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Install in OpenClaw
/install image-annotation-qc
Description
Image Annotation Quality Control Tool - Automatically detect quality issues in bounding box and polygon segmentation annotations, generate visual reports. Su...
README (SKILL.md)
Image Annotation QC Tool
Automatically detect quality issues in image annotations and generate detailed reports with visualization.
Features
- ✅ Multi-format Support: COCO / YOLO / VOC / LabelMe
- ✅ Auto-detection: Automatically identify format without specification
- ✅ Scene-specific QC: general / road / industrial / security
- ✅ Auto-save Reports: Save to
qc_report/directory automatically - ✅ Visualization: Generate images with error annotations (red boxes)
- ✅ Multi-format Reports: TXT / JSON / Excel
Quick Start
# Basic usage (auto-detect format)
python3 scripts/qc_tool.py -i \x3Cimage_dir> -a \x3Cannotation_dir>
# Specify format
python3 scripts/qc_tool.py -i ./images -a ./annotations -f labelme
# Industrial inspection scenario
python3 scripts/qc_tool.py -i ./images -a ./annotations -d industrial
# Sample 100 images
python3 scripts/qc_tool.py -i ./images -a ./annotations -s 100
# Specify output directory
python3 scripts/qc_tool.py -i ./images -a ./annotations -o ./my_report
# Generate Excel report
python3 scripts/qc_tool.py -i ./images -a ./annotations --formats txt json xlsx
Parameters
| Parameter | Short | Description | Default |
|---|---|---|---|
--image-dir |
-i |
Image directory | (required) |
--annotation |
-a |
Annotation file or directory | (required) |
--format |
-f |
Annotation format | auto |
--domain |
-d |
Application scenario | general |
--sample |
-s |
Sample size | all |
--output |
-o |
Output directory | ./qc_report |
--formats |
- | Report formats | txt json |
--no-visual |
- | Disable visualization | false |
Scenarios
- general: General purpose
- road: Autonomous driving (small objects, occlusions)
- industrial: Industrial inspection (micro-defects)
- security: Security monitoring
Output Example
╔══════════════════════════════════════════╗
║ Image Annotation QC ║
╠══════════════════════════════════════════╣
║ Images: 1000 Annotations: 4523 ║
║ Errors: 127 Quality Score: 85.5 ║
║ Accuracy: 97.2% ║
╚══════════════════════════════════════════╝
Output Files
After QC completes, the following files are generated in the annotation directory:
annotations/
├── qc_report/
│ ├── qc_report.txt # Text report
│ ├── qc_report.json # JSON detailed report
│ ├── qc_report.xlsx # Excel report (optional)
│ └── visual/ # Visualization images
│ ├── frame_001_qc.png
│ └── frame_002_qc.png
Error Types
| Error | Description | Weight |
|---|---|---|
| Missing | Obvious targets not labeled | 1.0 |
| Wrong | Annotation region doesn't match target | 0.8 |
| Offset | Box edge deviates >10px | 0.5 |
| Too Large | Box significantly larger than target | 0.3 |
| Too Small | Box doesn't fully contain target | 0.5 |
| Duplicate | Same object annotated multiple times | 0.5 |
| Label Error | Wrong class label | 0.8 |
Quality Score
Score = 100 × (annotations - errors×weight) / annotations
Grading:
- 90+: Excellent
- 80-89: Good
- 70-79: Pass
- \x3C70: Fail
Installation
pip install Pillow openpyxl
As Module
from qc_tool import AnnotationQC
qc = AnnotationQC('images/', 'annotations/', format='labelme')
data = qc.load_annotations()
result = qc.check_annotations(data)
qc.generate_report(result, formats=['txt', 'json'])
qc.visualize_errors(result)
print(f"Quality Score: {result.quality_score}")
Usage Guidance
This package appears to be what it says: a local image/annotation QC tool. Before running: (1) review and, if needed, run the script on a small sample dataset to confirm behavior; (2) note the script will create a qc_report/ folder under the annotation path and write text/json/xlsx and visualization PNGs; (3) install Pillow and openpyxl from PyPI if you trust that source; (4) if your images or annotations are sensitive, run the tool in an isolated environment since it will read those files locally; and (5) if you want extra assurance, skim the remainder of qc_tool.py (not fully shown in the truncated listing) to confirm there are no unexpected network operations or calls to external services.
Capability Analysis
Type: OpenClaw Skill
Name: image-annotation-qc
Version: 1.0.7
The skill bundle is a legitimate image annotation quality control tool. The primary script, scripts/qc_tool.py, implements standard logic for validating COCO, YOLO, VOC, and LabelMe formats, including bounding box and polygon checks, duplicate detection, and report generation using Pillow and openpyxl. No evidence of data exfiltration, malicious execution, or prompt injection was found.
Capability Assessment
Purpose & Capability
Name/description match the delivered artifacts: a Python script (qc_tool.py), sample annotation and report files, and SKILL.md describing how to run the QC. Required runtime is only python3 and the declared Python libraries (Pillow, openpyxl) which are reasonable for visualization and Excel output.
Instruction Scope
SKILL.md instructs the agent/user to run the included script against local image and annotation directories and to install Pillow/openpyxl. The instructions and script operate on local files, write reports to a qc_report/ folder, and do not direct data to external endpoints or attempt to read unrelated system paths.
Install Mechanism
There is no automated install spec; SKILL.md suggests pip installing Pillow and openpyxl. That is a standard, proportional dependency for image drawing and Excel output and does not involve arbitrary remote code downloads beyond PyPI.
Credentials
The skill declares no required environment variables, credentials, or config paths. The script only accesses provided image/annotation directories and writes output under qc_report/ — the access requested is proportional to the stated purpose.
Persistence & Privilege
The skill is not marked always:true and does not request persistent platform-level privileges. It creates output in the annotation directory (normal behavior) and does not attempt to modify other skills or global agent configuration.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install image-annotation-qc - After installation, invoke the skill by name or use
/image-annotation-qc - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.7
Remove billing, now completely free
v1.0.6
Fix security: remove embedded credentials
v1.0.5
Add SkillPay billing support
v1.0.4
Rebuild
v1.0.3
Revert to English description
v1.0.2
更口语化的描述
v1.0.1
Remove billing module, fix security warning
v1.0.0
Initial release
Metadata
Frequently Asked Questions
What is Image Annotation QC?
Image Annotation Quality Control Tool - Automatically detect quality issues in bounding box and polygon segmentation annotations, generate visual reports. Su... It is an AI Agent Skill for Claude Code / OpenClaw, with 353 downloads so far.
How do I install Image Annotation QC?
Run "/install image-annotation-qc" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Image Annotation QC free?
Yes, Image Annotation QC is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Image Annotation QC support?
Image Annotation QC is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Image Annotation QC?
It is built and maintained by Mingo_318 (@mingo-318); the current version is v1.0.7.
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