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Showing content from https://wicg.github.io/shape-detection-api/ below:

Accelerated Shape Detection in Images

Photos and images constitute the largest chunk of the Web, and many include recognisable features, such as human faces or barcordes/QR codes. Detecting these features is computationally expensive, but would lead to interesting use cases e.g. face tagging, or web URL redirection. While hardware manufacturers have been supporting these features for a long time, Web Apps do not yet have access to these hardware capabilities, which makes the use of computationally demanding libraries necessary.

Text Detection, despite being an interesting field, is not considered stable enough across neither computing platforms nor character sets to be standarized in the context of this document. For reference a sister informative specification is kept in [TEXT-DETECTION-API].

Please see the Readme/Explainer in the repository.

Individual browsers MAY provide Detectors indicating the availability of hardware providing accelerated operation.

Detecting features in an image occurs asynchronously, potentially communicating with acceleration hardware independent of the browser. Completion events use the shape detection task source.

This section is inspired by HTML Canvas 2D Context § image-sources-for-2d-rendering-contexts.

ImageBitmapSource allows objects implementing any of a number of interfaces to be used as image sources for the detection process.

• When an ImageBitmapSource object represents an HTMLImageElement, the element’s image must be used as the source image. Specifically, when an ImageBitmapSource object represents an animated image in an HTMLImageElement, the user agent must use the default image of the animation (the one that the format defines is to be used when animation is not supported or is disabled), or, if there is no such image, the first frame of the animation.

• When an ImageBitmapSource object represents an HTMLVideoElement, then the frame at the current playback position when the method with the argument is invoked must be used as the source image when processing the image, and the source image’s dimensions must be the intrinsic dimensions of the media resource (i.e. after any aspect-ratio correction has been applied).

• When an ImageBitmapSource object represents an HTMLCanvasElement, the element’s bitmap must be used as the source image.

When the UA is required to use a given type of ImageBitmapSource as input argument for the detect() method of whichever detector, it MUST run these steps:

• If any ImageBitmapSource have an effective script origin (origin) which is not the same as the Document’s effective script origin, then reject the Promise with a new DOMException whose name is SecurityError.

• If the ImageBitmapSource is an HTMLImageElement object that is in the Broken (HTML Standard §img-error) state, then reject the Promise with a new DOMException whose name is InvalidStateError, and abort any further steps.

• If the ImageBitmapSource is an HTMLImageElement object that is not fully decodable then reject the Promise with a new DOMException whose name is InvalidStateError, and abort any further steps

• If the ImageBitmapSource is an HTMLVideoElement object whose readyState attribute is either HAVE_NOTHING or HAVE_METADATA then reject the Promise with a new DOMException whose name is InvalidStateError, and abort any further steps.

• If the ImageBitmapSource argument is an HTMLCanvasElement whose bitmap’s origin-clean (HTML Standard §concept-canvas-origin-clean) flag is false, then reject the Promise with a new DOMException whose name is SecurityError, and abort any further steps.

Note that if the ImageBitmapSource is an object with either a horizontal dimension or a vertical dimension equal to zero, then the Promise will be simply resolved with an empty sequence of detected objects.

FaceDetector represents an underlying accelerated platform’s component for detection of human faces in images. It can be created with an optional Dictionary of FaceDetectorOptions. It provides a single detect() operation on an ImageBitmapSource which result is a Promise. This method MUST reject this promise in the cases detailed in § 2.1 Image sources for detection; otherwise it MAY queue a task that utilizes the OS/Platform resources to resolve the Promise with a Sequence of DetectedFaces, each one essentially consisting on and delimited by a boundingBox.

[Exposed=(Window,Worker),
 SecureContext]
interface FaceDetector {
  constructor(optional FaceDetectorOptions faceDetectorOptions = {});
  Promise<sequence<DetectedFace>> detect(ImageBitmapSource image);
};

FaceDetector(optional FaceDetectorOptions faceDetectorOptions)

Constructs a new FaceDetector with the optional faceDetectorOptions.

Detectors may potentially allocate and hold significant resources. Where possible, reuse the same

FaceDetector

for several detections.

detect(ImageBitmapSource image)

Tries to detect human faces in the ImageBitmapSource image. The detected faces, if any, are returned as a sequence of DetectedFaces.

dictionary FaceDetectorOptions {
  unsigned short maxDetectedFaces;
  boolean fastMode;
};

maxDetectedFaces, of type unsigned short

Hint to the UA to try and limit the amount of detected faces on the scene to this maximum number.

fastMode, of type boolean

Hint to the UA to try and prioritise speed over accuracy by e.g. operating on a reduced scale or looking for large features.

dictionary DetectedFace {
  required DOMRectReadOnly boundingBox;
  required sequence<Landmark>? landmarks;
};

boundingBox, of type DOMRectReadOnly

A rectangle indicating the position and extent of a detected feature aligned to the image axes.

