Python+OpenCV实现边缘检测与角点检测详解

import cv2 as cv
import matplotlib.pyplot as plt

# 读取图像
img = cv.imread('Lena.jpg')
lenna_img = cv.cvtColor(img, cv.COLOR_BGR2RGB)

# 图像转换成灰度图像
grayImage = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

'''
    Sobel算子
'''
x = cv.Sobel(grayImage, cv.CV_16S, 1, 0)     # 对x求一阶导数
y = cv.Sobel(grayImage, cv.CV_16S, 0, 1)    # 对y求一阶导数

absX = cv.convertScaleAbs(x)
absY = cv.convertScaleAbs(y)

Sobel = cv.addWeighted(absX, 0.5, absY, 0.5, 0)

'''
    Canny算子
'''

# 高斯滤波降噪
gaussian = cv.GaussianBlur(grayImage, (3, 3), 0)

# Canny算子
Canny = cv.Canny(gaussian, 50, 100)

# 显示正常中文的标签
plt.rcParams['font.sans-serif'] = ['SimHei']

titles = [u'(a)原始图像', u'(b)Sobel图像', u'(c)Canny图像']
images = [lenna_img, Sobel, Canny]

for i in range(3):
    plt.subplot(1, 3, i + 1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])
plt.show()

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

# 读取图像
img = cv.imread('Lena.jpg')
lenna_img = cv.cvtColor(img, cv.COLOR_BGR2RGB)

# 图像转换成灰度图像
grayImage = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
grayImage = np.float32(grayImage)

'''
    Harris算子
'''
harrisImage = cv.cornerHarris(grayImage, 2, 3, 0.04)

harrisImage = cv.dilate(harrisImage, None)

# 设置阈值
thresImage = 0.006 * harrisImage.max()

img[harrisImage > thresImage] = [255, 0, 0]

'''
    Susan算子
'''
def img_extraction(image):
    """
        img_extraction 函数利用susan角点检测算法，对图像进行处理
    """
    print("最小灰度值，%d" % image.min())
    print("最大灰度值，%d" % image.max())
    threshold_value = (int(image.max())-int(image.min())) / 10
    print("初始阈值为： %d" % threshold_value)

    offsetX = [
                -1, 0, 1,
            -2, -1, 0, 1, 2,
        -3, -2, -1, 0, 1, 2, 3,
        -3, -2, -1, 0, 1, 2, 3,
        -3, -2, -1, 0, 1, 2, 3,
            -2, -1, 0, 1, 2,
                -1, 0, 1
        ]
    offsetY = [
                -3, -3, -3,
            -2, -2, -2, -2, -2,
        -1, -1, -1, -1, -1, -1, -1,
             0, 0, 0, 0, 0, 0, 0,
             1, 1, 1, 1, 1, 1, 1,
                2, 2, 2, 2, 2,
                   3, 3, 3
        ]

    for i in range(3, image.shape[0] - 3):     # 利用圆形模板遍历图像，计算每点处的USAN值
        for j in range(3, image.shape[1] - 3):
            same = 0
            for k in range(0, 37):
                if abs(int(image[i + int(offsetY[k]), j + int(offsetX[k]), 0]) - int(image[i, j, 0])) < threshold_value:             # 计算相似度
                        same += 1

            if same < 18:
                image[i, j, 0] = 18 - same
                image[i, j, 1] = 18 - same
                image[i, j, 2] = 18 - same
            else:
                image[i, j, 0] = 0
                image[i, j, 1] = 0
                image[i, j, 2] = 0

    return image

def img_revise(image):
    """
        img_revise 函数用于对角点处理后的图像，进行非极大值抑制修正
    """

    X = [-1, -1, -1, 0, 0, 1, 1, 1]     # X轴偏移
    Y = [-1, 0, 1, -1, 1, -1, 0, 1]     # Y轴偏移
    for i in range(4, image.shape[0]-4):
        for j in range(4, image.shape[1]-4):
            flag = 0
            for k in range(0, 8):
                print(i)
                if image[i, j, 0] <= image[int(i + X[k]), int(j + Y[k]), 0]:
                    flag += 1
                    break

            if flag == 0:       # 判断是否是周围8个点中最大的值，是则保留
                image[i, j, 0] = 255
                image[i, j, 1] = 255
                image[i, j, 2] = 255
            else:
                image[i, j, 0] = 0
                image[i, j, 1] = 0
                image[i, j, 2] = 0

    return image

originImage = cv.imread('Lena.jpg')
originImage = cv.cvtColor(originImage, cv.COLOR_BGR2RGB)

# 图像转换成灰度图像
grayImage = np.float32(originImage)

# 调用susan角点检测算法
extraImage = img_extraction(originImage)

# 调用img_revise()非极大值抑制修正
susanImages = img_revise(extraImage)

# 显示正常中文的标签
plt.rcParams['font.sans-serif'] = ['SimHei']

titles = [u'(a)原始图像', u'(b)Harris图像', u'(c)Susan图像']
images = [lenna_img, img, susanImages]

for i in range(3):
    plt.subplot(1, 3, i + 1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])
plt.show()