← Back to Skills Marketplace
302
Downloads
0
Stars
0
Active Installs
1
Versions
Install in OpenClaw
/install annotation-visualizer
Description
Visualize bounding boxes and class labels on images with support for COCO, YOLO, VOC, and LabelMe annotation formats.
README (SKILL.md)
Annotation Visualizer
Visualize bounding boxes and labels on images. Supports COCO, YOLO, VOC, and LabelMe formats. Use when user wants to visualize annotations on images for quality checking or debugging.
Features
- Multi-format Support: COCO, YOLO, VOC, LabelMe
- Customizable Colors: Per-class colors or auto-generated
- Label Display: Show class names and confidence
- Box Styles: Filled or outline boxes
- Batch Processing: Visualize entire dataset
Usage
# Visualize YOLO annotations
python scripts/visualize.py yolo images/ labels/ output/
# Visualize COCO annotations
python scripts/visualize.py coco annotations.json images/ output/
# Custom colors and styles
python scripts/visualize.py yolo images/ labels/ output/ \
--colors red,green,blue \
--thickness 2 \
--fill
Examples
$ python scripts/visualize.py yolo ./images ./labels ./output
Processing 100 images...
✓ Saved visualization for image1.jpg -> output/image1.jpg
✓ Saved visualization for image2.jpg -> output/image2.jpg
...
Supported Formats
| Format | Input | Description |
|---|---|---|
| YOLO | .txt | YOLO darknet format |
| COCO | .json | COCO JSON annotation |
| VOC | .xml | Pascal VOC XML |
| LabelMe | .json | LabelMe JSON |
Installation
pip install pillow
Options
--colors: Comma-separated colors for each class--thickness: Box line thickness (default: 2)--fill: Fill boxes with semi-transparent color--show-label: Show class labels on boxes--font-size: Label font size (default: 16)
Usage Guidance
This skill appears coherent and limited to local image/annotation visualization. Before installing or running: 1) review the included script if you want to confirm behavior (it only reads files you point it at and writes output images); 2) run it in a sandbox or with non-sensitive images if you have policy concerns; 3) ensure pillow is installed (pip install pillow) and that input paths are correct. Minor notes: the script swallows some exceptions silently and defaults class names/IDs in simple ways, so check output for correctness on your datasets.
Capability Analysis
Type: OpenClaw Skill
Name: annotation-visualizer
Version: 1.0.0
The skill is a standard utility for visualizing bounding box annotations on images across multiple formats (YOLO, COCO, VOC, LabelMe). The implementation in `scripts/visualize.py` uses the Pillow library for image processing and contains no evidence of malicious intent, network activity, or unauthorized file access. The instructions in `SKILL.md` are consistent with the tool's purpose and do not contain any prompt injection attempts.
Capability Assessment
Purpose & Capability
Name/description (annotation visualization) matches the files and declared behavior. The included script implements YOLO/COCO/VOC/LabelMe parsing and image drawing, which is appropriate for the stated purpose.
Instruction Scope
SKILL.md instructs running the included script with local image/annotation paths and options. The runtime instructions and the script only read specified image/annotation directories and write output images; they do not request unrelated files, environment variables, or network endpoints.
Install Mechanism
No install spec; SKILL.md suggests installing pillow via pip which is proportional to drawing images. There are no downloads from external URLs or archive extraction steps.
Credentials
The skill declares no required environment variables, credentials, or config paths. The code does not read environment variables or secret files; requested resources are local image/annotation files as expected.
Persistence & Privilege
The skill is not always-enabled and does not request persistent platform privileges. It does not modify other skills or system-wide configurations.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install annotation-visualizer - After installation, invoke the skill by name or use
/annotation-visualizer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of Annotation Visualizer.
- Visualize bounding boxes and labels on images for COCO, YOLO, VOC, and LabelMe annotation formats.
- Supports customizable colors per class, label display (class names & confidence), and adjustable box styles (filled or outline).
- Enables batch processing to visualize entire datasets.
- Command-line interface with options for color, box thickness, fill, label display, and font size.
Metadata
Frequently Asked Questions
What is Annotation Visualizer?
Visualize bounding boxes and class labels on images with support for COCO, YOLO, VOC, and LabelMe annotation formats. It is an AI Agent Skill for Claude Code / OpenClaw, with 302 downloads so far.
How do I install Annotation Visualizer?
Run "/install annotation-visualizer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Annotation Visualizer free?
Yes, Annotation Visualizer is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Annotation Visualizer support?
Annotation Visualizer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Annotation Visualizer?
It is built and maintained by Mingo_318 (@mingo-318); the current version is v1.0.0.
More Skills