• ASIFT算法过程实现 --- 配置避坑指南

    常规的SIFT算法进行图像匹配的时候,只能进行两个摄像机夹角比较小的(最大是15°),拍摄的图像进行相机的图像匹配,但是针对于相机之间的夹角比较大的时候,上述的算法匹配就是会出现问题.为了解决上面的这个问题,使用了一种改进的算法匹配方式ASIFT算法进行匹配.具体这种算法的优点见博客论文:基于ASIFT算法特征匹配的研究算法特征匹配的研究-图像处理文档类资源-CSDN下载在进行相机图像匹配的时候，一般的情况下我们是使用的双目相机，但是二者的视角是几乎近似平行的，平时我们更多下载资源、学习资料请访问CSDN下载频道.https://download.csdn.net/download/m0_47489229/87108632配置过程出错:
    一开始我是看的这两篇博客,进行的配置ASIFT算法在WIN7 64位系统下利用VS2012生成_zz420521的博客-CSDN博客_demo_asift欢迎大家交流，转载时请注明原文出处：http://blog.csdn.net/zlr289652981/article/details/63684857传统的sift算法在视角变化不大的情况下，可以获得良好的匹配效果，但如果在视角变化偏大时，其匹配效果会急剧下降，甚至找不到匹配点。由Guoshen Yu和Jean-Michel Morel提出的ASIFT算法很好的解决了在大视角变化条件下的图像https://blog.csdn.net/zz420521/article/details/63686163搭建可随意更改路径的VS工程-以ASIFT算法为例_zz420521的博客-CSDN博客在前一篇的博文中，笔者介绍了如何利用Guoshen Yu和Jean-Michel Morel提出的ASIFT算法的windows下C++版本源码来构建工程，具体见链接：http://blog.csdn.net/zz420521/article/details/63686163但实际上，虽然工程是跑通了，但是里面还包含了许多我们不熟悉的或者不需要的东西，尤其是一些路径设置的问题，对工程里面的设置https://blog.csdn.net/zz420521/article/details/65437441?ops_request_misc=&request_id=&biz_id=102&utm_term=vs2017%E5%AE%9E%E7%8E%B0asift%E7%AE%97%E6%B3%95&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-0-65437441.nonecase&spm=1018.2226.3001.4187我是用的VS2017进行配置,已经配置好的是OpenCV3.6.4+contribe3.6.4,然后按照上面的步骤进行配置完成之后,发现有错误出现,当然我的需求是使用代码进行编制,在进行生成解决方案的时候会出现一个如下的错误:

    这里的问题貌似是需要进行提前配置Eigen和Boost库,但是我发现这个地方是直接使用一个exe文件进行操作的,并不是我所需要的.百度也没有发现想要解决的东西.

    后来查阅资料发现,就是代码形式的一般是相应的python形式的,但是在查询代码的过程之中,查到一个c++形式实现这个功能的代码,如下所示:

