Getting started¶

You’ll need a (scientific) Python distribution and a database backend. Currently we use Elasticsearch as a backend.

numpy, PIL, skimage, etc.¶

image_match requires several scientific Python packages. Although they can be installed and built individually, they are often bundled in a custom Python distribution, for instance Anaconda. Installation instructions can be found here.

You can set up image_match without a prebuilt distribution, but the performance may suffer. Note that scipy and numpy require many system-level dependencies that you made need to install first.

Elasticsearch¶

If you just want to generate and compare image signatures, you can skip this step. If you want to search over a corpus of millions or billions of image signatures, you will need a database backend. We built image_match around Elasticsearch. See download and installation instructions. We’re using Elasticsearch 2.2.1 in these examples.

Install image-match¶

Here are a few options:

Install with pip¶

$ pip install numpy
$ pip install scipy
$ pip install image_match

Build from source¶

Clone this repository:

$ git clone https://github.com/ascribe/image-match.git

Install image_match

From the project directory:
$ pip install numpy
$ pip install scipy
$ pip install .

Make sure elasticsearch is running (optional)¶

For example, on Ubuntu you can check with:

$ sudo service elasticsearch status

If it’s not running, simply run:

$ sudo service elasticsearch start

On OSX, to have launchd start elasticsearch, run :

$ brew services start elasticsearch

or simply run ,

$ elasticsearch

Docker¶

We have a Docker image that takes care of setting up image_match and elasticsearch. Consider it an alternative to the methods described above.

$ docker pull ascribe/image-match
$ docker run -it ascribe/image-match /bin/bash