I am to apply a warpperspective using opencv to mount a mosaic with varios images, but, i am with a very problem...
When i applied a cvWarpPerspective, a generate image don't show in window. Appear just a part of image and i need to know how to discover a coordinates (0,0) of my image after to apply a warpperspective. Is possible see, that in first image, a part of image is cut if to compare with a second image presented here.
Therefore, my problem is: how to discover coordinates of start after to apply a warpperspective ? I need help to solve this problem. How can i solve this problem using tool of opencv ? How can i solve this problem using opencv ?
This is my code:
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace cv;
void readme();
/** @function main */
int main( int argc, char** argv )
{
// Load the images
Mat image1= imread( "f.jpg");
Mat image2= imread( "e.jpg" );
Mat gray_image1;
Mat gray_image2;
// Convert to Grayscale
cvtColor( image1, gray_image1, CV_RGB2GRAY );
cvtColor( image2, gray_image2, CV_RGB2GRAY );
imshow("first image",image2);
imshow("second image",image1);
//-- Step 1: Detect the keypoints using SURF Detector
int minHessian = 100;
SurfFeatureDetector detector( minHessian );
std::vector< KeyPoint > keypoints_object, keypoints_scene;
detector.detect( gray_image1, keypoints_object );
detector.detect( gray_image2, keypoints_scene );
//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
Mat descriptors_object, descriptors_scene;
extractor.compute( gray_image1, keypoints_object, descriptors_object );
extractor.compute( gray_image2, 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 );
//-- Use 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]); }
}
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 );
}
// Find the Homography Matrix
Mat H = findHomography( obj, scene, CV_RANSAC);
// Use the Homography Matrix to warp the images
cv::Mat result;
warpPerspective(image1,result,H,cv::Size());
imshow("WARP", result);
cv::Mat half(result,cv::Rect(0,0,image2.cols,image2.rows));
image2.copyTo(half);
Mat key;
//drawKeypoints(image1,keypoints_scene,key,Scalar::all(-1), DrawMatchesFlags::DEFAULT );
//drawMatches(image2, keypoints_scene, image1, keypoints_object, matches, result);
imshow( "Result", result );
imwrite("teste.jpg", result);
waitKey(0);
return 0;
}
/** @function readme */
void readme()
{ std::cout << " Usage: Panorama < img1 > < img2 >" << std::endl; }
In this image appears a second image cut. See
I want that my image appears in this form:
