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    Detect faces

    Face Detectiondetects multiple faces within an image along with the associated key facial attributes such as emotional state or wearing headwear .

    Specific individual Facial Recognition is not supported.

    Try it for yourself

    If you're new to Google Cloud, create an account to evaluate how Cloud Vision API performs in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

    Try Cloud Vision API free

    Face detection requests

    Set up your Google Cloud project and authentication

    Detect Faces in a local image

    You can use the Vision API to perform feature detection on a local image file.

    For REST requests, send the contents of the image file as a base64 encoded string in the body of your request.

    For gcloud and client library requests, specify the path to a local image in your request.

    REST

    Before using any of the request data, make the following replacements:

    • BASE64_ENCODED_IMAGE : The base64 representation (ASCII string) of your binary image data. This string should look similar to the following string:
      • /9j/4QAYRXhpZgAA...9tAVx/zDQDlGxn//2Q==
      Visit the base64 encode topic for more information.
    • RESULTS_INT : (Optional) An integer value of results to return. If you omit the "maxResults" field and its value, the API returns the default value of 10 results. This field does not apply to the following feature types: TEXT_DETECTION , DOCUMENT_TEXT_DETECTION , or CROP_HINTS .
    • PROJECT_ID : Your Google Cloud project ID.

    HTTP method and URL:

    POST https://vision.googleapis.com/v1/images:annotate

    Request JSON body:

    {
  "requests": [
    {
      "image": {
        "content": " BASE64_ENCODED_IMAGE 
"
      },
      "features": [
        {
          "maxResults": RESULTS_INT 
,
          "type": "FACE_DETECTION"
        }
      ]
    }
  ]
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: PROJECT_ID 
" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https://vision.googleapis.com/v1/images:annotate"

    PowerShell

    Save the request body in a file named request.json , and execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = " PROJECT_ID 
" }

    Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://vision.googleapis.com/v1/images:annotate" | Select-Object -Expand Content

    If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format.

    A FACE_DETECTION response includes bounding boxes for all faces detected, landmarks detected on the faces (eyes, nose, mouth, etc.), and confidence ratings for face and image properties (joy, sorrow, anger, surprise, etc.).

    Go

    Before trying this sample, follow the Go setup instructions in the Vision quickstart using client libraries . For more information, see the Vision Go API reference documentation .

    To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // detectFaces gets faces from the Vision API for an image at the given file path. 
 func 
  
 detectFaces 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 file 
  
 string 
 ) 
  
 error 
  
 { 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 vision 
 . 
 NewImageAnnotatorClient 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 defer 
  
 client 
 . 
 Close 
 () 
  
 f 
 , 
  
 err 
  
 := 
  
 os 
 . 
 Open 
 ( 
 file 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 defer 
  
 f 
 . 
 Close 
 () 
  
 image 
 , 
  
 err 
  
 := 
  
 vision 
 . 
 NewImageFromReader 
 ( 
 f 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 annotations 
 , 
  
 err 
  
 := 
  
 client 
 . 
 DetectFaces 
 ( 
 ctx 
 , 
  
 image 
 , 
  
 nil 
 , 
  
 10 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 if 
  
 len 
 ( 
 annotations 
 ) 
  
 == 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "No faces found." 
 ) 
  
 } 
  
 else 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "Faces:" 
 ) 
  
 for 
  
 i 
 , 
  
 annotation 
  
 := 
  
 range 
  
 annotations 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "  Face" 
 , 
  
 i 
 ) 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "    Anger:" 
 , 
  
 annotation 
 . 
 AngerLikelihood 
 ) 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "    Joy:" 
 , 
  
 annotation 
 . 
 JoyLikelihood 
 ) 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "    Surprise:" 
 , 
  
 annotation 
 . 
 SurpriseLikelihood 
 ) 
  
 } 
  
 } 
  
 return 
  
 nil 
 }

    Java

    Before trying this sample, follow the Java setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Java reference documentation .

