An automated computer vision tool designed to detect and filter out blurry, poorly lit, or low-resolution images from datasets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install image-quality-filter
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install image-quality-filter using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Image Quality Filter?
The Image Quality Filter is a specialized utility designed to streamline the curation of visual data. By utilizing advanced computer vision techniques, this skill enables developers to automatically identify and manage media that fails to meet specific quality benchmarks. It is an indispensable part of the Openclaw Skills collection for those managing large-scale image repositories or preparing high-quality training data for machine learning models.
This skill provides a programmatic way to assess visual clarity and exposure, ensuring that only the best assets are retained. Whether you are dealing with motion blur, overexposure, or insufficient resolution, this tool offers a customizable framework to handle sub-par images through automated listing, relocation, or deletion workflows.
Image Quality Filter Use Cases
- Curating high-quality datasets for computer vision and AI model training.
- Automatically cleaning up photo uploads by removing out-of-focus or motion-blurred shots.
- Filtering digital archives to eliminate overexposed or underexposed imagery.
- Enforcing strict resolution requirements for web-ready digital assets.
How Image Quality Filter Works
- The skill crawls the target directory to identify all supported image formats for processing.
- It performs a blur analysis by calculating the Laplacian variance to assign a numerical clarity score.
- The system measures mean pixel intensity to determine if an image is too dark or too bright based on defined thresholds.
- File metadata is checked to ensure the image dimensions meet the minimum required resolution.
- Depending on the user-selected action, the skill outputs a report, moves the low-quality files to a quarantine folder, or deletes them entirely.
Image Quality Filter Setup
To get started with this component of Openclaw Skills, install the required dependencies using pip:
pip install pillow numpy opencv-python
You can then run a basic scan on your image folder with the following command:
python scripts/quality_filter.py scan /path/to/images/
Image Quality Filter Data Schema & Taxonomy
The skill evaluates images based on specific technical criteria and can organize the results into the following structure:
| Criterion | Measurement Method | Default Threshold |
|---|---|---|
| Blur | Laplacian Variance | < 100 |
| Brightness | Pixel Intensity (0-255) | 30 to 220 |
| Resolution | Pixel Dimensions | > 640x480 |
When using the move action, the skill organizes filtered images into a specified output directory, preserving the original file names for easy review.
Image Quality Filter Advanced Features
- Multi-parameter filtering allows simultaneous checks for blur, lighting, and size.
- Flexible action modes including
listfor auditing,movefor sorting, anddeletefor automated cleanup. - Custom threshold overrides to fine-tune the sensitivity of the detection algorithms for specific project needs.
- Efficient batch processing capabilities designed to handle thousands of images within the Openclaw Skills ecosystem.
SKILL.md
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