You can use ML Kit to recognize and decode barcodes.
Before you begin
- If you haven't already,
add Firebase to your Android project
.
- Add the dependencies for the ML Kit Android libraries to your module
(app-level) Gradle file (usually
app/build.gradle
):
apply plugin: 'com.android.application'
apply plugin: 'com.google.gms.google-services'
dependencies {
// ...
implementation 'com.google.firebase:firebase-ml-vision:24.0.3'
implementation 'com.google.firebase:firebase-ml-vision-barcode-model:16.0.1'
}
-
For ML Kit to accurately read barcodes, input images must contain
barcodes that are represented by sufficient pixel data.
The specific pixel data requirements are dependent on both the type of
barcode and the amount of data that is encoded in it (since most barcodes
support a variable length payload). In general, the smallest meaningful
unit of the barcode should be at least 2 pixels wide (and for
2-dimensional codes, 2 pixels tall).
For example, EAN-13 barcodes are made up of bars and spaces that are 1,
2, 3, or 4 units wide, so an EAN-13 barcode image ideally has bars and
spaces that are at least 2, 4, 6, and 8 pixels wide. Because an EAN-13
barcode is 95 units wide in total, the barcode should be at least 190
pixels wide.
Denser formats, such as PDF417, need greater pixel dimensions for
ML Kit to reliably read them. For example, a PDF417 code can have up to
34 17-unit wide "words" in a single row, which would ideally be at least
1156 pixels wide.
-
Poor image focus can hurt scanning accuracy. If you aren't getting
acceptable results, try asking the user to recapture the image.
-
For typical applications, it is recommended to provide a higher
resolution image (such as 1280x720 or 1920x1080), which makes barcodes
detectable from a larger distance away from the camera.
However, in applications where latency is critical, you can improve
performance by capturing images at a lower resolution, but requiring that
the barcode make up the majority of the input image. Also see
Tips to improve real-time performance
.
If you know which barcode formats you expect to read, you can improve the speed
of the barcode detector by configuring it to only detect those formats.
For example, to detect only Aztec code and QR codes, build a
FirebaseVisionBarcodeDetectorOptions
object as in the following example:
Java
FirebaseVisionBarcodeDetectorOptions options =
new FirebaseVisionBarcodeDetectorOptions.Builder()
.setBarcodeFormats(
FirebaseVisionBarcode.FORMAT_QR_CODE,
FirebaseVisionBarcode.FORMAT_AZTEC)
.build();
Kotlin+KTX
val options = FirebaseVisionBarcodeDetectorOptions.Builder()
.setBarcodeFormats(
FirebaseVisionBarcode.FORMAT_QR_CODE,
FirebaseVisionBarcode.FORMAT_AZTEC)
.build()
The following formats are supported:
- Code 128 (
FORMAT_CODE_128
)
- Code 39 (
FORMAT_CODE_39
)
- Code 93 (
FORMAT_CODE_93
)
- Codabar (
FORMAT_CODABAR
)
- EAN-13 (
FORMAT_EAN_13
)
- EAN-8 (
FORMAT_EAN_8
)
- ITF (
FORMAT_ITF
)
- UPC-A (
FORMAT_UPC_A
)
- UPC-E (
FORMAT_UPC_E
)
- QR Code (
FORMAT_QR_CODE
)
- PDF417 (
FORMAT_PDF417
)
- Aztec (
FORMAT_AZTEC
)
- Data Matrix (
FORMAT_DATA_MATRIX
)
2. Run the barcode detector
To recognize barcodes in an image, create a
FirebaseVisionImage
object
from either a
Bitmap
,
media.Image
,
ByteBuffer
, byte array, or a file on
the device. Then, pass the
FirebaseVisionImage
object to the
FirebaseVisionBarcodeDetector
's
detectInImage
method.
Create a
FirebaseVisionImage
object from your image.
-
To create a
FirebaseVisionImage
object from a
media.Image
object, such as when capturing an image from a
device's camera, pass the
media.Image
object and the image's
rotation to
FirebaseVisionImage.fromMediaImage()
.
