【YOLOv7 Model Beginner Series】LabelImg Annotation Tutorial 成大電機所 Smile Lab
"Step-by-Step Guide to Creating Training Data for YOLO Model:
1. Install Necessary Packages:
Start by installing the required packages using pip. Execute the following command in your terminal or command prompt:
pip install opencv-python
2. Set Up Webcam Capture:
Utilize OpenCV (cv2) to access your webcam and capture training data. Implement the code to open the camera and save frames/images for annotation.
3. Prepare LabelImg Tool:
Download and install LabelImg, a powerful annotation tool that simplifies the process of labeling objects in your training data.
4. Annotate Training Data:
With LabelImg, load the captured frames one by one, and carefully annotate the objects of interest by drawing bounding boxes around them.
5. Save Annotations:
After completing the annotations, save the bounding box coordinates and class labels in XML format for each image.
6. Generate YOLO-Compatible Format:
Convert the annotations from XML to YOLO format, which consists of a .txt file for each image containing the class index and normalized bounding box coordinates.
7. Train YOLO Model:
Finally, use the annotated training data to train your YOLO model for object detection tasks.
By following these steps, you will be able to create accurate and comprehensive training data for your YOLO model, enabling it to detect and localize objects effectively. Happy coding and best of luck with your YOLO project!"
【完整步驟！】如何使用LabelImg進行YOLO物件標註
1. 安裝必要套件：
開始之前，使用pip安裝所需的套件。在終端機或命令提示字元中執行以下指令：
pip install opencv-python
2. 設定攝影機捕捉：
利用OpenCV（cv2）來連接攝影機並拍攝訓練資料。編寫程式開啟攝影機，並將每一幀（影格）保存下來，以便進行後續的標註作業。
3. 準備LabelImg工具：
下載並安裝LabelImg工具，這是一個功能強大的標註工具，可以簡化標記訓練資料的流程。
4. 標註訓練資料：
使用LabelImg工具，依次載入每個保存的影格，並在感興趣的物件周圍畫上方框標示。
5. 儲存標註結果：
完成標註後，將每個影格的方框座標和類別標籤以XML格式儲存。
6. 轉換為YOLO格式：
將XML格式的標註轉換為YOLO格式，每個影格對應一個.txt檔，其中包含類別索引和標準化的方框座標。
7. 訓練YOLO模型：
最後，使用標註的訓練資料來訓練YOLO模型，以達到有效的物件偵測任務。
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