path: root/inference-engine/samples/common/samples/common.hpp

// Copyright (C) 2018 Intel Corporation
//
// SPDX-License-Identifier: Apache-2.0
//

/**
 * @brief a header file with common samples functionality
 * @file common.hpp
 */

#pragma once

#include <string>
#include <map>
#include <vector>
#include <list>
#include <limits>
#include <random>
#include <cctype>
#include <functional>
#include <time.h>
#include <iostream>
#include <fstream>
#include <iomanip>
#include <utility>

#include <algorithm>
#include <chrono>

#ifdef USE_OPENCV
    #include <opencv2/opencv.hpp>
#endif

#include <ie_plugin_dispatcher.hpp>
#include <ie_plugin_ptr.hpp>
#include <cpp/ie_cnn_net_reader.h>
#include <cpp/ie_infer_request.hpp>
#include <ie_device.hpp>
#include <ie_blob.h>

#ifndef UNUSED
  #ifdef WIN32
    #define UNUSED
  #else
    #define UNUSED  __attribute__((unused))
  #endif
#endif

/**
 * @brief Trims from both ends (in place)
 * @param s - string to trim
 * @return trimmed string
 */
inline std::string &trim(std::string &s) {
    s.erase(s.begin(), std::find_if(s.begin(), s.end(), std::not1(std::ptr_fun<int, int>(std::isspace))));
    s.erase(std::find_if(s.rbegin(), s.rend(), std::not1(std::ptr_fun<int, int>(std::isspace))).base(), s.end());
    return s;
}

/**
* @brief Converts string to TargetDevice
* @param deviceName - string value representing device
* @return TargetDevice value that corresponds to input string.
*         eDefault in case no corresponding value was found
*/
static InferenceEngine::TargetDevice getDeviceFromStr(const std::string &deviceName) {
    return InferenceEngine::TargetDeviceInfo::fromStr(deviceName);
}

/**
* @brief Loads plugin from directories
* @param pluginDirs - plugin paths
* @param plugin - plugin name
* @param device - device to infer on
* @return Plugin pointer
*/
static InferenceEngine::InferenceEnginePluginPtr selectPlugin(const std::vector<std::string> &pluginDirs,
                                                              const std::string &plugin,
                                                              InferenceEngine::TargetDevice device) {
    InferenceEngine::PluginDispatcher dispatcher(pluginDirs);

    if (!plugin.empty()) {
        return dispatcher.getPluginByName(plugin);
    } else {
        return dispatcher.getSuitablePlugin(device);
    }
}

/**
 * @brief Loads plugin from directories
 * @param pluginDirs - plugin paths
 * @param plugin - plugin name
 * @param device - string representation of device to infer on
 * @return Plugin pointer
 */
static UNUSED InferenceEngine::InferenceEnginePluginPtr selectPlugin(const std::vector<std::string> &pluginDirs,
                                                                     const std::string &plugin,
                                                                     const std::string &device) {
    return selectPlugin(pluginDirs, plugin, getDeviceFromStr(device));
}

/**
 * @brief Gets filename without extension
 * @param filepath - full file name
 * @return filename without extension
 */
static UNUSED std::string fileNameNoExt(const std::string &filepath) {
    auto pos = filepath.rfind('.');
    if (pos == std::string::npos) return filepath;
    return filepath.substr(0, pos);
}

/**
* @brief Get extension from filename
* @param filename - name of the file which extension should be extracted
* @return string with extracted file extension
*/
inline std::string fileExt(const std::string& filename) {
    auto pos = filename.rfind('.');
    if (pos == std::string::npos) return "";
    return filename.substr(pos + 1);
}

static UNUSED std::ostream &operator<<(std::ostream &os, const InferenceEngine::Version *version) {
    os << "\n\tAPI version ............ ";
    if (nullptr == version) {
        os << "UNKNOWN";
    } else {
        os << version->apiVersion.major << "." << version->apiVersion.minor;
        if (nullptr != version->buildNumber) {
            os << "\n\t" << "Build .................. " << version->buildNumber;
        }
        if (nullptr != version->description) {
            os << "\n\t" << "Description ....... " << version->description;
        }
    }
    return os;
}

