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HaardetectorUnitTest.cpp

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#ifdef __LINUX__
        #include "cob_people_detection/PeopleDetector.h"
        #include "cob_people_detection/PeopleDetectorControlFlow.h"
#else
        #include "cob_vision/cob_people_detection/common/include/cob_people_detection/PeopleDetector.h"
        #include "cob_vision/cob_people_detection/common/include/cob_people_detection/PeopleDetectorControlFlow.h"
#endif

#include <iostream>
#include <string>
#include <fstream>
#include <sstream>


template<typename T> inline T abs_(T x)
{
        return (x<0) ? -x : x;
}

bool checkRectangleCorrespondence(cv::Rect& gt, cv::Rect& measurement, double areaRatioThreshold)
{
        // overlapping area
        double dx = (min(gt.x+gt.width, measurement.x+measurement.width) - max(gt.x, measurement.x));
        double dy = (min(gt.y+gt.height, measurement.y+measurement.height) - max(gt.y, measurement.y));
        double overlapArea = dx * dy;

        // check area ratios
        double gtArea = gt.width*gt.height;
        double measurementArea = measurement.width*measurement.height;
        //std::cout << overlapArea/gtArea << "   " << overlapArea/measurementArea << "\n";

        if (dx<0 || dy<0 || (overlapArea/gtArea < areaRatioThreshold) || (overlapArea/measurementArea < areaRatioThreshold))
                return false;
        
        return true;
}


int main(int argc, char *argv[])
{
        if (argc != 4)
        {
                std::cout << "Usage of this tool:\nHaardetectorUnitTest <test_data_path> <detector_mode> <detector_path>\n";
                std::cout << " test_data_path\t=\tPath to the 'testData.txt' file with the ground truth (each line contains the filename of the respective image followed by the number of contained faces followed by the coordinates of these faces in the style x y width height, use spaces as separator)\n";
                std::cout << " detector_mode\t=\tswitches between range image detector (put a 0 as argument) and color image detector (put a 1 as argument)\n\n";
                std::cout << " detector_path\t=\tthe path to the range and color image haarcascades (the cacscades should be placed in a folder haarcascades/ , provide the path to that folder)\n\n";
                std::cout << "Example: HarrdetectorUnitTest ConfigurationFiles/TestData/ 0 ConfigurationFiles/\n\n";
                return ipa_Utils::RET_OK;
        }

        // detector mode (0=range, 1=color images)
        std::stringstream ss;
        ss << argv[2];
        int detectorMode;
        ss >> detectorMode;

        // initialize the people detector
        ipa_PeopleDetector::PeopleDetectorControlFlow peopleDetectorControlFlow;
        ipa_PeopleDetector::PeopleDetector peopleDetector;
        std::string directory = argv[3];
        std::string iniFileNameAndPath = directory + "peopleDetectorIni.xml";
        if (peopleDetector.Init(directory) & ipa_Utils::RET_FAILED)
        {       
                std::cerr << "ERROR - HaardetectorUnitTest:" << std::endl;
                std::cerr << "\t ... Could not initialize people detector library.\n";
                return ipa_Utils::RET_FAILED;
        }
        peopleDetectorControlFlow.m_PeopleDetector = &peopleDetector;
        if(peopleDetectorControlFlow.LoadParameters(iniFileNameAndPath.c_str()) & ipa_CameraSensors::RET_FAILED)
        {
                std::cerr << "ERROR - HaardetectorUnitTest:" << std::endl;
                std::cerr << "\t ... Error while loading configuration file '" << std::endl;
                std::cerr << "\t ... " << iniFileNameAndPath << "'.\n";
                return ipa_Utils::RET_FAILED;   
        }

        // try different parameter settings
        peopleDetector.m_range_drop_groups;
#ifndef __DEBUG__
        int* parameter = 0;
        if (detectorMode == 0)
                parameter = &(peopleDetector.m_range_drop_groups);      // range mode
        else
                parameter = &(peopleDetector.m_faces_drop_groups);      // color mode
        for (*parameter = 20; *parameter < 80; *parameter += 2)
        {
                if (detectorMode == 0)
                        std::cout << "\n\n\n---------------------------------------------------\nparam peopleDetector.m_range_drop_groups = " << peopleDetector.m_range_drop_groups << "\n";
                else
                        std::cout << "\n\n\n---------------------------------------------------\nparam peopleDetector.m_faces_drop_groups = " << peopleDetector.m_faces_drop_groups << "\n";
#endif

                // file containing the ground truth
                std::string testDataDirectory = argv[1];
                std::string filename = testDataDirectory + "testData.txt";

                // open the ground truth file
                std::fstream fin(filename.c_str(), std::fstream::in);
                if (!fin.is_open())
                {
                        std::cerr << "ERROR - HaardetectorUnitTest:\n";
                        std::cerr << "\t ... Could not open the ground truth file " << filename << ".\n";
                        return ipa_Utils::RET_FAILED;
                }