      import 
  
 com.google.cloud.vision.v1. AnnotateImageRequest 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. AnnotateImageResponse 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. BatchAnnotateImagesResponse 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. FaceAnnotation 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. Feature 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. Image 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. ImageAnnotatorClient 
 
 ; 
 import 
  
 com.google.protobuf. ByteString 
 
 ; 
 import 
  
 java.io.FileInputStream 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.ArrayList 
 ; 
 import 
  
 java.util.List 
 ; 
 public 
  
 class 
 DetectFaces 
  
 { 
  
 public 
  
 static 
  
 void 
  
 detectFaces 
 () 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 filePath 
  
 = 
  
 "path/to/your/image/file.jpg" 
 ; 
  
 detectFaces 
 ( 
 filePath 
 ); 
  
 } 
  
 // Detects faces in the specified local image. 
  
 public 
  
 static 
  
 void 
  
 detectFaces 
 ( 
 String 
  
 filePath 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 List<AnnotateImageRequest> 
  
 requests 
  
 = 
  
 new 
  
 ArrayList 
<> (); 
  
  ByteString 
 
  
 imgBytes 
  
 = 
  
  ByteString 
 
 . 
  readFrom 
 
 ( 
 new 
  
 FileInputStream 
 ( 
 filePath 
 )); 
  
  Image 
 
  
 img 
  
 = 
  
  Image 
 
 . 
 newBuilder 
 (). 
 setContent 
 ( 
 imgBytes 
 ). 
 build 
 (); 
  
  Feature 
 
  
 feat 
  
 = 
  
  Feature 
 
 . 
 newBuilder 
 (). 
 setType 
 ( 
  Feature 
 
 . 
 Type 
 . 
 FACE_DETECTION 
 ). 
 build 
 (); 
  
  AnnotateImageRequest 
 
  
 request 
  
 = 
  
  AnnotateImageRequest 
 
 . 
 newBuilder 
 (). 
 addFeatures 
 ( 
 feat 
 ). 
 setImage 
 ( 
 img 
 ). 
 build 
 (); 
  
 requests 
 . 
 add 
 ( 
 request 
 ); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  ImageAnnotatorClient 
 
  
 client 
  
 = 
  
  ImageAnnotatorClient 
 
 . 
 create 
 ()) 
  
 { 
  
  BatchAnnotateImagesResponse 
 
  
 response 
  
 = 
  
 client 
 . 
 batchAnnotateImages 
 ( 
 requests 
 ); 
  
 List<AnnotateImageResponse> 
  
 responses 
  
 = 
  
 response 
 . 
  getResponsesList 
 
 (); 
  
 for 
  
 ( 
  AnnotateImageResponse 
 
  
 res 
  
 : 
  
 responses 
 ) 
  
 { 
  
 if 
  
 ( 
 res 
 . 
 hasError 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Error: %s%n" 
 , 
  
 res 
 . 
 getError 
 (). 
 getMessage 
 ()); 
  
 return 
 ; 
  
 } 
  
 // For full list of available annotations, see http://g.co/cloud/vision/docs 
  
 for 
  
 ( 
  FaceAnnotation 
 
  
 annotation 
  
 : 
  
 res 
 . 
 getFaceAnnotationsList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "anger: %s%njoy: %s%nsurprise: %s%nposition: %s" 
 , 
  
 annotation 
 . 
 getAngerLikelihood 
 (), 
  
 annotation 
 . 
 getJoyLikelihood 
 (), 
  
 annotation 
 . 
 getSurpriseLikelihood 
 (), 
  
 annotation 
 . 
 getBoundingPoly 
 ()); 
  
 } 
  
 } 
  
 } 
  
 } 
 }

    Node.js

    Before trying this sample, follow the Node.js setup instructions in the Vision quickstart using client libraries . For more information, see the Vision Node.js API reference documentation .

