#include <stdio.h>

// Path for c_predict_api
#include </home/nvidia/incubator-mxnet/include/mxnet/c_predict_api.h>

#include <iostream>
#include <fstream>
#include <string>
#include <vector>

#include <opencv2/opencv.hpp>

const mx_float DEFAULT_MEAN = 117.0;

// Read file to buffer
class BufferFile {
 public :
    std::string file_path_;
    int length_;
    char* buffer_;

    explicit BufferFile(std::string file_path)
    :file_path_(file_path) {

        std::ifstream ifs(file_path.c_str(), std::ios::in | std::ios::binary);
        if (!ifs) {
            std::cerr << "Can't open the file. Please check " << file_path << ". \n";
            length_ = 0;
            buffer_ = NULL;
            return;
        }

        ifs.seekg(0, std::ios::end);
        length_ = ifs.tellg();
        ifs.seekg(0, std::ios::beg);
        std::cout << file_path.c_str() << " ... "<< length_ << " bytes\n";

        buffer_ = new char[sizeof(char) * length_];
        ifs.read(buffer_, length_);
        ifs.close();
    }

    int GetLength() {
        return length_;
    }
    char* GetBuffer() {
        return buffer_;
    }

    ~BufferFile() {
        if (buffer_) {
          delete[] buffer_;
          buffer_ = NULL;
        }
    }
};

void GetImageFile(const std::string image_file,
                  mx_float* image_data, const int channels,
                  const cv::Size resize_size, const mx_float* mean_data = NULL) {
    // Read all kinds of file into a BGR color 3 channels image
    cv::Mat im_ori = cv::imread(image_file, cv::IMREAD_COLOR);

    if (im_ori.empty()) {
        std::cerr << "Can't open the image. Please check " << image_file << ". \n";
        assert(false);
    }

    cv::Mat im;

    resize(im_ori, im, resize_size);

    int size = im.rows * im.cols * channels;

    mx_float* ptr_image_r = image_data;
    mx_float* ptr_image_g = image_data + size / 3;
    mx_float* ptr_image_b = image_data + size / 3 * 2;

    float mean_b, mean_g, mean_r;
    mean_b = mean_g = mean_r = DEFAULT_MEAN;

    for (int i = 0; i < im.rows; i++) {
        uchar* data = im.ptr<uchar>(i);

        for (int j = 0; j < im.cols; j++) {
            if (mean_data) {
                mean_r = *mean_data;
                if (channels > 1) {
                    mean_g = *(mean_data + size / 3);
                    mean_b = *(mean_data + size / 3 * 2);
                }
               mean_data++;
            }
            if (channels > 1) {
                *ptr_image_b++ = static_cast<mx_float>(*data++) - mean_b;
                *ptr_image_g++ = static_cast<mx_float>(*data++) - mean_g;
            }

            *ptr_image_r++ = static_cast<mx_float>(*data++) - mean_r;;
        }
    printf("get an image");
    }
}

// LoadSynsets
// Code from : https://github.com/pertusa/mxnet_predict_cc/blob/master/mxnet_predict.cc


void PrintOutputResult(const std::vector<float>& data) {

    float best_accuracy = 0.0;
    int best_idx = 0;

    for ( int i = 0; i < static_cast<int>(data.size()); i++ ) {
        printf("Accuracy[%d] = %.8f\n", i, data[i]);

        if ( data[i] > best_accuracy ) {
            best_accuracy = data[i];
            best_idx = i;
        }
    }

    printf("Best Result: id = %d, accuracy = %.8f\n", best_idx, best_accuracy);
}

int main(int argc, char* argv[]) {
    if (argc < 2) {
        std::cout << "No test image here." << std::endl
        << "Usage: ./image-classification-predict apple.jpg" << std::endl;
        return 0;
    }

    std::string test_file;
    test_file = std::string(argv[1]);

    // Models path for your model, you have to modify it
    std::string json_file = "lenet.json";
    std::string param_file = "lenet.params";


    BufferFile json_data(json_file);
    BufferFile param_data(param_file);

    // Parameters
    int dev_type = 2;  // 1: cpu, 2: gpu
    int dev_id = 0;  // arbitrary.
    mx_uint num_input_nodes = 1;  // 1 for feedforward
    const char* input_key[1] = {"data"};
    const char** input_keys = input_key;

    // Image size and channels
    int width = 32;
    int height = 32;
    int channels = 1;

    const mx_uint input_shape_indptr[2] = { 0, 4 };
    const mx_uint input_shape_data[4] = { 1,
                                        static_cast<mx_uint>(channels),
                                        static_cast<mx_uint>(height),
                                        static_cast<mx_uint>(width)};
    PredictorHandle pred_hnd = 0;

    if (json_data.GetLength() == 0 ||
        param_data.GetLength() == 0) {
        return -1;
    }

    // Create Predictor
    assert(0==MXPredCreate((const char*)json_data.GetBuffer(),
		(const char*)param_data.GetBuffer(),
		static_cast<size_t>(param_data.GetLength()),
		dev_type,
		dev_id,
		num_input_nodes,
		input_keys,
		input_shape_indptr,
		input_shape_data,
		&pred_hnd));
	assert(pred_hnd);

    int image_size = width * height * channels;

    // Read Mean Data
    const mx_float* nd_data = NULL;

    

    // Read Image Data
    std::vector<mx_float> image_data = std::vector<mx_float>(image_size);

    GetImageFile(test_file, image_data.data(),
                 channels, cv::Size(width, height));

    // Set Input Image
    MXPredSetInput(pred_hnd, "data", image_data.data(), image_size);

    // Do Predict Forward
    MXPredForward(pred_hnd);

    mx_uint output_index = 0;

    mx_uint *shape = 0;
    mx_uint shape_len;

    // Get Output Result
    MXPredGetOutputShape(pred_hnd, output_index, &shape, &shape_len);

    size_t size = 1;
    for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];

    std::vector<float> data(size);

    MXPredGetOutput(pred_hnd, output_index, &(data[0]), size);



    // Release Predictor
    MXPredFree(pred_hnd);



    // Print Output Data
    PrintOutputResult(data);

    return 0;
}