Image Annotation QC

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.

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

bash

# 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

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

text

╔══════════════════════════════════════════╗
║           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:

text

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

text

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

Grading:
- 90+: Excellent
- 80-89: Good
- 70-79: Pass
- <70: Fail

Installation

bash

pip install Pillow openpyxl

As Module

python

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}")