mirror of
https://github.com/lltcggie/waifu2x-caffe.git
synced 2025-06-26 05:32:47 +00:00
795 lines
21 KiB
C++
795 lines
21 KiB
C++
#include "waifu2x.h"
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#include <caffe/caffe.hpp>
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#include <cudnn.h>
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#include <mutex>
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#include <opencv2/opencv.hpp>
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#include <boost/filesystem.hpp>
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#include <boost/algorithm/string.hpp>
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#include <chrono>
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#include <cuda_runtime.h>
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#ifdef _MSC_VER
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#ifdef _DEBUG
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#pragma comment(lib, "libcaffed.lib")
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#pragma comment(lib, "libprotobufd.lib")
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#else
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#pragma comment(lib, "libcaffe.lib")
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#pragma comment(lib, "libprotobuf.lib")
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#endif
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#pragma comment(lib, "libprotoc.lib")
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#endif
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// 入力画像のオフセット
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const int offset = 0;
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// srcnn.prototxtで定義されたレイヤーの数
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const int layer_num = 7;
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const int ConvertMode = CV_RGB2YUV;
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const int ConvertInverseMode = CV_YUV2RGB;
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// 最低限必要なCUDAドライバーのバージョン
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const int MinCudaDriverVersion = 6050;
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static std::once_flag waifu2x_once_flag;
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static std::once_flag waifu2x_cudnn_once_flag;
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static std::once_flag waifu2x_cuda_once_flag;
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#ifndef CUDA_CHECK_WAIFU2X
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#define CUDA_CHECK_WAIFU2X(condition) \
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do { \
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cudaError_t error = condition; \
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if(error != cudaSuccess) throw error; \
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} while (0)
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#endif
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#define CUDA_HOST_SAFE_FREE(ptr) \
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do { \
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if (ptr) { \
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cudaFreeHost(ptr); \
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ptr = nullptr; \
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} \
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} while (0)
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#define SAFE_DELETE_WAIFU2X(ptr) \
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do { \
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if (ptr) { \
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delete [] ptr; \
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ptr = nullptr; \
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} \
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} while (0)
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namespace
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{
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class IgnoreErrorCV
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{
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private:
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static int handleError(int status, const char* func_name,
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const char* err_msg, const char* file_name,
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int line, void* userdata)
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{
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return 0;
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}
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public:
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IgnoreErrorCV()
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{
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cv::redirectError(handleError);
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}
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};
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IgnoreErrorCV g_IgnoreErrorCV;
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}
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Waifu2x::Waifu2x() : is_inited(false), isCuda(false), input_block(nullptr), dummy_data(nullptr), output_block(nullptr)
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{
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}
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Waifu2x::~Waifu2x()
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{
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destroy();
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}
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// 画像を読み込んで値を0.0f～1.0fの範囲に変換
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Waifu2x::eWaifu2xError Waifu2x::LoadMat(cv::Mat &float_image, const uint32_t* source, int width, int height)
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{
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float_image = cv::Mat(cv::Size(width, height), CV_MAKETYPE(CV_8U, 4));
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const auto LinePixel = float_image.step1() / float_image.channels();
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const auto Channel = float_image.channels();
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const auto Width = float_image.size().width;
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const auto Height = float_image.size().height;
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const uint8_t *sptr = (const uint8_t *)source;
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auto ptr = float_image.data;
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for (int i = 0; i < height; i++)
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{
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for (int j = 0; j < width; j++)
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{
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for (int ch = 0; ch < Channel; ch++)
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ptr[(i * LinePixel + j) * 4 + ch] = sptr[(i * width + j) * 4 + ch];
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}
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}
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// RGBだからBGRに変換
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/*
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for (int i = 0; i < height; i++)
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{
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for (int j = 0; j < width; j++)
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std::swap(ptr[(i * LinePixel + j) * 4 + 0], ptr[(i * LinePixel + j) * 4 + 2]);
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}
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*/
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cv::Mat convert;
