Is George Washington better looking on the dollar bill or represented by a word cloud built with the text of The Constitution of the USA?
A colleague recently asked me that exact question. If you want to be taken seriously in the data science world, you better be able to answer something like this!
I decided that it would be fun to show off a Python package by Andreas Mueller called word_cloud (here) to make a fun image with the text of the Constitution and an image of one of the Founding Fathers.
I must warn you, word clouds are like pie charts people like the way they look but clouds don’t provide much information. That said, this package is really neat because it allows you to easily turn text into images utilizing masks, colors, and numpy!
I’ll keep this post short, what you want to do is simple:
Select an image which you would like to mimic in both color and shape
Read your image into Python using numpy
Read your text into Python using open() and read()
After doing my post last month on OpenCV and face detection, I started looking into other algorithms used for pattern detection in images. As it turns out, Google has done a phenomenal job with their Vision API. It’s absolutely incredible the amount of information it can spit back to you by simply sending it a picture.
Also, it’s 100% free! I believe that includes 1000 images per month. Amazing!
OpenCV is an incredibly powerful tool to have in your toolbox. I have had a lot of success using it in Python but very little success in R. I haven’t done too much other than searching Google but it seems as if “imager” and “videoplayR” provide a lot of the functionality but not all of it.
I have never actually called Python functions from R before. Initially, I tried the “rPython” library – that has a lot of advantages, but was completely unnecessary for me so system() worked absolutely fine. While this example is extremely simple, it should help to illustrate how easy it is to utilize the power of Python from within R. I need to give credit to Harrison Kinsley for all of his efforts and work at PythonProgramming.net – I used a lot of his code and ideas for this post (especially the Python portion).