# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

import os
from pathlib import Path
from typing import Any

from ultralytics.solutions.solutions import BaseSolution, SolutionResults
from ultralytics.utils.plotting import save_one_box


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.
                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)
