a yfЖ@sdZddlZddlZddlmmZddlm Z ddl m Z ddl m Z ddlmZmZddlmZdd lmZmZmZmZmZmZmZmZmZdd lmZGd d d e ZGd ddeZ dS)a Generate predictions using the Segment Anything Model (SAM). SAM is an advanced image segmentation model offering features like promptable segmentation and zero-shot performance. This module contains the implementation of the prediction logic and auxiliary utilities required to perform segmentation using SAM. It forms an integral part of the Ultralytics framework and is designed for high-performance, real-time image segmentation tasks. N) LetterBox) BasePredictor)Results) DEFAULT_CFGops) select_device) batch_iteratorbatched_mask_to_boxbuild_all_layer_point_gridscalculate_stability_scoregenerate_crop_boxesis_box_near_crop_edgeremove_small_regionsuncrop_boxes_xyxy uncrop_masks) build_samc seZdZdZeddffdd ZddZddZd+d d Zd,d d Z d-ddZ d.ddZ ddZ ddZ fdd Zd!d"Zd#d$Zd%d&Zd'd(Zed/d)d*ZZS)0 Predictora Predictor class for SAM, enabling real-time image segmentation with promptable capabilities. This class extends BasePredictor and implements the Segment Anything Model (SAM) for advanced image segmentation tasks. It supports various input prompts like points, bounding boxes, and masks for fine-grained control over segmentation results. Attributes: args (SimpleNamespace): Configuration arguments for the predictor. model (torch.nn.Module): The loaded SAM model. device (torch.device): The device (CPU or GPU) on which the model is loaded. im (torch.Tensor): The preprocessed input image. features (torch.Tensor): Extracted image features. prompts (Dict): Dictionary to store various types of prompts (e.g., bboxes, points, masks). segment_all (bool): Flag to indicate if full image segmentation should be performed. mean (torch.Tensor): Mean values for image normalization. std (torch.Tensor): Standard deviation values for image normalization. Methods: preprocess: Prepares input images for model inference. pre_transform: Performs initial transformations on the input image. inference: Performs segmentation inference based on input prompts. prompt_inference: Internal function for prompt-based segmentation inference. generate: Generates segmentation masks for an entire image. setup_model: Initializes the SAM model for inference. get_model: Builds and returns a SAM model. postprocess: Post-processes model outputs to generate final results. setup_source: Sets up the data source for inference. set_image: Sets and preprocesses a single image for inference. get_im_features: Extracts image features using the SAM image encoder. set_prompts: Sets prompts for subsequent inference. reset_image: Resets the current image and its features. remove_small_regions: Removes small disconnected regions and holes from masks. Examples: >>> predictor = Predictor() >>> predictor.setup_model(model_path="sam_model.pt") >>> predictor.set_image("image.jpg") >>> masks, scores, boxes = predictor.generate() >>> results = predictor.postprocess((masks, scores, boxes), im, orig_img) NcsR|dur i}|tdddt|||d|j_d|_d|_i|_d|_ dS)aP Initialize the Predictor with configuration, overrides, and callbacks. Sets up the Predictor object for SAM (Segment Anything Model) and applies any configuration overrides or callbacks provided. Initializes task-specific settings for SAM, such as retina_masks being set to True for optimal results. Args: cfg (Dict): Configuration dictionary containing default settings. overrides (Dict | None): Dictionary of values to override default configuration. _callbacks (Dict | None): Dictionary of callback functions to customize behavior. Examples: >>> predictor = Predictor(cfg=DEFAULT_CFG) >>> predictor = Predictor(overrides={"imgsz": 640}) >>> predictor = Predictor(_callbacks={"on_predict_start": custom_callback}) NsegmentZpredict)taskmodeTF) updatedictsuper__init__argsZ retina_masksimfeaturesprompts segment_all)selfcfgZ overrides _callbacks __class__Z/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/models/sam/predict.pyrNszPredictor.