前言

1.抠图技术应用很广泛，比如证件照，美体，人体区域特殊处理，还有B站的字幕穿人效果等等。这些的关键技术都在于高精度高性能的分割算法。RobustVideoMatting是来自字节跳动视频人像抠图算法（RVM），专为稳定人物视频抠像设计。 不同于现有神经网络将每一帧作为单独图片处理，RVM 使用循环神经网络，在处理视频流时有时间记忆。RVM 可在任意视频上做实时高清人像抠图。
2.关于RobustVideoMatting算法和模型训练步骤可以直接转到官方的git:https://github.com/PeterL1n/RobustVideoMatting。这里只实现模型的C++推理与部署。
3.使用的开发环境是win10,显卡RTX3080，cuda11.2，cudnn8.1，OpenCV4.5,onnxruntime，IDE 是Vs2019。

一、模型与依赖

1.官方公布了很多种格式的模型，有mnn,ncnn，onnx等等，这里使用的是onnx这个模型，直接从这官方公布的地址（gym7）下载就可以了。

2.onnxruntime直接下载 官方编译好的release版本就可以使用了。

3.如果想用GPU进行推理，则要下载安装cuda，cudnn，具体安装方法网上有很多种，可以参考。

二、代码

1.推理代码

typedef struct MattingContentType
{
	Mat fgr_mat; 
	Mat pha_mat;
	Mat merge_mat;
	bool flag;

	MattingContentType() : flag(false)
	{};
} MattingContent;

class RobustVideoMatting
{
public:
	RobustVideoMatting(string model_path);
	void detect(const Mat& mat, MattingContent& content, float downsample_ratio);
private:
	Session* session_;
	Env env = Env(ORT_LOGGING_LEVEL_ERROR, "robustvideomatting");
	SessionOptions sessionOptions = SessionOptions();

	unsigned int num_inputs = 6;
	vector<const char*> input_node_names = {
		"src",
		"r1i",
		"r2i",
		"r3i",
		"r4i",
		"downsample_ratio"
	};
	vector<vector<int64_t>> dynamic_input_node_dims = {
		{1, 3, 1280, 720}, 
		{1, 1, 1,    1}, 
		{1, 1, 1,    1}, 
		{1, 1, 1,    1},
		{1, 1, 1,    1},
		{1} 
	};

	unsigned int num_outputs = 6;
	vector<const char*> output_node_names = {
		"fgr",
		"pha",
		"r1o",
		"r2o",
		"r3o",
		"r4o"
	};

	vector<float> dynamic_src_value_handler;
	vector<float> dynamic_r1i_value_handler = { 0.0f }; 
	vector<float> dynamic_r2i_value_handler = { 0.0f };
	vector<float> dynamic_r3i_value_handler = { 0.0f };
	vector<float> dynamic_r4i_value_handler = { 0.0f };
	vector<float> dynamic_dsr_value_handler = { 0.25f }; 
	int64_t value_size_of(const std::vector<int64_t>& dims);
	bool context_is_update = false;
	void normalize_(Mat img, vector<float>& output);
	vector<Ort::Value> transform(const Mat& mat);
	void generate_matting(vector<Ort::Value>& output_tensors, MattingContent& content);
	void update_context(vector<Ort::Value>& output_tensors);
};

RobustVideoMatting::RobustVideoMatting(string model_path)
{
	wstring widestr = wstring(model_path.begin(), model_path.end());
	sessionOptions.SetGraphOptimizationLevel(ORT_ENABLE_EXTENDED);
	session_ = new Session(env, widestr.c_str(), sessionOptions);
}

void RobustVideoMatting::normalize_(Mat img, vector<float>& output) 
{

	int row = img.rows;
	int col = img.cols;
	for (int c = 0; c < 3; c++)
	{
		for (int i = 0; i < row; i++) 
		{
			for (int j = 0; j < col; j++) 
			{
				float pix = img.ptr<uchar>(i)[j * 3 + 2 - c]; 
				output[c * row * col + i * col + j] = pix / 255.0;
			}
		}
	}
}

int64_t RobustVideoMatting::value_size_of(const std::vector<int64_t>& dims)
{
	if (dims.empty()) return 0;
	int64_t value_size = 1;
	for (const auto& size : dims) value_size *= size;
	return value_size;
}

vector<Ort::Value> RobustVideoMatting::transform(const Mat& mat)
{
	Mat src = mat.clone();
	const unsigned int img_height = mat.rows;
	const unsigned int img_width = mat.cols;
	vector<int64_t>& src_dims = dynamic_input_node_dims.at(0); 
	src_dims.at(2) = img_height;
	src_dims.at(3) = img_width;

