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参考资料 ultralytics/models/sam/predict.py

备注

该文件可在https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/models/ sam/predict .py。如果您发现问题,请通过提交 Pull Request🛠️ 帮助修复。谢谢🙏!



ultralytics.models.sam.predict.Predictor

垒球 BasePredictor

用于 Segment Anything Model (SAM) 的 Predictor 类,扩展了 BasePredictor。

该类提供了一个针对图像分割任务的模型推理接口。 它具有先进的架构和可提示的分割功能,便于灵活、实时地 生成掩码。该类能够处理各种类型的提示,如边界框、点和低分辨率遮罩、 点和低分辨率掩码。

属性

名称 类型 说明
cfg dict

配置字典指定模型和任务相关参数。

overrides dict

包含可覆盖默认配置的值的字典。

_callbacks dict

用户定义的回调函数字典,用于增强行为。

args namespace

用于存放命令行参数或其他操作变量的命名空间。

im Tensor

预处理后的输入图像tensor.

features Tensor

提取的图像特征用于推理。

prompts dict

各种提示类型(如边界框和点)的集合。

segment_all bool

标记,用于控制是分割图像中的所有对象,还是只分割指定的对象。

源代码 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)

使用配置、重载和回调初始化 Predictor。

该方法设置 Predictor 对象,并应用提供的任何配置重载或回调。该方法 初始化SAM 的特定任务设置,例如将 retina_masks 设置为 True 以获得最佳效果。

参数

名称 类型 说明 默认值
cfg dict

配置字典。

DEFAULT_CFG
overrides dict

用于覆盖默认配置的数值字典。

None
_callbacks dict

用于自定义行为的回调函数字典。

None
源代码 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)

使用 Segment Anything Model (SAM) 进行图像分割。

该功能通过利用SAM 的先进架构和实时性能功能,将整个图像分割成不同部分。 先进的架构和实时性能。它还可以选择对图像进行裁剪,以实现更精细的分割。

参数

名称 类型 说明 默认值
im Tensor

输入tensor ,表示尺寸为(N、C、H、W)的预处理图像。

所需
crop_n_layers int

指定图像裁剪附加遮罩预测的层数。 每层生成 2**i_ 层数的图像裁剪。

0
crop_overlap_ratio float

确定作物之间的重叠度。在后续图层中按比例缩小。

512 / 1500
crop_downscale_factor int

每层每边采样点数的缩放因子。

1
point_grids list[ndarray]

用于点采样的自定义网格,归一化为 [0,1]。 用于第 n 个作物层。

None
points_stride int

沿图像每边采样的点数。 与 "point_grids "排他。

32
points_batch_size int

同时处理的点数的批量大小。

64
conf_thres float

可信度阈值 [0,1],用于根据模型的掩膜质量预测进行过滤。

0.88
stability_score_thresh float

基于掩膜稳定性的掩膜过滤稳定性阈值 [0,1]。

0.95
stability_score_offset float

计算稳定性得分的偏移值。

0.95
crop_nms_thresh float

用于 NMS 的 IoU 截止值,以去除作物间的重复掩膜。

0.7

返回:

类型 说明
tuple

包含分段掩码、置信度得分和边界框的元组。

源代码 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)

根据给定的输入线索,使用当前加载的图像执行图像分割推理。这种 该方法利用SAM (分割任何模型)架构,包括图像编码器、提示编码器和掩码解码器。 掩码解码器组成的架构,用于实时和可提示的分割任务。

参数

名称 类型 说明 默认值
im Tensor

经过预处理的输入图像,格式为tensor ,形状为(N、C、H、W)。

所需
bboxes ndarray | List

形状为 (N, 4) 的边界框,格式为 XYXY。

None
points ndarray | List

表示物体位置的点,形状为 (N,2),单位为像素。

None
labels ndarray | List

点提示的标签,形状 (N,)。1 = 前景,0 = 背景。

None
masks ndarray

先前预测形状(N、H、W)的低分辨率掩码。SAM H=W=256。

None
multimask_output bool

返回多个掩码的标记。对于模棱两可的提示很有帮助。

False

返回:

类型 说明
tuple

包含以下三个元素 - np.ndarray:形状为 CxHxW 的输出掩码,其中 C 是生成的掩码数。 - np.ndarray:长度为 C 的数组,包含模型为每个掩码预测的质量分数。 - np.ndarray:用于后续推理的 CxHxW 形的低分辨率对数,其中 H=W=256.

