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Nguyễn Phước Thành
2025-08-06 21:44:39 +07:00
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README.md
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@@ -1,10 +1,24 @@
# ID Card Data Augmentation Pipeline # 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) ![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** ### **YOLO-based ID Card Detection**
- Automatic detection and cropping of ID cards from large images - 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 - **Random Cropping**: Simulates partially visible cards
- **Noise Addition**: Simulates worn-out cards - **Noise Addition**: Simulates worn-out cards
- **Partial Blockage**: Simulates occluded card details - **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 - **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 - **Grayscale Conversion**: Final preprocessing step for all images
### **Flexible Configuration** ### **Flexible Configuration**
- YAML-based configuration system - YAML-based configuration system
- Command-line argument overrides - Command-line argument overrides
- Environment-specific settings - Smart data strategy configuration
- Comprehensive logging - Comprehensive logging and statistics
## 📋 Requirements ## 📋 Requirements
@@ -44,6 +60,7 @@ pip install -r requirements.txt
- `Pillow>=8.3.0` - `Pillow>=8.3.0`
- `PyYAML>=5.4.0` - `PyYAML>=5.4.0`
- `ultralytics>=8.0.0` (for YOLO models) - `ultralytics>=8.0.0` (for YOLO models)
- `torch>=1.12.0` (for GPU acceleration)
## 🛠️ Installation ## 🛠️ Installation
@@ -69,115 +86,80 @@ data/weights/id_cards_yolov8n.pt
### **Basic Usage** ### **Basic Usage**
```bash ```bash
# Run with default configuration # Run with default configuration (3x multiplication)
python main.py 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 # Run with ID card detection enabled
python main.py --enable-id-detection 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** #### **Sampling Mode** (factor < 1.0)
```bash ```yaml
# Enable detection with custom model data_strategy:
python main.py --enable-id-detection --model-path "path/to/model.pt" multiplication_factor: 0.3 # Process 30% of input images
sampling:
# Adjust detection parameters method: "random" # random, stratified, uniform
python main.py --enable-id-detection --confidence 0.3 --crop-mode square preserve_distribution: true
# Set target size for cropped cards
python main.py --enable-id-detection --crop-target-size "640,640"
``` ```
- Input: 100 images → Select 30 images → Output: 100 images total
- Each selected image generates ~3-4 versions (including raw)
#### **Data Augmentation** #### **Multiplication Mode** (factor >= 1.0)
```bash ```yaml
# Customize augmentation parameters data_strategy:
python main.py --num-augmentations 5 --target-size "512,512" multiplication_factor: 3.0 # 3x dataset size
# Preview augmentation results
python main.py --preview
``` ```
- Input: 100 images → Process all → Output: 300 images total
- Each image generates 3 versions (1 raw + 2 augmented)
### **Configuration File** ### **Augmentation Strategy**
Edit `config/config.yaml` for persistent settings:
```yaml ```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: augmentation:
rotation: strategy:
enabled: true mode: "random_combine" # random_combine, sequential, individual
angles: [30, 60, 120, 150, 180, 210, 240, 300, 330] min_methods: 2 # Min augmentation methods per image
random_cropping: max_methods: 4 # Max augmentation methods per image
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 methods:
processing: rotation:
target_size: [640, 640] enabled: true
num_augmentations: 3 probability: 0.8 # 80% chance to be selected
save_format: "jpg" angles: [30, 60, 120, 150, 180, 210, 240, 300, 330]
quality: 95
random_cropping:
enabled: true
probability: 0.7
ratio_range: [0.7, 1.0]
# ... other methods with probabilities
``` ```
## 🔄 Workflow ## 🔄 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`: When `id_card_detection.enabled: true`:
1. **Input**: Large images containing multiple ID cards 1. **YOLO Detection**: Locate ID cards in large images
2. **YOLO Detection**: Locate and detect ID cards 2. **Cropping**: Extract individual ID cards with padding
3. **Cropping**: Extract individual ID cards with padding 3. **Output**: Cropped ID cards saved to `out/processed/`
4. **Output**: Cropped ID cards saved to `out/processed/`
#### **Step 2: Data Augmentation** #### **Step 3: Smart Augmentation**
1. **Input**: Original images OR cropped ID cards 1. **Raw Processing**: Always include original (resized + grayscale)
2. **Augmentation**: Apply 6 augmentation methods: 2. **Random Combination**: Select 2-4 augmentation methods randomly
- Rotation (9 different angles) 3. **Method Application**: Apply selected methods with probability weights
- Random cropping (70-100% ratio) 4. **Final Processing**: Grayscale conversion for all outputs
- 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
## 📊 Output Structure ## 📊 Output Structure
@@ -187,103 +169,144 @@ output_directory/
│ ├── id_card_001.jpg │ ├── id_card_001.jpg
│ ├── id_card_002.jpg │ ├── id_card_002.jpg
│ └── processing_summary.json │ └── processing_summary.json
├── im1__rotation_01.png # Augmented images ├── im1__raw_001.jpg # Raw processed images
├── im1__cropping_01.png ├── im1__aug_001.jpg # Augmented images (random combinations)
├── im1__noise_01.png ├── im1__aug_002.jpg
├── im1__blockage_01.png ├── im2__raw_001.jpg
├── im1__blurring_01.png ├── im2__aug_001.jpg
├── im1__brightness_contrast_01.png └── processing_summary.json
└── augmentation_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 ## 🎯 Use Cases
### **Training Data Generation** ### **Dataset Expansion**
```bash ```yaml
# Generate diverse training data # Triple your dataset size with balanced augmentation
python main.py --enable-id-detection --num-augmentations 10 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** ### **Quality Control**
```bash ```bash
# Preview results before processing # Preview results before full processing
python main.py --preview python main.py --preview
``` ```
### **Batch Processing**
```bash
# Process large datasets
python main.py --input-dir "large_dataset/" --output-dir "augmented_dataset/"
```
## ⚙️ Advanced Configuration ## ⚙️ Advanced Configuration
### **Custom Augmentation Parameters** ### **Augmentation Strategy Modes**
#### **Random Combination** (Recommended)
```yaml ```yaml
augmentation: augmentation:
rotation: strategy:
angles: [45, 90, 135, 180, 225, 270, 315] # Custom angles mode: "random_combine"
random_cropping: min_methods: 2
ratio_range: [0.8, 0.95] # Tighter cropping max_methods: 4
random_noise:
mean_range: [0.1, 0.5] # More noise
variance_range: [0.05, 0.15]
``` ```
Each image gets 2-4 randomly selected augmentation methods.