    1. #include 
    2. #include 
    3. #include 
    4. #include 
    5. #include 
    6. #include 
    7. #include 
    8.  
    9. using namespace std;
    10. using namespace cv;
    11.  
    12. static void help(char** argv)
    13. {
    14. 	cout
    15. 		<< "This is a sample usage of AffineFeature detector/extractor.\n"
    16. 		<< "And this is a C++ version of samples/python/asift.py\n"
    17. 		<< "Usage: " << argv[0] << "\n"
    18. 		<< "     [ --feature= ]         # Feature to use.\n"
    19. 		<< "     [ --flann ]                            # use Flann-based matcher instead of bruteforce.\n"
    20. 		<< "     [ --maxlines= ] # The maximum number of lines in visualizing the matching result.\n"
    21. 		<< "     [ --image1= ]\n"
    22. 		<< "     [ --image2= ] # Path to images to compare."
    23. 		<< endl;
    24. }
    25.  
    26. static double timer()
    27. {
    28. 	return getTickCount() / getTickFrequency();
    29. }
    30.  
    31. int main(int argc, char** argv)
    32. {
    33. 	vector fileName;
    34. 	cv::CommandLineParser parser(argc, argv,
    35. 		"{help h ||}"
    36. 		"{feature|brisk|}"
    37. 		"{flann||}"
    38. 		"{maxlines|50|}"
    39. 		"{image1|aero1.jpg|}{image2|aero3.jpg|}");
    40. 	if (parser.has("help"))
    41. 	{
    42. 		help(argv);
    43. 		return 0;
    44. 	}
    45. 	string feature = parser.get("feature");
    46. 	bool useFlann = parser.has("flann");
    47. 	int maxlines = parser.get<int>("maxlines");
    48. 	fileName.push_back(samples::findFile(parser.get("image1")));
    49. 	fileName.push_back(samples::findFile(parser.get("image2")));
    50. 	if (!parser.check())
    51. 	{
    52. 		parser.printErrors();
    53. 		cout << "See --help (or missing '=' between argument name and value?)" << endl;
    54. 		return 1;
    55. 	}
    56.  
    57. 	Mat img1 = imread(fileName[0], IMREAD_GRAYSCALE);
    58. 	Mat img2 = imread(fileName[1], IMREAD_GRAYSCALE);
    59. 	if (img1.empty())
    60. 	{
    61. 		cerr << "Image " << fileName[0] << " is empty or cannot be found" << endl;
    62. 		return 1;
    63. 	}
    64. 	if (img2.empty())
    65. 	{
    66. 		cerr << "Image " << fileName[1] << " is empty or cannot be found" << endl;
    67. 		return 1;
    68. 	}
    69.  
    70. 	Ptr backend;
    71. 	Ptr matcher;
    72.  
    73. 	if (feature == "sift")
    74. 	{
    75. 		backend = SIFT::create();
    76. 		if (useFlann)
    77. 			matcher = DescriptorMatcher::create("FlannBased");
    78. 		else
    79. 			matcher = DescriptorMatcher::create("BruteForce");
    80. 	}
    81. 	else if (feature == "orb")
    82. 	{
    83. 		backend = ORB::create();
    84. 		if (useFlann)
    85. 			matcher = makePtr(makePtr(6, 12, 1));
    86. 		else
    87. 			matcher = DescriptorMatcher::create("BruteForce-Hamming");
    88. 	}
    89. 	else if (feature == "brisk")
    90. 	{
    91. 		backend = BRISK::create();
    92. 		if (useFlann)
    93. 			matcher = makePtr(makePtr(6, 12, 1));
    94. 		else
    95. 			matcher = DescriptorMatcher::create("BruteForce-Hamming");
    96. 	}
    97. 	else
    98. 	{
    99. 		cerr << feature << " is not supported. See --help" << endl;
    100. 		return 1;
    101. 	}
    102.  
    103. 	cout << "extracting with " << feature << "..." << endl;
    104. 	Ptr ext = AffineFeature::create(backend);
    105. 	vector kp1, kp2;
    106. 	Mat desc1, desc2;
    107.  
    108. 	ext->detectAndCompute(img1, Mat(), kp1, desc1);
    109. 	ext->detectAndCompute(img2, Mat(), kp2, desc2);
    110. 	cout << "img1 - " << kp1.size() << " features, "
    111. 		<< "img2 - " << kp2.size() << " features"
    112. 		<< endl;
    113.  
    114. 	cout << "matching with " << (useFlann ? "flann" : "bruteforce") << "..." << endl;
    115. 	double start = timer();
    116. 	// match and draw
    117. 	vector< vector > rawMatches;
    118. 	vector p1, p2;
    119. 	vector<float> distances;
    120. 	matcher->knnMatch(desc1, desc2, rawMatches, 2);
    121. 	// filter_matches
    122. 	for (size_t i = 0; i < rawMatches.size(); i++)
    123. 	{
    124. 		const vector& m = rawMatches[i];
    125. 		if (m.size() == 2 && m[0].distance < m[1].distance * 0.75)
    126. 		{
    127. 			p1.push_back(kp1[m[0].queryIdx].pt);
    128. 			p2.push_back(kp2[m[0].trainIdx].pt);
    129. 			distances.push_back(m[0].distance);
    130. 		}
    131. 	}
    132. 	vector status;
    133. 	vector< pair > pointPairs;
    134. 	Mat H = findHomography(p1, p2, status, RANSAC);
    135. 	int inliers = 0;
    136. 	for (size_t i = 0; i < status.size(); i++)
    137. 	{
    138. 		if (status[i])
    139. 		{
    140. 			pointPairs.push_back(make_pair(p1[i], p2[i]));
    141. 			distances[inliers] = distances[i];
    142. 			// CV_Assert(inliers <= (int)i);
    143. 			inliers++;
    144. 		}
    145. 	}
    146. 	distances.resize(inliers);
    147.  
    148. 	cout << "execution time: " << fixed << setprecision(2) << (timer() - start) * 1000 << " ms" << endl;
    149. 	cout << inliers << " / " << status.size() << " inliers/matched" << endl;
    150.  
    151. 	cout << "visualizing..." << endl;
    152. 	vector<int> indices(inliers);
    153. 	cv::sortIdx(distances, indices, SORT_EVERY_ROW + SORT_ASCENDING);
    154.  
    155. 	// explore_match
    156. 	int h1 = img1.size().height;
    157. 	int w1 = img1.size().width;
    158. 	int h2 = img2.size().height;
    159. 	int w2 = img2.size().width;
    160. 	Mat vis = Mat::zeros(max(h1, h2), w1 + w2, CV_8U);
    161. 	img1.copyTo(Mat(vis, Rect(0, 0, w1, h1)));
    162. 	img2.copyTo(Mat(vis, Rect(w1, 0, w2, h2)));
    163. 	cvtColor(vis, vis, COLOR_GRAY2BGR);
    164.  
    165. 	vector corners(4);
    166. 	corners[0] = Point2f(0, 0);
    167. 	corners[1] = Point2f((float)w1, 0);
    168. 	corners[2] = Point2f((float)w1, (float)h1);
    169. 	corners[3] = Point2f(0, (float)h1);
    170. 	vector icorners;
    171. 	perspectiveTransform(corners, corners, H);
    172. 	transform(corners, corners, Matx23f(1, 0, (float)w1, 0, 1, 0));
    173. 	Mat(corners).convertTo(icorners, CV_32S);
    174. 	polylines(vis, icorners, true, Scalar(255, 255, 255));
    175.  
    176. 	for (int i = 0; i < min(inliers, maxlines); i++)
    177. 	{
    178. 		int idx = indices[i];
    179. 		const Point2f& pi1 = pointPairs[idx].first;
    180. 		const Point2f& pi2 = pointPairs[idx].second;
    181. 		circle(vis, pi1, 2, Scalar(0, 255, 0), -1);
    182. 		circle(vis, pi2 + Point2f((float)w1, 0), 2, Scalar(0, 255, 0), -1);
    183. 		line(vis, pi1, pi2 + Point2f((float)w1, 0), Scalar(0, 255, 0));
    184. 	}
    185. 	if (inliers > maxlines)
    186. 		cout << "only " << maxlines << " inliers are visualized" << endl;
    187. 	imshow("affine find_obj", vis);
    188.  
    189.  
    190. 	waitKey();
    191. 	cout << "done" << endl;
    192. 	return 0;
    193. }
    194.