__init__cCs|jdur|jSt|tj }|r^t||}|ddddfd}t|}t |}| |j }|j j rz|n|}|r||j|j}|S)a Preprocess the input image for model inference. This method prepares the input image by applying transformations and normalization. It supports both torch.Tensor and list of np.ndarray as input formats. Args: im (torch.Tensor | List[np.ndarray]): Input image(s) in BCHW tensor format or list of HWC numpy arrays. Returns: (torch.Tensor): The preprocessed image tensor, normalized and converted to the appropriate dtype. Examples: >>> predictor = Predictor() >>> image = torch.rand(1, 3, 640, 640) >>> preprocessed_image = predictor.preprocess(image) N.)rr)r isinstancetorchZTensornpstack pre_transformZ transposeZascontiguousarrayZ from_numpytodevicemodelfp16Zhalffloatmeanstd)r rZ not_tensorr%r%r& preprocessjs    zPredictor.preprocesscs8t|dksJdt|jjdddfdd|DS)a9 Perform initial transformations on the input image for preprocessing. This method applies transformations such as resizing to prepare the image for further preprocessing. Currently, batched inference is not supported; hence the list length should be 1. Args: im (List[np.ndarray]): List containing a single image in HWC numpy array format. Returns: (List[np.ndarray]): List containing the transformed image. Raises: AssertionError: If the input list contains more than one image. Examples: >>> predictor = Predictor() >>> image = np.random.rand(480, 640, 3) # Single HWC image >>> transformed = predictor.pre_transform([image]) >>> print(len(transformed)) 1 rz6SAM model does not currently support batched inferenceF)autocentercsg|]}|dqS))imager%.0xZ letterboxr%r& z+Predictor.pre_transform..)lenrrimgszr rr%r=r&r.szPredictor.pre_transformFc Os||jd|}|jd|}|jd|}|jd|}tdd|||fDrh|j|g|Ri|S|||||||S)a Perform image segmentation inference based on the given input cues, using the currently loaded image. This method leverages SAM's (Segment Anything Model) architecture consisting of image encoder, prompt encoder, and mask decoder for real-time and promptable segmentation tasks. Args: im (torch.Tensor): The preprocessed input image in tensor format, with shape (N, C, H, W). bboxes (np.ndarray | List | None): Bounding boxes with shape (N, 4), in XYXY format. points (np.ndarray | List | None): Points indicating object locations with shape (N, 2), in pixels. labels (np.ndarray | List | None): Labels for point prompts, shape (N,). 1 = foreground, 0 = background. masks (np.ndarray | None): Low-resolution masks from previous predictions, shape (N, H, W). For SAM H=W=256. multimask_output (bool): Flag to return multiple masks. Helpful for ambiguous prompts. *args (Any): Additional positional arguments. **kwargs (Any): Additional keyword arguments. Returns: (tuple): Contains the following three elements: - np.ndarray: The output masks in shape (C, H, W), 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 (C, H, W) for subsequent inference, where H=W=256. Examples: >>> predictor = Predictor() >>> predictor.setup_model(model_path="sam_model.pt") >>> predictor.set_image("image.jpg") >>> masks, scores, logits = predictor.inference(im, bboxes=[[0, 0, 100, 100]]) bboxespointsmaskslabelscss|]}|duVqdSNr%r;ir%r%r& r?z&Predictor.inference..)rpopallgenerateprompt_inference) r