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

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 np
import cv2
from matplotlib import pyplot as plt


# FIXME: doesn't work
def deskew():
    im_out = cv2.warpPerspective(img1,M,(img2.shape[1],img2.shape[0]))
    plt.imshow(im_out,'gray')
    plt.show()


# resizing images to improve speed
factor = 0.4
img1 = 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 = 0
index_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 = 10
if 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 keypoints
draw_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()

import numpy as np
import cv2
from matplotlib import pyplot as plt
import math


def 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: %.2fnCalculated rotation difference: %.2f" % (scaleRecovered,thetaRecovered))

    deskew()

else:
    print("Not  enough  matches are found   -   %d/%d" % (len(good),MIN_MATCH_COUNT))
    matchesMask = None