### **Performance Optimization** #### **Sequential Application**
```yaml ```yaml
performance: augmentation:
num_workers: 4 strategy:
prefetch_factor: 2 mode: "sequential"
pin_memory: true ```
use_gpu: false 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: The system provides detailed statistics:
- **File**: `logs/data_augmentation.log`
- **Console**: Real-time progress updates
- **Summary**: JSON files with processing statistics
### **Log Levels** ```json
- `INFO`: General processing information {
- `WARNING`: Non-critical issues (e.g., no cards detected) "input_images": 100,
- `ERROR`: Critical errors "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 ## 🔧 Troubleshooting
### **Common Issues** ### **Common Issues**
1. **No images detected** 1. **Low efficiency in sampling mode**
- Check input directory path - Increase `min_methods` or adjust `target_size`
- Verify image formats (jpg, png, bmp, tiff) - Check available augmentation methods
- Ensure images are not corrupted
2. **YOLO model not found** 2. **Memory issues with large datasets**
- Place model file at `data/weights/id_cards_yolov8n.pt` - Use sampling mode with lower factor
- Or specify custom path with `--model-path` - Reduce `target_size` resolution
- Enable `memory_efficient` mode
3. **Memory issues** 3. **Inconsistent augmentation results**
- Reduce `num_augmentations` - Set `random_seed` for reproducibility
- Use smaller `target_size` - Adjust method probabilities
- Enable GPU if available - Check `min_methods`/`max_methods` balance
### **Performance Tips** ### **Performance Tips**
- **GPU Acceleration**: Set `use_gpu: true` in config - **Sampling Mode**: Use for large datasets (>1000 images)
- **Batch Processing**: Use multiple workers for large datasets - **GPU Acceleration**: Enable for YOLO detection
- **Memory Management**: Process in smaller batches - **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 ## 🤝 Contributing
1. Fork the repository 1. Fork the repository
2. Create a feature branch 2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes 3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Add tests if applicable 4. Push to the branch (`git push origin feature/amazing-feature`)
5. Submit a pull request 5. Open a Pull Request
## 📄 License ## 📄 License
@@ -294,6 +317,7 @@ This project is licensed under the MIT License - see the LICENSE file for detail
- **YOLOv8**: Ultralytics for the detection framework - **YOLOv8**: Ultralytics for the detection framework
- **OpenCV**: Computer vision operations - **OpenCV**: Computer vision operations
- **NumPy**: Numerical computations - **NumPy**: Numerical computations
- **PyTorch**: Deep learning backend
--- ---

163
config/config.yaml
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@@ -1,5 +1,5 @@
# Data Augmentation Configuration # ID Card Data Augmentation Configuration v2.0
# Main configuration file for image data augmentation # Enhanced configuration with smart sampling, multiplication, and random method combination
# Paths configuration # Paths configuration
paths: paths:
@@ -7,72 +7,123 @@ paths:
output_dir: "out1" output_dir: "out1"
log_file: "logs/data_augmentation.log" log_file: "logs/data_augmentation.log"
# Data Sampling and Multiplication Strategy
data_strategy:
# Multiplication/Sampling factor:
# - If < 1.0 (e.g. 0.3): Random sampling 30% of input data to augment
# - If >= 1.0 (e.g. 2.0, 3.0): Multiply dataset size by 2x, 3x etc.
multiplication_factor: 0.3
# Random seed for reproducibility (null = random each run)
random_seed: null
# Sampling strategy for factor < 1.0
sampling:
method: "random" # random, stratified, uniform
preserve_distribution: true # Maintain file type distribution
# ID Card Detection configuration # ID Card Detection configuration
id_card_detection: id_card_detection:
enabled: false # Bật/tắt tính năng detect và crop ID cards enabled: false # Enable/disable YOLO detection and cropping
model_path: "data/weights/id_cards_yolov8n.pt" # Đường dẫn đến YOLO model model_path: "data/weights/id_cards_yolov8n.pt" # Path to YOLO model
confidence_threshold: 0.25 # Confidence threshold cho detection confidence_threshold: 0.25 # Detection confidence threshold
iou_threshold: 0.45 # IoU threshold cho NMS iou_threshold: 0.45 # IoU threshold for NMS
padding: 10 # Padding thêm xung quanh bbox padding: 10 # Extra padding around bounding box
crop_mode: "bbox" # Mode cắt: bbox, square, aspect_ratio crop_mode: "bbox" # Cropping mode: bbox, square, aspect_ratio
target_size: null # Kích thước target (width, height) hoặc null target_size: null # Target size (width, height) or null
save_original_crops: true # Có lưu ảnh gốc đã crop không save_original_crops: true # Save original cropped images
# Data augmentation parameters - ROTATION and RANDOM CROPPING # Augmentation Strategy - Random Combination of Methods
augmentation: augmentation:
# Geometric transformations # Strategy for combining augmentation methods
rotation: strategy:
enabled: true mode: "random_combine" # random_combine, sequential, individual
angles: [30, 60, 120, 150, 180, 210, 240, 300, 330] # Specific rotation angles min_methods: 2 # Minimum methods applied per image
probability: 1.0 # Always apply rotation max_methods: 4 # Maximum methods applied per image
allow_duplicates: false # Allow same method multiple times with different params
# Random cropping to simulate partially visible ID cards # Available augmentation methods with selection probabilities
random_cropping: methods:
enabled: true # Geometric transformations
ratio_range: [0.7, 1.0] # Crop ratio range (min, max) rotation:
probability: 1.0 # Always apply cropping enabled: true
probability: 0.8 # Selection probability for this method
angles: [30, 60, 120, 150, 180, 210, 240, 300, 330]
# Random noise to simulate worn-out ID cards # Random cropping to simulate partially visible ID cards
random_noise: random_cropping:
enabled: true enabled: true
mean_range: [0.0, 0.7] # Noise mean range (min, max) probability: 0.7
variance_range: [0.0, 0.1] # Noise variance range (min, max) ratio_range: [0.7, 1.0]
probability: 1.0 # Always apply noise
# Partial blockage to simulate occluded card details # Random noise to simulate worn-out ID cards
partial_blockage: random_noise:
enabled: true enabled: true
num_occlusions_range: [1, 100] # Number of occlusion lines (min, max) probability: 0.6