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float_image.convertTo(convert, CV_32F, 1.0 / 255.0);
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float_image.release();
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{
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// アルファチャンネル付きだったらα乗算済みにする
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std::vector<cv::Mat> planes;
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cv::split(convert, planes);
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cv::Mat w = planes[3];
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planes[0] = planes[0].mul(w);
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planes[1] = planes[1].mul(w);
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planes[2] = planes[2].mul(w);
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cv::merge(planes, convert);
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}
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float_image = convert;
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return eWaifu2xError_OK;
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}
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// 入力画像の(Photoshopでいう)キャンバスサイズをoutput_sizeの倍数に変更
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// 画像は左上配置、余白はcv::BORDER_REPLICATEで埋める
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Waifu2x::eWaifu2xError Waifu2x::PaddingImage(const cv::Mat &input, cv::Mat &output)
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{
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const auto h_blocks = (int)floor(input.size().width / output_size) + (input.size().width % output_size == 0 ? 0 : 1);
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const auto w_blocks = (int)floor(input.size().height / output_size) + (input.size().height % output_size == 0 ? 0 : 1);
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const auto height = offset + h_blocks * output_size + offset;
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const auto width = offset + w_blocks * output_size + offset;
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const auto pad_h1 = offset;
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const auto pad_w1 = offset;
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const auto pad_h2 = (height - offset) - input.size().width;
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const auto pad_w2 = (width - offset) - input.size().height;
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cv::copyMakeBorder(input, output, pad_w1, pad_w2, pad_h1, pad_h2, cv::BORDER_REPLICATE);
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return eWaifu2xError_OK;
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}
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// 画像をcv::INTER_NEARESTで二倍に拡大して、PaddingImage()でパディングする
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Waifu2x::eWaifu2xError Waifu2x::Zoom2xAndPaddingImage(const cv::Mat &input, cv::Mat &output, cv::Size_<int> &zoom_size)
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{
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zoom_size = input.size();
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zoom_size.width *= 2;
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zoom_size.height *= 2;
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cv::Mat zoom_image;
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cv::resize(input, zoom_image, zoom_size, 0.0, 0.0, cv::INTER_NEAREST);
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return PaddingImage(zoom_image, output);
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}
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// 入力画像をzoom_sizeの大きさにcv::INTER_CUBICで拡大し、色情報のみを残す
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Waifu2x::eWaifu2xError Waifu2x::CreateZoomColorImage(const cv::Mat &float_image, const cv::Size_<int> &zoom_size, std::vector<cv::Mat> &cubic_planes)
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{
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cv::Mat zoom_cubic_image;
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cv::resize(float_image, zoom_cubic_image, zoom_size, 0.0, 0.0, cv::INTER_CUBIC);
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cv::Mat converted_cubic_image;
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cv::cvtColor(zoom_cubic_image, converted_cubic_image, ConvertMode);
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zoom_cubic_image.release();
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cv::split(converted_cubic_image, cubic_planes);
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converted_cubic_image.release();
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// このY成分は使わないので解放
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cubic_planes[0].release();
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return eWaifu2xError_OK;
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}
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// モデルファイルからネットワークを構築
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// processでcudnnが指定されなかった場合はcuDNNが呼び出されないように変更する
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Waifu2x::eWaifu2xError Waifu2x::ConstractNet(boost::shared_ptr<caffe::Net<float>> &net, const std::string &model_path, const std::string ¶m_path, const std::string &process)
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{
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const std::string caffemodel_path = param_path + ".caffemodel";
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const std::string modelbin_path = model_path + ".protobin";
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FILE *fp = fopen(caffemodel_path.c_str(), "rb");
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const bool isModelExist = fp != nullptr;
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if (fp) fclose(fp);
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fp = fopen(modelbin_path.c_str(), "rb");
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const bool isModelBinExist = fp != nullptr;
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if (fp) fclose(fp);
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caffe::NetParameter param;
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if (isModelExist && isModelBinExist && caffe::ReadProtoFromBinaryFile(modelbin_path, ¶m))
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{
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const auto ret = SetParameter(param);
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if (ret != eWaifu2xError_OK)
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return ret;
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net = boost::shared_ptr<caffe::Net<float>>(new caffe::Net<float>(param));
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net->CopyTrainedLayersFrom(caffemodel_path);
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input_plane = param.input_dim(1);
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}
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else
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return eWaifu2xError_FailedConstructModel;
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return eWaifu2xError_OK;
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}
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Waifu2x::eWaifu2xError Waifu2x::SetParameter(caffe::NetParameter ¶m) const
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{
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param.mutable_state()->set_phase(caffe::TEST);
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{
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auto mid = param.mutable_input_dim();
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if (mid->size() != 4)
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return eWaifu2xError_FailedParseModelFile;
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*mid->Mutable(0) = batch_size;
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*mid->Mutable(2) = input_block_size;
