This project shows the different capabilities and responses of Google, Microsoft, and IBM Watson's vision APIs. It works in two extremely loosely connected parts: a batch processor and a results display.
Since node_modules is ignored by git, first create a node_modules directory, then mv rgbcolor.js node_modules/. to put that in the right place. You'll then need to run npm install to get the rest of what you need. There's a package.json here that will include all the requisite dependencies. (This will take a lot longer than you might think; the APIs often have to install lots of their own dependencies, and even compile things. You may need a FORTRAN compiler [seriously].)
You'll also need to start a local mongodb, as that's where we'll store our results. If you're on a modern Mac, you should be able to do this quite simply: mongod --rest --jsonp --config /usr/local/etc/mongod.conf. (The --rest and --jsonp flags will be used to serve results later on.)
image-test.js is a node application that will use a directory of images you define to blast each of the APIs with each of the images in that directory. Note: you will also need to zip your directory into a zipfile of the same name (i.e. if your directory is called "input", use zip -r input.zip input to make the appropriate archive).
To run the batch processor, just type node image-test.js <input-dir> with no slashes. At the moment, this only works if image-test.js is in the same directory as your input directory, so your command line will be something like node image-test.js pics.
At this time, image-test.js does not terminate on its own, because of async processing across several APIs being a hellish development issue. When the output of the log reads Google: inserting for pointer N (where N = the number of images in your directory) the program should hang; give it a sec to make sure Microsoft is also done (Watson works on the zip file and therefore returns much more predictably) and press ctrl-c to quit.
image-test.js puts its results into a mongo database, whose location is defined in mongourl at the top of the js file.
Note that you must provide the necessary auth tokens in the config.js file. A sample is provided here in the repo; for actual docs / keys, sign up for the relevant accounts (or see the NYTLABS Google Drive).
Here I cheated a bit: result.html is the file to serve, which reads from the mongo database to show the comparative tags / data. This file also needs access to your original input directory, as above, so that it can serve the images atop the data each API extracted for it.
I haven't done anything fancy to serve this file / these images, so your best bet is to simply go to the directory that contains result.html and your input directory and type python -m SimpleHTTPServer to start a local web server, then open localhost:8000/result.html to see the results.
In making this batch processor it quickly became clear that the actually useful version of this would be a single page, where a user could upload a picture, have that picture analyzed by all three APIs, and have the results come back right in the page. This requires a pretty significant refactoring, so I'll get to this next as a separate initiative. The batch version here is still useful, I think, especially for managing much larger groups of test images.