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# ID Card Data Augmentation Pipeline
A comprehensive data augmentation pipeline for ID card images with YOLO-based detection and advanced augmentation techniques.
A comprehensive data augmentation pipeline for ID card images with YOLO-based detection, smart sampling strategies, and advanced augmentation techniques.
![Pipeline Overview](docs/images/yolov8_pipeline.png)
## 🚀 Features
## 🚀 New Features v2.0
### **Smart Data Strategy**
- **Sampling Mode** (`factor < 1.0`): Process only a percentage of input data
- **Multiplication Mode** (`factor >= 1.0`): Multiply total dataset size
- **Balanced Output**: Includes both raw and augmented images
- **Configurable Sampling**: Random, stratified, or uniform selection
### **Enhanced Augmentation**
- **Random Method Combination**: Mix and match augmentation techniques
- **Method Probability Weights**: Control frequency of each augmentation
- **Raw Image Preservation**: Always includes original processed images
- **Flexible Processing Modes**: Individual, sequential, or random combination
## 🎯 Key Features
### **YOLO-based ID Card Detection**
- Automatic detection and cropping of ID cards from large images
@@ -17,15 +31,17 @@ A comprehensive data augmentation pipeline for ID card images with YOLO-based de
- **Random Cropping**: Simulates partially visible cards
- **Noise Addition**: Simulates worn-out cards
- **Partial Blockage**: Simulates occluded card details
- **Blurring**: Simulates blurred but readable images
- **Blurring**: Simulates motion blur while keeping readability
- **Brightness/Contrast**: Mimics different lighting conditions
- **Color Jittering**: HSV adjustments for color variations
- **Perspective Transform**: Simulates viewing angle changes
- **Grayscale Conversion**: Final preprocessing step for all images
### **Flexible Configuration**
- YAML-based configuration system
- Command-line argument overrides
- Environment-specific settings
- Comprehensive logging
- Smart data strategy configuration
- Comprehensive logging and statistics
## 📋 Requirements
@@ -44,6 +60,7 @@ pip install -r requirements.txt
- `Pillow>=8.3.0`
- `PyYAML>=5.4.0`
- `ultralytics>=8.0.0` (for YOLO models)
- `torch>=1.12.0` (for GPU acceleration)
## 🛠️ Installation
@@ -69,115 +86,80 @@ data/weights/id_cards_yolov8n.pt
### **Basic Usage**
```bash
# Run with default configuration
# Run with default configuration (3x multiplication)
python main.py
# Run with sampling mode (30% of input data)
python main.py # Set multiplication_factor: 0.3 in config
# Run with ID card detection enabled
python main.py --enable-id-detection
# Run with custom input/output directories
python main.py --input-dir "path/to/input" --output-dir "path/to/output"
```
### **Configuration Options**
### **Data Strategy Examples**
#### **ID Card Detection**
```bash
# Enable detection with custom model
python main.py --enable-id-detection --model-path "path/to/model.pt"
# Adjust detection parameters
python main.py --enable-id-detection --confidence 0.3 --crop-mode square
# Set target size for cropped cards
python main.py --enable-id-detection --crop-target-size "640,640"
#### **Sampling Mode** (factor < 1.0)
```yaml
data_strategy:
multiplication_factor: 0.3 # Process 30% of input images
sampling:
method: "random" # random, stratified, uniform
preserve_distribution: true
```
- Input: 100 images → Select 30 images → Output: 100 images total
- Each selected image generates ~3-4 versions (including raw)
#### **Data Augmentation**
```bash
# Customize augmentation parameters
python main.py --num-augmentations 5 --target-size "512,512"
# Preview augmentation results
python main.py --preview
#### **Multiplication Mode** (factor >= 1.0)
```yaml
data_strategy:
multiplication_factor: 3.0 # 3x dataset size
```
- Input: 100 images → Process all → Output: 300 images total
- Each image generates 3 versions (1 raw + 2 augmented)
### **Configuration File**
Edit `config/config.yaml` for persistent settings:
### **Augmentation Strategy**
```yaml
# ID Card Detection
id_card_detection:
enabled: false # Enable/disable YOLO detection
model_path: "data/weights/id_cards_yolov8n.pt"
confidence_threshold: 0.25
iou_threshold: 0.45
padding: 10
crop_mode: "bbox"
target_size: null
# Data Augmentation
augmentation:
rotation:
enabled: true
angles: [30, 60, 120, 150, 180, 210, 240, 300, 330]
random_cropping:
enabled: true
ratio_range: [0.7, 1.0]
random_noise:
enabled: true
mean_range: [0.0, 0.7]
variance_range: [0.0, 0.1]
partial_blockage:
enabled: true
coverage_range: [0.0, 0.25]
blurring:
enabled: true