If you use the
CameraX
library, the
OnImageCapturedListener
and
ImageAnalysis.Analyzer
classes calculate the rotation value
for you, so you just need to convert the rotation to one of ML Kit's
ROTATION_
constants before calling
FirebaseVisionImage.fromMediaImage()
:
Java
private class YourAnalyzer implements ImageAnalysis.Analyzer {
private int degreesToFirebaseRotation(int degrees) {
switch (degrees) {
case 0:
return FirebaseVisionImageMetadata.ROTATION_0;
case 90:
return FirebaseVisionImageMetadata.ROTATION_90;
case 180:
return FirebaseVisionImageMetadata.ROTATION_180;
case 270:
return FirebaseVisionImageMetadata.ROTATION_270;
default:
throw new IllegalArgumentException(
"Rotation must be 0, 90, 180, or 270.");
}
}
@Override
public void analyze(ImageProxy imageProxy, int degrees) {
if (imageProxy == null || imageProxy.getImage() == null) {
return;
}
Image mediaImage = imageProxy.getImage();
int rotation = degreesToFirebaseRotation(degrees);
FirebaseVisionImage image =
FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
// Pass image to an ML Kit Vision API
// ...
}
}
Kotlin+KTX
private class YourImageAnalyzer : ImageAnalysis.Analyzer {
private fun degreesToFirebaseRotation(degrees: Int): Int = when(degrees) {
0 -> FirebaseVisionImageMetadata.ROTATION_0
90 -> FirebaseVisionImageMetadata.ROTATION_90
180 -> FirebaseVisionImageMetadata.ROTATION_180
270 -> FirebaseVisionImageMetadata.ROTATION_270
else -> throw Exception("Rotation must be 0, 90, 180, or 270.")
}
override fun analyze(imageProxy: ImageProxy?, degrees: Int) {
val mediaImage = imageProxy?.image
val imageRotation = degreesToFirebaseRotation(degrees)
if (mediaImage != null) {
val image = FirebaseVisionImage.fromMediaImage(mediaImage, imageRotation)
// Pass image to an ML Kit Vision API
// ...
}
}
}
If you don't use a camera library that gives you the image's rotation, you
can calculate it from the device's rotation and the orientation of camera
sensor in the device:
Java
private static final SparseIntArray ORIENTATIONS = new SparseIntArray();
static {
ORIENTATIONS.append(Surface.ROTATION_0, 90);
ORIENTATIONS.append(Surface.ROTATION_90, 0);
ORIENTATIONS.append(Surface.ROTATION_180, 270);
ORIENTATIONS.append(Surface.ROTATION_270, 180);
}
/**
* Get the angle by which an image must be rotated given the device's current
* orientation.
*/
@RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
private int getRotationCompensation(String cameraId, Activity activity, Context context)
throws CameraAccessException {
// Get the device's current rotation relative to its "native" orientation.
// Then, from the ORIENTATIONS table, look up the angle the image must be
// rotated to compensate for the device's rotation.
int deviceRotation = activity.getWindowManager().getDefaultDisplay().getRotation();
int rotationCompensation = ORIENTATIONS.get(deviceRotation);
// On most devices, the sensor orientation is 90 degrees, but for some
// devices it is 270 degrees. For devices with a sensor orientation of
// 270, rotate the image an additional 180 ((270 + 270) % 360) degrees.
