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Merge branch 'merging' of https://github.com/UCBerkeley-Spring2022-CS284A-Project/HDRplus into merging

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Haohua-Lyu 3 years ago
parent c185ba3b21 e44cc5eea0
commit 4112f3b125
2 changed files with 55 additions and 294 deletions
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55
include/hdrplus/utility.h
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@ -287,4 +287,59 @@ void extract_rgb_fmom_bayer( const cv::Mat& bayer_img, \
} }
} }
/**
* @brief Extract RGB channel seprately from bayer image
*
* @tparam T data tyoe of bayer image.
* @return vector of RGB image. OpenCV internally maintain reference count.
* Thus this step won't create deep copy overhead.
*
* @example extract_rgb_fmom_bayer<uint16_t>( bayer_img, rgb_vector_container );
*/
template <typename T>
void extract_rgb_fmom_bayer( const cv::Mat& bayer_img, \
cv::Mat& red_img, cv::Mat& green_img1, cv::Mat& green_img2, cv::Mat& blue_img )
{
const T* bayer_img_ptr = (const T*)bayer_img.data;
int bayer_width = bayer_img.size().width;
int bayer_height = bayer_img.size().height;
int bayer_step = bayer_img.step1();
if ( bayer_width % 2 != 0 || bayer_height % 2 != 0 )
{
throw std::runtime_error("Bayer image data size incorrect, must be multiplier of 2\n");
}
// RGB image is half the size of bayer image
int rgb_width = bayer_width / 2;
int rgb_height = bayer_height / 2;
red_img.create( rgb_height, rgb_width, bayer_img.type() );
green_img1.create( rgb_height, rgb_width, bayer_img.type() );
green_img2.create( rgb_height, rgb_width, bayer_img.type() );
blue_img.create( rgb_height, rgb_width, bayer_img.type() );
int rgb_step = red_img.step1();
T* r_img_ptr = (T*)red_img.data;
T* g1_img_ptr = (T*)green_img1.data;
T* g2_img_ptr = (T*)green_img2.data;
T* b_img_ptr = (T*)blue_img.data;
for ( int rgb_row_i = 0; rgb_row_i < rgb_height; rgb_row_i++ )
{
int rgb_row_i_offset = rgb_row_i * rgb_step;
// Every RGB row corresbonding to two Bayer image row
int bayer_row_i_offset1 = ( rgb_row_i * 2 + 0 ) * bayer_step; // For RG
int bayer_row_i_offset2 = ( rgb_row_i * 2 + 1 ) * bayer_step; // For GB
for ( int rgb_col_j = 0; rgb_col_j < rgb_width; rgb_col_j++ )
{
r_img_ptr[ rgb_row_i_offset + rgb_col_j ] = bayer_img_ptr[ bayer_row_i_offset1 + ( rgb_col_j * 2 + 0 ) ];
g1_img_ptr[ rgb_row_i_offset + rgb_col_j ] = bayer_img_ptr[ bayer_row_i_offset1 + ( rgb_col_j * 2 + 1 ) ];
g2_img_ptr[ rgb_row_i_offset + rgb_col_j ] = bayer_img_ptr[ bayer_row_i_offset2 + ( rgb_col_j * 2 + 0 ) ];
b_img_ptr[ rgb_row_i_offset + rgb_col_j ] = bayer_img_ptr[ bayer_row_i_offset2 + ( rgb_col_j * 2 + 1 ) ];
}
}
}
} // namespace hdrplus } // namespace hdrplus

294
src/merge.cpp
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@ -391,298 +391,4 @@ namespace hdrplus
} }
std::pair<double, double> merge::getNoiseParams( int ISO, \
int white_level, \
double black_level )
{
// 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<ushort> channels[4];
for (int y = 0; y < reference_image.rows; ++y) {
for (int x = 0; x < reference_image.cols; ++x) {
if (y % 2 == 0) {
if (x % 2 == 0) {
channels[0].push_back(reference_image.at<ushort>(y, x));
} else {
channels[1].push_back(reference_image.at<ushort>(y, x));
}
else {
if (x % 2 == 0) {
channels[2].push_back(reference_image.at<ushort>(y, x));
}
else {
channels[3].push_back(reference_image.at<ushort>(y, x));
}
}
}
}
// For each channel, perform denoising and merge
for (int i = 0; i < 4; ++i) {
// Get channel mat
cv::Mat channel_i(reference_image.rows / 2, reference_image.cols / 2, CV_16U, channels[i].data());
// cv::imwrite("ref" + std::to_string(i) + ".jpg", channel_i);
// Apply merging on the channel
//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<ushort> alt_img_channel = getChannels(alt_image); //get channel array from alternate image
cv::Mat alt_channel_i(alt_image.rows / 2, alt_image.cols / 2, CV_16U, alt_img_channel[i].data());
alternate_channel_i_list.push_back(alt_channel_i)
}
}
/////
//cv::Mat merged_channel = processChannel(burst_images, alignments, channel_i, lambda_shot, lambda_read);
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
channels[i] = merged_channel.reshape(1, merged_channel.total());
}
// Write all channels back to a bayer mat
std::vector<ushort> merged_raw;
for (int y = 0; y < reference_image.rows; ++y) {
for (int x = 0; x < reference_image.cols; ++x) {
if (y % 2 == 0) {
if (x % 2 == 0) {
merged_raw.push_back(channels[0][(y / 2) * (reference_image.cols / 2) + (x / 2)]);
}
else {
merged_raw.push_back(channels[1][(y / 2) * (reference_image.cols / 2) + (x / 2)]);
}
}
else {
if (x % 2 == 0) {
merged_raw.push_back(channels[2][(y / 2) * (reference_image.cols / 2) + (x / 2)]);
}
else {
merged_raw.push_back(channels[3][(y / 2) * (reference_image.cols / 2) + (x / 2)]);
}
}
}
}
// Create merged mat
cv::Mat merged(reference_image.rows, reference_image.cols, CV_16U, merged_raw.data());
// cv::imwrite("merged.jpg", merged);
// 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_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<ushort> 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 } // namespace hdrplus
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