Automatically Filter Image Uploads According to their NSFW Score

Our colleague Vincent just published an interesting blog post on dev.to on how to automatically filter images uploads (GIF included) according to their NSFW score via the PixLab NSFW API endpoint and apply a blur filter if adult, nudity or gory details is detected. Find out more information via the following links:

ASCII ART Camera Effect Model Now Available on the Unity Asset Store

The PixLab development team is thrilled to announce the immediate availability of the ASCII ART Camera Model in the Unity Asset Store!

ASCII Camera let you transform your input camera stream, video frames or static images/textures into ASCII glyphs & printable characters at real-time.

ASCII Camera Effect

Real-Time performance (even on low end Android devices) of the ASCII Camera asset is achieved via pixel intensity comparison inside internal nodes of a single decision tree. The Unity implementation is based on this paper.

ASCII Camera in the Asset Store

Finally, the ASCII Camera documentation, demo & source code are available via the following links:

PixLab on Social Media Platforms

Follow PixLab on social media to keep up-to-date with the latest company news, research highlights and benefit from a range of useful resources including (but not limited to) our brand new API services such the state-of-the-art Passports & ID Cards scanning API, the new facial recognition engine which achieve 99.8% success ratio and many more API endpoints for building intelligent applications.

PixLab Logo

Don't forget! You can instantly reach our support team via the PixLab dashboard and we always guaranty a response in 48 business hours timeframe for your integration and support assistance!

Full Scan Support for India Aadhar ID Card

The PixLab OCR team is pleased to announce that is now fully support scanning India Aadhar ID Cards besides Malaysia (MyKad) and Singapore identity cards as well governments issued Passports from all over the world via the /docscan API endpoint.

When invoked, the /docscan API endpoint shall Extract (crop) any detected face and transform the raw Aadhar ID card content such as holder name, gender, date of birth, ID number, etc. into a JSON object ready to be consumed by your app.

Below, a typical output result of the /docscan API endpoint for a Aadhar ID card input sample:

Input Aadhar ID Card

ID card specimen

Extracted Aadhar Card Fields

extracted fields

The same API call applies for Passports as well different ID cards from supported countries (you just specify the country name or ISO code):

Input Passport Specimen

Passport Specimen

Extracted MRZ Fields

MRZ Fields

The code samples used to achieve such result are available to consult via the following Github links:

Face extraction is automatically performed using the /facedetect API endpoint. If you are dealing with PDF documents, you can convert them at first to raw images via the /pdftoimg endpoint.

Finally, the official endpoint documentation is available to consult at pixlab.io/cmd?id=docscan and a set of working samples in various programming language are available at the PixLab samples pages.

SOD CV/ML Library 1.1.8 Released

The PixLab development team is pleased to announce the immediate availability of the 1.1.8 release of our Embedded Computer Vision & Machine Learning library SOD.

SOD Face detection

SOD is an embedded, modern, cross-platform, computer vision and machine learning C/C++ library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. At PixLab, we believe SOD is:

  • Suitable for deep learning on limited computational resource, embedded systems and IoT devices.
  • Easy to integrate with existing code bases. Interpolatable with OpenCV and/or any other proprietary API.

SOD is shipped with a real-time face detection & tracking model (download link) that has been ported to Unity, Unreal Engine and WebAssembly.

Finally, you can find out more information about the SOD project via the following links: