【OpenCV】透视变换 Perspective Transformation(续)
透视变换的原理和矩阵求解请参见前一篇《透视变换 Perspective Transformation》。在OpenCV中也实现了透视变换的公式求解和变换函数。
求解变换公式的函数:
- Mat getPerspectiveTransform(const Point2f src[], const Point2f dst[])
输入原始图像和变换之后的图像的对应4个点,便可以得到变换矩阵。之后用求解得到的矩阵输入perspectiveTransform便可以对一组点进行变换:
- void perspectiveTransform(InputArray src, OutputArray dst, InputArray m)
注意这里src和dst的输入并不是图像,而是图像对应的坐标。应用前一篇的例子,做个相反的变换:
- int main( )
- {
- Mat img=imread("boy.png");
- int img_height = img.rows;
- int img_width = img.cols;
- vector<Point2f> corners(4);
- corners[0] = Point2f(0,0);
- corners[1] = Point2f(img_width-1,0);
- corners[2] = Point2f(0,img_height-1);
- corners[3] = Point2f(img_width-1,img_height-1);
- vector<Point2f> corners_trans(4);
- corners_trans[0] = Point2f(150,250);
- corners_trans[1] = Point2f(771,0);
- corners_trans[2] = Point2f(0,img_height-1);
- corners_trans[3] = Point2f(650,img_height-1);
- Mat transform = getPerspectiveTransform(corners,corners_trans);
- cout<<transform<<endl;
- vector<Point2f> ponits, points_trans;
- for(int i=0;i<img_height;i++){
- for(int j=0;j<img_width;j++){
- ponits.push_back(Point2f(j,i));
- }
- }
- perspectiveTransform( ponits, points_trans, transform);
- Mat img_trans = Mat::zeros(img_height,img_width,CV_8UC3);
- int count = 0;
- for(int i=0;i<img_height;i++){
- uchar* p = img.ptr<uchar>(i);
- for(int j=0;j<img_width;j++){
- int y = points_trans[count].y;
- int x = points_trans[count].x;
- uchar* t = img_trans.ptr<uchar>(y);
- t[x*3] = p[j*3];
- t[x*3+1] = p[j*3+1];
- t[x*3+2] = p[j*3+2];
- count++;
- }
- }
- imwrite("boy_trans.png",img_trans);
- return 0;
- }
得到变换之后的图片:
注意这种将原图变换到对应图像上的方式会有一些没有被填充的点,也就是右图中黑色的小点。解决这种问题一是用差值的方式,再一种比较简单就是不用原图的点变换后对应找新图的坐标,而是直接在新图上找反向变换原图的点。说起来有点绕口,具体见前一篇《透视变换 Perspective Transformation》的代码应该就能懂啦。
除了getPerspectiveTransform()函数,OpenCV还提供了findHomography()的函数,不是用点来找,而是直接用透视平面来找变换公式。这个函数在特征匹配的经典例子中有用到,也非常直观:
- int main( int argc, char** argv )
- {
- Mat img_object = imread( argv[1], IMREAD_GRAYSCALE );
- Mat img_scene = imread( argv[2], IMREAD_GRAYSCALE );
- if( !img_object.data || !img_scene.data )
- { std::cout<< " --(!) Error reading images " << std::endl; return -1; }
- //-- Step 1: Detect the keypoints using SURF Detector
- int minHessian = 400;
- SurfFeatureDetector detector( minHessian );
- std::vector<KeyPoint> keypoints_object, keypoints_scene;
- detector.detect( img_object, keypoints_object );
- detector.detect( img_scene, keypoints_scene );
- //-- Step 2: Calculate descriptors (feature vectors)
- SurfDescriptorExtractor extractor;
- Mat descriptors_object, descriptors_scene;
- extractor.compute( img_object, keypoints_object, descriptors_object );
- extractor.compute( img_scene, keypoints_scene, descriptors_scene );
- //-- Step 3: Matching descriptor vectors using FLANN matcher
- FlannBasedMatcher matcher;
- std::vector< DMatch > matches;
- matcher.match( descriptors_object, descriptors_scene, matches );
- double max_dist = 0; double min_dist = 100;
- //-- Quick calculation of max and min distances between keypoints
- for( int i = 0; i < descriptors_object.rows; i++ )
- { double dist = matches[i].distance;
- if( dist < min_dist ) min_dist = dist;
- if( dist > max_dist ) max_dist = dist;
- }
- printf("-- Max dist : %f \n", max_dist );
- printf("-- Min dist : %f \n", min_dist );
- //-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
- std::vector< DMatch > good_matches;
- for( int i = 0; i < descriptors_object.rows; i++ )
- { if( matches[i].distance < 3*min_dist )
- { good_matches.push_back( matches[i]); }
- }
- Mat img_matches;
- drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
- good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
- vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
- //-- Localize the object from img_1 in img_2
- std::vector<Point2f> obj;
- std::vector<Point2f> scene;
- for( size_t i = 0; i < good_matches.size(); i++ )
- {
- //-- Get the keypoints from the good matches
- obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
- scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
- }
- Mat H = findHomography( obj, scene, RANSAC );
- //-- Get the corners from the image_1 ( the object to be "detected" )
- std::vector<Point2f> obj_corners(4);
- obj_corners[0] = Point(0,0); obj_corners[1] = Point( img_object.cols, 0 );
- obj_corners[2] = Point( img_object.cols, img_object.rows ); obj_corners[3] = Point( 0, img_object.rows );
- std::vector<Point2f> scene_corners(4);
- perspectiveTransform( obj_corners, scene_corners, H);
- //-- Draw lines between the corners (the mapped object in the scene - image_2 )
- Point2f offset( (float)img_object.cols, 0);
- line( img_matches, scene_corners[0] + offset, scene_corners[1] + offset, Scalar(0, 255, 0), 4 );
- line( img_matches, scene_corners[1] + offset, scene_corners[2] + offset, Scalar( 0, 255, 0), 4 );
- line( img_matches, scene_corners[2] + offset, scene_corners[3] + offset, Scalar( 0, 255, 0), 4 );
- line( img_matches, scene_corners[3] + offset, scene_corners[0] + offset, Scalar( 0, 255, 0), 4 );
- //-- Show detected matches
- imshow( "Good Matches & Object detection", img_matches );
- waitKey(0);
- return 0;
- }
代码运行效果:
findHomography()函数直接通过两个平面上相匹配的特征点求出变换公式,之后代码又对原图的四个边缘点进行变换,在右图上画出对应的矩形。这个图也很好地解释了所谓透视变换的“Viewing Plane”。
(转载请注明作者和出处:http://blog.csdn.net/xiaowei_cqu 未经允许请勿用于商业用途)
【OpenCV】透视变换 Perspective Transformation(续)相关推荐
- 【OpenCV】透视变换 Perspective Transformation
透视变换的原理和矩阵求解请参见前一篇<透视变换 Perspective Transformation>.在OpenCV中也实现了透视变换的公式求解和变换函数. 求解变换公式的函数: Mat ...
