/*
 * Copyright 2024 KylinSoft Co., Ltd.
 *
 * 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 <https://www.gnu.org/licenses/>.
 */
#pragma once

#include <dirent.h>
#include <kylin-ai/ai-engine/aiengine.h>
#include <triton/client/http_client.h>

#include <chrono>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <opencv2/opencv.hpp>

#include "base64_transformer.h"
#include "common.h"

namespace tc = triton::client;

#define FAIL_IF_ERR(X, MSG)                                              \
    {                                                                    \
        tc::Error err = (X);                                             \
        if (!err.IsOk()) {                                               \
            std::cerr << "error: " << (MSG) << ": " << err << std::endl; \
            exit(1);                                                     \
        }                                                                \
    }

// using namespace std;
// using namespace Ort;
// using namespace cv;

class Document_Dewarp {
public:
    Document_Dewarp() {};
    ImgBase64Result detect(cv::Mat& m_srcImg, const std::unique_ptr<triton::client::InferenceServerHttpClient>& client,
                           const std::string& model_name_, std::string imgFormat);

private:
    int inputWidth = 992;
    int inputHeight = 992;
    cv::Mat preprocess(cv::Mat& m_srcImg);

    std::vector<float> input_image_;
    std::vector<std::string> input_names;
    std::vector<std::string> output_names;

    int col_gap = 0;
    int row_gap = 0;
    int fiducial_point_gaps[8] = {1, 2, 3, 5, 6, 10, 15, 30};
    int fiducial_point_num[8] = {31, 16, 11, 7, 6, 4, 3, 2};

    cv::Mat flatByRegressWithClassiy_multiProcess(std::vector<int>& vec_segment, std::array<int, 2>& segment_shape,
                                                  std::vector<float>& vec_regress, std::array<int, 4>& regress_shape,
                                                  cv::Mat& srcImg);
    cv::Mat linearInterpolation(const cv::Mat& tshape, const cv::Mat& sshape, const cv::Mat& grid_x,
                                const cv::Mat& grid_y);
    void mesgrid(int xstart, int xend, int xpointnum, int ystart, int yend, int ypointnum, cv::Mat& x, cv::Mat& y);
};

class DdcpTritonClient {
public:
    DdcpTritonClient(const std::string& url);
    ImgBase64Result DdcpRequestByPath(std::string ImagePath);
    ImgBase64Result DdcpRequestByImage(cv::Mat Image, std::string imgFormat);
    ImgBase64Result DdcpRequestByBase64(const char* ImageData);
    tc::Error LoadModel();
    tc::Error RemoveModel();
    std::string GetConfigValue(const char* key, const char* engine);
    Document_Dewarp UNet_;

private:
    int inputWidth = 992;
    int inputHeight = 992;
    std::string model_name_;
    std::unique_ptr<tc::InferenceServerHttpClient> client_;  // Triton client as a member variable
};
