#include <opencv2/opencv.hpp> // all opencv header
#include <vector>
#include <utility>
#include "hdrplus/merge.h"
#include "hdrplus/burst.h"
#include "hdrplus/utility.h"

namespace hdrplus
{

    void merge::process(hdrplus::burst& burst_images, \
        std::vector<std::vector<std::vector<std::pair<int, int>>>>& alignments)
    {
        // 4.1 Noise Parameters and RMS
        // Noise parameters calculated from baseline ISO noise parameters
        double lambda_shot, lambda_read;
        std::tie(lambda_shot, lambda_read) = burst_images.bayer_images[burst_images.reference_image_idx].get_noise_params();

        // 4.2-4.4 Denoising and Merging

        // Get padded bayer image
        cv::Mat reference_image = burst_images.bayer_images_pad[burst_images.reference_image_idx];
        cv::imwrite("ref.jpg", reference_image);

        // Get raw channels
        std::vector<cv::Mat> channels(4, cv::Mat::zeros(reference_image.rows / 2, reference_image.cols / 2, CV_16U));
        hdrplus::extract_rgb_fmom_bayer<uint16_t>(reference_image, channels[0], channels[1], channels[2], channels[3]);

        // For each channel, perform denoising and merge
        for (int i = 0; i < 4; ++i) {
            // Get channel mat
            cv::Mat channel_i = channels[i];
            // cv::imwrite("ref" + std::to_string(i) + ".jpg", channel_i);
            
            //we should be getting the individual channel in the same place where we call the processChannel function with the reference channel in its arguments
            //possibly we could add another argument in the processChannel function which is the channel_i for the alternate image. maybe using a loop to cover all the other images

            //create list of channel_i of alternate images:
            std::vector<cv::Mat> alternate_channel_i_list;
            for (int j = 0; j < burst_images.num_images; j++) {
                if (j != burst_images.reference_image_idx) {

                    //get alternate image
                    cv::Mat alt_image = burst_images.bayer_images_pad[j];
                    std::vector<cv::Mat> alt_channels(4, cv::Mat::zeros(reference_image.rows / 2, reference_image.cols / 2, CV_16U));
                    hdrplus::extract_rgb_fmom_bayer<uint16_t>(alt_image, alt_channels[0], alt_channels[1], alt_channels[2], alt_channels[3]);

                    alternate_channel_i_list.push_back(alt_channels[i]);
                }
            }

            // Apply merging on the channel
            cv::Mat merged_channel = processChannel(burst_images, alignments, channel_i, alternate_channel_i_list, lambda_shot, lambda_read);
            // cv::imwrite("merged" + std::to_string(i) + ".jpg", merged_channel);

            // Put channel raw data back to channels
            merged_channel.convertTo(channels[i], CV_16U);
        }

        // Write all channels back to a bayer mat
        cv::Mat merged(reference_image.rows, reference_image.cols, CV_16U);
        int x, y;
        for (y = 0; y < reference_image.rows; ++y){
            uint16_t* row = merged.ptr<uint16_t>(y);
            if (y % 2 == 0){
                uint16_t* i0 = channels[0].ptr<uint16_t>(y / 2);
                uint16_t* i1 = channels[1].ptr<uint16_t>(y / 2);

                for (x = 0; x < reference_image.cols;){
                    //R
                    row[x] = i0[x / 2];
                    x++;

                    //G1
                    row[x] = i1[x / 2];
                    x++;
                }
            }
            else {
                uint16_t* i2 = channels[2].ptr<uint16_t>(y / 2);
                uint16_t* i3 = channels[3].ptr<uint16_t>(y / 2);

                for(x = 0; x < reference_image.cols;){
                    //G2
                    row[x] = i2[x / 2];
                    x++;

                    //B
                    row[x] = i3[x / 2];
                    x++;
                }
            }
        }

        // Remove padding
        std::vector<int> padding = burst_images.padding_info_bayer;
        cv::Range horizontal = cv::Range(padding[2], reference_image.cols - padding[3]);
        cv::Range vertical = cv::Range(padding[0], reference_image.rows - padding[1]);
        burst_images.merged_bayer_image = merged(vertical, horizontal);
    }

    std::vector<cv::Mat> merge::getReferenceTiles(cv::Mat reference_image) {
        std::vector<cv::Mat> reference_tiles;
        for (int y = 0; y < reference_image.rows - offset; y += offset) {
            for (int x = 0; x < reference_image.cols - offset; x += offset) {
                cv::Mat tile = reference_image(cv::Rect(x, y, TILE_SIZE, TILE_SIZE));
                reference_tiles.push_back(tile);
            }
        }
        return reference_tiles;
    }

    cv::Mat merge::mergeTiles(std::vector<cv::Mat> tiles, int num_rows, int num_cols) {
        // 1. get all four subsets: original (evenly split), horizontal overlapped,
        // vertical overlapped, 2D overlapped
        std::vector<std::vector<cv::Mat>> tiles_original;
        for (int y = 0; y < num_rows / offset - 1; y += 2) {
            std::vector<cv::Mat> row;
            for (int x = 0; x < num_cols / offset - 1; x += 2) {
                row.push_back(tiles[y * (num_cols / offset - 1) + x]);
            }
            tiles_original.push_back(row);
        }

