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 LabelMe 工具进行标注，然后使用 labelme2yolov8 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 # 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使用以下python脚本进行训练生成onnx文件：
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文件进行目标检测的实现代码：
namespace {
constexpr bool cuda_enabled{ false };
constexpr int image_size[2]{ 640, 640 }; // {height,width}, input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 6, 8400)
constexpr float model_score_threshold{ 0.45 }; // confidence threshold
constexpr float model_nms_threshold{ 0.50 }; // iou 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;
}
void draw_boxes(const std::vector<std::string>& classes, const std::vector<int>& ids, const std::vector<float>& confidences,
const std::vector<cv::Rect>& boxes, const std::string& name, cv::Mat& frame)
{
if (ids.size() != confidences.size() || ids.size() != boxes.size() || confidences.size() != boxes.size()) {
std::cerr << "Error: their lengths are inconsistent: " << ids.size() << ", " << confidences.size() << ", " << boxes.size() << std::endl;
return;
}
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);
for (auto i = 0; i < ids.size(); ++i) {
auto color = cv::Scalar(dis(gen), dis(gen), dis(gen));
cv::rectangle(frame, boxes[i], color, 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, color, 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);
}
//cv::imshow("Inference", frame);
//cv::waitKey(-1);
std::string path(result_dir);
path += "/" + name;
cv::imwrite(path, frame);
}
std::wstring ctow(const char* str)
{
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 resize_scales{ 1. };
if (src.cols >= src.rows) {
resize_scales = src.cols * 1.f / image_size[1];
cv::resize(dst, dst, cv::Size(image_size[1], static_cast<int>(src.rows / resize_scales)));
} else {
resize_scales = src.rows * 1.f / image_size[0];
cv::resize(dst, dst, cv::Size(static_cast<int>(src.cols / resize_scales), image_size[0]));
}
cv::Mat tmp = cv::Mat::zeros(image_size[0], image_size[1], CV_8UC3);
dst.copyTo(tmp(cv::Rect(0, 0, dst.cols, dst.rows)));
dst = tmp;
return resize_scales;
}
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 post_process(const float* data, int rows, int stride, float xfactor, float yfactor, const std::vector<std::string>& classes,
cv::Mat& frame, const std::string& name)
{
std::vector<int> class_ids;
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
for (auto i = 0; i < rows; ++i) {
const float* classes_scores = data + 4;
cv::Mat scores(1, classes.size(), CV_32FC1, (float*)classes_scores);
cv::Point class_id;
double max_class_score;
cv::minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
if (max_class_score > model_score_threshold) {
confidences.push_back(max_class_score);
class_ids.push_back(class_id.x);
float x = data[0];
float y = data[1];
float w = data[2];
float h = data[3];
int left = int((x - 0.5 * w) * xfactor);
int top = int((y - 0.5 * h) * yfactor);
int width = int(w * xfactor);
int height = int(h * yfactor);
boxes.push_back(cv::Rect(left, top, width, height));
}
data += stride;
}
std::vector<int> nms_result;
cv::dnn::NMSBoxes(boxes, confidences, model_score_threshold, model_nms_threshold, nms_result);
std::vector<int> ids;
std::vector<float> confs;
std::vector<cv::Rect> rects;
for (size_t i = 0; i < nms_result.size(); ++i) {
ids.emplace_back(class_ids[nms_result[i]]);
confs.emplace_back(confidences[nms_result[i]]);
rects.emplace_back(boxes[nms_result[i]]);
}
draw_boxes(classes, ids, confs, rects, name, frame);
}
} // namespace
int test_yolov8_detect_onnxruntime()
{
// reference: ultralytics/examples/YOLOv8-ONNXRuntime-CPP
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());
Ort::RunOptions options(nullptr);
std::unique_ptr<float[]> blob(new float[image_size[0] * image_size[1] * 3]);
std::vector<int64_t> input_node_dims{ 1, 3, image_size[1], image_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;
}
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;
auto resize_scales = 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 * image_size[1] * image_size[0], input_node_dims.data(), input_node_dims.size());
auto output_tensors = session.Run(options, input_node_names.data(), &input_tensor, 1, output_node_names.data(), output_node_names.size());
Ort::TypeInfo type_info = output_tensors.front().GetTypeInfo();
auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
std::vector<int64_t> output_node_dims = tensor_info.GetShape();
auto output = output_tensors.front().GetTensorMutableData<float>();
int stride_num = output_node_dims[1];
int signal_result_num = output_node_dims[2];
cv::Mat raw_data = cv::Mat(stride_num, signal_result_num, CV_32F, output);
raw_data = raw_data.t();
float* data = (float*)raw_data.data;
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(data, signal_result_num, stride_num, resize_scales, resize_scales, classes, frame, key);
}
}
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；
5.yolov8中提供的sample有问题，需要作调整。
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
其中一幅图像的检测结果如下图所示：
GitHub：https://github.com/fengbingchun/NN_Test