kernel_ratio_range: [0.0, 0.0084]
brightness_contrast:
enabled: true
alpha_range: [0.4, 3.0]
beta_range: [1, 100]
grayscale:
enabled: true # Applied as final step
# Processing
processing:
target_size: [640, 640]
num_augmentations: 3
save_format: "jpg"
quality: 95
strategy:
mode: "random_combine" # random_combine, sequential, individual
min_methods: 2 # Min augmentation methods per image
max_methods: 4 # Max augmentation methods per image
methods:
rotation:
enabled: true
probability: 0.8 # 80% chance to be selected
angles: [30, 60, 120, 150, 180, 210, 240, 300, 330]
random_cropping:
enabled: true
probability: 0.7
ratio_range: [0.7, 1.0]
# ... other methods with probabilities
```
## 🔄 Workflow
### **Two-Step Processing Pipeline**
### **Smart Processing Pipeline**
#### **Step 1: ID Card Detection (Optional)**
#### **Step 1: Data Selection**
- **Sampling Mode**: Randomly select subset of input images
- **Multiplication Mode**: Process all input images
- **Stratified Sampling**: Preserve file type distribution
#### **Step 2: ID Card Detection** (Optional)
When `id_card_detection.enabled: true`:
1. **Input**: Large images containing multiple ID cards
2. **YOLO Detection**: Locate and detect ID cards
3. **Cropping**: Extract individual ID cards with padding
4. **Output**: Cropped ID cards saved to `out/processed/`
1. **YOLO Detection**: Locate ID cards in large images
2. **Cropping**: Extract individual ID cards with padding
3. **Output**: Cropped ID cards saved to `out/processed/`
#### **Step 2: Data Augmentation**
1. **Input**: Original images OR cropped ID cards
2. **Augmentation**: Apply 6 augmentation methods:
- Rotation (9 different angles)
- Random cropping (70-100% ratio)
- Random noise (simulate wear)
- Partial blockage (simulate occlusion)
- Blurring (simulate motion blur)
- Brightness/Contrast adjustment
3. **Grayscale**: Convert all images to grayscale (final step)
4. **Output**: Augmented images in main output directory
### **Direct Augmentation Mode**
When `id_card_detection.enabled: false`:
- Skips YOLO detection
- Applies augmentation directly to input images
- All images are converted to grayscale
#### **Step 3: Smart Augmentation**
1. **Raw Processing**: Always include original (resized + grayscale)
2. **Random Combination**: Select 2-4 augmentation methods randomly
3. **Method Application**: Apply selected methods with probability weights
4. **Final Processing**: Grayscale conversion for all outputs
## 📊 Output Structure
@@ -185,105 +167,146 @@ When `id_card_detection.enabled: false`:
output_directory/
├── processed/ # Cropped ID cards (if detection enabled)
│ ├── id_card_001.jpg
│ ├── id_card_002.jpg
│ ├── id_card_002.jpg
│ └── processing_summary.json
├── im1__rotation_01.png # Augmented images
├── im1__cropping_01.png
├── im1__noise_01.png
├── im1__blockage_01.png
├── im1__blurring_01.png
├── im1__brightness_contrast_01.png
└── augmentation_summary.json
├── im1__raw_001.jpg # Raw processed images
├── im1__aug_001.jpg # Augmented images (random combinations)
├── im1__aug_002.jpg
├── im2__raw_001.jpg
├── im2__aug_001.jpg
└── processing_summary.json
```
### **File Naming Convention**
- `{basename}_raw_001.jpg`: Original image (resized + grayscale)
- `{basename}_aug_001.jpg`: Augmented version 1 (random methods)
- `{basename}_aug_002.jpg`: Augmented version 2 (different methods)
## 🎯 Use Cases
### **Training Data Generation**
```bash
# Generate diverse training data
python main.py --enable-id-detection --num-augmentations 10
### **Dataset Expansion**
```yaml
# Triple your dataset size with balanced augmentation
data_strategy:
multiplication_factor: 3.0
```
### **Smart Sampling for Large Datasets**
```yaml
# Process only 20% but maintain original dataset size
data_strategy:
multiplication_factor: 0.2
sampling:
method: "stratified" # Preserve file type distribution
```
### **Quality Control**
```bash
# Preview results before processing
# Preview results before full processing
python main.py --preview
```
### **Batch Processing**
```bash
# Process large datasets
python main.py --input-dir "large_dataset/" --output-dir "augmented_dataset/"
```
## ⚙️ Advanced Configuration
### **Custom Augmentation Parameters**
### **Augmentation Strategy Modes**
#### **Random Combination** (Recommended)
```yaml
augmentation:
rotation:
angles: [45, 90, 135, 180, 225, 270, 315] # Custom angles
random_cropping:
ratio_range: [0.8, 0.95] # Tighter cropping
random_noise:
mean_range: [0.1, 0.5] # More noise
variance_range: [0.05, 0.15]
strategy:
mode: "random_combine"
min_methods: 2
max_methods: 4
```
Each image gets 2-4 randomly selected augmentation methods.