/**
 * @class PluginVersion
 * @brief A PluginVersion class stores plugin version and initialization status
 */
struct PluginVersion : public InferenceEngine::Version {
    bool initialized = false;

    explicit PluginVersion(const InferenceEngine::Version *ver) {
        if (nullptr == ver) {
            return;
        }
        InferenceEngine::Version::operator=(*ver);
        initialized = true;
    }

    operator bool() const noexcept {
        return initialized;
    }
};

static UNUSED std::ostream &operator<<(std::ostream &os, const PluginVersion &version) {
    os << "\tPlugin version ......... ";
    if (!version) {
        os << "UNKNOWN";
    } else {
        os << version.apiVersion.major << "." << version.apiVersion.minor;
    }

    os << "\n\tPlugin name ............ ";
    if (!version || version.description == nullptr) {
        os << "UNKNOWN";
    } else {
        os << version.description;
    }

    os << "\n\tPlugin build ........... ";
    if (!version || version.buildNumber == nullptr) {
        os << "UNKNOWN";
    } else {
        os << version.buildNumber;
    }

    return os;
}

inline void printPluginVersion(InferenceEngine::InferenceEnginePluginPtr ptr, std::ostream& stream) {
    const PluginVersion *pluginVersion;
    ptr->GetVersion((const InferenceEngine::Version*&)pluginVersion);
    stream << pluginVersion << std::endl;
}

static UNUSED std::vector<std::vector<size_t>> blobToImageOutputArray(InferenceEngine::TBlob<float>::Ptr output,
                                                                      size_t *pWidth, size_t *pHeight,
                                                                      size_t *pChannels) {
    std::vector<std::vector<size_t>> outArray;
    size_t W = output->dims().at(0);
    size_t H = output->dims().at(1);
    size_t C = output->dims().at(2);

    // Get classes
    const float *outData = output->data();
    for (unsigned h = 0; h < H; h++) {
        std::vector<size_t> row;
        for (unsigned w = 0; w < W; w++) {
            float max_value = outData[h * W + w];
            size_t index = 0;
            for (size_t c = 1; c < C; c++) {
                size_t dataIndex = c * H * W + h * W + w;
                if (outData[dataIndex] > max_value) {
                    index = c;
                    max_value = outData[dataIndex];
                }
            }
            row.push_back(index);
        }
        outArray.push_back(row);
    }

    if (pWidth != nullptr) *pWidth = W;
    if (pHeight != nullptr) *pHeight = H;
    if (pChannels != nullptr) *pChannels = C;

    return outArray;
}

/**
 * @class Color
 * @brief A Color class stores channels of a given color
 */
class Color {
private:
    unsigned char _r;
    unsigned char _g;
    unsigned char _b;

public:
    /**
     * A default constructor.
     * @param r - value for red channel
     * @param g - value for green channel
     * @param b - value for blue channel
     */
    Color(unsigned char r,
          unsigned char g,
          unsigned char b) : _r(r), _g(g), _b(b) {}

    inline unsigned char red() {
        return _r;
    }

    inline unsigned char blue() {
        return _b;
    }

    inline unsigned char green() {
        return _g;
    }
};

// TODO : keep only one version of writeOutputBMP

/**
 * @brief Writes output data to image
 * @param name - image name
 * @param data - output data
 * @param classesNum - the number of classes
 * @return false if error else true
 */
static UNUSED void writeOutputBmp(std::vector<std::vector<size_t>> data, size_t classesNum, std::ostream &outFile) {
    unsigned int seed = (unsigned int) time(NULL);
    // Known colors for training classes from Cityscape dataset
    static std::vector<Color> colors = {
        {128, 64,  128},
        {232, 35,  244},
        {70,  70,  70},
        {156, 102, 102},
        {153, 153, 190},
        {153, 153, 153},
        {30,  170, 250},
        {0,   220, 220},
        {35,  142, 107},
        {152, 251, 152},
        {180, 130, 70},
        {60,  20,  220},
        {0,   0,   255},
        {142, 0,   0},
        {70,  0,   0},
        {100, 60,  0},
        {90,  0,   0},
        {230, 0,   0},
        {32,  11,  119},
        {0,   74,  111},
        {81,  0,   81}
    };

    while (classesNum > colors.size()) {
        static std::mt19937 rng(seed);
        std::uniform_int_distribution<int> dist(0, 255);
        Color color(dist(rng), dist(rng), dist(rng));
        colors.push_back(color);
    }

    unsigned char file[14] = {
            'B', 'M',           // magic
            0, 0, 0, 0,         // size in bytes
            0, 0,               // app data
            0, 0,               // app data
            40 + 14, 0, 0, 0      // start of data offset
    };
    unsigned char info[40] = {
            40, 0, 0, 0,        // info hd size
            0, 0, 0, 0,         // width
            0, 0, 0, 0,         // height
            1, 0,               // number color planes
            24, 0,              // bits per pixel
            0, 0, 0, 0,         // compression is none
            0, 0, 0, 0,         // image bits size
            0x13, 0x0B, 0, 0,   // horz resolution in pixel / m
            0x13, 0x0B, 0, 0,   // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi)
            0, 0, 0, 0,         // #colors in palette
            0, 0, 0, 0,         // #important colors
    };

    auto height = data.size();
    auto width = data.at(0).size();

    if (height > (size_t) std::numeric_limits<int32_t>::max || width > (size_t) std::numeric_limits<int32_t>::max) {
        THROW_IE_EXCEPTION << "File size is too big: " << height << " X " << width;
    }

