/*

* Copyright (c) 2012. Philipp Wagner <bytefish[at]gmx[dot]de>.

* Released to public domain under terms of the BSD Simplified license.

*

* Redistribution and use in source and binary forms, with or without

* modification, are permitted provided that the following conditions are met:

* * Redistributions of source code must retain the above copyright

* notice, this list of conditions and the following disclaimer.

* * Redistributions in binary form must reproduce the above copyright

* notice, this list of conditions and the following disclaimer in the

* documentation and/or other materials provided with the distribution.

* * Neither the name of the organization nor the names of its contributors

* may be used to endorse or promote products derived from this software

* without specific prior written permission.

*

* See <http://www.opensource.org/licenses/bsd-license>

*/

#include <opencv2/core/core.hpp>

#include <opencv2/imgproc/imgproc.hpp>

#include <opencv2/highgui/highgui.hpp>

#include <iostream>

#include <fstream>

#include <sstream>

using namespace cv;

using namespace std;

// Normalizes a given image into a value range between 0 and 255.

Mat norm_0_255(const Mat& src) {

// Create and return normalized image:

Mat dst;

switch(src.channels()) {

case 1:

cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);

break;

case 3:

cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3);

break;

default:

src.copyTo(dst);

break;

}

return dst;

}

//

// Calculates the TanTriggs Preprocessing as described in:

//

// Tan, X., and Triggs, B. "Enhanced local texture feature sets for face

// recognition under difficult lighting conditions.". IEEE Transactions

// on Image Processing 19 (2010), 1635–650.

//

// Default parameters are taken from the paper.

//

Mat tan_triggs_preprocessing(InputArray src,

float alpha = 0.1, float tau = 10.0, float gamma = 0.2, int sigma0 = 1,

int sigma1 = 2) {

// Convert to floating point:

Mat X = src.getMat();

X.convertTo(X, CV_32FC1);

// Start preprocessing:

Mat I;

pow(X, gamma, I);

// Calculate the DOG Image:

{

Mat gaussian0, gaussian1;

// Kernel Size:

int kernel_sz0 = (3*sigma0);

int kernel_sz1 = (3*sigma1);

// Make them odd for OpenCV:

kernel_sz0 += ((kernel_sz0 % 2) == 0) ? 1 : 0;

kernel_sz1 += ((kernel_sz1 % 2) == 0) ? 1 : 0;

GaussianBlur(I, gaussian0, Size(kernel_sz0,kernel_sz0), sigma0, sigma0, BORDER_REPLICATE);

GaussianBlur(I, gaussian1, Size(kernel_sz1,kernel_sz1), sigma1, sigma1, BORDER_REPLICATE);

subtract(gaussian0, gaussian1, I);

}

{

double meanI = 0.0;

{

Mat tmp;

pow(abs(I), alpha, tmp);

meanI = mean(tmp).val[0];

}

I = I / pow(meanI, 1.0/alpha);

}

{

double meanI = 0.0;

{

Mat tmp;

pow(min(abs(I), tau), alpha, tmp);

meanI = mean(tmp).val[0];

}

I = I / pow(meanI, 1.0/alpha);

}

// Squash into the tanh:

{

Mat exp_x, exp_negx;

exp( I / tau, exp_x );

exp( -I / tau, exp_negx );

divide( exp_x - exp_negx, exp_x + exp_negx, I );

I = tau * I;

}

return I;

}

int main(int argc, const char *argv[]) {

// Get filename to the source image:

if (argc != 2) {

cout << "usage: " << argv[0] << " <image.ext>" << endl;

exit(1);

}

// Load image & get skin proportions:

Mat image = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);

// Calculate the TanTriggs Preprocessed image with default parameters:

Mat preprocessed = tan_triggs_preprocessing(image);

// Draw it on screen:

imshow("Original Image", image);

imshow("TanTriggs Preprocessed Image", norm_0_255(preprocessed));

// Show the images:

waitKey(0);

// Success!

return 0;

}