OpenCV图像处理教程钱银中课后答案

单元1参考答案1.B2.A3..D4.C5.B6.A7.B8.D9.C10.B11.A12.B13.D14.C15.D16.D17.B18.C单元2参考答案习题参考答案：一．选择题1.C2.A3.B4.D5.C6.B7.D8.C9.B10.C11.A12.B13.C14.C15.C16.A17.B18.C19.D20.A

21.D22.B23.D24.A25.编写程序，读取一个彩色图像，分别提取并显示其中的R,G,B灰度图像；importcv2img=cv2.imread('dog.jpg')cv2.imshow("Color",img)cv2.imshow("0",img[:,:,0])cv2.imshow("1",img[:,:,1])cv2.imshow("2",img[:,:,2])cv2.waitKey(0)26. 编写程序，读取一个彩色图像，分别转换为灰度图像、HSV图像和L*a*b*图像，显示转化后的目标图像；importcv2img=cv2.imread('flower.png')cv2.imshow("Flower",img)hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)cv2.imshow("hsv0",hsv[:,:,0])cv2.imshow("hsv1",hsv[:,:,1])cv2.imshow("hsv0",hsv[:,:,2])lab=cv2.cvtColor(img,cv2.COLOR_BGR2LAB)cv2.imshow("lab0",lab[:,:,0])cv2.imshow("lab1",lab[:,:,1])cv2.imshow("lab0",lab[:,:,2])cv2.waitKey(0)27. 编写程序，读取一个图像，查看这个图像中矩阵元素测数据类型，这个图像的宽度和高度；importcv2im=cv2.imread("flower.png")print(im.dtype)print(im.shape)28. 编写程序，使用手机拍摄一个自己个人的图像，从图像中截取人脸窗口图像。importcv2img=cv2.imread('me.jpg')cv2.imshow("0",img)img1=img[a:b,c:d,:]#a,b,c,d根据人脸位置确定cv2.imshow("1",img1)cv2.imwrite("a.jpg",img1)单元3参考答案1.D2.C3.D4.A5.B6.C7.A8.B9.A10.A11.B12.D13.C14.A15.B16.D17.编写程序，读取一个图像，在图像的最中间画一条垂直线。importcv2img=cv2.imread("imgs/bike.jpg")h,w,_=img.shapecv2.line(img,(w//2,0),(w//2,h),(255,255,255),2)cv2.imshow("0",img)cv2.waitKey(0)cv2.destroyAllWindows()18.编写程序，读取一幅图像，在图像最中间画一个半径100的圆。importcv2img=cv2.imread("imgs/bike.jpg")h,w,_=img.shapecv2.circle(img,(w//2,h//2),100,(0,0,255),2)cv2.imshow("0",img)cv2.waitKey(0)cv2.destroyAllWindows()19.编写程序，在黑色背景上画一个白色实心椭圆。importcv2importnumpyasnpimg=np.zeros((300,600,3),dtype="uint8")cv2.ellipse(img,(300,150),(100,50),0,0,360,(255,255,255),-1)cv2.imshow("0",img)cv2.waitKey(0)cv2.destroyAllWindows()20.编写程序，在图像中间位置，用斜体字书写“Python!”importcv2img=cv2.imread("imgs/bike.jpg")h,w,_=img.shapecv2.putText(img,'Python!',(w//2-100,h//2-30),cv2.FONT_ITALIC,1.5,(255,0,0))cv2.imshow("0",img)cv2.waitKey(0)cv2.destroyAllWindows()单元4参考答案1.B2.A3.D4.C5.A6.C7.B8.D9.B10.A11.D12.D13.B14.C15.B16.A17.C18.D19.D20.B21.A22.读取一个图像，宽度方向放大1.8倍、高度方向放大1.5倍，显示缩放后的图像。importcv2img=cv2.imread("imgs/cameraman.jpg")cv2.imshow("0",img)img1=cv2.resize(img,(100,150))cv2.imshow("1",img1)img2=cv2.resize(img,None,fx=1.8,fy=1.5)cv2.imshow('img2',img2)cv2.waitKey(0)cv2.destroyAllWindows()23.读取一个图像，向上平移50，向右平移80，显示平移后的图像。importnumpyasnpimportcv2img=cv2.imread("imgs/lena.png")cv2.imshow("0",img)M=np.float32([[1,0,50],[0,1,80]])#仿射变换矩阵s=cv2.warpAffine(img,M,(img.shape[1],img.shape[0]))cv2.imshow("1",s)s=cv2.warpAffine(img,M,(img.shape[1],img.shape[0]),borderMode=cv2.BORDER_REPLICATE)cv2.imshow("2",s)cv2.waitKey(0)cv2.destroyAllWindows()24.读取一个图像，围绕图像左上角沿逆时针方向旋转20°，显示旋转后的图像。importcv2image=cv2.imread("imgs/lena.png")cv2.imshow("0",image)(h,w)=image.shape[:2]center=(0,0)M=cv2.getRotationMatrix2D(center,20,1)r=cv2.warpAffine(image,M,(w,h))cv2.imshow("1",r)cv2.waitKey(0)25.读取一个图像，显示10个在-15°-15°随机旋转的图像。importcv2importmatplotlib.pyplotaspltimage=cv2.imread("imgs/lena.png",cv2.IMREAD_GRAYSCALE)cv2.imshow("0",image)(h,w)=image.shape[:2]center=(w//2,h//2)plt.figure(figsize=(12,4))j=1foriinrange(-15,16,10):M=cv2.getRotationMatrix2D(center,i,1)r=cv2.warpAffine(image,M,(w,h))plt.subplot(2,4,j)j=j+1plt.xticks([]),plt.yticks([])plt.imshow(r)plt.show()26.