    int padSize = static_cast<int>(4 - (width * 3) % 4) % 4;
    int sizeData = static_cast<int>(width * height * 3 + height * padSize);
    int sizeAll = sizeData + sizeof(file) + sizeof(info);

    file[2] = (unsigned char) (sizeAll);
    file[3] = (unsigned char) (sizeAll >> 8);
    file[4] = (unsigned char) (sizeAll >> 16);
    file[5] = (unsigned char) (sizeAll >> 24);

    info[4] = (unsigned char) (width);
    info[5] = (unsigned char) (width >> 8);
    info[6] = (unsigned char) (width >> 16);
    info[7] = (unsigned char) (width >> 24);

    int32_t negativeHeight = -(int32_t) height;
    info[8] = (unsigned char) (negativeHeight);
    info[9] = (unsigned char) (negativeHeight >> 8);
    info[10] = (unsigned char) (negativeHeight >> 16);
    info[11] = (unsigned char) (negativeHeight >> 24);

    info[20] = (unsigned char) (sizeData);
    info[21] = (unsigned char) (sizeData >> 8);
    info[22] = (unsigned char) (sizeData >> 16);
    info[23] = (unsigned char) (sizeData >> 24);

    outFile.write(reinterpret_cast<char *>(file), sizeof(file));
    outFile.write(reinterpret_cast<char *>(info), sizeof(info));

    unsigned char pad[3] = {0, 0, 0};

    for (size_t y = 0; y < height; y++) {
        for (size_t x = 0; x < width; x++) {
            unsigned char pixel[3];
            size_t index = data.at(y).at(x);
            pixel[0] = colors.at(index).red();
            pixel[1] = colors.at(index).green();
            pixel[2] = colors.at(index).blue();
            outFile.write(reinterpret_cast<char *>(pixel), 3);
        }
        outFile.write(reinterpret_cast<char *>(pad), padSize);
    }
}

/**
* @brief Writes output data to BMP image
* @param name - image name
* @param data - output data
* @param height - height of the target image
* @param width - width of the target image
* @return false if error else true
*/
static UNUSED bool writeOutputBmp(std::string name, unsigned char *data, size_t height, size_t width) {
    std::ofstream outFile;
    outFile.open(name, std::ofstream::binary);
    if (!outFile.is_open()) {
        return false;
    }

    unsigned char file[14] = {
        'B', 'M',           // magic
        0, 0, 0, 0,         // size in bytes
        0, 0,               // app data
        0, 0,               // app data
        40 + 14, 0, 0, 0      // start of data offset
    };
    unsigned char info[40] = {
        40, 0, 0, 0,        // info hd size
        0, 0, 0, 0,         // width
        0, 0, 0, 0,         // height
        1, 0,               // number color planes
        24, 0,              // bits per pixel
        0, 0, 0, 0,         // compression is none
        0, 0, 0, 0,         // image bits size
        0x13, 0x0B, 0, 0,   // horz resolution in pixel / m
        0x13, 0x0B, 0, 0,   // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi)
        0, 0, 0, 0,         // #colors in palette
        0, 0, 0, 0,         // #important colors
    };

    if (height > (size_t)std::numeric_limits<int32_t>::max || width > (size_t)std::numeric_limits<int32_t>::max) {
        THROW_IE_EXCEPTION << "File size is too big: " << height << " X " << width;
    }

    int padSize = static_cast<int>(4 - (width * 3) % 4) % 4;
    int sizeData = static_cast<int>(width * height * 3 + height * padSize);
    int sizeAll = sizeData + sizeof(file) + sizeof(info);

    file[2] = (unsigned char)(sizeAll);
    file[3] = (unsigned char)(sizeAll >> 8);
    file[4] = (unsigned char)(sizeAll >> 16);
    file[5] = (unsigned char)(sizeAll >> 24);

    info[4] = (unsigned char)(width);
    info[5] = (unsigned char)(width >> 8);
    info[6] = (unsigned char)(width >> 16);
    info[7] = (unsigned char)(width >> 24);

    int32_t negativeHeight = -(int32_t)height;
    info[8] = (unsigned char)(negativeHeight);
    info[9] = (unsigned char)(negativeHeight >> 8);
    info[10] = (unsigned char)(negativeHeight >> 16);
    info[11] = (unsigned char)(negativeHeight >> 24);

    info[20] = (unsigned char)(sizeData);
    info[21] = (unsigned char)(sizeData >> 8);
    info[22] = (unsigned char)(sizeData >> 16);
    info[23] = (unsigned char)(sizeData >> 24);

    outFile.write(reinterpret_cast<char *>(file), sizeof(file));
    outFile.write(reinterpret_cast<char *>(info), sizeof(info));

    unsigned char pad[3] = { 0, 0, 0 };

    for (size_t y = 0; y < height; y++) {
        for (size_t x = 0; x < width; x++) {
            unsigned char pixel[3];
            pixel[0] = data[y * width * 3 + x * 3];
            pixel[1] = data[y * width * 3 + x * 3 + 1];
            pixel[2] = data[y * width * 3 + x * 3 + 2];

            outFile.write(reinterpret_cast<char *>(pixel), 3);
        }
        outFile.write(reinterpret_cast<char *>(pad), padSize);
    }
    return true;
}