landmarks, of type sequence<Landmark>, nullable

A series of features of interest related to the detected feature.

dictionary Landmark {
  required sequence<Point2D> locations;
  LandmarkType type;
};

locations, of type sequence<Point2D>

A point in the center of the detected landmark, or a sequence of points defining the vertices of a simple polygon surrounding the landmark in either a clockwise or counter-clockwise direction.

type, of type LandmarkType

Type of the landmark, if known.

enum LandmarkType {
  "mouth",
  "eye",
  "nose"
};

mouth

The landmark is identified as a human mouth.

eye

The landmark is identified as a human eye.

nose

The landmark is identified as a human nose.

Consider adding attributes such as, e.g.:

[SameObject] readonly attribute unsigned long id;

to DetectedFace.

BarcodeDetector represents an underlying accelerated platform’s component for detection of linear or two-dimensional barcodes in images. It provides a single detect() operation on an ImageBitmapSource which result is a Promise. This method MUST reject this Promise in the cases detailed in § 2.1 Image sources for detection; otherwise it MAY queue a task using the OS/Platform resources to resolve the Promise with a sequence of DetectedBarcodes, each one essentially consisting on and delimited by a boundingBox and a series of Point2Ds, and possibly a rawValue decoded DOMString.

[Exposed=(Window,Worker),
 SecureContext]
interface BarcodeDetector {
  constructor(optional BarcodeDetectorOptions barcodeDetectorOptions = {});
  static Promise<sequence<BarcodeFormat>> getSupportedFormats();

  Promise<sequence<DetectedBarcode>> detect(ImageBitmapSource image);
};

BarcodeDetector(optional BarcodeDetectorOptions barcodeDetectorOptions)

Constructs a new BarcodeDetector with barcodeDetectorOptions.

• If barcodeDetectorOptions.formats is present and empty, then throw a new TypeError.

• If barcodeDetectorOptions.formats is present and contains unknown, then throw a new TypeError.

Detectors may potentially allocate and hold significant resources. Where possible, reuse the same

BarcodeDetector

for several detections.

getSupportedFormats()

This method, when invoked, MUST return a new Promise promise and run the following steps in parallel:

1. Let supportedFormats be a new Array.
2. If the UA does not support barcode detection, queue a global task on the relevant global object of this using the shape detection task source to resolve promise with supportedFormats and abort these steps.
3. Enumerate the BarcodeFormats that the UA understands as potentially detectable in images. Add these to supportedFormats.

   The UA cannot give a definitive answer as to whether a given barcode format will always be recognized on an image due to e.g. positioning of the symbols or encoding errors. If a given barcode symbology is not in supportedFormats array, however, it should not be detectable whatsoever.

4. Queue a global task on the relevant global object of this using the shape detection task source to resolve promise with supportedFormats.

detect(ImageBitmapSource image)

Tries to detect barcodes in the ImageBitmapSource image.

dictionary BarcodeDetectorOptions {
  sequence<BarcodeFormat> formats;
};

formats, of type sequence<BarcodeFormat>

A series of BarcodeFormats to search for in the subsequent detect() calls. If not present then the UA SHOULD search for all supported formats.

Limiting the search to a particular subset of supported formats is likely to provide better performance.

dictionary DetectedBarcode {
  required DOMRectReadOnly boundingBox;
  required DOMString rawValue;
  required BarcodeFormat format;
  required sequence<Point2D> cornerPoints;
};

boundingBox, of type DOMRectReadOnly

A rectangle indicating the position and extent of a detected feature aligned to the image

rawValue, of type DOMString

String decoded from the barcode. This value might be multiline.

format, of type BarcodeFormat

Detect BarcodeFormat.

cornerPoints, of type sequence<Point2D>

A sequence of corner points of the detected barcode, in clockwise direction and starting with top-left. This is not necessarily a square due to possible perspective distortions.

enum BarcodeFormat {
  "aztec",
  "code_128",
  "code_39",
  "code_93",
  "codabar",
  "data_matrix",
  "ean_13",
  "ean_8",
  "itf",
  "pdf417",
  "qr_code",
  "unknown",
  "upc_a",
  "upc_e"
};

aztec

This entry represents a square two-dimensional matrix following [iso24778] and with a square bullseye pattern at their centre, thus resembling an Aztec pyramid. Does not require a surrounding blank zone.

code_128

Code 128 is a linear (one-dimensional), bidirectionally-decodable, self-checking barcode following [iso15417] and able to encode all 128 characters of ASCII (hence the naming).

code_39

This part talks about the Code 39 barcode. It is a discrete and variable-length barcode type. [iso16388]

code_93

Code 93 is a linear, continuous symbology with a variable length following [bc5]. It offers a larger information density than Code 128 and the visually similar Code 39. Code 93 is used primarily by Canada Post to encode supplementary delivery information.

