You can use ML Kit to recognize and decode barcodes.
Before you begin
- If you have not already added Firebase to your app, do so by following the
steps in the
getting started guide
.
- Include the ML Kit libraries in your Podfile:
pod 'Firebase/MLVision'
pod 'Firebase/MLVisionBarcodeModel'
After you install or update your project's Pods, be sure to open your Xcode
project using its
.xcworkspace
.
- In your app, import Firebase:
Objective-C
@import Firebase;
-
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
VisionBarcodeDetectorOptions
object as in the
following example:
Swift
let format = VisionBarcodeFormat.all
let barcodeOptions = VisionBarcodeDetectorOptions(formats: format)
The following formats are supported:
- Code128
- Code39
- Code93
- CodaBar
- EAN13
- EAN8
- ITF
- UPCA
- UPCE
- QRCode
- PDF417
- Aztec
- DataMatrix
Objective-C
FIRVisionBarcodeDetectorOptions *options =
[[FIRVisionBarcodeDetectorOptions alloc]
initWithFormats: FIRVisionBarcodeFormatQRCode | FIRVisionBarcodeFormatAztec];
The following formats are supported:
- Code 128 (
FIRVisionBarcodeFormatCode128
)
- Code 39 (
FIRVisionBarcodeFormatCode39
)
- Code 93 (
FIRVisionBarcodeFormatCode93
)
- Codabar (
FIRVisionBarcodeFormatCodaBar
)
- EAN-13 (
FIRVisionBarcodeFormatEAN13
)
- EAN-8 (
FIRVisionBarcodeFormatEAN8
)
- ITF (
FIRVisionBarcodeFormatITF
)
- UPC-A (
FIRVisionBarcodeFormatUPCA
)
- UPC-E (
FIRVisionBarcodeFormatUPCE
)
- QR Code (
FIRVisionBarcodeFormatQRCode
)
- PDF417 (
FIRVisionBarcodeFormatPDF417
)
- Aztec (
FIRVisionBarcodeFormatAztec
)
- Data Matrix (
FIRVisionBarcodeFormatDataMatrix
)
2. Run the barcode detector
To scan barcodes in an image, pass the image as a
UIImage
or a
CMSampleBufferRef
to the
VisionBarcodeDetector
's
detect(in:)
method:
- Get an instance of
VisionBarcodeDetector
:
Swift
lazy var vision = Vision.vision()
let barcodeDetector = vision.barcodeDetector(options: barcodeOptions)
Objective-C
FIRVision *vision = [FIRVision vision];
FIRVisionBarcodeDetector *barcodeDetector = [vision barcodeDetector];
// Or, to change the default settings:
// FIRVisionBarcodeDetector *barcodeDetector =
// [vision barcodeDetectorWithOptions:options];
-
Create a
VisionImage
object using a
UIImage
or a
CMSampleBufferRef
.
To use a
UIImage
:
- If necessary, rotate the image so that its
imageOrientation
property is
.up
.
- Create a
VisionImage
object using the correctly-rotated
UIImage
. Do not specify any rotation metadata—the default
value,
.topLeft
, must be used.
Swift
let image = VisionImage(image: uiImage)
Objective-C
FIRVisionImage *image = [[FIRVisionImage alloc] initWithImage:uiImage];
To use a
CMSampleBufferRef
:
-
Create a
VisionImageMetadata
object that specifies the
orientation of the image data contained in the
CMSampleBufferRef
buffer.