/**
* @brief Adds colored rectangles to the image
* @param data - data where rectangles are put
* @param height - height of the rectangle
* @param width - width of the rectangle
* @param rectangles - vector points for the rectangle, should be 4x compared to num classes
* @param classes - vector of classes
*/
static UNUSED void addRectangles(unsigned char *data, size_t height, size_t width, std::vector<int> rectangles, std::vector<int> classes) {
    std::vector<Color> colors = {
        { 128, 64,  128 },
        { 232, 35,  244 },
        { 70,  70,  70 },
        { 156, 102, 102 },
        { 153, 153, 190 },
        { 153, 153, 153 },
        { 30,  170, 250 },
        { 0,   220, 220 },
        { 35,  142, 107 },
        { 152, 251, 152 },
        { 180, 130, 70 },
        { 60,  20,  220 },
        { 0,   0,   255 },
        { 142, 0,   0 },
        { 70,  0,   0 },
        { 100, 60,  0 },
        { 90,  0,   0 },
        { 230, 0,   0 },
        { 32,  11,  119 },
        { 0,   74,  111 },
        { 81,  0,   81 }
    };
    if (rectangles.size() % 4 != 0 || rectangles.size() / 4 != classes.size()) {
        return;
    }

    for (size_t i = 0; i < classes.size(); i++) {
        int x = rectangles.at(i * 4);
        int y = rectangles.at(i * 4 + 1);
        int w = rectangles.at(i * 4 + 2);
        int h = rectangles.at(i * 4 + 3);

        if (x < 0) x = 0;
        if (y < 0) y = 0;
        if (w < 0) w = 0;
        if (h < 0) h = 0;

        if (x >= width) { x = width - 1; w = 0; }
        if (y >= height) { y = height - 1; h = 0; }

        if (x + w >= width) { w = width - x - 1; }
        if (y + h >= height) { h = height - y - 1; }

        size_t shift_first = y*width * 3;
        size_t shift_second = (y + h)*width * 3;
        int cls = classes.at(i) % colors.size();
        for (int i = x; i < x + w; i++) {
            data[shift_first + i * 3] = colors.at(cls).red();
            data[shift_first + i * 3 + 1] = colors.at(cls).green();
            data[shift_first + i * 3 + 2] = colors.at(cls).blue();
            data[shift_second + i * 3] = colors.at(cls).red();
            data[shift_second + i * 3 + 1] = colors.at(cls).green();
            data[shift_second + i * 3 + 2] = colors.at(cls).blue();
        }

        shift_first = x * 3;
        shift_second = (x + w) * 3;
        for (int i = y; i < y + h; i++) {
            data[shift_first + i*width * 3] = colors.at(cls).red();
            data[shift_first + i*width * 3 + 1] = colors.at(cls).green();
            data[shift_first + i*width * 3 + 2] = colors.at(cls).blue();
            data[shift_second + i*width * 3] = colors.at(cls).red();
            data[shift_second + i*width * 3 + 1] = colors.at(cls).green();
            data[shift_second + i*width * 3 + 2] = colors.at(cls).blue();
        }
    }
}



/**
 * Write output data to image
 * \param name - image name
 * \param data - output data
 * \param classesNum - the number of classes
 * \return false if error else true
 */

static UNUSED bool writeOutputBmp(unsigned char *data, size_t height, size_t width, std::ostream &outFile) {
    unsigned char file[14] = {
            'B', 'M',           // magic
            0, 0, 0, 0,         // size in bytes
            0, 0,               // app data
            0, 0,               // app data
            40+14, 0, 0, 0      // start of data offset
    };
    unsigned char info[40] = {
            40, 0, 0, 0,        // info hd size
            0, 0, 0, 0,         // width
            0, 0, 0, 0,         // height
            1, 0,               // number color planes
            24, 0,              // bits per pixel
            0, 0, 0, 0,         // compression is none
            0, 0, 0, 0,         // image bits size
            0x13, 0x0B, 0, 0,   // horz resolution in pixel / m
            0x13, 0x0B, 0, 0,   // vert resolution (0x03C3 = 96 dpi, 0x0B13 = 72 dpi)
            0, 0, 0, 0,         // #colors in palette
            0, 0, 0, 0,         // #important colors
    };

    if (height > (size_t)std::numeric_limits<int32_t>::max || width > (size_t)std::numeric_limits<int32_t>::max) {
        THROW_IE_EXCEPTION << "File size is too big: " << height << " X " << width;
    }

