TL;DR The Google Summer of Code period ends, and am glad that I am able to meet all the goals and develop something productive for the Drupal community. In this blog post, I will be sharing the details of the project, the functionality of the module and its current status.
I am glad that I was one of the lucky students who were selected to be a part of the Google Summer of Code 2016 program for the project “Integrate Google Cloud Vision API to Drupal 8”. The project was under the mentorship of Naveen Valecha, Christian López Espínola and Eugene Ilyin. Under their mentoring and guidance, I am able meet all the goals and develop something productive for the Drupal community.
Google Cloud Vision API bring to picture the automated content analysis of the images. The API can not only detect objects ranging from animals to famous monuments, but also detects faces on emotions. In addition, the API can also help censor images, extract text from images, detect logos and landmarks, and even the attributes of the image itself, for instance the dominant color in the image. Thus, it can serve as a powerful content analysis tool for images.
Now let us see how can we put the module to use, i.e. what are its use cases. To start with, the Google Vision API module allows Taxonomy tagging of image files using Label Detection.
Label Detection classifies the images into a number of general purpose categories. For example, classifying a war scenario to war, troop, soldiers, transport, etc. based on the surroundings in the images. This feature of the module is especially important to filter the images based on some tags.
Second feature listing our use case is the Safe Search Detection. It quickly identifies and detects the presence of any explicit or violent contents in an image which are not fit for display.
When this feature is enabled in the module, the Safe Search technique validates any image for explicit/violent contents. If found, these images are asked for moderation, and are not allowed to be uploaded on the site, thus keeping the site clean.
Continuing with the other use cases, the third one is Filling the Alternate Text field of an image file.
Label, Logo, Landmark and Optical Character Detection feature of the Google Cloud Vision API have been used to implement this use case. Based on the choice entered by the end user, he/she can have the Alternate Text for any image auto filled by one of the four above-mentioned options. The choice “Label Detection” would fill the field with the first value returned in the API response. “Logo Detection” identifies the logos of famous brands, and can be used to fill the field accordingly. Likewise, “Landmark Detection” identifies the monuments and structures, ranging from natural to man-made; and “Optical Character Detection” detects and identifies the texts within an image, and fills the Alternate Text field accordingly.
Next comes the User Emotion Detection feature.
This feature is especially important in cases of new account creation. On enabling this feature, it would detect the emotion of the user in the profile picture and notify the new user if he/she seems to be unhappy in the image, prompting them to upload a happy one.
Lastly, the module also allows Displaying the similar image files.
Based on the dominant color component (Red, Green or Blue), the module quickly groups all the images which share the same color component, and display them under the “Similar Content” tab in the form of a list. Each item links itself to the image file itself, and is named as per the filename saved by the user.
Users should note here that by “similar contents”, we do not mean that the images would resemble each other always. Instead we mean here the same dominant color components.
All the details of my work, the interesting facts and features have been shared on the Drupal Planet.
Please watch this video to know more on how to use the above-mentioned use cases in proper way.
This is the complete picture of the Google Vision API module developed during the Google Summer of Code phase (May 23, 2016- August 23, 2016).
With this, the three wonderful months of Google Summer of Code phase comes to an end, enriching me with lots of experiences, meeting great persons and working with them. In addition of giving me an asset, it also boosted and enhanced my skills. I learnt a lot of new techniques, which probably, I would not have learnt otherwise. The use of services and dependency injection, constraints and validators, controllers, automated tests and the introduction to concepts of entities and entity types to name a few.
I would put to use these concepts in best possible way, and try to contribute to the Drupal community with my best efforts.
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