rrCrDrFrEmultimask_outputrkwargsr%r%r& inferenceszPredictor.inferencecCs|jdur||n|j}|jddjdd|jdd}} |jrLdn t| d|d| d|d} |durtj|tj|j d}|j dkr|dn|}|durt |jd}tj|tj |j d}|| 9}|dddddf|dddf}}|dur>> predictor = Predictor() >>> im = torch.rand(1, 3, 1024, 1024) >>> bboxes = [[100, 100, 200, 200]] >>> masks, scores, logits = predictor.prompt_inference(im, bboxes=bboxes) Nrrr)?dtyper0rDboxesrE)image_embeddingsimage_pesparse_prompt_embeddingsdense_prompt_embeddingsrO)rget_im_featuresbatchshaperminr+ as_tensorfloat32r0ndimr,onesint32 unsqueezer1Zprompt_encoderZ mask_decoder get_dense_peflatten)r rrCrDrFrErOr src_shape dst_shapersparse_embeddingsdense_embeddings pred_masks pred_scoresr%r%r&rNs6(, (    zPredictor.prompt_inferencerg?r @)\(?ffffff?ffffff?c - Csddl} d|_|jdd\} }t| |f||\}}|durHt|||}ggggf\}}}}t||D]\}}|\}}}}||||}}tj|||jd}t ||gg}t j |d||||f| |fddd }|||} ggg}!}"}#t || D]\}$|j||$dd \}%}&t j |%d||fddd d}%|&|k}'|%|'|&|'}%}&t|%|jj| }(|(| k}'|%|'|&|'}%}&|%|jjk}%t|%})t|)|dd|| g}*t|*s|)|*|%|*|&|*})}%}&|!|%|#|)|"|&qt|!}!t|#}#t|"}"| j|#|"|jj}+t|#|+|}#t|!|+|| |}!|"|+}"||!||#||"||t|!qft|}t|}t|}t|}t|d krd |},| j||,| }+||+||+||+}}}|||fS) a Perform image segmentation using the Segment Anything Model (SAM). This method segments an entire image into constituent parts by leveraging SAM's advanced architecture and real-time performance capabilities. It can optionally work on image crops for finer segmentation. Args: im (torch.Tensor): Input tensor representing the preprocessed image with shape (N, C, H, W). crop_n_layers (int): Number of layers for additional mask predictions on image crops. crop_overlap_ratio (float): Overlap between crops, scaled down in subsequent layers. crop_downscale_factor (int): Scaling factor for sampled points-per-side in each layer. point_grids (List[np.ndarray] | None): Custom grids for point sampling normalized to [0,1]. points_stride (int): Number of points to sample along each side of the image. points_batch_size (int): Batch size for the number of points processed simultaneously. conf_thres (float): Confidence threshold [0,1] for filtering based on mask quality prediction. stability_score_thresh (float): Stability threshold [0,1] for mask filtering based on stability. stability_score_offset (float): Offset value for calculating stability score. crop_nms_thresh (float): IoU cutoff for NMS to remove duplicate masks between crops. Returns: (Tuple[torch.Tensor, torch.Tensor, torch.Tensor]): A tuple containing: - pred_masks (torch.Tensor): Segmented masks with shape (N, H, W). - pred_scores (torch.Tensor): Confidence scores for each mask with shape (N,). - pred_bboxes (torch.Tensor): Bounding boxes for each mask with shape (N, 4). Examples: >>> predictor = Predictor() >>> im = torch.rand(1, 3, 1024, 1024) # Example input image >>> masks, scores, boxes = predictor.generate(im) rNTr))r0.ZbilinearF)rZ align_corners)rDrOr) torchvisionrr]r r zipr+tensorr0r,arrayFZ interpolater rNr r1mask_thresholdr r3rrLappendcatrnmsrZiourrexpandr@)-r rZ crop_n_layersZcrop_overlap_ratioZcrop_downscale_factorZ point_gridsZ points_strideZpoints_batch_sizeZ conf_thresZstability_score_threshZstability_score_offsetZcrop_nms_threshrtZihiwZ crop_regionsZ layer_idxsrlrm pred_bboxesZ region_areasZ crop_regionZ layer_idxx1y1Zx2y2whZareaZ points_scaleZcrop_imZpoints_for_imageZ crop_masksZ crop_scoresZ crop_bboxesrDZ pred_maskZ pred_scoreidxZstability_scoreZ pred_bboxZ keep_maskkeepscoresr%r%r&rMsj,  (                 zPredictor.generateTcCst|jj|d}|dur |}||||_||_tgd ddd||_ tgd ddd||_ d|j_ d|j_ d|j_d|j_d |_dS) ah 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. Examples: >>> predictor = Predictor() >>> predictor.setup_model(model=sam_model, verbose=True) )verboseN)g33333^@gR]@gRY@r'r)g(\2M@g(\L@gL@FroT)rrr0 get_modelevalr/r1r+rvviewr4r5ptZtritonZstrider2Z done_warmup)r r1rr0r%r%r& setup_models   zPredictor.setup_modelcCs t|jjS)zRRetrieves or builds the Segment Anything Model (SAM) for image segmentation tasks.rrr1r r%r%r&rszPredictor.get_modelc CsV|dd\}}|jr|dnd}ttddtt|D}t|tsTt|}g}t |g||j dD]\} } } t| dkrd} ntj | d | j ddddd} | |jjk} |durtj|j dd| | j dd}nt| }tjt|tj|jd} tj||dddf| dddfgd d }|t| | || |d qld|_|S) a Post-processes SAM's inference outputs to generate object detection masks and bounding boxes. This method scales masks and boxes to the original image size and applies a threshold to the mask predictions. It leverages SAM's advanced architecture for real-time, promptable segmentation tasks. Args: preds (Tuple[torch.Tensor]): The output from SAM model inference, containing: - pred_masks (torch.Tensor): Predicted masks with shape (N, 1, H, W). - pred_scores (torch.Tensor): Confidence scores for each mask with shape (N, 1). - pred_bboxes (torch.Tensor, optional): Predicted bounding boxes if segment_all is True. img (torch.Tensor): The processed input image tensor with shape (C, H, W). orig_imgs (List[np.ndarray] | torch.Tensor): The original, unprocessed images. Returns: (List[Results]): List of Results objects containing detection masks, bounding boxes, and other metadata for each processed image. Examples: >>> predictor = Predictor() >>> preds = predictor.inference(img) >>> results = predictor.postprocess(preds, img, orig_imgs) Nr)css|]}t|VqdSrG)strrHr%r%r&rJr?z(Predictor.postprocess..rF)paddingrSr'dim)pathnamesrErV)rr enumerateranger@r*listrZconvert_torch2numpy_batchrur\Z scale_masksr3r]r1ryZ scale_boxesr r+Zarangercr0r{rzr) r predsZimgZ orig_imgsrlrmrrresultsrEZorig_imgZimg_pathclsr%r%r& postprocesss&   & $,zPredictor.postprocesscs|durt|dS)a Sets up the data source for inference. This method configures the data source from which images will be fetched for inference. It supports various input types such as image files, directories, video files, and other compatible data sources. Args: source (str | Path | None): The path or identifier for the image data source. Can be a file path, directory path, URL, or other supported source types. Examples: >>> predictor = Predictor() >>> predictor.setup_source("path/to/images") >>> predictor.setup_source("video.mp4") >>> predictor.setup_source(None) # Uses default source if available Notes: - If source is None, the method may use a default source if configured. - The method adapts to different source types and prepares them for subsequent inference steps. - Supported source types may include local files, directories, URLs, and video streams. N)r setup_source)r sourcer#r%r&rszPredictor.setup_sourcecCsd|jdur|jdd||t|jdks6Jd|jD]"}||d}|||_q`q>> predictor = Predictor() >>> predictor.set_image("path/to/image.jpg") >>> predictor.set_image(cv2.imread("path/to/image.jpg")) Notes: - This method should be called before performing inference on a new image. - The extracted features are stored in the `self.features` attribute for later use. Nr1r,`set_image` only supports setting one image!r1rrr@Zdatasetr6r[rr r9r\rr%r%r& set_images     zPredictor.set_imagecCsPt|jttfr$|jd|jdks6Jd|jd|j|j|j|S)z[Extracts image features using the SAM model's image encoder for subsequent mask prediction.rrz3SAM models only support square image size, but got .)r*rAtuplerr1 set_imgszZ image_encoderrBr%r%r&r[s zPredictor.get_im_featurescCs ||_dS)z1Sets prompts for subsequent inference operations.N)r)r