opencv/samples/ocl/hog.cpp

#include <iostream>
#include <fstream>
#include <string>
#include <sstream>
#include <iomanip>
#include <stdexcept>
#include <opencv2/core/utility.hpp>
#include "opencv2/ocl.hpp"
#include "opencv2/highgui.hpp"

using namespace std;
using namespace cv;

bool help_showed = false;

class Args
{
public:
    Args();
    static Args read(int argc, char** argv);

    string src;
    bool src_is_video;
    bool src_is_camera;
    int camera_id;

    bool write_video;
    string dst_video;
    double dst_video_fps;

    bool make_gray;

    bool resize_src;
    int width, height;

    double scale;
    int nlevels;
    int gr_threshold;

    double hit_threshold;
    bool hit_threshold_auto;

    int win_width;
    int win_stride_width, win_stride_height;

    bool gamma_corr;
};

class App
{
public:
    App(const Args& s);
    void run();

    void handleKey(char key);

    void hogWorkBegin();
    void hogWorkEnd();
    string hogWorkFps() const;

    void workBegin();
    void workEnd();
    string workFps() const;

    string message() const;

// This function test if gpu_rst matches cpu_rst.
// If the two vectors are not equal, it will return the difference in vector size
// Else if will return 
// (total diff of each cpu and gpu rects covered pixels)/(total cpu rects covered pixels)
    double checkRectSimilarity(Size sz, 
                               std::vector<Rect>& cpu_rst, 
                               std::vector<Rect>& gpu_rst);
private:
    App operator=(App&);

    Args args;
    bool running;

    bool use_gpu;
    bool make_gray;
    double scale;
    int gr_threshold;
    int nlevels;
    double hit_threshold;
    bool gamma_corr;

    int64 hog_work_begin;
    double hog_work_fps;

    int64 work_begin;
    double work_fps;
};

static void printHelp()
{
    cout << "Histogram of Oriented Gradients descriptor and detector sample.\n"
         << "\nUsage: hog_gpu\n"
         << "  (<image>|--video <vide>|--camera <camera_id>) # frames source\n"
         << "  [--make_gray <true/false>] # convert image to gray one or not\n"
         << "  [--resize_src <true/false>] # do resize of the source image or not\n"
         << "  [--width <int>] # resized image width\n"
         << "  [--height <int>] # resized image height\n"
         << "  [--hit_threshold <double>] # classifying plane distance threshold (0.0 usually)\n"
         << "  [--scale <double>] # HOG window scale factor\n"
         << "  [--nlevels <int>] # max number of HOG window scales\n"
         << "  [--win_width <int>] # width of the window (48 or 64)\n"
         << "  [--win_stride_width <int>] # distance by OX axis between neighbour wins\n"
         << "  [--win_stride_height <int>] # distance by OY axis between neighbour wins\n"
         << "  [--gr_threshold <int>] # merging similar rects constant\n"
         << "  [--gamma_correct <int>] # do gamma correction or not\n"
         << "  [--write_video <bool>] # write video or not\n"
         << "  [--dst_video <path>] # output video path\n"
         << "  [--dst_video_fps <double>] # output video fps\n";
    help_showed = true;
}

int main(int argc, char** argv)
{
    try
    {
        if (argc < 2)
            printHelp();
        Args args = Args::read(argc, argv);
        if (help_showed)
            return -1;
        App app(args);
        app.run();
    }
    catch (const Exception& e) { return cout << "error: "  << e.what() << endl, 1; }
    catch (const exception& e) { return cout << "error: "  << e.what() << endl, 1; }
    catch(...) { return cout << "unknown exception" << endl, 1; }
    return 0;
}


Args::Args()
{
    src_is_video = false;
    src_is_camera = false;
    camera_id = 0;

    write_video = false;
    dst_video_fps = 24.;

    make_gray = false;

    resize_src = false;
    width = 640;
    height = 480;

    scale = 1.05;
    nlevels = 13;
    gr_threshold = 8;
    hit_threshold = 1.4;
    hit_threshold_auto = true;

    win_width = 48;
    win_stride_width = 8;
    win_stride_height = 8;

    gamma_corr = true;
}


Args Args::read(int argc, char** argv)
{
    Args args;
    for (int i = 1; i < argc; i++)
    {
        if (string(argv[i]) == "--make_gray") args.make_gray = (string(argv[++i]) == "true");
        else if (string(argv[i]) == "--resize_src") args.resize_src = (string(argv[++i]) == "true");
        else if (string(argv[i]) == "--width") args.width = atoi(argv[++i]);
        else if (string(argv[i]) == "--height") args.height = atoi(argv[++i]);
        else if (string(argv[i]) == "--hit_threshold")
        {
            args.hit_threshold = atof(argv[++i]);
            args.hit_threshold_auto = false;
        }
        else if (string(argv[i]) == "--scale") args.scale = atof(argv[++i]);
        else if (string(argv[i]) == "--nlevels") args.nlevels = atoi(argv[++i]);
        else if (string(argv[i]) == "--win_width") args.win_width = atoi(argv[++i]);
        else if (string(argv[i]) == "--win_stride_width") args.win_stride_width = atoi(argv[++i]);
        else if (string(argv[i]) == "--win_stride_height") args.win_stride_height = atoi(argv[++i]);
        else if (string(argv[i]) == "--gr_threshold") args.gr_threshold = atoi(argv[++i]);
        else if (string(argv[i]) == "--gamma_correct") args.gamma_corr = (string(argv[++i]) == "true");
        else if (string(argv[i]) == "--write_video") args.write_video = (string(argv[++i]) == "true");
        else if (string(argv[i]) == "--dst_video") args.dst_video = argv[++i];
        else if (string(argv[i]) == "--dst_video_fps") args.dst_video_fps = atof(argv[++i]);
        else if (string(argv[i]) == "--help") printHelp();
        else if (string(argv[i]) == "--video") { args.src = argv[++i]; args.src_is_video = true; }
        else if (string(argv[i]) == "--camera") { args.camera_id = atoi(argv[++i]); args.src_is_camera = true; }
        else if (args.src.empty()) args.src = argv[i];
        else throw runtime_error((string("unknown key: ") + argv[i]));
    }
    return args;
}


App::App(const Args& s)
{
    args = s;
    cout << "\nControls:\n"
         << "\tESC - exit\n"
         << "\tm - change mode GPU <-> CPU\n"
         << "\tg - convert image to gray or not\n"
         << "\t1/q - increase/decrease HOG scale\n"
         << "\t2/w - increase/decrease levels count\n"
         << "\t3/e - increase/decrease HOG group threshold\n"
         << "\t4/r - increase/decrease hit threshold\n"
         << endl;

    use_gpu = true;
    make_gray = args.make_gray;
    scale = args.scale;
    gr_threshold = args.gr_threshold;
    nlevels = args.nlevels;

    if (args.hit_threshold_auto)
        args.hit_threshold = args.win_width == 48 ? 1.4 : 0.;
    hit_threshold = args.hit_threshold;

    gamma_corr = args.gamma_corr;

    if (args.win_width != 64 && args.win_width != 48)
        args.win_width = 64;

    cout << "Scale: " << scale << endl;
    if (args.resize_src)
        cout << "Resized source: (" << args.width << ", " << args.height << ")\n";
    cout << "Group threshold: " << gr_threshold << endl;
    cout << "Levels number: " << nlevels << endl;
    cout << "Win width: " << args.win_width << endl;
    cout << "Win stride: (" << args.win_stride_width << ", " << args.win_stride_height << ")\n";
    cout << "Hit threshold: " << hit_threshold << endl;
    cout << "Gamma correction: " << gamma_corr << endl;
    cout << endl;
}


void App::run()
{
    std::vector<ocl::Info> oclinfo;
    ocl::getDevice(oclinfo);
    running = true;
    cv::VideoWriter video_writer;

    Size win_size(args.win_width, args.win_width * 2); //(64, 128) or (48, 96)
    Size win_stride(args.win_stride_width, args.win_stride_height);

