Utilizes OpenCV 4.8.0 with the extra CUDA modules, in combination with the vision AI model YOLOv5 (YOLOv5l6 specifically) to process images to identify and categorize objects. Uses Qt as the GUI framework for user-friendly interaction with the application.
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Project Setup:
- Configure the development environment with C++, OpenCV, and Qt.
- Create a new C++ project within the chosen IDE, Visual Studio 2022, in this case.
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Image Processing:
- Utilize OpenCV to perform image processing tasks.
- Implement techniques like object detection and segmentation to identify and categorize products.
- Apply preprocessing methods to enhance image quality and reduce noise.
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User Interface:
- Create a user interface using a Qt as a GUI for interaction.
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Image Capture (Optional):
- Integrate camera modules or webcams to capture product images directly into the application.
- Use OpenCV to preprocess captured images before storing them in the database.
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Product Categorization:
- Develop algorithms to categorize products based on image-extracted information.
- Match features with existing product categories and update the database accordingly.
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Testing and Debugging:
- Thoroughly test the application with various products and images.
- Debug and address any issues arising during testing, ensuring proper image processing and database functionality.
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Documentation and Deployment:
- Document the application code and its components, providing instructions for setup and usage.
- Deploy the application in a testing environment, ensuring stability were the application to be deployed.
- Programming Language: C++
- Image Processing: OpenCV, YOLOv5
- User Interface: Qt