	std::vector<int64_t>& r1i_dims = dynamic_input_node_dims.at(1); 
	std::vector<int64_t>& r2i_dims = dynamic_input_node_dims.at(2); 
	std::vector<int64_t>& r3i_dims = dynamic_input_node_dims.at(3); 
	std::vector<int64_t>& r4i_dims = dynamic_input_node_dims.at(4);
	std::vector<int64_t>& dsr_dims = dynamic_input_node_dims.at(5); 

	int64_t src_value_size = this->value_size_of(src_dims); 
	int64_t r1i_value_size = this->value_size_of(r1i_dims); 
	int64_t r2i_value_size = this->value_size_of(r2i_dims); 
	int64_t r3i_value_size = this->value_size_of(r3i_dims); 
	int64_t r4i_value_size = this->value_size_of(r4i_dims); 
	int64_t dsr_value_size = this->value_size_of(dsr_dims); 

	dynamic_src_value_handler.resize(src_value_size);
	this->normalize_(src, dynamic_src_value_handler);
	std::vector<Ort::Value> input_tensors;
	auto allocator_info = MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU);

	input_tensors.push_back(Value::CreateTensor<float>(allocator_info, 
		dynamic_src_value_handler.data(), dynamic_src_value_handler.size(), src_dims.data(), src_dims.size()));
	input_tensors.push_back(Value::CreateTensor<float>(allocator_info, 
		dynamic_r1i_value_handler.data(), r1i_value_size, r1i_dims.data(), r1i_dims.size()));
	input_tensors.push_back(Value::CreateTensor<float>(allocator_info, 
		dynamic_r2i_value_handler.data(), r2i_value_size, r2i_dims.data(), r2i_dims.size()));
	input_tensors.push_back(Value::CreateTensor<float>(allocator_info, 
		dynamic_r3i_value_handler.data(), r3i_value_size, r3i_dims.data(), r3i_dims.size()));
	input_tensors.push_back(Value::CreateTensor<float>(allocator_info, 
		dynamic_r4i_value_handler.data(), r4i_value_size, r4i_dims.data(), r4i_dims.size()));
	input_tensors.push_back(Value::CreateTensor<float>(allocator_info, 
		dynamic_dsr_value_handler.data(), dsr_value_size, dsr_dims.data(), dsr_dims.size()));
	return input_tensors;
}

void RobustVideoMatting::generate_matting(std::vector<Ort::Value>& output_tensors, MattingContent& content)
{
	Ort::Value& fgr = output_tensors.at(0); 
	Ort::Value& pha = output_tensors.at(1); 
	auto fgr_dims = fgr.GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();
	auto pha_dims = pha.GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();
	const unsigned int height = fgr_dims.at(2);
	const unsigned int width = fgr_dims.at(3); 
	const unsigned int channel_step = height * width;

	float* fgr_ptr = fgr.GetTensorMutableData<float>();
	float* pha_ptr = pha.GetTensorMutableData<float>();
	Mat rmat(height, width, CV_32FC1, fgr_ptr);
	Mat gmat(height, width, CV_32FC1, fgr_ptr + channel_step);
	Mat bmat(height, width, CV_32FC1, fgr_ptr + 2 * channel_step);
	Mat pmat(height, width, CV_32FC1, pha_ptr);
	rmat *= 255.;
	bmat *= 255.;
	gmat *= 255.;
	Mat rest = 1. - pmat;
	Mat mbmat = bmat.mul(pmat) + rest * 153.;
	Mat mgmat = gmat.mul(pmat) + rest * 255.;
	Mat mrmat = rmat.mul(pmat) + rest * 120.;
	std::vector<Mat> fgr_channel_mats, merge_channel_mats;
	fgr_channel_mats.push_back(bmat);
	fgr_channel_mats.push_back(gmat);
	fgr_channel_mats.push_back(rmat);
	merge_channel_mats.push_back(mbmat);
	merge_channel_mats.push_back(mgmat);
	merge_channel_mats.push_back(mrmat);

	content.pha_mat = pmat;
	merge(fgr_channel_mats, content.fgr_mat);
	merge(merge_channel_mats, content.merge_mat);
	content.fgr_mat.convertTo(content.fgr_mat, CV_8UC3);
	content.merge_mat.convertTo(content.merge_mat, CV_8UC3);

	content.flag = true;
}

void RobustVideoMatting::update_context(std::vector<Ort::Value>& output_tensors)
{
	Ort::Value& r1o = output_tensors.at(2);
	Ort::Value& r2o = output_tensors.at(3); 
	Ort::Value& r3o = output_tensors.at(4); 
	Ort::Value& r4o = output_tensors.at(5); 
	auto r1o_dims = r1o.GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();
	auto r2o_dims = r2o.GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();
	auto r3o_dims = r3o.GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();
	auto r4o_dims = r4o.GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();

