使用YOLOv8来训练一个钢材表面缺陷检测数据集，并使用OpenCV和Tkinter构建一个GUI应用程序来进行检测 ——钢材缺陷检测识别+数据集+代码

 steel_defect_dataset/

    
 ├── train/
    
 │   ├── images/
    
 │   └── labels/
    
 ├── valid/
    
 │   ├── images/
    
 │   └── labels/
    
 └── data.yaml  # 数据配置文件
    
    
    
    
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 train: ./train/images/  # 训练集图像路径

    
 val: ./valid/images/  # 验证集图像路径
    
 test: ./test/images/  # 测试集图像路径（如果有）
    
  
    
 # Classes
    
 nc: 6  # 类别数量
    
 names: ['crazing', 'inclusion', 'patches', 'pitted_surface', 'rolled-in_scale', 'scratches']  # 类别名称列表
    
    
    
    
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 pip install opencv-python-headless  # 如果不需要显示窗口，可以使用headless版本

    
 pip install opencv-python            # 如果需要显示窗口
    
 pip install ultralytics
    
 pip install tk
    
    
    
    
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 import tkinter as tk

    
 from tkinter import filedialog
    
 import cv2
    
 import numpy as np
    
 from ultralytics import YOLO
    
  
    
 # 加载模型
    
 model = YOLO("path/to/best.pt")  # 替换为实际的模型路径
    
  
    
 def detect_image(image_path):
    
     results = model.predict(source=image_path, conf=0.5)
    
     result = results[0]
    
     boxes = result.boxes.xyxy.cpu().numpy().astype(int)
    
     labels = result.names
    
     for box in boxes:
    
     cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
    
     cv2.putText(image, labels[int(result.boxes.cls[box])], (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
    
     cv2.imshow("Detected Image", image)
    
     cv2.waitKey(0)
    
     cv2.destroyAllWindows()
    
  
    
 def detect_video(video_path):
    
     cap = cv2.VideoCapture(video_path)
    
     while True:
    
     ret, frame = cap.read()
    
     if not ret:
    
         break
    
     results = model.predict(source=frame, conf=0.5)
    
     result = results[0]
    
     boxes = result.boxes.xyxy.cpu().numpy().astype(int)
    
     labels = result.names
    
     for box in boxes:
    
         cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
    
         cv2.putText(frame, labels[int(result.boxes.cls[box])], (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
    
     cv2.imshow("Detected Video", frame)
    
     if cv2.waitKey(1) & 0xFF == ord('q'):
    
         break
    
     cap.release()
    
     cv2.destroyAllWindows()
    
  
    
 def detect_camera():
    
     cap = cv2.VideoCapture(0)
    
     while True:
    
     ret, frame = cap.read()
    
     if not ret:
    
         break
    
     results = model.predict(source=frame, conf=0.5)
    
     result = results[0]
    
     boxes = result.boxes.xyxy.cpu().numpy().astype(int)
    
     labels = result.names
    
     for box in boxes:
    
         cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
    
         cv2.putText(frame, labels[int(result.boxes.cls[box])], (box[0], box[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
    
     cv2.imshow("Camera Detection", frame)
    
     if cv2.waitKey(1) & 0xFF == ord('q'):
    
         break
    
     cap.release()
    
     cv2.destroyAllWindows()
    
  
    
 def select_image():
    
     file_path = filedialog.askopenfilename()
    
     if file_path:
    
     image = cv2.imread(file_path)
    
     detect_image(image)
    
  
    
 def select_video():
    
     file_path = filedialog.askopenfilename()
    
     if file_path:
    
     detect_video(file_path)
    
  
    
 # 创建主窗口
    
 root = tk.Tk()
    
 root.title("钢材表面缺陷检测")
    
  
    
 # 创建按钮
    
 btn_image = tk.Button(root, text="选择图片", command=select_image)
    
 btn_video = tk.Button(root, text="选择视频", command=select_video)
    
 btn_camera = tk.Button(root, text="启动摄像头", command=detect_camera)
    
  
    
 # 布局
    
 btn_image.pack(pady=10)
    
 btn_video.pack(pady=10)
    
 btn_camera.pack(pady=10)
    
  
    
 root.mainloop()
    
    
    
    
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