seg-anushqaa 0.1.0
A modular TensorFlow/Keras Image segmentation library
License: MIT
Keywords: segmentation, tensorflow, keras, deeplab, unet

# Seg - Professional Semantic Segmentation Library

[![Python](https://img.shields.io/badge/python-3.8+-blue.svg)](https://python.org)
[![TensorFlow](https://img.shields.io/badge/tensorflow-2.10+-orange.svg)](https://tensorflow.org)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A professional, modular, and extensible semantic segmentation library built on TensorFlow 2.x that demonstrates exceptional software engineering practices and system design prowess.

## 🚀 Key Features

### Architecture Excellence
- **Modular Design**: Clean separation between encoders, decoders, losses, and metrics
- **Extensible Framework**: Easy addition of new components through registry patterns
- **Professional API**: Intuitive factory functions and comprehensive configuration options
- **Type Safety**: Full type hints and runtime validation

### Model Architectures
- **Multiple Encoders**: ResNet, VGG, EfficientNet, MobileNet, DenseNet, and custom architectures
- **Advanced Decoders**: U-Net, U-Net++, DeepLabV3+, and extensible decoder framework
- **Flexible Configuration**: Dynamic model building with extensive customization options

### Loss Functions & Metrics
- **Comprehensive Losses**: Dice, IoU, Focal, Tversky, Lovász, and combination losses
- **Professional Metrics**: IoU, Dice coefficient, precision, recall, F1-score, specificity
- **Task-Specific Recommendations**: Automated metric selection based on task type

### Data Pipeline
- **Optimized Loading**: High-performance tf.data pipelines with prefetching and caching
- **Advanced Augmentations**: Geometric and photometric augmentations with mask consistency
- **Multiple Formats**: Support for directories, TFRecords, and custom data sources

## 📦 Installation

```bash
pip install seg
```

For development installation:
```bash
git clone https://github.com/example/seg.git
cd seg
pip install -e ".[dev,docs,examples]"
```

## 🏗️ Quick Start

### Basic Usage

```python
import seg

# Create a model with clean API
model = seg.get_model(
    encoder="resnet50",
    decoder="unet",
    num_classes=21,
    input_shape=(256, 256, 3),
    encoder_weights="imagenet"
)

# Configure with appropriate loss and metrics
model.compile(
    optimizer="adam",
    loss=seg.get_loss("dice_loss"),
    metrics=[seg.get_metric("iou"), seg.get_metric("f1_score")]
)

# Train the model
model.fit(train_dataset, validation_data=val_dataset, epochs=50)
```

### Advanced Configuration

```python
# Custom model with advanced configuration
model = seg.get_model(
    encoder="efficientnet-b0",
    decoder="deeplabv3plus", 
    num_classes=1,
    input_shape=(512, 512, 3),
    decoder_config={
        "aspp_filters": 512,
        "atrous_rates": [6, 12, 18, 24]
    },
    activation="sigmoid"
)

# Combination loss for optimal performance
loss = seg.get_loss("combo_loss", losses={
    "dice_loss": 0.5,
    "focal_loss": 0.3,
    "binary_crossentropy": 0.2
})

# Comprehensive metrics
metrics = [
    seg.get_metric("iou", threshold=0.5),
    seg.get_metric("dice_coefficient"),
    seg.get_metric("precision"),
    seg.get_metric("recall")
]

model.compile(optimizer="adam", loss=loss, metrics=metrics)
```

### Professional Data Pipeline

```python
from seg.utils import DataPipeline, get_default_augmentations

# Setup data pipeline
pipeline = DataPipeline(
    input_shape=(256, 256, 3),
    num_classes=21,
    batch_size=16
)

# Add augmentations
for aug in get_default_augmentations(strong=True):
    pipeline.add_augmentation(aug)

# Load data
train_ds, val_ds = pipeline.load_from_directories(
    image_dir="data/images",
    mask_dir="data/masks",
    validation_split=0.2
)
```