    To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Imports the Google Cloud client library 
 const 
  
 vision 
  
 = 
  
 require 
 ( 
 ' @google-cloud/vision 
' 
 ); 
 // Creates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 vision 
 . 
  ImageAnnotatorClient 
 
 (); 
 async 
  
 function 
  
 detectFaces 
 () 
  
 { 
  
 /** 
 * TODO(developer): Uncomment the following line before running the sample. 
 */ 
  
 // const fileName = 'Local image file, e.g. /path/to/image.png'; 
  
 const 
  
 [ 
 result 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 faceDetection 
 ( 
 fileName 
 ); 
  
 const 
  
 faces 
  
 = 
  
  result 
 
 . 
 faceAnnotations 
 ; 
  
 console 
 . 
 log 
 ( 
 'Faces:' 
 ); 
  
 faces 
 . 
 forEach 
 (( 
 face 
 , 
  
 i 
 ) 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  Face # 
 ${ 
 i 
  
 + 
  
 1 
 } 
 :` 
 ); 
  
 console 
 . 
 log 
 ( 
 `    Joy: 
 ${ 
 face 
 . 
 joyLikelihood 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `    Anger: 
 ${ 
 face 
 . 
 angerLikelihood 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `    Sorrow: 
 ${ 
 face 
 . 
 sorrowLikelihood 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `    Surprise: 
 ${ 
 face 
 . 
 surpriseLikelihood 
 } 
 ` 
 ); 
  
 }); 
 } 
 detectFaces 
 ();

    Python

    Before trying this sample, follow the Python setup instructions in the Vision quickstart using client libraries . For more information, see the Vision Python API reference documentation .

    To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      def 
  
 detect_faces 
 ( 
 path 
 ): 
  
 """Detects faces in an image.""" 
 from 
  
 google.cloud 
  
 import 
 vision 
 client 
 = 
 vision 
 . 
  ImageAnnotatorClient 
 
 () 
 with 
 open 
 ( 
 path 
 , 
 "rb" 
 ) 
 as 
 image_file 
 : 
 content 
 = 
 image_file 
 . 
 read 
 () 
 image 
 = 
 vision 
 . 
  Image 
 
 ( 
 content 
 = 
 content 
 ) 
 response 
 = 
 client 
 . 
 face_detection 
 ( 
 image 
 = 
 image 
 ) 
 faces 
 = 
 response 
 . 
 face_annotations 
 # Names of likelihood from google.cloud.vision.enums 
 likelihood_name 
 = 
 ( 
 "UNKNOWN" 
 , 
 "VERY_UNLIKELY" 
 , 
 "UNLIKELY" 
 , 
 "POSSIBLE" 
 , 
 "LIKELY" 
 , 
 "VERY_LIKELY" 
 , 
 ) 
 print 
 ( 
 "Faces:" 
 ) 
 for 
 face 
 in 
 faces 
 : 
 print 
 ( 
 f 
 "anger: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 anger_likelihood 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "joy: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 joy_likelihood 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "surprise: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 surprise_likelihood 
 ] 
 } 
 " 
 ) 
 vertices 
 = 
 [ 
 f 
 "( 
 { 
 vertex 
 . 
 x 
 } 
 , 
 { 
 vertex 
 . 
 y 
 } 
 )" 
 for 
 vertex 
 in 
 face 
 . 
 bounding_poly 
 . 
 vertices 
 ] 
 print 
 ( 
 "face bounds: 
 {} 
 " 
 . 
 format 
 ( 
 "," 
 . 
 join 
 ( 
 vertices 
 ))) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

    Additional languages

    C#: Please follow the C# setup instructions on the client libraries page and then visit the Vision reference documentation for .NET.

    PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Vision reference documentation for PHP.

    Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Vision reference documentation for Ruby.

    Detect Faces in a remote image

    You can use the Vision API to perform feature detection on a remote image file that is located in Cloud Storage or on the Web. To send a remote file request, specify the file's Web URL or Cloud Storage URI in the request body.

    REST

    Before using any of the request data, make the following replacements:

    • CLOUD_STORAGE_IMAGE_URI : the path to a valid image file in a Cloud Storage bucket. You must at least have read privileges to the file. Example: 
      • gs://cloud-samples-data/vision/face/faces.jpeg
    • RESULTS_INT : (Optional) An integer value of results to return. If you omit the "maxResults" field and its value, the API returns the default value of 10 results. This field does not apply to the following feature types: TEXT_DETECTION , DOCUMENT_TEXT_DETECTION , or CROP_HINTS .
    • PROJECT_ID : Your Google Cloud project ID.

