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Welcome to the AN-Augment Wiki

AN-Augment (Advanced and Novel Augmentation) is a Python library offering advanced and innovative data augmentation techniques for diverse domains, from medical imaging to environmental data. It enhances dataset diversity, improving model robustness and performance across applications.

Augmentations

AN-Augment provides a wide range of built-in augmentations to enhance your datasets across multiple domains:

Core Features:

• Fine-Grained Control: Adjustable parameters for precise transformation intensity and probability.
• Batch Processing & Randomization: Apply augmentations to image/data batches with flexible randomization settings.
• Visualization Tools: Easily visualize original and augmented data side-by-side for quick quality checks.

Multi-Domain Augmentations:

• Medical Imaging:

  • Augment Metadata for Labels and Masks: Streamline label and mask processing during augmentation.
  • Add GAN-Based Synthetic Image Generation: Generate synthetic medical images to enrich datasets.
  • Random Block Masks for Occlusion Simulation: Simulate occlusions for robustness testing.
  • 3D Elastic Transformations for Volumetric Data: Apply elastic deformations for 3D medical volumes.
  • Fourier Transform Filters for Frequency-Based Augmentation: Enhance feature learning with frequency-domain transformations.
  • Contrast-Enhanced Region Augmentation: Improve model sensitivity to contrast-specific areas.
  • Simulated Lesions/Anomalies Augmentation: Add realistic lesions to improve rare anomaly detection.

• Mechanical Simulations:

  • Add Stress-Based Transformations: Simulate structural stress conditions for robustness.
  • Dynamic Scaling and Rotation: Introduce scaling and rotational effects to mechanical structures.
  • Apply Structural Noise Augmentation: Add noise specific to mechanical patterns to enrich datasets.
  • Load Simulation Augmentation: Simulate load-based distortions and deformations.

• Environmental Data:

  • Simulate Cloud Occlusions: Apply cloud-like occlusions for satellite and environmental imagery.
  • Add Noise for Sensor Simulation: Introduce sensor-based noise patterns for realism.
  • Spatial Scaling and Shifting: Apply shifts and scaling to spatial data.
  • Enhance Terrain Patterns: Augment terrain-based features with synthetic distortions.

• Water Systems:

  • Wave Simulation Augmentation: Generate wave-like distortions for water bodies.
  • Flow Distortion Augmentation: Simulate flow patterns in water datasets.
  • Add Scaling Factors for Dynamic Modeling: Apply scaling for dynamic hydrodynamic modeling.
  • Turbulence Simulation: Introduce turbulence effects for water-based data.

Installation

To install AN-Augment, use pip:

pip install an-augment

Alternatively, you can install the latest development version from the repository:

git clone /~https://github.com/lunovian/an-augment.git
cd an-augment
pip install -r requirements.txt

Usage

Here’s a quick example of how to use AN-Augment for medical imaging augmentations:

import cv2
import matplotlib.pyplot as plt
from an_augment.default import scale, flip, noise, random_rotation, random_crop, intensity, elastic_deformation, occlusion, blur

# Load the image
image_path = "images/mri.jpg"
image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
if image is None:
    raise FileNotFoundError(f"The image at path '{image_path}' could not be loaded. Ensure the path is correct.")

image = image / 255.0  # Normalize the image to [0, 1]

# Define augmentation parameters
params = {
    'blur': {'blur_radius': 2},
    'elastic_deformation': {'alpha': 30, 'sigma': 4},
    'flip': {'flip_horizontal': True, 'flip_vertical': False},
    'intensity': {'brightness_factor': 1.2, 'contrast_factor': 1.3},
    'noise': {'noise_type': 'gaussian', 'noise_intensity': 0.1},
    'occlusion': {'mask_shape': 'rectangle', 'mask_size_range': (0.1, 0.2)},
    'random_rotation': {'angle_range': (-15, 15)},
    'random_crop': {'crop_size': (0.8, 0.8), 'scaling_factor': 1.0},
    'scale': {'scale_factor': 0.8}
}

# Apply augmentations (manually applying each augmentation)
try:
    augmented_image = blur(image, **params['blur'])
    augmented_image = elastic_deformation(image, **params['elastic_deformation'])
    augmented_image = flip(augmented_image, **params['flip'])
    augmented_image = intensity(augmented_image, **params['intensity'])
    augmented_image = noise(augmented_image, **params['noise'])
    augmented_image = occlusion(augmented_image, **params['occlusion'])
    augmented_image = random_rotation(augmented_image, **params['random_rotation'])
    augmented_image = random_crop(augmented_image, **params['random_crop'])
    augmented_image = scale(augmented_image, **params['scale'])
except Exception as e:
    raise RuntimeError(f"An error occurred while applying augmentations: {e}")

# Display original and augmented images
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.title("Original Image")
plt.imshow(image, cmap="gray")
plt.axis("off")

plt.subplot(1, 2, 2)
plt.title("Augmented Image")
plt.imshow(augmented_image, cmap="gray")
plt.axis("off")

plt.tight_layout()
plt.show()

Here is an example of the original image and the augmented result:

Original Image	Augmented Image

For other domains (e.g., mechanical or environmental), the process is similar—just import the relevant augmentation module (e.g., MechanicalAugmentation, EnvironmentalAugmentation, etc.).

Contributing

We welcome contributions! If you'd like to help expand AN-Augment:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Submit a pull request with detailed explanations.

Support

If you encounter any issues or have questions, please check:

• GitHub Issues
• Frequently Asked Questions

Let me know if you'd like to adjust or expand any section!