一、Canny边缘提取步骤

文中用python实现canny算子，Canny算子的步骤为：

• 1）图像灰度预处理
• 2）对每个像素求梯度
• 3）求每个点处最大梯度的编码
• 4）非极大值抑制，保证梯度编码的唯一性。
• 5）通过阈值，将边缘像素抽取出来；

二、代码

        通过下列代码学习，可以了解canny算子全过程；并按自己的理解进行修改学习： 

# !/usr/bin/env python
# -*- coding: utf-8 -*-

import cv2
import matplotlib.pyplot as plt
import numpy as np
import math

# img = plt.imread('d:\\women.png')
img = plt.imread('../image/1104C.jpg')

sigma1 = sigma2 = 1
sum = 0

gaussian = np.zeros([5, 5])
for i in range(5):
    for j in range(5):
        gaussian[i, j] = math.exp(-1 / 2 * (np.square(i - 3) / np.square(sigma1)  # 生成二维高斯分布矩阵
                                            + (np.square(j - 3) / np.square(sigma2)))) / (2 * math.pi * sigma1 * sigma2)
        sum = sum + gaussian[i, j]

gaussian = gaussian / sum


# print(gaussian)

def rgb2gray(rgb):
    return np.dot(rgb[..., :3], [0.299, 0.587, 0.114])


# step1.高斯滤波
gray = rgb2gray(img)
W, H = gray.shape
# new_gray = np.zeros([W - 5, H - 5])
# for i in range(W - 5):
#     for j in range(H - 5):
#         new_gray[i, j] = np.sum(gray[i:i + 5, j:j + 5] * gaussian)  # 与高斯矩阵卷积实现滤波

new_gray = cv2.GaussianBlur(gray, (5, 5), 0)


# step2.增强 通过求梯度幅值
W1, H1 = new_gray.shape
dx = np.zeros([W1 - 1, H1 - 1])
dy = np.zeros([W1 - 1, H1 - 1])
d = np.zeros([W1 - 1, H1 - 1])
for i in range(W1 - 1):
    for j in range(H1 - 1):
        dx[i, j] = new_gray[i, j + 1] - new_gray[i, j]
        dy[i, j] = new_gray[i + 1, j] - new_gray[i, j]
        d[i, j]  = np.sqrt(np.square(dx[i, j]) + np.square(dy[i, j]))  # 图像梯度幅值作为图像强度值

# plt.imshow(d, cmap="gray")
# setp3.非极大值抑制 NMS

W2, H2 = d.shape
NMS = np.copy(d)
NMS[0, :] = NMS[W2 - 1, :] = NMS[:, 0] = NMS[:, H2 - 1] = 0
for i in range(1, W2 - 1):
    for j in range(1, H2 - 1):

        if d[i, j] == 0:
            NMS[i, j] = 0
        else:
            gradX = dx[i, j]
            gradY = dy[i, j]
            gradTemp = d[i, j]

            # 如果Y方向幅度值较大
            if np.abs(gradY) > np.abs(gradX):
                weight = np.abs(gradX) / np.abs(gradY)
                grad2 = d[i - 1, j]
                grad4 = d[i + 1, j]
                # 如果x,y方向梯度符号相同
                if gradX * gradY > 0:
                    grad1 = d[i - 1, j - 1]
                    grad3 = d[i + 1, j + 1]
                # 如果x,y方向梯度符号相反
                else:
                    grad1 = d[i - 1, j + 1]
                    grad3 = d[i + 1, j - 1]

            # 如果X方向幅度值较大
            else:
                weight = np.abs(gradY) / np.abs(gradX)
                grad2 = d[i, j - 1]
                grad4 = d[i, j + 1]
                # 如果x,y方向梯度符号相同
                if gradX * gradY > 0:
                    grad1 = d[i + 1, j - 1]
                    grad3 = d[i - 1, j + 1]
                # 如果x,y方向梯度符号相反
                else:
                    grad1 = d[i - 1, j - 1]
                    grad3 = d[i + 1, j + 1]

            gradTemp1 = weight * grad1 + (1 - weight) * grad2
            gradTemp2 = weight * grad3 + (1 - weight) * grad4
            if gradTemp >= gradTemp1 and gradTemp >= gradTemp2:
                NMS[i, j] = gradTemp
            else:
                NMS[i, j] = 0

# plt.imshow(NMS, cmap = "gray")


# step4. 双阈值算法检测、连接边缘
W3, H3 = NMS.shape
DT = np.zeros([W3, H3])
# 定义高低阈值
TL = 0.31 * np.max(NMS)
TH = 0.4 * np.max(NMS)

for i in range(1, W3 - 1):
    for j in range(1, H3 - 1):
        if (NMS[i, j] < TL):
            DT[i, j] = 0
        elif (NMS[i, j] > TH):
            DT[i, j] = 255
        elif ((NMS[i - 1, j - 1:j + 1] < TH).any() or (NMS[i + 1, j - 1:j + 1]).any()
              or (NMS[i, [j - 1, j + 1]] < TH).any()):
            DT[i, j] = 255
# newDT = DT.astype(np.uint8)

cv2.imshow( "gray",DT)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imwrite("parts4.jpg",DT)

三、结果展示