# FU Berlin - Image Processing SS 18

The assignments will be published to this repository.
All assignments will be IPython Notebooks. That you have to complete.

## Work flow

The rough workflow is:
1. You clone this repository.
2. Edit the exercises.
3. Push it to your private repository.
4. I fetch your code when the assignment is due. (Every Wednesday at 8:00 a.m.)
5. You fetch the latest assignments from this repository.

It is required to use private git repositories.
The university offers free private repositories [here](https://git.imp.fu-berlin.de/)
or you can get [5 GitHub repositories for free](https://education.github.com/) as a student.

First clone this repository:

```
$ git clone --origin upstream https://github.com/BildverarbeitungSS18/Hausaufgaben
```

Get into the new folder

```
$ cd Hausaufgaben
```

Add your remote

```
$ git remote add origin <your git repo url>
```

Please clear the notebook's output before committing. Otherwise the repository
size can get pretty big.
The best thing is to setup a `pre-commit` hook that removes the outputs before
the files are committed:

```
$ ln -s ../../nb_strip_output.py .git/hooks/pre-commit
```

Otherwise you manually clean the output with `Cell -> All Output -> Clear` or
use the `nb_strip_output.py <filename>` script.

To get the latest assignments into your repository see [how to sync a fork](https://help.github.com/articles/syncing-a-fork/).

Paste a link to your repository into the kvv assignment box.
Make sure that I have read and write rights on your repository.

## Docker

There exists a script for a [docker](https://www.docker.com/) image.
All the libraries we will use are included in this image.
It is recommended to use the image, but you are free to setup the environment
for yourself.

First [install docker](https://docs.docker.com/engine/installation/) on your computer.

Build the image:

```
$ ./docker_build.sh
```
The build script will create a user with your username and uid inside the image.
It may take some minutes until the image is built.

If you don't have a bash you can manually edit the `Dockerfile.sample`.
See the docker documentation for [details](https://docs.docker.com/)

To run the image:

```
$ ./docker_start.sh
```

Now visit [localhost:8888](http://localhost:8888). Jupyter Notebook
should be ready to use.