pywayne-cv-tools

Read and write OpenCV FileStorage YAML data with nested structures and numpy arrays.

8|Updated Feb 2, 2026

Search Tags:#yaml #parsing #opencv #numpy #filestorage #cv2

Ready to run

npx skills add https://github.com/wangyendt/wayne-skills --skill pywayne-cv-tools

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pywayne-cv-tools
Source: https://github.com/wangyendt/wayne-skills/tree/main/pywayne/cv/tools
Command: npx skills add https://github.com/wangyendt/wayne-skills --skill pywayne-cv-tools

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

OpenCV YAML I/O utilities provide simple, reliable read and write access to OpenCV's FileStorage YAML data, with support for nested structures, numpy arrays, and common cv2 types.

Core Features & Use Cases

Read/write OpenCV YAML files using read_cv_yaml and write_cv_yaml
Handle nested dictionaries, lists, and matrix data via cv2.FileNode parsing
Suitable for calibration, configuration, and experiment data in vision projects

Quick Start

Use read_cv_yaml to load a calibration config or write_cv_yaml to persist detector parameters for a vision pipeline.

Frequently Asked Questions about pywayne-cv-tools

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I read and write OpenCV YAML files with nested dictionaries and numpy arrays?▼

Use read_cv_yaml and write_cv_yaml utilities to parse cv2.FileNode maps, sequences, and matrices for reliable OpenCV YAML I/O. These functions safely handle nested dictionaries, lists, and numpy arrays for vision pipelines.

What is the best way to parse cv2.FileNode data into Python objects?▼

Parsing cv2.FileNode data involves converting maps and sequences into nested Python dictionaries and lists. This approach extracts matrix data directly into numpy arrays, providing a reliable Python interface for OpenCV FileStorage YAML configurations.

Can I use this approach to save calibration configuration data for computer vision projects?▼

Yes, OpenCV YAML I/O utilities are specifically suitable for calibration configuration and experiment data in vision projects. You can persist detector parameters and calibration configs by writing nested structures and numpy arrays to FileStorage YAML.

Does OpenCV FileStorage YAML parsing support complex nested structures?▼

OpenCV FileStorage YAML parsing supports complex nested structures by recursively traversing cv2.FileNode maps and sequences. It reliably handles nested dictionaries, lists, and cv2 matrix data, ensuring robust configuration management for OpenCV workflows.

Why does my OpenCV YAML parsing fail when handling nested numpy arrays?▼

OpenCV YAML parsing can fail with nested numpy arrays if cv2.FileNode types are not properly mapped to Python objects. Using dedicated I/O utilities ensures matrices, sequences, and nested maps are correctly serialized and deserialized without data loss.