使用onnxruntime加载YOLOv8生成的onnx文件进行实例分割_如何使用onnx文件处理图片

      在网上下载了60多幅包含西瓜和冬瓜的图像组成melon数据集，使用 EISeg 工具进行标注，然后使用 eiseg2yolov8 脚本将.json文件转换成YOLOv8支持的.txt文件，并自动生成YOLOv8支持的目录结构，包括melon.yaml文件，其内容如下：

path: ../datasets/melon_seg # dataset root dir
train: images/train # train images (relative to 'path')
val: images/val  # val images (relative to 'path')
test: # test images (optional)
 
# Classes
names:
  0: watermelon
  1: wintermelon

      对melon数据集进行训练的Python实现如下：最终生成的模型文件有best.pt、best.onnx、best.torchscript

import argparse
import colorama
from ultralytics import YOLO
 
def parse_args():
	parser = argparse.ArgumentParser(description="YOLOv8 train")
	parser.add_argument("--yaml", required=True, type=str, help="yaml file")
	parser.add_argument("--epochs", required=True, type=int, help="number of training")
	parser.add_argument("--task", required=True, type=str, choices=["detect", "segment"], help="specify what kind of task")
 
	args = parser.parse_args()
	return args
 
def train(task, yaml, epochs):
	if task == "detect":
		model = YOLO("yolov8n.pt") # load a pretrained model
	elif task == "segment":
		model = YOLO("yolov8n-seg.pt") # load a pretrained model
	else:
		print(colorama.Fore.RED + "Error: unsupported task:", task)
		raise
 
	results = model.train(data=yaml, epochs=epochs, imgsz=640) # train the model
 
	metrics = model.val() # It'll automatically evaluate the data you trained, no arguments needed, dataset and settings remembered
 
	model.export(format="onnx") #, dynamic=True) # export the model, cannot specify dynamic=True, opencv does not support
	# model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
	model.export(format="torchscript") # libtorch
 
if __name__ == "__main__":
	colorama.init()
	args = parse_args()
 
	train(args.task, args.yaml, args.epochs)
 
	print(colorama.Fore.GREEN + "====== execution completed ======")

      以下是使用onnxruntime接口加载onnx文件进行实例分割的C++实现代码：

namespace {
 
constexpr bool cuda_enabled{ false };
constexpr int input_size[2]{ 640, 640 }; // {height,width}, input shape (1, 3, 640, 640) BCHW and output shape(s): detect:(1,6,8400); segment:(1,38,8400),(1,32,160,160)
constexpr float confidence_threshold{ 0.45 }; // confidence threshold
constexpr float iou_threshold{ 0.50 }; // iou threshold
constexpr float mask_threshold{ 0.50 }; // segment mask threshold
 
#ifdef _MSC_VER
constexpr char* onnx_file{ "../../../data/best.onnx" };
constexpr char* torchscript_file{ "../../../data/best.torchscript" };
constexpr char* images_dir{ "../../../data/images/predict" };
constexpr char* result_dir{ "../../../data/result" };
constexpr char* classes_file{ "../../../data/images/labels.txt" };
#else
constexpr char* onnx_file{ "data/best.onnx" };
constexpr char* torchscript_file{ "data/best.torchscript" };
constexpr char* images_dir{ "data/images/predict" };
constexpr char* result_dir{ "data/result" };
constexpr char* classes_file{ "data/images/labels.txt" };
#endif
 
std::vector<std::string> parse_classes_file(const char* name)
{
	std::vector<std::string> classes;
 
	std::ifstream file(name);
	if (!file.is_open()) {
		std::cerr << "Error: fail to open classes file: " << name << std::endl;
		return classes;
	}
	
	std::string line;
	while (std::getline(file, line)) {
		auto pos = line.find_first_of(" ");
		classes.emplace_back(line.substr(0, pos));
	}
 
	file.close();
	return classes;
}
 
auto get_dir_images(const char* name)
{
	std::map<std::string, std::string> images; // image name, image path + image name
 
	for (auto const& dir_entry : std::filesystem::directory_iterator(name)) {
		if (dir_entry.is_regular_file())
			images[dir_entry.path().filename().string()] = dir_entry.path().string();
	}
 