     在执行的时候,发现代码报错:

    还是老样子,这里的报错原因是我的库是opencv3.6.4,但是支持这个地方的是opencv4.5.0版本以上,还必须要配置contribe,所以一切还得重新弄.
    我在使用配置vs2015+cmake3.24.0+opencv4.5.3+contribe4.5.3过程又出现了之前的老问题,解决方案失败,就是像下面的这个所示:

    这个过程就是我是连接的外网,发现还是不行,就是需要下载很多很多的配置文件,将这些配置文件需要一个一个下载下来才可以,感觉还是比较困难的.然后就接着找博客,我发现了这一篇博客.史上最全最详细讲解 VS2015+OpenCV4.5.1+OpenCV-4.5.1_Contribute+CUDA_元宇宙MetaAI的博客-CSDN博客视频正在录制请稍等VS2015+opencv4.1+opencv4.1_contributeCmake官网CMake Warning at cmake/OpenCVDownload.cmake:202 (message)如何去掉cmake编译OpenCV时的Cmake Warning：“OpenCVGenSetupVars.cmake:54https://blog.csdn.net/CSS360/article/details/117871722?ops_request_misc=&request_id=&biz_id=102&utm_term=vs2015%20%20opencv4.5.1&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-3-117871722.142%5Ev66%5Econtrol,201%5Ev3%5Eadd_ask,213%5Ev2%5Et3_esquery_v2&spm=1018.2226.3001.4187

    我的电脑已经是使用vs2017配置好别的环境了,所以只能是用vs2015进行配置新的,参考上面的操作步骤,里面的东西在他的百度网盘里面下载.cache文件添加进去,就可以了.
    可以个锤子,还是不行.....................................................................................................................

    前面的配置过程都是有问题的,就是我走过的坑.

    第一种配置

    发现要安装之前还要安装一个什么CUDA什么鬼,这具体是个什么,我现在也不知道,需要在网上进行发查找,那就接着安装...

    win10 vs2015 cmake3.18.0 opencv4.5.1 opencv_contrib4.5.1 编译攻略_dragon_perfect的博客-CSDN博客_cmake编译opencv4.5.1一、前期准备：需要下载的部分有四个：OpenCV, OpenCV_contrib, CMake，VisualStudio1.　下载OpenCV and OpenCV_contrib，要求是匹配的同版本，并解压缩存储到同一文件夹下；下载OpenCV链接：https://opencv.org/releases/下载OpenCV_contrib链接 ：https://github.com/opencv/opencv_contrib/releases2. CMake的下载下载链接：ht...https://blog.csdn.net/zhulong1984/article/details/112728038https://www.baidu.com/link?url=STTSCR6UH-Zr7kAZXC8sNy3h9MSenc5VtvTHiSWMeQ30_MXa0-zrnioqai1bvwuNYjH2s4jpYwHufK5vrhn-LF0xMpJ8zL9pltVB_gINiJe&wd=&eqid=a6ef9f51000221a300000006637e2a66https://www.baidu.com/link?url=STTSCR6UH-Zr7kAZXC8sNy3h9MSenc5VtvTHiSWMeQ30_MXa0-zrnioqai1bvwuNYjH2s4jpYwHufK5vrhn-LF0xMpJ8zL9pltVB_gINiJe&wd=&eqid=a6ef9f51000221a300000006637e2a66我在看完上面两个博客的介绍之后,发现配置之中不存在什么CUDA的配置,也就是说我没有安装CUDA,那就接着CSDN,github看不懂,智力有限.那么接下来请看下面的这个博文,进行配置CUDA配置过程.
    win10+cuda11.0+vs2019安装教程_离墨猫的博客-CSDN博客_cuda vs转自：https://www.jianshu.com/p/1fd15d2408bf?utm_campaign=hugo第一步：检查显卡支持的cuda版本1.第一种方法：win+R打开cmd，输入nvidia-smi，我的显卡是nvidia 2070super，支持的cuda版本是11.0图1 cmd查看显卡支持的cuda版本2.第二种方法：搜索框输入nvidia,出现nvidia控制面板，打开帮助中的系统信息，选择组件，出现cuda版本信息。第二步：安装vs2019.https://blog.csdn.net/syz201558503103/article/details/114867877?ops_request_misc=&request_id=&biz_id=102&utm_term=vs%20cuda%E5%A6%82%E4%BD%95%E5%AE%89%E8%A3%85&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-3-114867877.pc_v2_rank_dl_default&spm=1018.2226.3001.4449但是我看了一些实验室配的电脑的显卡,只有一个核显,没有独显,所以就是没有什么NVDIA配置,就没有什么CUDA配置.

    到这个地方思路有断了,那么我可不可以不使用cuda的加速了.又开始了在vs2015上的配置过程,但是还是生成解决方案的时候,爆出了很多的错误.
    实在没有办法了,只能在VS2017上进行配置了,突然发现可以,生成解决方案和install都没有爆出错误,很玄学. 然后我执行的代码把原来的修改了一下