    int padSize  = static_cast<int>(4 - (width * 3) % 4) % 4;
    int sizeData = static_cast<int>(width * height * 3 + height * padSize);
    int sizeAll  = sizeData + sizeof(file) + sizeof(info);

    file[ 2] = (unsigned char)(sizeAll      );
    file[ 3] = (unsigned char)(sizeAll >>  8);
    file[ 4] = (unsigned char)(sizeAll >> 16);
    file[ 5] = (unsigned char)(sizeAll >> 24);

    info[ 4] = (unsigned char)(width      );
    info[ 5] = (unsigned char)(width >>  8);
    info[ 6] = (unsigned char)(width >> 16);
    info[ 7] = (unsigned char)(width >> 24);

    int32_t negativeHeight = -(int32_t)height;
    info[ 8] = (unsigned char)(negativeHeight      );
    info[ 9] = (unsigned char)(negativeHeight >>  8);
    info[10] = (unsigned char)(negativeHeight >> 16);
    info[11] = (unsigned char)(negativeHeight >> 24);

    info[20] = (unsigned char)(sizeData      );
    info[21] = (unsigned char)(sizeData >>  8);
    info[22] = (unsigned char)(sizeData >> 16);
    info[23] = (unsigned char)(sizeData >> 24);

    outFile.write(reinterpret_cast<char*>(file), sizeof(file));
    outFile.write(reinterpret_cast<char*>(info), sizeof(info));

    unsigned char pad[3] = {0, 0, 0};

    for (size_t y = 0; y < height; y++) {
        for (size_t x = 0; x < width; x++) {
            unsigned char pixel[3];
            pixel[0] = data[y*width*3 + x*3];
            pixel[1] = data[y*width*3 + x*3 + 1];
            pixel[2] = data[y*width*3 + x*3 + 2];
            outFile.write(reinterpret_cast<char *>(pixel), 3);
        }
        outFile.write(reinterpret_cast<char *>(pad), padSize);
    }

    return true;
}

inline double getDurationOf(std::function<void()> func) {
    auto t0 = std::chrono::high_resolution_clock::now();
    func();
    auto t1 = std::chrono::high_resolution_clock::now();
    std::chrono::duration<float> fs = t1 - t0;
    return std::chrono::duration_cast<std::chrono::duration<double, std::ratio<1, 1000>>>(fs).count();
}


static UNUSED void printPerformanceCounts(const std::map<std::string, InferenceEngine::InferenceEngineProfileInfo>& performanceMap,
                                          std::ostream &stream,
                                          bool bshowHeader = true) {
    long long totalTime = 0;
    // Print performance counts
    if (bshowHeader) {
        stream << std::endl << "performance counts:" << std::endl << std::endl;
    }
    for (const auto & it : performanceMap) {
        std::string toPrint(it.first);
        const int maxLayerName = 30;

        if (it.first.length() >= maxLayerName) {
            toPrint  = it.first.substr(0, maxLayerName - 4);
            toPrint += "...";
        }


        stream << std::setw(maxLayerName) << std::left << toPrint;
        switch (it.second.status) {
        case InferenceEngine::InferenceEngineProfileInfo::EXECUTED:
            stream << std::setw(15) << std::left << "EXECUTED";
            break;
        case InferenceEngine::InferenceEngineProfileInfo::NOT_RUN:
            stream << std::setw(15) << std::left << "NOT_RUN";
            break;
        case InferenceEngine::InferenceEngineProfileInfo::OPTIMIZED_OUT:
            stream << std::setw(15) << std::left << "OPTIMIZED_OUT";
            break;
        }
        stream << std::setw(30) << std::left << "layerType: " + std::string(it.second.layer_type) + " ";
        stream << std::setw(20) << std::left << "realTime: " + std::to_string(it.second.realTime_uSec);
        stream << std::setw(20) << std::left << " cpu: "  + std::to_string(it.second.cpu_uSec);
        stream << " execType: " << it.second.exec_type << std::endl;
        if (it.second.realTime_uSec > 0) {
            totalTime += it.second.realTime_uSec;
        }
    }
    stream << std::setw(20) << std::left << "Total time: " + std::to_string(totalTime) << " microseconds" << std::endl;
}

static UNUSED void printPerformanceCounts(InferenceEngine::InferRequest request, std::ostream &stream) {
    auto perfomanceMap = request.GetPerformanceCounts();
    printPerformanceCounts(perfomanceMap, stream);
}

/**
 * @deprecated
 */
static UNUSED void printPerformanceCountsPlugin(InferenceEngine::InferenceEnginePluginPtr plugin, std::ostream &stream) {
    std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> perfomanceMap;
    plugin->GetPerformanceCounts(perfomanceMap, nullptr);
    printPerformanceCounts(perfomanceMap, stream);
}