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*mid->Mutable(3) = input_block_size;
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}
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for (int i = 0; i < param.layer_size(); i++)
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{
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caffe::LayerParameter *layer_param = param.mutable_layer(i);
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const std::string& type = layer_param->type();
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if (type == "Convolution")
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{
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if (process == "cudnn")
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layer_param->mutable_convolution_param()->set_engine(caffe::ConvolutionParameter_Engine_CUDNN);
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else
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layer_param->mutable_convolution_param()->set_engine(caffe::ConvolutionParameter_Engine_CAFFE);
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}
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else if (type == "ReLU")
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{
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if (process == "cudnn")
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layer_param->mutable_relu_param()->set_engine(caffe::ReLUParameter_Engine_CUDNN);
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else
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layer_param->mutable_relu_param()->set_engine(caffe::ReLUParameter_Engine_CAFFE);
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}
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}
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return eWaifu2xError_OK;
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}
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// ネットワークを使って画像を再構築する
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Waifu2x::eWaifu2xError Waifu2x::ReconstructImage(boost::shared_ptr<caffe::Net<float>> net, cv::Mat &im)
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{
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const auto Height = im.size().height;
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const auto Width = im.size().width;
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const auto Line = im.step1();
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assert(Width % output_size == 0);
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assert(Height % output_size == 0);
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assert(im.channels() == 1 || im.channels() == 3);
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cv::Mat outim(im.rows, im.cols, im.type());
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// float *imptr = (float *)im.data;
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float *imptr = (float *)outim.data;
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try
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{
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auto input_blobs = net->input_blobs();
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auto input_blob = net->input_blobs()[0];
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input_blob->Reshape(batch_size, input_plane, input_block_size, input_block_size);
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assert(im.channels() == input_plane);
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assert(input_blob->shape(1) == input_plane);
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const int WidthNum = Width / output_size;
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const int HeightNum = Height / output_size;
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const int BlockNum = WidthNum * HeightNum;
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const int input_block_plane_size = input_block_size * input_block_size * input_plane;
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const int output_block_plane_size = output_block_size * output_block_size * input_plane;
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const int output_padding = inner_padding + outer_padding - layer_num;
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// 画像は(消費メモリの都合上)output_size*output_sizeに分けて再構築する
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for (int num = 0; num < BlockNum; num += batch_size)
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{
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const int processNum = (BlockNum - num) >= batch_size ? batch_size : BlockNum - num;
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if (processNum < batch_size)
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input_blob->Reshape(processNum, input_plane, input_block_size, input_block_size);
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for (int n = 0; n < processNum; n++)
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{
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const int wn = (num + n) % WidthNum;
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const int hn = (num + n) / WidthNum;
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const int w = wn * output_size;
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const int h = hn * output_size;
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if (w + crop_size <= Width && h + crop_size <= Height)
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{
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int x, y;
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x = w - inner_padding;
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y = h - inner_padding;
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int width, height;
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width = crop_size + inner_padding * 2;
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height = crop_size + inner_padding * 2;
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int top, bottom, left, right;
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top = outer_padding;
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bottom = outer_padding;
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left = outer_padding;
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right = outer_padding;
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if (x < 0)
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{
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left += -x;
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width -= -x;
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x = 0;
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}
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if (x + width > Width)
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{
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right += (x + width) - Width;
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width = Width - x;
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}
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if (y < 0)
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{
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top += -y;
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height -= -y;
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y = 0;
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}
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if (y + height > Height)
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{
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bottom += (y + height) - Height;
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height = Height - y;
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}
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cv::Mat someimg = im(cv::Rect(x, y, width, height));
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cv::Mat someborderimg;
