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+/*
+ * graycoprops
+ *
+ * graycoprops in scilab
+ * Please refer to :
+ * http://www.cse.unsw.edu.au/~icml2002/workshops/MLCV02/MLCV02-Bevk.pdf, p.3.
+ */
+
+// Created by Samiran Roy, mail: samiranroy@cse.iitb.ac.in
+// An implementation of graycoprops method of matlab
+// Usage:
+// graycoprops(GLCM,property) : Calculates the Property of the input Gray level co-occurence matrix
+
+// Known Changes from Matlab:
+/*
+ * 1) None, as of now - Matching exactly - but does not use structures - for properties
+ */
+
+#include <numeric>
+#include <math.h>
+#include <vector>
+#include "opencv2/core/core.hpp"
+#include "opencv2/highgui/highgui.hpp"
+#include "opencv2/opencv.hpp"
+#include <iostream>
+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"
+
+double return_contrast(Mat image)
+
+{
+ double contrast = 0;
+
+ for (int i = 0; i < image.rows; i++) {
+ for (int j = 0; j < image.cols; j++) {
+ contrast = contrast + (pow((i - j), 2) * image.at<double>(i, j));
+ }
+ }
+ return contrast;
+}
+
+double return_correlation(Mat image)
+
+{
+ double correlation = 0;
+
+ std::vector<int> mg_rows;
+ std::vector<int> mg_cols;
+
+ // Meshgrid of row and column indices
+ for (int i = 0; i < image.rows; i++) {
+ for (int j = 0; j < image.cols; j++) {
+ mg_rows.push_back(j);
+ mg_cols.push_back(i);
+ }
+ }
+
+ // for (int i=0; i<rows.size();i++)
+ // sciprint("%d\n", rows[i]);
+
+ Mat flat_image = image.reshape(1, 1);
+
+ double mean_row = 0;
+ double mean_col = 0;
+ double std_row = 0;
+ double std_col = 0;
+
+ for (int i = 0; i < mg_rows.size(); i++)
+ mean_row = mean_row + mg_rows[i] * flat_image.at<double>(i);
+
+ for (int i = 0; i < mg_cols.size(); i++)
+ mean_col = mean_col + mg_cols[i] * flat_image.at<double>(i);
+
+ for (int i = 0; i < mg_rows.size(); i++)
+ std_row =
+ std_row + (pow(mg_rows[i] - mean_row, 2) * flat_image.at<double>(i));
+
+ std_row = sqrt(std_row);
+
+ for (int i = 0; i < mg_cols.size(); i++)
+ std_col =
+ std_col + (pow(mg_cols[i] - mean_col, 2) * flat_image.at<double>(i));
+
+ std_col = sqrt(std_col);
+
+ for (int i = 0; i < mg_rows.size(); i++)
+ correlation =
+ correlation + ((mg_rows[i] - mean_row) * (mg_cols[i] - mean_col) *
+ flat_image.at<double>(i));
+
+ double denom = std_col * std_row;
+ if (denom != 0) {
+ correlation = correlation / denom;
+ }
+
+ // sciprint("\n");
+
+ // sciprint("MR: %f",mean_row);
+ // sciprint("MC: %f",mean_col);
+
+ // sciprint("SR: %f",std_row);
+ // sciprint("SC: %f",std_col);
+ return correlation;
+}
+
+double return_energy(Mat image)
+
+{
+ double energy = 0;
+
+ for (int i = 0; i < image.rows; i++) {
+ for (int j = 0; j < image.cols; j++) {
+ energy = energy + pow(image.at<double>(i, j), 2);
+ }
+ }
+ return energy;
+}
+
+double return_homogeneity(Mat image)
+
+{
+ double homogeneity = 0;
+
+ for (int i = 0; i < image.rows; i++) {
+ for (int j = 0; j < image.cols; j++) {
+ homogeneity = homogeneity + (image.at<double>(i, j) / (1 + abs(i - j)));
+ }
+ }
+ return homogeneity;
+}
+
+int opencv_graycoprops(char *fname, unsigned long fname_len) {
+ SciErr sciErr;
+ int intErr = 0;
+
+ int *piAddr1 = NULL;
+
+ int error;
+
+ // String holding the second argument
+ int iRet = 0;
+ char *pstData = NULL;
+
+ // Checking input argument
+ CheckInputArgument(pvApiCtx, 1, 2);
+ CheckOutputArgument(pvApiCtx, 1, 1);
+
+ // Get input image
+
+ Mat image;
+ retrieveImage(image, 1);
+
+ // converting image to float
+
+ image.convertTo(image, CV_64FC1, 1, 0);
+
+ // for (int i = 0; i < image.rows; i++) {
+ // for (int j = 0; j < image.cols; j++) {
+ // sciprint("%f ", image.at<double>(i, j));
+ // }
+
+ // sciprint("\n");
+ // }
+
+ // Error Checks
+
+ if (image.channels() == 1) {
+ // Normalizing image
+
+ double s = cv::sum(image)[0];
+ image = image / s;
+
+ sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr1);
+
+ if (sciErr.iErr) {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ if (isStringType(pvApiCtx, piAddr1)) {
+ if (isScalar(pvApiCtx, piAddr1)) {
+ iRet = getAllocatedSingleString(pvApiCtx, piAddr1, &pstData);
+ }
+ }
+ if (strcmp(pstData, "contrast") == 0) {
+ double contrast = return_contrast(image);
+ intErr =
+ createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, contrast);
+ if (intErr) return intErr;
+
+ // Assigning the list as the Output Variable
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1;
+ // Returning the Output Variables as arguments to the Scilab environment
+ ReturnArguments(pvApiCtx);
+ return 0;
+ }
+
+ else if (strcmp(pstData, "correlation") == 0) {
+ double correlation = return_correlation(image);
+
+ intErr = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1,
+ correlation);
+ if (intErr) return intErr;
+
+ // Assigning the list as the Output Variable