    1. #include 
    2. #include 
    3. #include 
    4. #include 
    5. #include 
    6. #include 
    7. #include 
    8.  
    9. using namespace std;
    10. using namespace cv;
    11.  
    12. static void help(char** argv)
    13. {
    14. 	cout
    15. 		<< "This is a sample usage of AffineFeature detector/extractor.\n"
    16. 		<< "And this is a C++ version of samples/python/asift.py\n"
    17. 		<< "Usage: " << argv[0] << "\n"
    18. 		<< "     [ --feature= ]         # Feature to use.\n"
    19. 		<< "     [ --flann ]                            # use Flann-based matcher instead of bruteforce.\n"
    20. 		<< "     [ --maxlines= ] # The maximum number of lines in visualizing the matching result.\n"
    21. 		<< "     [ --image1= ]\n"
    22. 		<< "     [ --image2= ] # Path to images to compare."
    23. 		<< endl;
    24. }
    25.  
    26. static double timer()
    27. {
    28. 	return getTickCount() / getTickFrequency();
    29. }
    30.  
    31. int main(int argc, char** argv)
    32. {
    33. 	vector fileName;
    34. 	cv::CommandLineParser parser(argc, argv,
    35. 		"{help h ||}"
    36. 		"{feature|brisk|}"
    37. 		"{flann||}"
    38. 		"{maxlines|50|}"
    39. 		"{image1|aero1.jpg|}{image2|aero3.jpg|}");
    40. 	if (parser.has("help"))
    41. 	{
    42. 		help(argv);
    43. 		return 0;
    44. 	}
    45. 	string feature = parser.get("feature");
    46. 	bool useFlann = parser.has("flann");
    47. 	int maxlines = parser.get<int>("maxlines");
    48. 	fileName.push_back(samples::findFile(parser.get("image1")));
    49. 	fileName.push_back(samples::findFile(parser.get("image2")));
    50. 	if (!parser.check())
    51. 	{
    52. 		parser.printErrors();
    53. 		cout << "See --help (or missing '=' between argument name and value?)" << endl;
    54. 		return 1;
    55. 	}
    56. 	
    57. 	Mat img1 = imread("茉莉清茶1.jpg", IMREAD_GRAYSCALE);
    58. 	Mat img2 = imread("茉莉清茶2.jpg", IMREAD_GRAYSCALE);
    59. 	if (img1.empty())
    60. 	{
    61. 		cerr << "Image1 "  << " is empty or cannot be found" << endl;
    62. 		return 1;
    63. 	}
    64. 	if (img2.empty())
    65. 	{
    66. 		cerr << "Image2 "  << " is empty or cannot be found" << endl;
    67. 		return 1;
    68. 	}
    69.  
    70. 	Ptr backend;
    71. 	Ptr matcher;
    72.  
    73. 	if (feature == "sift")
    74. 	{
    75. 		backend = SIFT::create();
    76. 		if (useFlann)
    77. 			matcher = DescriptorMatcher::create("FlannBased");
    78. 		else
    79. 			matcher = DescriptorMatcher::create("BruteForce");
    80. 	}
    81. 	else if (feature == "orb")
    82. 	{
    83. 		backend = ORB::create();
    84. 		if (useFlann)
    85. 			matcher = makePtr(makePtr(6, 12, 1));
    86. 		else
    87. 			matcher = DescriptorMatcher::create("BruteForce-Hamming");
    88. 	}
    89. 	else if (feature == "brisk")
    90. 	{
    91. 		backend = BRISK::create();
    92. 		if (useFlann)
    93. 			matcher = makePtr(makePtr(6, 12, 1));
    94. 		else
    95. 			matcher = DescriptorMatcher::create("BruteForce-Hamming");
    96. 	}
    97. 	else
    98. 	{
    99. 		cerr << feature << " is not supported. See --help" << endl;
    100. 		return 1;
    101. 	}
    102.  
    103. 	cout << "extracting with " << feature << "..." << endl;
    104. 	Ptr ext = AffineFeature::create(backend);
    105. 	vector kp1, kp2;
    106. 	Mat desc1, desc2;
    107.  
    108. 	ext->detectAndCompute(img1, Mat(), kp1, desc1);
    109. 	ext->detectAndCompute(img2, Mat(), kp2, desc2);
    110. 	cout << "img1 - " << kp1.size() << " features, "
    111. 		<< "img2 - " << kp2.size() << " features"
    112. 		<< endl;
    113.  
    114. 	cout << "matching with " << (useFlann ? "flann" : "bruteforce") << "..." << endl;
    115. 	double start = timer();
    116. 	// match and draw
    117. 	vector< vector > rawMatches;
    118. 	vector p1, p2;
    119. 	vector<float> distances;
    120. 	matcher->knnMatch(desc1, desc2, rawMatches, 2);
    121. 	// filter_matches
    122. 	for (size_t i = 0; i < rawMatches.size(); i++)
    123. 	{
    124. 		const vector& m = rawMatches[i];
    125. 		if (m.size() == 2 && m[0].distance < m[1].distance * 0.75)
    126. 		{
    127. 			p1.push_back(kp1[m[0].queryIdx].pt);
    128. 			p2.push_back(kp2[m[0].trainIdx].pt);
    129. 			distances.push_back(m[0].distance);
    130. 		}
    131. 	}
    132. 	vector status;
    133. 	vector< pair > pointPairs;
    134. 	Mat H = findHomography(p1, p2, status, RANSAC);
    135. 	int inliers = 0;
    136. 	for (size_t i = 0; i < status.size(); i++)
    137. 	{
    138. 		if (status[i])
    139. 		{
    140. 			pointPairs.push_back(make_pair(p1[i], p2[i]));
    141. 			distances[inliers] = distances[i];
    142. 			// CV_Assert(inliers <= (int)i);
    143. 			inliers++;
    144. 		}
    145. 	}
    146. 	distances.resize(inliers);
    147.  
    148. 	cout << "execution time: " << fixed << setprecision(2) << (timer() - start) * 1000 << " ms" << endl;
    149. 	cout << inliers << " / " << status.size() << " inliers/matched" << endl;
    150.  
    151. 	cout << "visualizing..." << endl;
    152. 	vector<int> indices(inliers);
    153. 	cv::sortIdx(distances, indices, SORT_EVERY_ROW + SORT_ASCENDING);
    154.  
    155. 	// explore_match
    156. 	int h1 = img1.size().height;
    157. 	int w1 = img1.size().width;
    158. 	int h2 = img2.size().height;
    159. 	int w2 = img2.size().width;
    160. 	Mat vis = Mat::zeros(max(h1, h2), w1 + w2, CV_8U);
    161. 	img1.copyTo(Mat(vis, Rect(0, 0, w1, h1)));
    162. 	img2.copyTo(Mat(vis, Rect(w1, 0, w2, h2)));
    163. 	cvtColor(vis, vis, COLOR_GRAY2BGR);
    164.  
    165. 	vector corners(4);
    166. 	corners[0] = Point2f(0, 0);
    167. 	corners[1] = Point2f((float)w1, 0);
    168. 	corners[2] = Point2f((float)w1, (float)h1);
    169. 	corners[3] = Point2f(0, (float)h1);
    170. 	vector icorners;
    171. 	perspectiveTransform(corners, corners, H);
    172. 	transform(corners, corners, Matx23f(1, 0, (float)w1, 0, 1, 0));
    173. 	Mat(corners).convertTo(icorners, CV_32S);
    174. 	polylines(vis, icorners, true, Scalar(255, 255, 255));
    175.  
    176. 	for (int i = 0; i < min(inliers, maxlines); i++)
    177. 	{
    178. 		int idx = indices[i];
    179. 		const Point2f& pi1 = pointPairs[idx].first;
    180. 		const Point2f& pi2 = pointPairs[idx].second;
    181. 		circle(vis, pi1, 2, Scalar(0, 255, 0), -1);
    182. 		circle(vis, pi2 + Point2f((float)w1, 0), 2, Scalar(0, 255, 0), -1);
    183. 		line(vis, pi1, pi2 + Point2f((float)w1, 0), Scalar(0, 255, 0));
    184. 	}
    185. 	if (inliers > maxlines)
    186. 		cout << "only " << maxlines << " inliers are visualized" << endl;
    187. 	imshow("affine find_obj", vis);
    188.  
    189.  
    190. 	waitKey();
    191. 	cout << "done" << endl;
    192. 	return 0;
    193. }

