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https://github.com/sadeepj/crfasrnn_keras

CRF-RNN Keras/Tensorflow version
https://github.com/sadeepj/crfasrnn_keras

crf-as-rnn crf-rnn-keras crf-rnn-tensorflow crfasrnn crfasrnn-keras crfasrnn-tensorflow image-segmentation keras semantic-segmentation tensorflow

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CRF-RNN Keras/Tensorflow version

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README 

        
# CRF-RNN for Semantic Image Segmentation - Keras/Tensorflow version

![sample](sample.png)



Live demo:              [http://crfasrnn.torr.vision](http://crfasrnn.torr.vision) 


PyTorch version:    [http://github.com/sadeepj/crfasrnn_pytorch](http://github.com/sadeepj/crfasrnn_pytorch)


Caffe version:         [http://github.com/torrvision/crfasrnn](http://github.com/torrvision/crfasrnn)



This repository contains Keras/Tensorflow code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper [Conditional Random Fields as Recurrent Neural Networks](http://www.robots.ox.ac.uk/~szheng/papers/CRFasRNN.pdf). The [online demo](http://crfasrnn.torr.vision) of this project won the Best Demo Prize at ICCV 2015. Results of this Keras/Tensorflow code are identical to that of the Caffe and PyTorch based versions above.



If you use this code/model for your research, please cite the following paper:

```

@inproceedings{crfasrnn_ICCV2015,

    author = {Shuai Zheng and Sadeep Jayasumana and Bernardino Romera-Paredes and Vibhav Vineet and

    Zhizhong Su and Dalong Du and Chang Huang and Philip H. S. Torr},

    title  = {Conditional Random Fields as Recurrent Neural Networks},

    booktitle = {International Conference on Computer Vision (ICCV)},

    year   = {2015}

}

```



## Installation Guide



### Step 1: Clone the repository

```

$ git clone https://github.com/sadeepj/crfasrnn_keras.git

```

The root directory of the clone will be referred to as `crfasrnn_keras` hereafter.



### Step 2: Install dependencies



**Note**: If you are using a Python virtualenv, make sure it is activated before running each command in this guide.



Use the `requirements.txt` file (or `requirements_gpu.txt`, if you have a GPU device) in this repository to install all the dependencies via `pip`:

```

$ cd crfasrnn_keras

$ pip install -r requirements.txt  # If you have a GPU device, use requirements_gpu.txt instead

```

As you can notice from the contents of `requirements.txt`, we only depend on `tensorflow`, `keras`, and `h5py`. Additionally, `Pillow` is required for running the demo.

After installing the dependencies, run the following commands to make sure they are properly installed:

```

$ python

>>> import tensorflow

>>> import keras

```

You should not see any errors while importing `tensorflow` and `keras` above.



### Step 3: Build CRF-RNN custom op C++ code



Run `make` inside the `crfasrnn_keras/src/cpp` directory:

```

$ cd crfasrnn_keras/src/cpp

$ make

``` 

Note that the `python` command in the console should refer to the Python interpreter associated with your Tensorflow installation before running the `make` command above.



You will get a new file named `high_dim_filter.so` from this build. If it fails, refer to the official Tensorflow guide for [building a custom op](https://www.tensorflow.org/extend/adding_an_op#build_the_op_library) for help.



**Note**: This make script works on Linux and macOS, but not on Windows OS. If you are on Windows, please check [this issue](https://github.com/tensorflow/models/issues/1103) and the comments therein for build instructions. The official Tensorflow guide for building a custom op does not yet include build instructions for Windows.



### Step 4: Download the pre-trained model weights



Download the model weights from [here](https://github.com/sadeepj/crfasrnn_keras/releases/download/v1.0/crfrnn_keras_model.h5) and place it in the `crfasrnn_keras` directory with the file name `crfrnn_keras_model.h5`.



### Step 5: Run the demo

```

$ cd crfasrnn_keras

$ python run_demo.py

```

If all goes well, you will see the segmentation results in a file named "labels.png".



## Notes

1. Current implementation of the CrfRnnLayer only supports batch_size == 1

2. An experimental GPU version of the CrfRnnLayer that has been tested on CUDA 9 and Tensorflow 1.7 only, is available under the `gpu_support` branch. This code was contributed by [thwjoy](https://github.com/thwjoy).