coverage_range: [0.0, 0.25] # Coverage ratio (min, max) mean_range: [0.0, 0.7]
variance_range: [0.0, 0.1] # Line thickness variance (min, max) variance_range: [0.0, 0.1]
probability: 1.0 # Always apply blockage
# Blurring to simulate blurred card images that are still readable # Partial blockage to simulate occluded card details
blurring: partial_blockage:
enabled: true enabled: true
kernel_ratio_range: [0.0, 0.0084] # Kernel ratio range (min, max) probability: 0.5
probability: 1.0 # Always apply blurring num_occlusions_range: [1, 100]
coverage_range: [0.0, 0.25]
variance_range: [0.0, 0.1]
# Brightness and contrast adjustment to mimic different environmental lighting conditions # Blurring to simulate motion blur while keeping readability
brightness_contrast: blurring:
enabled: true enabled: true
alpha_range: [0.4, 3.0] # Contrast range (min, max) probability: 0.6
beta_range: [1, 100] # Brightness range (min, max) kernel_ratio_range: [0.0, 0.0084]
probability: 1.0 # Always apply brightness/contrast adjustment
# Grayscale transformation as final step (applied to all augmented images) # Brightness and contrast adjustment for lighting variations
grayscale: brightness_contrast:
enabled: true enabled: true
probability: 1.0 # Always apply grayscale as final step probability: 0.7
alpha_range: [0.4, 3.0]
beta_range: [1, 100]
# Color space transformations
color_jitter:
enabled: true
probability: 0.4
brightness_range: [0.8, 1.2]
contrast_range: [0.8, 1.2]
saturation_range: [0.8, 1.2]
hue_range: [-0.1, 0.1]
# Perspective transformation for viewing angle simulation
perspective:
enabled: false
probability: 0.3
distortion_scale: 0.2
# Final processing (always applied to all outputs)
final_processing:
# Grayscale transformation as final preprocessing step
grayscale:
enabled: true
probability: 1.0 # Always apply to ensure consistency
# Quality enhancement (future feature)
quality_enhancement:
enabled: false
sharpen: 0.1
denoise: false
# Processing configuration # Processing configuration
processing: processing:
target_size: [640, 640] # [width, height] - Increased for better coverage target_size: [640, 640] # [width, height] - Target resolution
batch_size: 32 batch_size: 32
num_augmentations: 3 # number of augmented versions per image
save_format: "jpg" save_format: "jpg"
quality: 95 quality: 95
# Advanced processing options
preserve_original: false # Whether to save original images
parallel_processing: true # Enable parallel processing
memory_efficient: true # Optimize memory usage
# Supported image formats # Supported image formats
supported_formats: supported_formats:
- ".jpg" - ".jpg"
@@ -83,7 +134,7 @@ supported_formats:
# Logging configuration # Logging configuration
logging: logging:
level: "INFO" # DEBUG, INFO, WARNING, ERROR level: "INFO" # Available levels: DEBUG, INFO, WARNING, ERROR
format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s" format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
handlers: handlers:
- type: "file" - type: "file"
@@ -92,7 +143,7 @@ logging:
# Performance settings # Performance settings
performance: performance:
num_workers: 4 num_workers: 4 # Number of parallel workers
prefetch_factor: 2 prefetch_factor: 2 # Data prefetching factor
pin_memory: true pin_memory: true # Pin memory for GPU transfer
use_gpu: false use_gpu: false # Enable GPU acceleration

61
main.py
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@@ -214,11 +214,11 @@ def preview_augmentation(input_dir: Path, output_dir: Path, config: Dict[str, An
else: else:
print("⚠️ No ID cards detected, proceeding with normal augmentation") print("⚠️ No ID cards detected, proceeding with normal augmentation")
# Normal augmentation (fallback) # Normal augmentation (fallback) with new logic
augmented_paths = augmenter.augment_image_file( augmented_paths = augmenter.augment_image_file(
image_files[0], image_files[0],
output_dir, output_dir,
num_augmentations=3 num_target_images=3
) )
if augmented_paths: if augmented_paths:
@@ -270,6 +270,7 @@ def main():
processing_config = config_manager.get_processing_config() processing_config = config_manager.get_processing_config()
augmentation_config = config_manager.get_augmentation_config() augmentation_config = config_manager.get_augmentation_config()
logging_config = config_manager.get_logging_config() logging_config = config_manager.get_logging_config()
data_strategy_config = config.get("data_strategy", {})
# Setup logging # Setup logging
logger = setup_logging(logging_config.get("level", "INFO")) logger = setup_logging(logging_config.get("level", "INFO"))
@@ -324,10 +325,20 @@ def main():
logger.error(f"No images found in {input_dir}") logger.error(f"No images found in {input_dir}")
sys.exit(1) sys.exit(1)
# Get data strategy parameters
multiplication_factor = data_strategy_config.get("multiplication_factor", 3.0)
random_seed = data_strategy_config.get("random_seed")
logger.info(f"Found {len(image_files)} images to process") logger.info(f"Found {len(image_files)} images to process")
logger.info(f"Output directory: {output_dir}") logger.info(f"Output directory: {output_dir}")
logger.info(f"Number of augmentations per image: {processing_config.get('num_augmentations', 3)}") logger.info(f"Data strategy: multiplication_factor = {multiplication_factor}")
if multiplication_factor < 1.0:
logger.info(f"SAMPLING MODE: Will process {multiplication_factor*100:.1f}% of input images")
else:
logger.info(f"MULTIPLICATION MODE: Target {multiplication_factor}x dataset size")
logger.info(f"Target size: {processing_config.get('target_size', [224, 224])}") logger.info(f"Target size: {processing_config.get('target_size', [224, 224])}")
if random_seed:
logger.info(f"Random seed: {random_seed}")
# Process with ID detection if enabled # Process with ID detection if enabled
if id_detection_config.get('enabled', False): if id_detection_config.get('enabled', False):
@@ -360,24 +371,52 @@ def main():
target_size=id_detection_config.get('target_size'), target_size=id_detection_config.get('target_size'),
padding=id_detection_config.get('padding', 10) padding=id_detection_config.get('padding', 10)
) )
# Bước 2: Augment các card đã crop # Bước 2: Augment các card đã crop với strategy mới
logger.info("Step 2: Augment cropped ID cards...") logger.info("Step 2: Augment cropped ID cards with smart strategy...")