        std::vector<std::vector<cv::Mat>> tiles_horizontal;
        for (int y = 0; y < num_rows / offset - 1; y += 2) {
            std::vector<cv::Mat> row;
            for (int x = 1; x < num_cols / offset - 1; x += 2) {
                row.push_back(tiles[y * (num_cols / offset - 1) + x]);
            }
            tiles_horizontal.push_back(row);
        }

        std::vector<std::vector<cv::Mat>> tiles_vertical;
        for (int y = 1; y < num_rows / offset - 1; y += 2) {
            std::vector<cv::Mat> row;
            for (int x = 0; x < num_cols / offset - 1; x += 2) {
                row.push_back(tiles[y * (num_cols / offset - 1) + x]);
            }
            tiles_vertical.push_back(row);
        }

        std::vector<std::vector<cv::Mat>> tiles_2d;
        for (int y = 1; y < num_rows / offset - 1; y += 2) {
            std::vector<cv::Mat> row;
            for (int x = 1; x < num_cols / offset - 1; x += 2) {
                row.push_back(tiles[y * (num_cols / offset - 1) + x]);
            }
            tiles_2d.push_back(row);
        }

        // 2. Concatenate the four subsets
        cv::Mat img_original = cat2Dtiles(tiles_original);
        cv::Mat img_horizontal = cat2Dtiles(tiles_horizontal);
        cv::Mat img_vertical = cat2Dtiles(tiles_vertical);
        cv::Mat img_2d = cat2Dtiles(tiles_2d);

        // 3. Add the four subsets together
        img_original(cv::Rect(offset, 0, num_cols - TILE_SIZE, num_rows)) += img_horizontal;
        img_original(cv::Rect(0, offset, num_cols, num_rows - TILE_SIZE)) += img_vertical;
        img_original(cv::Rect(offset, offset, num_cols - TILE_SIZE, num_rows - TILE_SIZE)) += img_2d;

        return img_original;
    }

    cv::Mat merge::processChannel(hdrplus::burst& burst_images, \
        std::vector<std::vector<std::vector<std::pair<int, int>>>>& alignments, \
        cv::Mat channel_image, \
        std::vector<cv::Mat> alternate_channel_i_list,\
        float lambda_shot, \
        float lambda_read) {
        // Get tiles of the reference image
        std::vector<cv::Mat> reference_tiles = getReferenceTiles(channel_image);

        // Get noise variance (sigma**2 = lambda_shot * tileRMS + lambda_read)
        std::vector<float> noise_variance = getNoiseVariance(reference_tiles, lambda_shot, lambda_read);

        // Apply FFT on reference tiles (spatial to frequency)
        std::vector<cv::Mat> reference_tiles_DFT;
        for (auto ref_tile : reference_tiles) {
            cv::Mat ref_tile_DFT;
            ref_tile.convertTo(ref_tile_DFT, CV_32F);
            cv::dft(ref_tile_DFT, ref_tile_DFT, cv::DFT_SCALE | cv::DFT_COMPLEX_OUTPUT);
            reference_tiles_DFT.push_back(ref_tile_DFT);
        }

        // TODO: 4.2 Temporal Denoising
        //std::vector<cv::Mat> temporal_denoised_tiles = temporal_denoise(reference_tiles, reference_tiles_DFT, noise_varaince)
     

        // TODO: 4.3 Spatial Denoising

        ////adding after here
        
        //std::vector<cv::Mat> spatial_denoised_tiles = spatial_denoise( reference_tiles,  reference_tiles_DFT, noise_varaince)
        //apply the cosineWindow2D over the merged_channel_tiles_spatial and reconstruct the image
        //reference_tiles = spatial_denoised_tiles; //now reference tiles are temporally and spatially denoised
        ////

        // Apply IFFT on reference tiles (frequency to spatial)
        std::vector<cv::Mat> denoised_tiles;
        for (auto dft_tile : reference_tiles_DFT) {
            cv::Mat denoised_tile;
            cv::dft(dft_tile, denoised_tile, cv::DFT_INVERSE | cv::DFT_REAL_OUTPUT);
            denoised_tile.convertTo(denoised_tile, CV_16U);
            denoised_tiles.push_back(denoised_tile);
        }
        reference_tiles = denoised_tiles;

        // 4.4 Cosine Window Merging
        // Process tiles through 2D cosine window
        std::vector<cv::Mat> windowed_tiles;
        for (auto tile : reference_tiles) {
            windowed_tiles.push_back(cosineWindow2D(tile));
        }

        // Merge tiles
        return mergeTiles(windowed_tiles, channel_image.rows, channel_image.cols);
    }
        