    HTTP method and URL:

    POST https://vision.googleapis.com/v1/images:annotate

    Request JSON body:

    {
  "requests": [
    {
      "image": {
        "source": {
          "imageUri": " CLOUD_STORAGE_IMAGE_URI 
"
        }
       },
       "features": [
         {
           "maxResults": RESULTS_INT 
,
           "type": "FACE_DETECTION"
         }
       ]
    }
  ]
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: PROJECT_ID 
" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d @request.json \
    "https://vision.googleapis.com/v1/images:annotate"

    PowerShell

    Save the request body in a file named request.json , and execute the following command:

    $cred = gcloud auth print-access-token
    $headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = " PROJECT_ID 
" }

    Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -ContentType: "application/json; charset=utf-8" `
    -InFile request.json `
    -Uri "https://vision.googleapis.com/v1/images:annotate" | Select-Object -Expand Content

    If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format.

    A FACE_DETECTION response includes bounding boxes for all faces detected, landmarks detected on the faces (eyes, nose, mouth, etc.), and confidence ratings for face and image properties (joy, sorrow, anger, surprise, etc.).

    Go

    Before trying this sample, follow the Go setup instructions in the Vision quickstart using client libraries . For more information, see the Vision Go API reference documentation .

    To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // detectFaces gets faces from the Vision API for an image at the given file path. 
 func 
  
 detectFacesURI 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 file 
  
 string 
 ) 
  
 error 
  
 { 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 vision 
 . 
 NewImageAnnotatorClient 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 image 
  
 := 
  
 vision 
 . 
 NewImageFromURI 
 ( 
 file 
 ) 
  
 annotations 
 , 
  
 err 
  
 := 
  
 client 
 . 
 DetectFaces 
 ( 
 ctx 
 , 
  
 image 
 , 
  
 nil 
 , 
  
 10 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 if 
  
 len 
 ( 
 annotations 
 ) 
  
 == 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "No faces found." 
 ) 
  
 } 
  
 else 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "Faces:" 
 ) 
  
 for 
  
 i 
 , 
  
 annotation 
  
 := 
  
 range 
  
 annotations 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "  Face" 
 , 
  
 i 
 ) 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "    Anger:" 
 , 
  
 annotation 
 . 
 AngerLikelihood 
 ) 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "    Joy:" 
 , 
  
 annotation 
 . 
 JoyLikelihood 
 ) 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "    Surprise:" 
 , 
  
 annotation 
 . 
 SurpriseLikelihood 
 ) 
  
 } 
  
 } 
  
 return 
  
 nil 
 }

    Java

    Before trying this sample, follow the Java setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Java reference documentation .

      import 
  
 com.google.cloud.vision.v1. AnnotateImageRequest 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. AnnotateImageResponse 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. BatchAnnotateImagesResponse 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. FaceAnnotation 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. Feature 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. Image 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. ImageAnnotatorClient 
 
 ; 
 import 
  
 com.google.cloud.vision.v1. ImageSource 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.ArrayList 
 ; 
 import 
  
 java.util.List 
 ; 
 public 
  
 class 
 DetectFacesGcs 
  
 { 
  
 public 
  
 static 
  
 void 
  
 detectFacesGcs 
 () 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 filePath 
  
 = 
  
 "gs://your-gcs-bucket/path/to/image/file.jpg" 
 ; 
  
 detectFacesGcs 
 ( 
 filePath 
 ); 
  
 } 
  
 // Detects faces in the specified remote image on Google Cloud Storage. 
  
 public 
  
 static 
  
 void 
  
 detectFacesGcs 
 ( 
 String 
  
 gcsPath 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 List<AnnotateImageRequest> 
  
 requests 
  
 = 
  
 new 
  
 ArrayList 
<> (); 
  
