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/*********************************************************************************
*Author : Kevin George
*
*-> To execute, estimateGeometricTransform(I1,I2)
* where I1 & I2 are images
*
*This program only gives an affine transformation matrix
*********************************************************************************/
#include <numeric>
#include <string.h>
#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/opencv.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include <iostream>
#include <math.h>
#include <vector>
using namespace cv;
using namespace std;
extern "C"
{
#include "api_scilab.h"
#include "Scierror.h"
#include "BOOL.h"
#include <localization.h>
#include "sciprint.h"
#include "../common.h"
// # include "../common.cpp"
int opencv_estimateGeometricTransform(char *fname, unsigned long fname_len)
{
//-> Error Management variables
SciErr sciErr;
int intErr=0;
//-> Mat containers for images
Mat image_1;
Mat image_2;
//-> Address of Various Arguments
int *piAddr = NULL;
//-> Local variables
int minHessian = 400; //-> For SURF algorithm
int num_InputArgs;
//-> KeyPoint is data structure to store a point feature
// detected by a keypoint detector(OpenCV)
vector<KeyPoint> keypoints_1;
vector<KeyPoint> keypoints_2;
Mat descriptors_1, descriptors_2;
Mat img_matches;
vector< DMatch > good_matches;
vector< DMatch > matches;
vector<Point2f> a,b;
double max_dist = 0; double min_dist = 100;
/*double *indexPairs = NULL;
double *matchMetric = NULL;
double size=1;
int iRows, iCols; */
//KeyPoint *X = new KeyPoint; //-> Remember to free this Kevin
//-> Checks the number of arguments
//-> pvApiCtx is a Scilab environment pointer
CheckInputArgument(pvApiCtx, 1, 5); //Check on Number of Input Arguments
CheckOutputArgument(pvApiCtx, 1, 5); //Check on Number of Output Arguments
//-> Read Image
retrieveImage( image_1, 1);
retrieveImage(image_2, 2);
//-> Count number of input arguments
//num_InputArgs = *getNbInputArgument(pvApiCtx);
//-> Based on number of input arguments
//***************************************************** Actual Processing *************************************************************
//-- Step 1: Calculate keypoints
SurfFeatureDetector detector( minHessian );
detector.detect( image_1, keypoints_1 );
detector.detect( image_2, keypoints_2 );
//-- Step 2: Calculate descriptors (feature vectors)
SurfDescriptorExtractor extractor;
extractor.compute( image_1, keypoints_1, descriptors_1 );
extractor.compute( image_2, keypoints_2, descriptors_2 );
//-- Step 3: Matching descriptor vectors using FLANN matcher
FlannBasedMatcher matcher;
matcher.match( descriptors_1, descriptors_2, matches );
//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < descriptors_1.rows; i++ )
{
double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
for( int i = 0; i < descriptors_1.rows; i++ )
{
if( matches[i].distance <= max(2*min_dist, 0.02) )
{ good_matches.push_back( matches[i]); }
}
//queryidx- keypoints1
//traindidx- keypoints2
for(int i=0;i<3;i++)
{
a.push_back( keypoints_1[good_matches[i].queryIdx].pt );
b.push_back( keypoints_2[good_matches[i].trainIdx].pt );
}
Mat M = getAffineTransform( Mat(a), Mat(b) );
/* double output[M.rows][M.cols];
for(int i=0; i<M.rows; ++i)
{
const double* Mi = M.ptr<double>(i);
for (int j = 0; j < M.cols; j++)
{
output[i][j] = Mi[j];
}
} */
double *output = NULL;
output = (double *)malloc(sizeof(double)*M.rows*M.cols);
//-> Accessing elements of Mat object M
for(int i=0; i<M.rows; ++i)
{
const double* Mi = M.ptr<double>(i);
for (int j = 0; j < M.cols; j++)
{
output[i+j] = Mi[j];
}
}
sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1, M.rows, M.cols, output);
if(sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
//-> Returning Output
AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1;
ReturnArguments(pvApiCtx);
return 0;
}
}
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