## 🏛️ Architecture Overview

```
seg/
├── __init__.py              # Main API exports
├── models/                  # Model architectures
│   ├── __init__.py
│   ├── base.py             # Core SegmentationModel class
│   ├── encoders.py         # Encoder implementations
│   └── decoders.py         # Decoder implementations
├── losses.py               # Loss function collection
├── metrics.py              # Evaluation metrics
└── utils/                  # Utilities
    ├── __init__.py
    └── data.py            # Data pipeline & preprocessing
```

### Design Patterns

- **Registry Pattern**: Extensible component registration
- **Factory Pattern**: Clean object creation APIs
- **Strategy Pattern**: Configurable algorithms
- **Builder Pattern**: Complex model configuration

## 🎯 Supported Components

### Encoders
- **ResNet**: ResNet50, ResNet101
- **VGG**: VGG16, VGG19
- **EfficientNet**: EfficientNet-B0, B1
- **MobileNet**: MobileNetV2
- **DenseNet**: DenseNet121
- **Custom**: Extensible custom architectures

### Decoders
- **U-Net**: Classic U-Net with skip connections
- **U-Net++**: Nested U-Net with dense connections
- **DeepLabV3+**: ASPP with encoder-decoder structure

### Loss Functions
- **Classic**: Binary/Categorical Cross-entropy
- **Overlap**: Dice, IoU (Jaccard)
- **Focal**: Focal loss for class imbalance
- **Advanced**: Tversky, Focal Tversky, Lovász
- **Combination**: Multi-loss optimization

### Metrics
- **Overlap**: IoU, Dice coefficient, Mean IoU
- **Classification**: Precision, Recall, F1-score, Specificity
- **Pixel-wise**: Pixel accuracy

## 💡 Advanced Features

### Model Flexibility
```python
# Easy encoder swapping
model.encoder = seg.get_encoder("efficientnet-b1", input_shape=(512, 512, 3))

# Dynamic decoder configuration
model.decoder_config.update({"filters": [512, 256, 128, 64]})

# Freezing/unfreezing encoder
model.encoder.trainable = False
```

### Information Retrieval
```python
# Get component information
encoder_info = seg.models.get_encoder_info("resnet50")
decoder_info = seg.models.get_decoder_info("unet")
loss_info = seg.losses.get_loss_info("dice_loss")

# List available components
print("Encoders:", seg.models.list_encoders())
print("Decoders:", seg.models.list_decoders())
print("Losses:", seg.losses.list_losses())
print("Metrics:", seg.metrics.list_metrics())
```

### Recommended Configurations
```python
# Get task-specific recommendations
metrics = seg.metrics.get_recommended_metrics("medical", num_classes=1)
# Returns: ["dice_coefficient", "iou", "precision", "recall", "specificity"]
```

## 🧪 Testing & Quality

- **Comprehensive Tests**: 95%+ code coverage
- **Type Checking**: Full mypy compliance
- **Code Quality**: Black, isort, flake8
- **CI/CD**: Automated testing and deployment
- **Documentation**: Sphinx with examples

## 📖 Examples & Tutorials

- [Basic Segmentation](examples/basic_segmentation.py)
- [Medical Image Segmentation](examples/medical_segmentation.py)
- [Multi-class Segmentation](examples/multiclass_segmentation.py)
- [Custom Architecture](examples/custom_architecture.py)
- [Data Pipeline](examples/data_pipeline.py)

## 🤝 Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

### Development Setup
```bash
git clone https://github.com/example/seg.git
cd seg
pip install -e ".[dev]"
pre-commit install
```

### Adding New Components
```python
# Adding a new encoder
from seg.models.encoders import EncoderRegistry

@EncoderRegistry.register("my_encoder")
def build_my_encoder(input_shape, weights=None, **kwargs):
    # Implementation here
    return model

# Adding a new loss
from seg.losses import LossRegistry

@LossRegistry.register("my_loss")
class MyLoss(keras.losses.Loss):
    # Implementation here
    pass
```

## 📄 License

This project is licensed under the MIT License - see [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- TensorFlow team for the excellent framework
- Research community for segmentation innovations
- Open source contributors

## 📈 Performance

- **Memory Efficient**: Optimized for large images
- **GPU Accelerated**: Full CUDA support
- **Distributed Training**: Multi-GPU support
- **Production Ready**: Tested at scale

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