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/***************************************************
Author : Rohit Suri
***************************************************/
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/ml/ml.hpp>
using namespace std;
using namespace cv;
extern "C"
{
#include "api_scilab.h"
#include "Scierror.h"
#include "BOOL.h"
#include <localization.h>
#include "sciprint.h"
#include "../common.h"
int opencv_predict(char *fname, unsigned long fname_len)
{
// Error management variables
SciErr sciErr;
//------Local variables------//
int upright = 1;
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
Ptr<DescriptorExtractor> extractor = new SurfDescriptorExtractor(1, 4, 2, 1, int(upright));
BOWImgDescriptorExtractor bowDE(extractor, matcher);
SurfFeatureDetector detector(1, 4, 2, 1, int(upright));
char *classifierLocation = NULL;
Mat dictionary,features;
double response;
vector<KeyPoint> keyPoints;
CvSVM svm;
int dictionarySize;
int *piAddr = NULL;
int *piChild = NULL;
int iRows, iCols;
char **pstData = NULL;
int *piLen = NULL;
char **classifierDescription = NULL;
int classifierDescriptionCount;
char *bagOfFeaturesLocation = NULL;
int descriptionCount;
Mat input;
//------Check number of parameters------//
CheckInputArgument(pvApiCtx, 2, 2);
CheckOutputArgument(pvApiCtx, 1, 1);
//------Get input arguments------//
retrieveImage(input,2);
sciErr = getVarAddressFromPosition(pvApiCtx, 1, &piAddr);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
if(!isListType(pvApiCtx, piAddr))
{
Scierror(999, "Error: The input argument #1 is not of type classifier.\n");
return 0;
}
// Extracting object type and checking if type is classifier
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, NULL, NULL);
if(sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
piLen = (int*)malloc(sizeof(int) * iRows * iCols);
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, NULL);
if(sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
pstData = (char**)malloc(sizeof(char*) * iRows * iCols);
for(int iter = 0 ; iter < iRows * iCols ; iter++)
{
pstData[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination
}
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 1, &iRows, &iCols, piLen, pstData);
if(sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
if(!(strcmp(pstData[0],"classifier")==0))
{
Scierror(999, "Error: The input argument #1 is not of type classifier.\n");
return 0;
}
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, NULL, NULL);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
piLen = (int*) malloc(sizeof(int) * iRows * iCols);
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, NULL);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
pstData = (char**) malloc(sizeof(char*) * iRows * iCols);
for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++)
{
pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1);
}
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, pstData);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
if(iRows!=1 || iCols!=1)
{
Scierror(999, "1x1 Matrix expected for classifier argument.");
return 0;
}
classifierLocation = pstData[0];
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, NULL, NULL);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
piLen = (int*) malloc(sizeof(int) * iRows * iCols);
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, NULL);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
pstData = (char**) malloc(sizeof(char*) * iRows * iCols);
for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++)
{
pstData[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1);
}
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 3, &iRows, &iCols, piLen, pstData);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
if(iRows!=1 || iCols!=1)
{
Scierror(999, "1x1 Matrix expected for bagOfFeatures argument.");
return 0;
}
bagOfFeaturesLocation = pstData[0];
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, NULL, NULL);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
piLen = (int*) malloc(sizeof(int) * iRows * iCols);
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, NULL);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
classifierDescription = (char**) malloc(sizeof(char*) * iRows * iCols);
for(int iterPstData = 0; iterPstData < iRows * iCols; iterPstData++)
{
classifierDescription[iterPstData] = (char*) malloc(sizeof(char) * piLen[iterPstData] + 1);
}
sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 4, &iRows, &iCols, piLen, classifierDescription);
if (sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
//------Actual processing------//
FileStorage fs(bagOfFeaturesLocation, FileStorage::READ);
fs["dictionary"] >> dictionary;
fs.release();
dictionarySize = dictionary.rows;
bowDE.setVocabulary(dictionary);
svm.load(classifierLocation);
detector.detect(input, keyPoints);
bowDE.compute(input, keyPoints, features);
response = svm.predict(features);
//------Create output arguments------//
sciErr = createMatrixOfString(pvApiCtx, nbInputArgument(pvApiCtx)+1, 1, 1, &classifierDescription[(int)response]);
if(sciErr.iErr)
{
printError(&sciErr, 0);
return 0;
}
//------Return Arguments------//
AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1;
ReturnArguments(pvApiCtx);
return 0;
}
/* ==================================================================== */
}
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