Opencv计算机视觉入门——图像的处理（一）计算机图像处理

接上次的笔记，开始图像操作的第二段学习旅程~~~
图像的处理（一）图像阈值拿到一张图像，图像是由众多像素点组成的，我们要对每一个像素点的值进行判断，用像素点的值与阈值进行比较，对于大于或小于阈值分别做不同的处理。

ret,dist = cv2.threshold(src,thresh,maxval,type)

src：输入图，只能输入单通道图像，通常来说为灰度图
dist：输出图
thresh：阈值
maxval：当像素值超过了阈值（或者小于阈值，根据type来决定），所赋予的值
type：二值化操作的类型，包括以下5种类型：cv2.THRESH_BINARY; cv2.THRESH_BINARY_INV; cv2.THRESH_TRUNC; cv2.THRESH_TOZERO; cv2.THRESH_TOZERO_INV
cv2.THRESH_BINARY：超过阈值部分取maxval（最大值），否则取0
cv2.THRESH_BINARY_INV：THRESH_BINARY的反转，即超过阈值部分取0，否则取maxval（最大值）
cv2.THRESH_TRUNC：大于阈值部分设为阈值，否则不变
cv2.THRESH_TOZERO：大于阈值部分不改变，否则设为0
cv2.THRESH_TOZERO_INV：THRESH_TOZERO的反转

#图像阈值 import matplotlib.pyplot as plt ret, thresh1 = cv2.threshold(img_gray,127,255,cv2.THRESH_BINARY) ret, thresh2 = cv2.threshold(img_gray,127,255,cv2.THRESH_BINARY_INV) ret, thresh3 = cv2.threshold(img_gray,127,255,cv2.THRESH_TRUNC) ret, thresh4 = cv2.threshold(img_gray,127,255,cv2.THRESH_TOZERO) ret, thresh5 = cv2.threshold(img_gray,127,255,cv2.THRESH_TOZERO_INV)title = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV'] images = [img,thresh1,thresh2,thresh3,thresh4,thresh5] for i in range(6): plt.subplot(2,3,i + 1),plt.imshow(images[i],'gray') plt.title(title[i]) plt.xticks() plt.yticks() plt.show()

图像平滑

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当前输入数据，如上lena图像上有很多噪音点，现在我们想通过滤波、平滑处理等操作尽可能的去掉这些噪音点。

#图像平滑 img = cv2.imread('lenaNoise.png') cv2.imshow('image',img) cv2.waitKey(0) cv2.destroyAllWindows()#均值滤波 #简单的平均卷积操作 blur = cv2.blur(img,(3,3)) cv2.imshow('blur',blur) cv2.waitKey(0) cv2.destroyAllWindows()#方框滤波 #基本和滤波一样，可以选择归一化 box = cv2.boxFilter(img,-1,(3,3),normalize=True) cv2.imshow('box',box) cv2.waitKey(0) cv2.destroyAllWindows()#方框滤波 #基本和滤波一样，可以选择归一化,容易越界 box = cv2.boxFilter(img,-1,(3,3),normalize=False) cv2.imshow('box',box) cv2.waitKey(0) cv2.destroyAllWindows()#高斯滤波 #高斯模糊的卷积核里的数值是满足高斯分布的，相当于更重视中间的 aussian = cv2.GaussianBlur(img,(5,5),1) cv2.imshow('aussian',aussian) cv2.waitKey(0) cv2.destroyAllWindows()#中值滤波 #相当于用中值代替,利用中值滤波处理后，椒盐噪声几乎完全被去除掉 median = cv2.medianBlur(img,5) cv2.imshow('median',median) cv2.waitKey(0) cv2.destroyAllWindows()#展示所有的 res = np.hstack((blur,aussian,median)) print(res) cv2.imshow('median vs average',res) cv2.waitKey(0) cv2.destroyAllWindows()

均值滤波相当于低通滤波，有将图像模糊化的趋势，对椒盐噪声计基本无能为力。中值滤波的优点是可以很好的过滤掉椒盐噪声，缺点是易造成图像的不连续性。
形态学-腐蚀操作

#形态学—腐蚀操作 img = cv2.imread('dige.png') cv2.imshow('image',img) cv2.waitKey(0) cv2.destroyAllWindows()kernel_1 = np.ones((3,3),np.uint8) erosion = cv2.erode(img,kernel_1,iterations=1) cv2.imshow('erosion',erosion) cv2.waitKey(0) cv2.destroyAllWindows()pie = cv2.imread('pie.png') cv2.imshow('pie',pie) cv2.waitKey(0) cv2.destroyAllWindows()kernel_2 = np.ones((30,30),np.uint8) erosion_1 = cv2.erode(pie,kernel_2,iterations=1) erosion_2 = cv2.erode(pie,kernel_2,iterations=2) erosion_3 = cv2.erode(pie,kernel_2,iterations=3) res = np.hstack((erosion_1,erosion_2,erosion_3)) cv2.imshow('res',res) cv2.waitKey(0) cv2.destroyAllWindows()

