python-tensorflow-opencv《计算机视觉的深度学习实践》代码笔记

1、ython-tensorflow-opencv计算机视觉的深度学习实践代码笔记python-tensorflow-opencv计算机视觉的深度学习实践代码笔记01 课程概述tf_test:# 创建2个矩阵，前者12列，后者21列，然后矩阵相乘：matrix1 =tf.constant(3,3)# 上边的操作是定义图，然后会话Session去计算：with tf.Session() as sess:print(- )print(matrix1)Tensor(Const_2:0, shape(1, 2), dtypeint32)opencv_test:import cv2 as cv#释放窗cv.

2、destroyAllWindows()02图像预处理f1.py程序：import cv2img =cv2.imread(D:/pictures/opencv.png,0) #直接读为灰度图像for i in range(2000): #添加点噪声blur_1 =cv2.GaussianBlur(img,(5,5),0)blur_2 =cv2.medianBlur(img,5)plt.subplot(1,3,1),plt.imshow(img,gray),plt.title(img)#默认彩，另种彩bgrplt.subplot(1,3,2),plt.imshow(blur_1,gray),plt

3、.title(blur1)plt.subplot(1,3,3),plt.imshow(blur_2,gray),plt.title(blur2)plt.show()f1.py结果：f2.py程序：import cv2 as cvfrom matplotlib import pyplot as pltimg =cv.imread(D:/pictures/dave.png,0)plt.subplot(2,2,4),plt.imshow(sobely,cmap =gray)plt.title(Sobel Y), plt.xticks(), plt.yticks()plt.show()f2.py结果：

4、fft.py程序：import cv2 as cvplt.subplot(121),plt.imshow(img, cmap =gray)plt.title(Input Image), plt.xticks(), plt.yticks()plt.subplot(122),plt.imshow(magnitude_spectrum, cmap =gray)plt.title(Magnitude Spectrum), plt.xticks(), plt.yticks()plt.show()fft.py结果：clahe.py程序：import cv2import matplotlib.pyplot

5、as pltimg =cv2.imread(D:/pictures/timg.jpg,0) #直接读为灰度图像res =cv2.equalizeHist(img)#直接直图均衡化clahe =cv2.createCLAHE(clipLimit2,tileGridSize(10,10)cl1 =clahe.apply(img)# 限制对度适应直图均衡plt.show()clahe.py结果：filter_1.ipynb程序：kernel =np.ones(5,5),np.float32)/25dst =cv.filter2D(img,-1,kernel)plt.show()kernelfilte

6、r_1.ipynb结果：array(0.04, 0.04, 0.04, 0.04, 0.04,0.04, 0.04, 0.04, 0.04, 0.04,0.04, 0.04, 0.04, 0.04, 0.04,0.04, 0.04, 0.04, 0.04, 0.04,0.04, 0.04, 0.04, 0.04, 0.04, dtype=float32)03图像特征与描述imagestiching.py程序：def stitch(self, images, ratio0.75, reprojThresh4.0,showMatchesFalse):#获取输图# 匹配两张图的所有特征点，返回匹配结

7、果# 如果返回结果为空，没有匹配成功的特征点，退出算法if M is None:return None# 否则，提取匹配结果# 将图进视变换，result是变换后图result =cv2.warpPerspective(imageA, H, (imageA.shape1 +imageB.shape1, imageA.shape0)# 将图传result图最左端# 检测是否需要显图匹配# 成匹配图vis =self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)# 返回结果return (result, vis)# 返回匹配结果r

8、eturn resultdef detectAndDescribe(self, image):# 将彩图转换成灰度图gray =cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)descriptor =cv2.xfeatures2d.SIFT_create()# 检测SIFT特征点，并计算描述(kps, features) =descriptor.detectAndCompute(image, None)# 将结果转换成NumPy数组kps =np.float32(kp.pt for kp in kps)# 返回特征点集，及对应的描述特征return (kps, f

9、eatures)def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):# 建暴匹配器matcher =cv2.DescriptorMatcher_create (BruteForce)# 使检测来图的SIFT特征匹配对，K=2rawMatches =matcher.knnMatch(featuresA, featuresB, 2)# 当最近距离跟次近距离的值于ratio值时，保留此匹配对if len(m) =2 and m0.distance m1.distance *ratio:# 存储

10、两个点在featuresA, featuresB中的索引值matches.append(m0.trainIdx, m0.queryIdx)# 当筛选后的匹配对于4时，计算视变换矩阵# 获取匹配对的点坐标# 计算视变换矩阵# 返回结果# 如果匹配对于4时，返回None# 联合遍历，画出匹配对for (trainIdx, queryIdx), s) in zip(matches, status):# 当点对匹配成功时，画到可视化图上if s =1:# 画出匹配对return vis# 读取拼接图# 把图拼接成全景图stitcher =Stitcher()(result, vis) =stitche

11、r.stitch(imageA, imageB, showMatchesTrue)# 显所有图cv2.destroyAllWindows()imagestiching.py结果：harris_corner.py程序：import numpy as npimport cv2 as cvfilename =./chessboard.pngimg =cv.imread(filename)cv.imwrite(D:/dst.jpg,img)if cv.waitKey(0) &0 xff =27:cv.destroyAllWindows()harris_corner.py结果：surf.py程序：imp

12、ort numpy as npimport cv2 as cvimg =cv.imread(./butterfly.jpg,0)surf =cv.xfeatures2d.SURF_create(400)surf.setHessianThreshold(50000)kp, des =surf.detectAndCompute(img,None)cv.waitKey(0)cv.destroyAllWindows()surf.py结果：sift.py程序：import cv2 as cvimgcv.drawKeypoints(gray,kp,img)cv.destroyAllWindows()sif

13、t.py结果：orb.py程序：import cv2 as cvimg3 =cv.drawMatches(img1,kp1,img2,kp2,matches:20,None, flags2)plt.imshow(img3),plt.show()orb.py结果：laplacian_sharpen.py程序：# 为了减少计算的维度，因此将图像转为灰度图img_gray =img.convert(L)# 得到转换后灰度图的像素矩阵img_arr =np.array(img_gray)h =img_arr.shape0 # w =img_arr.shape1 # 列# 拉普拉斯算锐化图像，阶微分new_img_arr =np.zeros(h, w) # 拉普拉斯锐化后的图像像素矩阵for i in range(2, h-1):laplace_img_arr =np.zeros(h, w)