算法简介
CodeFormer是一种基于AI技术深度学习的人脸复原模型，由南洋理工大学和商汤科技联合研究中心联合开发，它能够接收模糊或马赛克图像作为输入，并生成更清晰的原始图像。算法源码地址：https://github.com/sczhou/CodeFormer
Face Restoration
Face Color Enhancement and Restoration
Face Inpainting
模型部署
如果想用C++进行模型推理部署，首先要把模型转换成onnx，转成onnx就可以使用onnxruntime c++库进行部署，或者使用OpenCV的DNN也可以。
1、可在以下地址下载模型：https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0
2、下载CodeFormer源码，在工程目录下添加onnx转换python代码
import torch
from basicsr.utils.registry import ARCH_REGISTRY
if __name__ == '__main__':
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
net = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9,
connect_list=['32', '64', '128', '256']).to(device)
# ckpt_path = 'weights/CodeFormer/codeformer.pth'
ckpt_path = './codeformer.pth'
checkpoint = torch.load(ckpt_path)['params_ema']
net.load_state_dict(checkpoint)
net.eval()
input_tensor = torch.zeros((1, 3, 512, 512)).to(device)
torch.onnx.export(
net, # 模型实例
input_tensor, # 输入张量
"./codeformer.onnx", # 输出的ONNX模型路径
export_params=True, # 是否包含模型参数
opset_version=11, # ONNX操作集版本
do_constant_folding=True, # 是否进行常量折叠优化
input_names=['input'], # 输入名称
output_names=['output'], # 输出名称
dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}} # 声明动态轴
)
3、采用onnxruntime加载模型，示例代码如下
#define _CRT_SECURE_NO_WARNINGS
#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("codeformer.onnx");
string imgpath = "input.png";
Mat srcimg = imread(imgpath);
Mat dstimg = mynet.detect(srcimg);
resize(dstimg, dstimg, Size(srcimg.cols, srcimg.rows), INTER_LINEAR);
//imwrite("result.jpg", dstimg)
namedWindow("srcimg", WINDOW_NORMAL);
imshow("srcimg", srcimg);
namedWindow("dstimg", WINDOW_NORMAL);
imshow("dstimg", dstimg);
waitKey(0);
destroyAllWindows();
}
