Flutter Plugin for Google ML Kit Vision

google ml kit vision plugin

a flutter plugin to use the capabilities of on-device google ml kit vision apis

usage

to use this plugin, add google_ml_vision as a dependency in your pubspec.yaml file.

using an ml vision detector

1. create a googlevisionimage.

create a googlevisionimage object from your image. to create a googlevisionimage from an image file object:

final file imagefile = getimagefile();
final googlevisionimage visionimage = googlevisionimage.fromfile(imagefile);

2. create an instance of a detector.

final barcodedetector barcodedetector = googlevision.instance.barcodedetector();
final facedetector facedetector = googlevision.instance.facedetector();
final imagelabeler labeler = googlevision.instance.imagelabeler();
final textrecognizer textrecognizer = googlevision.instance.textrecognizer();

you can also configure all detectors, except textrecognizer, with desired options.

final imagelabeler labeler = googlevision.instance.imagelabeler(
  imagelabeleroptions(confidencethreshold: 0.75),
);

3. call detectinimage() or processimage() with visionimage.

final list<barcode> barcodes = await barcodedetector.detectinimage(visionimage);
final list<face> faces = await facedetector.processimage(visionimage);
final list<imagelabel> labels = await labeler.processimage(visionimage);
final visiontext visiontext = await textrecognizer.processimage(visionimage);

4. extract data.

a. extract barcodes.

for (barcode barcode in barcodes) {
  final rectangle<int> boundingbox = barcode.boundingbox;
  final list<point<int>> cornerpoints = barcode.cornerpoints;

  final string rawvalue = barcode.rawvalue;

  final barcodevaluetype valuetype = barcode.valuetype;

  // see api reference for complete list of supported types
  switch (valuetype) {
    case barcodevaluetype.wifi:
      final string ssid = barcode.wifi.ssid;
      final string password = barcode.wifi.password;
      final barcodewifiencryptiontype type = barcode.wifi.encryptiontype;
      break;
    case barcodevaluetype.url:
      final string title = barcode.url.title;
      final string url = barcode.url.url;
      break;
  }
}

b. extract faces.

for (face face in faces) {
  final rectangle<int> boundingbox = face.boundingbox;

  final double roty = face.headeulerangley; // head is rotated to the right roty degrees
  final double rotz = face.headeuleranglez; // head is tilted sideways rotz degrees

  // if landmark detection was enabled with facedetectoroptions (mouth, ears,
  // eyes, cheeks, and nose available):
  final facelandmark leftear = face.getlandmark(facelandmarktype.leftear);
  if (leftear != null) {
    final point<double> leftearpos = leftear.position;
  }

  // if classification was enabled with facedetectoroptions:
  if (face.smilingprobability != null) {
    final double smileprob = face.smilingprobability;
  }

  // if face tracking was enabled with facedetectoroptions:
  if (face.trackingid != null) {
    final int id = face.trackingid;
  }
}

c. extract labels.

for (imagelabel label in labels) {
  final string text = label.text;
  final string entityid = label.entityid;
  final double confidence = label.confidence;
}

d. extract text.

string text = visiontext.text;
for (textblock block in visiontext.blocks) {
  final rect boundingbox = block.boundingbox;
  final list<offset> cornerpoints = block.cornerpoints;
  final string text = block.text;
  final list<recognizedlanguage> languages = block.recognizedlanguages;

  for (textline line in block.lines) {
    // same getters as textblock
    for (textelement element in line.elements) {
      // same getters as textblock
    }
  }
}

5. release resources with close().

barcodedetector.close();
facedetector.close();
labeler.close();
textrecognizer.close();

getting started

see the example directory for a complete sample app using google machine learning.