源代码 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)

对SAM 的推理输出进行后处理,生成物体检测掩码和边界框。

该方法根据原始图像大小缩放掩码和方框,并对掩码预测应用阈值。 SAM 模型采用先进的架构和可提示的分割任务来实现实时性能。

参数

名称 类型 说明 默认值
preds tuple

SAM 模型推理的输出结果,包含掩码、分数和可选的边界框。

所需
img Tensor

经过处理的输入图像tensor 。

所需
orig_imgs list | Tensor

未经处理的原始图像。

所需

返回:

类型 说明
list

包含检测掩码、边界框和其他元数据的结果对象列表。

源代码 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)

对输入图像进行初始变换,以便进行预处理。

该方法应用变换(如调整大小),为进一步预处理图像做好准备。 目前不支持分批推理,因此列表长度应为 1。

参数

名称 类型 说明 默认值
im List[ndarray]

包含 HWC numpy 数组格式图像的列表。

所需

返回:

类型 说明
List[ndarray]

转换图像列表。

源代码 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)

对输入图像进行预处理,以便进行模型推理。

该方法通过应用变换和归一化处理来准备输入图像。 它支持torch.Tensor 和 np.ndarray 列表两种输入格式。

参数

名称 类型 说明 默认值
im Tensor | List[ndarray]

BCHWtensor 格式或 HWC numpy 数组列表。

所需

返回:

类型 说明
Tensor

预处理后的图像tensor 。

源代码 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)

基于边界框、点和遮罩等线索进行图像分割推理的内部功能。 利用SAM 的专业架构进行基于提示的实时分割。

参数

名称 类型 说明 默认值
im Tensor

经过预处理的输入图像,格式为tensor ,形状为(N、C、H、W)。

所需
bboxes ndarray | List

形状为 (N, 4) 的边界框,格式为 XYXY。

None
points ndarray | List

表示物体位置的点,形状为 (N,2),单位为像素。

None
labels ndarray | List

点提示的标签,形状 (N,)。1 = 前景,0 = 背景。

None
masks ndarray

先前预测形状(N、H、W)的低分辨率掩码。SAM H=W=256。

None
multimask_output bool

返回多个掩码的标记。对于模棱两可的提示很有帮助。

False

返回:

类型 说明
tuple

包含以下三个元素 - np.ndarray:形状为 CxHxW 的输出掩码,其中 C 是生成的掩码数。 - np.ndarray:长度为 C 的数组,包含模型为每个掩码预测的质量分数。 - np.ndarray:用于后续推理的 CxHxW 形的低分辨率对数,其中 H=W=256.

源代码 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

对任何分段模型 (SAM) 生成的分段掩码进行后处理。具体来说,该 功能会从输入掩码中移除小的断开区域和洞,然后执行非最大化抑制(NMS 抑制 (NMS) 以消除任何新创建的重复方框。

参数

名称 类型 说明 默认值
masks Tensor

tensor 包含要处理的掩码。形状应为 (N、H、W),其中 N 是掩码的数量,H 是高度,W 是宽度。 是掩码的数量,H 是高度,W 是宽度。

所需
min_area int

删除断开区域和孔洞的最小面积。默认为 0。

0
nms_thresh float

NMS 算法的 IoU 阈值。默认为 0.7。

0.7

返回:

类型 说明
tuple([Tensor, List[int]])
  • new_masks (torch.Tensor):经过处理并去除小区域的蒙版。形状为(N、H、W)。
  • keep(List[int]):NMS 后剩余掩码的索引,可用于筛选方框。
源代码 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()

将图像及其特征重置为 "无"。

源代码 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)

预处理和设置用于推理的单个图像。

如果模型尚未初始化,该函数将对模型进行设置,并将数据源配置为指定的图像、 并预处理图像以进行特征提取。一次只能设置一幅图像。

参数

名称 类型 说明 默认值
image str | ndarray

字符串形式的图像文件路径,或 cv2 读取的 np.ndarray 图像。

所需

加薪:

类型 说明
AssertionError

如果设置了多个图像。

源代码 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)

提前设置提示。

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

setup_model(model, verbose=True)

初始化用于推理的分段 Anything Model (SAM)。

该方法通过将SAM 模型分配给适当的设备并初始化必要的 参数和其他Ultralytics 兼容性设置。

参数

名称 类型 说明 默认值
model Module

预先训练的SAM 模型。如果无,将根据配置建立模型。

所需
verbose bool

如果为 True,则打印所选设备信息。

True

属性

名称 类型 说明
model Module

分配给所选设备用于推理的SAM 模型。

device device

分配模型和张量的设备。

mean Tensor

图像正常化的平均值。

std Tensor

图像正常化的标准偏差值。

源代码 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)

为推理设置数据源。

该方法可配置获取图像进行推理的数据源。数据源可以是 目录、视频文件或其他类型的图像数据源。

参数

名称 类型 说明 默认值
source str | Path

用于推理的图像数据源路径。

所需
源代码 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)





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (5), Burhan-Q (1)