读取一个图像，执行仿射变换。importnumpyasnpimportcv2img=cv2.imread("imgs/swan.jpg")cv2.imshow("0",img)src=np.float32([[0,0],[0,110],[120,0]])dst=np.float32([[0,0],[10,80],[90,20]])M=cv2.getAffineTransform(src,dst,)img1=cv2.warpAffine(img,M,(img.shape[1],img.shape[0]))cv2.imshow("1",img1)cv2.waitKey()cv2.destroyAllWindows()27读取一个图像，执行透视变换。importnumpyasnpimportcv2image=cv2.imread("imgs/road.jpeg")cv2.imshow("0",image)(h,w)=image.shape[:2]src=np.float32([[0,0],[368,395],[209,218],[336,230]])dst=np.float32([[50,395],[368,395],[50,230],[336,230]])M=cv2.getPerspectiveTransform(src,dst)w=cv2.warpPerspective(image,M,(w,h))cv2.imshow("1",w)cv2.waitKey(0)单元5参考答案1.A2.D3.C4.B5.C6.A7.B8.B9.A10.D11.B12.A13.C14.D15.A16.C17.A18.D19.C20.编写程序，读取一个彩色图像，分别用3X3,5X5,7X7大小的核执行均值平滑。importcv2img=cv2.imread("a.jpg")img1=cv2.blur(img,(3,3))img2=cv2.blur(img,(5,5))img3=cv2.blur(img,(7,7))cv2.imshow("0",img)cv2.imshow("1",img1)cv2.imshow("2",img2)cv2.imshow("3",img3)cv2.waitKey()cv2.destroyAllWindows()21.编写程序，读取一个彩色图像，分别用3X3,9X9,15X15大小的核执行高斯平滑。importcv2img=cv2.imread("a.jpg")img1=cv2.GaussianBlur(img,(3,3),0)img2=cv2.GaussianBlur(img,(9,9),0)img3=cv2.GaussianBlur(img,(15,15),0)cv2.imshow("0",img)cv2.imshow("1",img1)cv2.imshow("2",img2)cv2.imshow("3",img3)cv2.waitKey()cv2.destroyAllWindows()22.编写程序，读取一个彩色图像，分别用3X3,5X5,7X7大小的核执行中值平滑。importcv2img=cv2.imread("a.jpg")img1=cv2.medianBlur(img,3)img2=cv2.medianBlur(img,5)img3=cv2.medianBlur(img,7)cv2.imshow("0",img)cv2.imshow("1",img1)cv2.imshow("2",img2)cv2.imshow("3",img3)cv2.waitKey()cv2.destroyAllWindows()23.编写程序，定义一个5X5均值核，使用二维卷积，对输入图像执行均值平滑。importcv2importnumpyasnpimg=cv2.imread("a.jpg")k=np.ones((5,5),np.float32)/25img2=cv2.filter2D(img,-1,k)cv2.imshow("2",img2)cv2.waitKey()cv2.destroyAllWindows()24.编写程序，定义一个差分核，对输入图像执行差分滤波，提取图像中的垂直边缘。importcv2importnumpyasnpimg=cv2.imread("imgs/lena.png",cv2.IMREAD_GRAYSCALE)k1=np.array([(1,0,-1),(1,0,-1),(1,0,-1)],np.float32)img1=cv2.filter2D(img,-1,k1)cv2.imshow("0",img)cv2.imshow("1",img1)cv2.waitKey()cv2.destroyAllWindows()单元6参考答案1.C2.A3.A4.D5.C6.D7.B8.A9.C10.D11.B12.D13.C14.A15.C16.C17.B18.A19. 编写程序，读取一个灰度图像，使用Sobel算子检测边缘，显示两个方向以及合并以后的检测结果.importcv2importnumpyasnpimg=cv2.imread("a.png",cv2.IMREAD_GRAYSCALE)cv2.imshow("Image",img)sx=cv2.Sobel(img,cv2.CV_64F,1,0)sy=cv2.Sobel(img,cv2.CV_64F,0,1)sx=np.uint8(np.absolute(sx))sy=np.uint8(np.absolute(sy))s=cv2.bitwise_or(sx,sy)cv2.imshow("x",sx)cv2.imshow("y",sy)cv2.imshow("s",s)cv2.waitKey(0)20. 编写程序，在灰度值相等的画布图像上绘制一条水平线和垂直线，分别用两种Sobel算子检测边缘，显示两个方向以及合并以后的检测结果.importcv2importnumpyasnpimg=np.zeros((512,512,3),np.uint8)cv2.line(img,(0,200),(511,200),(255,255,255),20)cv2.line(img,(200,0),(200,511),(255,255,255),20)cv2.imshow("Image",img)sx=cv2.Sobel(img,cv2.CV_64F,1,0)sy=cv2.Sobel(img,cv2.CV_64F,0,1)sx=np.uint8(np.absolute(sx))sy=np.uint8(np.absolute(sy))s=cv2.bitwise_or(sx,sy)#通过位或运算合并两个方向的检测结果cv2.imshow("x",sx)cv2.imshow("y",sy)cv2.imshow("s",s)cv2.waitKey(0)

