Fi NddlmZddlmZddlmZmZddlmZGddeZ y))Path)Any) BaseSolutionSolutionResults) save_one_boxc8eZdZdZdeddffd ZdefdZxZS) ObjectCropperaA 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|d i||jd|_t |jj dd|jdr8|j jd|jdd|jd<d|_|jd |_ |jd |_ y ) a6Initialize the ObjectCropper class for cropping objects from detected bounding boxes. Args: **kwargs (Any): Keyword arguments passed to the parent class and used for configuration including: - crop_dir (str): Path to the directory for saving cropped object images. crop_dirT)parentsexist_okshowz?show=True is not supported for ObjectCropper; saving crops to 'z'.FriouconfN) super__init__CFGr rmkdirLOGGERwarningcrop_idxrr)selfr __class__s f/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/solutions/object_cropper.pyrzObjectCropper.__init__ s "6",  T]]!!$!> 88F  KK  "abfboboappr s t$DHHV  88E?HHV$ c .|jd5|jj||j|j|j |j ddd}|jjj|_ dddjD]T}|xjdz c_ t|j|t|jd|jdz d Vt!||j S#1swYxYw) a9Crop 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!rrrboxesclstolistclssrrxyxyrr r)rim0resultsboxs rprocesszObjectCropper.process2s ^^A  3jj(( YYHHxx) )G ))002DI 3== C MMQ M $--(U4==/+FF   st}}MM+ 3 3s A9D  D) __name__ __module__ __qualname____doc__rrrr4 __classcell__)rs@rr r s)*%%%$%No%Nrr N) pathlibrtypingrultralytics.solutions.solutionsrrultralytics.utils.plottingrr rrrr>s#I3MNLMNr