	return images;
}
 
std::wstring ctow(const char* str)
{
	//std::wstring_convert<std::codecvt_utf8<wchar_t>>().from_bytes(std::string); // std::string -> std::wstring
	constexpr size_t len{ 128 };
	wchar_t wch[len];
	swprintf(wch, len, L"%hs", str);
 
	return std::wstring(wch);
}
 
float image_preprocess(const cv::Mat& src, cv::Mat& dst)
{
	cv::cvtColor(src, dst, cv::COLOR_BGR2RGB);
 
	float scalex = src.cols * 1.f / input_size[1];
	float scaley = src.rows * 1.f / input_size[0];
 
	if (scalex > scaley)
		cv::resize(dst, dst, cv::Size(input_size[1], static_cast<int>(src.rows / scalex)));
	else
		cv::resize(dst, dst, cv::Size(static_cast<int>(src.cols / scaley), input_size[0]));
 
	cv::Mat tmp = cv::Mat::zeros(input_size[0], input_size[1], CV_8UC3);
	dst.copyTo(tmp(cv::Rect(0, 0, dst.cols, dst.rows)));
	dst = tmp;
 
	return (scalex > scaley) ? scalex : scaley;
}
 
template<typename T>
void image_to_blob(const cv::Mat& src, T* blob)
{
	for (auto c = 0; c < 3; ++c) {
		for (auto h = 0; h < src.rows; ++h) {
			for (auto w = 0; w < src.cols; ++w) {
				blob[c * src.rows * src.cols + h * src.cols + w] = (src.at<cv::Vec3b>(h, w)[c]) / 255.f;
			}
		}
	}
}
 
void get_masks(const cv::Mat& features, const cv::Mat& proto, const std::vector<int>& output1_sizes, const cv::Mat& frame, const cv::Rect box, cv::Mat& mk)
{
	const cv::Size shape_src(frame.cols, frame.rows), shape_input(input_size[1], input_size[0]), shape_mask(output1_sizes[3], output1_sizes[2]);
	
	cv::Mat res = (features * proto).t();
	res = res.reshape(1, { shape_mask.height, shape_mask.width });
	// apply sigmoid to the mask
	cv::exp(-res, res);
	res = 1.0 / (1.0 + res);
	cv::resize(res, res, shape_input);
 
	float scalex = shape_src.width * 1.0 / shape_input.width;
	float scaley = shape_src.height * 1.0 / shape_input.height;
	cv::Mat tmp;
	if (scalex > scaley)
		cv::resize(res, tmp, cv::Size(shape_src.width, static_cast<int>(shape_input.height * scalex)));
	else
		cv::resize(res, tmp, cv::Size(static_cast<int>(shape_input.width * scaley), shape_src.height));
 
	cv::Mat dst = tmp(cv::Rect(0, 0, shape_src.width, shape_src.height));
	mk = dst(box) > mask_threshold;
}
 
void draw_boxes_mask(const std::vector<std::string>& classes, const std::vector<int>& ids, const std::vector<float>& confidences,
	const std::vector<cv::Rect>& boxes, const std::vector<cv::Mat>& masks, const std::string& name, cv::Mat& frame)
{
	std::cout << "image name: " << name << ", number of detections: " << ids.size() << std::endl;
 
	std::random_device rd;
	std::mt19937 gen(rd());
	std::uniform_int_distribution<int> dis(100, 255);
	cv::Mat mk = frame.clone();
 
	std::vector<cv::Scalar> colors;
	for (auto i = 0; i < classes.size(); ++i)
		colors.emplace_back(cv::Scalar(dis(gen), dis(gen), dis(gen)));
 
	for (auto i = 0; i < ids.size(); ++i) {
		cv::rectangle(frame, boxes[i], colors[ids[i]], 2);
 
		std::string class_string = classes[ids[i]] + ' ' + std::to_string(confidences[i]).substr(0, 4);
		cv::Size text_size = cv::getTextSize(class_string, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);
		cv::Rect text_box(boxes[i].x, boxes[i].y - 40, text_size.width + 10, text_size.height + 20);
 
		cv::rectangle(frame, text_box, colors[ids[i]], cv::FILLED);
		cv::putText(frame, class_string, cv::Point(boxes[i].x + 5, boxes[i].y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);
 
		mk(boxes[i]).setTo(colors[ids[i]], masks[i]);
	}
 
	cv::addWeighted(frame, 0.5, mk, 0.5, 0, frame);
 
