de estabilidad [0,1] para el filtrado de máscaras basado en la estabilidad de la máscara.

0.95
stability_score_offset float

Valor de compensación para calcular la puntuación de estabilidad.

0.95
crop_nms_thresh float

Corte de IoU para que el SMN elimine las máscaras duplicadas entre cultivos.

0.7

Devuelve:

Tipo Descripción
tuple

Una tupla que contiene máscaras segmentadas, puntuaciones de confianza y cuadros delimitadores.

Código fuente en ultralytics/models/sam/predict.py
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 function 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 dimensions (N, C, H, W).
        crop_n_layers (int): Specifies the number of layers for additional mask predictions on image crops.
                             Each layer produces 2**i_layer number of image crops.
        crop_overlap_ratio (float): Determines the overlap between crops. Scaled down in subsequent layers.
        crop_downscale_factor (int): Scaling factor for the number of sampled points-per-side in each layer.
        point_grids (list[np.ndarray], optional): Custom grids for point sampling normalized to [0,1].
                                                  Used in the nth crop layer.
        points_stride (int, optional): Number of points to sample along each side of the image.
                                       Exclusive with 'point_grids'.
        points_batch_size (int): Batch size for the number of points processed simultaneously.
        conf_thres (float): Confidence threshold [0,1] for filtering based on the model's mask quality prediction.
        stability_score_thresh (float): Stability threshold [0,1] for mask filtering based on mask 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:
        (tuple): A tuple containing segmented masks, confidence scores, and bounding boxes.
    """
    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(len(crop_masks)))

    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

inference(im, bboxes=None, points=None, labels=None, masks=None, multimask_output=False, *args, **kwargs)

Realiza la inferencia de segmentación de la imagen basándote en las claves de entrada dadas, utilizando la imagen cargada actualmente. Este método de método aprovecha la arquitectura SAM(Segment Anything Model), que consiste en un codificador de imágenes, un codificador de indicaciones y un decodificador de máscaras para tareas de segmentación en tiempo real y con indicaciones. decodificador de máscaras para tareas de segmentación en tiempo real y con avisos.

Parámetros:

Nombre Tipo Descripción Por defecto
im Tensor

La imagen de entrada preprocesada en formato tensor , con forma (N, C, H, W).

necesario
bboxes ndarray | List

Cajas delimitadoras con forma (N, 4), en formato XYXY.

None
points ndarray | List

Puntos que indican la ubicación de los objetos con forma (N, 2), en píxeles.

None
labels ndarray | List

Etiquetas para indicaciones de puntos, forma (N, ). 1 = primer plano, 0 = segundo plano.

None
masks ndarray

Máscaras de baja resolución de la forma de predicciones anteriores (N,H,W). Para SAM H=W=256.

None
multimask_output bool

Bandera para devolver varias máscaras. Útil para indicaciones ambiguas.

False

Devuelve:

Tipo Descripción
tuple

Contiene los tres elementos siguientes - np.ndarray: Las máscaras de salida en forma CxHxW, donde C es el número de máscaras generadas. - np.ndarray: Una matriz de longitud C que contiene las puntuaciones de calidad predichas por el modelo para cada máscara. - np.ndarray: Logits de baja resolución de forma CxHxW para la inferencia posterior, donde H=W=256.

Código fuente en ultralytics/models/sam/predict.py
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, optional): Bounding boxes with shape (N, 4), in XYXY format.
        points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels.
        labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background.
        masks (np.ndarray, optional): Low-resolution masks from previous predictions shape (N,H,W). For SAM H=W=256.
        multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts.

    Returns:
        (tuple): Contains the following three elements.
            - np.ndarray: The output masks in shape CxHxW, where C is the number of generated masks.
            - np.ndarray: An array of length C containing quality scores predicted by the model for each mask.
            - np.ndarray: Low-resolution logits of shape CxHxW for subsequent inference, where H=W=256.
    """
    # 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)

    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)

postprocess(preds, img, orig_imgs)

Postprocesa los resultados de la inferencia de SAM para generar máscaras de detección de objetos y cuadros delimitadores.

El método escala las máscaras y las cajas al tamaño de la imagen original y aplica un umbral a las predicciones de las máscaras. El modelo SAM utiliza una arquitectura avanzada y tareas de segmentación incitables para lograr un rendimiento en tiempo real.

