Annotation Visualizer

Visualize bounding boxes and class labels on images with support for COCO, YOLO, VOC, and LabelMe annotation formats.

Security Scan

The skill is internally consistent: it only reads local annotation/image files, draws boxes, and writes output images; it asks for no credentials, has no install script, and its instructions match the included code.

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.

Assessment

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.

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)