基于CodeFormer使用C++实现图片模糊变清晰，去除马赛克等效果（代码完整）

#include <iostream> #include <fstream> #include <numeric> #include <opencv2/imgproc.hpp> #include <opencv2/highgui.hpp> //#include <cuda_provider_factory.h> ///nvidia-cuda加速 #include <onnxruntime_cxx_api.h> using namespace cv; using namespace std; using namespace Ort; class CodeFormer { public: CodeFormer(string modelpath); Mat detect(Mat cv_image); private: void preprocess(Mat srcimg); vector<float> input_image_; vector<double> input2_tensor; int inpWidth; int inpHeight; int outWidth; int outHeight; float min_max[2] = { -1,1 }; //存储初始化获得的可执行网络 Env env = Env(ORT_LOGGING_LEVEL_ERROR, "CodeFormer"); Ort::Session* ort_session = nullptr; SessionOptions sessionOptions = SessionOptions(); vector<char*> input_names; vector<char*> output_names; vector<vector<int64_t>> input_node_dims; // >=1 outputs vector<vector<int64_t>> output_node_dims; // >=1 outputs }; CodeFormer::CodeFormer(string model_path) { //OrtStatus* status = OrtSessionOptionsAppendExecutionProvider_CUDA(sessionOptions, 0); ///nvidia-cuda加速 sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_BASIC); std::wstring widestr = std::wstring(model_path.begin(), model_path.end()); ///如果在windows系统就这么写 ort_session = new Session(env, widestr.c_str(), sessionOptions); ///如果在windows系统就这么写 ///ort_session = new Session(env, model_path.c_str(), sessionOptions); ///如果在linux系统，就这么写 size_t numInputNodes = ort_session->GetInputCount(); size_t numOutputNodes = ort_session->GetOutputCount(); AllocatorWithDefaultOptions allocator; for (int i = 0; i < numInputNodes; i++) { input_names.push_back(ort_session->GetInputName(i, allocator)); Ort::TypeInfo input_type_info = ort_session->GetInputTypeInfo(i); auto input_tensor_info = input_type_info.GetTensorTypeAndShapeInfo(); auto input_dims = input_tensor_info.GetShape(); input_node_dims.push_back(input_dims); } for (int i = 0; i < numOutputNodes; i++) { output_names.push_back(ort_session->GetOutputName(i, allocator)); Ort::TypeInfo output_type_info = ort_session->GetOutputTypeInfo(i); auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo(); auto output_dims = output_tensor_info.GetShape(); output_node_dims.push_back(output_dims); } this->inpHeight = input_node_dims[0][2]; this->inpWidth = input_node_dims[0][3]; this->outHeight = output_node_dims[0][2]; this->outWidth = output_node_dims[0][3]; input2_tensor.push_back(0.5); } void CodeFormer::preprocess(Mat srcimg) { Mat dstimg; cvtColor(srcimg, dstimg, COLOR_BGR2RGB); resize(dstimg, dstimg, Size(this->inpWidth, this->inpHeight), INTER_LINEAR); this->input_image_.resize(this->inpWidth * this->inpHeight * dstimg.channels()); int k = 0; for (int c = 0; c < 3; c++) { for (int i = 0; i < this->inpHeight; i++) { for (int j = 0; j < this->inpWidth; j++) { float pix = dstimg.ptr<uchar>(i)[j * 3 + c]; this->input_image_[k] = (pix / 255.0 - 0.5) / 0.5; k++; } } } } Mat CodeFormer::detect(Mat srcimg) { int im_h = srcimg.rows; int im_w = srcimg.cols; this->preprocess(srcimg); array<int64_t, 4> input_shape_{ 1, 3, this->inpHeight, this->inpWidth }; vector<int64_t> input2_shape_ = { 1 }; auto allocator_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU); vector<Value> ort_inputs; ort_inputs.push_back(Value::CreateTensor<float>(allocator_info, input_image_.data(), input_image_.size(), input_shape_.data(), input_shape_.size())); ort_inputs.push_back(Value::CreateTensor<double>(allocator_info, input2_tensor.data(), input2_tensor.size(), input2_shape_.data(), input2_shape_.size())); vector<Value> ort_outputs = ort_session->Run(RunOptions{ nullptr }, input_names.data(), ort_inputs.data(), ort_inputs.size(), output_names.data(), output_names.size()); ////post_process float* pred = ort_outputs[0].GetTensorMutableData<float>(); //////Mat mask(outHeight, outWidth, CV_32FC3, pred); /////经过试验,直接这样赋值,是不行的 const unsigned int channel_step = outHeight * outWidth; vector<Mat> channel_mats; Mat rmat(outHeight, outWidth, CV_32FC1, pred); // R Mat gmat(outHeight, outWidth, CV_32FC1, pred + channel_step); // G Mat bmat(outHeight, outWidth, CV_32FC1, pred + 2 * channel_step); // B channel_mats.push_back(rmat); channel_mats.push_back(gmat); channel_mats.push_back(bmat); Mat mask; merge(channel_mats, mask); // CV_32FC3 allocated ///不用for循环遍历Mat里的每个像素值,实现numpy.clip函数 mask.setTo(this->min_max[0], mask < this->min_max[0]); mask.setTo(this->min_max[1], mask > this->min_max[1]); ////也可以用threshold函数,阈值类型THRESH_TOZERO_INV mask = (mask - this->min_max[0]) / (this->min_max[1] - this->min_max[0]); mask *= 255.0; mask.convertTo(mask, CV_8UC3); cvtColor(mask, mask, COLOR_BGR2RGB); return mask; } int main() { CodeFormer mynet("../ONNX_Photo_Restoration/codeformer.onnx"); string imgpath = "../ONNX_Photo_Restoration/image/4.png"; Mat srcimg = imread(imgpath); Mat dstimg = mynet.detect(srcimg); resize(dstimg, dstimg, Size(srcimg.cols, srcimg.rows), INTER_LINEAR); imwrite("../ONNX_Photo_Restoration/result/result.jpg", dstimg); namedWindow("srcimg", WINDOW_NORMAL); imshow("srcimg", srcimg); namedWindow("dstimg", WINDOW_NORMAL); imshow("dstimg", dstimg); waitKey(0); destroyAllWindows(); }