─░├žeri─če ge├ž

Referans i├žin ultralytics/models/sam/predict.py

Not

Bu dosya https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/models/ sam/predict .py adresinde mevcuttur. Bir sorun tespit ederseniz l├╝tfen bir ├çekme ─░ste─či ­čŤá´ŞĆ ile katk─▒da bulunarak d├╝zeltilmesine yard─▒mc─▒ olun. Te┼čekk├╝rler ­čÖĆ!



ultralytics.models.sam.predict.Predictor

├ťsler: BasePredictor

Segment Anything Model i├žin Predictor s─▒n─▒f─▒ (SAM), BasePredictor'─▒ geni┼čletir.

S─▒n─▒f, g├Âr├╝nt├╝ segmentasyon g├Ârevlerine uyarlanm─▒┼č model ├ž─▒kar─▒m─▒ i├žin bir aray├╝z sa─člar. Geli┼čmi┼č mimarisi ve uyar─▒labilir segmentasyon yetenekleri ile esnek ve ger├žek zamanl─▒ maske olu┼čturma. S─▒n─▒f, s─▒n─▒rlay─▒c─▒ kutular gibi ├že┼čitli istem t├╝rleriyle ├žal─▒┼čabilir, noktalar ve d├╝┼č├╝k ├ž├Âz├╝n├╝rl├╝kl├╝ maskeler.

Nitelikler:

─░sim Tip A├ž─▒klama
cfg dict

Model ve g├Ârevle ilgili parametreleri belirten yap─▒land─▒rma s├Âzl├╝─č├╝.

overrides dict

Varsay─▒lan yap─▒land─▒rmay─▒ ge├žersiz k─▒lan de─čerleri i├žeren s├Âzl├╝k.

_callbacks dict

Davran─▒┼č─▒ art─▒rmak i├žin kullan─▒c─▒ tan─▒ml─▒ geri arama i┼člevleri s├Âzl├╝─č├╝.

args namespace

Komut sat─▒r─▒ arg├╝manlar─▒n─▒ veya di─čer i┼člemsel de─či┼čkenleri tutmak i├žin ad alan─▒.

im Tensor

├ľn i┼člemden ge├žirilmi┼č giri┼č g├Âr├╝nt├╝s├╝ tensor.

features Tensor

├ç─▒kar─▒m i├žin kullan─▒lan ├ž─▒kar─▒lm─▒┼č g├Âr├╝nt├╝ ├Âzellikleri.

prompts dict

S─▒n─▒rlay─▒c─▒ kutular ve noktalar gibi ├že┼čitli istem t├╝rlerinin toplanmas─▒.

segment_all bool

G├Âr├╝nt├╝deki t├╝m nesnelerin mi yoksa yaln─▒zca belirtilen nesnelerin mi segmentlere ayr─▒laca─č─▒n─▒ kontrol etmek i├žin bayrak.

Kaynak kodu 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)

Predictor'─▒ yap─▒land─▒rma, ge├žersiz k─▒lmalar ve geri ├ža─č─▒rmalarla ba┼člat─▒n.

Y├Ântem, Predictor nesnesini ayarlar ve sa─članan t├╝m yap─▒land─▒rma ge├žersiz k─▒lmalar─▒n─▒ veya geri aramalar─▒ uygular. Bu SAM i├žin g├Âreve ├Âzg├╝ ayarlar─▒ ba┼člat─▒r, ├Ârne─čin en iyi sonu├žlar i├žin retina_masks True olarak ayarlan─▒r.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
cfg dict

Yap─▒land─▒rma s├Âzl├╝─č├╝.

DEFAULT_CFG
overrides dict

Varsay─▒lan yap─▒land─▒rmay─▒ ge├žersiz k─▒lmak i├žin de─čerler s├Âzl├╝─č├╝.

None
_callbacks dict

Davran─▒┼č─▒ ├Âzelle┼čtirmek i├žin geri arama i┼člevleri s├Âzl├╝─č├╝.

