A little while ago I did a brief tutorial of the Google Vision API using RoogleVision created by Mark Edmonson. I couldn’t find anything similar to that in R for the Microsoft Cognitive Services API so I thought I would give it a shot. I whipped this example together quickly to give it a proof-of-concept but I could certainly see myself building an R package to support this (unless someone can point to one – and please do if one exists)!
A quick example, sending this image retrieved the location of the human face and created a caption! Here’s my dog lined up next to his doppelganger:
The disastrous impact of recent hurricanes, Harvey and Irma, generated a large influx of data within the online community. I was curious about the history of hurricanes and tropical storms so I found a data set on data.world and started some basic Exploratory data analysis (EDA).
EDA is crucial to starting any project. Through EDA you can start to identify errors & inconsistencies in your data, find interesting patterns, see correlations and start to develop hypotheses to test. For most people, basic spreadsheets and charts are handy and provide a great place to start. They are an easy-to-use method to manipulate and visualize your data quickly. Data scientists may cringe at the idea of using a graphical user interface (GUI) to kick-off the EDA process but those tools are very effective and efficient when used properly. However, if you’re reading this, you’re probably trying to take EDA to the next level. The best way to learn is to get your hands dirty, let’s get started.
In the coming months I’ll be digging into the immigration enforcement data posted on data.world. I encourage anyone to take this data and either add to the project or to do something on their own. I will be bringing in external data sources to merge as well (which I did for this first plot).
If you’re only here for a “high-level nugget” of information, the basic thing you can see is:
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).