1 // OpenCV_Align.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。
  2 //
  3  
  4 #include "pch.h"   //opencv4.0要去掉
  5 #include <iostream>
  6  
  7 #include <opencv2/opencv.hpp>
  8 #include "opencv2/xfeatures2d.hpp"
  9 #include "opencv2/features2d.hpp"
 10  
 11 using namespace std;
 12 using namespace cv;
 13 using namespace cv::xfeatures2d;
 14  
 15 //最大特征点数
 16 const int MAX_FEATURES = 500;
 17 //好的特征点数
 18 const float GOOD_MATCH_PERCENT = 0.15f;
 19  
 20 /**
 21  * @brief 图像对齐
 22  *
 23  * @param im1 对齐图像
 24  * @param im2 模板图像
 25  * @param im1Reg 输出图像
 26  * @param h
 27  */
 28 void alignImages(Mat &im1, Mat &im2, Mat &im1Reg, Mat &h)
 29 {
 30     // Convert images to grayscale
 31     Mat im1Gray, im2Gray;
 32     //转换为灰度图
 33     cvtColor(im1, im1Gray, CV_BGR2GRAY);   //opencv4.0这里CV_BGR2GRAY要改成COLOR_BGR2GRAY
 34     cvtColor(im2, im2Gray, CV_BGR2GRAY);
 35  
 36     // Variables to store keypoints and descriptors
 37     //关键点
 38     std::vector<KeyPoint> keypoints1, keypoints2;
 39     //特征描述符
 40     Mat descriptors1, descriptors2;
 41  
 42     // Detect ORB features and compute descriptors. 计算ORB特征和描述子
 43     Ptr<Feature2D> orb = ORB::create(MAX_FEATURES);
 44     orb->detectAndCompute(im1Gray, Mat(), keypoints1, descriptors1);
 45     orb->detectAndCompute(im2Gray, Mat(), keypoints2, descriptors2);
 46  
 47     // Match features. 特征点匹配
 48     std::vector<DMatch> matches;
 49     //汉明距离进行特征点匹配
 50     Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce-Hamming");
 51     matcher->match(descriptors1, descriptors2, matches, Mat());
 52  
 53     // Sort matches by score 按照特征点匹配结果从优到差排列
 54     std::sort(matches.begin(), matches.end());
 55  
 56     // Remove not so good matches 移除不好的特征点
 57     const int numGoodMatches = matches.size() * GOOD_MATCH_PERCENT;
 58     matches.erase(matches.begin() + numGoodMatches, matches.end());
 59  
 60     // Draw top matches
 61     Mat imMatches;
 62     //画出特征点匹配图
 63     drawMatches(im1, keypoints1, im2, keypoints2, matches, imMatches);
 64     imwrite("matches.jpg", imMatches);             //两图之间的特征点匹配关系图
 65  
 66     // Extract location of good matches
 67     std::vector<Point2f> points1, points2;
 68  
 69     //保存对应点
 70     for (size_t i = 0; i < matches.size(); i++)
 71     {
 72         //queryIdx是对齐图像的描述子和特征点的下标。
 73         points1.push_back(keypoints1[matches[i].queryIdx].pt);
 74         //queryIdx是是样本图像的描述子和特征点的下标。
 75         points2.push_back(keypoints2[matches[i].trainIdx].pt);
 76     }
 77  
 78     // Find homography 计算Homography，RANSAC随机抽样一致性算法
 79     h = findHomography(points1, points2, RANSAC);
 80  
 81     // Use homography to warp image 映射
 82     warpPerspective(im1, im1Reg, h, im2.size());
 83 }
 84  
 85 int main()
 86 {
 87     // Read reference image 读取参考图像
 88     string refFilename("./image/form.jpg");        //基准图像
 89     cout << "Reading reference image : " << refFilename << endl;
 90     Mat imReference = imread(refFilename);
 91  
 92     // Read image to be aligned 读取对准图像
 93     string imFilename("./image/scanned-form.jpg");   //待校准图像
 94     cout << "Reading image to align : " << imFilename << endl;
 95     Mat im = imread(imFilename);
 96  
 97     // Registered image will be resotred in imReg.
 98     // The estimated homography will be stored in h.
 99     //结果图像，单应性矩阵
100     Mat imReg, h;
101  
102     // Align images
103     cout << "Aligning images ..." << endl;
104     alignImages(im, imReference, imReg, h);
105  
106     // Write aligned image to disk.
107     string outFilename("aligned.jpg");   //校准图
108     cout << "Saving aligned image : " << outFilename << endl;
109     imwrite(outFilename, imReg);
110  
111     // Print estimated homography
112     cout << "Estimated homography : 
" << h << endl;
113     return 0;
114 }

 1 from __future__ import print_function
 2 import cv2
 3 import numpy as np
 4  
 5  
 6 MAX_MATCHES = 500
 7 GOOD_MATCH_PERCENT = 0.15
 8  
 9  
10 def alignImages(im1, im2):
11  
12   # Convert images to grayscale
13   im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
14   im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
15   
16   # Detect ORB features and compute descriptors.
17   orb = cv2.ORB_create(MAX_MATCHES)
18   keypoints1, descriptors1 = orb.detectAndCompute(im1Gray, None)
19   keypoints2, descriptors2 = orb.detectAndCompute(im2Gray, None)
20   
21   # Match features.
22   matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
23   matches = matcher.match(descriptors1, descriptors2, None)
24   
25   # Sort matches by score
26   matches.sort(key=lambda x: x.distance, reverse=False)
27  
28   # Remove not so good matches
29   numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
30   matches = matches[:numGoodMatches]
31  
32   # Draw top matches
33   imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
34   cv2.imwrite("matches.jpg", imMatches)
35   
36   # Extract location of good matches
37   points1 = np.zeros((len(matches), 2), dtype=np.float32)
38   points2 = np.zeros((len(matches), 2), dtype=np.float32)
39  
40   for i, match in enumerate(matches):
41     points1[i, :] = keypoints1[match.queryIdx].pt
42     points2[i, :] = keypoints2[match.trainIdx].pt
43   
44   # Find homography
45   h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
46  
47   # Use homography
48   height, width, channels = im2.shape
49   im1Reg = cv2.warpPerspective(im1, h, (width, height))
50   
51   return im1Reg, h
52  
53  
54 if __name__ == '__main__':
55   
56   # Read reference image
57   refFilename = "./image/form.jpg"
58   print("Reading reference image : ", refFilename)
59   imReference = cv2.imread(refFilename, cv2.IMREAD_COLOR)
60  
61   # Read image to be aligned
62   imFilename = "./image/scanned-form.jpg"
63   print("Reading image to align : ", imFilename);  
64   im = cv2.imread(imFilename, cv2.IMREAD_COLOR)
65   
66   print("Aligning images ...")
67   # Registered image will be resotred in imReg. 
68   # The estimated homography will be stored in h. 
69   imReg, h = alignImages(im, imReference)
70   
71   # Write aligned image to disk. 
72   outFilename = "aligned.jpg"
73   print("Saving aligned image : ", outFilename); 
74   cv2.imwrite(outFilename, imReg)
75  
76   # Print estimated homography
77   print("Estimated homography : 
",  h)