/*
 * Copyright (c) 2009 Silvia Ramis, Marta Ramis
 * Copyright (c) 2016 Jose-Luis Lisani <joseluis.lisani@uib.es>
 * Copyright (c) 2019 Jose-Luis Lisani <joseluis.lisani@uib.es>
 * All rights reserved.
 *
 * This code was originally written by Silvia Ramis and Marta Ramis
 * for their Final Project on Computer Sciences Engineering (2009)
 * under the supervision of J.L. Lisani
 * Some parts of the code were written by J.L. Lisani
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <dirent.h>

#include "library/parser.h"
#include "library/lib_facedet.h"

//Main function: read arguments and call face detection function
//Input:
//- input image (PNG format)
//- name of folder containing learned classifiers
//- maximum number of false positives in output result
//Output:
//- raw detections after simplification (displayed in PNG image)
//- raw detections after simplification (text file)
//- detections after rejection of unstable detections and simplification (displayed in PNG image)
//- detections after rejection of unstable detections and simplification (text file)
int main(int argc, char **argv)
{

    std::vector<ParStruct *> parameters;
    ParStruct pInput = {(char *) "input", NULL, (char *) "input image"};
    parameters.push_back(&pInput);
    ParStruct pClass = {(char *) "classifiers", NULL, (char *) "folder containing learned image classifiers"};
    parameters.push_back(&pClass);
    ParStruct pOutput = {(char *) "output", NULL, (char *) "root name of output files"};
    parameters.push_back(&pOutput);
    
    std::vector <OptStruct *> options;
    OptStruct oN  = {(char *) "n:", 0, (char *) "1", NULL, (char *) "Maximum number of false positives"}; 
    options.push_back(&oN);
    OptStruct oS  = {(char *) "s:", 0, (char *) "24", NULL, (char *) "Minimum size (pixels) of detected faces"}; 
    options.push_back(&oS);
    OptStruct oL  = {(char *) "l:", 0, (char *) "220", NULL, (char *) "Maximum size (pixels) of detected faces"}; 
    options.push_back(&oL);
    OptStruct oZ  = {(char *) "z:", 0, (char *) "1", NULL, (char *) "Size increase factor of checked subwindows (if 1 check all subwindows in size range)"};
    options.push_back(&oZ);

    if (!parsecmdline((char *) "facedet", (char *) "A contrario face detection", \
                  argc, argv, options, parameters))
        return EXIT_FAILURE;
    
    const char *namein = pInput.value;
    const char *namedirclass = pClass.value;
    const char *nameout = pOutput.value;
    
    //Parameters of the method
    //default value of parameter for 4th classifier (final output of detector):
    float nfa2 = atof(oN.value);
    //Minimum size (pixels) of detected faces: sizeMin x sizeMin
    int sizeMin = atoi(oS.value);
    //Maximum size (pixels) of detected faces: sizeMax x sizeMax
    int sizeMax = atoi(oL.value);
    //Factor of decrease/increase of zoom factors for resizing sub-windows 
    //to standard face size (24 x 24)
    float zStep = atof(oZ.value); 

    
    //name of classifiers in namedirclass folder
    char nameclass1[200];
    char nameclass2[200];
    char nameclass3[200];
    char nameclass4[200];
    sprintf(nameclass1, "%s/%s", namedirclass, "classifier5.txt");
    sprintf(nameclass2, "%s/%s", namedirclass, "classifier10.txt");
    sprintf(nameclass3, "%s/%s", namedirclass, "classifier80.txt");
    sprintf(nameclass4, "%s/%s", namedirclass, "classifier200.txt");
    
    //default parameters for the definition of the detection thresholds
    //of the classifier
    float prc1=20.0f; //parameter for 1st classifier (keep 20% best detections)
    float prc2=5.0f; //parameter for 2nd classifier (keep 5% best detections)
    float nfa1=100.0f;//parameter for 3rd classifier (allow up to 100 false
                      //positives)
    
    //call detection function
    detectfaces(namein, nameclass1, nameclass2, nameclass3, nameclass4,
                nameout, sizeMin, sizeMax, zStep, prc1/100.0f, prc2/100.0f, nfa1, nfa2);


    return EXIT_SUCCESS;
}