Full instructions for repainting the past
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bucketset
colorization
getframes
normalisecolor
tweetlistener
tweetpic
.gitignore
.travis.yml
CONTRIBUTING.md
LICENSE
README.md
bucketset.yml
build.sh
colorisation-architecture.png
colourise_frames.py
deploy.sh
requirements.txt
split_frames.py
stack.yml
twitter_stack.yml

README.md

repaint-the-past

Deployment

Minio

You'll need a Minio server running to store the images in.

  • Run Minio once and capture the secret/access keys and inject into the command above.
$ docker run -ti --rm minio/minio server /data
...
AccessKey: ZBPIIAOCJRY9QLUVEHQO
SecretKey: vMIoCaBu9sSg4ODrSkbD9CGXtq0TTpq6kq7psLuE
...

Hit Control+C and set up two environmental variables:

export MINIO_ACCESS_KEY="ZBPIIAOCJRY9QLUVEHQO"
export MINIO_SECRET_KEY="vMIoCaBu9sSg4ODrSkbD9CGXtq0TTpq6kq7psLuE"

We found running a single-container minio server was the easiest way as I had issues when running the distributed version. Once Minio is deployed, go and create a new bucket called colorization. This is where the images will be stored.

$ docker run -dp 9000:9000 \
  --restart always --name minio \
  -e "MINIO_ACCESS_KEY=$MINIO_ACCESS_KEY" \
  -e "MINIO_SECRET_KEY=$MINIO_SECRET_KEY" \
  minio/minio server /data

You can optionally add a bind-mount too by adding the option: -v /mnt/data:/data -v /mnt/config:/root/.minio

OpenFaaS functions

Firstly, you'll need to make sure the OpenFaaS gateway is configured to have larger read & write timeouts as the colorise function can sometimes take a few seconds to return.

$ docker service update func_gateway \
  --env-add "write_timeout=60" \
  --env-add "read_timeout=60"

Create credentials.yml with these contents:

environment:
  minio_secret_key: <minio secret key>
  minio_access_key: <minio access key>
  minio_authority: <host:port>

Do not include HTTP in the minio_authority.

For example:

environment:
  minio_secret_key: vMIoCaBu9sSg4ODrSkbD9CGXtq0TTpq6kq7psLuE
  minio_access_key: ZBPIIAOCJRY9QLUVEHQO
  minio_authority: 192.168.0.10:9000

And now deploy the OpenFaaS functions

$ faas-cli deploy -f stack.yml

Configuration

There are three modes supported by the colorise function, as documented below.

minio

Uses minio object storage for saving images and is enabled by setting minio_authority as above. When in this mode, input to the function must be JSON in the following format:

{
  "output_filename": "output.jpg",
  "image": "input.jpg"
}

This will retrieve input.jpg from the minio bucket colorization, colourise that image and then upload it to the same bucket with a name of output.jpg.

binary_mode

Pure binary is used for input & output. Enabled by not setting the minio_authority env var. Simply POST the raw image and pipe the output back into a file.

url_mode

POST a URL as input and receive binary output. Enabled by setting env var url_mode to "true"

Other options

There is also the ability to pass the image through imagemagick to remove some of the saturation. This can be enabled by setting the env var normalise_enabled to "true".

Invocation

Configure Minio (object storage)

  • Download the CLI

Download the Minio client from: https://github.com/minio/mc

  • Add the Docker container running Minio as a host
$ export IP=192.168.0.1   # replace with the Docker host IP
$ mc config host add minios3 http://$IP:9000 $MINIO_ACCESS_KEY $MINIO_SECRET_KEY
  • Create a bucket
$ mc mb minios3/colorization
Bucket created successfully minios3/colorization.
  • Upload an image to your minio bucket:
$ curl -sL https://static.pexels.com/photos/276374/pexels-photo-276374.jpeg > test_image_bw.jpg && \
  http_proxy="" mc cp test_image_bw.jpg minios3/colorization
  • Then call the function:
$ http_proxy="" curl -d '{"image": "test_image_bw.jpg"}' \
  http://127.0.0.1:8080/function/colorization

{"duration": 8.719741106033325, "image": "1508788935770_output.jpg"}

The returned image is the path to the converted image in minio.

So copy it back to your host:

$ mc cp minios3/colorization/1508788935770_output.jpg .
$ open 1508788935770_output.jpg

Congratulations - you just recoloured the past! Read on for how we hooked this into a Twitter bot.

Twitter extension

If you want to replicate our DockerCon demo which listens for tweets matching a certain criteria & then replies with the colourised image, follow the instructions below.

You need to deploy the tweetlistener service. This will listen for tweets matching a certain criteria and then forward the requests into the colorise function. Make sure you only deploy one replica of tweetlistener because otherwise you'll get duplicate events.

Create tweetlistener.envs with the following contents:

# tweetlistener.envs (add your values below)
minio_access_key=<minio access key>
minio_secret_key=<minio secret key>
minio_authority=<host:port>
consumer_key=<twitter consumer key>
consumer_secret=<twitter consumer secret>
access_token=<twitter token>
access_token_secret=<twitter token secret>
$ docker service create --name tweetlistener \
  --env-file tweetlistener.envs \
  --network func_functions \
  developius/tweetlistener:latest

Update credentials.yml to add your Twitter details

environment:
  minio_secret_key: <minio secret key>
  minio_access_key: <minio access key>
  minio_authority: <host:port>
  consumer_key: <twitter consumer key>
  consumer_secret: <twitter consumer secret>
  access_token: <twitter token>
  access_token_secret: <twitter token secret>

Then re-deploy

$ faas-cli deploy -f twitter_stack.yml

Tweet it

You can now tweet @colorisebot (or your own twitter account) with your image and see the data flow through the functions. Depending on the underlying infrastructure, it should take about 10s for the whole flow from tweeting the image, to receiving the tweeted reply.

Colourising video

We've shown how we can colourise photos, but our original goal was to colourise video. There are a couple of scripts we've written to do this.

Prerequisite

You need to install some dependencies before you can run the code.

$ pip install -r requirements.txt

Splitting frames

First, modify line 2 of split_frames.py to use the path to your video file.

Now you can run the script which splits up all the frames & outputs them into frames/ (make sure this folder exists).

$ python split_frames.py

Colourising the frames

Next you need to run the actual colourisation code. To do this, you must first start up the docker container which will run the colourisation.

$ docker run -a STDERR -e write_timeout=60 -e read_timeout=60 -p 8080:8080 --rm developius/openfaas-colorization:0.1

Now you can run colourise_frames.py to generate the colourised frames.

$ python colourise_frames.py

This will create all the colourised frames in the folder output/ (make sure this one exists too).

Stitch them back together

The final step is to stitch them back together with ffmpeg. ffmpeg can be installed via brew with brew install ffmpeg and is available on most other platforms too.

$ ffmpeg -framerate 24 -i output/%05d.jpg output.mp4

Now check your current directory for a file name output.mp4. Whoop, you just colourised the past, again!