rr%r%r& set_promptsszPredictor.set_promptscCsd|_d|_dS)zRResets the current image and its features, clearing them for subsequent inference.N)rrrr%r%r& reset_imageszPredictor.reset_imagec Csddl}t|dkr|Sg}g}|D]p}|tj}t||dd\}}| }t||dd\}}|on| }|t | d|t |q$t j |dd}t|} |j| t ||} || j|j|jd| fS)a Remove small disconnected regions and holes from segmentation masks. This function performs post-processing on segmentation masks generated by the Segment Anything Model (SAM). It removes small disconnected regions and holes from the input masks, and then performs Non-Maximum Suppression (NMS) to eliminate any newly created duplicate boxes. Args: masks (torch.Tensor): Segmentation masks to be processed, with shape (N, H, W) where N is the number of masks, H is height, and W is width. min_area (int): Minimum area threshold for removing disconnected regions and holes. Regions smaller than this will be removed. nms_thresh (float): IoU threshold for the NMS algorithm to remove duplicate boxes. Returns: (tuple): - new_masks (torch.Tensor): Processed masks with small regions removed, shape (N, H, W). - keep (List[int]): Indices of remaining masks after NMS, for filtering corresponding boxes. Examples: >>> masks = torch.rand(5, 640, 640) > 0.5 # 5 random binary masks >>> new_masks, keep = remove_small_regions(masks, min_area=100, nms_thresh=0.7) >>> print(f"Original masks: {masks.shape}, Processed masks: {new_masks.shape}") >>> print(f"Indices of kept masks: {keep}") rNZholes)rZislandsr)r0rT)rtr@cpunumpyZastyper,Zuint8rrzr+r_rdr3r{r rr|r/r0rT) rEZmin_areaZ nms_threshrtZ new_masksrmaskchangedZ unchangedrVrr%r%r&r"s"  zPredictor.remove_small_regions)NNNNF)NNNNF) rrnrNrorprqrrrrrs)T)rrs)__name__ __module__ __qualname____doc__rrr6r.rQrNrMrrrrrr[rr staticmethodr __classcell__r%r%r#r&r#s6*! ( E q 3 !rc@s:eZdZdZgdZddZddd Zd d Zd d ZdS) SAM2Predictora SAM2Predictor class for advanced image segmentation using Segment Anything Model 2 architecture. This class extends the base Predictor class to implement SAM2-specific functionality for image segmentation tasks. It provides methods for model initialization, feature extraction, and prompt-based inference. Attributes: _bb_feat_sizes (List[Tuple[int, int]]): Feature sizes for different backbone levels. model (torch.nn.Module): The loaded SAM2 model. device (torch.device): The device (CPU or GPU) on which the model is loaded. features (Dict[str, torch.Tensor]): Cached image features for efficient inference. segment_all (bool): Flag to indicate if all segments should be predicted. prompts (Dict): Dictionary to store various types of prompts for inference. Methods: get_model: Retrieves and initializes the SAM2 model. prompt_inference: Performs image segmentation inference based on various prompts. set_image: Preprocesses and sets a single image for inference. get_im_features: Extracts and processes image features using SAM2's image encoder. Examples: >>> predictor = SAM2Predictor(cfg) >>> predictor.set_image("path/to/image.jpg") >>> bboxes = [[100, 100, 200, 200]] >>> masks, scores, _ = predictor.prompt_inference(predictor.im, bboxes=bboxes) >>> print(f"Predicted {len(masks)} masks with average score {scores.mean():.2f}") ))r)r)rprpcCs t|jjS)z[Retrieves and initializes the Segment Anything Model 2 (SAM2) for image segmentation tasks.rrr%r%r&r|szSAM2Predictor.get_modelNFr'c sp|jdur||n|j}|jddjdd|jdd} } |jrLdn t| d| d| d| d} |durtj|tj|j d}|j dkr|dn|}|durt |jd}tj|tj |j d}|| 9}|dddf|dddf}}|durtj|tj|j d}|j dkr(|dn|}| ddd| }tjddggtj |j dt|d} |durtj||gdd }tj| |gdd }n || }}|durtj|tj|j dd}|dur||fnd}|jj|d|d \} }|duo|djddk}fd d |d