import cv2
import numpy as np

class PREPROCESSING_MODE:
    CANNY = "CANNY"
    THRES = "THRES"

class Preprocessing_parameters:
    def __init__(self, low_threshold: int=130,
                 high_threshold: int=150, 
                 kernel_size: int=19, 
                 threshold: int=3,
                 gaussian_kernel: tuple[int, int]=(3, 3)
        ) -> None:
        self.canny_low_threshold = low_threshold
        self.canny_high_threshold = high_threshold
        self.thres_kernel_size = kernel_size
        self.thres_threshold = threshold
        self.gaussian_kernel = gaussian_kernel

def canny_edge_dection(frame: np.ndarray, parameters: Preprocessing_parameters) -> np.ndarray:
    frame_proc = cv2.GaussianBlur(frame, parameters.gaussian_kernel, 0)
    return cv2.Canny(
        frame_proc, 
        parameters.canny_low_threshold, parameters.canny_high_threshold
    )

def thresholding_edge_detection(frame: np.ndarray, parameters: Preprocessing_parameters) -> np.ndarray:
    frame_proc = cv2.GaussianBlur(frame, parameters.gaussian_kernel, 0)
    return cv2.adaptiveThreshold(
        frame_proc, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 
        parameters.thres_kernel_size, parameters.thres_threshold                            
    )

def edge_dection(frame: np.ndarray, mode: PREPROCESSING_MODE, param: Preprocessing_parameters) -> np.ndarray:    
    if mode == PREPROCESSING_MODE.CANNY:
        return canny_edge_dection(frame, param)
    elif mode == PREPROCESSING_MODE.THRES:
        return thresholding_edge_detection(frame, param)
    else:
        raise ValueError("Invalid mode")