augmenter = DataAugmentation(augmentation_config) augmenter = DataAugmentation(augmentation_config)
augmenter.batch_augment(
# Truyền full config để augmenter có thể access data_strategy
augmenter.config.update({"data_strategy": data_strategy_config})
augment_results = augmenter.batch_augment(
processed_dir, processed_dir,
output_dir, output_dir,
num_augmentations=processing_config.get("num_augmentations", 3) multiplication_factor=multiplication_factor,
random_seed=random_seed
) )
# Log results
if augment_results:
logger.info(f"Augmentation Summary:")
logger.info(f" Input images: {augment_results.get('input_images', 0)}")
logger.info(f" Selected for processing: {augment_results.get('selected_images', 0)}")
logger.info(f" Target total: {augment_results.get('target_total', 0)}")
logger.info(f" Actually generated: {augment_results.get('actual_generated', 0)}")
logger.info(f" Efficiency: {augment_results.get('efficiency', 0):.1%}")
else: else:
# Augment trực tiếp ảnh gốc # Augment trực tiếp ảnh gốc với strategy mới
logger.info("Starting normal batch augmentation (direct augmentation)...") logger.info("Starting smart batch augmentation (direct augmentation)...")
augmenter = DataAugmentation(augmentation_config) augmenter = DataAugmentation(augmentation_config)
augmenter.batch_augment(
# Truyền full config để augmenter có thể access data_strategy
augmenter.config.update({"data_strategy": data_strategy_config})
augment_results = augmenter.batch_augment(
input_dir, input_dir,
output_dir, output_dir,
num_augmentations=processing_config.get("num_augmentations", 3) multiplication_factor=multiplication_factor,
random_seed=random_seed
) )
# Log results
if augment_results:
logger.info(f"Augmentation Summary:")
logger.info(f" Input images: {augment_results.get('input_images', 0)}")
logger.info(f" Selected for processing: {augment_results.get('selected_images', 0)}")
logger.info(f" Target total: {augment_results.get('target_total', 0)}")
logger.info(f" Actually generated: {augment_results.get('actual_generated', 0)}")
logger.info(f" Efficiency: {augment_results.get('efficiency', 0):.1%}")
logger.info("Data processing completed successfully") logger.info("Data processing completed successfully")
if __name__ == "__main__": if __name__ == "__main__":

630
src/data_augmentation.py
View File

@@ -7,6 +7,7 @@ from pathlib import Path
from typing import List, Tuple, Optional, Dict, Any from typing import List, Tuple, Optional, Dict, Any
import random import random
import math import math
import logging
from image_processor import ImageProcessor from image_processor import ImageProcessor
from utils import load_image, save_image, create_augmented_filename, print_progress from utils import load_image, save_image, create_augmented_filename, print_progress
@@ -22,6 +23,7 @@ class DataAugmentation:
""" """
self.config = config or {} self.config = config or {}
self.image_processor = ImageProcessor() self.image_processor = ImageProcessor()
self.logger = logging.getLogger(__name__)
def random_crop_preserve_quality(self, image: np.ndarray, crop_ratio_range: Tuple[float, float] = (0.7, 1.0)) -> np.ndarray: def random_crop_preserve_quality(self, image: np.ndarray, crop_ratio_range: Tuple[float, float] = (0.7, 1.0)) -> np.ndarray:
""" """
@@ -363,21 +365,306 @@ class DataAugmentation:
return result return result
def augment_single_image(self, image: np.ndarray, num_augmentations: int = None) -> List[np.ndarray]: def augment_single_image(self, image: np.ndarray, num_target_images: int = None) -> List[np.ndarray]:
""" """
Apply each augmentation method separately to create independent augmented versions Apply random combination of augmentation methods to create diverse augmented versions
Args: Args:
image: Input image image: Input image
num_augmentations: Number of augmented versions to create per method num_target_images: Number of target augmented images to generate
Returns: Returns:
List of augmented images (each method creates separate versions) List of augmented images with random method combinations
""" """
num_augmentations = num_augmentations or 3 # Default value num_target_images = num_target_images or 3 # Default value
# Get strategy config
strategy_config = self.config.get("strategy", {})
methods_config = self.config.get("methods", {})
final_config = self.config.get("final_processing", {})
mode = strategy_config.get("mode", "random_combine")
min_methods = strategy_config.get("min_methods", 2)
max_methods = strategy_config.get("max_methods", 4)
if mode == "random_combine":
return self._augment_random_combine(image, num_target_images, methods_config, final_config, min_methods, max_methods)
elif mode == "sequential":
return self._augment_sequential(image, num_target_images, methods_config, final_config)
elif mode == "individual":
return self._augment_individual_legacy(image, num_target_images)
else:
# Fallback to legacy method
return self._augment_individual_legacy(image, num_target_images)
def _augment_random_combine(self, image: np.ndarray, num_target_images: int,
methods_config: dict, final_config: dict,
min_methods: int, max_methods: int) -> List[np.ndarray]:
"""Apply random combination of methods"""
augmented_images = [] augmented_images = []
# Get configuration # Get enabled methods with their probabilities
available_methods = []
for method_name, method_config in methods_config.items():
if method_config.get("enabled", False):
available_methods.append((method_name, method_config))
if not available_methods:
self.logger.warning("No augmentation methods enabled!")
