hed-tensor 1.0.0

Holistic Edge Detection (HED) model for TensorFlow/Keras with bundled pretrained weights

HED-Tensor: Holistic Edge Detection

A TensorFlow/Keras implementation of Holistically-Nested Edge Detection (HED) with pretrained weights included. Use it as easily as VGG16 or ResNet!

🚀 Quick Start

Installation

pip install hed-tensor

Usage (3 lines!)

from hed_tensor import HED, detect_edges

model = HED()  # Weights automatically load!
edges = detect_edges('your_image.jpg', model=model)

That's it! No need to download weights separately. 🎉

📦 Features

✅ Pretrained weights included - No extra downloads needed
✅ Simple API - Just like using VGG16 or ResNet
✅ Multiple input formats - Works with file paths, PIL Images, or NumPy arrays
✅ Batch processing - Process multiple images efficiently
✅ TensorFlow 2.x - Modern and fast

🎯 Examples

Basic Usage

from hed_tensor import HED, detect_edges
from PIL import Image

# Load model (weights auto-load)
model = HED()

# Detect edges
edges = detect_edges('image.jpg', model=model)

# Save result
Image.fromarray(edges).save('edges.png')

Using NumPy arrays

import numpy as np
from hed_tensor import HED, detect_edges

model = HED()
image_array = np.array(Image.open('image.jpg'))
edges = detect_edges(image_array, model=model)

Batch Processing

from hed_tensor import batch_detect_edges, HED

model = HED()
image_paths = ['img1.jpg', 'img2.jpg', 'img3.jpg']

edge_maps = batch_detect_edges(
    image_paths, 
    model=model, 
    output_dir='output_edges'
)

Load Custom Weights

# Use your own trained weights
model = HED(weights='path/to/your/checkpoint.h5')

# Or skip loading weights
model = HED(weights=None)

📋 Requirements

• Python >= 3.7
• TensorFlow >= 2.4.0
• NumPy >= 1.19.0
• Pillow >= 8.0.0

🎓 Citation

If you use this package in your research, please cite the original HED paper:

@inproceedings{xie2015holistically,
  title={Holistically-nested edge detection},
  author={Xie, Saining and Tu, Zhuowen},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={1395--1403},
  year={2015}
}

📄 License

MIT License

👨‍💻 Author

Mohammad Saad Nathani
📧 [e-mail]

🤝 Contributing

Contributions are welcome!

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