- 【图像处理】透视变换 Perspective Transformation
透视变换(Perspective Transformation)是将图片投影到一个新的视平面(Viewing Plane),也称作投影映射(Projective Mapping).通用的变换公式为: ...
- 【CG】透视变换(Perspective Transformation)
起源 透视与消失点 单点透视 - 1 个消失点 两点透视 - 2个消失点 三点透视 - 3个消失点 透视投影变换 透视变换矩阵 透视变换矩阵与 Homography.相机内参的关系 透视与车载环境的联 ...
- 【图像处理】透视变换 Perspective Transformation(含续加部分)
本文博文地址:https://blog.csdn.net/xiaowei_cqu/article/details/26471527#commentsedit 续文博文地址:https://blog.c ...
- 【图像处理】透视变换 Perspective Transformation(小细节修正和推导流程补充)
这部分的推导,首先是求解∆x3,∆y3,然后通过得到的方程组,求解可得a13,a23,然后通过∆x1,∆x2分别求得a21,a11,同样的通过∆y1,∆y2求得a22和a12,至此透视变换矩阵所有元素 ...
- 透视变换(perspective transformation)
透视变换(Perspective Transformation)是将图片投影到一个新的视平面(Viewing Plane),也称作投影映射(Projective Mapping).如下图所示 透视变换 ...
- 详解 OpenCV 透视变换原理 及 实例
OpenCV提供了两种图片变换的方式:仿射变换和透视变换,两者的区别很容易区分, 前者是将矩形的图片变成平行四边形 后者是将图片变成梯形 这两种变换虽然都有各自的应用场景,但在实际的图片变换中由于透视 ...
- Opencv透视变换——cv::getPerspectiveTransform()与cv::warpPerspective()详解
透视变换(Perspective Transformation)是将成像投影到一个新的视平面(Viewing Plane),也称作投影映射(Projective Mapping).如图1,通过透视变换 ...
- 图像处理中的投影变换(Perspective Transformation)
透视变换(Perspective Transformation)是将图片投影到一个新的视平面(Viewing Plane),也称作投影映射(Projective Mapping).通用的变换公式为: ...
最新文章
- 阿里三面被挂,幸获内推,历经5轮终于拿到口碑offer
- JavaScript 技术篇-一段js代码展示可以随鼠标移动变换样式的卡通人物,动态女生眼睛跟着鼠转动
- HTML5-WebSocket实现对服务器CPU实时监控
- xml.modify() 实例演示(二)
- Shiro安全框架-简介
- Day12-date time
- C/C++语言里的near和far是什么意思?
- gin源码解析(1) - gin 与 net/http 的关系
- ssh: Could not resolve hostname gitcafe.com: nodename nor servname provided, or not known
- java 含有package cmd_如何在命令提示符下编译运行含有Package的java文件
- cjson使用_LD3320语音识别模块:LDV7模块使用详解
- rep( )函数--R语言
- java实现小程序订阅消息推送(附源码)
- (Windows) CodeBlocks 下载
- mysql卸载不干净 linux,CentOS下如何完全卸载MySQL?解决卸载不干净的问题
- 常用计算机英文缩写,常用计算机英文缩写含义
- 图片压缩工具ImageOptim
- [转载]美国夫妻生活如何在钱上更亲密
- mmc0: Too large timeout requested for CMD25
- win10桌面图标全部变成白色的怎么办
热门文章
- 星型模型和雪花型模型比较
- Ubuntu18.04安装Dlib-19.19.0成功
- 比较简单的c语言编辑器,大佬们,小菜鸟想问一问用vc编译器做简易画图软件
- Shell遍历hadoop目录的批量操作
- 《啊哈!算法》笔记_Day02
- python无法调用pygame,python-pycharm无法识别pygame程序包
- win10安装ensp启动40_acer e5-475g笔记本如何安装win10系统【图文教程】
- 校园计算机故障解决方论文法,计算机故障与处理-计算机专业毕业论文.pdf
- 实现JavaScript数组拍平/扁平化的方法
- dell电脑重装系统no bootable devices found