21. 编写程序，在灰度值相等的画布图像上绘制一个圆，分别用两种Sobel算子检测边缘，显示两个方向以及合并以后的检测结果.importcv2importnumpyasnpimg=np.zeros((512,512,3),np.uint8)cv2.circle(img,(256,256),100,(255,255,255),10)cv2.imshow("Image",img)sx=cv2.Sobel(img,cv2.CV_64F,1,0)sy=cv2.Sobel(img,cv2.CV_64F,0,1)sx=np.uint8(np.absolute(sx))sy=np.uint8(np.absolute(sy))s=cv2.bitwise_or(sx,sy)

cv2.imshow("x",sx)cv2.imshow("y",sy)cv2.imshow("s",s)cv2.waitKey(0)22. 编写程序，读取一个灰度图像，使用Scharr算子检测边缘，显示两个方向以及合并以后的检测结果.importcv2importnumpyasnpimg=cv2.imread("imgs/lena.png")sx=cv2.Scharr(img,cv2.CV_64F,1,0)sy=cv2.Scharr(img,cv2.CV_64F,0,1)sx=np.uint8(np.absolute(sx))sy=np.uint8(np.absolute(sy))s=cv2.bitwise_or(sx,sy)cv2.imshow("x",sx)cv2.imshow("y",sy)cv2.imshow("s",s)cv2.waitKey(0)23. 编写程序，在灰度值相等的画布图像上绘制一个圆，分别用两种Scharr算子检测边缘，显示两个方向以及合并以后的检测结果.importcv2importnumpyasnpimg=np.zeros((512,512,3),np.uint8)cv2.circle(img,(256,256),100,(255,255,255),10)cv2.imshow("Image",img)sx=cv2.Scharr(img,cv2.CV_64F,1,0)sy=cv2.Scharr(img,cv2.CV_64F,0,1)sx=np.uint8(np.absolute(sx))sy=np.uint8(np.absolute(sy))s=cv2.bitwise_or(sx,sy)

