Implement improved zoom augmentations through albumentations module

[Copilot]

This PR implements a comprehensive, modular augmentation system to address the core challenge of generalizing across sensors and acquisition conditions in airborne biodiversity observation. The new system moves augmentations from inline implementation to a dedicated module with configurable zoom augmentations for improved multi-resolution training.

Key Features

🔧 Modular Augmentation System

• Created dedicated src/deepforest/augmentations.py module with 10+ augmentations
• Moved from inline implementation in datasets/training.py to reusable module
• Supports flexible configuration via strings, lists, or parameter dictionaries

🔍 Zoom Augmentations for Multi-Resolution Training

Implements the specifically requested augmentations:

• Downscale: Simulates different acquisition heights/resolutions (scale_range parameter)
• RandomSizedBBoxSafeCrop: Crops at different scales while preserving bounding boxes
• PadIfNeeded: Ensures minimum image sizes for consistent processing

⚙️ Flexible Configuration Options

# Config file approach
train:
  augmentations: ["HorizontalFlip", "Downscale", "RandomBrightnessContrast"]
  
# Or with custom parameters
train:
  augmentations:
    Downscale: {scale_range: [0.25, 0.75], p: 0.5}
    RandomSizedBBoxSafeCrop: {height: 400, width: 400, p: 0.3}

# Runtime configuration
config_args = {"train": {"augmentations": ["Downscale", "PadIfNeeded"]}}
model = main.deepforest(config_args=config_args)

# Direct parameter override
ds = model.load_dataset(csv_file, augmentations=["HorizontalFlip", "Blur"])

🔄 Full Backward Compatibility

• Existing code continues to work unchanged
• Default behavior (augment=True) still uses HorizontalFlip
• Custom transform functions still supported via transforms parameter

Example Usage for Multi-Resolution Training

# Configuration optimized for airborne biodiversity observation
config_args = {
    "train": {
        "augmentations": {
            # Simulate different acquisition heights/resolutions
            "Downscale": {"scale_range": (0.25, 0.75), "p": 0.5},
            
            # Crop at different scales while preserving objects
            "RandomSizedBBoxSafeCrop": {"height": 400, "width": 400, "p": 0.3},
            
            # Ensure minimum image size
            "PadIfNeeded": {"min_height": 400, "min_width": 400, "p": 1.0},
            
            # Basic augmentations
            "HorizontalFlip": {"p": 0.5},
            "RandomBrightnessContrast": {"p": 0.3}
        }
    }
}

model = main.deepforest(config_args=config_args)

Changes Made

1. New Module: src/deepforest/augmentations.py with configurable transform system
2. Config Support: Added augmentations field to train section in config.yaml
3. Enhanced Dataset: Updated BoxDataset to accept augmentations configuration
4. Updated Main: Modified train_dataloader() to pass config-based augmentations
5. Comprehensive Testing: 13 unit tests + 4 integration tests ensuring functionality
6. Documentation: Complete rewrite of augmentation section in training guide
7. Demo Script: Practical examples showing zoom augmentation usage

Testing

• ✅ All existing tests pass (backward compatibility verified)
• ✅ 13 new augmentation tests covering all functionality
• ✅ 4 integration tests validating config-based usage
• ✅ Manual testing confirms multi-resolution training works correctly

Benefits for Airborne Biodiversity Observation

This implementation directly addresses the stated challenge by providing:

• Improved generalization across different sensors and resolutions
• Better handling of varying object sizes and focal views
• Enhanced robustness to acquisition condition variations
• Easy experimentation with different augmentation strategies
• Low data regime optimization through effective data augmentation

Fixes #735.

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[bw4sz]

Add blur augmentations as well.

[Copilot]

Add blur augmentations as well.

Added comprehensive blur augmentations including GaussianBlur, MedianBlur, MotionBlur, ZoomBlur, and AdvancedBlur. These are particularly relevant for airborne biodiversity observation as they simulate camera movement, atmospheric effects, sensor noise, and zoom effects during image acquisition. All blur augmentations are configurable via the existing config system and include comprehensive tests. See commit b00d498.