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// 画像を中央にパディング。余白はcv::BORDER_REPLICATEで埋める
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// 実はimで画素が存在する部分は余白と認識されないが、inner_paddingがlayer_numでouter_paddingが1以上ならそこの部分の画素は結果画像として取り出す部分には影響しない
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cv::copyMakeBorder(someimg, someborderimg, top, bottom, left, right, cv::BORDER_REPLICATE);
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someimg.release();
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// 画像を直列に変換
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{
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float *fptr = input_block + (input_block_plane_size * n);
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const float *uptr = (const float *)someborderimg.data;
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const auto Line = someborderimg.step1();
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if (someborderimg.channels() == 1)
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{
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if (input_block_size == Line)
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memcpy(fptr, uptr, input_block_size * input_block_size * sizeof(float));
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else
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{
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for (int i = 0; i < input_block_size; i++)
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memcpy(fptr + i * input_block_size, uptr + i * Line, input_block_size * sizeof(float));
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}
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}
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else
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{
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const auto LinePixel = someborderimg.step1() / someborderimg.channels();
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const auto Channel = someborderimg.channels();
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const auto Width = someborderimg.size().width;
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const auto Height = someborderimg.size().height;
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for (int i = 0; i < Height; i++)
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{
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for (int j = 0; j < LinePixel; j++)
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{
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for (int ch = 0; ch < Channel; ch++)
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fptr[(ch * Height + i) * Width + j] = uptr[(i * LinePixel + j) * Channel + ch];
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}
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}
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/*
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{
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cv::Mat im(someborderimg.size(), CV_32F, fptr, Width * sizeof(float));
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cv::Mat write_iamge;
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im.convertTo(write_iamge, CV_8U, 255.0);
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im.release();
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if (!cv::imwrite("test_in.png", write_iamge))
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return eWaifu2xError_FailedOpenOutputFile;
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}
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*/
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}
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}
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}
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}
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assert(input_blob->count() == input_block_plane_size * processNum);
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// ネットワークに画像を入力
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input_blob->set_cpu_data(input_block);
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// 計算
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auto out = net->ForwardPrefilled(nullptr);
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auto b = out[0];
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assert(b->count() == output_block_plane_size * processNum);
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const float *ptr = nullptr;
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if (caffe::Caffe::mode() == caffe::Caffe::CPU)
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ptr = b->cpu_data();
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else
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ptr = b->gpu_data();
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caffe::caffe_copy(output_block_plane_size * processNum, ptr, output_block);
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for (int n = 0; n < processNum; n++)
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{
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const int wn = (num + n) % WidthNum;
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const int hn = (num + n) / WidthNum;
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const int w = wn * output_size;
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const int h = hn * output_size;
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const float *fptr = output_block + (output_block_plane_size * n);
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// 結果を出力画像にコピー
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if (outim.channels() == 1)
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{
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for (int i = 0; i < crop_size; i++)
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memcpy(imptr + (h + i) * Line + w, fptr + (i + output_padding) * output_block_size + output_padding, crop_size * sizeof(float));
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}
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else
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{
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const auto LinePixel = outim.step1() / outim.channels();
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const auto Channel = outim.channels();
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for (int i = 0; i < crop_size; i++)
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{
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for (int j = 0; j < crop_size; j++)
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{
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for (int ch = 0; ch < Channel; ch++)
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imptr[((h + i) * LinePixel + (w + j)) * Channel + ch] = fptr[(ch * output_block_size + i + output_padding) * output_block_size + j + output_padding];
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}
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}
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/*
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{
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cv::Mat im(someborderimg.size(), CV_32F, fptr, Width * sizeof(float));
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cv::Mat write_iamge;
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im.convertTo(write_iamge, CV_8U, 255.0);
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im.release();
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if (!cv::imwrite("test_in.png", write_iamge))
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return eWaifu2xError_FailedOpenOutputFile;
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}
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*/
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}
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}
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}
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}
|
||
catch (...)
