This is the repository for shape-detection-api, an experimental API for detecting Shapes (e.g. Faces, Barcodes, Text) in live or still images on the Web by using accelerated hardware/OS resources.

You're welcome to contribute! Let's make the Web rock our socks off!

Photos and images constitute the largest chunk of the Web, and many include recognisable features, such as human faces, text or QR codes. Detecting these features is computationally expensive, but would lead to interesting use cases e.g. face tagging or detection of high saliency areas. Users interacting with WebCams or other Video Capture Devices have become accustomed to camera-like features such as the ability to focus directly on human faces on the screen of their devices. This is particularly true in the case of mobile devices, where hardware manufacturers have long been supporting these features. Unfortunately, Web Apps do not yet have access to these hardware capabilities, which makes the use of computationally demanding libraries necessary.

QR/barcode/text detection can be used for:

• user identification/registration, e.g. for voting purposes;
• eCommerce, e.g. Walmart Pay;
• Augmented Reality overlay, e.g. here;
• Driving online-to-offline engagement, fighting fakes etc.

Face detection can be used for:

• producing fun effects, e.g. Snapchat Lenses;
• giving hints to encoders or auto focus routines;
• user name tagging;
• enhance accesibility by e.g. making objects appear larger as the user gets closer like HeadTrackr;
• speeding up Face Recognition by indicating the areas of the image where faces are present.

Some Web Apps -gasp- run Detection in Javascript. A performance comparison of some such libraries can be found here (note that this performance evaluation does not include e.g. WebCam image acquisition and/or canvas interactions).

Samsung Browser has a private API (click to unfold "Overview for Android", then search for "QR code reader").

TODO: compare a few JS/native libraries in terms of size and performance. A performance and detection comparison of some popular JS QR code scanners can be found here. zxingjs2 has a list of some additional JS libraries.

Android Native Apps usually integrate ZXing (which amounts to adding ~560KB when counting core.jar, android-core.jar and android-integration.jar)).

OCR reader in Javascript are north of 1MB of size ()

Face Detection is an expensive operation due to the algorithmic complexity. Many requests, or demanding systems like a live stream feed with a certain frame rate, could slow down the whole system or greatly increase power consumption.

What platforms support what detector?

Android provides both a stand alone software face detector and a interface to the hardware ones.

The availability of the actual hardware detection depends on the actual chip; according to the market share in 1H 2016 Qualcomm, MediaTek, Samsung and HiSilicon are the largest individual OEMs and they all have support for Face Detection (all the top-10 phones are covered as well):

• Qualcomm Snapdragon chipset family supports it since ~2013 as part of their ISP.
• MediaTek as part of CorePilot 2.0 (introduced in 2015).
• Samsung Exynos (at least 2013).
• Huawei HiSilicon Kirin950 since 2015 (this fabless manufacturer is relatively new).
• It is worth noting that ARM acquired Apical in 2016 for its computer vision expertise.

Barcode/QR and Text detection is available via Google Play Services barcode and text, respectively.

Mac OS X/iOS provides CIDetector and Vision Framework for Face, QR, Text and Rectangle detection in software or hardware.

Apple has supported Face Detection in hardware since the Apple A5 processor introduced in 2011.

Windows 10 has a FaceDetector class and support for Text Detection OCR.

The rendered version of this site can be found in https://wicg.github.io/shape-detection-api (if that's not alive for some reason try the rawgit rendering).

https://wicg.github.io/shape-detection-api/#examples

To compile, run:

curl https://api.csswg.org/bikeshed/ -F file=@index.bs -F force=1 > index.html

if the produced file has a strange size (i.e. zero), then something went terribly wrong; run instead

curl https://api.csswg.org/bikeshed/ -F file=@index.bs -F output=err

and try to figure out why bikeshed did not like the .bs :'(