/**
 * @brief This class represents an object that is found by an object detection net
 */
class DetectedObject {
public:
    int objectType;
    float xmin, xmax, ymin, ymax, prob;
    bool difficult;

    DetectedObject(int objectType, float xmin, float ymin, float xmax, float ymax, float prob, bool difficult = false)
        : objectType(objectType), xmin(xmin), xmax(xmax), ymin(ymin), ymax(ymax), prob(prob), difficult(difficult) {
    }

    DetectedObject(const DetectedObject& other) = default;

    static float ioU(const DetectedObject& detectedObject1_, const DetectedObject& detectedObject2_) {
        // Add small space to eliminate empty squares
        float epsilon = 0;  // 1e-5f;

        DetectedObject detectedObject1(detectedObject1_.objectType,
                (detectedObject1_.xmin - epsilon),
                (detectedObject1_.ymin - epsilon),
                (detectedObject1_.xmax- epsilon),
                (detectedObject1_.ymax- epsilon), detectedObject1_.prob);
        DetectedObject detectedObject2(detectedObject2_.objectType,
                (detectedObject2_.xmin + epsilon),
                (detectedObject2_.ymin + epsilon),
                (detectedObject2_.xmax),
                (detectedObject2_.ymax), detectedObject2_.prob);

        if (detectedObject1.objectType != detectedObject2.objectType) {
            // objects are different, so the result is 0
            return 0.0f;
        }

        if (detectedObject1.xmax < detectedObject1.xmin) return 0.0;
        if (detectedObject1.ymax < detectedObject1.ymin) return 0.0;
        if (detectedObject2.xmax < detectedObject2.xmin) return 0.0;
        if (detectedObject2.ymax < detectedObject2.ymin) return 0.0;


        float xmin = (std::max)(detectedObject1.xmin, detectedObject2.xmin);
        float ymin = (std::max)(detectedObject1.ymin, detectedObject2.ymin);
        float xmax = (std::min)(detectedObject1.xmax, detectedObject2.xmax);
        float ymax = (std::min)(detectedObject1.ymax, detectedObject2.ymax);

        // Caffe adds 1 to every length if the box isn't normalized. So do we...
        float addendum;
        if (xmax > 1 || ymax > 1)
            addendum = 1;
        else
            addendum = 0;

        // intersection
        float intr;
        if ((xmax >= xmin) && (ymax >= ymin)) {
            intr = (addendum + xmax - xmin) * (addendum + ymax - ymin);
        } else {
            intr = 0.0f;
        }

        // union
        float square1 = (addendum + detectedObject1.xmax - detectedObject1.xmin) * (addendum + detectedObject1.ymax - detectedObject1.ymin);
        float square2 = (addendum + detectedObject2.xmax - detectedObject2.xmin) * (addendum + detectedObject2.ymax - detectedObject2.ymin);

        float unn = square1 + square2 - intr;

        return static_cast<float>(intr) / unn;
    }

    DetectedObject scale(float scale_x, float scale_y) const {
        return DetectedObject(objectType, xmin * scale_x, ymin * scale_y, xmax * scale_x, ymax * scale_y, prob, difficult);
    }
};

class ImageDescription {
public:
    const std::list<DetectedObject> alist;
    const bool check_probs;

    explicit ImageDescription(const std::list<DetectedObject> &alist, bool check_probs = false)
            : alist(alist), check_probs(check_probs) {
    }

    static float ioUMultiple(const ImageDescription &detectedObjects, const ImageDescription &desiredObjects) {
        const ImageDescription *detectedObjectsSmall, *detectedObjectsBig;
        bool check_probs = desiredObjects.check_probs;

        if (detectedObjects.alist.size() < desiredObjects.alist.size()) {
            detectedObjectsSmall = &detectedObjects;
            detectedObjectsBig = &desiredObjects;
        } else {
            detectedObjectsSmall = &desiredObjects;
            detectedObjectsBig = &detectedObjects;
        }

        std::list<DetectedObject> doS = detectedObjectsSmall->alist;
        std::list<DetectedObject> doB = detectedObjectsBig->alist;

        float fullScore = 0.0f;
        while (doS.size() > 0) {
            float score = 0.0f;
            std::list<DetectedObject>::iterator bestJ = doB.end();
            for (auto j = doB.begin(); j != doB.end(); j++) {
                float curscore = DetectedObject::ioU(*doS.begin(), *j);
                if (score < curscore) {
                    score = curscore;
                    bestJ = j;
                }
            }

            float coeff = 1.0;
            if (check_probs) {
                if (bestJ != doB.end()) {
                    DetectedObject test = *bestJ;
                    DetectedObject test1 = *doS.begin();
                    float mn = std::min((*bestJ).prob, (*doS.begin()).prob);
                    float mx = std::max((*bestJ).prob, (*doS.begin()).prob);

                    coeff = mn/mx;
                }
            }

            doS.pop_front();
            if (bestJ != doB.end()) doB.erase(bestJ);
            fullScore += coeff * score;
        }
        fullScore /= detectedObjectsBig->alist.size();


        return fullScore;
    }

    ImageDescription scale(float scale_x, float scale_y) const {
        std::list<DetectedObject> slist;
        for (auto& dob : alist) {
            slist.push_back(dob.scale(scale_x, scale_y));
        }
        return ImageDescription(slist, check_probs);
    }
};

struct AveragePrecisionCalculator {
private:
    enum MatchKind {
        TruePositive, FalsePositive
    };