D}|jj|dd|jj| ||||d\}}}}|dd|ddfS)a Performs image segmentation inference based on various prompts using SAM2 architecture. This method leverages the Segment Anything Model 2 (SAM2) to generate segmentation masks for input images based on provided prompts such as bounding boxes, points, or existing masks. It supports both single and multi-object prediction scenarios. Args: im (torch.Tensor): Preprocessed input image tensor with shape (N, C, H, W). bboxes (np.ndarray | List[List[float]] | None): Bounding boxes in XYXY format with shape (N, 4). points (np.ndarray | List[List[float]] | None): Object location points with shape (N, 2), in pixels. labels (np.ndarray | List[int] | None): Point prompt labels with shape (N,). 1 = foreground, 0 = background. masks (np.ndarray | None): Low-resolution masks from previous predictions with shape (N, H, W). multimask_output (bool): Flag to return multiple masks for ambiguous prompts. img_idx (int): Index of the image in the batch to process. Returns: (tuple): Tuple containing: - np.ndarray: Output masks with shape (C, H, W), where C is the number of generated masks. - np.ndarray: Quality scores for each mask, with length C. - np.ndarray: Low-resolution logits with shape (C, 256, 256) for subsequent inference. Examples: >>> predictor = SAM2Predictor(cfg) >>> image = torch.rand(1, 3, 640, 640) >>> bboxes = [[100, 100, 200, 200]] >>> masks, scores, logits = predictor.prompt_inference(image, bboxes=bboxes) >>> print(f"Generated {masks.shape[0]} masks with average score {scores.mean():.2f}") Notes: - The method supports batched inference for multiple objects when points or bboxes are provided. - Input prompts (bboxes, points) are automatically scaled to match the input image dimensions. - When both bboxes and points are provided, they are merged into a single 'points' input for the model. References: - SAM2 Paper: [Add link to SAM2 paper when available] Nrrr)rRrSr'r(rrUcsg|]}|dqS)r)rd)r;Z feat_levelimg_idxr%r&r>r?z2SAM2Predictor.prompt_inference..high_res_feats image_embed)rWrXrYrZrOZ repeat_imagehigh_res_features)rr[r\r]rr^r+r_r`r0rarbrcrrvr}r@r{rdr1Zsam_prompt_encoderZsam_mask_decoderrerf)r rrCrDrFrErOrrrgrhriZ bbox_labelsrjrkZ batched_moderrlrm_r%rr&rNsP/(," &      zSAM2Predictor.prompt_inferencecCsd|jdur|jdd||t|jdks6Jd|jD]"}||d}|||_q`q>> predictor = SAM2Predictor() >>> predictor.set_image("path/to/image.jpg") >>> predictor.set_image(np.array([...])) # Using a numpy array Notes: - This method must be called before performing any inference on a new image. - The method caches the extracted features for efficient subsequent inferences on the same image. - Only one image can be set at a time. To process multiple images, call this method for each new image. Nrrrrrr%r%r&rs     zSAM2Predictor.set_imagecstjttfr$jdjdks6JdjdjjfdddD_j|}j|\}}}}jj r|djj |d<d dt |d d djd d dDd d d}|d|d dd S) zMExtracts image features from the SAM image encoder for subsequent processing.rrz5SAM 2 models only support square image size, but got rcs g|]fddjDqS)csg|]}|dqS)r%r:rIr%r&r> r?z..)rA)r;rrr&r> r?z1SAM2Predictor.get_im_features..)rr)rr'cSs.g|]&\}}|dddjddg|RqS)rr)rr')Zpermuter)r;ZfeatZ feat_sizer%r%r&r>sN)rr) r*rArrr1r_bb_feat_sizesZ forward_imageZ_prepare_backbone_featuresZdirectly_add_no_mem_embedZ no_mem_embedru)r rZ backbone_outrZ vision_featsZfeatsr%rr&r[s$  zSAM2Predictor.get_im_features)NNNNFr') rrrrrrrNrr[r%r%r%r&rXs d r)!rrr,r+Ztorch.nn.functionalnnZ functionalrxZultralytics.data.augmentrZultralytics.engine.predictorrZultralytics.engine.resultsrZultralytics.utilsrrZultralytics.utils.torch_utilsrZamgr r r r r rrrrbuildrrrr%r%r%r&s      , 9