|
||
{
|
||
return eWaifu2xError_FailedProcessCaffe;
|
||
}
|
||
|
||
im = outim;
|
||
|
||
return eWaifu2xError_OK;
|
||
}
|
||
|
||
Waifu2x::eWaifu2xError Waifu2x::init(int argc, char** argv, const std::string &Mode, const int NoiseLevel, const std::string &ModelDir, const std::string &Process,
|
||
const int CropSize, const int BatchSize)
|
||
{
|
||
Waifu2x::eWaifu2xError ret;
|
||
|
||
if (is_inited)
|
||
return eWaifu2xError_OK;
|
||
|
||
try
|
||
{
|
||
mode = Mode;
|
||
noise_level = NoiseLevel;
|
||
model_dir = ModelDir;
|
||
process = Process;
|
||
|
||
crop_size = CropSize;
|
||
batch_size = BatchSize;
|
||
|
||
inner_padding = layer_num;
|
||
outer_padding = 1;
|
||
|
||
output_size = crop_size - offset * 2;
|
||
input_block_size = crop_size + (inner_padding + outer_padding) * 2;
|
||
original_width_height = 128 + layer_num * 2;
|
||
|
||
output_block_size = crop_size + (inner_padding + outer_padding - layer_num) * 2;
|
||
|
||
std::call_once(waifu2x_once_flag, [argc, argv]()
|
||
{
|
||
assert(argc >= 1);
|
||
|
||
int tmpargc = 1;
|
||
char* tmpargvv[] = { argv[0] };
|
||
char** tmpargv = tmpargvv;
|
||
// glog等の初期化
|
||
caffe::GlobalInit(&tmpargc, &tmpargv);
|
||
});
|
||
|
||
const auto cuDNNCheckStartTime = std::chrono::system_clock::now();
|
||
|
||
if (process == "gpu")
|
||
process = "cudnn";
|
||
|
||
const auto cuDNNCheckEndTime = std::chrono::system_clock::now();
|
||
|
||
boost::filesystem::path mode_dir_path(model_dir);
|
||
if (!mode_dir_path.is_absolute()) // model_dirが相対パスなら絶対パスに直す
|
||
{
|
||
// まずはカレントディレクトリ下にあるか探す
|
||
mode_dir_path = boost::filesystem::absolute(model_dir);
|
||
if (!boost::filesystem::exists(mode_dir_path) && argc >= 1) // 無かったらargv[0]から実行ファイルのあるフォルダを推定し、そのフォルダ下にあるか探す
|
||
{
|
||
boost::filesystem::path a0(argv[0]);
|
||
if (a0.is_absolute())
|
||
mode_dir_path = a0.branch_path() / model_dir;
|
||
}
|
||
}
|
||
|
||
if (!boost::filesystem::exists(mode_dir_path))
|
||
return eWaifu2xError_FailedOpenModelFile;
|
||
|
||
if (process == "cpu")
|
||
{
|
||
caffe::Caffe::set_mode(caffe::Caffe::CPU);
|
||
isCuda = false;
|
||
}
|
||
else
|
||
{
|
||
caffe::Caffe::set_mode(caffe::Caffe::GPU);
|
||
isCuda = true;
|
||
}
|
||
|
||
if (mode == "noise" || mode == "noise_scale" || mode == "auto_scale")
|
||
{
|
||
const std::string model_path = (mode_dir_path / "srcnn.prototxt").string();
|
||
const std::string param_path = (mode_dir_path / ("noise" + std::to_string(noise_level) + "_model.json")).string();
|
||
|
||
ret = ConstractNet(net_noise, model_path, param_path, process);
|
||
if (ret != eWaifu2xError_OK)
|
||
return ret;
|
||
}
|
||
|
||
if (mode == "scale" || mode == "noise_scale" || mode == "auto_scale")
|
||
{
|
||
const std::string model_path = (mode_dir_path / "srcnn.prototxt").string();
|
||
const std::string param_path = (mode_dir_path / "scale2.0x_model.json").string();