return [image.copy() for _ in range(num_target_images)]
for i in range(num_target_images):
# Decide number of methods for this image
num_methods = random.randint(min_methods, min(max_methods, len(available_methods)))
# Select methods based on probability
selected_methods = self._select_methods_by_probability(available_methods, num_methods)
# Apply selected methods in sequence
augmented = image.copy()
method_names = []
for method_name, method_config in selected_methods:
if random.random() < method_config.get("probability", 0.5):
augmented = self._apply_single_method(augmented, method_name, method_config)
method_names.append(method_name)
# Apply final processing
augmented = self._apply_final_processing(augmented, final_config)
# Resize preserving aspect ratio
target_size = self.image_processor.target_size
if target_size:
augmented = self.resize_preserve_aspect(augmented, target_size)
augmented_images.append(augmented)
return augmented_images
def _select_methods_by_probability(self, available_methods: List[Tuple], num_methods: int) -> List[Tuple]:
"""Select methods based on their probability weights"""
# Create weighted list
weighted_methods = []
for method_name, method_config in available_methods:
probability = method_config.get("probability", 0.5)
weighted_methods.append((method_name, method_config, probability))
# Sort by probability (highest first) and select top candidates
weighted_methods.sort(key=lambda x: x[2], reverse=True)
# Use weighted random selection
selected = []
remaining_methods = weighted_methods.copy()
for _ in range(num_methods):
if not remaining_methods:
break
# Calculate cumulative probabilities
total_prob = sum(method[2] for method in remaining_methods)
if total_prob == 0:
# If all probabilities are 0, select randomly
selected_method = random.choice(remaining_methods)
else:
rand_val = random.uniform(0, total_prob)
cumulative_prob = 0
selected_method = None
for method in remaining_methods:
cumulative_prob += method[2]
if rand_val <= cumulative_prob:
selected_method = method
break
if selected_method is None:
selected_method = remaining_methods[-1]
selected.append((selected_method[0], selected_method[1]))
remaining_methods.remove(selected_method)
return selected
def _apply_single_method(self, image: np.ndarray, method_name: str, method_config: dict) -> np.ndarray:
"""Apply a single augmentation method"""
try:
if method_name == "rotation":
angles = method_config.get("angles", [30, 60, 90, 120, 150, 180, 210, 240, 300, 330])
angle = random.choice(angles)
return self.rotate_image_preserve_quality(image, angle)
elif method_name == "random_cropping":
ratio_range = method_config.get("ratio_range", (0.7, 1.0))
return self.random_crop_preserve_quality(image, ratio_range)
elif method_name == "random_noise":
mean_range = method_config.get("mean_range", (0.0, 0.7))
variance_range = method_config.get("variance_range", (0.0, 0.1))
return self.add_random_noise_preserve_quality(image, mean_range, variance_range)
elif method_name == "partial_blockage":
num_range = method_config.get("num_occlusions_range", (1, 100))
coverage_range = method_config.get("coverage_range", (0.0, 0.25))
variance_range = method_config.get("variance_range", (0.0, 0.1))
return self.add_partial_blockage_preserve_quality(image, num_range, coverage_range, variance_range)
elif method_name == "blurring":
kernel_range = method_config.get("kernel_ratio_range", (0.0, 0.0084))
return self.apply_blurring_preserve_quality(image, kernel_range)
elif method_name == "brightness_contrast":
alpha_range = method_config.get("alpha_range", (0.4, 3.0))
beta_range = method_config.get("beta_range", (1, 100))
return self.adjust_brightness_contrast_preserve_quality(image, alpha_range, beta_range)
elif method_name == "color_jitter":
return self.apply_color_jitter(image, method_config)
elif method_name == "perspective":
distortion_scale = method_config.get("distortion_scale", 0.2)
return self.apply_perspective_transform(image, distortion_scale)
else:
return image
except Exception as e:
print(f"Error applying method {method_name}: {e}")
return image
def _apply_final_processing(self, image: np.ndarray, final_config: dict) -> np.ndarray:
"""Apply final processing steps - ALWAYS applied to all outputs"""
# Grayscale conversion - ALWAYS applied if enabled
grayscale_config = final_config.get("grayscale", {})
if grayscale_config.get("enabled", False):
# Always apply grayscale, no random check
image = self.convert_to_grayscale_preserve_quality(image)
# Quality enhancement (future feature)
quality_config = final_config.get("quality_enhancement", {})
if quality_config.get("enabled", False):
# TODO: Implement quality enhancement
pass
return image
def apply_color_jitter(self, image: np.ndarray, config: dict) -> np.ndarray:
"""
Apply color jittering (brightness, contrast, saturation, hue adjustments)
Args:
image: Input image
config: Color jitter configuration
Returns:
Color-jittered image
"""
# Get parameters
brightness_range = config.get("brightness_range", [0.8, 1.2])
contrast_range = config.get("contrast_range", [0.8, 1.2])
saturation_range = config.get("saturation_range", [0.8, 1.2])
hue_range = config.get("hue_range", [-0.1, 0.1])
# Convert to HSV for saturation and hue adjustments
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV).astype(np.float32)
# Apply brightness (adjust V channel)
brightness_factor = random.uniform(brightness_range[0], brightness_range[1])
hsv[:, :, 2] = np.clip(hsv[:, :, 2] * brightness_factor, 0, 255)
# Apply saturation (adjust S channel)
saturation_factor = random.uniform(saturation_range[0], saturation_range[1])
hsv[:, :, 1] = np.clip(hsv[:, :, 1] * saturation_factor, 0, 255)
# Apply hue shift (adjust H channel)
hue_shift = random.uniform(hue_range[0], hue_range[1]) * 179 # OpenCV hue range is 0-179
hsv[:, :, 0] = (hsv[:, :, 0] + hue_shift) % 180
# Convert back to RGB
result = cv2.cvtColor(hsv.astype(np.uint8), cv2.COLOR_HSV2RGB)
# Apply contrast (after converting back to RGB)
contrast_factor = random.uniform(contrast_range[0], contrast_range[1])
