hVSSKrSSKJr SSKJr SSKJrJr SSKJ r "SS\5r g)N)Path)Any) BaseSolutionSolutionResults) save_one_boxcF^\rSrSrSrS\SS4U4SjjrS\4SjrSr U=r $) ObjectCropper a A class to manage the cropping of detected objects in a real-time video stream or images. This class extends the BaseSolution class and provides functionality for cropping objects based on detected bounding boxes. The cropped images are saved to a specified directory for further analysis or usage. Attributes: crop_dir (str): Directory where cropped object images are stored. crop_idx (int): Counter for the total number of cropped objects. iou (float): IoU (Intersection over Union) threshold for non-maximum suppression. conf (float): Confidence threshold for filtering detections. Methods: process: Crop detected objects from the input image and save them to the output directory. Examples: >>> cropper = ObjectCropper() >>> frame = cv2.imread("frame.jpg") >>> processed_results = cropper.process(frame) >>> print(f"Total cropped objects: {cropper.crop_idx}") kwargsreturnNc >[TU]"S0UD6 URSUl[R R UR5(d [R"UR5 URS(a(URRSUR35 SUl URSUl URSUl g) a  Initialize the ObjectCropper class for cropping objects from detected bounding boxes. Args: **kwargs (Any): Keyword arguments passed to the parent class and used for configuration. crop_dir (str): Path to the directory for saving cropped object images. crop_dirshowzTshow=True disabled for crop solution, results will be saved in the directory named: riouconfN) super__init__CFGrospathexistsmkdirLOGGERwarningcrop_idxrr)selfr __class__s ^/home/james-whalen/.local/lib/python3.13/site-packages/ultralytics/solutions/object_cropper.pyrObjectCropper.__init__"s "6", ww~~dmm,, HHT]] # 88F  KK  fgkgtgtfuv  88E?HHV$ c (URS URRUURURUR UR SSS9SnURRR5Ul SSS5 WRHRnU=RS- sl [URU[UR5SURS3- S S 9 MT [!XRS 9$!,(df  N=f) a Crop detected objects from the input image and save them as separate images. Args: im0 (np.ndarray): The input image containing detected objects. Returns: (SolutionResults): A SolutionResults object containing the total number of cropped objects and processed image. Examples: >>> cropper = ObjectCropper() >>> frame = cv2.imread("image.jpg") >>> results = cropper.process(frame) >>> print(f"Total cropped objects: {results.total_crop_objects}") rdeviceF)classesrrr#verboseNcrop_z.jpgT)fileBGR)plot_imtotal_crop_objects) profilersmodelpredictr$rrrboxesclstolistclssrrxyxyrrr)rim0resultsboxs rprocessObjectCropper.process7s"^^A jj(( YYHHxx) )G ))002DI==C MMQ M $--(U4==/+FF  !s}}MM+ s A6D D)r2rrrr) __name__ __module__ __qualname____firstlineno____doc__rrrr7__static_attributes__ __classcell__)rs@rr r s/,%%%*&No&N&Nr!r ) rpathlibrtypingrultralytics.solutions.solutionsrrultralytics.utils.plottingrr rr!rrDs& I3RNLRNr!