|
||
|
||
ret = ConstractNet(net_scale, model_path, param_path, process);
|
||
if (ret != eWaifu2xError_OK)
|
||
return ret;
|
||
}
|
||
|
||
const int input_block_plane_size = input_block_size * input_block_size * input_plane;
|
||
const int output_block_plane_size = output_block_size * output_block_size * input_plane;
|
||
|
||
if (isCuda)
|
||
{
|
||
CUDA_CHECK_WAIFU2X(cudaHostAlloc(&input_block, sizeof(float) * input_block_plane_size * batch_size, cudaHostAllocWriteCombined));
|
||
CUDA_CHECK_WAIFU2X(cudaHostAlloc(&dummy_data, sizeof(float) * input_block_plane_size * batch_size, cudaHostAllocWriteCombined));
|
||
CUDA_CHECK_WAIFU2X(cudaHostAlloc(&output_block, sizeof(float) * output_block_plane_size * batch_size, cudaHostAllocDefault));
|
||
}
|
||
else
|
||
{
|
||
input_block = new float[input_block_plane_size * batch_size];
|
||
dummy_data = new float[input_block_plane_size * batch_size];
|
||
output_block = new float[output_block_plane_size * batch_size];
|
||
}
|
||
|
||
for (size_t i = 0; i < input_block_plane_size * batch_size; i++)
|
||
dummy_data[i] = 0.0f;
|
||
|
||
is_inited = true;
|
||
}
|
||
catch (...)
|
||
{
|
||
return eWaifu2xError_InvalidParameter;
|
||
}
|
||
|
||
return eWaifu2xError_OK;
|
||
}
|
||
|
||
void Waifu2x::destroy()
|
||
{
|
||
net_noise.reset();
|
||
net_scale.reset();
|
||
|
||
if (isCuda)
|
||
{
|
||
CUDA_HOST_SAFE_FREE(input_block);
|
||
CUDA_HOST_SAFE_FREE(dummy_data);
|
||
CUDA_HOST_SAFE_FREE(output_block);
|
||
}
|
||
else
|
||
{
|
||
SAFE_DELETE_WAIFU2X(input_block);
|
||
SAFE_DELETE_WAIFU2X(dummy_data);
|
||
SAFE_DELETE_WAIFU2X(output_block);
|
||
}
|
||
|
||
is_inited = false;
|
||
}
|
||
|
||
Waifu2x::eWaifu2xError Waifu2x::waifu2x(int factor, const uint32_t* source, uint32_t* dest, int width, int height)
|
||
{
|
||
Waifu2x::eWaifu2xError ret;
|
||
|
||
if (!is_inited)
|
||
return eWaifu2xError_NotInitialized;
|
||
|
||
cv::Mat float_image;
|
||
ret = LoadMat(float_image, source, width, height);
|
||
if (ret != eWaifu2xError_OK)
|
||
return ret;
|
||
|
||
cv::Mat im;
|
||
if (input_plane == 1)
|
||
return eWaifu2xError_NotInitialized;
|
||
else
|
||
{
|
||
std::vector<cv::Mat> planes;
|
||
cv::split(float_image, planes);
|
||
|
||
if (float_image.channels() == 4)
|
||
planes.resize(3);
|
||
|
||
// BGRからRGBにする
|
||
//std::swap(planes[0], planes[2]);
|
||
|
||
cv::merge(planes, im);
|
||
}
|
||
cv::Size_<int> image_size = im.size();
|
||
|
||
const bool isReconstructNoise = mode == "noise" || mode == "noise_scale" || mode == "auto_scale";
|
||
const bool isReconstructScale = mode == "scale" || mode == "noise_scale";
|
||
|
||
if (isReconstructNoise)
|
||
{
|
||
PaddingImage(im, im);
|
||
|
||
ret = ReconstructImage(net_noise, im);
|
||
if (ret != eWaifu2xError_OK)
|
||
return ret;
|
||
|
||
// パディングを取り払う
|
||
im = im(cv::Rect(offset, offset, image_size.width, image_size.height));