To get the image orientation:
Swift
func imageOrientation(
deviceOrientation: UIDeviceOrientation,
cameraPosition: AVCaptureDevice.Position
) -> VisionDetectorImageOrientation {
switch deviceOrientation {
case .portrait:
return cameraPosition == .front ? .leftTop : .rightTop
case .landscapeLeft:
return cameraPosition == .front ? .bottomLeft : .topLeft
case .portraitUpsideDown:
return cameraPosition == .front ? .rightBottom : .leftBottom
case .landscapeRight:
return cameraPosition == .front ? .topRight : .bottomRight
case .faceDown, .faceUp, .unknown:
return .leftTop
}
}
Objective-C
- (FIRVisionDetectorImageOrientation)
imageOrientationFromDeviceOrientation:(UIDeviceOrientation)deviceOrientation
cameraPosition:(AVCaptureDevicePosition)cameraPosition {
switch (deviceOrientation) {
case UIDeviceOrientationPortrait:
if (cameraPosition == AVCaptureDevicePositionFront) {
return FIRVisionDetectorImageOrientationLeftTop;
} else {
return FIRVisionDetectorImageOrientationRightTop;
}
case UIDeviceOrientationLandscapeLeft:
if (cameraPosition == AVCaptureDevicePositionFront) {
return FIRVisionDetectorImageOrientationBottomLeft;
} else {
return FIRVisionDetectorImageOrientationTopLeft;
}
case UIDeviceOrientationPortraitUpsideDown:
if (cameraPosition == AVCaptureDevicePositionFront) {
return FIRVisionDetectorImageOrientationRightBottom;
} else {
return FIRVisionDetectorImageOrientationLeftBottom;
}
case UIDeviceOrientationLandscapeRight:
if (cameraPosition == AVCaptureDevicePositionFront) {
return FIRVisionDetectorImageOrientationTopRight;
} else {
return FIRVisionDetectorImageOrientationBottomRight;
}
default:
return FIRVisionDetectorImageOrientationTopLeft;
}
}
Then, create the metadata object:
Swift
let cameraPosition = AVCaptureDevice.Position.back // Set to the capture device you used.
let metadata = VisionImageMetadata()
metadata.orientation = imageOrientation(
deviceOrientation: UIDevice.current.orientation,
cameraPosition: cameraPosition
)
Objective-C
FIRVisionImageMetadata *metadata = [[FIRVisionImageMetadata alloc] init];
AVCaptureDevicePosition cameraPosition =
AVCaptureDevicePositionBack; // Set to the capture device you used.
metadata.orientation =
[self imageOrientationFromDeviceOrientation:UIDevice.currentDevice.orientation
cameraPosition:cameraPosition];
- Create a
VisionImage
object using the
CMSampleBufferRef
object and the rotation metadata:
Swift
let image = VisionImage(buffer: sampleBuffer)
image.metadata = metadata
Objective-C
FIRVisionImage *image = [[FIRVisionImage alloc] initWithBuffer:sampleBuffer];
image.metadata = metadata;
-
Then, pass the image to the
detect(in:)
method:
Swift
barcodeDetector.detect(in: visionImage) { features, error in
guard error == nil, let features = features, !features.isEmpty else {
// ...
return
}
// ...
}
Objective-C
[barcodeDetector detectInImage:image
completion:^(NSArray<FIRVisionBarcode *> *barcodes,
NSError *error) {
if (error != nil) {
return;
} else if (barcodes != nil) {
// Recognized barcodes
// ...
}
}];
If the barcode recognition operation succeeds, the detector returns an array of
VisionBarcode
objects. Each
VisionBarcode
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:
Swift
for barcode in barcodes {
let corners = barcode.cornerPoints
let displayValue = barcode.displayValue
let rawValue = barcode.rawValue
let valueType = barcode.valueType
switch valueType {
case .wiFi:
let ssid = barcode.wifi!.ssid
let password = barcode.wifi!.password
let encryptionType = barcode.wifi!.type
case .URL:
let title = barcode.url!.title
let url = barcode.url!.url
default:
// See API reference for all supported value types
}
}
Objective-C
for (FIRVisionBarcode *barcode in barcodes) {
NSArray *corners = barcode.cornerPoints;
NSString *displayValue = barcode.displayValue;
NSString *rawValue = barcode.rawValue;
FIRVisionBarcodeValueType valueType = barcode.valueType;
switch (valueType) {
case FIRVisionBarcodeValueTypeWiFi:
// ssid = barcode.wifi.ssid;
// password = barcode.wifi.password;
// encryptionType = barcode.wifi.type;
break;
case FIRVisionBarcodeValueTypeURL:
// url = barcode.URL.url;
// title = barcode.URL.title;
break;
// ...
default:
break;
}
}
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.
The named capture session presets—
AVCaptureSessionPresetDefault
,
AVCaptureSessionPresetLow
,
AVCaptureSessionPresetMedium
,
and so on)—are not recommended, however, as they can map to
unsuitable resolutions on some devices. Instead, use the specific presets
such as
AVCaptureSessionPreset1280x720
.
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. See the
previewOverlayView
and
FIRDetectionOverlayView
classes in the showcase sample app for an example.