【python-opencv】 sobel算子 图像边缘检测 图像二值化处理

文章目录一、rgb空间下的sobel边缘检测（1）代码（2）结果二、hsv-s空间下的加入图像腐蚀和图像膨胀的sobel边缘检测（1）代码（2）结果三、hsv-s空间下的加入图像腐蚀和图像膨胀，并进行孔洞填充后的sobel边缘检测（1）代码（2）结果 一、rgb空间下的sobel边缘检测 （1）代码 # -*- coding: utf-8 -*- import cv2 import matplotlib.pyplot as plt # 读取图像 # 此处注意cv2.imread读出来的是bgr格式要转为rgb img = cv2.imread('7.jpg') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 裁剪图片 img = cv2.resize(img, (0, 0), fx=0.2, fy=0.2, interpolation=cv2.INTER_NEAREST)[25:25 + 700, 210:210 + 760] plt.imshow(img) # 高斯模糊去噪 img = cv2.medianBlur(img, 7) # 灰度化处理图像 grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Sobel算子 x = cv2.Sobel(grayImage, cv2.CV_16S, 1, 0) # 对x求一阶导 y = cv2.Sobel(grayImage, cv2.CV_16S, 0, 1) # 对y求一阶导 absX = cv2.convertScaleAbs(x) absY = cv2.convertScaleAbs(y) Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0) # 显示图形 plt.imshow(Sobel, 'gray') plt.show() （2）结果 可以看到效果不是很好，所以接下来会在hsv空间下改进

二、hsv-s空间下的加入图像腐蚀和图像膨胀的sobel边缘检测 （1）代码 # -*- coding: utf-8 -*- import cv2 import numpy as np def sobel_cal(img): # 裁剪图片 img = cv2.resize(img, (0, 0), fx=0.2, fy=0.2, interpolation=cv2.INTER_NEAREST)[25:25 + 700, 210:210 + 760] # plt.imshow(img) # 高斯模糊去噪 # img = cv2.medianBlur(img, 7) hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) H, S, V = cv2.split(hsv) lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) lab_l = lab[..., 0] lab_a = lab[..., 1] lab_b = lab[..., 2] # cv2.imshow("image1", lab_b) # Sobel算子 x = cv2.Sobel(S, cv2.CV_16S, 1, 0) # 对x求一阶导 y = cv2.Sobel(S, cv2.CV_16S, 0, 1) # 对y求一阶导 absX = cv2.convertScaleAbs(x) absY = cv2.convertScaleAbs(y) sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0) ## b.设置卷积核5*5s kernel = np.ones((1, 1), np.uint8) ## 图像的膨胀 dst = cv2.dilate(sobel, kernel) ## c.图像的腐蚀，默认迭代次数 erosion = cv2.erode(dst, kernel) out = erosion # out = fillHole(dst) return img, out if __name__ == '__main__': # 读取图像 # 此处注意cv2.imread读出来的是bgr格式要转为rgb img = cv2.imread('7.jpg') img, out = sobel_cal(img) # 显示图形 cv2.imshow('out', out) cv2.waitKey() （2）结果 可以看到很好的将边缘都检测出来了，效果很明显，纹理也都很清晰
三、hsv-s空间下的加入图像腐蚀和图像膨胀，并进行孔洞填充后的sobel边缘检测 （1）代码 # -*- coding: utf-8 -*- import cv2 import numpy as np def fillHole(im_in): # Threshold. # Set values equal to or above 220 to 0. # Set values below 220 to 255. th, im_th = cv2.threshold(im_in, 40, 255, cv2.THRESH_BINARY) # Copy the thresholded image. im_floodfill = im_th.copy() # print(im_th) # Mask used to flood filling. # Notice the size needs to be 2 pixels than the image. h, w = im_th.shape[:2] mask = np.zeros((h + 2, w + 2), np.uint8) # Floodfill from point (0, 0) cv2.floodFill(im_floodfill, mask, (0, 0), 255) # Invert floodfilled image im_floodfill_inv = cv2.bitwise_not(im_floodfill) # Combine the two images to get the foreground. im_out = im_th | im_floodfill_inv return im_out def baweraopen(image, size): ''' @image:单通道二值图，数据类型uint8 @size:欲去除区域大小(黑底上的白区域) ''' output = image.copy() nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(image) for i in range(1, nlabels - 1): regions_size = stats[i, 4] if regions_size < size: x0 = stats[i, 0] y0 = stats[i, 1] x1 = stats[i, 0] + stats[i, 2] y1 = stats[i, 1] + stats[i, 3] for row in range(y0, y1): for col in range(x0, x1): if labels[row, col] == i: output[row, col] = 0 return output def sobel_cal(img): # 裁剪图片 img = cv2.resize(img, (0, 0), fx=0.2, fy=0.2, interpolation=cv2.INTER_NEAREST)[25:25 + 700, 210:210 + 760] # plt.imshow(img) # 高斯模糊去噪 # img = cv2.medianBlur(img, 7) hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) H, S, V = cv2.split(hsv) lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) lab_l = lab[..., 0] lab_a = lab[..., 1] lab_b = lab[..., 2] # cv2.imshow("image1", lab_b) color_space = S # Sobel算子 x = cv2.Sobel(color_space, cv2.CV_16S, 1, 0) # 对x求一阶导 y = cv2.Sobel(color_space, cv2.CV_16S, 0, 1) # 对y求一阶导 absX = cv2.convertScaleAbs(x) absY = cv2.convertScaleAbs(y) sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0) # b.设置卷积核5*5 kernel = np.ones((2, 2), np.uint8) # 腐蚀的作用说白了就是让暗的区域变大，而膨胀的作用就是让亮的区域变大 # 图像的膨胀 dst = cv2.dilate(sobel, kernel) # 空洞填充 out = fillHole(dst) # c.图像的腐蚀，默认迭代次数 erosion = cv2.erode(out, kernel) # 图像的膨胀 dst = cv2.dilate(erosion, kernel) # 去除小的斑点 out = baweraopen(dst, 300) return img, out if __name__ == '__main__': # 读取图像 # 此处注意cv2.imread读出来的是bgr格式要转为rgb img = cv2.imread('7.jpg') # 高斯模糊去噪 img = cv2.medianBlur(img, 7) img, out = sobel_cal(img) # 显示图形 # cv2.imwrite('10.jpg', img) cv2.imshow('out', out) cv2.waitKey() （2）结果 可以发现，进行孔洞填充和不进行孔洞填充，对图片进行二值化处理后的效果差距明显。

作者：miracleo_

图像二值化 边缘检测 sobel 二值化 opencv Python