name: image-annotation-qc description: Image Annotation Quality Control Tool - Automatically detect quality issues in bounding box and polygon segmentation annotations, generate visual reports. Supports COCO/YOLO/VOC/LabelMe formats, suitable for autonomous driving, industrial inspection, security monitoring and other AI training data quality control scenarios. metadata: {"openclaw":{"emoji":"🔍","requires":{"bins":["python3"],"pip":["Pillow","openpyxl"]}}}

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 <image_dir> -a <annotation_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

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

Quality Score

Score = 100 × (annotations - errors×weight) / annotations

Grading:
- 90+: Excellent
- 80-89: Good
- 70-79: Pass
- <70: 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}")