    // Create HOG descriptors and detectors here
    vector<float> detector;
    if (win_size == Size(64, 128))
        detector = cv::ocl::HOGDescriptor::getPeopleDetector64x128();
    else
        detector = cv::ocl::HOGDescriptor::getPeopleDetector48x96();

    cv::ocl::HOGDescriptor gpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9,
                                   cv::ocl::HOGDescriptor::DEFAULT_WIN_SIGMA, 0.2, gamma_corr,
                                   cv::ocl::HOGDescriptor::DEFAULT_NLEVELS);
    cv::HOGDescriptor cpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9, 1, -1,
                              HOGDescriptor::L2Hys, 0.2, gamma_corr, cv::HOGDescriptor::DEFAULT_NLEVELS);
    gpu_hog.setSVMDetector(detector);
    cpu_hog.setSVMDetector(detector);

    while (running)
    {
        VideoCapture vc;
        Mat frame;

        if (args.src_is_video)
        {
            vc.open(args.src.c_str());
            if (!vc.isOpened())
                throw runtime_error(string("can't open video file: " + args.src));
            vc >> frame;
        }
        else if (args.src_is_camera)
        {
            vc.open(args.camera_id);
            if (!vc.isOpened())
            {
                stringstream msg;
                msg << "can't open camera: " << args.camera_id;
                throw runtime_error(msg.str());
            }
            vc >> frame;
        }
        else
        {
            frame = imread(args.src);
            if (frame.empty())
                throw runtime_error(string("can't open image file: " + args.src));
        }

        Mat img_aux, img, img_to_show;
        ocl::oclMat gpu_img;

        // Iterate over all frames
        bool verify = false;
        while (running && !frame.empty())
        {
            workBegin();

            // Change format of the image
            if (make_gray) cvtColor(frame, img_aux, COLOR_BGR2GRAY);
            else if (use_gpu) cvtColor(frame, img_aux, COLOR_BGR2BGRA);
            else frame.copyTo(img_aux);

            // Resize image
            if (args.resize_src) resize(img_aux, img, Size(args.width, args.height));
            else img = img_aux;
            img_to_show = img;

            gpu_hog.nlevels = nlevels;
            cpu_hog.nlevels = nlevels;

            vector<Rect> found;

            // Perform HOG classification
            hogWorkBegin();
            if (use_gpu)
            {
                gpu_img.upload(img);
                gpu_hog.detectMultiScale(gpu_img, found, hit_threshold, win_stride,
                                         Size(0, 0), scale, gr_threshold);
                if (!verify)
                {
                    // verify if GPU output same objects with CPU at 1st run
                    verify = true;
                    vector<Rect> ref_rst;
                    cvtColor(img, img, COLOR_BGRA2BGR);
                    cpu_hog.detectMultiScale(img, ref_rst, hit_threshold, win_stride,
                                              Size(0, 0), scale, gr_threshold-2);
                    double accuracy = checkRectSimilarity(img.size(), ref_rst, found);
                    cout << "\naccuracy value: " << accuracy << endl;           
                } 
           }
            else cpu_hog.detectMultiScale(img, found, hit_threshold, win_stride,
                                          Size(0, 0), scale, gr_threshold);
            hogWorkEnd();

            // Draw positive classified windows
            for (size_t i = 0; i < found.size(); i++)
            {
                Rect r = found[i];
                rectangle(img_to_show, r.tl(), r.br(), Scalar(0, 255, 0), 3);
            }

            if (use_gpu)
                putText(img_to_show, "Mode: GPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
            else
                putText(img_to_show, "Mode: CPU", Point(5, 25), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
            putText(img_to_show, "FPS (HOG only): " + hogWorkFps(), Point(5, 65), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
            putText(img_to_show, "FPS (total): " + workFps(), Point(5, 105), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
            imshow("opencv_gpu_hog", img_to_show);

            if (args.src_is_video || args.src_is_camera) vc >> frame;

            workEnd();

            if (args.write_video)
            {
                if (!video_writer.isOpened())
                {
                    video_writer.open(args.dst_video, VideoWriter::fourcc('x','v','i','d'), args.dst_video_fps,
                                      img_to_show.size(), true);
                    if (!video_writer.isOpened())
                        throw std::runtime_error("can't create video writer");
                }

