# crfasrnn_keras

**Repository Path**: DwRolin/crfasrnn_keras

## Basic Information

- **Project Name**: crfasrnn_keras
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-13
- **Last Updated**: 2021-09-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CRF-RNN for Semantic Image Segmentation - Keras/Tensorflow version
![sample](sample.png)

<b>Live demo:</b> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [http://crfasrnn.torr.vision](http://crfasrnn.torr.vision) <br/>
<b>PyTorch version:</b> &nbsp;&nbsp;&nbsp;[http://github.com/sadeepj/crfasrnn_pytorch](http://github.com/sadeepj/crfasrnn_pytorch)<br/>
<b>Caffe version:</b>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; [http://github.com/torrvision/crfasrnn](http://github.com/torrvision/crfasrnn)<br/>

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://goo.gl/ciEYZi) or [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).