	dynamic_input_node_dims.at(1) = r1o_dims;
	dynamic_input_node_dims.at(2) = r2o_dims;
	dynamic_input_node_dims.at(3) = r3o_dims;
	dynamic_input_node_dims.at(4) = r4o_dims;

	int64_t new_r1i_value_size = this->value_size_of(r1o_dims); 
	int64_t new_r2i_value_size = this->value_size_of(r2o_dims); 
	int64_t new_r3i_value_size = this->value_size_of(r3o_dims);
	int64_t new_r4i_value_size = this->value_size_of(r4o_dims); 
	dynamic_r1i_value_handler.resize(new_r1i_value_size);
	dynamic_r2i_value_handler.resize(new_r2i_value_size);
	dynamic_r3i_value_handler.resize(new_r3i_value_size);
	dynamic_r4i_value_handler.resize(new_r4i_value_size);
	float* new_r1i_value_ptr = r1o.GetTensorMutableData<float>();
	float* new_r2i_value_ptr = r2o.GetTensorMutableData<float>();
	float* new_r3i_value_ptr = r3o.GetTensorMutableData<float>();
	float* new_r4i_value_ptr = r4o.GetTensorMutableData<float>();
	std::memcpy(dynamic_r1i_value_handler.data(), 
		new_r1i_value_ptr, new_r1i_value_size * sizeof(float));
	std::memcpy(dynamic_r2i_value_handler.data(), 
		new_r2i_value_ptr, new_r2i_value_size * sizeof(float));
	std::memcpy(dynamic_r3i_value_handler.data(), 
		new_r3i_value_ptr, new_r3i_value_size * sizeof(float));
	std::memcpy(dynamic_r4i_value_handler.data(), 
		new_r4i_value_ptr, new_r4i_value_size * sizeof(float));
	context_is_update = true;
}

void RobustVideoMatting::detect(const Mat& mat, MattingContent& content, float downsample_ratio)
{
	if (mat.empty()) return;

	dynamic_dsr_value_handler.at(0) = downsample_ratio;

	std::vector<Ort::Value> input_tensors = this->transform(mat);

	auto output_tensors = session_->Run(
		Ort::RunOptions{ nullptr }, input_node_names.data(),
		input_tensors.data(), num_inputs, output_node_names.data(),
		num_outputs
	);

	this->generate_matting(output_tensors, content);

	context_is_update = false; 
	this->update_context(output_tensors);
}

2.对图像中的人像进行抠图：

void detect_image(const cv::Mat& cv_src, string model_path)
{
	const float downsample_ratio = 0.2;
	RobustVideoMatting rvm(model_path);
	MattingContent content;
	rvm.detect(cv_src, content, downsample_ratio);

	namedWindow("src", 0);
	imshow("src", cv_src);

	namedWindow("matting", 0);
	imshow("matting", content.pha_mat * 255.);
	namedWindow("merge", 0);
	imshow("merge", content.merge_mat);
	waitKey(0);
}

执行效果：


3.对视频进行人像抠图

void detect_video(const std::string video_path, string model_path)
{
	const float downsample_ratio = 0.25;
	RobustVideoMatting rvm(model_path);

	cv::VideoCapture video_capture(video_path);
	const unsigned int width = video_capture.get(cv::CAP_PROP_FRAME_WIDTH);
	const unsigned int height = video_capture.get(cv::CAP_PROP_FRAME_HEIGHT);
	const unsigned int frame_count = video_capture.get(cv::CAP_PROP_FRAME_COUNT);
	if (!video_capture.isOpened())
	{
		return;
	}

	cv::Mat cv_src;
	while (video_capture.read(cv_src))
	{
		MattingContent content;
		rvm.detect(cv_src, content, downsample_ratio);

		namedWindow("src", 0);
		imshow("src", cv_src);

		namedWindow("matting", WINDOW_NORMAL);
		imshow("matting", content.pha_mat);
		namedWindow("merge", WINDOW_NORMAL);
		imshow("merge", content.merge_mat);
		waitKey(10);
	}
}

执行效果：

三、源码

1.源码地址：https://download.csdn.net/download/matt45m/86821062
2.下载源码后解压，使用vs2019打开，images和video是测试的图像和视频，lib里面有使用到的依赖库，include依赖的头文件。

3.配置include和lib路径

4.添加lib，把lib目录下所有.lib后缀的添加到依赖项。

5.运行配置