Parámetros:

Nombre Tipo Descripción Por defecto
preds tuple

El resultado de la inferencia del modelo SAM , que contiene máscaras, puntuaciones y cuadros delimitadores opcionales.

necesario
img Tensor

La imagen de entrada procesada tensor.

necesario
orig_imgs list | Tensor

Las imágenes originales, sin procesar.

necesario

Devuelve:

Tipo Descripción
list

Lista de objetos Resultados que contienen máscaras de detección, cuadros delimitadores y otros metadatos.

Código fuente en ultralytics/models/sam/predict.py
def postprocess(self, preds, img, orig_imgs):
    """
    Post-processes SAM's inference outputs to generate object detection masks and bounding boxes.

    The method scales masks and boxes to the original image size and applies a threshold to the mask predictions.
    The SAM model uses advanced architecture and promptable segmentation tasks to achieve real-time performance.

    Args:
        preds (tuple): The output from SAM model inference, containing masks, scores, and optional bounding boxes.
        img (torch.Tensor): The processed input image tensor.
        orig_imgs (list | torch.Tensor): The original, unprocessed images.

    Returns:
        (list): List of Results objects containing detection masks, bounding boxes, and other metadata.
    """
    # (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(len(pred_masks))))

    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, masks in enumerate([pred_masks]):
        orig_img = orig_imgs[i]
        if pred_bboxes is not None:
            pred_bboxes = ops.scale_boxes(img.shape[2:], pred_bboxes.float(), orig_img.shape, padding=False)
            cls = torch.arange(len(pred_masks), dtype=torch.int32, device=pred_masks.device)
            pred_bboxes = torch.cat([pred_bboxes, pred_scores[:, None], cls[:, None]], dim=-1)

        masks = ops.scale_masks(masks[None].float(), orig_img.shape[:2], padding=False)[0]
        masks = masks > self.model.mask_threshold  # to bool
        img_path = self.batch[0][i]
        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

pre_transform(im)

Realiza transformaciones iniciales en la imagen de entrada para preprocesarla.

El método aplica transformaciones, como el cambio de tamaño, para preparar la imagen para su posterior preprocesamiento. Actualmente no se admite la inferencia por lotes, por lo que la longitud de la lista debe ser 1.

Parámetros:

Nombre Tipo Descripción Por defecto
im List[ndarray]

Lista que contiene imágenes en formato de matriz numpy HWC.

necesario

Devuelve:

Tipo Descripción
List[ndarray]

Lista de imágenes transformadas.

Código fuente en ultralytics/models/sam/predict.py
def pre_transform(self, im):
    """
    Perform initial transformations on the input image for preprocessing.

    The 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 images in HWC numpy array format.

    Returns:
        (List[np.ndarray]): List of transformed images.
    """
    assert len(im) == 1, "SAM model does not currently support batched inference"
    letterbox = LetterBox(self.args.imgsz, auto=False, center=False)
    return [letterbox(image=x) for x in im]

preprocess(im)

Preprocesa la imagen de entrada para la inferencia del modelo.

El método prepara la imagen de entrada aplicando transformaciones y normalización. Admite tanto torch.Tensor como listas de np.ndarray como formatos de entrada.

Parámetros:

Nombre Tipo Descripción Por defecto
im Tensor | List[ndarray]

Formato BCHW tensor o lista de matrices numpy HWC.

necesario

Devuelve:

Tipo Descripción
Tensor

La imagen preprocesada tensor.

Código fuente en ultralytics/models/sam/predict.py
def preprocess(self, im):
    """
    Preprocess the input image for model inference.

    The 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]): BCHW tensor format or list of HWC numpy arrays.

    Returns:
        (torch.Tensor): The preprocessed image tensor.
    """
    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)
    im = im.half() if self.model.fp16 else im.float()
    if not_tensor:
        im = (im - self.mean) / self.std
    return im

prompt_inference(im, bboxes=None, points=None, labels=None, masks=None, multimask_output=False)

Función interna para la inferencia de segmentación de imágenes basada en pistas como cuadros delimitadores, puntos y máscaras. Aprovecha la arquitectura especializada de SAM para la segmentación en tiempo real basada en indicaciones.

Parámetros:

Nombre Tipo Descripción Por defecto
im Tensor

La imagen de entrada preprocesada en formato tensor , con forma (N, C, H, W).

necesario
bboxes ndarray | List

Cajas delimitadoras con forma (N, 4), en formato XYXY.

None
points ndarray | List

Puntos que indican la ubicación de los objetos con forma (N, 2), en píxeles.

