This is the author's reference implementation of the single-image HDR reconstruction using TensorFlow described in: "LANet: A Luminance Attentive Network with Scale Invariance for HDR Image Reconstruction"
The network architecture details are shown in "model.py" and the data processing is in "utils.py".
- Python3
- numpy
- OpenCV >= 3.4
- Tensorflow == 1.13.1
- TensorLayer == 1.11.1
The pretrained LANet checkpoints can be found in the checkpoints folder on Google Drive. The pretrained panoLANet checkpoints can be found in the checkpoints folder on Google Drive.
- Run your own images (using our trained LANet):
cd LANet
python ./src/main.py --phase test --gpu 0 --checkpoint_dir ./checkpoint_LANet/ --test_dir ./test/ --out_dir ./out/
- Run your own panoramas (using our trained panoLANet):
cd panoLANet
python ./src/main.py --phase test --gpu 0 --checkpoint_dir ./checkpoint_panoLANet/ --test_dir ./test/ --out_dir ./out/
Parameters and their description:
checkpoint_dir
: path to the trained models.
test_dir
: input images directory. This project provides a few sample images.
out_dir
: path to output directory.
See main.py for more settable parameters.