    貌似是没有配置相应的cuda加速,然后代码就是一直处于卡顿的状态.

    因为ASIFT本来就是比较慢的,那只能慢慢等啦.反正我的CPU已经跑疯了. 

    这个过程是需要等待很长的时间才可以出现结果的,害,慢慢等吧.受不了了,等的时间实在太长了,等了半个小时还不行,只能是把像素降低.输出结果如下所示:

    然后这个过程使用的VS2017+cmake+opencv4.5.1+contribe4.5.1(cuda没有配置),我就放在下面的百度网盘连接里了.

    链接：https://pan.baidu.com/s/1hPGGiupQW-1khpiCe4QHGw?pwd=1111 
    提取码：1111 

    第二种配置

    这里说的配置就是前面什么一个博客VS2012之中进行配置的一个过程,不知道为什么就是我的VS2017这个方式总是有问题,所以我使用了VS2015进行了配置.

    将这个里面的代码进行改正,为什么呢?因为我看了看之前使用cmd进行操作的时候,这里会输出一定的函数,然后这里argc集合的第一个函数就是exe文件,所以第一个我就不用管,剩下的值我是自己进行配置的,由于像素的限制,就是输入的图片像素是需要进行改变的,变成一定的大小,这个是很容易实现的.后面的代码我是改成了这样的,就直接可以自己使用了.

