dataflow-cv 0.6.2

A computer vision dataset processing library

DataFlow-CV

Where Vibe Coding meets CV data. 🌊 Convert & visualize datasets. Built with the flow of Claude Code.

A computer vision dataset processing library for seamless format conversion and visualization between LabelMe, COCO, and YOLO annotation formats. Designed for researchers and developers working with multi-format annotation pipelines.

Features

• Bidirectional Conversion: Convert between LabelMe, COCO, and YOLO formats in any direction
• Multi-format Support: Handle object detection bounding boxes and instance segmentation polygons
• Lossless Round-trip: Preserve original coordinates through conversion chains
• Visualization: Visualize annotations with OpenCV, supporting both display and save modes
• Command-line Interface: User-friendly CLI with convert and visualize subcommands
• Python API: Programmatic access for integration into larger pipelines
• Verbose Logging: Detailed logging with file output for debugging
• Cross-platform: Full support for Windows, Linux, and macOS

Table of Contents

• DataFlow-CV
  • Features
  • Table of Contents
  • Installation
    • From PyPI
    • From Source
    • Optional Dependencies
  • Quick Start
    • Command-line Interface
      • Format Conversion
      • Visualization
    • Python API
  • Documentation
    • Key Concepts
  • Development
    • Testing
    • Linting and Formatting
    • Project Structure
  • Contributing
  • License
  • Acknowledgments

Installation

From PyPI

pip install dataflow-cv

From Source

# Clone the repository
git clone https://github.com/zjykzj/DataFlow-CV.git
cd DataFlow-CV

# Regular installation
pip install .

# Editable installation (for development)
pip install -e .

Note: When installed in editable mode, use python -m dataflow.cli instead of the dataflow-cv command.

Optional Dependencies

• pycocotools: Required for COCO RLE segmentation support

  pip install pycocotools
  

Quick Start

Command-line Interface

All required parameters (image directories, label directories, class files, output paths) are positional arguments for better usability. Use --help on any subcommand for detailed usage.

Format Conversion

# YOLO to COCO
dataflow-cv convert yolo2coco images/ yolo_labels/ classes.txt coco_annotations.json

# With RLE encoding
dataflow-cv convert yolo2coco images/ yolo_labels/ classes.txt coco_annotations.json --do-rle

# YOLO to LabelMe
dataflow-cv convert yolo2labelme images/ yolo_labels/ classes.txt labelme_json/

# COCO to YOLO
dataflow-cv convert coco2yolo coco_annotations.json yolo_labels/

# COCO to LabelMe
dataflow-cv convert coco2labelme coco_annotations.json labelme_json/

# LabelMe to YOLO
dataflow-cv convert labelme2yolo labelme_json/ classes.txt yolo_labels/

# LabelMe to COCO
dataflow-cv convert labelme2coco labelme_json/ classes.txt coco_annotations.json

# With RLE encoding
dataflow-cv convert labelme2coco labelme_json/ classes.txt coco_annotations.json --do-rle

# Enable verbose logging
dataflow-cv convert yolo2coco images/ yolo_labels/ classes.txt coco_annotations.json --verbose

Visualization

# Visualize YOLO annotations
dataflow-cv visualize yolo images/ yolo_labels/ classes.txt --save visualized/

# Visualize COCO annotations
dataflow-cv visualize coco images/ coco_annotations.json --save visualized/

# Visualize LabelMe annotations
dataflow-cv visualize labelme images/ labelme_json/ --save visualized/

# Enable verbose logging for detailed debug output
dataflow-cv visualize yolo --verbose images/ yolo_labels/ classes.txt --save visualized/

Python API

from dataflow.convert import YoloAndCocoConverter
from dataflow.visualize import YOLOVisualizer

# Convert YOLO to COCO
converter = YoloAndCocoConverter(source_to_target=True, verbose=True, strict_mode=True)
result = converter.convert(
    source_path="yolo_labels/",
    target_path="coco_annotations.json",
    class_file="classes.txt",
    image_dir="images/",
    do_rle=False  # Set to True for RLE encoding
)

# Visualize YOLO annotations
visualizer = YOLOVisualizer(
    label_dir="yolo_labels/",
    image_dir="images/",
    class_file="classes.txt",
    is_show=True,
    is_save=True,
    output_dir="visualized/",
    verbose=True,
    strict_mode=True
)
result = visualizer.visualize()

See the samples/ directory for complete examples:

• samples/visualize/yolo_demo.py - YOLO visualization example
• samples/visualize/labelme_demo.py - LabelMe visualization example
• samples/visualize/coco_demo.py - COCO visualization example
• samples/convert/ - Conversion examples

Documentation

• CLAUDE.md: Detailed architecture and development guide
• docs/formats/: Format specifications (YOLO, COCO, LabelMe)
• docs/specs/: Module specifications and design documents
• CHANGELOG.md: Version history and breaking changes

Key Concepts

• Normalized Coordinates: All internal coordinates are in 0-1 range
• Original Data Preservation: Lossless round-trip conversion through OriginalData system
• Strict Mode: Validation errors raise exceptions (default: enabled in CLI, can be disabled via strict_mode=False parameter in Python API)
• Verbose Logging: Detailed debug logs saved to files when --verbose is used. The CLI prints "Verbose log saved to:
• Keyboard Shortcuts: During visualization, press q or ESC to exit early; any other key continues
• Missing Image Handling: Missing images are skipped with warnings, allowing processing to continue
• RLE Mask Visualization: COCO RLE masks are displayed with semi-transparent fills for better visibility
• Color Management: Each class ID gets a unique color from a palette of 1000 distinct colors for consistent visualization

Development

For detailed developer guidance including advanced test commands, debugging, and architecture overview, see CLAUDE.md.

Testing

# Run all tests
pytest

# Run tests with coverage
pytest --cov=dataflow

# Run specific test module
pytest tests/convert/test_yolo_and_coco.py

Linting and Formatting

# Install development dependencies
pip install -e .[dev]

# Format code
black dataflow tests samples

# Sort imports
isort dataflow tests samples

# Type checking
mypy dataflow

# Linting
flake8 dataflow tests samples

Project Structure

dataflow/
├── label/           # Annotation handlers (YOLO, LabelMe, COCO)
├── convert/         # Format converters
├── visualize/       # Visualization modules
├── util/           # Utilities (logging, file operations)
└── cli/            # Command-line interface
tests/              # Comprehensive test suite
samples/            # Usage examples
assets/             # Sample data for testing

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Before contributing, review CLAUDE.md for architecture and development patterns.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add or update tests as needed
5. Ensure code passes formatting and linting checks
6. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the creators of LabelMe, COCO, and YOLO formats for establishing these annotation standards
• Built with OpenCV, NumPy, and Click
• Inspired by the need for seamless format conversion in multi-tool CV pipelines