    /**
     * Here we count all TP and FP matches for all the classes in all the images.
     */
    std::map<int, std::vector<std::pair<double, MatchKind>>> matches;

    std::map<int, int> N;

    double threshold;

    static bool SortBBoxDescend(const DetectedObject& bbox1, const DetectedObject& bbox2) {
      return bbox1.prob > bbox2.prob;
    }

    static bool SortPairDescend(const std::pair<double, MatchKind>& p1, const std::pair<double, MatchKind>& p2) {
      return p1.first > p2.first;
    }

public:
    explicit AveragePrecisionCalculator(double threshold) : threshold(threshold) { }

    // gt_bboxes -> des
    // bboxes -> det

    void consumeImage(const ImageDescription &detectedObjects, const ImageDescription &desiredObjects) {
            // Collecting IoU values
        int tp = 0, fp = 0;

        std::vector<bool> visited(desiredObjects.alist.size(), false);
        std::vector<DetectedObject> bboxes{ std::begin(detectedObjects.alist), std::end(detectedObjects.alist) };
        std::sort(bboxes.begin(), bboxes.end(), SortBBoxDescend);


        for (auto&& detObj : bboxes) {
                // Searching for the best match to this detection

            // Searching for desired object
            float overlap_max = -1;
            int jmax = -1;
            auto desmax = desiredObjects.alist.end();

            int j = 0;
            for (auto desObj = desiredObjects.alist.begin(); desObj != desiredObjects.alist.end(); desObj++, j++) {
                double iou = DetectedObject::ioU(detObj, *desObj);
                if (iou > overlap_max) {
                    overlap_max = iou;
                    jmax = j;
                    desmax = desObj;
                }
            }

            MatchKind mk;
            if (overlap_max >= threshold) {
                if (!desmax->difficult) {
                    if (!visited[jmax]) {
                        mk = TruePositive;
                        visited[jmax] = true;
                    } else {
                        mk = FalsePositive;
                    }
                    matches[detObj.objectType].push_back(std::make_pair(detObj.prob, mk));
                }
            } else {
                mk = FalsePositive;
                matches[detObj.objectType].push_back(std::make_pair(detObj.prob, mk));
            }
        }

        for (auto desObj = desiredObjects.alist.begin(); desObj != desiredObjects.alist.end(); desObj++) {
            if (!desObj->difficult) {
                N[desObj->objectType]++;
                }
            }
        }

    std::map<int, double> calculateAveragePrecisionPerClass() const {
        /**
         * Precision-to-TP curve per class (a variation of precision-to-recall curve without dividing into N)
         */
        std::map<int, std::map<int, double>> precisionToTP;


        std::map<int, double> res;

        double AP = 0;
        double q = 0;
        for (auto m : matches) {
            // Sorting
            std::sort(m.second.begin(), m.second.end(), SortPairDescend);

            int clazz = m.first;
            int TP = 0, FP = 0;

            std::vector<double> prec;
            std::vector<double> rec;

            for (auto mm : m.second) {
                // Here we are descending in a probability value
                MatchKind mk = mm.second;
                if (mk == TruePositive) TP++;
                else if (mk == FalsePositive) FP++;

                double precision = static_cast<double>(TP) / (TP + FP);
                double recall = 0;
                if (N.find(clazz) != N.end()) {
                    recall = static_cast<double>(TP) / N.at(clazz);
                }

                prec.push_back(precision);
                rec.push_back(recall);
            }

            int num = rec.size();

            // 11point from Caffe
            double ap = 0;
            std::vector<float> max_precs(11, 0.);
            int start_idx = num - 1;
            for (int j = 10; j >= 0; --j) {
                for (int i = start_idx; i >= 0; --i) {
                    if (rec[i] < j / 10.) {
                        start_idx = i;
                        if (j > 0) {
                            max_precs[j-1] = max_precs[j];
                        }
                        break;
                    } else {
                        if (max_precs[j] < prec[i]) {
                            max_precs[j] = prec[i];
                        }
                    }
                }
            }
            for (int j = 10; j >= 0; --j) {
                ap += max_precs[j] / 11;
            }
            res[clazz] = ap;
        }

        return res;
    }
};