|
||
}
|
||
|
||
const int scale2 = ceil(log2((double)factor));
|
||
const double shrinkRatio = (double)factor / std::pow(2.0, (double)scale2);
|
||
|
||
if (isReconstructScale)
|
||
{
|
||
bool isError = false;
|
||
for (int i = 0; i < scale2; i++)
|
||
{
|
||
Zoom2xAndPaddingImage(im, im, image_size);
|
||
|
||
ret = ReconstructImage(net_scale, im);
|
||
if (ret != eWaifu2xError_OK)
|
||
return ret;
|
||
|
||
// パディングを取り払う
|
||
im = im(cv::Rect(offset, offset, image_size.width, image_size.height));
|
||
}
|
||
}
|
||
|
||
cv::Mat process_image;
|
||
if (input_plane == 1)
|
||
{
|
||
// 再構築した輝度画像とCreateZoomColorImage()で作成した色情報をマージして通常の画像に変換し、書き込む
|
||
|
||
std::vector<cv::Mat> color_planes;
|
||
CreateZoomColorImage(float_image, image_size, color_planes);
|
||
|
||
float_image.release();
|
||
|
||
color_planes[0] = im;
|
||
im.release();
|
||
|
||
cv::Mat converted_image;
|
||
cv::merge(color_planes, converted_image);
|
||
color_planes.clear();
|
||
|
||
cv::cvtColor(converted_image, process_image, ConvertInverseMode);
|
||
converted_image.release();
|
||
}
|
||
else
|
||
{
|
||
std::vector<cv::Mat> planes;
|
||
cv::split(im, planes);
|
||
|
||
// RGBからBGRに直す
|
||
//std::swap(planes[0], planes[2]);
|
||
|
||
cv::merge(planes, process_image);
|
||
}
|
||
|
||
cv::Mat alpha;
|
||
if (float_image.channels() == 4)
|
||
{
|
||
std::vector<cv::Mat> planes;
|
||
cv::split(float_image, planes);
|
||
alpha = planes[3];
|
||
|
||
cv::resize(alpha, alpha, image_size, 0.0, 0.0, cv::INTER_CUBIC);
|
||
}
|
||
|
||
// アルファチャンネルがあったら、アルファを付加してカラーからアルファの影響を抜く
|
||
if (!alpha.empty())
|
||
{
|
||
std::vector<cv::Mat> planes;
|
||
cv::split(process_image, planes);
|
||
process_image.release();
|
||
|
||
planes.push_back(alpha);
|
||
|
||
cv::Mat w2 = planes[3];
|
||
|
||
planes[0] = (planes[0]).mul(1.0 / w2);
|
||
planes[1] = (planes[1]).mul(1.0 / w2);
|
||
planes[2] = (planes[2]).mul(1.0 / w2);
|
||
|
||
cv::merge(planes, process_image);
|
||
}
|
||
|
||
const cv::Size_<int> ns(image_size.width * shrinkRatio, image_size.height * shrinkRatio);
|
||
if (image_size.width != ns.width || image_size.height != ns.height)
|
||
cv::resize(process_image, process_image, ns, 0.0, 0.0, cv::INTER_LINEAR);
|
||
|
||
cv::Mat write_iamge;
|
||
process_image.convertTo(write_iamge, CV_8U, 255.0);
|
||
process_image.release();
|
||
|
||
/*
|
||
ret = WriteMat(write_iamge, output_file);
|
||
if (ret != eWaifu2xError_OK)
|
||
return ret;
|
||
|
||
write_iamge.release();
|
||
*/
|
||
|
||
{
|
||
const auto width = write_iamge.size().width;
|
||
const auto stride = write_iamge.step1();
|
||
for (int i = 0; i < write_iamge.size().height; i++)
|
||
memcpy(dest + width * i, write_iamge.data + stride * i, stride);
|
||
}
|
||
|
||
return eWaifu2xError_OK;
|
||
}
|
||
|
||
const std::string& Waifu2x::used_process() const
|
||
{
|
||
return process;
|
||
}
|