    // std::vector<cv::Mat> temporal_denoise(std::vector<cv::Mat> reference_tiles, std::vector<cv::Mat> reference_tiles_DFT, std::vector<float> noise_varaince) {
    //     //goal: temporially denoise using the weiner filter
    //     //input:
    //     //1. array of 2D dft tiles of the reference image
    //     //2. array of 2D dft tiles ocf the aligned alternate image
    //     //3. estimated noise varaince
    //     //4. temporal factor
    //     //return: merged image patches dft
        
        
        
    //     //tile_size = TILE_SIZE;
        
    //     double temporal_factor = 8.0 //8 by default

    //     double temporal_noise_scaling = (pow(TILE_SIZE,2) * (1.0/16*2))*temporal_factor;

    //     //start calculating the merged image tiles fft
     
        
    //     //get the tiles of the alternate image as a list

    //     std::vector<std::vector<cv::Mat>> alternate_channel_i_tile_list; //list of alt channel tiles
    //     std::vector<std::vector<cv::Mat>> alternate_tiles_DFT_list; //list of alt channel tiles

    //     for (auto alt_img_channel : alternate_channel_i_list) {
    //         std::vector<uint16_t> alt_img_channel_tile = getReferenceTiles(alt_img_channel); //get tiles from alt image
    //         alternate_channel_i_tile_list.push_back(alt_img_channel_tile)

    //         std::vector<cv::Mat> alternate_tiles_DFT_list;
    //         for (auto alt_tile : alt_img_channel_tile) {
    //             cv::Mat alt_tile_DFT;
    //             alt_tile.convertTo(alt_tile_DFT, CV_32F);
    //             cv::dft(alt_tile_DFT, alt_tile_DFT, cv::DFT_SCALE | cv::DFT_COMPLEX_OUTPUT);
    //             alternate_tiles_DFT_list.push_back(alt_tile_DFT);
    //         }
    //         alternate_tiles_DFT_list.push_back(alternate_tiles_DFT);
    //     }

    //     //get the dft of the alternate image
    //     //std::vector<cv::Mat> alternate_tiles_DFT;




    //     //std::vector<cv::Mat> tile_differences = reference_tiles_DFT - alternate_tiles_DFT_list;

    //     //find reference_tiles_DFT - alternate_tiles_DFT_list
    //     std::vector<std::vector<cv::Mat>> tile_difference_list; //list of tile differences
    //     for (auto individual_alternate_tile_DFT : alternate_tiles_DFT_list) {
    //         std::vector<cv::Mat> single_tile_difference = reference_tiles_DFT - individual_alternate_tile_DFT;
    //         tile_difference_list.push_back(single_tile_difference);
    //     }


    //    // std::vector<cv::Mat> tile_sq_asolute_diff = tile_differences; //squared absolute difference is tile_differences.real**2 + tile_differnce.imag**2; //also tile_dist
        
    //     std::vector<cv::Mat> tile_sq_asolute_diff = tile_differences; //squared absolute difference is tile_differences.real**2 + tile_differnce.imag**2; //also tile_dist

    //     //get the real and imaginary components
    //     /*
    //     std::vector<std::vector<cv::Mat>> absolute_difference_list;
    //      for (auto individual_difference : tile_difference_list) {
    //         for (int i =0; i < individual_difference.rows; i++ ) {
    //             std::complex<double>* row_ptr = tile_sq_asolute_diff.ptr<std::complex<double>>(i);
    //             for (int j = 0; j< individual_difference.cols*individual_difference.channels(); j++) {
    //                  row_ptr = math.pow(individual_difference.at<std::complex<double>>(i,j).real(),2)+math.pow(individual_difference.at<std::complex<double>>(i,j).imag(),2); //.real and .imag
    //             }
    //         }

    //         //std::vector<cv::Mat> single_tile_difference = individual_difference.at<std::complex<double>>(0,0).real(); //.real and .imag
    //         absolute_difference_list.push_back(single_tile_difference);
    //     }
    //     */

    //     //find the squared absolute difference across all the tiles


    //     std::vector<cv::Mat>  A = tile_sq_asolute_diff/(tile_sq_asolute_diff+noise_variance)

    //     std::vector<cv::Mat> merged_image_tiles_fft = alternate_tiles_DFT_list + A * tile_differences;

    //     return merged_image_tiles_fft

    // }

    // std::vector<cv::Mat> spatial_denoise(std::vector<cv::Mat> reference_tiles, std::vector<cv::Mat> reference_tiles_DFT, std::vector<float> noise_varaince) {
        
    //     double spatial_factor = 1; //to be added
    //     double spatial_noise_scaling = (pow(TILE_SIZE,2) * (1.0/16*2))*spatial_factor;

    //     //calculate the spatial denoising
    //     spatial_tile_dist = reference_tiles.real**2 + reference_tiles.imag**2;
    //     std::vector<cv::Mat> WienerCoeff = denoised_tiles*spatial_noise_scaling*noise_variance;

    //     merged_channel_tiles_spatial = reference_tiles*spatial_tile_dist/(spatial_tile_dist+WienerCoeff)

    // }
    

} // namespace hdrplus