result = cv2.convertScaleAbs(result, alpha=contrast_factor, beta=0)
return result
def apply_perspective_transform(self, image: np.ndarray, distortion_scale: float = 0.2) -> np.ndarray:
"""
Apply perspective transformation to simulate viewing angle changes
Args:
image: Input image
distortion_scale: Scale of perspective distortion (0.0 to 1.0)
Returns:
Perspective-transformed image
"""
height, width = image.shape[:2]
# Define source points (corners of original image)
src_points = np.float32([
[0, 0],
[width-1, 0],
[width-1, height-1],
[0, height-1]
])
# Add random distortion to destination points
max_distortion = min(width, height) * distortion_scale
dst_points = np.float32([
[random.uniform(0, max_distortion), random.uniform(0, max_distortion)],
[width-1-random.uniform(0, max_distortion), random.uniform(0, max_distortion)],
[width-1-random.uniform(0, max_distortion), height-1-random.uniform(0, max_distortion)],
[random.uniform(0, max_distortion), height-1-random.uniform(0, max_distortion)]
])
# Calculate perspective transformation matrix
matrix = cv2.getPerspectiveTransform(src_points, dst_points)
# Apply transformation
result = cv2.warpPerspective(image, matrix, (width, height),
borderMode=cv2.BORDER_CONSTANT,
borderValue=(255, 255, 255))
return result
def _augment_sequential(self, image: np.ndarray, num_target_images: int,
methods_config: dict, final_config: dict) -> List[np.ndarray]:
"""Apply methods in sequence (pipeline style)"""
augmented_images = []
# Get enabled methods
enabled_methods = [
(name, config) for name, config in methods_config.items()
if config.get("enabled", False)
]
for i in range(num_target_images):
augmented = image.copy()
# Apply all enabled methods in sequence
for method_name, method_config in enabled_methods:
if random.random() < method_config.get("probability", 0.5):
augmented = self._apply_single_method(augmented, method_name, method_config)
# Apply final processing
augmented = self._apply_final_processing(augmented, final_config)
# Resize preserving aspect ratio
target_size = self.image_processor.target_size
if target_size:
augmented = self.resize_preserve_aspect(augmented, target_size)
augmented_images.append(augmented)
return augmented_images
def _augment_individual_legacy(self, image: np.ndarray, num_target_images: int) -> List[np.ndarray]:
"""Legacy individual method application (backward compatibility)"""
# This is the old implementation for backward compatibility
augmented_images = []
# Get old-style configuration
rotation_config = self.config.get("rotation", {}) rotation_config = self.config.get("rotation", {})
cropping_config = self.config.get("random_cropping", {}) cropping_config = self.config.get("random_cropping", {})
noise_config = self.config.get("random_noise", {}) noise_config = self.config.get("random_noise", {})
@@ -386,177 +673,272 @@ class DataAugmentation:
blurring_config = self.config.get("blurring", {}) blurring_config = self.config.get("blurring", {})
brightness_contrast_config = self.config.get("brightness_contrast", {}) brightness_contrast_config = self.config.get("brightness_contrast", {})
# Configuration parameters # Apply individual methods (old logic)
angles = rotation_config.get("angles", [30, 60, 120, 150, 180, 210, 240, 300, 330]) methods = [
crop_ratio_range = cropping_config.get("ratio_range", (0.7, 1.0)) ("rotation", rotation_config, self.rotate_image_preserve_quality),
mean_range = noise_config.get("mean_range", (0.0, 0.7)) ("cropping", cropping_config, self.random_crop_preserve_quality),
variance_range = noise_config.get("variance_range", (0.0, 0.1)) ("noise", noise_config, self.add_random_noise_preserve_quality),
num_occlusions_range = blockage_config.get("num_occlusions_range", (1, 100)) ("blockage", blockage_config, self.add_partial_blockage_preserve_quality),
coverage_range = blockage_config.get("coverage_range", (0.0, 0.25)) ("blurring", blurring_config, self.apply_blurring_preserve_quality),
blockage_variance_range = blockage_config.get("variance_range", (0.0, 0.1)) ("brightness_contrast", brightness_contrast_config, self.adjust_brightness_contrast_preserve_quality)
kernel_ratio_range = blurring_config.get("kernel_ratio_range", (0.0, 0.0084)) ]
alpha_range = brightness_contrast_config.get("alpha_range", (0.4, 3.0))
beta_range = brightness_contrast_config.get("beta_range", (1, 100))
# Apply each method separately to create independent versions for method_name, method_config, method_func in methods:
if method_config.get("enabled", False):
for i in range(num_target_images):
augmented = image.copy()
# Apply single method with appropriate parameters
if method_name == "rotation":
angles = method_config.get("angles", [30, 60, 90, 120, 150, 180, 210, 240, 300, 330])
angle = random.choice(angles)
augmented = method_func(augmented, angle)
elif method_name == "cropping":
ratio_range = method_config.get("ratio_range", (0.7, 1.0))
augmented = method_func(augmented, ratio_range)
# Add other method parameter handling as needed
# 1. Rotation only # Resize preserving aspect ratio
if rotation_config.get("enabled", False): target_size = self.image_processor.target_size
for i in range(num_augmentations): if target_size:
augmented = image.copy() augmented = self.resize_preserve_aspect(augmented, target_size)
angle = random.choice(angles)
augmented = self.rotate_image_preserve_quality(augmented, angle)
# Resize preserving aspect ratio augmented_images.append(augmented)
target_size = self.image_processor.target_size
if target_size:
augmented = self.resize_preserve_aspect(augmented, target_size)
augmented_images.append(augmented) # Apply grayscale to all images
# 2. Random cropping only
if cropping_config.get("enabled", False):
for i in range(num_augmentations):
augmented = image.copy()
augmented = self.random_crop_preserve_quality(augmented, crop_ratio_range)
# Resize preserving aspect ratio
target_size = self.image_processor.target_size
if target_size:
augmented = self.resize_preserve_aspect(augmented, target_size)
augmented_images.append(augmented)
# 3. Random noise only
if noise_config.get("enabled", False):