    1. // Copyright (c) 2008-2011, Guoshen Yu 
    2. // Copyright (c) 2008-2011, Jean-Michel Morel 
    3. //
    4. // WARNING: 
    5. // This file implements an algorithm possibly linked to the patent
    6. //
    7. // Jean-Michel Morel and Guoshen Yu, Method and device for the invariant 
    8. // affine recognition recognition of shapes (WO/2009/150361), patent pending. 
    9. //
    10. // This file is made available for the exclusive aim of serving as
    11. // scientific tool to verify of the soundness and
    12. // completeness of the algorithm description. Compilation,
    13. // execution and redistribution of this file may violate exclusive
    14. // patents rights in certain countries.
    15. // The situation being different for every country and changing
    16. // over time, it is your responsibility to determine which patent
    17. // rights restrictions apply to you before you compile, use,
    18. // modify, or redistribute this file. A patent lawyer is qualified
    19. // to make this determination.
    20. // If and only if they don't conflict with any patent terms, you
    21. // can benefit from the following license terms attached to this
    22. // file.
    23. //
    24. // This program is provided for scientific and educational only:
    25. // you can use and/or modify it for these purposes, but you are
    26. // not allowed to redistribute this work or derivative works in
    27. // source or executable form. A license must be obtained from the
    28. // patent right holders for any other use.
    29. //
    30. // 
    31. //*----------------------------- demo_ASIFT  --------------------------------*/
    32. // Detect corresponding points in two images with the ASIFT method. 
    33.  
    34. // Please report bugs and/or send comments to Guoshen Yu yu@cmap.polytechnique.fr
    35. // 
    36. // Reference: J.M. Morel and G.Yu, ASIFT: A New Framework for Fully Affine Invariant Image 
    37. //            Comparison, SIAM Journal on Imaging Sciences, vol. 2, issue 2, pp. 438-469, 2009. 
    38. // Reference: ASIFT online demo (You can try ASIFT with your own images online.) 
    39. //			  http://www.ipol.im/pub/algo/my_affine_sift/
    40. /*---------------------------------------------------------------------------*/
    41.  
    42. #include 
    43. #include 
    44. #include 
    45. #include 
    46. #include 
    47. using namespace std;
    48.  
    49. #ifdef _OPENMP
    50. #include 
    51. #endif
    52.  
    53. #include "demo_lib_sift.h"
    54. #include "io_png/io_png.h"
    55.  
    56.  
    57. #include "library.h"
    58. #include "frot.h"
    59. #include "fproj.h"
    60. #include "compute_asift_keypoints.h"
    61. #include "compute_asift_matches.h"
    62.  
    63. # define IM_X 800//800
    64. # define IM_Y 600//600
    65.  
    66. int main(int argc, char **argv)
    67. {			
    68. 	argc = 8;
    69. 	cout << "?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????" << endl;
    70. 	/*
    71.     if ((argc != 8) && (argc != 9)) {
    72.         std::cerr << " ******************************************************************************* " << std::endl
    73. 				  << " ***************************  ASIFT image matching  **************************** " << std::endl
    74. 				  << " ******************************************************************************* " << std::endl
    75. 				  << "Usage: " << argv[0] << " imgIn1.png imgIn2.png imgOutVert.png imgOutHori.png " << std::endl
    76. 										  << "           matchings.txt keys1.txt keys2.txt [Resize option: 0/1] " << std::endl
    77. 									      << "- imgIn1.png, imgIn2.png: input images (in PNG format). " << std::endl
    78. 										  << "- imgOutVert.png, imgOutHori.png: output images (vertical/horizontal concatenated, " << std::endl
    79. 				                          << "  in PNG format.) The detected matchings are connected by write lines." << std::endl
    80. 										  << "- matchings.txt: coordinates of matched points (col1, row1, col2, row2). " << std::endl
    81. 										  << "- keys1.txt keys2.txt: ASIFT keypoints of the two images." << std::endl
    82. 										  << "- [optional 0/1]. 1: input images resize to 800x600 (default). 0: no resize. " << std::endl 
    83.    				  << " ******************************************************************************* " << std::endl
    84. 				  << " *********************  Jean-Michel Morel, Guoshen Yu, 2010 ******************** " << std::endl
    85. 				  << " ******************************************************************************* " << std::endl;
    86.         return 1;
    87.     }
    88. 	*/
    89. 	
    90. 	 Input
    91. 	// Read image1
    92. 	
    93. 	
    94. 	
    95. 	argv[1] = "C:\\Users\\td\\Desktop\\xianmu\\new_stereo_match\\book3.png";
    96. 	argv[2] = "C:\\Users\\td\\Desktop\\xianmu\\new_stereo_match\\book4.png";
    97.  
    98. 	argv[3] = "C:\\Users\\td\\Desktop\\xianmu\\new_stereo_match\\bookshuchu1.png";
    99. 	argv[4] = "C:\\Users\\td\\Desktop\\xianmu\\new_stereo_match\\bookshuchu2.png";
    100.  
    101. 	size_t w1 , h1 ;
    102. 	size_t w2 , h2 ;
    103.  
    104.     float * iarr1;
    105. 	//argv[1] = "C:\\Users\\td\\Desktop\\xianmu\\new_stereo_match\\adam1.png";
    106.  
    107.     if (NULL == (iarr1 = read_png_f32_gray(argv[1], &w1, &h1))) {
    108. 		cout << iarr1 << endl;
    109.  
    110.         std::cerr << "Unable to load image file " << argv[1] << std::endl;
    111.         return 1;
    112.     }
    113.     std::vector<float> ipixels1(iarr1, iarr1 + w1 * h1);
    114.  
    115. 	free(iarr1); 
    116. 	
    117. 	// Read image2
    118.     float * iarr2;
    119. 	//argv[2] = "C:\\Users\\td\\Desktop\\xianmu\\new_stereo_match\\adam2.png";
    120.     if (NULL == (iarr2 = read_png_f32_gray(argv[2], &w2, &h2))) {
    121.         std::cerr << "Unable to load image file " << argv[2] << std::endl;
