Reference for ultralytics/solutions/object_cropper.py

class ObjectCropper(BaseSolution):
    """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}")
    """

    def __init__(self, **kwargs: Any) -> None:
        """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 including:
                - crop_dir (str): Path to the directory for saving cropped object images.
        """
        super().__init__(**kwargs)

        self.crop_dir = self.CFG["crop_dir"]  # Directory for storing cropped detections
        if not os.path.exists(self.crop_dir):
            os.mkdir(self.crop_dir)  # Create directory if it does not exist
        if self.CFG["show"]:
            self.LOGGER.warning(
                f"show=True disabled for crop solution, results will be saved in the directory named: {self.crop_dir}"
            )
        self.crop_idx = 0  # Initialize counter for total cropped objects
        self.iou = self.CFG["iou"]
        self.conf = self.CFG["conf"]

def process(self, im0) -> SolutionResults:
    """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}")
    """
    with self.profilers[0]:
        results = self.model.predict(
            im0,
            classes=self.classes,
            conf=self.conf,
            iou=self.iou,
            device=self.CFG["device"],
            verbose=False,
        )[0]
        self.clss = results.boxes.cls.tolist()  # required for logging only.

    for box in results.boxes:
        self.crop_idx += 1
        save_one_box(
            box.xyxy,
            im0,
            file=Path(self.crop_dir) / f"crop_{self.crop_idx}.jpg",
            BGR=True,
        )

    # Return SolutionResults
    return SolutionResults(plot_im=im0, total_crop_objects=self.crop_idx)