【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 ", max_dist ）;
printf（"-- Min dist : %f ", 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”。--------------------------------------分割线 --------------------------------------Ubuntu Linux下安装OpenCV2.4.1所需包 http://www.linuxidc.com/Linux/2012-08/68184.htmUbuntu 12.04 安装 OpenCV2.4.2 http://www.linuxidc.com/Linux/2012-09/70158.htmCentOS下OpenCV无法读取视频文件 http://www.linuxidc.com/Linux/2011-07/39295.htmUbuntu 12.04下安装OpenCV 2.4.5总结 http://www.linuxidc.com/Linux/2013-06/86704.htmUbuntu 10.04中安装OpenCv2.1九步曲 http://www.linuxidc.com/Linux/2010-09/28678.htm基于QT和OpenCV的人脸识别系统 http://www.linuxidc.com/Linux/2011-11/47806.htm[翻译]Ubuntu 14.04, 13.10 下安装 OpenCV 2.4.9 http://www.linuxidc.com/Linux/2014-12/110045.htm--------------------------------------分割线 --------------------------------------OpenCV的详细介绍：请点这里
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