hdr-net 源码

# Deep Bilateral Learning for Real-Time Image Enhancements Siggraph 2017 Visit our [Project Page](https://groups.csail.mit.edu/graphics/hdrnet/). [Michael Gharbi](https://mgharbi.com) Jiawen Chen Jonathan T. Barron Samuel W. Hasinoff Fredo Durand Maintained by Michael Gharbi (<gharbi@mit.edu>) Tested on Python 2.7, Ubuntu 14.0, gcc-4.8. ## Disclaimer This is not an official Google product. ## Setup ### Dependencies To install the Python dependencies, run: cd hdrnet pip install -r requirements.txt ### Build Our network requires a custom Tensorflow operator to "slice" in the bilateral grid. To build it, run: cd hdrnet make To build the benchmarking code, run: cd benchmark make Note that the benchmarking code requires a frozen and optimized model. Use `hdrnet/bin/scripts/optimize_graph.sh` and `hdrnet/bin/freeze.py to produce these`. To build the Android demo, see dedicated section below. ### Test Run the test suite to make sure the BilateralSlice operator works correctly: cd hdrnet py.test test ### Download pretrained models We provide a set of pretrained models. One of these is included in the repo (see `pretrained_models/local_laplacian_sample`). To download the rest of them run: cd pretrained_models ./download.py ## Usage To train a model, run the following command: ./hdrnet/bin/train.py <checkpoint_dir> <path/to_training_data/filelist.txt> Look at `sample_data/identity/` for a typical structure of the training data folder. You can monitor the training process using Tensorboard: tensorboard --logdir <checkpoint_dir> To run a trained model on a novel image (or set of images), use: ./hdrnet/bin/run.py <checkpoint_dir> <path/to_eval_data> <output_dir> To prepare a model for use on mobile, freeze the graph, and optimize the network: ./hdrnet/bin/freeze_graph.py <checkpoint_dir> ./hdrnet/bin/scripts/optimize_graph.sh <checkpoint_dir> You will need to change the `${TF_BASE}` environment variable in `./hdrnet/bin/scripts/optimize_graph.sh` and compile the necessary tensorflow command line tools for this (automated in the script). ## Android prototype We will add it to this repo soon. ## Known issues and limitations * The BilateralSliceApply operation is GPU only at this point. We do not plan on releasing a CPU implementation. * The provided pre-trained models were updated from an older version and might slightly differ from the models used for evaluation in the paper. * The pre-trained HDR+ model expects as input a specially formatted 16-bit linear input. In summary, starting from Bayer RAW: 1. Subtract black level. 2. Apply white balance channel gains. 3. Demosaic to RGB. 4. Apply lens shading correction (aka vignetting correction). Our Android demo approximates this by undoing the RGB->YUV conversion and white balance, and tone mapping performed by the Qualcomm SOC. It results in slightly different colors than that on the test set. If you run our HDR+ model on an sRGB input, it may produce uncanny colors.