使用OpenCV和SIFT/SURF对扫描的图像进行歪斜校正以匹配原始图像 [英] Deskewing scanned image to match original image using OpenCV and SIFT/SURF

问题描述

我有一个数字形式的原始页面，以及同一页面的多个扫描版本.我的目标是使扫描的页面偏斜，以使其与原始页面尽可能匹配.我知道我可以按照此处所述使用概率霍夫变换a>用于固定旋转，但是由于某些人将页面缩放为其他纸张格式，因此扫描的纸张尺寸也有所不同.我认为，OpenCV中的findHomography()函数与SIFT/SURF中的关键点结合在一起，正是我需要解决的这个问题.但是，我只是无法使我的deskew()函数正常工作.

I have an original page in digital form and several scanned versions of the same page. My goal is to deskew the scanned pages such that they match the original page as much as possible. I know that I could use the Probabilistic Hough Transform as described here for fixing the rotation but the scanned papers also differ in size as some people scaled the page to a different paper format. I think that the findHomography() function in OpenCV in combination with the keypoints from SIFT/SURF are exactly what I need to solve this problem. However, I just can't get my deskew() function to work.

我的大部分代码来自以下两个来源: 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 .

Most of my code stems from the following two sources: http://www.learnopencv.com/homography-examples-using-opencv-python-c/ and 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", 0), None, fx=factor, fy=factor, 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, 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, h - 1], [w - 1, 0]]).reshape(-1, 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, 255, 0),  # draw  matches in  green   color
                   singlePointColor=None,
                   matchesMask=matchesMask,  # draw only    inliers
                   flags=2)
img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params)
plt.imshow(img3, 'gray')
plt.show()

推荐答案

原来，我非常接近解决自己的问题的方法. 这是我的代码的工作版本:

Turns out I was very close to solving my own problem. Here's the working version of my code:

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, None)
kp2, des2 = surf.detectAndCompute(skewed_image, 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, 1, 2)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 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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