FPhcZdZddlmZddlmZddlmZddlmZddl m Z Gdd eZ y ) a> SAM model interface. This module provides an interface to the Segment Anything Model (SAM) from Ultralytics, designed for real-time image segmentation tasks. The SAM model allows for promptable segmentation with unparalleled versatility in image analysis, and has been trained on the SA-1B dataset. It features zero-shot performance capabilities, enabling it to adapt to new image distributions and tasks without prior knowledge. Key Features: - Promptable segmentation - Real-time performance - Zero-shot transfer capabilities - Trained on SA-1B dataset )Path)Model) model_info) build_sam Predictorc`eZdZdZd d fd Zd defdZd fd Zd dZddZ e dZ xZ S)SAMa/ SAM (Segment Anything Model) interface class. SAM is designed for promptable real-time image segmentation. It can be used with a variety of prompts such as bounding boxes, points, or labels. The model has capabilities for zero-shot performance and is trained on the SA-1B dataset. cp|r"t|jdvr tdt||dy)a& Initializes the SAM model with a pre-trained model file. Args: model (str): Path to the pre-trained SAM model file. File should have a .pt or .pth extension. Raises: NotImplementedError: If the model file extension is not .pt or .pth. )z.ptz.pthz8SAM prediction requires pre-trained *.pt or *.pth model.segment)modeltaskN)rsuffixNotImplementedErrorsuper__init__)selfr __class__s gC:\Users\daisl\Desktop\realtime-object-detection\venv\Lib\site-packages\ultralytics/models/sam/model.pyrz SAM.__init__#s7 T%[''>%&`a a u95weightsc$t||_y)z Loads the specified weights into the SAM model. Args: weights (str): Path to the weights file. task (str, optional): Task name. Defaults to None. N)rr)rrrs r_loadz SAM._load1sw' rc tdddd}|j|t|||}t | ||fd|i|S)a Performs segmentation prediction on the given image or video source. Args: source (str): Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object. stream (bool, optional): If True, enables real-time streaming. Defaults to False. bboxes (list, optional): List of bounding box coordinates for prompted segmentation. Defaults to None. points (list, optional): List of points for prompted segmentation. Defaults to None. labels (list, optional): List of labels for prompted segmentation. Defaults to None. Returns: (list): The model predictions. g?r predicti)confrmodeimgsz)bboxespointslabelsprompts)dictupdaterr) rsourcestreamr r!r"kwargs overridesr#rs rrz SAM.predict;sJd$O  i fVFCwvvIwI&IIrc 0|j|||||fi|S)a} Alias for the 'predict' method. Args: source (str): Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object. stream (bool, optional): If True, enables real-time streaming. Defaults to False. bboxes (list, optional): List of bounding box coordinates for prompted segmentation. Defaults to None. points (list, optional): List of points for prompted segmentation. Defaults to None. labels (list, optional): List of labels for prompted segmentation. Defaults to None. Returns: (list): The model predictions. )r)rr&r'r r!r"r(s r__call__z SAM.__call__Ns"t||FFFFFMfMMrc2t|j||S)aq Logs information about the SAM model. Args: detailed (bool, optional): If True, displays detailed information about the model. Defaults to False. verbose (bool, optional): If True, displays information on the console. Defaults to True. Returns: (tuple): A tuple containing the model's information. )detailedverbose)rr)rr-r.s rinfozSAM.info^s$**xIIrcddtiiS)z Provides a mapping from the 'segment' task to its corresponding 'Predictor'. Returns: (dict): A dictionary mapping the 'segment' task to its corresponding 'Predictor'. r predictorr)rs rtask_mapz SAM.task_mapksK344r)zsam_b.pt)returnN)N)FNNN)NFNNN)FT) __name__ __module__ __qualname____doc__rstrrrr+r/propertyr2 __classcell__)rs@rr r s? 6(S(J&N J55rr N) r7pathlibrultralytics.engine.modelrultralytics.utils.torch_utilsrbuildrrr r rrr@s) *4Y5%Y5r