None
Kaynak kodu 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)

Segment Anything Model'i (SAM) kullanarak g├Âr├╝nt├╝ segmentasyonu ger├žekle┼čtirin.

Bu i┼člev, SAM'un geli┼čmi┼č mimarisinden yararlanarak g├Âr├╝nt├╝n├╝n tamam─▒n─▒ olu┼čturan par├žalara ay─▒r─▒r ve ger├žek zamanl─▒ performans yetenekleri. ─░ste─če ba─čl─▒ olarak daha ince segmentasyon i├žin g├Âr├╝nt├╝ k─▒rpmalar─▒ ├╝zerinde ├žal─▒┼čabilir.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
im Tensor

Giri┼č tensor ├Ânceden i┼členmi┼č g├Âr├╝nt├╝y├╝ (N, C, H, W) boyutlar─▒yla temsil eder.

gerekli
crop_n_layers int

G├Âr├╝nt├╝ k─▒rpmalar─▒nda ek maske tahminleri i├žin katman say─▒s─▒n─▒ belirtir. Her katman 2**i_layer say─▒da g├Âr├╝nt├╝ k─▒rp─▒nt─▒s─▒ ├╝retir.

0
crop_overlap_ratio float

Mahsuller aras─▒ndaki ├Ârt├╝┼čmeyi belirler. Sonraki katmanlarda k├╝├ž├╝lt├╝lm├╝┼čt├╝r.

512 / 1500
crop_downscale_factor int

Her katmanda her bir taraf i├žin ├Ârneklenen nokta say─▒s─▒ i├žin ├Âl├žeklendirme fakt├Âr├╝.

1
point_grids list[ndarray]

Nokta ├Ârnekleme i├žin [0,1]'e normalle┼čtirilmi┼č ├Âzel ─▒zgaralar. N'inci ├╝r├╝n katman─▒nda kullan─▒l─▒r.

None
points_stride int

G├Âr├╝nt├╝n├╝n her bir kenar─▒ boyunca ├Ârneklenecek nokta say─▒s─▒. 'point_grids' ile ├Âzel.

32
points_batch_size int

Ayn─▒ anda i┼členen nokta say─▒s─▒ i├žin toplu i┼č boyutu.

64
conf_thres float

Modelin maske kalitesi tahminine dayal─▒ filtreleme i├žin g├╝ven e┼či─či [0,1].

0.88
stability_score_thresh float

Maske kararl─▒l─▒─č─▒na dayal─▒ maske filtreleme i├žin kararl─▒l─▒k e┼či─či [0,1].

0.95
stability_score_offset float

Kararl─▒l─▒k puan─▒n─▒ hesaplamak i├žin ofset de─čeri.

0.95
crop_nms_thresh float

Mahsuller aras─▒nda yinelenen maskeleri kald─▒rmak i├žin NMS i├žin IoU kesme.

0.7

─░ade:

Tip A├ž─▒klama
tuple

B├Âl├╝mlere ayr─▒lm─▒┼č maskeleri, g├╝ven puanlar─▒n─▒ ve s─▒n─▒rlay─▒c─▒ kutular─▒ i├žeren bir tuple.

Kaynak kodu 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)

O anda y├╝kl├╝ olan g├Âr├╝nt├╝y├╝ kullanarak, verilen girdi ipu├žlar─▒na dayal─▒ olarak g├Âr├╝nt├╝ segmentasyonu ├ž─▒kar─▒m─▒ ger├žekle┼čtirin. Bu y├Ântemi, SAM'un (Segment Anything Model) g├Âr├╝nt├╝ kodlay─▒c─▒, istem kodlay─▒c─▒ ve Ger├žek zamanl─▒ ve h─▒zl─▒ segmentasyon g├Ârevleri i├žin maske kod ├ž├Âz├╝c├╝.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
im Tensor

tensor format─▒nda, (N, C, H, W) ┼čeklinde ├Ânceden i┼členmi┼č giri┼č g├Âr├╝nt├╝s├╝.

gerekli
bboxes ndarray | List

XYXY bi├žiminde (N, 4) ┼čeklinde s─▒n─▒rlay─▒c─▒ kutular.