                if (make_gray) cvtColor(img_to_show, img, COLOR_GRAY2BGR);
                else cvtColor(img_to_show, img, COLOR_BGRA2BGR);

                video_writer << img;
            }

            handleKey((char)waitKey(3));
        }
    }
}


void App::handleKey(char key)
{
    switch (key)
    {
    case 27:
        running = false;
        break;
    case 'm':
    case 'M':
        use_gpu = !use_gpu;
        cout << "Switched to " << (use_gpu ? "CUDA" : "CPU") << " mode\n";
        break;
    case 'g':
    case 'G':
        make_gray = !make_gray;
        cout << "Convert image to gray: " << (make_gray ? "YES" : "NO") << endl;
        break;
    case '1':
        scale *= 1.05;
        cout << "Scale: " << scale << endl;
        break;
    case 'q':
    case 'Q':
        scale /= 1.05;
        cout << "Scale: " << scale << endl;
        break;
    case '2':
        nlevels++;
        cout << "Levels number: " << nlevels << endl;
        break;
    case 'w':
    case 'W':
        nlevels = max(nlevels - 1, 1);
        cout << "Levels number: " << nlevels << endl;
        break;
    case '3':
        gr_threshold++;
        cout << "Group threshold: " << gr_threshold << endl;
        break;
    case 'e':
    case 'E':
        gr_threshold = max(0, gr_threshold - 1);
        cout << "Group threshold: " << gr_threshold << endl;
        break;
    case '4':
        hit_threshold+=0.25;
        cout << "Hit threshold: " << hit_threshold << endl;
        break;
    case 'r':
    case 'R':
        hit_threshold = max(0.0, hit_threshold - 0.25);
        cout << "Hit threshold: " << hit_threshold << endl;
        break;
    case 'c':
    case 'C':
        gamma_corr = !gamma_corr;
        cout << "Gamma correction: " << gamma_corr << endl;
        break;
    }
}


inline void App::hogWorkBegin() { hog_work_begin = getTickCount(); }

inline void App::hogWorkEnd()
{
    int64 delta = getTickCount() - hog_work_begin;
    double freq = getTickFrequency();
    hog_work_fps = freq / delta;
}

inline string App::hogWorkFps() const
{
    stringstream ss;
    ss << hog_work_fps;
    return ss.str();
}


inline void App::workBegin() { work_begin = getTickCount(); }

inline void App::workEnd()
{
    int64 delta = getTickCount() - work_begin;
    double freq = getTickFrequency();
    work_fps = freq / delta;
}

inline string App::workFps() const
{
    stringstream ss;
    ss << work_fps;
    return ss.str();
}

double App::checkRectSimilarity(Size sz, 
                                std::vector<Rect>& ob1, 
                                std::vector<Rect>& ob2)
{
    double final_test_result = 0.0;
    size_t sz1 = ob1.size();
    size_t sz2 = ob2.size();

    if(sz1 != sz2)
        return sz1 > sz2 ? (double)(sz1 - sz2) : (double)(sz2 - sz1);
    else
    {
        cv::Mat cpu_result(sz, CV_8UC1);
        cpu_result.setTo(0);

        for(vector<Rect>::const_iterator r = ob1.begin(); r != ob1.end(); r++)
        {      
            cv::Mat cpu_result_roi(cpu_result, *r);
            cpu_result_roi.setTo(1);
            cpu_result.copyTo(cpu_result);
        }
        int cpu_area = cv::countNonZero(cpu_result > 0);

        cv::Mat gpu_result(sz, CV_8UC1);
        gpu_result.setTo(0);
        for(vector<Rect>::const_iterator r2 = ob2.begin(); r2 != ob2.end(); r2++)
        {
            cv::Mat gpu_result_roi(gpu_result, *r2);
            gpu_result_roi.setTo(1);
            gpu_result.copyTo(gpu_result);
        }

        cv::Mat result_;
        multiply(cpu_result, gpu_result, result_);
        int result = cv::countNonZero(result_ > 0);

        final_test_result = 1.0 - (double)result/(double)cpu_area;
    }
    return final_test_result;

}