|h VddlZddlmZddlmZddlmZmZddlm Z GddeZ y)N)Path)Any) BaseSolutionSolutionResults) save_one_boxc8eZdZdZdeddffd ZdefdZxZS) ObjectCroppera 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 t|di||jd|_tj j |jst j|j|jdr(|jjd|jd|_ |jd|_ |jd|_ y) a3 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__CFGr ospathexistsmkdirLOGGERwarningcrop_idxrr)selfr __class__s c/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/solutions/object_cropper.pyrzObjectCropper.__init__"s "6", ww~~dmm, HHT]] # 88F  KK  fgkgtgtfuv  88E?HHV$ c |jd5|jj||j|j|j |j ddd}dddjD]T}|xjdz c_t|j|t|jd|jdz d Vt||j S#1swYxYw) aE Crop detected objects from the input image and save them as separate images. Args: im0 (numpy.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"rrrboxesrrxyxyrr r)rim0resultsboxs rprocesszObjectCropper.process7s"^^A  jj(( YYHHxx) )G == C MMQ M $--(U4==/+FF   st}}MM)  s AC""C+) __name__ __module__ __qualname____doc__rrrr2 __classcell__)rs@rr r s),%%%*%No%Nrr ) rpathlibrtypingrultralytics.solutions.solutionsrrultralytics.utils.plottingrr rrrr<s& I3QNLQNr