形态学-膨胀操作

#形态学—膨胀操作 img = cv2.imread('dige.png') cv2.imshow('image',img) cv2.waitKey(0) cv2.destroyAllWindows()kernel = np.ones((3,3),np.uint8) dige_erosion = cv2.erode(img,kernel,iterations=1) cv2.imshow('erosion',dige_erosion) cv2.waitKey(0) cv2.destroyAllWindows()kernel = np.ones((3,3),np.uint8) dige_dilate = cv2.dilate(dige_erosion,kernel,iterations=1) cv2.imshow('dilate',dige_dilate) cv2.waitKey(0) cv2.destroyAllWindows()pie = cv2.imread('pie.png') kernel = np.ones((30,30),np.uint8) dilate_1 = cv2.dilate(pie,kernel,iterations=1) dilate_2 = cv2.dilate(pie,kernel,iterations=2) dilate_3 = cv2.dilate(pie,kernel,iterations=3) res = np.hstack((dilate_1,dilate_2,dilate_3)) cv2.imshow('res',res) cv2.waitKey(0) cv2.destroyAllWindows()

开运算与闭运算开运算：先腐蚀，再膨胀
闭运算：先膨胀，再腐蚀

#开运算与闭运算 #开：先腐蚀，再膨胀 img = cv2.imread('dige.png') kernel = np.ones((3,3),np.uint8) opening = cv2.morphologyEx(img,cv2.MORPH_OPEN,kernel) cv2.imshow('opening',opening) cv2.waitKey(0) cv2.destroyAllWindows()#闭：先膨胀，再腐蚀 img =cv2.imread('dige.png') kernel = np.ones((3,3),np.uint8) closing = cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel) cv2.imshow('closing',closing) cv2.waitKey(0) cv2.destroyAllWindows()#梯度运算 #梯度=膨胀-腐蚀 pie = cv2.imread('pie.png') kernel = np.ones((7,7),np.uint8) dilate = cv2.dilate(pie,kernel,iterations=5) erossion = cv2.erode(pie,kernel,iterations=5)res = np.hstack((dilate,erossion)) cv2.imshow('res',res) cv2.waitKey(0) cv2.destroyAllWindows()gradient = cv2.morphologyEx(pie,cv2.MORPH_GRADIENT,kernel) cv2.imshow('gradient',gradient) cv2.waitKey(0) cv2.destroyAllWindows()#礼帽和黑帽 #礼帽=原始输入-开运算结果 #黑帽=闭运算-原始输入#礼帽 img = cv2.imread('dige.png') kernel = np.ones((5,5),np.uint8) tophat = cv2.morphologyEx(img,cv2.MORPH_TOPHAT,kernel) cv2.imshow('topcat',tophat) cv2.waitKey(0) cv2.destroyAllWindows() #黑帽 img = cv2.imread('dige.png') kernel = np.ones((5,5),np.uint8) blackhat = cv2.morphologyEx(img,cv2.MORPH_BLACKHAT,kernel) cv2.imshow('blackhat',blackhat) cv2.waitKey(0) cv2.destroyAllWindows()

图像梯度-Sobel算子

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梯度简单来说就是求导。OpenCV提供了三种不同的梯度滤波器，或者说高通滤波器：Sobel，Scharr和Lapacian。Sobel，Scharr其实就是求一阶或二阶导。Scharr是对Sobel的部分优化。Laplacian是求二阶导。

#图像梯度-Sobel算子 img = cv2.imread('pie.png',cv2.IMREAD_GRAYSCALE) cv2.imshow('img',img) cv2.waitKey(0) cv2.destroyAllWindows()

dist = cv2.Sobel(src,ddepth,dx,dy,ksize)

【Opencv计算机视觉入门——图像的处理（一）】ddepth:图像深度
dx和dy分别表示水平和竖直方向
ksize是Sobel算子的大小

#图像梯度-Sobel算子 img = cv2.imread('pie.png',cv2.IMREAD_GRAYSCALE) cv2.imshow('img',img) cv2.waitKey(0) cv2.destroyAllWindows()def cv_show(img,name): cv2.imshow(name,img) cv2.waitKey(0) cv2.destroyAllWindows()#cv2.CV_64F,64F代表每一个像素点元素占64位浮点数 sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3) sobelx = cv2.convertScaleAbs(sobelx) cv_show(sobelx,'sobelx')#白到黑是正数，黑到白是负数，所有的负数都会被截断为0，所以要取绝对值 sobely = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3) sobely = cv2.convertScaleAbs(sobely) cv_show(sobely,'sobely')sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3) sobely = cv2.convertScaleAbs(sobely) cv_show(sobely,'sobely')#分别计算x和y，再求和 sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0) cv_show(sobelxy,'sobelxy')img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE) sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3) sobelx = cv2.convertScaleAbs(sobelx) sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3) sobely = cv2.convertScaleAbs(sobely) sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0) cv_show(sobelxy,'sobelxy')#整体计算存在重影,不建议 img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE) sobelxy = cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3) sobelxy = cv2.convertScaleAbs(sobelxy) cv_show(sobelxy,'sobelxy')

图像梯度-Scharr算子

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图像梯度-laplacian算子

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拉普拉斯算子可以使用二阶导数的形式定义，可假设其离散实现类似于二阶Sobel导数。

#不同算子的差异 img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE) sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3) sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3) sobelx = cv2.convertScaleAbs(sobelx) sobely = cv2.convertScaleAbs(sobely) sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)scharrx = cv2.Scharr(img,cv2.CV_64F,1,0) scharry = cv2.Scharr(img,cv2.CV_64F,0,1) scharrx = cv2.convertScaleAbs(scharrx) scharry = cv2.convertScaleAbs(scharry) scharrxy = cv2.addWeighted(scharrx,0.5,scharry,0.5,0)laplacian = cv2.Laplacian(img,cv2.CV_64F) laplacian = cv2.convertScaleAbs(laplacian)res = np.hstack((img,sobelxy,scharrxy,laplacian)) cv_show(res,'res')