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‎blog17-sobel/Lena.png‎

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‎blog17-sobel/blog17-image02.py‎

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# -*- coding: utf-8 -*-
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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#读取图像
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img = cv2.imread('lena.png')
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lenna_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
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#灰度化处理图像
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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#Prewitt算子
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kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]],dtype=int)
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kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]],dtype=int)
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x = cv2.filter2D(grayImage, cv2.CV_16S, kernelx)
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y = cv2.filter2D(grayImage, cv2.CV_16S, kernely)
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#转uint8
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Prewitt = cv2.addWeighted(absX,0.5,absY,0.5,0)
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#用来正常显示中文标签
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plt.rcParams['font.sans-serif']=['SimHei']
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#显示图形
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titles = [u'原始图像', u'Prewitt算子']
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images = [lenna_img, Prewitt]
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for i in xrange(2):
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plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')
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plt.title(titles[i])
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plt.xticks([]),plt.yticks([])
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plt.show()

‎blog17-sobel/blog17-image03.py‎

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# -*- coding: utf-8 -*-
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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#读取图像
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img = cv2.imread('lena.png')
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lenna_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
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#灰度化处理图像
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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#Sobel算子
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x = cv2.Sobel(grayImage, cv2.CV_16S, 1, 0) #对x求一阶导
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y = cv2.Sobel(grayImage, cv2.CV_16S, 0, 1) #对y求一阶导
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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#用来正常显示中文标签
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plt.rcParams['font.sans-serif']=['SimHei']
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#显示图形
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titles = [u'原始图像', u'Sobel算子']
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images = [lenna_img, Sobel]
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for i in xrange(2):
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plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')
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plt.title(titles[i])
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plt.xticks([]),plt.yticks([])
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plt.show()

‎blog17-sobel/blog17-image04.py‎

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# -*- coding: utf-8 -*-
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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#读取图像
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img = cv2.imread('lena.png')
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lenna_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
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#灰度化处理图像
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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#拉普拉斯算法
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dst = cv2.Laplacian(grayImage, cv2.CV_16S, ksize = 3)
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Laplacian = cv2.convertScaleAbs(dst)
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#用来正常显示中文标签
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plt.rcParams['font.sans-serif']=['SimHei']
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#显示图形
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titles = [u'原始图像', u'Laplacian算子']
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images = [lenna_img, Laplacian]
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for i in xrange(2):
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plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')
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plt.title(titles[i])
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plt.xticks([]),plt.yticks([])
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plt.show()

‎blog17-sobel/blog17-image05.py‎

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# -*- coding: utf-8 -*-
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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#读取图像
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img = cv2.imread('lena.png')
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lenna_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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#灰度化处理图像
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grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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#高斯滤波
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gaussianBlur = cv2.GaussianBlur(grayImage, (3,3), 0)
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#阈值处理
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ret, binary = cv2.threshold(gaussianBlur, 127, 255, cv2.THRESH_BINARY)
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#Roberts算子
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kernelx = np.array([[-1,0],[0,1]], dtype=int)
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kernely = np.array([[0,-1],[1,0]], dtype=int)
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x = cv2.filter2D(binary, cv2.CV_16S, kernelx)
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y = cv2.filter2D(binary, cv2.CV_16S, kernely)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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#Prewitt算子
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kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]], dtype=int)
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kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]], dtype=int)
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x = cv2.filter2D(binary, cv2.CV_16S, kernelx)
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y = cv2.filter2D(binary, cv2.CV_16S, kernely)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Prewitt = cv2.addWeighted(absX,0.5,absY,0.5,0)
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#Sobel算子
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x = cv2.Sobel(binary, cv2.CV_16S, 1, 0)
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y = cv2.Sobel(binary, cv2.CV_16S, 0, 1)
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absX = cv2.convertScaleAbs(x)
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absY = cv2.convertScaleAbs(y)
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Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
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#拉普拉斯算法
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dst = cv2.Laplacian(binary, cv2.CV_16S, ksize = 3)
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Laplacian = cv2.convertScaleAbs(dst)
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#效果图
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titles = ['Source Image', 'Binary Image', 'Roberts Image',
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'Prewitt Image','Sobel Image', 'Laplacian Image']
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images = [lenna_img, binary, Roberts, Prewitt, Sobel, Laplacian]
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for i in np.arange(6):
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plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
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plt.title(titles[i])
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plt.xticks([]),plt.yticks([])
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plt.show()

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