  ImageSource 
 
  
 imgSource 
  
 = 
  
  ImageSource 
 
 . 
 newBuilder 
 (). 
  setGcsImageUri 
 
 ( 
 gcsPath 
 ). 
 build 
 (); 
  
  Image 
 
  
 img 
  
 = 
  
  Image 
 
 . 
 newBuilder 
 (). 
  setSource 
 
 ( 
 imgSource 
 ). 
 build 
 (); 
  
  Feature 
 
  
 feat 
  
 = 
  
  Feature 
 
 . 
 newBuilder 
 (). 
 setType 
 ( 
  Feature 
 
 . 
 Type 
 . 
 FACE_DETECTION 
 ). 
 build 
 (); 
  
  AnnotateImageRequest 
 
  
 request 
  
 = 
  
  AnnotateImageRequest 
 
 . 
 newBuilder 
 (). 
 addFeatures 
 ( 
 feat 
 ). 
 setImage 
 ( 
 img 
 ). 
 build 
 (); 
  
 requests 
 . 
 add 
 ( 
 request 
 ); 
  
 // Initialize client that will be used to send requests. This client only needs to be created 
  
 // once, and can be reused for multiple requests. After completing all of your requests, call 
  
 // the "close" method on the client to safely clean up any remaining background resources. 
  
 try 
  
 ( 
  ImageAnnotatorClient 
 
  
 client 
  
 = 
  
  ImageAnnotatorClient 
 
 . 
 create 
 ()) 
  
 { 
  
  BatchAnnotateImagesResponse 
 
  
 response 
  
 = 
  
 client 
 . 
 batchAnnotateImages 
 ( 
 requests 
 ); 
  
 List<AnnotateImageResponse> 
  
 responses 
  
 = 
  
 response 
 . 
  getResponsesList 
 
 (); 
  
 for 
  
 ( 
  AnnotateImageResponse 
 
  
 res 
  
 : 
  
 responses 
 ) 
  
 { 
  
 if 
  
 ( 
 res 
 . 
 hasError 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Error: %s%n" 
 , 
  
 res 
 . 
 getError 
 (). 
 getMessage 
 ()); 
  
 return 
 ; 
  
 } 
  
 // For full list of available annotations, see http://g.co/cloud/vision/docs 
  
 for 
  
 ( 
  FaceAnnotation 
 
  
 annotation 
  
 : 
  
 res 
 . 
 getFaceAnnotationsList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "anger: %s%njoy: %s%nsurprise: %s%nposition: %s" 
 , 
  
 annotation 
 . 
 getAngerLikelihood 
 (), 
  
 annotation 
 . 
 getJoyLikelihood 
 (), 
  
 annotation 
 . 
 getSurpriseLikelihood 
 (), 
  
 annotation 
 . 
 getBoundingPoly 
 ()); 
  
 } 
  
 } 
  
 } 
  
 } 
 }

    Node.js

    Before trying this sample, follow the Node.js setup instructions in the Vision quickstart using client libraries . For more information, see the Vision Node.js API reference documentation .

    To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Imports the Google Cloud client libraries 
 const 
  
 vision 
  
 = 
  
 require 
 ( 
 ' @google-cloud/vision 
' 
 ); 
 // Creates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 vision 
 . 
  ImageAnnotatorClient 
 
 (); 
 /** 
 * TODO(developer): Uncomment the following lines before running the sample. 
 */ 
 // const bucketName = 'Bucket where the file resides, e.g. my-bucket'; 
 // const fileName = 'Path to file within bucket, e.g. path/to/image.png'; 
 // Performs face detection on the gcs file 
 const 
  
