dataflow-cv 0.2.0

A data processing library for computer vision datasets

DataFlow-CV

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

A data processing library for computer vision datasets, focusing on format conversion and visualization between LabelMe, COCO, and YOLO formats. Provides both a CLI and Python API.

Table of Contents

• DataFlow-CV
  • Table of Contents
  • Project Structure
  • Requirements
  • License

Project Structure

dataflow/
├── __init__.py              # Package exports and convenience functions
├── cli.py                   # Command-line interface
├── config.py                # Configuration management
├── convert/                 # Format conversion module
│   ├── __init__.py
│   ├── base.py             # Converter base class
│   ├── coco_to_yolo.py     # COCO to YOLO converter
│   └── yolo_to_coco.py     # YOLO to COCO converter
tests/
├── __init__.py
├── convert/                # Conversion tests
│   ├── __init__.py
│   ├── test_coco_to_yolo.py
│   └── test_yolo_to_coco.py
├── run_tests.py           # Test runner
samples/
├── __init__.py
├── cli/                   # CLI usage examples
│   ├── __init__.py
│   └── convert/
│       ├── cli_coco_to_yolo.py
│       └── cli_yolo_to_coco.py
└── api/                   # Python API examples
    ├── __init__.py
    └── convert/
        ├── api_coco_to_yolo.py
        └── api_yolo_to_coco.py

Requirements

Core Dependencies

• Python 3.8 or higher
• Linux environment (POSIX compatible, assumes POSIX paths)
• click >= 8.1.0 – CLI framework
• numpy >= 2.0.0 – numerical operations
• opencv-python >= 4.8.0 – image processing (optional, used for some image operations)
• Pillow >= 10.0.0 – image reading (optional, used for reading image dimensions)

Quick Start

Installation

# Install
pip install .

Command Line Usage

Global options: --verbose (-v) for progress output, --overwrite to replace existing files.

# COCO to YOLO conversion
dataflow convert coco2yolo annotations.json output_dir/

# YOLO to COCO conversion
dataflow convert yolo2coco images/ labels/ classes.names output.json

# Show configuration
dataflow config

# Get help
dataflow --help
dataflow convert coco2yolo --help

See the CLI Reference below for detailed usage.

Python API Usage

import dataflow

# COCO to YOLO
result = dataflow.coco_to_yolo("annotations.json", "output_dir")
print(f"Processed {result['images_processed']} images")

# YOLO to COCO
result = dataflow.yolo_to_coco("images/", "labels/", "classes.names", "output.json")
print(f"Generated {result['annotations_processed']} annotations")

CLI Reference

The CLI follows a hierarchical structure: dataflow <main‑task> <sub‑task> [arguments]. Global options can be placed before the main task.

Global Options

• --verbose, -v: Enable verbose output (progress information)
• --overwrite: Overwrite existing files

Conversion Commands

COCO to YOLO

dataflow convert coco2yolo COCO_JSON_PATH OUTPUT_DIR [--segmentation]

• COCO_JSON_PATH: Path to COCO JSON annotation file
• OUTPUT_DIR: Directory where labels/ and class.names will be created
• --segmentation, -s: Handle segmentation annotations (polygon format)

YOLO to COCO

dataflow convert yolo2coco IMAGE_DIR YOLO_LABELS_DIR YOLO_CLASS_PATH COCO_JSON_PATH

• IMAGE_DIR: Directory containing image files
• YOLO_LABELS_DIR: Directory containing YOLO label files (.txt)
• YOLO_CLASS_PATH: Path to YOLO class names file (e.g., class.names)
• COCO_JSON_PATH: Path to save COCO JSON file

Configuration Command

dataflow config

Shows the current configuration (file extensions, default values, CLI context).

Getting Help

dataflow --help
dataflow convert --help
dataflow convert coco2yolo --help
dataflow convert yolo2coco --help

Running Tests

# Run all tests
python tests/run_tests.py

# Run specific test
python tests/run_tests.py --test TestCocoToYoloConverter

# With verbose output
python tests/run_tests.py -v

Examples

Check the samples/ directory for detailed usage examples:

• samples/cli/convert/ - CLI usage examples
• samples/api/convert/ - Python API examples

License

MIT License © 2026 zjykzj