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1;
+ // Returning the Output Variables as arguments to the Scilab environment
+ ReturnArguments(pvApiCtx);
+ return 0;
+ } else if (strcmp(pstData, "energy") == 0) {
+ double energy = return_energy(image);
+
+ intErr =
+ createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1, energy);
+ if (intErr) return intErr;
+
+ // Assigning the list as the Output Variable
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1;
+ // Returning the Output Variables as arguments to the Scilab environment
+ ReturnArguments(pvApiCtx);
+ return 0;
+ }
+
+ else if (strcmp(pstData, "homogeneity") == 0) {
+ double homogeneity = return_homogeneity(image);
+
+ intErr = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx) + 1,
+ homogeneity);
+ if (intErr) return intErr;
+
+ // Assigning the list as the Output Variable
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx) + 1;
+ // Returning the Output Variables as arguments to the Scilab environment
+ ReturnArguments(pvApiCtx);
+ return 0;
+ }
+
+ else {
+ sciprint("\nUnknown Parameter Name: %s\n", pstData);
+ return 0;
+ }
+ }
+
+ else if (image.channels() == 3) {
+ sciErr = getVarAddressFromPosition(pvApiCtx, 2, &piAddr1);
+
+ if (sciErr.iErr) {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ if (isStringType(pvApiCtx, piAddr1)) {
+ if (isScalar(pvApiCtx, piAddr1)) {
+ iRet = getAllocatedSingleString(pvApiCtx, piAddr1, &pstData);
+ }
+ }
+ if (strcmp(pstData, "contrast") == 0) {
+ vector<Mat> rgb;
+ split(image, rgb);
+
+ double *contrast = (double *)malloc(sizeof(double) * 3);
+ double s;
+
+ for (int i = 0; i < 3; i++) {
+ s = cv::sum(rgb[i])[0];
+ rgb[i] = rgb[i] / s;
+ contrast[2-i] = return_contrast(rgb[i]);
+ }
+
+ sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx), 1, 3,
+ contrast);
+
+ free(contrast);
+
+ if (sciErr.iErr) {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ // Assigning the list as the Output Variable
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx);
+ // Returning the Output Variables as arguments to the Scilab environment
+ ReturnArguments(pvApiCtx);
+ return 0;
+ }
+
+ else if (strcmp(pstData, "correlation") == 0) {
+ vector<Mat> rgb;
+ split(image, rgb);
+
+ double *correlation = (double *)malloc(sizeof(double) * 3);
+ double s;
+
+ for (int i = 0; i < 3; i++) {
+ s = cv::sum(rgb[i])[0];
+ rgb[i] = rgb[i] / s;
+ correlation[2-i] = return_correlation(rgb[i]);
+ }
+
+ sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx), 1, 3,
+ correlation);
+
+ free(correlation);
+
+ if (sciErr.iErr) {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ // Assigning the list as the Output Variable
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx);
+ // Returning the Output Variables as arguments to the Scilab environment
+ ReturnArguments(pvApiCtx);
+ return 0;
+ }
+
+ else if (strcmp(pstData, "energy") == 0) {
+ vector<Mat> rgb;
+ split(image, rgb);
+
+ double *energy = (double *)malloc(sizeof(double) * 3);
+ double s;
+
+ for (int i = 0; i < 3; i++) {
+ s = cv::sum(rgb[i])[0];
+ rgb[i] = rgb[i] / s;
+ energy[2-i] = return_energy(rgb[i]);
+ }
+
+ sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx), 1, 3,
+ energy);
+
+ free(energy);
+
+ if (sciErr.iErr) {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ // Assigning the list as the Output Variable
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx);
+ // Returning the Output Variables as arguments to the Scilab environment
+ ReturnArguments(pvApiCtx);
+ return 0;
+ }
+
+ else if (strcmp(pstData, "homogeneity") == 0) {
+ vector<Mat> rgb;
+ split(image, rgb);
+
+ double *homogeneity = (double *)malloc(sizeof(double) * 3);
+ double s;
+
+ for (int i = 0; i < 3; i++) {
+ s = cv::sum(rgb[i])[0];
+ rgb[i] = rgb[i] / s;
+ homogeneity[2-i] = return_homogeneity(rgb[i]);
+ }
+
+ sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx), 1, 3,
+ homogeneity);
+
+ free(homogeneity);
+
+ if (sciErr.iErr) {
+ printError(&sciErr, 0);
+ return 0;
+ }
+
+ // Assigning the list as the Output Variable
+ AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx);
+ // Returning the Output Variables as arguments to the Scilab environment
+ ReturnArguments(pvApiCtx);
+ return 0;
+ }
+ else {
+ sciprint("\nUnknown Parameter Name: %s\n", pstData);
+ return 0;
+ }
+
+ } else {
+ sciprint("Invalid number of channels in the image(must be 1 or 3)\n");
+ return 0;
+ }
+
+ // sciprint("\n");
+
+ // for (int i = 0; i < new_image.rows; i++) {
+ // for (int j = 0; j < new_image.cols; j++) {
+ // sciprint("%f ", new_image.at<double>(i,j));
+
+ // }
+
+ // sciprint("\n");
+ // }
+
+ // new_image is sent to scilab as output
+}
+/* ==================================================================== */
+}
+
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