This repository serves as a personal learning journal for machine learning and deep learning, documenting practical operations and theoretical knowledge. It targets individuals studying computer vision, image processing, and related ML/DL concepts, offering a collection of implemented algorithms and study notes.

How It Works

The project showcases various computer vision techniques implemented using OpenCV and potentially TensorFlow. It covers traditional image processing methods like Gabor filters, perspective transforms for document correction, blur detection via Laplacian variance, and barcode localization using morphological operations. The approach emphasizes practical application and understanding the underlying mathematical principles.

Quick Start & Requirements

• Install: pip install opencv-python (and potentially TensorFlow).
• Prerequisites: Python 3, OpenCV, potentially TensorFlow. Specific scripts may require additional libraries or datasets.
• Demo: Run main.py for the "tech cat" example, 几何矫正.py for scanning correction, detect_blur.py for blur detection, and others as indicated in the table.

Highlighted Details

• Implements a "tech cat" using Gabor filters and sliders.
• Replicates scanning app correction features using perspective transforms.
• Offers blur detection via Laplacian variance.
• Includes a GIF-to-text converter.
• Demonstrates barcode detection and object size measurement.

Maintenance & Community

No specific information on contributors, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The repository does not specify a license.

Limitations & Caveats

Several sections are marked as "未完成" (unfinished), including deep learning theory, TensorFlow implementations, and specific research areas like image retrieval and segmentation. The project appears to be a personal learning log rather than a production-ready library.