codabar

Codabar is a linear barcode symbology developed in 1972 by Pitney Bowes Corp. (

data_matrix

Data Matrix is an orientation-independent two-dimensional barcode composed of black and white modules arranged in either a square or rectangular pattern following [iso16022].

ean_13

EAN-13 is a linear barcode based on the UPC-A standard and defined in [iso15420]. It was originally developed by the International Article Numbering Association (EAN) in Europe as a superset of the original 12-digit Universal Product Code (UPC) system developed in the United States (UPC-A codes are represented in EAN-13 with the first character set to 0).

ean_8

EAN-8 is a linear barcode defined in [iso15420] and derived from EAN-13.

itf

ITF14 barcode is the GS1 implementation of an Interleaved 2 of 5 bar code to encode a Global Trade Item Number. It is continuous, self-checking, bidirectionally decodable and it will always encode 14 digits. was once used in the package delivery industry but replaced by Code 128. [bc2]

pdf417

PDF417 refers to a continuous two-dimensional barcode symbology format with multiple rows and columns, bi-directionally decodable and according to the Standard [iso15438].

qr_code

QR Code is a two-dimensional barcode respecting the Standard [iso18004]. The information encoded can be text, URL or other data.

unknown

This value is used by the platform to signify that it does not know or specify which barcode format is being detected or supported.

upc_a

UPC-A is one of the most common linear barcode types and is widely applied to retail in the United States. Define in [iso15420], it represents digits by strips of bars and spaces, each digit being associated to a unique pattern of 2 bars and 2 spaces, both of variable width. UPC-A can encode 12 digits that are uniquely assigned to each trade item, and it’ss technically a subset of EAN-13 (UPC-A codes are represented in EAN-13 with the first character set to 0).

upc_e

UPC-E Barcode is a variation of UPC-A defined in [iso15420], compressing out unnecessary zeros for a more compact barcode.

This section is non-normative.

This interface reveals information about the contents of an image source. It is critical for implementations to ensure that it cannot be used to bypass protections that would otherwise protect an image source from inspection. § 2.1 Image sources for detection describes the algorithm to accomplish this.

By providing high-performance shape detection capabilities this interface allows developers to run image analysis tasks on the local device. This offers a privacy advantage over offloading computation to a remote system. Developers should consider the results returned by this interface as privacy sensitive as the original image from which they were derived.

This section is non-normative.

Slightly modified/extended versions of these examples (and more) can be found in e.g. this codepen collection.

The following example can also be found in e.g.

with minimal modifications.

if (window.FaceDetector == undefined) {
  console.error('Face Detection not supported on this platform');
}
if (window.BarcodeDetector == undefined) {
  console.error('Barcode Detection not supported on this platform');
}

The following example can also be found in e.g.

(or

, with landmarks overlay).

let faceDetector = new FaceDetector({fastMode: true, maxDetectedFaces: 1});
// Assuming |theImage| is e.g. a <img> content, or a Blob.

faceDetector.detect(theImage)
.then(detectedFaces => {
  for (const face of detectedFaces) {
    console.log(
        ' Face @ (${face.boundingBox.x}, ${face.boundingBox.y}),' +
        ' size ${face.boundingBox.width}x${face.boundingBox.height}');
  }
}).catch(() => {
  console.error("Face Detection failed, boo.");
})

The following example can also be found in e.g.

.

let barcodeDetector = new BarcodeDetector();
// Assuming |theImage| is e.g. a <img> content, or a Blob.

barcodeDetector.detect(theImage)
.then(detectedCodes => {
  for (const barcode of detectedCodes) {
    console.log(' Barcode ${barcode.rawValue}' +
        ' @ (${barcode.boundingBox.x}, ${barcode.boundingBox.y}) with size' +
        ' ${barcode.boundingBox.width}x${barcode.boundingBox.height}');
  }
}).catch(() => {
  console.error("Barcode Detection failed, boo.");
})

Conformance requirements are expressed with a combination of descriptive assertions and RFC 2119 terminology. The key words “MUST”, “MUST NOT”, “REQUIRED”, “SHALL”, “SHALL NOT”, “SHOULD”, “SHOULD NOT”, “RECOMMENDED”, “MAY”, and “OPTIONAL” in the normative parts of this document are to be interpreted as described in RFC 2119. However, for readability, these words do not appear in all uppercase letters in this specification.

All of the text of this specification is normative except sections explicitly marked as non-normative, examples, and notes. [RFC2119]

Examples in this specification are introduced with the words “for example” or are set apart from the normative text with class="example", like this:

This is an example of an informative example.

Informative notes begin with the word “Note” and are set apart from the normative text with class="note", like this:

Note, this is an informative note.

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