/*
 * Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a
 * copy of this software and associated documentation files (the "Software"),
 * to deal in the Software without restriction, including without limitation
 * the rights to use, copy, modify, merge, publish, distribute, sublicense,
 * and/or sell copies of the Software, and to permit persons to whom the
 * Software is furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
 * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
 * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 * DEALINGS IN THE SOFTWARE.
 *
 * Edited by Marcos Luciano
 * https://www.github.com/marcoslucianops
 */

#ifndef _YOLO_H_
#define _YOLO_H_

#include "layers/convolutional_layer.h"
#include "layers/implicit_layer.h"
#include "layers/channels_layer.h"
#include "layers/shortcut_layer.h"
#include "layers/route_layer.h"
#include "layers/upsample_layer.h"
#include "layers/maxpool_layer.h"
#include "layers/reorgv5_layer.h"

#include "nvdsinfer_custom_impl.h"

struct NetworkInfo
{
    std::string inputBlobName;
    std::string networkType;
    std::string configFilePath;
    std::string wtsFilePath;
    std::string int8CalibPath;
    std::string deviceType;
    uint numDetectedClasses;
    int clusterMode;
    std::string networkMode;
};

struct TensorInfo
{
    std::string blobName;
    uint gridSizeX {0};
    uint gridSizeY {0};
    uint numBBoxes {0};
    float scaleXY;
    std::vector<float> anchors;
    std::vector<int> mask;
};

class Yolo : public IModelParser {
public:
    Yolo(const NetworkInfo& networkInfo);

    ~Yolo() override;

    bool hasFullDimsSupported() const override { return false; }

    const char* getModelName() const override {
        return m_ConfigFilePath.empty() ? m_NetworkType.c_str() : m_ConfigFilePath.c_str();
    }

    NvDsInferStatus parseModel(nvinfer1::INetworkDefinition& network) override;

    nvinfer1::ICudaEngine *createEngine (nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config);

protected:
    const std::string m_InputBlobName;
    const std::string m_NetworkType;
    const std::string m_ConfigFilePath;
    const std::string m_WtsFilePath;
    const std::string m_Int8CalibPath;
    const std::string m_DeviceType;
    const uint m_NumDetectedClasses;
    const int m_ClusterMode;
    const std::string m_NetworkMode;

    uint m_InputH;
    uint m_InputW;
    uint m_InputC;
    uint64_t m_InputSize;
    uint m_NumClasses;
    uint m_LetterBox;
    uint m_NewCoords;
    uint m_YoloCount;
    float m_IouThreshold;
    float m_ScoreThreshold;
    uint m_TopK;

    std::vector<TensorInfo> m_YoloTensors;
    std::vector<std::map<std::string, std::string>> m_ConfigBlocks;
    std::vector<std::map<std::string, std::string>> m_ConfigNMSBlocks;
    std::vector<nvinfer1::Weights> m_TrtWeights;

private:
    NvDsInferStatus buildYoloNetwork(std::vector<float>& weights, nvinfer1::INetworkDefinition& network);

    std::vector<std::map<std::string, std::string>> parseConfigFile(const std::string cfgFilePath);

    void parseConfigBlocks();

    void parseConfigNMSBlocks();

    void destroyNetworkUtils();
};

#endif // _YOLO_H_