Opencv计算机视觉入门——图像的处理（一）

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

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

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)

    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

图像梯度-laplacian算子

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')