None
points ndarray | List

Piksel cinsinden (N, 2) ┼čeklindeki nesne konumlar─▒n─▒ g├Âsteren noktalar.

None
labels ndarray | List

Nokta istemleri i├žin etiketler, ┼čekil (N, ). 1 = ├Ân plan, 0 = arka plan.

None
masks ndarray

├ľnceki tahminlerden elde edilen d├╝┼č├╝k ├ž├Âz├╝n├╝rl├╝kl├╝ maskeler (N,H,W) ┼čeklindedir. SAM i├žin H=W=256.

None
multimask_output bool

Birden fazla maske d├Ând├╝rmek i├žin i┼čaretleyin. Belirsiz istemler i├žin faydal─▒d─▒r.

False

─░ade:

Tip A├ž─▒klama
tuple

A┼ča─č─▒daki ├╝├ž ├Â─čeyi i├žerir. - np.ndarray: CxHxW ┼čeklinde ├ž─▒kt─▒ maskeleri, burada C ├╝retilen maske say─▒s─▒d─▒r. - np.ndarray: Her maske i├žin model taraf─▒ndan tahmin edilen kalite puanlar─▒n─▒ i├žeren C uzunlu─čunda bir dizi. - np.ndarray: Sonraki ├ž─▒kar─▒m i├žin CxHxW ┼čeklinde d├╝┼č├╝k ├ž├Âz├╝n├╝rl├╝kl├╝ logitler, burada H=W=256'd─▒r.

Kaynak kodu 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)

Nesne alg─▒lama maskeleri ve s─▒n─▒rlay─▒c─▒ kutular olu┼čturmak i├žin SAM'un ├ž─▒kar─▒m ├ž─▒kt─▒lar─▒n─▒ sonradan i┼čler.

Y├Ântem, maskeleri ve kutular─▒ orijinal g├Âr├╝nt├╝ boyutuna g├Âre ├Âl├žeklendirir ve maske tahminlerine bir e┼čik uygular. SAM modeli, ger├žek zamanl─▒ performans elde etmek i├žin geli┼čmi┼č mimari ve h─▒zl─▒ segmentasyon g├Ârevleri kullan─▒r.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
preds tuple

SAM model ├ž─▒kar─▒m─▒n─▒n maskeler, puanlar ve iste─če ba─čl─▒ s─▒n─▒rlay─▒c─▒ kutular i├žeren ├ž─▒kt─▒s─▒.

gerekli
img Tensor

─░┼členmi┼č giri┼č g├Âr├╝nt├╝s├╝ tensor.

gerekli
orig_imgs list | Tensor

Orijinal, i┼členmemi┼č g├Âr├╝nt├╝ler.

gerekli

─░ade:

Tip A├ž─▒klama
list

Alg─▒lama maskeleri, s─▒n─▒rlay─▒c─▒ kutular ve di─čer meta verileri i├žeren Sonu├ž nesnelerinin listesi.

Kaynak kodu 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)

├ľn i┼čleme i├žin giri┼č g├Âr├╝nt├╝s├╝ ├╝zerinde ilk d├Ân├╝┼č├╝mleri ger├žekle┼čtirin.

Y├Ântem, g├Âr├╝nt├╝y├╝ daha ileri ├Ân i┼člemlere haz─▒rlamak i├žin yeniden boyutland─▒rma gibi d├Ân├╝┼č├╝mler uygular. ┼×u anda, toplu ├ž─▒kar─▒m desteklenmemektedir; bu nedenle liste uzunlu─ču 1 olmal─▒d─▒r.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
im List[ndarray]

HWC numpy dizisi bi├žiminde g├Âr├╝nt├╝ler i├žeren liste.

gerekli

─░ade:

Tip A├ž─▒klama
List[ndarray]

D├Ân├╝┼čt├╝r├╝lm├╝┼č g├Âr├╝nt├╝lerin listesi.

