python – 使用OpenCV和SIFTSURF校正扫描图像以匹配原始图像

概述我有一个数字形式的原始页面和同一页面的几个扫描版本.我的目标是对扫描的页面进行校正,使其尽可能与原始页面匹配.我知道我可以使用 here所述的概率霍夫变换来固定旋转但扫描的纸张尺寸也不同,因为有些人将页面缩放到不同的纸张格式.我认为OpenCV中的findHomography()函数与SIFT / SURF的关键点组合正是我解决这个问题所需要的.但是,我只是无法让我的deskew()函数工作. 我 我有一个数字形式的原始页面和同一页面的几个扫描版本.我的目标是对扫描的页面进行校正,使其尽可能与原始页面匹配.我知道我可以使用 here所述的概率霍夫变换来固定旋转但扫描的纸张尺寸也不同,因为有些人将页面缩放到不同的纸张格式.我认为OpenCV中的findHomography()函数与SIFT / SURF的关键点组合正是我解决这个问题所需要的.但是,我只是无法让我的deskew()函数工作.

我的大多数代码源于以下两个来源：
http://www.learnopencv.com/homography-examples-using-opencv-python-c/和http://docs.opencv.org/3.1.0/d1/de0/tutorial_py_feature_homography.html.

import numpy as npimport cv2from matplotlib import pyplot as plt# FIXME: doesn't workdef deskew():    im_out = cv2.warpPerspective(img1,M,(img2.shape[1],img2.shape[0]))    plt.imshow(im_out,'gray')    plt.show()# resizing images to improve speedfactor = 0.4img1 = cv2.resize(cv2.imread("image.png",0),None,fx=factor,fy=factor,interpolation=cv2.INTER_CUBIC)img2 = cv2.resize(cv2.imread("imageSkewed.png",interpolation=cv2.INTER_CUBIC)surf = cv2.xfeatures2d.SURF_create()kp1,des1 = surf.detectAndCompute(img1,None)kp2,des2 = surf.detectAndCompute(img2,None)FLANN_INDEX_KDTREE = 0index_params = dict(algorithm=FLANN_INDEX_KDTREE,trees=5)search_params = dict(checks=50)flann = cv2.FlannBasedMatcher(index_params,search_params)matches = flann.knnMatch(des1,des2,k=2)# store all the good matches as per Lowe's ratio test.good = []for m,n in matches:    if m.distance < 0.7 * n.distance:        good.append(m)MIN_MATCH_COUNT = 10if len(good) > MIN_MATCH_COUNT:    src_pts = np.float32([kp1[m.queryIDx].pt for m in good                          ]).reshape(-1,1,2)    dst_pts = np.float32([kp2[m.trainIDx].pt for m in good                          ]).reshape(-1,2)    M,mask = cv2.findHomography(src_pts,dst_pts,cv2.RANSAC,5.0)    matchesMask = mask.ravel().toList()    h,w = img1.shape    pts = np.float32([[0,0],[0,h - 1],[w - 1,0]]).reshape(-1,2)    dst = cv2.perspectivetransform(pts,M)    deskew()    img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3,cv2.liNE_AA)else:    print("Not  enough  matches are found   -   %d/%d" % (len(good),MIN_MATCH_COUNT))    matchesMask = None# show matching keypointsdraw_params = dict(matchcolor=(0,# draw  matches in  green   color                   singlePointcolor=None,matchesMask=matchesMask,# draw only    inlIErs                   flags=2)img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,**draw_params)plt.imshow(img3,'gray')plt.show()

解决方法 结果我非常接近解决我自己的问题.
这是我的代码的工作版本：

import numpy as npimport cv2from matplotlib import pyplot as pltimport mathdef deskew():    im_out = cv2.warpPerspective(skewed_image,np.linalg.inv(M),(orig_image.shape[1],orig_image.shape[0]))    plt.imshow(im_out,'gray')    plt.show()orig_image = cv2.imread(r'image.png',0)skewed_image = cv2.imread(r'imageSkewed.png',0)surf = cv2.xfeatures2d.SURF_create(400)kp1,des1 = surf.detectAndCompute(orig_image,des2 = surf.detectAndCompute(skewed_image,5.0)    # see https://ch.mathworks.com/help/images/examples/find-image-rotation-and-scale-using-automated-feature-matching.HTML for details    ss = M[0,1]    sc = M[0,0]    scaleRecovered = math.sqrt(ss * ss + sc * sc)    thetaRecovered = math.atan2(ss,sc) * 180 / math.pi    print("Calculated scale difference: %.2f\ncalculated rotation difference: %.2f" % (scaleRecovered,thetaRecovered))    deskew()else:    print("Not  enough  matches are found   -   %d/%d" % (len(good),MIN_MATCH_COUNT))    matchesMask = None

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