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    Detect Web entities and pages

    Web Detectiondetects Web references to an image.

    Carnaval image
    Image credit : Quinten de Graaf on Unsplash .

    Category
    Responses
    Web entities
    • entityId : /m/02p7_j8, score : 1.3225499, description : Carnival in Rio de Janeiro
    • entityId : /m/06gmr, score : 1.1684971, description : Rio de Janeiro
    • entityId : /m/04cx88, score : 1.05945, description : Brazilian Carnival
    ...
    Full matching images
    • url : https://1000lugaresparair.files.wordpress.com/2017/11/quinten-de-graaf-278848.jpg
    • url : https://freewalkingtourrotterdam.com/wp-content/uploads/2017/07/quinten-de-graaf-278848.jpg
    ...
    Partial matching images
    • url : https://www.linnanneito.fi/wp-content/uploads/sambakarnevaali-riossa.jpg
    • url : https://static.airhelp.com/wp-content/uploads/2019/02/26105557/two-women-in-carnival-costumes.jpg
    ...
    Pages with matching images
    • url : https://travelnoire.com/best-carnival-celebrations-around-the-world/,
      pageTitle : Best \u003cb\u003eCarnival\u003c/b\u003e Celebrations Around The World - Travel Noire,
      fullMatchingImages : [{ url : https://travelnoire.com/wp-content/uploads/2019/02/quinten-de-graaf-278848-unsplash.jpg}]
    • url : https://bespokebrazil.com/rio-carnival-2019/,
      pageTitle : Visit \u003cb\u003eRio Carnival 2019\u003c/b\u003e with the Brazil Specialists - Bespoke Brazil,
      partialMatchingImages : [{ url : https://bespoke-brazil-2018-bespokebrazil.netdna-ssl.com/wp-content/uploads/2019/01/Carnival-1.jpg}]
    ...
    Visually similar images
    • url : https://www.brazilbookers.com/_images/photos/rio-carnival-images/rio-carnival-2016-carnival-date.jpg
    • url : https://image.redbull.com/rbcom/010/2017-02-08/1331843859949_3/0100/0/1/watch-rio-carnival-2017-live-on-red-bull-tv.jpg
    ...
    Best guess labels
    rio carnival 2019 dancers

    Web detection requests

    Set up your Google Cloud project and authentication

    Detect Web entities with 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": "WEB_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.

    Response:

    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 .

      // detectWeb gets image properties from the Vision API for an image at the given file path. 
 func 
  
 detectWeb 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 file 
  
 string 
 ) 
  
 error 
  
 { 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 vision 
 . 
 NewImageAnnotatorClient 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 f 
 , 
  
 err 
  
 := 
  
 os 
 . 
 Open 
 ( 
 file 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 defer 
  
 f 
 . 
 Close 
 () 
  
 image 
 , 
  
 err 
  
 := 
  
 vision 
 . 
 NewImageFromReader 
 ( 
 f 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 web 
 , 
  
 err 
  
 := 
  
 client 
 . 
 DetectWeb 
 ( 
 ctx 
 , 
  
 image 
 , 
  
 nil 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "Web properties:" 
 ) 
  
 if 
  
 len 
 ( 
 web 
 . 
 FullMatchingImages 
 ) 
  
 != 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\tFull image matches:" 
 ) 
  
 for 
  
 _ 
 , 
  
 full 
  
 := 
  
 range 
  
 web 
 . 
 FullMatchingImages 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\t\t%s\n" 
 , 
  
 full 
 . 
 Url 
 ) 
  
 } 
  
 } 
  
 if 
  
 len 
 ( 
 web 
 . 
 PagesWithMatchingImages 
 ) 
  
 != 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\tPages with this image:" 
 ) 
  
 for 
  
 _ 
 , 
  
 page 
  
 := 
  
 range 
  
 web 
 . 
 PagesWithMatchingImages 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\t\t%s\n" 
 , 
  
 page 
 . 
 Url 
 ) 
  
 } 
  
 } 
  
 if 
  
 len 
 ( 
 web 
 . 
 WebEntities 
 ) 
  
