Přejít na konzoli

Scan Barcodes with ML Kit on iOS

You can use ML Kit to recognize and decode barcodes.

See the ML Kit Material Design showcase app and the ML Kit quickstart sample on GitHub for examples of this API in use.

Before you begin

  1. If you have not already added Firebase to your app, do so by following the steps in the getting started guide.
  2. 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.
  3. In your app, import Firebase:

    Swift

    import Firebase

    Objective-C

    @import Firebase;

Input image guidelines

  • 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.

1. Configure the barcode detector

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:

  1. 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];
    
  2. Create a VisionImage object using a UIImage or a CMSampleBufferRef.

    To use a UIImage:

    1. If necessary, rotate the image so that its imageOrientation property is .up.
    2. 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:

    1. 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];
    2. 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;
  3. 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
        // ...
      }
    }];
    

3. Get information from 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;
   }
 }

Tips to improve real-time performance

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.

Next steps

See the ML Kit Material Design showcase app and the ML Kit quickstart sample on GitHub for examples of this API in use.