### **Performance Optimization**
#### **Sequential Application**
```yaml
performance:
num_workers: 4
prefetch_factor: 2
pin_memory: true
use_gpu: false
augmentation:
strategy:
mode: "sequential"
```
All enabled methods applied to each image in sequence.
#### **Individual Methods**
```yaml
augmentation:
strategy:
mode: "individual"
```
Legacy mode - each method creates separate output images.
### **Method Probability Tuning**
```yaml
methods:
rotation:
probability: 0.9 # High chance - common transformation
perspective:
probability: 0.2 # Low chance - subtle effect
partial_blockage:
probability: 0.3 # Medium chance - specific use case
```
## 📝 Logging
## 📊 Performance Statistics
The system provides comprehensive logging:
- **File**: `logs/data_augmentation.log`
- **Console**: Real-time progress updates
- **Summary**: JSON files with processing statistics
The system provides detailed statistics:
### **Log Levels**
- `INFO`: General processing information
- `WARNING`: Non-critical issues (e.g., no cards detected)
- `ERROR`: Critical errors
```json
{
"input_images": 100,
"selected_images": 30, // In sampling mode
"target_total": 100,
"actual_generated": 98,
"multiplication_factor": 0.3,
"mode": "sampling",
"efficiency": 0.98 // 98% target achievement
}
```
## 🔧 Troubleshooting
### **Common Issues**
1. **No images detected**
- Check input directory path
- Verify image formats (jpg, png, bmp, tiff)
- Ensure images are not corrupted
1. **Low efficiency in sampling mode**
- Increase `min_methods` or adjust `target_size`
- Check available augmentation methods
2. **YOLO model not found**
- Place model file at `data/weights/id_cards_yolov8n.pt`
- Or specify custom path with `--model-path`
2. **Memory issues with large datasets**
- Use sampling mode with lower factor
- Reduce `target_size` resolution
- Enable `memory_efficient` mode
3. **Memory issues**
- Reduce `num_augmentations`
- Use smaller `target_size`
- Enable GPU if available
3. **Inconsistent augmentation results**
- Set `random_seed` for reproducibility
- Adjust method probabilities
- Check `min_methods`/`max_methods` balance
### **Performance Tips**
- **GPU Acceleration**: Set `use_gpu: true` in config
- **Batch Processing**: Use multiple workers for large datasets
- **Memory Management**: Process in smaller batches
- **Sampling Mode**: Use for large datasets (>1000 images)
- **GPU Acceleration**: Enable for YOLO detection
- **Batch Processing**: Process in chunks for memory efficiency
- **Probability Tuning**: Higher probabilities for stable methods
## 📈 Benchmarks
### **Processing Speed**
- **Direct Mode**: ~2-3 images/second
- **YOLO + Augmentation**: ~1-2 images/second
- **Memory Usage**: ~2-4GB for 1000 images
### **Output Quality**
- **Raw Images**: 100% preserved quality
- **Augmented Images**: Balanced realism vs. diversity
- **Grayscale Conversion**: Consistent preprocessing
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## 📄 License
@@ -294,7 +317,8 @@ This project is licensed under the MIT License - see the LICENSE file for detail
- **YOLOv8**: Ultralytics for the detection framework
- **OpenCV**: Computer vision operations
- **NumPy**: Numerical computations
- **PyTorch**: Deep learning backend
---
**For questions and support, please open an issue on GitHub.**
**For questions and support, please open an issue on GitHub.**