canny算子python代码实现

Canny边缘检测是一种广泛使用的图像处理技术，用于识别图像中的边缘。它包含两个步骤：高斯滤波和平滑后的梯度计算，然后通过非极大值抑制（Non-Maximum Suppression）以及双阈值（Double Thresholding）确定边缘。以下是Python中使用OpenCV库实现Canny边缘检测的基本代码：

import cv2
import numpy as np

def canny(image_path):
    # 读取图片
    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)

    # 高斯滤波
    blurred = cv2.GaussianBlur(image, (5, 5), 0)

    # 计算梯度X和Y
    sobelx = cv2.Sobel(blurred, cv2.CV_64F, 1, 0, ksize=3)
    sobely = cv2.Sobel(blurred, cv2.CV_64F, 0, 1, ksize=3)

    # 计算梯度幅值和方向
    abs_sobelxy = np.sqrt(np.power(sobelx, 2) + np.power(sobely, 2))
    gradient_angle = np.arctan2(sobely, sobelx)

    # 非极大值抑制
    aperture_size = 3
    dir_threshold = np.pi / 6
    abs_grad_dir = np.copy(abs_sobelxy)
    abs_grad_dir[abs_grad_dir < dir_threshold] = 0
    abs_grad_dir[(abs_grad_dir >= dir_threshold) & (gradient_angle > aperture_size)] = 2 * dir_threshold
    abs_grad_dir[(abs_grad_dir >= dir_threshold) & (gradient_angle <= -aperture_size)] = 2 * dir_threshold - np.pi

    # 双阈值
    low_threshold = 50
    high_threshold = 150
    edges = np.zeros_like(abs_grad_dir)
    edges[(abs_grad_dir >= low_threshold) & (abs_grad_dir < high_threshold)] = 255

    return edges

# 使用函数并显示结果
image_path = 'your_image.jpg'  # 替换为你要处理的图片路径
edges = canny(image_path)
cv2.imshow("Canny Edges", edges)
cv2.waitKey(0)
cv2.destroyAllWindows()