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-rw-r--r--sci_gateway1/cpp/opencv_showMatchedFeatures.cpp275
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diff --git a/sci_gateway1/cpp/opencv_showMatchedFeatures.cpp b/sci_gateway1/cpp/opencv_showMatchedFeatures.cpp
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+/*********************************************************************************
+*Author : Kevin George
+*
+*-> To execute, showMatchedFeatures(I1,I2)
+* where I1 & I2 are images
+*
+*********************************************************************************/
+#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_showMatchedFeatures(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;
+ double max_dist = 0; double min_dist = 100;
+ int *outList = NULL;
+ unsigned char *red = NULL;
+ unsigned char *green = NULL;
+ unsigned char *blue = NULL;
+ /*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, 2, 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
+
+
+//****************************************************** Retrieval of 3rd,4th,5th args *******************************************************************
+
+ /*
+ /*************** Size of good matches **************
+ sciErr = getVarAddressFromPosition(pvApiCtx, 3, &piAddr);
+ if (sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ sciErr = getScalarDouble(pvApiCtx, piAddr, &size);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ if(getScalarDouble(pvApiCtx, piAddr, &size))
+ {
+ Scierror(999,"Cannot Read Upright Value\n");
+ return 0;
+ }
+
+ vector <DMatch> good_matches(size);
+
+
+ /*************** For indexPairs *************
+ sciErr = getVarAddressFromPosition(pvApiCtx, 4, &piAddr);
+ if (sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &indexPairs);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+
+ //-> Extract X and Ycoordinates of KeyPoints
+
+ //X->pt = Point2f(keypoints[i],keypoints[iRows+i]);
+ //keypoints_1.push_back(*X);
+
+
+ /*************** For matchMetric **************
+ sciErr = getVarAddressFromPosition(pvApiCtx, 5, &piAddr);
+ if (sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ sciErr = getMatrixOfDouble(pvApiCtx, piAddr, &iRows, &iCols, &matchMetric);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ for(int i=0; i< size; i++)
+ {
+ good_matches[i].queryIdx = indexPairs[i];
+ good_matches[i].trainIdx = indexPairs[ static_cast<int>(size)+i];
+ good_matches[i].distance = matchMetric[i];
+
+ }
+ */
+
+//***************************************************** 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) )
+ if( matches[i].distance <= (2*min_dist) )
+ { good_matches.push_back( matches[i]); }
+ }
+
+ //-> Draw only "good" matches
+ drawMatches( image_1, keypoints_1, image_2, keypoints_2,
+ good_matches,img_matches, Scalar::all(-1), Scalar::all(-1),
+ vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
+
+ //-> Show detected matches
+ //imshow( "Good Matches", img_matches );
+ //waitKey(0);
+
+ if( img_matches.channels() == 1)
+ {
+ sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 1, &outList);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+ red = (unsigned char *)malloc(sizeof(unsigned char)*img_matches.rows*img_matches.cols);
+
+ for(int k=0;k<img_matches.rows;k++)
+ for(int p=0;p<img_matches.cols;p++)
+ red[k+img_matches.rows*p]=img_matches.at<uchar>(k, p);
+
+ sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, img_matches.rows, img_matches.cols, red);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+ free(red);
+ }
+
+ else
+ {
+ sciErr = createList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, 3, &outList);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ red = (unsigned char *)malloc(sizeof(unsigned char)*img_matches.rows*img_matches.cols);
+ green = (unsigned char *)malloc(sizeof(unsigned char)*img_matches.rows*img_matches.cols);
+ blue = (unsigned char *)malloc(sizeof(unsigned char)*img_matches.rows*img_matches.cols);
+
+ for(int k=0;k<img_matches.rows;k++)
+ {
+ for(int p=0;p<img_matches.cols;p++)
+ {
+ Vec3b intensity = img_matches.at<Vec3b>(k, p);
+ red[k+img_matches.rows*p]=intensity.val[2];
+ green[k+img_matches.rows*p]=intensity.val[1];
+ blue[k+img_matches.rows*p]=intensity.val[0];
+ }
+ }
+
+ sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 1, img_matches.rows, img_matches.cols, red);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+ sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 2, img_matches.rows, img_matches.cols, green);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+ sciErr = createMatrixOfUnsignedInteger8InList(pvApiCtx, nbInputArgument(pvApiCtx) + 1, outList, 3, img_matches.rows, img_matches.cols, blue);
+ if(sciErr.iErr)
+ {
+ printError(&sciErr, 0);
+ return 0;
+ }
+ free(red);
+ free(green);
+ free(blue);
+
+ }
+
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1;
+ ReturnArguments(pvApiCtx);
+ return 0;
+ }
+
+}
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