CameraManager cameraManager = (CameraManager) context.getSystemService(CAMERA_SERVICE);
int sensorOrientation = cameraManager
.getCameraCharacteristics(cameraId)
.get(CameraCharacteristics.SENSOR_ORIENTATION);
rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360;
// Return the corresponding FirebaseVisionImageMetadata rotation value.
int result;
switch (rotationCompensation) {
case 0:
result = FirebaseVisionImageMetadata.ROTATION_0;
break;
case 90:
result = FirebaseVisionImageMetadata.ROTATION_90;
break;
case 180:
result = FirebaseVisionImageMetadata.ROTATION_180;
break;
case 270:
result = FirebaseVisionImageMetadata.ROTATION_270;
break;
default:
result = FirebaseVisionImageMetadata.ROTATION_0;
Log.e(TAG, "Bad rotation value: " + rotationCompensation);
}
return result;
}
Kotlin+KTX
private val ORIENTATIONS = SparseIntArray()
init {
ORIENTATIONS.append(Surface.ROTATION_0, 90)
ORIENTATIONS.append(Surface.ROTATION_90, 0)
ORIENTATIONS.append(Surface.ROTATION_180, 270)
ORIENTATIONS.append(Surface.ROTATION_270, 180)
}
/**
* Get the angle by which an image must be rotated given the device's current
* orientation.
*/
@RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
@Throws(CameraAccessException::class)
private fun getRotationCompensation(cameraId: String, activity: Activity, context: Context): Int {
// Get the device's current rotation relative to its "native" orientation.
// Then, from the ORIENTATIONS table, look up the angle the image must be
// rotated to compensate for the device's rotation.
val deviceRotation = activity.windowManager.defaultDisplay.rotation
var rotationCompensation = ORIENTATIONS.get(deviceRotation)
// On most devices, the sensor orientation is 90 degrees, but for some
// devices it is 270 degrees. For devices with a sensor orientation of
// 270, rotate the image an additional 180 ((270 + 270) % 360) degrees.
val cameraManager = context.getSystemService(CAMERA_SERVICE) as CameraManager
val sensorOrientation = cameraManager
.getCameraCharacteristics(cameraId)
.get(CameraCharacteristics.SENSOR_ORIENTATION)!!
rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360
// Return the corresponding FirebaseVisionImageMetadata rotation value.
val result: Int
when (rotationCompensation) {
0 -> result = FirebaseVisionImageMetadata.ROTATION_0
90 -> result = FirebaseVisionImageMetadata.ROTATION_90
180 -> result = FirebaseVisionImageMetadata.ROTATION_180
270 -> result = FirebaseVisionImageMetadata.ROTATION_270
else -> {
result = FirebaseVisionImageMetadata.ROTATION_0
Log.e(TAG, "Bad rotation value: $rotationCompensation")
}
}
return result
}
Then, pass the
media.Image
object and the
rotation value to
FirebaseVisionImage.fromMediaImage()
:
Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
Kotlin+KTX
val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
- To create a
FirebaseVisionImage
object from a file URI, pass
the app context and file URI to
FirebaseVisionImage.fromFilePath()
. This is useful when you
use an
ACTION_GET_CONTENT
intent to prompt the user to select
an image from their gallery app.
Java
FirebaseVisionImage image;
try {
image = FirebaseVisionImage.fromFilePath(context, uri);
} catch (IOException e) {
e.printStackTrace();
}
Kotlin+KTX
val image: FirebaseVisionImage
try {
image = FirebaseVisionImage.fromFilePath(context, uri)
} catch (e: IOException) {
e.printStackTrace()
}
- To create a
FirebaseVisionImage
object from a
ByteBuffer
or a byte array, first calculate the image
rotation as described above for
media.Image
input.
Then, create a
FirebaseVisionImageMetadata
object
that contains the image's height, width, color encoding format,
and rotation:
Java
FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder()
.setWidth(480) // 480x360 is typically sufficient for
.setHeight(360) // image recognition
.setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
.setRotation(rotation)
.build();
Kotlin+KTX
val metadata = FirebaseVisionImageMetadata.Builder()
.setWidth(480) // 480x360 is typically sufficient for
.setHeight(360) // image recognition
.setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
.setRotation(rotation)
.build()
Use the buffer or array, and the metadata object, to create a
FirebaseVisionImage
object:
Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
// Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
Kotlin+KTX
val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata)
// Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
- To create a
FirebaseVisionImage
object from a
Bitmap
object:
Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
The image represented by the
Bitmap
object must
be upright, with no additional rotation required.
