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 | 1 | initial version |
Here i will explain method to decompose the decompose the transformation matrix H , as in the following two articles
Math
,code
here it's my trial code
//read the input image
Mat img_object = imread( strObjectFile, CV_LOAD_IMAGE_GRAYSCALE );
Mat img_scene = imread( strSceneFile, CV_LOAD_IMAGE_GRAYSCALE );
Mat img_scene_color = imread( strSceneFile, CV_LOAD_IMAGE_COLOR );
if( img_scene.empty() || img_object.empty())
{
return ERROR_READ_FILE;
}
//Step 1 Find the object in the scene and find H matrix
//-- 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 );
//-- 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 );
//-- 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;
}
//-- 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 );
//Draw matched points
imwrite("c:\\temp\\Matched_Pints.png",img_matches);
//-- Localize the object
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for( int 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, CV_RANSAC );
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 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 )
line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
//-- Show detected matches
//imshow( "Good Matches & Object detection", img_matches );
imwrite("c:\\temp\\Object_detection_result.png",img_matches);
//Step 2 correct the scene scale and rotation and locate object in the recovered scene
Mat img_Recovered;
//1-decompose find the H matrix
float a = H.at<double>(0,0);
float b = H.at<double>(0,1);
float c = H.at<double>(0,2);
float d = H.at<double>(1,0);
float e = H.at<double>(1,1);
float f = H.at<double>(1,2);
float p = sqrt(a*a + b*b);
float r = (a*e - b*d)/(p);
float q = (a*d+b*e)/(a*e - b*d);
Point2f translation(c,f);
Point2f scale(p,r);
float shear = q;
float theta = atan2(b,a);
double thetaRadian = theta * 180 / CV_PI ;
//rotate and scale the image scene
Size ImgSize = Size(img_scene.cols, img_scene.rows);//initial size
Point2f pt(img_scene.cols/2., img_scene.rows/2.);//initial center
Mat rotMat = getRotationMatrix2D(pt,-thetaRadian,scale.x);
//Calculate the new image size to avoid truncation of some parts from the scene
cv::Rect bbox = cv::RotatedRect(pt,img_scene.size(), -thetaRadian).boundingRect();
// adjust transformation matrix and destination matrix to hold the new size
rotMat.at<double>(0,2) += bbox.width/2.0 - pt.x;
rotMat.at<double>(1,2) += bbox.height/2.0 - pt.y;
ImgSize = bbox.size();
warpAffine(img_scene_color, img_Recovered, rotMat, ImgSize,INTER_LANCZOS4,BORDER_CONSTANT,Scalar(255));
//now get the coordinates of logo (object) detected in the step1 but in the recoverd image (waraped)
std::vector<Point2f> obj_corners_wraped(4);
transform(scene_corners, obj_corners_wraped, rotMat);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_Recovered, obj_corners_wraped[0] , obj_corners_wraped[1] , Scalar(0, 255, 0), 4 );
line( img_Recovered, obj_corners_wraped[1] , obj_corners_wraped[2] , Scalar( 0, 255, 0), 4 );
line( img_Recovered, obj_corners_wraped[2] , obj_corners_wraped[3] , Scalar( 0, 255, 0), 4 );
line( img_Recovered, obj_corners_wraped[3] , obj_corners_wraped[0] , Scalar( 0, 255, 0), 4 );
imwrite("c:\\temp\\Object_detection_Recoverd.png",img_Recovered);
and here it's the required image and rectangle of the object is detected
| | 2 | No.2 Revision |
Here i will explain method to decompose the decompose the transformation matrix H , as in the following two articles
Math
,code
here it's my trial code
//read the input image
Mat img_object = imread( strObjectFile, CV_LOAD_IMAGE_GRAYSCALE );
Mat img_scene = imread( strSceneFile, CV_LOAD_IMAGE_GRAYSCALE );
Mat img_scene_color = imread( strSceneFile, CV_LOAD_IMAGE_COLOR );
if( img_scene.empty() || img_object.empty())
{
return ERROR_READ_FILE;
}
//Step 1 Find the object in the scene and find H matrix
//-- 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 );
//-- 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 );
//-- 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;
}
//-- 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 );
//Draw matched points
imwrite("c:\\temp\\Matched_Pints.png",img_matches);
//-- Localize the object
std::vector<Point2f> obj;
std::vector<Point2f> scene;
for( int 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, CV_RANSAC );
//-- Get the corners from the image_1 ( the object to be "detected" )
std::vector<Point2f> obj_corners(4);
obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 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 )
line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
//-- Show detected matches
//imshow( "Good Matches & Object detection", img_matches );
imwrite("c:\\temp\\Object_detection_result.png",img_matches);
//Step 2 correct the scene scale and rotation and locate object in the recovered scene
Mat img_Recovered;
//1-decompose find the H matrix
float a = H.at<double>(0,0);
float b = H.at<double>(0,1);
float c = H.at<double>(0,2);
float d = H.at<double>(1,0);
float e = H.at<double>(1,1);
float f = H.at<double>(1,2);
float p = sqrt(a*a + b*b);
float r = (a*e - b*d)/(p);
float q = (a*d+b*e)/(a*e - b*d);
Point2f translation(c,f);
Point2f scale(p,r);
float shear = q;
float theta = atan2(b,a);
double thetaRadian = theta * 180 / CV_PI ;
//rotate and scale the image scene
Size ImgSize = Size(img_scene.cols, img_scene.rows);//initial size
Point2f pt(img_scene.cols/2., img_scene.rows/2.);//initial center
Mat rotMat = getRotationMatrix2D(pt,-thetaRadian,scale.x);
//Calculate the new image size to avoid truncation of some parts from the scene
cv::Rect bbox = cv::RotatedRect(pt,img_scene.size(), -thetaRadian).boundingRect();
// adjust transformation matrix and destination matrix to hold the new size
rotMat.at<double>(0,2) += bbox.width/2.0 - pt.x;
rotMat.at<double>(1,2) += bbox.height/2.0 - pt.y;
ImgSize = bbox.size();
warpAffine(img_scene_color, img_Recovered, rotMat, ImgSize,INTER_LANCZOS4,BORDER_CONSTANT,Scalar(255));
//now get the coordinates of logo (object) detected in the step1 but in the recoverd image (waraped)
std::vector<Point2f> obj_corners_wraped(4);
transform(scene_corners, obj_corners_wraped, rotMat);
//-- Draw lines between the corners (the mapped object in the scene - image_2 )
line( img_Recovered, obj_corners_wraped[0] , obj_corners_wraped[1] , Scalar(0, 255, 0), 4 );
line( img_Recovered, obj_corners_wraped[1] , obj_corners_wraped[2] , Scalar( 0, 255, 0), 4 );
line( img_Recovered, obj_corners_wraped[2] , obj_corners_wraped[3] , Scalar( 0, 255, 0), 4 );
line( img_Recovered, obj_corners_wraped[3] , obj_corners_wraped[0] , Scalar( 0, 255, 0), 4 );
imwrite("c:\\temp\\Object_detection_Recoverd.png",img_Recovered);
and here it's the required image and rectangle of the object is detected