Imagga Featured Hack: Hipster Bar

Hipster bar is where only hipsters are allowed! How do you reinforce that? With a physical doorman who’s job is to ruthlessly send back guys without beards, or, in the case of the Max Dovey’s project - using Imagga’s image recognition technology. The hipster bar was open to the public for the duration of WdW Festival 2015.

Let’s get into the details of this quite unique usage of Imagga’s powerful image recognition technology. To enter the bar, you need to stand in front of camera that snaps a photo of you and then sends it to Imagga servers. Then the tech analyses your look and as result returns how certain the system is you are a hipster.
If you are found over 90% hipster, the door of the bar will open and you can join great company of people that are hipster enough.

Hipster is quite loose term and usually is used to describe a subculture of people who attempt to keep up to date with the latest trend and remain 'hip'. These are men and women in their 20's and 30's that value progressive politics and independent thinking, and often have appetite for art and indie-rock & counterculture. Of course being hipster includes certain look - thick rimmed glasses, tight-fitting jeans, old-school sneakers,  side-swept bangs and beards (men only).

Max Dovey, an artist from Rotterdam, who initiated the project, sourced thousands of images of hipsters to be used by our team to build a special hipster deep learning mode.  The specific classifier was able to easily distinguish between snaps of hipsters and all the rest. Here’s how it actually worked:

https://vimeo.com/139604496

Have another crazy idea? Don’t hesitate to try it out - with our custom training only the sky is the limit... if you are hip enough ;)


Batch Image Processing From Local Folder Using Imagga API

Batch Upload of Photos for Image Recognition

This blog post is part of series on How-Tos for those of you who are not quite experienced and need a bit of help to set up and use properly our powerful image recognition APIs.

In this one we will help you to batch process (using our Tagging or Color extraction API) a whole folder of photos, that reside on your local computer. To make that possible we’ve written a short script in the programming language Python: https://bitbucket.org/snippets/imaggateam/LL6dd

Feel free to reuse or modify it. Here’s a short explanation what it does. The script requires the Python package, which you can install using this guide.

It uses requests’ HTTPBasicAuth to initialize a Basic authentication used in Imagga’s API from a given API_KEY and API_SECRET which you have to manually set in the first lines of the script.

There are three main functions in the script - upload_image, tag_image, extract_colors.

    • upload_image(image_path) - uploads your file to our API using the content endpoint, the argument image_path is the path to the file in your local file system. The function returns the content id associated with the image.
  • tag_image(image, content_id=False, verbose=False, language='en') - the function tags a given image using Imagga’s Tagging API. You can provide an image url or a content id (from upload_image) to the ‘image’ argument but you will also have to set content_id=True. By setting the verbose argument to True, the returned tags will also contain their origin (whether it is coming from machine learning recognition or from additional analysis). The last parameter is ‘language’ if you want your output tags to be translated in one of Imagga’s supported 50 (+1) languages. You can find the supported languages from here - http://docs.imagga.com/#auto-tagging
  • extract_colors(image, content_id=False) - using this function you can extract colors from your image using our Color Extraction API. Just like the tag_image function, you can provide an image URL or a content id (by also setting content_id argument to True).

Script usage:

Note: You need to install the Python package requests in order to use the script. You can find installation notes here.

You have to manually set the API_KEY and API_SECRET variables found in the first lines of the script by replacing YOUR_API_KEY and YOUR_API_SECRET with your API key and secret.

Usage (in your terminal or CMD):

python tag_images.py <input_folder> <output_folder> --language=<language> --verbose=<verbose> --merged-output=<merged_output> --include-colors=<include_colors>

The script has two required - <input_folder>, <output_folder> and four optional arguments - <language>, <verbose>, <merged_output>, <include_colors>.

  • <input_folder> - required, the input folder containing the images you would like to tag.
  • <output_folder> - required, the output folder where the tagging JSON response will be saved.
  • <language> - optional, default: en, the output tags will be translated in the given language (a list of supported languages can be found here: http://docs.imagga.com/#auto-tagging)
  • <verbose> - optional, default: False, if True the output tags will contain an origin key (whether it is coming from machine learning recognition or from additional analysis)
  • <include_colors> - optional, default: False, if True the output will also contain color extraction results for each image.
  • <merged_output> - optional, default: False, if True the output will be merged in a JSON single file, otherwise - separate JSON files for each image.

Faster & Better Image Tagging

Image Auto tagging

Dealing with images is still complicated and time consuming process. You never take the time to organize that vacation photos or add tags to your growing image collection. We can do that automatically for you thanks to our auto-tagging technology. There are two ways if we want to do it really fast – to get more powerful machines or to speed up the algorithms responsible for this. Actually we did both, but lets pay some attention to our improved image tagging algorithms. Tagging is faster than ever!

GPU comes to help image recognition and thanks to it we are able to do things impossible (or ridiculously expensive) several years ago. We just ported all components of our tagging to take advantage of GPU acceleration and the result is about 5 times faster tagging! GPU is a bliss for image intensive technologies as ours. Actually the biggest portion of the time we need  for tagging an image now  is used for retrieving the image to be tagged.

With significantly faster tagging API now we can handle even larger image volumes and this can be done simultaneously for hundreds of users. Real time tagging is made very attractive and we are already testing with several clients.

Intrigued how fast the tagging is now? Why don’t you sing up now and test it yourself!


Autotag.me New Video

AutotagMe Video

AutoTag.me is an online tool that magically tags your images and makes them easier to organize and find! We’ve been working hard on the technology part that allows the actual photo tagging automation. We’ve seen great interest in the technology and  believe there’s great potential in auto-tagging – saving time and money, more discoverable image content, more precious moments visualized and well preserved.

AutoTag.me helps you tag your images so you can later organize them without the hustle and bustle of manual tagging.  You will be able to submit your images from sources like Flick, Dropbox and Facebook or any given image or folder on your hard drive. Then our service will automatically analyze the pixel content of the submitted images  and come up with tags that describe the concept, the composition, the objects and the colors found in each of the images. You can then add easily add or remove tags and save the text set within the image itself or export it to some popular online photo service.

Watch our short video to see what to expect. We are very excited about Autotag.me and can’t wait to see you using it and returning some valuable feedback.