Get an instance of
FirebaseVisionBarcodeDetector
:
Java
FirebaseVisionBarcodeDetector detector = FirebaseVision.getInstance()
.getVisionBarcodeDetector();
// Or, to specify the formats to recognize:
// FirebaseVisionBarcodeDetector detector = FirebaseVision.getInstance()
// .getVisionBarcodeDetector(options);
Kotlin+KTX
val detector = FirebaseVision.getInstance()
.visionBarcodeDetector
// Or, to specify the formats to recognize:
// val detector = FirebaseVision.getInstance()
// .getVisionBarcodeDetector(options)
Finally, pass the image to the
detectInImage
method:
Java
Task<List<FirebaseVisionBarcode>> result = detector.detectInImage(image)
.addOnSuccessListener(new OnSuccessListener<List<FirebaseVisionBarcode>>() {
@Override
public void onSuccess(List<FirebaseVisionBarcode> barcodes) {
// Task completed successfully
// ...
}
})
.addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
// Task failed with an exception
// ...
}
});
Kotlin+KTX
val result = detector.detectInImage(image)
.addOnSuccessListener { barcodes ->
// Task completed successfully
// ...
}
.addOnFailureListener {
// Task failed with an exception
// ...
}
If the barcode recognition operation succeeds, a list of
FirebaseVisionBarcode
objects will be passed to the success listener. Each
FirebaseVisionBarcode
object represents a barcode that was detected in the
image. For each barcode, you can get its bounding coordinates in the input
image, as well as the raw data encoded by the barcode. Also, if the barcode
detector was able to determine the type of data encoded by the barcode, you can
get an object containing parsed data.
For example:
Java
for (FirebaseVisionBarcode barcode: barcodes) {
Rect bounds = barcode.getBoundingBox();
Point[] corners = barcode.getCornerPoints();
String rawValue = barcode.getRawValue();
int valueType = barcode.getValueType();
// See API reference for complete list of supported types
switch (valueType) {
case FirebaseVisionBarcode.TYPE_WIFI:
String ssid = barcode.getWifi().getSsid();
String password = barcode.getWifi().getPassword();
int type = barcode.getWifi().getEncryptionType();
break;
case FirebaseVisionBarcode.TYPE_URL:
String title = barcode.getUrl().getTitle();
String url = barcode.getUrl().getUrl();
break;
}
}
Kotlin+KTX
for (barcode in barcodes) {
val bounds = barcode.boundingBox
val corners = barcode.cornerPoints
val rawValue = barcode.rawValue
val valueType = barcode.valueType
// See API reference for complete list of supported types
when (valueType) {
FirebaseVisionBarcode.TYPE_WIFI -> {
val ssid = barcode.wifi!!.ssid
val password = barcode.wifi!!.password
val type = barcode.wifi!!.encryptionType
}
FirebaseVisionBarcode.TYPE_URL -> {
val title = barcode.url!!.title
val url = barcode.url!!.url
}
}
}
If you want to scan barcodes in a real-time application, follow these
guidelines to achieve the best framerates:
-
Don't capture input at the camera’s native resolution. On some devices,
capturing input at the native resolution produces extremely large (10+
megapixels) images, which results in very poor latency with no benefit to
accuracy. Instead, only request the size from the camera that is required
for barcode detection: usually no more than 2 megapixels.
If scanning speed is important, you can further lower the image capture
resolution. However, bear in mind the minimum barcode size requirements
outlined above.
- Throttle calls to the detector. If a new video frame becomes
available while the detector is running, drop the frame.
- If you are using the output of the detector to overlay graphics on
the input image, first get the result from ML Kit, then render the image
and overlay in a single step. By doing so, you render to the display surface
only once for each input frame.
-
If you use the Camera2 API, capture images in
ImageFormat.YUV_420_888
format.
If you use the older Camera API, capture images in
ImageFormat.NV21
format.