    122.         return 1;
    123.     }
    124.     std::vector<float> ipixels2(iarr2, iarr2 + w2 * h2);
    125. 	free(iarr2); 
    126. 	cout << "?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????" << endl;
    127. 	
    128.  
    129. 	/ Resize the images to area wS*hW in remaining the apsect-ratio	
    130. 	/ Resize if the resize flag is not set or if the flag is set unequal to 0
    131.  
    132.  
    133.  
    134. 	float wS = IM_X;
    135. 	float hS = IM_Y;
    136. 	
    137. 	float zoom1=0, zoom2=0;	
    138. 	int wS1=0, hS1=0, wS2=0, hS2=0;
    139. 	vector<float> ipixels1_zoom, ipixels2_zoom;	
    140. 		
    141. 	int flag_resize = 1;
    142. 	if (argc == 9)
    143. 	{	
    144. 		flag_resize = atoi(argv[8]);
    145. 	}
    146. 	
    147. 	if ((argc == 8) || (flag_resize != 0))
    148. 	{
    149. 		cout << "WARNING: The input images are resized to " << wS << "x" << hS << " for ASIFT. " << endl 
    150. 		<< "         But the results will be normalized to the original image size." << endl << endl;
    151. 		
    152. 		float InitSigma_aa = 1.6;
    153. 		
    154. 		float fproj_p, fproj_bg;
    155. 		char fproj_i;
    156. 		float *fproj_x4, *fproj_y4;
    157. 		int fproj_o;
    158.  
    159. 		fproj_o = 3;
    160. 		fproj_p = 0;
    161. 		fproj_i = 0;
    162. 		fproj_bg = 0;
    163. 		fproj_x4 = 0;
    164. 		fproj_y4 = 0;
    165. 				
    166. 		float areaS = wS * hS;
    167.  
    168. 		// Resize image 1 
    169. 		float area1 = w1 * h1;
    170. 		zoom1 = sqrt(area1/areaS);
    171. 		
    172. 		wS1 = (int) (w1 / zoom1);
    173. 		hS1 = (int) (h1 / zoom1);
    174. 		
    175. 		int fproj_sx = wS1;
    176. 		int fproj_sy = hS1;     
    177. 		
    178. 		float fproj_x1 = 0;
    179. 		float fproj_y1 = 0;
    180. 		float fproj_x2 = wS1;
    181. 		float fproj_y2 = 0;
    182. 		float fproj_x3 = 0;	     
    183. 		float fproj_y3 = hS1;
    184. 		
    185. 		/* Anti-aliasing filtering along vertical direction */
    186. 		if ( zoom1 > 1 )
    187. 		{
    188. 			float sigma_aa = InitSigma_aa * zoom1 / 2;
    189. 			GaussianBlur1D(ipixels1,w1,h1,sigma_aa,1);
    190. 			GaussianBlur1D(ipixels1,w1,h1,sigma_aa,0);
    191. 		}
    192. 			
    193. 		// simulate a tilt: subsample the image along the vertical axis by a factor of t.
    194. 		ipixels1_zoom.resize(wS1*hS1);
    195. 		fproj (ipixels1, ipixels1_zoom, w1, h1, &fproj_sx, &fproj_sy, &fproj_bg, &fproj_o, &fproj_p, 
    196. 			   &fproj_i , fproj_x1 , fproj_y1 , fproj_x2 , fproj_y2 , fproj_x3 , fproj_y3, fproj_x4, fproj_y4); 
    197. 		
    198. 		
    199. 		// Resize image 2 
    200. 		float area2 = w2 * h2;
    201. 		zoom2 = sqrt(area2/areaS);
    202. 				
    203. 		wS2 = (int) (w2 / zoom2);
    204. 		hS2 = (int) (h2 / zoom2);
    205. 		
    206. 		fproj_sx = wS2;
    207. 		fproj_sy = hS2;     
    208. 		
    209. 		fproj_x2 = wS2;
    210. 		fproj_y3 = hS2;
    211. 		
    212. 		/* Anti-aliasing filtering along vertical direction */
    213. 		if ( zoom1 > 1 )
    214. 		{
    215. 			float sigma_aa = InitSigma_aa * zoom2 / 2;
    216. 			GaussianBlur1D(ipixels2,w2,h2,sigma_aa,1);
    217. 			GaussianBlur1D(ipixels2,w2,h2,sigma_aa,0);
    218. 		}
    219. 			
    220. 		// simulate a tilt: subsample the image along the vertical axis by a factor of t.
    221. 		ipixels2_zoom.resize(wS2*hS2);
    222. 		fproj (ipixels2, ipixels2_zoom, w2, h2, &fproj_sx, &fproj_sy, &fproj_bg, &fproj_o, &fproj_p, 
    223. 			   &fproj_i , fproj_x1 , fproj_y1 , fproj_x2 , fproj_y2 , fproj_x3 , fproj_y3, fproj_x4, fproj_y4); 
    224. 	}
    225. 	else 
    226. 	{
    227. 		ipixels1_zoom.resize(w1*h1);	
    228. 		ipixels1_zoom = ipixels1;
    229. 		wS1 = w1;
    230. 		hS1 = h1;
    231. 		zoom1 = 1;
    232. 		
    233. 		ipixels2_zoom.resize(w2*h2);	
    234. 		ipixels2_zoom = ipixels2;
    235. 		wS2 = w2;
    236. 		hS2 = h2;
    237. 		zoom2 = 1;
    238. 	}
    239.  
    240. 	
    241. 	/ Compute ASIFT keypoints
    242. 	// number N of tilts to simulate t = 1, \sqrt{2}, (\sqrt{2})^2, ..., {\sqrt{2}}^(N-1)
    243. 	int num_of_tilts1 = 7;
    244. 	int num_of_tilts2 = 7;
    245. //	int num_of_tilts1 = 1;
    246. //	int num_of_tilts2 = 1;
    247. 	int verb = 0;
    248. 	// Define the SIFT parameters
    249. 	siftPar siftparameters;	
    250. 	default_sift_parameters(siftparameters);
    251.  
    252. 	vector< vector< keypointslist > > keys1;		
    253. 	vector< vector< keypointslist > > keys2;	
    254. 	
    255. 	int num_keys1=0, num_keys2=0;
    256. 	
    257. 	
    258. 	cout << "Computing keypoints on the two images..." << endl;
    259. 	time_t tstart, tend;	
    260. 	tstart = time(0);
    261.  
    262. 	num_keys1 = compute_asift_keypoints(ipixels1_zoom, wS1, hS1, num_of_tilts1, verb, keys1, siftparameters);
    263. 	num_keys2 = compute_asift_keypoints(ipixels2_zoom, wS2, hS2, num_of_tilts2, verb, keys2, siftparameters);
    264. 	
    265. 	tend = time(0);
    266. 	cout << "Keypoints computation accomplished in " << difftime(tend, tstart) << " seconds." << endl;
    267. 	
    268. 	 Match ASIFT keypoints
    269. 	int num_matchings;
    270. 	matchingslist matchings;	
    271. 	cout << "Matching the keypoints..." << endl;
    272. 	tstart = time(0);
    273. 	num_matchings = compute_asift_matches(num_of_tilts1, num_of_tilts2, wS1, hS1, wS2, 
    274. 										  hS2, verb, keys1, keys2, matchings, siftparameters);
    275. 	tend = time(0);
    276. 	cout << "Keypoints matching accomplished in " << difftime(tend, tstart) << " seconds." << endl;
    277. 	
    278. 	/ Output image containing line matches (the two images are concatenated one above the other)
    279. 	int band_w = 20; // insert a black band of width band_w between the two images for better visibility
    280. 	
    281. 	int wo =  MAX(w1,w2);
    282. 	int ho = h1+h2+band_w;
    283. 	
    284. 	float *opixelsASIFT = new float[wo*ho];
    285. 	
    286. 	for(int j = 0; j < (int) ho; j++)