/**
* @brief Adds colored rectangles to the image
* @param data - data where rectangles are put
* @param height - height of the rectangle
* @param width - width of the rectangle
* @param detectedObjects - vector of detected objects
*/
static UNUSED void addRectangles(unsigned char *data, size_t height, size_t width, std::vector<DetectedObject> detectedObjects) {
    std::vector<Color> colors = {
        { 128, 64,  128 },
        { 232, 35,  244 },
        { 70,  70,  70 },
        { 156, 102, 102 },
        { 153, 153, 190 },
        { 153, 153, 153 },
        { 30,  170, 250 },
        { 0,   220, 220 },
        { 35,  142, 107 },
        { 152, 251, 152 },
        { 180, 130, 70 },
        { 60,  20,  220 },
        { 0,   0,   255 },
        { 142, 0,   0 },
        { 70,  0,   0 },
        { 100, 60,  0 },
        { 90,  0,   0 },
        { 230, 0,   0 },
        { 32,  11,  119 },
        { 0,   74,  111 },
        { 81,  0,   81 }
    };

    for (size_t i = 0; i < detectedObjects.size(); i++) {
        int cls = detectedObjects[i].objectType % colors.size();

        int xmin = detectedObjects[i].xmin * width;
        int xmax = detectedObjects[i].xmax * width;
        int ymin = detectedObjects[i].ymin * height;
        int ymax = detectedObjects[i].ymax * height;

        size_t shift_first = ymin*width * 3;
        size_t shift_second = ymax*width * 3;
        for (int x = xmin; x < xmax; x++) {
            data[shift_first + x * 3] = colors.at(cls).red();
            data[shift_first + x * 3 + 1] = colors.at(cls).green();
            data[shift_first + x * 3 + 2] = colors.at(cls).blue();
            data[shift_second + x * 3] = colors.at(cls).red();
            data[shift_second + x * 3 + 1] = colors.at(cls).green();
            data[shift_second + x * 3 + 2] = colors.at(cls).blue();
        }

        shift_first = xmin * 3;
        shift_second = xmax * 3;
        for (int y = ymin; y < ymax; y++) {
            data[shift_first + y*width * 3] = colors.at(cls).red();
            data[shift_first + y*width * 3 + 1] = colors.at(cls).green();
            data[shift_first + y*width * 3 + 2] = colors.at(cls).blue();
            data[shift_second + y*width * 3] = colors.at(cls).red();
            data[shift_second + y*width * 3 + 1] = colors.at(cls).green();
            data[shift_second + y*width * 3 + 2] = colors.at(cls).blue();
        }
    }
}

#ifdef USE_OPENCV
/**
* @brief Sets image data stored in cv::Mat object to a given Blob object.
* @param orig_image - given cv::Mat object with an image data.
* @param blob - Blob object which to be filled by an image data.
* @param batchIndex - batch index of an image inside of the blob.
*/
template <typename T>
void matU8ToBlob(const cv::Mat& orig_image, InferenceEngine::Blob::Ptr& blob, int batchIndex = 0) {
    InferenceEngine::SizeVector blobSize = blob->getTensorDesc().getDims();
    const size_t width = blobSize[3];
    const size_t height = blobSize[2];
    const size_t channels = blobSize[1];
    T* blob_data = blob->buffer().as<T*>();

    cv::Mat resized_image(orig_image);
    if (width != orig_image.size().width || height!= orig_image.size().height) {
        cv::resize(orig_image, resized_image, cv::Size(width, height));
    }

    int batchOffset = batchIndex * width * height * channels;

    for (size_t c = 0; c < channels; c++) {
        for (size_t  h = 0; h < height; h++) {
            for (size_t w = 0; w < width; w++) {
                blob_data[batchOffset + c * width * height + h * width + w] =
                        resized_image.at<cv::Vec3b>(h, w)[c];
            }
        }
    }
}

/**
 * @brief Wraps data stored inside of a passed cv::Mat object by new Blob pointer.
 * @note: No memory allocation is happened. The blob just points to already existing
 *        cv::Mat data.
 * @param mat - given cv::Mat object with an image data.
 * @return resulting Blob pointer.
 */
static InferenceEngine::Blob::Ptr wrapMat2Blob(const cv::Mat &mat) {
    size_t channels = mat.channels();
    size_t height = mat.size().height;
    size_t width = mat.size().width;

    size_t strideH = mat.step.buf[0];
    size_t strideW = mat.step.buf[1];

    bool is_dense =
            strideW == channels &&
            strideH == channels * width;

    if (!is_dense) THROW_IE_EXCEPTION
                << "Doesn't support conversion from not dense cv::Mat";

    InferenceEngine::TensorDesc tDesc(InferenceEngine::Precision::U8,
                                      {1, channels, height, width},
                                      InferenceEngine::Layout::NHWC);

    return InferenceEngine::make_shared_blob<uint8_t>(tDesc, mat.data);
}
#endif