/***************************************************
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"
    int opencv_evaluate(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,inp,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;
        int *count = NULL;
        char **classifierDescription = NULL;
        int classifierDescriptionCount;
        char **description = NULL;
        char ***location = NULL;
        char *bagOfFeaturesLocation = NULL;
        int descriptionCount;
        double *confMatrix = NULL;
        char *fileName = NULL;
        double avgAccuracy = 0;
        //------Check number of parameters------//
        CheckInputArgument(pvApiCtx, 2, 2);
        CheckOutputArgument(pvApiCtx, 1, 1);

        //------Get input arguments------//
        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;
        }
        classifierDescriptionCount = iRows;
        // Second Argument
        sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr);
        if (sciErr.iErr)
        {
            printError(&sciErr, 0);
            return 0;
        }
        if(!isListType(pvApiCtx, piAddr))
        {
            Scierror(999, "Error: The input argument #2 is not of type imageSet.\n");
            return 0;
        }

        // Extracting object type and checking if type is imageSet
        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],"imageSet")==0))
        {
            Scierror(999, "Error: The input argument #2 is not of type imageSet.\n");
            return 0;
        }

        // Extracting Description attribute of input argument
        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;
        }

        description = (char**)malloc(sizeof(char*) * iRows * iCols);

        for(int iter = 0 ; iter < iRows * iCols ; iter++)
        {
            description[iter] = (char*)malloc(sizeof(char) * (piLen[iter] + 1));//+ 1 for null termination
        }

        sciErr = getMatrixOfStringInList(pvApiCtx, piAddr, 2, &iRows, &iCols, piLen, description);
        if(sciErr.iErr)
        {
            printError(&sciErr, 0);
            return 0;
        }
        descriptionCount = iRows;

        // Extracting Count attribute of input argument
        sciErr = getMatrixOfInteger32InList(pvApiCtx, piAddr, 3, &iRows, &iCols, &count);
        if(sciErr.iErr)
        {
            printError(&sciErr, 0);
            return 0;
        }

        location = (char***) malloc(sizeof(char**) * descriptionCount);
        sciErr = getListItemAddress(pvApiCtx, piAddr, 4, &piChild);
        if(sciErr.iErr)
        {
            printError(&sciErr, 0);
            return 0;
        }
        for(int iter = 1; iter<=descriptionCount; iter++)
        {
            sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, NULL, NULL);
            if(sciErr.iErr)
            {
                printError(&sciErr, 0);
                return 0;
            }

            piLen = (int*)malloc(sizeof(int) * iRows * iCols);

            sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, NULL);
            if(sciErr.iErr)
            {
                printError(&sciErr, 0);
                return 0;
            }

            location[iter-1] = (char**)malloc(sizeof(char*) * iRows * iCols);

            for(int colIter = 0 ; colIter < iRows * iCols ; colIter++)
            {
                location[iter-1][colIter] = (char*)malloc(sizeof(char) * (piLen[colIter] + 1));//+ 1 for null termination
            }

            sciErr = getMatrixOfStringInList(pvApiCtx, piChild, iter, &iRows, &iCols, piLen, location[iter-1]);
            if(sciErr.iErr)
            {
                printError(&sciErr, 0);
                return 0;
            }
        }
        //------Actual processing------//
        if(descriptionCount != classifierDescriptionCount)
        {
            Scierror(999, "Error: The input imageSet does not match the classifier's training set.\n");
            return 0;
        }
        for(int i=0; i<descriptionCount; i++)
        {
            if(strcmp(classifierDescription[i],description[i])!=0)
            {
                Scierror(999, "Error: The input imageSet does not match the classifier's training set.\n");
                return 0;
            }
        }
        sciprint("Evaluating image category classifier for %d categories.\n",descriptionCount);
        sciprint("--------------------------------------------------------\n\n");
        for(int i=0;i<descriptionCount;i++)
        {
            sciprint("# Category %d: %s\n",i+1,description[i]);
        }
        sciprint("\n");
        confMatrix = (double*) malloc(sizeof(double)*descriptionCount*descriptionCount);
        for(int i=0;i<descriptionCount*descriptionCount;i++)
        {
            confMatrix[i]=0;
        }
        FileStorage fs(bagOfFeaturesLocation, FileStorage::READ);
        fs["dictionary"] >> dictionary;
        fs.release();
        dictionarySize = dictionary.rows;
        bowDE.setVocabulary(dictionary);
        svm.load(classifierLocation);
        for(int i=0; i<descriptionCount;i++)
        {
            sciprint("# Evaluating %d images from category %d ...",count[i],i+1);
            for(int j=0; j<count[i]; j++)
            {
                features.release();
                keyPoints.clear();
                fileName = location[i][j];
                inp = imread(fileName);
                detector.detect(inp, keyPoints);
                bowDE.compute(inp, keyPoints, features);
                response = svm.predict(features);
                confMatrix[i+(int)response*descriptionCount]++;
            }
            sciprint("done.\n");
        }
        sciprint("\n");
        sciprint("\t PREDICTED\n");
        sciprint("KNOWN\t|");
        for(int i=0;i<descriptionCount;i++)
        {
            sciprint(" %d\t",i+1);
        }
        sciprint("\n");
        for(int i=0; i<descriptionCount;i++)
        {
            sciprint("%d\t|",i+1);
            for(int j=0; j<descriptionCount; j++)
            {
                confMatrix[i+descriptionCount*j] /= count[i];
                sciprint(" %.2f\t",confMatrix[i+descriptionCount*j]);
            }
            sciprint("\n");
        }
        sciprint("\n");
        for(int i=0;i<descriptionCount*descriptionCount;i=i+descriptionCount+1)
        {
            avgAccuracy += confMatrix[i];
        }
        sciprint("# Average accuracy: %f\n",avgAccuracy/descriptionCount);
        //------Create output arguments------//
        sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1, descriptionCount, descriptionCount, confMatrix);
        if(sciErr.iErr)
        {
            printError(&sciErr, 0);
            return 0;
        }
        //------Return Arguments------//
        AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1;
        ReturnArguments(pvApiCtx);
        return 0;
    }
    /* ==================================================================== */
}