    287. 		for(int i = 0; i < (int) wo; i++)  opixelsASIFT[j*wo+i] = 255;		
    288. 	
    289. 	/// Copy both images to output
    290. 	for(int j = 0; j < (int) h1; j++)
    291. 		for(int i = 0; i < (int) w1; i++)  opixelsASIFT[j*wo+i] = ipixels1[j*w1+i];				
    292. 	
    293. 	for(int j = 0; j < (int) h2; j++)
    294. 		for(int i = 0; i < (int) (int)w2; i++)  opixelsASIFT[(h1 + band_w + j)*wo + i] = ipixels2[j*w2 + i];	
    295. 	
    296. 	 Draw matches
    297. 	matchingslist::iterator ptr = matchings.begin();
    298. 	for(int i=0; i < (int) matchings.size(); i++, ptr++)
    299. 	{		
    300. 		draw_line(opixelsASIFT, (int) (zoom1*ptr->first.x), (int) (zoom1*ptr->first.y), 
    301. 				  (int) (zoom2*ptr->second.x), (int) (zoom2*ptr->second.y) + h1 + band_w, 255.0f, wo, ho);		
    302. 	}
    303. 			
    304. 	/ Save imgOut	
    305. 	write_png_f32(argv[3], opixelsASIFT, wo, ho, 1);
    306. 	
    307. 	delete[] opixelsASIFT; /*memcheck*/
    308. 	
    309. 	/// Output image containing line matches (the two images are concatenated one aside the other)
    310. 	int woH =  w1+w2+band_w;
    311. 	int hoH = MAX(h1,h2);
    312. 	
    313. 	float *opixelsASIFT_H = new float[woH*hoH];
    314. 	
    315. 	for(int j = 0; j < (int) hoH; j++)
    316. 		for(int i = 0; i < (int) woH; i++)  opixelsASIFT_H[j*woH+i] = 255;
    317. 	
    318. 	/// Copy both images to output
    319. 	for(int j = 0; j < (int) h1; j++)
    320. 		for(int i = 0; i < (int) w1; i++)  opixelsASIFT_H[j*woH+i] = ipixels1[j*w1+i];				
    321. 	
    322. 	for(int j = 0; j < (int) h2; j++)
    323. 		for(int i = 0; i < (int) w2; i++)  opixelsASIFT_H[j*woH + w1 + band_w + i] = ipixels2[j*w2 + i];	
    324.  
    325. 	
    326. 	 Draw matches
    327. 	matchingslist::iterator ptrH = matchings.begin();
    328. 	for(int i=0; i < (int) matchings.size(); i++, ptrH++)
    329. 	{		
    330. 		draw_line(opixelsASIFT_H, (int) (zoom1*ptrH->first.x), (int) (zoom1*ptrH->first.y), 
    331. 				  (int) (zoom2*ptrH->second.x) + w1 + band_w, (int) (zoom2*ptrH->second.y), 255.0f, woH, hoH);		
    332. 	}
    333. 	
    334. 	/ Save imgOut	
    335. 	write_png_f32(argv[4], opixelsASIFT_H, woH, hoH, 1);
    336. 	
    337. 	delete[] opixelsASIFT_H; /*memcheck*/
    338. 	
    339. 	// Write the coordinates of the matched points (row1, col1, row2, col2) to the file argv[5]
    340. 	std::ofstream file(argv[5]);
    341. 	if (file.is_open())
    342. 	{		
    343. 		// Write the number of matchings in the first line
    344. 		file << num_matchings << std::endl;
    345. 		
    346. 		matchingslist::iterator ptr = matchings.begin();
    347. 		for(int i=0; i < (int) matchings.size(); i++, ptr++)		
    348. 		{
    349. 			file << zoom1*ptr->first.x << "  " << zoom1*ptr->first.y << "  " <<  zoom2*ptr->second.x << 
    350. 			"  " <<  zoom2*ptr->second.y << std::endl;
    351. 		}		
    352. 	}
    353. 	else 
    354. 	{
    355. 		std::cerr << "Unable to open the file matchings."; 
    356. 	}
    357.  
    358. 	file.close();
    359.  
    360.  
    361. 	
    362. 	// Write all the keypoints (row, col, scale, orientation, desciptor (128 integers)) to 
    363. 	// the file argv[6] (so that the users can match the keypoints with their own matching algorithm if they wish to)
    364. 	// keypoints in the 1st image
    365. 	std::ofstream file_key1(argv[6]);
    366. 	if (file_key1.is_open())
    367. 	{
    368. 		// Follow the same convention of David Lowe: 
    369. 		// the first line contains the number of keypoints and the length of the desciptors (128)
    370. 		file_key1 << num_keys1 << "  " << VecLength << "  " << std::endl;
    371. 		for (int tt = 0; tt < (int) keys1.size(); tt++)
    372. 		{
    373. 			for (int rr = 0; rr < (int) keys1[tt].size(); rr++)
    374. 			{
    375. 				keypointslist::iterator ptr = keys1[tt][rr].begin();
    376. 				for(int i=0; i < (int) keys1[tt][rr].size(); i++, ptr++)	
    377. 				{
    378. 					file_key1 << zoom1*ptr->x << "  " << zoom1*ptr->y << "  " << zoom1*ptr->scale << "  " << ptr->angle;
    379. 					
    380. 					for (int ii = 0; ii < (int) VecLength; ii++)
    381. 					{
    382. 						file_key1 << "  " << ptr->vec[ii];
    383. 					}
    384. 					
    385. 					file_key1 << std::endl;
    386. 				}
    387. 			}	
    388. 		}
    389. 	}
    390. 	else 
    391. 	{
    392. 		std::cerr << "Unable to open the file keys1."; 
    393. 	}
    394.  
    395. 	file_key1.close();
    396. 	
    397. 	// keypoints in the 2nd image
    398. 	std::ofstream file_key2(argv[7]);
    399. 	if (file_key2.is_open())
    400. 	{
    401. 		// Follow the same convention of David Lowe: 
    402. 		// the first line contains the number of keypoints and the length of the desciptors (128)
    403. 		file_key2 << num_keys2 << "  " << VecLength << "  " << std::endl;
    404. 		for (int tt = 0; tt < (int) keys2.size(); tt++)
    405. 		{
    406. 			for (int rr = 0; rr < (int) keys2[tt].size(); rr++)
    407. 			{
    408. 				keypointslist::iterator ptr = keys2[tt][rr].begin();
    409. 				for(int i=0; i < (int) keys2[tt][rr].size(); i++, ptr++)	
    410. 				{
    411. 					file_key2 << zoom2*ptr->x << "  " << zoom2*ptr->y << "  " << zoom2*ptr->scale << "  " << ptr->angle;
    412. 					
    413. 					for (int ii = 0; ii < (int) VecLength; ii++)
    414. 					{
    415. 						file_key2 << "  " << ptr->vec[ii];
    416. 					}					
    417. 					file_key2 << std::endl;
    418. 				}
    419. 			}	
    420. 		}
    421. 	}
    422. 	else 
    423. 	{
    424. 		std::cerr << "Unable to open the file keys2."; 
    425. 	}
    426. 	file_key2.close();
    427. 	
    428.     return 0;
    429. }

     原始图片实在是太大了,我就把他的像素变小了一点点,然后输出的结果如下所示: 

    终于弄好了,太难了,花费了几天........................................
    上面的两个代码我是同时运行的,总体是第一个方式比较快,需要进行降低像素.

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  • 原文地址：https://blog.csdn.net/m0_47489229/article/details/127997036