Scan Barcodes with ML Kit on iOS

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:

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:

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 
  
 // ... 
  
 } 
 }];

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.

Scan Barcodes with ML Kit on iOS Stay organized with collections Save and categorize content based on your preferences.

Before you begin

Swift

Objective-C

Input image guidelines

1. Configure the barcode detector

Swift

Objective-C

2. Run the barcode detector

Swift

Objective-C

Swift

Objective-C

Swift

Objective-C

Swift

Objective-C

Swift

Objective-C

Swift

Objective-C

3. Get information from barcodes

Swift

Objective-C

Tips to improve real-time performance

Scan Barcodes with ML Kit on iOS