Kaynak kodu 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)

Model ├ž─▒kar─▒m─▒ i├žin giri┼č g├Âr├╝nt├╝s├╝n├╝ ├Ân i┼čleme tabi tutun.

Y├Ântem, d├Ân├╝┼č├╝mler ve normalle┼čtirme uygulayarak giri┼č g├Âr├╝nt├╝s├╝n├╝ haz─▒rlar. Girdi bi├žimleri olarak hem torch.Tensor hem de np.ndarray listesini destekler.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
im Tensor | List[ndarray]

BCHW tensor bi├žimi veya HWC numpy dizilerinin listesi.

gerekli

─░ade:

Tip A├ž─▒klama
Tensor

├ľnceden i┼členmi┼č g├Âr├╝nt├╝ tensor.

Kaynak kodu 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)

S─▒n─▒rlay─▒c─▒ kutular, noktalar ve maskeler gibi ipu├žlar─▒na dayal─▒ g├Âr├╝nt├╝ segmentasyonu ├ž─▒kar─▒m─▒ i├žin dahili i┼člev. ─░stem tabanl─▒, ger├žek zamanl─▒ segmentasyon i├žin SAM'un ├Âzel mimarisinden yararlan─▒r.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
im Tensor

tensor format─▒nda, (N, C, H, W) ┼čeklinde ├Ânceden i┼členmi┼č giri┼č g├Âr├╝nt├╝s├╝.

gerekli
bboxes ndarray | List

XYXY bi├žiminde (N, 4) ┼čeklinde s─▒n─▒rlay─▒c─▒ kutular.

None
points ndarray | List

Piksel cinsinden (N, 2) ┼čeklindeki nesne konumlar─▒n─▒ g├Âsteren noktalar.

None
labels ndarray | List

Nokta istemleri i├žin etiketler, ┼čekil (N, ). 1 = ├Ân plan, 0 = arka plan.

None
masks ndarray

├ľnceki tahminlerden elde edilen d├╝┼č├╝k ├ž├Âz├╝n├╝rl├╝kl├╝ maskeler (N,H,W) ┼čeklindedir. SAM i├žin H=W=256.

None
multimask_output bool

Birden fazla maske d├Ând├╝rmek i├žin i┼čaretleyin. Belirsiz istemler i├žin faydal─▒d─▒r.

False

─░ade:

Tip A├ž─▒klama
tuple

A┼ča─č─▒daki ├╝├ž ├Â─čeyi i├žerir. - np.ndarray: CxHxW ┼čeklinde ├ž─▒kt─▒ maskeleri, burada C ├╝retilen maske say─▒s─▒d─▒r. - np.ndarray: Her maske i├žin model taraf─▒ndan tahmin edilen kalite puanlar─▒n─▒ i├žeren C uzunlu─čunda bir dizi. - np.ndarray: Sonraki ├ž─▒kar─▒m i├žin CxHxW ┼čeklinde d├╝┼č├╝k ├ž├Âz├╝n├╝rl├╝kl├╝ logitler, burada H=W=256'd─▒r.

Kaynak kodu 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

Segment Anything Model (SAM) taraf─▒ndan olu┼čturulan segmentasyon maskeleri ├╝zerinde post-processing ger├žekle┼čtirin. ├ľzellikle, bu i┼člevi, giri┼č maskelerinden k├╝├ž├╝k ba─člant─▒s─▒z b├Âlgeleri ve delikleri kald─▒r─▒r ve ard─▒ndan Maksimum Olmayan Yeni olu┼čturulan yinelenen kutular─▒ ortadan kald─▒rmak i├žin Bast─▒rma (NMS).

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
masks Tensor

─░┼členecek maskeleri i├žeren bir tensor . ┼×ekil (N, H, W) olmal─▒d─▒r, burada N maske say─▒s─▒, H y├╝kseklik ve W geni┼čliktir.

gerekli
min_area int

Ba─člant─▒s─▒ kesilen b├Âlgelerin ve deliklerin kald─▒r─▒laca─č─▒ minimum alan. Varsay─▒lan de─čer 0'd─▒r.

