Deep Dream For Everyone (if you use osx :D)

Minimal Python/TensorFlow implementation of the DeepDream algorithm originally created by Alexander Mordvintsev.

Setup and Installation

First you need to create a virutal environment because by default all versions of python install packages globally & this is very bad. Consider you use sudo and install some python2.7 version of of a package that OSx uses for some random important task: you just potentially broke it. Ergo, you can create virtualenvs in one of two ways. You can safely install the package virtualenv globally using pip install virtualenv or use built in python 3 flag to create an env.

Note, this package will not run on python 3.8. Its a tensorflow logging error due to the old version of tensorflow I suspect. I did not write this python code however, but ported it from a jupiter notebook and while tensorflow versions greater than 1.14 may work, anything as of version 2.0 will not. Too many breaking changes. It wouldn't take long to fix the issues as most of the functions have simply been moved to new modules or classes.

Before we start install our python dependencies we will need to install caffe and those instructions are a bit rusty, at least for the OSx link provided. Thus, I will walk you through the rough bits so you don't have issues with missing homebrew packages and installation issues.

be aware that these are only the steps for OSx

First you need to install homebrew, so paste the following line into your terminal.

$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Now brew is installed. You may need to execute the command $SHELL before the brew command is recognized, or you just open a new terminal and the brew command will be available. Test it by running brew and hitting enter. Onto installing Caffe...

Caffe first says to perform some steps for general dependencies. However, only the first command works because brew moved the science keg. So follow these instructions and you should be good to go.

$ brew install -vd snappy leveldb gflags glog szip lmdb

# need the homebrew science source for OpenCV and hdf5
# which no longer exists where Caffe states in homebrew/science
# but now exists in brewsci/science

$ brew tap brewsci/science
$ brew install hdf5 opencv

The docs mention setting some compiler flags but aren't needed for the various installations I've attempted on an array of macs with mojava or catalina at least. Lastly, run this:

$ brew install protobuf boost

By the way, if you have an NVidia card, see the top of the Caffe link about installing CUDA. Otherwise, neglect it. This script will run without it, but will use CPU instead of GPU.

Configuring python virtual environments.

If you already have virtualenv installed you can still use it, you specify which version of python you want to use when you create the virtualenv, by using -p or --python flag, like so:

$ virtualenv -p python3 venv

Next you need to activate your virtual env:

$ source venv/bin/activate

If you do not have virtualenv installed python3 gives you a crafty builtin way to do this automatically by passing a flag. Make sure you are in the root directory of your project and run these commands to install a python3 virtual env and activate it.

$ python3 -m venv venv
$ source venv/bin/activate

You will know that your inside your new virtual environment because the terminal line(s) are prepended with something akin to this:

(venv) $

When you are done with running your python code later, you can stop the virtual environment by simply running

(venv) $ deactivate

# now your terminal no longer shows (venv)
$ ...

Installing dependencies

In standard python projects all dependencies are installed with pip and saved to a file named requirements.txt. This has been a python standard for as long as I can remember. Therefore, all you need to before being able to run the script is execute this command in your activated python3 virtual environment.

(venv) $ pip install -r requirements.txt

It will take some time, but it will install all of those packages to the venv/lib/python3.x/site-packages but you should never modify these.

Now you can run your project

Script Usage

To use on the predefined a specific image:

(venv) $ python deep_dream.py <path_to_the_image>

or to use sample data that comes with the project:

(venv) $ python deep_dream.py

to perform on the random image.

How does it work?

We are using Inception5h model which was designed to classify images.

During the classification process we are providing input images and using gradient descent to adapt weights to the images through filters.

DeepDream algorithm does the opposite. It adapts the input images to match the network weights with gradient ascent which results in visualizing network filters on the input images giving them psychodelic look.