opencv进阶 ——（九）图像处理之人脸修复祛马赛克算法CodeFormer

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();
}