	//cv::imshow("Inference", frame);
	//cv::waitKey(-1);
 
	std::string path(result_dir);
	cv::imwrite(path + "/" + name, frame);
}
 
void post_process_mask(const cv::Mat& output0, const cv::Mat& output1, const std::vector<int>& output1_sizes, const std::vector<std::string>& classes, const std::string& name, cv::Mat& frame)
{
	std::vector<int> class_ids;
	std::vector<float> confidences;
	std::vector<cv::Rect> boxes;
	std::vector<std::vector<float>> masks;
 
	float scalex = frame.cols * 1.f / input_size[1]; // note: image_preprocess function
	float scaley = frame.rows * 1.f / input_size[0];
	auto scale = (scalex > scaley) ? scalex : scaley;
 
	const float* data = (float*)output0.data;
	for (auto i = 0; i < output0.rows; ++i) {
		cv::Mat scores(1, classes.size(), CV_32FC1, (float*)data + 4);
		cv::Point class_id;
		double max_class_score;
 
		cv::minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
 
		if (max_class_score > confidence_threshold) {
			confidences.emplace_back(max_class_score);
			class_ids.emplace_back(class_id.x);
			masks.emplace_back(std::vector<float>(data + 4 + classes.size(), data + output0.cols)); // 32
 
			float x = data[0];
			float y = data[1];
			float w = data[2];
			float h = data[3];
 
			int left = std::max(0, std::min(int((x - 0.5 * w) * scale), frame.cols));
			int top = std::max(0, std::min(int((y - 0.5 * h) * scale), frame.rows));
			int width = std::max(0, std::min(int(w * scale), frame.cols - left));
			int height = std::max(0, std::min(int(h * scale), frame.rows - top));
			boxes.emplace_back(cv::Rect(left, top, width, height));
		}
 
		data += output0.cols;
	}
 
	std::vector<int> nms_result;
	cv::dnn::NMSBoxes(boxes, confidences, confidence_threshold, iou_threshold, nms_result);
 
	cv::Mat proto = output1.reshape(0, { output1_sizes[1], output1_sizes[2] * output1_sizes[3] });
 
	std::vector<int> ids;
	std::vector<float> confs;
	std::vector<cv::Rect> rects;
	std::vector<cv::Mat> mks;
	for (size_t i = 0; i < nms_result.size(); ++i) {
		auto index = nms_result[i];
		ids.emplace_back(class_ids[index]);
		confs.emplace_back(confidences[index]);
		boxes[index] = boxes[index] & cv::Rect(0, 0, frame.cols, frame.rows);
 
		cv::Mat mk;
		get_masks(cv::Mat(masks[index]).t(), proto, output1_sizes, frame, boxes[index], mk);
		mks.emplace_back(mk);
		rects.emplace_back(boxes[index]);
	}
 
	draw_boxes_mask(classes, ids, confs, rects, mks, name, frame);
}
 
} // namespace
 
int test_yolov8_segment_onnxruntime()
{
	try {
		Ort::Env env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "Yolo");
		Ort::SessionOptions session_option;
 
		if (cuda_enabled) {
			OrtCUDAProviderOptions cuda_option;
			cuda_option.device_id = 0;
			session_option.AppendExecutionProvider_CUDA(cuda_option);
		}
 
		session_option.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
		session_option.SetIntraOpNumThreads(1);
		session_option.SetLogSeverityLevel(3);
 