None
labels ndarray | List

Etiquetas para indicaciones de puntos, forma (N, ). 1 = primer plano, 0 = segundo plano.

None
masks ndarray

Máscaras de baja resolución de la forma de predicciones anteriores (N,H,W). Para SAM H=W=256.

None
multimask_output bool

Bandera para devolver varias máscaras. Útil para indicaciones ambiguas.

False

Devuelve:

Tipo Descripción
tuple

Contiene los tres elementos siguientes - np.ndarray: Las máscaras de salida en forma CxHxW, donde C es el número de máscaras generadas. - np.ndarray: Una matriz de longitud C que contiene las puntuaciones de calidad predichas por el modelo para cada máscara. - np.ndarray: Logits de baja resolución de forma CxHxW para la inferencia posterior, donde H=W=256.

Código fuente en ultralytics/models/sam/predict.py
def prompt_inference(self, im, bboxes=None, points=None, labels=None, masks=None, multimask_output=False):
    """
    Internal function for image segmentation inference based on cues like bounding boxes, points, and masks.
    Leverages SAM's specialized architecture for prompt-based, real-time segmentation.

    Args:
        im (torch.Tensor): The preprocessed input image in tensor format, with shape (N, C, H, W).
        bboxes (np.ndarray | List, optional): Bounding boxes with shape (N, 4), in XYXY format.
        points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels.
        labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background.
        masks (np.ndarray, optional): Low-resolution masks from previous predictions shape (N,H,W). For SAM H=W=256.
        multimask_output (bool, optional): Flag to return multiple masks. Helpful for ambiguous prompts.

    Returns:
        (tuple): Contains the following three elements.
            - np.ndarray: The output masks in shape CxHxW, where C is the number of generated masks.
            - np.ndarray: An array of length C containing quality scores predicted by the model for each mask.
            - np.ndarray: Low-resolution logits of shape CxHxW for subsequent inference, where H=W=256.
    """
    features = self.model.image_encoder(im) if self.features is None else self.features

    src_shape, dst_shape = self.batch[1][0].shape[:2], im.shape[2:]
    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=torch.float32, 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[0])
        labels = torch.as_tensor(labels, dtype=torch.int32, device=self.device)
        points *= r
        # (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=torch.float32, device=self.device)
        bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes
        bboxes *= r
    if masks is not None:
        masks = torch.as_tensor(masks, dtype=torch.float32, device=self.device).unsqueeze(1)

    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)

remove_small_regions(masks, min_area=0, nms_thresh=0.7) staticmethod

Realiza el postprocesado de las máscaras de segmentación generadas por el Modelo de Todo Segmento (SAM). En concreto, esta función elimina las pequeñas regiones desconectadas y los agujeros de las máscaras de entrada y, a continuación, realiza una Supresión No Máxima (NMS) para eliminar los recuadros duplicados de nueva creación. (NMS) para eliminar los recuadros duplicados de nueva creación.

Parámetros:

Nombre Tipo Descripción Por defecto
masks Tensor

Un tensor que contenga las máscaras que se van a procesar. La forma debe ser (N, H, W), donde N es el número de máscaras, H es la altura y W es la anchura.

necesario
min_area int

El área mínima por debajo de la cual se eliminarán las regiones desconectadas y los agujeros. Por defecto es 0.

0
nms_thresh float

El umbral de IoU para el algoritmo NMS. Por defecto es 0,7.

0.7

Devuelve:

Tipo Descripción
tuple([Tensor, List[int]])
  • nuevas_máscaras (torch.Tensor): Las máscaras procesadas con las regiones pequeñas eliminadas. La forma es (N, H, W).
  • mantener (Lista[int]): Los índices de las máscaras restantes post-NMS, que pueden utilizarse para filtrar las cajas.
Código fuente en ultralytics/models/sam/predict.py
@staticmethod
def remove_small_regions(masks, min_area=0, nms_thresh=0.7):
    """
    Perform post-processing on segmentation masks generated by the Segment Anything Model (SAM). Specifically, this
    function 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): A tensor containing the masks to be processed. Shape should be (N, H, W), where N is
                              the number of masks, H is height, and W is width.
        min_area (int): The minimum area below which disconnected regions and holes will be removed. Defaults to 0.
        nms_thresh (float): The IoU threshold for the NMS algorithm. Defaults to 0.7.

