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网站建设优化一年赚几十万,厦门软件开发培训机构,找企业做网站,软件开发自学网轴承缺陷数据集#xff0c;3659张#xff0c;提供yolo和voc两种 所有代码仅供参考 轴承缺陷数据集#xff0c;3659张#xff0c;提供yolo和voc两种标注方式 8类#xff0c;标注数量#xff1a; Casting_burr#xff1a;铸造毛刺#xff0c;750 crack#xff1a;裂纹3659张提供yolo和voc两种所有代码仅供参考轴承缺陷数据集3659张提供yolo和voc两种标注方式8类标注数量Casting_burr铸造毛刺750crack裂纹1675scratch划痕2446pit凹坑843Polished_casting抛光铸件2180strain应变94unpolished_casting未抛光铸件742burr毛刺3image num图像数量3659数据准备、模型训练、评估和推理。整个项目结构和代码完整项目结构bearing_defect_detection/ ├── main.py ├── train.py ├── evaluate.py ├── infer.py ├── datasets/ │ ├── bearing_defects/ │ │ ├── Annotations/ │ │ ├── ImageSets/ │ │ │ └── Main/ │ │ │ ├── train.txt │ │ │ └── val.txt │ │ └── JPEGImages/ ├── best_bearing_defects.pt ├── requirements.txt └── data.yaml文件内容requirements.txtopencv-python torch1.9 ultralytics PyQt5data.yamltrain:./datasets/bearing_defects/JPEGImages/trainval:./datasets/bearing_defects/JPEGImages/valtest:./datasets/bearing_defects/JPEGImages/testnc:8names:[Casting_burr,crack,scratch,pit,Polished_casting,strain,unpolished_casting,burr]convert_voc_to_yolo.pyimportosimportxml.etree.ElementTreeasETimportshutilimportcv2defxml_to_yolo(xml_file,image_width,image_height):yolo_labels[]treeET.parse(xml_file)roottree.getroot()forobjinroot.findall(object):labelobj.find(name).text bboxobj.find(bndbox)xminint(bbox.find(xmin).text)yminint(bbox.find(ymin).text)xmaxint(bbox.find(xmax).text)ymaxint(bbox.find(ymax).text)x_center(xminxmax)/2.0/image_width y_center(yminymax)/2.0/image_height width(xmax-xmin)/image_width height(ymax-ymin)/image_height class_id{Casting_burr:0,crack:1,scratch:2,pit:3,Polished_casting:4,strain:5,unpolished_casting:6,burr:7}[label]yolo_labels.append(f{class_id}{x_center}{y_center}{width}{height})return\n.join(yolo_labels)defsplit_dataset(image_dir,annotations_dir,output_dir,train_ratio0.8):images[fforfinos.listdir(image_dir)iff.endswith(.jpg)]num_trainint(len(images)*train_ratio)train_imagesimages[:num_train]val_imagesimages[num_train:]withopen(os.path.join(output_dir,ImageSets/Main/train.txt),w)asf:f.write(\n.join([os.path.splitext(img)[0]forimgintrain_images]))withopen(os.path.join(output_dir,ImageSets/Main/val.txt),w)asf:f.write(\n.join([os.path.splitext(img)[0]forimginval_images]))defconvert_dataset(voc_dir,yolo_dir):annotations_diros.path.join(voc_dir,Annotations)images_diros.path.join(voc_dir,JPEGImages)yolo_labels_diros.path.join(yolo_dir,labels)os.makedirs(yolo_labels_dir,exist_okTrue)os.makedirs(os.path.join(yolo_dir,images),exist_okTrue)os.makedirs(os.path.join(yolo_dir,images/train),exist_okTrue)os.makedirs(os.path.join(yolo_dir,images/val),exist_okTrue)os.makedirs(os.path.join(yolo_dir,ImageSets/Main),exist_okTrue)split_dataset(images_dir,annotations_dir,yolo_dir)forfilenameinos.listdir(annotations_dir):iffilename.endswith(.xml):xml_fileos.path.join(annotations_dir,filename)image_filenameos.path.splitext(filename)[0].jpgimage_pathos.path.join(images_dir,image_filename)imagecv2.imread(image_path)image_height,image_width,_image.shape yolo_labelxml_to_yolo(xml_file,image_width,image_height)txt_filenameos.path.splitext(filename)[0].txttxt_fileos.path.join(yolo_labels_dir,txt_filename)withopen(txt_file,w)asf:f.write(yolo_label)# Copy image to YOLO directorybase_image_diros.path.join(yolo_dir,images)ifimage_filename.split(.)[0]in[line.strip()forlineinopen(os.path.join(yolo_dir,ImageSets/Main/train.txt))]:target_image_diros.path.join(base_image_dir,train)else:target_image_diros.path.join(base_image_dir,val)shutil.copy(image_path,target_image_dir)# 示例用法convert_dataset(./datasets/bearing_defects,./datasets/bearing_defects_yolo)train.pyimporttorchfromultralyticsimportYOLO# 设置随机种子以保证可重复性torch.manual_seed(42)# 定义数据集路径dataset_configdata.yaml# 加载预训练的YOLOv8n模型modelYOLO(yolov8n.pt)# 训练模型resultsmodel.train(datadataset_config,epochs50,imgsz640,batch16,namebearing_defects,projectruns/train)# 评估模型metricsmodel.val()# 保存最佳模型权重best_model_weightsruns/train/bearing_defects/weights/best.ptprint(f最佳模型权重已保存到{best_model_weights})evaluate.pyfromultralyticsimportYOLO# 初始化YOLOv8模型modelYOLO(runs/train/bearing_defects/weights/best.pt)# 评估模型metricsmodel.val()# 打印评估结果print(metrics)infer.pyimportsysimportcv2importnumpyasnpfromultralyticsimportYOLOfromPyQt5.QtWidgetsimportQApplication,QMainWindow,QFileDialog,QMessageBox,QLabel,QPushButtonfromPyQt5.QtGuiimportQImage,QPixmapfromPyQt5.QtCoreimportQTimerclassMainWindow(QMainWindow):def__init__(self):super(MainWindow,self).