C++ AnimeGAN实现照片一键动漫化

 #include  #include  #include  //模型路径 std::string celeba_model = "models/Celeba.bin"; std::string celeba_param = "models/Celeba.param"; std::string face_v1_model = "models/FacePointV1.bin"; std::string face_v1_param = "models/FacePointV1.param"; std::string face_v2_model = "models/FacePointV2.bin"; std::string face_v2_param = "models/FacePointV2.param"; //读取模型 int readModels(ncnn::Net& ncnn_net, std::string param_path, std::string model_path, bool use_gpu = true); //推理，target_w，target_h为推理尺寸，越往大的改，细节保留就越多，也越吃GPU算力 int animeInference(const ncnn::Net& ncnn_net, const cv::Mat& cv_src, cv::Mat& cv_dst,int target_w = 512, int target_h = 512) { cv::Mat cv_backup = cv_src.clone(); const int w = cv_src.cols; const int h = cv_src.rows; const float mean_vals[3] = { 127.5f, 127.5f,  127.5f }; const float norm_vals[3] = { 1 / 127.5f, 1 / 127.5f, 1 / 127.5f }; ncnn::Mat in = ncnn::Mat::from_pixels_resize(cv_backup.data, ncnn::Mat::PIXEL_BGR2RGB, w, h, target_w, target_h); in.substract_mean_normalize(mean_vals, norm_vals); ncnn::Mat out; ncnn::Extractor ex = ncnn_net.create_extractor(); ex.input("input", in); ex.extract("out", out); cv::Mat result(out.h, out.w, CV_32FC3); for (int i = 0; i (h, w)[2 - i] = out_data[h * out.h + w]; } } } cv::Mat result8U(out.h, out.w, CV_8UC3); result.convertTo(result8U, CV_8UC3, 255.0/2, 255.0/2); result8U.copyTo(cv_dst); cv::resize(cv_dst, cv_dst, cv_src.size()); return 0; } //调用，三种风格 int styletransferResult(const cv::Mat& cv_src, std::vector& cv_dsts, ncnn::Net& face_v1_net, ncnn::Net& face_v2_net, ncnn::Net& celeba_net) { animeInference(face_v1_net, cv_src, cv_dsts.at(0)); animeInference(face_v2_net, cv_src, cv_dsts.at(1)); animeInference(celeba_net, cv_src, cv_dsts.at(2)); } int main(int argc, char** argv) { //图像路径 std::string path = "images"; std::vector filenames; cv::glob(path, filenames, false); ncnn::Net face_v1_net, face_v2_net, celeba_net; readModels(face_v1_net, face_v1_param, face_v1_model); readModels(face_v2_net, face_v1_param, face_v1_model); readModels(celeba_net, celeba_param, celeba_model); for(auto v : filenames) { cv::Mat cv_src = cv::imread(v, 1); std::vector cv_dsts(3); double start = static_cast(cv::getTickCount()); styletransferResult(cv_src, cv_dsts, face_v1_net, face_v2_net, celeba_net); double time = ((double)cv::getTickCount() - start) / cv::getTickFrequency(); std::cout << "time:" << time << "(s)" << std::endl; cv_src.push_back(cv_dsts.at(0)); cv_dsts.at(1).push_back(cv_dsts.at(2)); cv::Mat des; des.create(cv_src.rows, cv_src.cols*2, cv_src.type()); cv::Mat r1 = des(cv::Rect(0, 0, cv_src.cols, cv_src.rows)); cv_src.copyTo(r1); cv::Mat r2 = des(cv::Rect(cv_src.cols, 0, cv_src.cols,cv_src.rows)); cv_dsts.at(1).copyTo(r2); imshow("style", des); cv::imwrite(std::to_string(j)+".jpg-600", des); // cv::waitKey(); } return 0; } int readModels(ncnn::Net& ncnn_net,std::string param_path,std::string model_path, bool use_gpu) { bool has_gpu = false; #if NCNN_VULKAN ncnn::create_gpu_instance(); has_gpu = ncnn::get_gpu_count() > 0; #endif bool to_use_gpu = has_gpu && use_gpu; ncnn_net.opt.use_vulkan_compute = to_use_gpu; int rp = ncnn_net.load_param(param_path.c_str()); int rb = ncnn_net.load_model(model_path.c_str()); if (rp <0 || rb < 0) { return 1; } 0; pre>