cv2.imshow("x",sx)cv2.imshow("y",sy)cv2.imshow("s",s)cv2.waitKey(0)24. 编写程序，在灰度值相等的画布图像上绘制一条水平线和垂直线，用Laplacian算子检测边缘.importcv2importnumpyasnpimg=np.zeros((512,512,3),np.uint8)cv2.line(img,(0,200),(511,200),(255,255,255),20)cv2.line(img,(200,0),(200,511),(255,255,255),20)cv2.imshow("Image",img)s=cv2.Laplacian(img,cv2.CV_64F)cv2.imshow("s",s)cv2.waitKey(0)25. 编写程序，对有噪声的图像执行高斯平滑，然后用Canny算子检测边缘.#Example6_9importcv2image=cv2.imread("imgs/cat.jpg")gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)cv2.imshow("Original",image)gray=cv2.GaussianBlur(gray,(5,5),0)cv2.imshow("Blurred",gray)can=cv2.Canny(gray,100,200)cv2.imshow("Canny",can)cv2.waitKey(0)cv2.imwrite("imgs/output/cat2.jpg",can)cv2.destroyAllWindows()单元7参考答案1.B2.D3.C4.B5.A6.B7.D8.A9.C10.D11.B12.C13.A14.B15.A16.D17.C18.B19.D20.A21. 读取一个二值图像，分别用3X3,5X5,7X7的椭圆结构元执行腐蚀处理。importcv2s=[(3,3),(5,5),(7,7)]img=cv2.imread("imgs/m.jpg",cv2.IMREAD_GRAYSCALE)cv2.imshow("Input",img)foriinrange(len(s)):

e=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,s[i])

img2=cv2.morphologyEx(img,cv2.MORPH_ERODE,e)

cv2.imshow(str(i+1),img2)cv2.waitKey(0)cv2.destroyAllWindows()22. 读取一个灰度图像，分别用3X3,5X5,7X7的椭圆结构元执行膨胀处理。importcv2s=[(3,3),(5,5),(7,7)]img=cv2.imread("imgs/m.jpg",cv2.IMREAD_GRAYSCALE)cv2.imshow("Input",img)foriinrange(len(s)):e=cv2.getStructuringElement(cv2.MORPH_ELLIPSE,s[i])img2=cv2.morphologyEx(img,cv2.MORPH_DILATE,e)cv2.imshow(str(i+1),img2)cv2.waitKey(0)cv2.destroyAllWindows()23. 读取一个二值图像，分别用3X3,9X9,13X13的十字型结构元执行开运算处理。importcv2s=[(3,3),(9,9),(13,13)]img=cv2.imread("imgs/m.jpg",cv2.IMREAD_GRAYSCALE)cv2.imshow("Input",img)foriinrange(len(s)):

e=cv2.getStructuringElement(cv2.MORPH_CROSS,s[i])

img2=cv2.morphologyEx(img,cv2.MORPH_OPEN,e)

cv2.imshow(str(i+1),img2)cv2.waitKey(0)cv2.destroyAllWindows()24. 读取一个灰度图像，分别用3X3,9X9,13X13的十字型结构元执行闭运算处理。importcv2s=[(3,3),(9,9),(13,13)]img=cv2.imread("imgs/m.jpg",cv2.IMREAD_GRAYSCALE)cv2.imshow("Input",img)foriinrange(len(s)):e=cv2.getStructuringElement(cv2.MORPH_CROSS,s[i])img2=cv2.morphologyEx(img,cv2.MORPH_CLOSE,e)cv2.imshow(str(i+1),img2)cv2.waitKey(0)cv2.destroyAllWindows()25. 读取一个有白色物体的二值图像，分别用3X3,5X5,7X11的矩形结构形态学梯度处理。importcv2s=[(3,3),(5,5),(7,11)]img=cv2.imread("imgs/m.jpg",cv2.IMREAD_GRAYSCALE)cv2.imshow("Input",img)foriinrange(len(s)):

e=cv2.getStructuringElement(cv2.MORPH_RECT,s[i])

img2=cv2.morphologyEx(img,cv2.MORPH_GRADIENT,e)

cv2.imshow(str(i+1),img2)cv2.waitKey(0)cv2.destroyAllWindows()26. 读取一个灰度图像，分别用3X3,5X5,7X9的矩形结构顶帽运算处理。importcv2s=[(3,3),(5,5),(7,9)]img=cv2.imread("imgs/m.jpg",cv2.IMREAD_GRAYSCALE)cv2.imshow("Input",img)foriinrange(len(s)):e=cv2.get