__init__()self.setWindowTitle(轴承缺陷检测)self.setGeometry(100,100,800,600)# 初始化YOLOv8模型self.modelYOLO(runs/train/bearing_defects/weights/best.pt)# 设置类别名称self.class_names[Casting_burr,crack,scratch,pit,Polished_casting,strain,unpolished_casting,burr]# 创建界面元素self.label_displayQLabel(self)self.label_display.setGeometry(10,10,780,400)self.button_select_imageQPushButton(选择图片,self)self.button_select_image.setGeometry(10,420,150,30)self.button_select_image.clicked.connect(self.select_image)self.button_select_videoQPushButton(选择视频,self)self.button_select_video.setGeometry(170,420,150,30)self.button_select_video.clicked.connect(self.select_video)self.button_start_cameraQPushButton(开始摄像头,self)self.button_start_camera.setGeometry(330,420,150,30)self.button_start_camera.clicked.connect(self.start_camera)self.button_stop_cameraQPushButton(停止摄像头,self)self.button_stop_camera.setGeometry(490,420,150,30)self.button_stop_camera.clicked.connect(self.stop_camera)self.timerQTimer()self.timer.timeout.connect(self.update_frame)self.capNoneself.results[]defselect_image(self):optionsQFileDialog.Options()file_path,_QFileDialog.getOpenFileName(self,选择图片,,图片 (*.jpg *.jpeg *.png);;所有文件 (*),optionsoptions)iffile_path:self.process_image(file_path)defprocess_image(self,image_path):framecv2.imread(image_path)resultsself.model(frame)annotated_frameself.draw_annotations(frame,results)self.display_image(annotated_frame)self.results.append((image_path,annotated_frame))defselect_video(self):optionsQFileDialog.Options()file_path,_QFileDialog.getOpenFileName(self,选择视频,,视频 (*.mp4 *.avi);;所有文件 (*),optionsoptions)iffile_path:self.process_video(file_path)defprocess_video(self,video_path):self.capcv2.VideoCapture(video_path)whileself.cap.isOpened():ret,frameself.cap.read()ifnotret:breakresultsself.model(frame)annotated_frameself.draw_annotations(frame,results)self.display_image(annotated_frame)self.results.append((video_path,annotated_frame))ifcv2.waitKey(1)0xFFord(q):breakself.cap.release()defstart_camera(self):self.capcv2.VideoCapture(0)self.timer.start(30)defstop_camera(self):self.timer.stop()ifself.capisnotNone:self.cap.release()self.label_display.clear()defupdate_frame(self):ret,frameself.cap.read()ifnotret:returnresultsself.model(frame)annotated_frameself.draw_annotations(frame,results)self.display_image(annotated_frame)self.results.append((camera,annotated_frame))defdraw_annotations(self,frame,results):forresultinresults:boxesresult.boxes.cpu().numpy()forboxinboxes:rbox.xyxy[0].astype(int)clsint(box.cls[0])confbox.conf[0]labelf{self.class_names[cls]}{conf:.2f}color(0,255,0)cv2.rectangle(frame,(r[0],r[1]),(r[2],r[3]),color,2)cv2.putText(frame,label,(r[0],r[1]-10),cv2.FONT_HERSHEY_SIMPLEX,0.9,color,2)returnframedefdisplay_image(self,frame):rgb_imagecv2.cvtColor(frame,cv2.COLOR_BGR2RGB)h,w,chrgb_image.shape bytes_per_linech*w qt_imageQImage(rgb_image.data,w,h,bytes_per_line,QImage.Format_RGB888)pixmapQPixmap.fromImage(qt_image)self.label_display.setPixmap(pixmap.scaled(self.label_display.width(),self.label_display.height()))if__name____main__:appQApplication(sys.argv)windowMainWindow()window.show()sys.exit(app.exec_())运行步骤总结克隆项目仓库如果有的话gitclone https://github.com/yourusername/bearing_defect_detection.gitcdbearing_defect_detection安装依赖项pipinstall-r requirements.txt转换数据集格式python convert_voc_to_yolo.py训练模型python train.py评估模型python evaluate.py运行推理界面python infer.py操作界面选择图片进行检测。选择视频进行检测。使用摄像头进行实时检测。结果展示你可以通过以下方式查看演示视频用上述步骤运行infer.py并按照界面上的按钮操作。希望这些详细的信息和代码能够帮助你顺利实施和优化你的轴承缺陷检测系统。如果有其他需求或问题请随时告知详细解释requirements.txt列出项目所需的所有Python包及其版本。data.yaml配置数据集路径和类别信息用于YOLOv8模型训练。convert_voc_to_yolo.py将VOC格式的数据集转换为YOLO格式。读取XML标注文件并将其转换为YOLO所需的TXT标签格式。同时将数据集分为训练集和验证集。train.py加载预训练的YOLOv8模型并使用自定义数据集进行训练。训练完成后评估模型并保存最佳模型权重。evaluate.py加载训练好的YOLOv8模型并对验证集进行评估打印评估结果。infer.py创建一个GUI应用程序支持选择图片、视频或使用摄像头进行实时检测并显示检测结果。