for i in range(num_augmentations):
augmented = image.copy()
augmented = self.add_random_noise_preserve_quality(augmented, mean_range, variance_range)
# Resize preserving aspect ratio
target_size = self.image_processor.target_size
if target_size:
augmented = self.resize_preserve_aspect(augmented, target_size)
augmented_images.append(augmented)
# 4. Partial blockage only
if blockage_config.get("enabled", False):
for i in range(num_augmentations):
augmented = image.copy()
augmented = self.add_partial_blockage_preserve_quality(augmented, num_occlusions_range, coverage_range, blockage_variance_range)
# Resize preserving aspect ratio
target_size = self.image_processor.target_size
if target_size:
augmented = self.resize_preserve_aspect(augmented, target_size)
augmented_images.append(augmented)
# 5. Blurring only
if blurring_config.get("enabled", False):
for i in range(num_augmentations):
augmented = image.copy()
augmented = self.apply_blurring_preserve_quality(augmented, kernel_ratio_range)
# Resize preserving aspect ratio
target_size = self.image_processor.target_size
if target_size:
augmented = self.resize_preserve_aspect(augmented, target_size)
augmented_images.append(augmented)
# 6. Brightness/Contrast only
if brightness_contrast_config.get("enabled", False):
for i in range(num_augmentations):
augmented = image.copy()
augmented = self.adjust_brightness_contrast_preserve_quality(augmented, alpha_range, beta_range)
# Resize preserving aspect ratio
target_size = self.image_processor.target_size
if target_size:
augmented = self.resize_preserve_aspect(augmented, target_size)
augmented_images.append(augmented)
# 7. Apply grayscale as final step to ALL augmented images
if grayscale_config.get("enabled", False): if grayscale_config.get("enabled", False):
for i in range(len(augmented_images)): for i in range(len(augmented_images)):
augmented_images[i] = self.convert_to_grayscale_preserve_quality(augmented_images[i]) augmented_images[i] = self.convert_to_grayscale_preserve_quality(augmented_images[i])
return augmented_images return augmented_images
def augment_image_file(self, image_path: Path, output_dir: Path, num_augmentations: int = None) -> List[Path]: def augment_image_file(self, image_path: Path, output_dir: Path, num_target_images: int = None) -> List[Path]:
""" """
Augment a single image file and save results with quality preservation Augment a single image file and save results with quality preservation
Args: Args:
image_path: Path to input image image_path: Path to input image
output_dir: Output directory for augmented images output_dir: Output directory for augmented images
num_augmentations: Number of augmented versions to create per method num_target_images: Number of target augmented images to generate
Returns: Returns:
List of paths to saved augmented images List of paths to saved augmented images
""" """
# Load image without resizing to preserve original quality # Load image without resizing to preserve original quality
image = load_image(image_path, None) # Load original size image = load_image(image_path, None)
if image is None: if image is None:
return [] return []
# Apply augmentations # Apply augmentations
augmented_images = self.augment_single_image(image, num_augmentations) augmented_images = self.augment_single_image(image, num_target_images)
# Save augmented images with method names # Save augmented images
saved_paths = [] saved_paths = []
method_names = ["rotation", "cropping", "noise", "blockage", "blurring", "brightness_contrast", "grayscale"]
method_index = 0
for i, aug_image in enumerate(augmented_images): for i, aug_image in enumerate(augmented_images):
# Determine method name based on index base_name = image_path.stem
method_name = method_names[method_index // num_augmentations] if method_index // num_augmentations < len(method_names) else "aug" output_filename = f"{base_name}_aug_{i+1:03d}.jpg"
output_path = output_dir / output_filename
# Create output filename with method name
output_filename = create_augmented_filename(image_path, (i % num_augmentations) + 1, method_name)
output_path = output_dir / output_filename.name
# Save image
if save_image(aug_image, output_path): if save_image(aug_image, output_path):
saved_paths.append(output_path) saved_paths.append(output_path)
method_index += 1 return saved_paths
def augment_image_file_with_raw(self, image_path: Path, output_dir: Path,
num_total_versions: int = None) -> List[Path]:
"""
Augment a single image file including raw/original version
Args:
image_path: Path to input image
output_dir: Output directory for all image versions
num_total_versions: Total number of versions (including raw)
Returns:
List of paths to saved images (raw + augmented)
"""
# Load original image
image = load_image(image_path, None)
if image is None:
return []
saved_paths = []
base_name = image_path.stem
# Always save raw version first (resized but not augmented)
if num_total_versions > 0:
raw_image = image.copy()
# Apply final processing (grayscale) but no augmentation
final_config = self.config.get("final_processing", {})
raw_image = self._apply_final_processing(raw_image, final_config)
# Resize to target size
target_size = self.image_processor.target_size
if target_size:
raw_image = self.resize_preserve_aspect(raw_image, target_size)
# Save raw version
raw_filename = f"{base_name}_raw_001.jpg"
raw_path = output_dir / raw_filename
if save_image(raw_image, raw_path):
saved_paths.append(raw_path)
# Generate augmented versions for remaining slots
num_augmented = max(0, num_total_versions - 1)
if num_augmented > 0:
augmented_images = self.augment_single_image(image, num_augmented)
for i, aug_image in enumerate(augmented_images):
aug_filename = f"{base_name}_aug_{i+1:03d}.jpg"
aug_path = output_dir / aug_filename
if save_image(aug_image, aug_path):
saved_paths.append(aug_path)
return saved_paths return saved_paths
def batch_augment(self, input_dir: Path, output_dir: Path, num_augmentations: int = None) -> Dict[str, List[Path]]: def batch_augment(self, input_dir: Path, output_dir: Path,