                // statistics
                enum {TRUE_POS=0, FALSE_NEG, FALSE_POS, TRUE_NEG};
                std::vector<int> stats(4, 0);
                int numberNegativeImages = 0;   // number of negative images (without any face) in the test set
                int numberGroundtruthFaces = 0; // total number of faces in the whole test set

                // open each image of the ground truth, run the detector and examine the detection results
                while (!fin.eof())
                {
                        // filename of the image, load image
                        std::string imageFilename;
                        fin >> imageFilename;
                        if (imageFilename=="") break;
                        imageFilename = testDataDirectory + imageFilename;
                        cv::Mat img = cv::imread(imageFilename, -1);

                        //for (int v=0; v<img.rows; v++)
                        //{
                        //      for (int u=0; u<img.cols; u++)
                        //      {
                        //              if (img.at<uchar>(v,u) == 0) img.at<uchar>(v,u) = 255;
                        //      }
                        //}

        #ifdef __DEBUG__
                        cv::Mat dispImg;
                        cv::cvtColor(img, dispImg, CV_GRAY2BGR, 3);
        #endif

                        // the ratio of overlapping area to ground truth area or to detected area must not fall below this threshold otherwise it is no detection
                        const double maxFaceDistance = 0.6;

                        // number of contained faces
                        int numFaces = 0;
                        fin >> numFaces;
                        numberGroundtruthFaces += numFaces;
                        if (numFaces==0) numberNegativeImages++;

                        // detect faces
                        std::vector<cv::Rect> faces;
                        if (detectorMode==0)
                                peopleDetector.DetectRangeFace(img, faces, false);      // range images
                        else
                                peopleDetector.DetectColorFaces(img, faces);    // color images

                        // check for each ground truth face whether it was found by the detector
                        std::vector<bool> truePositiveDetection(faces.size(), false);   // stores when a detected face turns out to be a true positive
                        for (int gtfaceIndex=0; gtfaceIndex<numFaces; gtfaceIndex++)
                        {
                                // read the ground truth faces from file
                                cv::Rect faceGt;
                                fin >> faceGt.x >> faceGt.y >> faceGt.width >> faceGt.height;

        #ifdef __DEBUG__
                                cv::rectangle(dispImg, faceGt, CV_RGB(0,255,0), 2);
        #endif

                                // compare with each detected face
                                bool truePositiveFound = false;
                                for (int faceIndex=0; faceIndex<(int)faces.size(); faceIndex++)
                                {
                                        if (checkRectangleCorrespondence(faceGt, faces[faceIndex], maxFaceDistance) == true)
                                        {
                                                // both rectangles correspond
                                                if (truePositiveFound == false) stats[TRUE_POS]++;
                                                truePositiveDetection[faceIndex] = true;
                                                truePositiveFound = true;
                                        }
                                }

                                // complete statistics - could the ground truth be detected in the image
                                if (truePositiveFound == false)
                                        stats[FALSE_NEG]++;
                        }

                        // complete statistics - check for false positives
                        for (int faceIndex=0; faceIndex<(int)faces.size(); faceIndex++)
                                if (truePositiveDetection[faceIndex]==false)
                                        stats[FALSE_POS]++;

                        // complete statistics - check for true negative (no face in ground truth and no detections)
                        if (numFaces==0 && faces.size()==0)
                                stats[TRUE_NEG]++;

        #ifdef __DEBUG__
                        std::cout << stats[TRUE_POS] << "\t" << stats[FALSE_NEG] << "\t" << stats[FALSE_POS] << "\t" << stats[TRUE_NEG] << "\t\t" << numberGroundtruthFaces << "\t" << numberNegativeImages << "\n";
                        for (int faceIndex=0; faceIndex<(int)faces.size(); faceIndex++)
                                cv::rectangle(dispImg, faces[faceIndex], CV_RGB(0,0,255), 2);
                        cv::imshow("Groundtruth (green) vs detections (blue)", dispImg);
                        cv::waitKey();
        #endif
                }
                fin.close();

                // output results
                std::cout << "-----------\nStatistics:\n-----------\nTruePos\tFalseNeg\tFalsePos\tTrueNeg\t\tNumFaces\tNumNegatives\n";
                std::cout << stats[TRUE_POS] << "\t" << stats[FALSE_NEG] << "\t\t" << stats[FALSE_POS] << "\t\t" << stats[TRUE_NEG] << "\t\t" << numberGroundtruthFaces << "\t\t" << numberNegativeImages << "\n";
                std::cout << "\nPositive detection rate (#truePositives/#NumFaces): " << (double)stats[TRUE_POS]/(double)numberGroundtruthFaces << "\n";
                std::cout << "\nFalse positive rate (#falsePositives/#NumFaces): " << (double)stats[FALSE_POS]/(double)numberGroundtruthFaces << "\n";
                std::cout << "\nNegative detection rate (#trueNegatives/#NumNegatives): " << (double)stats[TRUE_NEG]/(double)numberNegativeImages << "\n";
#ifndef __DEBUG__
        }
#endif

        getchar();

        return ipa_Utils::RET_OK;
}

---

cob_people_detection
Author(s): Richard Bormann
autogenerated on Tue Mar 5 11:50:09 2013