def crop_dataset_by_bounding_boxes_and_save(
    dataset: LightlyDataset,
    output_dir: str,
    bounding_boxes_list_list: List[List[BoundingBox]],
    class_indices_list_list: List[List[int]],
    class_names: List[str] = None,
) -> List[List[str]]:
    """Crops all images in a dataset by the bounding boxes and saves them in the output dir

    Args:
        dataset:
            The dataset with the images to be cropped. Must contain M images.
        output_dir:
            The output directory to saved the cropped images to.
        bounding_boxes_list_list:
            The bounding boxes of the detections for each image. Must have M sublists, one for each image.
            Each sublist contains the bounding boxes for each detection, thus N_m elements.
        class_indices_list_list:
            The object class ids of the detections for each image. Must have M sublists, one for each image.
            Each sublist contains the bounding boxes for each detection, thus N_m elements.
        class_names:
            The names of the classes, used to map the class id to the class name.


    Returns:
        The filepaths to all saved cropped images. Has M sublists, one for each image.
        Each sublist contains the filepath of the crop each detection, thus N_m elements.

    """
    filenames_images = dataset.get_filenames()
    if len(filenames_images) != len(bounding_boxes_list_list) or len(
        filenames_images
    ) != len(class_indices_list_list):
        raise ValueError(
            "There must be one bounding box and class index list for each image in the datasets,"
            "but the lengths dont align."
        )

    cropped_image_filepath_list_list: List[List[str]] = []

    print(f"Cropping objects out of {len(filenames_images)} images...")
    for filename_image, class_indices, bounding_boxes in tqdm(
        zip(filenames_images, class_indices_list_list, bounding_boxes_list_list)
    ):
        if not len(class_indices) == len(bounding_boxes):
            warnings.warn(
                UserWarning(
                    f"Length of class indices ({len(class_indices)} does not equal length of bounding boxes"
                    f"({len(bounding_boxes)}. This is an error in the input arguments. "
                    f"Skipping this image {filename_image}."
                )
            )
            continue

        filepath_image = dataset.get_filepath_from_filename(filename_image)
        filepath_image_base, image_extension = os.path.splitext(filepath_image)

        filepath_out_dir = os.path.join(output_dir, filename_image).replace(
            image_extension, ""
        )
        Path(filepath_out_dir).mkdir(parents=True, exist_ok=True)

        image = Image.open(filepath_image)

        cropped_images_filepaths = []
        # For every image, crop out multiple cropped images, one for each
        # bounding box
        for index, (class_index, bbox) in enumerate(
            (zip(class_indices, bounding_boxes))
        ):
            # determine the filename and filepath of the cropped image
            if class_names:
                class_name = class_names[class_index]
            else:
                class_name = f"class{class_index}"
            cropped_image_last_filename = f"{index}_{class_name}{image_extension}"
            cropped_image_filepath = os.path.join(
                filepath_out_dir, cropped_image_last_filename
            )

            # crop out the image and save it
            w, h = image.size
            crop_box = (w * bbox.x0, h * bbox.y0, w * bbox.x1, h * bbox.y1)
            crop_box = tuple(int(i) for i in crop_box)
            cropped_image = image.crop(crop_box)
            cropped_image.save(cropped_image_filepath)

            # add the filename of the cropped image to the corresponding list
            cropped_image_filename: str = os.path.join(
                filename_image.replace(image_extension, ""), cropped_image_last_filename
            )
            cropped_images_filepaths.append(cropped_image_filename)

        cropped_image_filepath_list_list.append(cropped_images_filepaths)

    return cropped_image_filepath_list_list
Crops all images in a dataset by the bounding boxes and saves them in the output dir

    Args:
        dataset:
            The dataset with the images to be cropped. Must contain M images.
        output_dir:
            The output directory to saved the cropped images to.
        bounding_boxes_list_list:
            The bounding boxes of the detections for each image. Must have M sublists, one for each image.
            Each sublist contains the bounding boxes for each detection, thus N_m elements.
        class_indices_list_list:
            The object class ids of the detections for each image. Must have M sublists, one for each image.
            Each sublist contains the bounding boxes for each detection, thus N_m elements.
        class_names:
            The names of the classes, used to map the class id to the class name.


    Returns:
        The filepaths to all saved cropped images. Has M sublists, one for each image.
        Each sublist contains the filepath of the crop each detection, thus N_m elements.

    
lightly/utils/cropping/crop_image_by_bounding_boxes.py