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做网站国内好的服务器,网站建设方案word,wordpress 导出word,做ppt好的网站在网上下载了60多幅包含西瓜和冬瓜的图像组成melon数据集#xff0c;使用 LabelMe 工具进行标注#xff0c;然后使用 labelme2yolov8 脚本将json文件转换成YOLOv8支持的.txt文件#xff0c;并自动生成YOLOv8支持的目录结构#xff0c;包括melon.yaml文件#xff0c;其内容…      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: watermelon1: wintermelon 使用以下python脚本进行训练生成torchscript文件 import argparse import colorama from ultralytics import YOLOdef parse_args():parser argparse.ArgumentParser(descriptionYOLOv8 object detect)parser.add_argument(–yaml, requiredTrue, typestr, helpyaml file)parser.add_argument(–epochs, requiredTrue, typeint, helpnumber of training)args parser.parse_args()return argsdef train(yaml, epochs):model YOLO(yolov8n.pt) # load a pretrained modelresults model.train(datayaml, epochsepochs, imgsz640) # train the modelmetrics model.val() # Itll automatically evaluate the data you trained, no arguments needed, dataset and settings rememberedmodel.export(formatonnx) #, dynamicTrue) # export the model, cannot specify dynamicTrue, opencv does not support# model.export(formatonnx, opset12, simplifyTrue, dynamicFalse, imgsz640)model.export(formattorchscript) # libtorchif name main:colorama.init()args parse_args()train(args.yaml, args.epochs)print(colorama.Fore.GREEN execution completed ) 以下是使用libtorch接口加载torchscript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 }; #endifstd::vectorstd::string parse_classes_file(const char* name) {std::vectorstd::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::mapstd::string, std::string images; // image name, image path image namefor (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::vectorstd::string classes, const std::vectorint ids, const std::vectorfloat confidences,const std::vectorcv::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_distributionint 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); }float letter_box(const cv::Mat src, cv::Mat dst, const std::vectorint imgsz) {if (src.cols imgsz[1] src.rows imgsz[0]) {if (src.data dst.data) {return 1.;} else {dst src.clone();return 1.;}}auto resize_scale std::min(imgsz[0] * 1. / src.rows, imgsz[1] * 1. / src.cols);int new_shape_w std::round(src.cols * resize_scale);int new_shape_h std::round(src.rows * resize_scale);float padw (imgsz[1] - new_shape_w) / 2.;float padh (imgsz[0] - new_shape_h) / 2.;int top std::round(padh - 0.1);int bottom std::round(padh 0.1);int left std::round(padw - 0.1);int right std::round(padw 0.1);cv::resize(src, dst, cv::Size(new_shape_w, new_shape_h), 0, 0, cv::INTER_AREA);cv::copyMakeBorder(dst, dst, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(114.));return resize_scale; }torch::Tensor xywh2xyxy(const torch::Tensor x) {auto y torch::empty_like(x);auto dw x.index({ …, 2 }).div(2);auto dh x.index({ …, 3 }).div(2);y.indexput({ …, 0 }, x.index({ …, 0 }) - dw);y.indexput({ …, 1 }, x.index({ …, 1 }) - dh);y.indexput({ …, 2 }, x.index({ …, 0 }) dw);y.indexput({ …, 3 }, x.index({ …, 1 }) dh);return y; }// reference: https://github.com/pytorch/vision/blob/main/torchvision/csrc/ops/cpu/nms_kernel.cpp torch::Tensor nms(const torch::Tensor bboxes, const torch::Tensor scores, float iou_threshold) {if (bboxes.numel() 0)return torch::empty({ 0 }, bboxes.options().dtype(torch::kLong));auto x1_t bboxes.select(1, 0).contiguous();auto y1_t bboxes.select(1, 1).contiguous();auto x2_t bboxes.select(1, 2).contiguous();auto y2_t bboxes.select(1, 3).contiguous();torch::Tensor areas_t (x2_t - x1_t) * (y2_t - y1_t);auto order_t std::get1(scores.sort(/stable/true, /dim/0, /* descending*/true));auto ndets bboxes.size(0);torch::Tensor suppressed_t torch::zeros({ ndets }, bboxes.options().dtype(torch::kByte));torch::Tensor keep_t torch::zeros({ ndets }, bboxes.options().dtype(torch::kLong));auto suppressed suppressed_t.data_ptruint8_t();auto keep keep_t.data_ptrint64_t();auto order order_t.data_ptrint64_t();auto x1 x1_t.data_ptrfloat();auto y1 y1_t.data_ptrfloat();auto x2 x2_t.data_ptrfloat();auto y2 y2_t.data_ptrfloat();auto areas areas_t.data_ptrfloat();int64_t num_to_keep 0;for (int64_t _i 0; _i ndets; _i) {auto i order[_i];if (suppressed[i] 1)continue;keep[num_to_keep] i;auto ix1 x1[i];auto iy1 y1[i];auto ix2 x2[i];auto iy2 y2[i];auto iarea areas[i];for (int64_t _j _i 1; _j ndets; _j) {auto j order[_j];if (suppressed[j] 1)continue;auto xx1 std::max(ix1, x1[j]);auto yy1 std::max(iy1, y1[j]);auto xx2 std::min(ix2, x2[j]);auto yy2 std::min(iy2, y2[j]);auto w std::max(static_castfloat(0), xx2 - xx1);auto h std::max(static_castfloat(0), yy2 - yy1);auto inter w * h;auto ovr inter / (iarea areas[j] - inter);if (ovr iou_threshold)suppressed[j] 1;}}return keep_t.narrow(0, 0, num_to_keep); }torch::Tensor non_max_suppression(torch::Tensor prediction, float conf_thres 0.25, float iou_thres 0.45, int max_det 300) {using torch::indexing::Slice;using torch::indexing::None;auto bs prediction.size(0);auto nc prediction.size(1) - 4;auto nm prediction.size(1) - nc - 4;auto mi 4 nc;auto xc prediction.index({ Slice(), Slice(4, mi) }).amax(1) conf_thres;prediction prediction.transpose(-1, -2);prediction.indexput({ …, Slice({None, 4}) }, xywh2xyxy(prediction.index({ …, Slice(None, 4) })));std::vectortorch::Tensor output;for (int i 0; i bs; i) {output.push_back(torch::zeros({ 0, 6 nm }, prediction.device()));}for (int xi 0; xi prediction.size(0); xi) {auto x prediction[xi];x x.index({ xc[xi] });auto x_split x.split({ 4, nc, nm }, 1);auto box x_split[0], cls x_split[1], mask x_split[2];auto [conf, j] cls.max(1, true);x torch::cat({ box, conf, j.toType(torch::kFloat), mask }, 1);x x.index({ conf.view(-1) conf_thres });int n x.size(0);if (!n) { continue; }// NMSauto c x.index({ Slice(), Slice{5, 6} }) * 7680;auto boxes x.index({ Slice(), Slice(None, 4) }) c;auto scores x.index({ Slice(), 4 });auto i nms(boxes, scores, iou_thres);i i.index({ Slice(None, max_det) });output[xi] x.index({ i });}return torch::stack(output); }} // namespaceint test_yolov8_detect_libtorch() {// reference: ultralytics/examples/YOLOv8-LibTorch-CPP-Inferenceif (auto flag torch::cuda::is_available(); flag true)std::cout cuda is available std::endl;elsestd::cout cuda is not available std::endl;torch::Device device(torch::cuda::is_available() ? torch::kCUDA : torch::kCPU);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;}std::cout classes: ;for (const auto val : classes) {std::cout val ;}std::cout std::endl;try {// load modeltorch::jit::script::Module model;if (torch::cuda::is_available() true)model torch::jit::load(torchscript_file, torch::kCUDA);elsemodel torch::jit::load(torchscript_file, torch::kCPU);model.eval();// note: cpu is normal; gpu is abnormal: the model may not be fully placed on the gpu // model torch::jit::load(file); model.to(torch::kCUDA) model torch::jit::load(file, torch::kCUDA)// model.to(device, torch::kFloat32);for (const auto [key, val] : get_dir_images(images_dir)) {// load image and preprocesscv::Mat frame cv::imread(val, cv::IMREAD_COLOR);if (frame.empty()) {std::cerr Warning: unable to load image: val std::endl;continue;}cv::Mat bgr;letter_box(frame, bgr, {image_size[0], image_size[1]});torch::Tensor tensor torch::from_blob(bgr.data, { bgr.rows, bgr.cols, 3 }, torch::kByte).to(device);tensor tensor.toType(torch::kFloat32).div(255);tensor tensor.permute({ 2, 0, 1 });tensor tensor.unsqueeze(0);std::vectortorch::jit::IValue inputs{ tensor };// inferencetorch::Tensor output model.forward(inputs).toTensor().cpu();// NMSauto keep non_max_suppression(output, 0.1f, 0.1f, 300)[0];std::vectorint ids;std::vectorfloat confidences;std::vectorcv::Rect boxes;for (auto i 0; i keep.size(0); i) {int x1 keep[i][0].item().toFloat();int y1 keep[i][1].item().toFloat();int x2 keep[i][2].item().toFloat();int y2 keep[i][3].item().toFloat();boxes.emplace_back(cv::Rect(x1, y1, x2 - x1, y2 - y1));confidences.emplace_back(keep[i][4].item().toFloat());ids.emplace_back(keep[i][5].item().toInt());}draw_boxes(classes, ids, confidences, boxes, key, bgr);}} catch (const c10::Error e) {std::cerr Error: e.msg() std::endl;}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libtorch版本为2.2.2 2.通过函数torch::cuda::is_available()判断执行cpu还是gpu 3.通过非cmake构建项目时调用torch::cuda::is_available()时即使在gpu下也会返回false解决方法项目属性链接器 – 命令行其他选项中添加如下语句 /INCLUDE:?warp_sizecudaatYAHXZ 4.gpu下语句model.to(torch::kCUDA)有问题好像并不能将模型全部放置在gpu上应调整为如下语句 model torch::jit::load(torchscript_file, torch::kCUDA); 执行结果如下图所示同样的预测图像集结果不如使用 opencv dnn 方法好它们的前处理和后处理方式不同 其中一幅图像的检测结果如下图所示 GitHubhttps://github.com/fengbingchun/NN_Test