panoramic view

[jax666999]

I've been tasked with righting a program that accepts three images and combines them into a panoramic view I'm having trouble getting started any advice?

[SamuelAdams]

I'd love to see your program when you finish.

I have thought about how to do this and I assume you will start with A1 location with the 1st image, and compare a block of pixels to A1 B1 C1 of the 2nd image. If a 90% match is not found, try using A2, B2, C2 of the 2nd image until the best match is found.
A123456789
B123456789
C123456789
D123456789
E123456789

Unless you find a better way, I would start with just 2 images, then modify that code to work with more than 2 images.

[jax666999]

This is what I've got so far I'm working on adapting it to take three images but there is one function giving me trouble?

void read();
 

int main( int argc, char** argv )
{
 if( argc != 3 )
 { 
	 read(); return -1; 
 }
 

 Mat image1 = imread(argv[1]);
 Mat image2 = imread(argv[2]);
 Mat image3 = imread(argv[3]);
 Mat grey_image1;
 Mat grey_image2;
 Mat grey_image3;
 
 cvtColor( image1, grey_image1, CV_RGB2GRAY );
 cvtColor( image2, grey_image2, CV_RGB2GRAY );
 cvtColor( image3, grey_image3, CV_RGB2GRAY );
 
 imshow("first image",image1);
 imshow("second image",image2);
 imshow("third image",image3);
 
if(!grey_image1.data || !grey_image2.data || !grey_image3.data)
 { 
	 std::cout<< " --(!) Error reading images " << std::endl; return -1;
 }
 
 int minHessian = 400;
 
SurfFeatureDetector detector( minHessian );
 
std::vector< KeyPoint > keypoints_object, keypoints_scene;
 
 detector.detect( grey_image1, keypoints_object );
 detector.detect( grey_image2, keypoints_scene );
 
 SurfDescriptorExtractor extractor;
 
Mat descriptors_object, descriptors_scene;
 
extractor.compute( grey_image1, keypoints_object, descriptors_object );
 extractor.compute( grey_image2, keypoints_scene, descriptors_scene );
 
 FlannBasedMatcher matcher;
 std::vector< DMatch > matches;
 matcher.match( descriptors_object, descriptors_scene, matches );
 
double max_dist = 0; double min_dist = 100;


 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 );
 

 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++ )
 {
 
 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 );
 
 cv::Mat result;
 warpPerspective(image1,result,H,cv::Size(image1.cols+image2.cols,image1.rows));
 cv::Mat half(result,cv::Rect(0,0,image2.cols,image2.rows));
 image2.copyTo(half);
 imshow( "Result", result );
 
 waitKey(0);
 return 0;
 }
 

 void read()
 { 
	 std::cout << " Usage: Panorama < img1 > < img2 > <img3>" << std::endl;
 }

line 86 findHomography throughs an error and I'd like to resolve it before I finish adapting for more images.