v1

def apply_thresholding(
    image: np.ndarray, threshold_type: str, thresh_value: int, max_value: int
) -> np.ndarray:
    """
    Applies the specified thresholding to the image.

    Args:
        image (np.ndarray): Input image in grayscale.
        threshold_type (str): Type of thresholding ('binary', 'binary_inv', 'trunc', 'tozero', 'tozero_inv', 'adaptive_mean', 'adaptive_gaussian', 'otsu').
        thresh_value (int, optional): Threshold value.
        max_value (int, optional): Maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding types.

    Returns:
        np.ndarray: Image with thresholding applied.
    """
    if threshold_type == "binary":
        _, thresh_image = cv2.threshold(
            image, thresh_value, max_value, cv2.THRESH_BINARY
        )
    elif threshold_type == "binary_inv":
        _, thresh_image = cv2.threshold(
            image, thresh_value, max_value, cv2.THRESH_BINARY_INV
        )
    elif threshold_type == "trunc":
        _, thresh_image = cv2.threshold(
            image, thresh_value, max_value, cv2.THRESH_TRUNC
        )
    elif threshold_type == "tozero":
        _, thresh_image = cv2.threshold(
            image, thresh_value, max_value, cv2.THRESH_TOZERO
        )
    elif threshold_type == "tozero_inv":
        _, thresh_image = cv2.threshold(
            image, thresh_value, max_value, cv2.THRESH_TOZERO_INV
        )
    elif threshold_type == "adaptive_mean":
        thresh_image = cv2.adaptiveThreshold(
            image, max_value, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2
        )
    elif threshold_type == "adaptive_gaussian":
        thresh_image = cv2.adaptiveThreshold(
            image,
            max_value,
            cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
            cv2.THRESH_BINARY,
            11,
            2,
        )
    elif threshold_type == "otsu":
        _, thresh_image = cv2.threshold(
            image, 0, max_value, cv2.THRESH_BINARY + cv2.THRESH_OTSU
        )
    else:
        raise ValueError(f"Unknown threshold type: {threshold_type}")

    return thresh_image