    Returns:
        (tuple([torch.Tensor, List[int]])):
            - new_masks (torch.Tensor): The processed masks with small regions removed. Shape is (N, H, W).
            - keep (List[int]): The indices of the remaining masks post-NMS, which can be used to filter the boxes.
    """
    import torchvision  # scope for faster 'import ultralytics'

    if len(masks) == 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

reset_image()

Restablece la imagen y sus características a Ninguno.

Código fuente en ultralytics/models/sam/predict.py
def reset_image(self):
    """Resets the image and its features to None."""
    self.im = None
    self.features = None

set_image(image)

Preprocesa y fija una sola imagen para la inferencia.

Esta función configura el modelo si no está ya inicializado, configura la fuente de datos a la imagen especificada, y preprocesa la imagen para la extracción de características. Sólo se puede configurar una imagen a la vez.

Parámetros:

Nombre Tipo Descripción Por defecto
image str | ndarray

Ruta del archivo de imagen como cadena, o una imagen np.ndarray leída por cv2.

necesario

Aumenta:

Tipo Descripción
AssertionError

Si se establece más de una imagen.

Código fuente en ultralytics/models/sam/predict.py
def set_image(self, image):
    """
    Preprocesses and sets a single image for inference.

    This function sets up the model if not already initialized, configures the data source to the specified image,
    and preprocesses the image for feature extraction. Only one image can be set at a time.

    Args:
        image (str | np.ndarray): Image file path as a string, or a np.ndarray image read by cv2.

    Raises:
        AssertionError: If more than one image is set.
    """
    if self.model is None:
        model = build_sam(self.args.model)
        self.setup_model(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.model.image_encoder(im)
        self.im = im
        break

set_prompts(prompts)

Establece avisos con antelación.

Código fuente en ultralytics/models/sam/predict.py
def set_prompts(self, prompts):
    """Set prompts in advance."""
    self.prompts = prompts

setup_model(model, verbose=True)

Inicializa el Modelo de Todo Segmento (SAM) para la inferencia.

Este método configura el modelo SAM asignándolo al dispositivo adecuado e inicializando los parámetros necesarios para la normalización de la imagen y otros ajustes de compatibilidad de . para la normalización de la imagen y otros ajustes de compatibilidad de Ultralytics .

Parámetros:

Nombre Tipo Descripción Por defecto
model Module

Un modelo preentrenado SAM . Si es Ninguno, se construirá un modelo basado en la configuración.

necesario
verbose bool

Si es Verdadero, imprime la información del dispositivo seleccionado.

True

Atributos:

Nombre Tipo Descripción
model Module

El modelo SAM asignado al dispositivo elegido para la inferencia.

device device

El dispositivo al que se asignan el modelo y los tensores.

mean Tensor

Los valores medios para la normalización de la imagen.

std Tensor

Los valores de la desviación típica para la normalización de la imagen.

Código fuente en ultralytics/models/sam/predict.py
def setup_model(self, model, verbose=True):
    """
    Initializes 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): A pre-trained SAM model. If None, a model will be built based on configuration.
        verbose (bool): If True, prints selected device information.

    Attributes:
        model (torch.nn.Module): The SAM model allocated to the chosen device for inference.
        device (torch.device): The device to which the model and tensors are allocated.
        mean (torch.Tensor): The mean values for image normalization.
        std (torch.Tensor): The standard deviation values for image normalization.
    """
    device = select_device(self.args.device, verbose=verbose)
    if model is None:
        model = build_sam(self.args.model)
    model.eval()
    self.model = model.to(device)
    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 = False
    self.done_warmup = True

setup_source(source)

Configura la fuente de datos para la inferencia.

Este método configura la fuente de datos de la que se obtendrán las imágenes para la inferencia. La fuente puede ser un directorio, un archivo de vídeo u otros tipos de fuentes de datos de imágenes.

Parámetros:

Nombre Tipo Descripción Por defecto
source str | Path

La ruta a la fuente de datos de la imagen para la inferencia.

necesario
Código fuente en ultralytics/models/sam/predict.py
def setup_source(self, source):
    """
    Sets up the data source for inference.

    This method configures the data source from which images will be fetched for inference. The source could be a
    directory, a video file, or other types of image data sources.

    Args:
        source (str | Path): The path to the image data source for inference.
    """
    if source is not None:
        super().setup_source(source)





Creado 2023-11-12, Actualizado 2024-05-18
Autores: glenn-jocher (4), Burhan-Q (1)