 [ 
 result 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 faceDetection 
 ( 
 `gs:// 
 ${ 
 bucketName 
 } 
 / 
 ${ 
 fileName 
 } 
 ` 
 ); 
 const 
  
 faces 
  
 = 
  
  result 
 
 . 
 faceAnnotations 
 ; 
 console 
 . 
 log 
 ( 
 'Faces:' 
 ); 
 faces 
 . 
 forEach 
 (( 
 face 
 , 
  
 i 
 ) 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  Face # 
 ${ 
 i 
  
 + 
  
 1 
 } 
 :` 
 ); 
  
 console 
 . 
 log 
 ( 
 `    Joy: 
 ${ 
 face 
 . 
 joyLikelihood 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `    Anger: 
 ${ 
 face 
 . 
 angerLikelihood 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `    Sorrow: 
 ${ 
 face 
 . 
 sorrowLikelihood 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `    Surprise: 
 ${ 
 face 
 . 
 surpriseLikelihood 
 } 
 ` 
 ); 
 });

    Python

    Before trying this sample, follow the Python setup instructions in the Vision quickstart using client libraries . For more information, see the Vision Python API reference documentation .

    To authenticate to Vision, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      def 
  
 detect_faces_uri 
 ( 
 uri 
 ): 
  
 """Detects faces in the file located in Google Cloud Storage or the web.""" 
 from 
  
 google.cloud 
  
 import 
 vision 
 client 
 = 
 vision 
 . 
  ImageAnnotatorClient 
 
 () 
 image 
 = 
 vision 
 . 
  Image 
 
 () 
 image 
 . 
 source 
 . 
 image_uri 
 = 
 uri 
 response 
 = 
 client 
 . 
 face_detection 
 ( 
 image 
 = 
 image 
 ) 
 faces 
 = 
 response 
 . 
 face_annotations 
 # Names of likelihood from google.cloud.vision.enums 
 likelihood_name 
 = 
 ( 
 "UNKNOWN" 
 , 
 "VERY_UNLIKELY" 
 , 
 "UNLIKELY" 
 , 
 "POSSIBLE" 
 , 
 "LIKELY" 
 , 
 "VERY_LIKELY" 
 , 
 ) 
 print 
 ( 
 "Faces:" 
 ) 
 for 
 face 
 in 
 faces 
 : 
 print 
 ( 
 f 
 "anger: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 anger_likelihood 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "joy: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 joy_likelihood 
 ] 
 } 
 " 
 ) 
 print 
 ( 
 f 
 "surprise: 
 { 
 likelihood_name 
 [ 
 face 
 . 
 surprise_likelihood 
 ] 
 } 
 " 
 ) 
 vertices 
 = 
 [ 
 f 
 "( 
 { 
 vertex 
 . 
 x 
 } 
 , 
 { 
 vertex 
 . 
 y 
 } 
 )" 
 for 
 vertex 
 in 
 face 
 . 
 bounding_poly 
 . 
 vertices 
 ] 
 print 
 ( 
 "face bounds: 
 {} 
 " 
 . 
 format 
 ( 
 "," 
 . 
 join 
 ( 
 vertices 
 ))) 
 if 
 response 
 . 
 error 
 . 
 message 
 : 
 raise 
 Exception 
 ( 
 " 
 {} 
 \n 
 For more info on error messages, check: " 
 "https://cloud.google.com/apis/design/errors" 
 . 
 format 
 ( 
 response 
 . 
 error 
 . 
 message 
 ) 
 )

    gcloud

    To perform face detection, use the gcloud ml vision detect-faces command as shown in the following example:

    gcloud  
ml  
vision  
detect-faces  
 gs://cloud-samples-data/vision/face/faces.jpeg

    Additional languages

    C#: Please follow the C# setup instructions on the client libraries page and then visit the Vision reference documentation for .NET.

    PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Vision reference documentation for PHP.

    Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Vision reference documentation for Ruby.

    Try it

    Try face detection below. You can use the image specified already ( gs://cloud-samples-data/vision/face/faces.jpeg ) or specify your own image in its place. Send the request by selecting Execute.

    Request body:

    {
  "requests": [
    {
      "features": [
        {
          "maxResults": 10,
          "type": "FACE_DETECTION"
        }
      ],
      "image": {
        "source": {
          "imageUri": "gs://cloud-samples-data/vision/face/faces.jpeg"
        }
      }
    }
  ]
}
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