用小波变换优化一下canny算法编写python代码

好的，关于用小波变换优化Canny算法的Python代码，以下是我给您提供的代码：

import cv2
import pywt
import numpy as np


def canny_edge_detection(img, sigma=0.33):
    # 小波变换进行图像去噪
    coeffs = pywt.dwtn(img, 'haar')
    cA = coeffs['aaa']
    cH = coeffs['aha']
    cV = coeffs['ava']
    cD = coeffs['ada']
    threshold = sigma * np.mean(cA.ravel())
    cA = pywt.threshold(cA, threshold, mode='soft')
    cH = pywt.threshold(cH, threshold, mode='soft')
    cV = pywt.threshold(cV, threshold, mode='soft')
    cD = pywt.threshold(cD, threshold, mode='soft')
    coeffs['aaa'] = cA
    coeffs['aha'] = cH
    coeffs['ava'] = cV
    coeffs['ada'] = cD
    denoised_img = pywt.idwtn(coeffs, 'haar')

    # Canny算法进行边缘检测
    v = np.median(denoised_img)
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))
    edges = cv2.Canny(denoised_img, lower, upper)

    return edges

您可以将需要进行边缘检测的图像传入该函数，函数会返回一个经过小波变换去噪和Canny算法边缘检测后的图像。该代码中sigma参数控制Canny算法中阈值的上下限，最终的阈值值为图像的中位数加上/减去sigma倍的图像中位数差。明白了吗？