0
nms_thresh float

NMS algoritmas─▒ i├žin IoU e┼či─či. Varsay─▒lan de─čer 0,7'dir.

0.7

─░ade:

Tip A├ž─▒klama
tuple([Tensor, List[int]])
  • new_masks (torch.Tensor): K├╝├ž├╝k b├Âlgeleri kald─▒r─▒lm─▒┼č i┼členmi┼č maskeler. ┼×ekil (N, H, W) ┼čeklindedir.
  • keep (Liste[int]): Kutular─▒ filtrelemek i├žin kullan─▒labilecek NMS sonras─▒ kalan maskelerin indisleri.
Kaynak kodu 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()

G├Âr├╝nt├╝y├╝ ve ├Âzelliklerini Yok olarak s─▒f─▒rlar.

Kaynak kodu 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)

├ç─▒kar─▒m i├žin tek bir g├Âr├╝nt├╝y├╝ ├Ân i┼čler ve ayarlar.

Bu fonksiyon, ├Ânceden ba┼člat─▒lmam─▒┼čsa modeli kurar, veri kayna─č─▒n─▒ belirtilen g├Âr├╝nt├╝ye g├Âre yap─▒land─▒r─▒r, ve ├Âzellik ├ž─▒kar─▒m─▒ i├žin g├Âr├╝nt├╝y├╝ ├Ân i┼čleme tabi tutar. Bir seferde yaln─▒zca bir g├Âr├╝nt├╝ ayarlanabilir.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
image str | ndarray

Bir dize olarak g├Âr├╝nt├╝ dosyas─▒ yolu veya cv2 taraf─▒ndan okunan bir np.ndarray g├Âr├╝nt├╝s├╝.

gerekli

Zamlar:

Tip A├ž─▒klama
AssertionError

Birden fazla resim ayarlanm─▒┼čsa.

Kaynak kodu 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)

─░stemleri ├Ânceden ayarlay─▒n.

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

setup_model(model, verbose=True)

├ç─▒kar─▒m i├žin Segment Anything Model'i (SAM) ba┼člat─▒r.

Bu y├Ântem, SAM modelini uygun cihaza tahsis ederek ve gerekli ayg─▒tlar─▒ ba┼člatarak kurar. g├Âr├╝nt├╝ normalle┼čtirme ve di─čer Ultralytics uyumluluk ayarlar─▒ i├žin parametreler.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
model Module

├ľnceden e─čitilmi┼č bir SAM modeli. Yok ise, yap─▒land─▒rmaya dayal─▒ bir model olu┼čturulacakt─▒r.

gerekli
verbose bool

True ise, se├žilen cihaz bilgilerini yazd─▒r─▒r.

True

Nitelikler:

─░sim Tip A├ž─▒klama
model Module

├ç─▒kar─▒m i├žin se├žilen cihaza tahsis edilen SAM modeli.

device device

Model ve tens├Ârlerin tahsis edildi─či cihaz.

mean Tensor

G├Âr├╝nt├╝ normalizasyonu i├žin ortalama de─čerler.

std Tensor

G├Âr├╝nt├╝ normalizasyonu i├žin standart sapma de─čerleri.

Kaynak kodu 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)

├ç─▒kar─▒m i├žin veri kayna─č─▒n─▒ ayarlar.

Bu y├Ântem, g├Âr├╝nt├╝lerin ├ž─▒kar─▒m i├žin al─▒naca─č─▒ veri kayna─č─▒n─▒ yap─▒land─▒r─▒r. Kaynak bir dizini, bir video dosyas─▒ veya di─čer g├Âr├╝nt├╝ veri kaynaklar─▒ t├╝rleri.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
source str | Path

├ç─▒kar─▒m i├žin g├Âr├╝nt├╝ veri kayna─č─▒na giden yol.

gerekli
Kaynak kodu 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)