multiplication_factor: float = None, random_seed: int = None) -> Dict[str, List[Path]]:
""" """
Augment all images in a directory Augment images in a directory with smart sampling and multiplication strategy
Args: Args:
input_dir: Input directory containing images input_dir: Input directory containing images
output_dir: Output directory for augmented images output_dir: Output directory for augmented images
num_augmentations: Number of augmented versions per image multiplication_factor:
- If < 1.0: Sample percentage of input data to augment
- If >= 1.0: Target multiplication factor for output data size
random_seed: Random seed for reproducibility
Returns: Returns:
Dictionary mapping original images to their augmented versions Dictionary containing results and statistics
""" """
from utils import get_image_files from utils import get_image_files
image_files = get_image_files(input_dir) # Set random seed for reproducibility
if random_seed is not None:
random.seed(random_seed)
np.random.seed(random_seed)
# Get all input images
all_image_files = get_image_files(input_dir)
if not all_image_files:
print("No images found in input directory")
return {}
# Get multiplication factor from config if not provided
if multiplication_factor is None:
data_strategy = self.config.get("data_strategy", {})
multiplication_factor = data_strategy.get("multiplication_factor", 3.0)
print(f"Found {len(all_image_files)} total images")
print(f"Multiplication factor: {multiplication_factor}")
# Determine sampling strategy
if multiplication_factor < 1.0:
# Sampling mode: Take a percentage of input data
num_selected = int(len(all_image_files) * multiplication_factor)
selected_images = self._sample_images(all_image_files, num_selected)
target_total_images = len(all_image_files) # Keep original dataset size
images_per_input = max(1, target_total_images // len(selected_images))
print(f"SAMPLING MODE: Selected {len(selected_images)} images ({multiplication_factor*100:.1f}%)")
print(f"Target: {target_total_images} total images, {images_per_input} per selected image")
else:
# Multiplication mode: Multiply dataset size
selected_images = all_image_files
target_total_images = int(len(all_image_files) * multiplication_factor)
images_per_input = max(1, target_total_images // len(selected_images))
print(f"MULTIPLICATION MODE: Processing all {len(selected_images)} images")
print(f"Target: {target_total_images} total images ({multiplication_factor}x original), {images_per_input} per image")
# Process selected images
results = {} results = {}
total_generated = 0
print(f"Found {len(image_files)} images to augment") for i, image_path in enumerate(selected_images):
print_progress(i + 1, len(selected_images), f"Processing {image_path.name}")
for i, image_path in enumerate(image_files): # Calculate number of versions for this image (including raw)
print_progress(i + 1, len(image_files), "Augmenting images") remaining_images = target_total_images - total_generated
remaining_inputs = len(selected_images) - i
total_versions_needed = min(images_per_input, remaining_images)
# Augment single image # Always include raw image, then augmented ones
augmented_paths = self.augment_image_file(image_path, output_dir, num_augmentations) augmented_paths = self.augment_image_file_with_raw(
image_path, output_dir, total_versions_needed
)
if augmented_paths: if augmented_paths:
results[str(image_path)] = augmented_paths results[str(image_path)] = augmented_paths
total_generated += len(augmented_paths)
print(f"\nAugmented {len(results)} images successfully") # Generate summary
return results summary = {
"input_images": len(all_image_files),
"selected_images": len(selected_images),
"target_total": target_total_images,
"actual_generated": total_generated,
"multiplication_factor": multiplication_factor,
"mode": "sampling" if multiplication_factor < 1.0 else "multiplication",
"results": results,
"efficiency": total_generated / target_total_images if target_total_images > 0 else 0
}
print(f"\n✅ Augmentation completed!")
print(f"Generated {total_generated} images from {len(selected_images)} selected images")
print(f"Target vs Actual: {target_total_images} → {total_generated} ({summary['efficiency']:.1%} efficiency)")
return summary
def _sample_images(self, image_files: List[Path], num_selected: int) -> List[Path]:
"""Sample images from the input list based on strategy"""
data_strategy = self.config.get("data_strategy", {})
sampling_config = data_strategy.get("sampling", {})
method = sampling_config.get("method", "random")
preserve_distribution = sampling_config.get("preserve_distribution", True)
if method == "random":
# Simple random sampling
return random.sample(image_files, min(num_selected, len(image_files)))
elif method == "stratified" and preserve_distribution:
# Stratified sampling by file extension
extension_groups = {}
for img_file in image_files:
ext = img_file.suffix.lower()
if ext not in extension_groups:
extension_groups[ext] = []
extension_groups[ext].append(img_file)
selected = []
for ext, files in extension_groups.items():
# Sample proportionally from each extension group
group_size = max(1, int(num_selected * len(files) / len(image_files)))
group_selected = random.sample(files, min(group_size, len(files)))
selected.extend(group_selected)
# If we have too few, add more randomly
if len(selected) < num_selected:
remaining = [f for f in image_files if f not in selected]
additional = random.sample(remaining,
min(num_selected - len(selected), len(remaining)))
selected.extend(additional)
return selected[:num_selected]
elif method == "uniform":
# Uniform sampling - evenly spaced
if num_selected >= len(image_files):
return image_files
step = len(image_files) / num_selected
indices = [int(i * step) for i in range(num_selected)]
return [image_files[i] for i in indices]
else:
# Fallback to random
return random.sample(image_files, min(num_selected, len(image_files)))
def get_augmentation_summary(self, results: Dict[str, List[Path]]) -> Dict[str, Any]: def get_augmentation_summary(self, results: Dict[str, List[Path]]) -> Dict[str, Any]:
""" """