		Ort::Session session(env, ctow(onnx_file).c_str(), session_option);
		Ort::AllocatorWithDefaultOptions allocator;
		std::vector<const char*> input_node_names, output_node_names;
		std::vector<std::string> input_node_names_, output_node_names_;
 
		for (auto i = 0; i < session.GetInputCount(); ++i) {
			Ort::AllocatedStringPtr input_node_name = session.GetInputNameAllocated(i, allocator);
			input_node_names_.emplace_back(input_node_name.get());
		}
 
		for (auto i = 0; i < session.GetOutputCount(); ++i) {
			Ort::AllocatedStringPtr output_node_name = session.GetOutputNameAllocated(i, allocator);
			output_node_names_.emplace_back(output_node_name.get());
		}
 
		for (auto i = 0; i < input_node_names_.size(); ++i)
			input_node_names.emplace_back(input_node_names_[i].c_str());
		for (auto i = 0; i < output_node_names_.size(); ++i)
			output_node_names.emplace_back(output_node_names_[i].c_str());
 
		std::unique_ptr<float[]> blob(new float[input_size[0] * input_size[1] * 3]);
		std::vector<int64_t> input_node_dims{ 1, 3, input_size[1], input_size[0] };
 
		auto classes = parse_classes_file(classes_file);
		if (classes.size() == 0) {
			std::cerr << "Error: fail to parse classes file: " << classes_file << std::endl;
			return -1;
		}
 
		if (!std::filesystem::exists(result_dir)) {
			std::filesystem::create_directories(result_dir);
		}
 
		for (const auto& [key, val] : get_dir_images(images_dir)) {
			cv::Mat frame = cv::imread(val, cv::IMREAD_COLOR);
			if (frame.empty()) {
				std::cerr << "Warning: unable to load image: " << val << std::endl;
				continue;
			}
 
			auto tstart = std::chrono::high_resolution_clock::now();
			cv::Mat rgb;
			image_preprocess(frame, rgb);
			image_to_blob(rgb, blob.get());
			Ort::Value input_tensor = Ort::Value::CreateTensor<float>(
				Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeCPU), blob.get(), 3 * input_size[1] * input_size[0], input_node_dims.data(), input_node_dims.size());
			auto output_tensors = session.Run(Ort::RunOptions{nullptr}, input_node_names.data(), &input_tensor, input_node_names.size(), output_node_names.data(), output_node_names.size());
			if (output_tensors.size() != 2) {
				std::cerr << "Error: output must have 2 layers: " << output_tensors.size() << std::endl;
				return -1;
			}
 
			// output0
			std::vector<int64_t> output0_node_dims = output_tensors[0].GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();
			auto output0 = output_tensors[0].GetTensorMutableData<float>();
			cv::Mat data0 = cv::Mat(output0_node_dims[1], output0_node_dims[2], CV_32F, output0);
			data0 = data0.t();
 
			// output1
			std::vector<int64_t> output1_node_dims = output_tensors[1].GetTypeInfo().GetTensorTypeAndShapeInfo().GetShape();
			auto output1 = output_tensors[1].GetTensorMutableData<float>();
			std::vector<int> sizes;
			for (auto val : output1_node_dims)
				sizes.emplace_back(val);
			cv::Mat data1 = cv::Mat(sizes, CV_32F, output1);
 
			auto tend = std::chrono::high_resolution_clock::now();
			std::cout << "elapsed millisenconds: " << std::chrono::duration_cast<std::chrono::milliseconds>(tend - tstart).count() << " ms" << std::endl;
 
			post_process_mask(data0, data1, sizes, classes, key, frame);
		}
	}
	catch (const std::exception& e) {
		std::cerr << "Error: " << e.what() << std::endl;
		return -1;
	}
 
	return 0;
}

      labels.txt文件内容如下：仅2类

watermelon 0
wintermelon 1

      说明：

      1.这里使用的onnxruntime版本为1.18.0；

      2.windows下，onnxruntime库在debug和release为同一套库，在debug和release下均可执行；

      3.通过指定变量cuda_enabled判断走cpu还是gpu流程 ；

      4.windows下，onnxruntime中有些接口参数为wchar_t*，而linux下为char*，因此在windows下需要单独做转换，这里通过ctow函数实现从char*到wchar_t的转换。

      执行结果如下图所示：同样的预测图像集，与opencv dnn结果相似，它们具有相同的后处理流程；下面显示的耗时是在cpu下，gpu下仅20毫秒左右

      其中一幅图像的分割结果如下图所示：

      GitHub：https://github.com/fengbingchun/NN_Test