 != 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\tEntities:" 
 ) 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\t\tEntity\t\tScore\tDescription" 
 ) 
  
 for 
  
 _ 
 , 
  
 entity 
  
 := 
  
 range 
  
 web 
 . 
 WebEntities 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\t\t%-14s\t%-2.4f\t%s\n" 
 , 
  
 entity 
 . 
 EntityId 
 , 
  
 entity 
 . 
 Score 
 , 
  
 entity 
 . 
 Description 
 ) 
  
 } 
  
 } 
  
 if 
  
 len 
 ( 
 web 
 . 
 BestGuessLabels 
 ) 
  
 != 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\tBest guess labels:" 
 ) 
  
 for 
  
 _ 
 , 
  
 label 
  
 := 
  
 range 
  
 web 
 . 
 BestGuessLabels 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\t\t%s\n" 
 , 
  
 label 
 . 
 Label 
 ) 
  
 } 
  
 } 
  
 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.Feature 
 ; 
 import 
  
 com.google.cloud.vision.v1.Feature.Type 
 ; 
 import 
  
 com.google.cloud.vision.v1.Image 
 ; 
 import 
  
 com.google.cloud.vision.v1.ImageAnnotatorClient 
 ; 
 import 
  
 com.google.cloud.vision.v1.WebDetection 
 ; 
 import 
  
 com.google.cloud.vision.v1.WebDetection.WebEntity 
 ; 
 import 
  
 com.google.cloud.vision.v1.WebDetection.WebImage 
 ; 
 import 
  
 com.google.cloud.vision.v1.WebDetection.WebLabel 
 ; 
 import 
  
 com.google.cloud.vision.v1.WebDetection.WebPage 
 ; 
 import 
  
 com.google.protobuf.ByteString 
 ; 
 import 
  
 java.io.FileInputStream 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.ArrayList 
 ; 
 import 
  
 java.util.List 
 ; 
 public 
  
 class 
 DetectWebDetections 
  
 { 
  
 public 
  
 static 
  
 void 
  
 detectWebDetections 
 () 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 filePath 
  
 = 
  
 "path/to/your/image/file.jpg" 
 ; 
  
 detectWebDetections 
 ( 
 filePath 
 ); 
  
 } 
  
 // Finds references to the specified image on the web. 
  
 public 
  
 static 
  
 void 
  
 detectWebDetections 
 ( 
 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 
 ( 
 Type 
 . 
 WEB_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 
 ; 
  
 } 
  
 // Search the web for usages of the image. You could use these signals later 
  
 // for user input moderation or linking external references. 
  
 // For a full list of available annotations, see http://g.co/cloud/vision/docs 
  
 WebDetection 
  
 annotation 
  
 = 
  
 res 
 . 
 getWebDetection 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Entity:Id:Score" 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "===============" 
 ); 
  
 for 
  
 ( 
 WebEntity 
  
 entity 
  
 : 
  
 annotation 
 . 
 getWebEntitiesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
  
 entity 
 . 
 getDescription 
 () 
  
 + 
  
 " : " 
  
 + 
  
 entity 
 . 
 getEntityId 
 () 
  
 + 
  
 " : " 
  
 + 
  
 entity 
 . 
 getScore 
 ()); 
  
 } 
  
 for 
  
 ( 
 WebLabel 
  
 label 
  
 : 
  
 annotation 
 . 
 getBestGuessLabelsList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "%nBest guess label: %s" 
 , 
  
 label 
 . 
 getLabel 
 ()); 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "%nPages with matching images: Score%n==" 
 ); 
  
 for 
  
 ( 
 WebPage 
  
 page 
  
 : 
  
 annotation 
 . 
 getPagesWithMatchingImagesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 page 
 . 
 getUrl 
 () 
  
 + 
  
 " : " 
  
 + 
  
 page 
 . 
 getScore 
 ()); 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "%nPages with partially matching images: Score%n==" 
 ); 
  
 for 
  
 ( 
 WebImage 
  
 image 
  
 : 
  
 annotation 
 . 
 getPartialMatchingImagesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 image 
 . 
 getUrl 
 () 
  
 + 
  
 " : " 
  
 + 
  
 image 
 . 
 getScore 
 ()); 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "%nPages with fully matching images: Score%n==" 
 ); 
  
 for 
  
 ( 
 WebImage 
  
 image 
  
 : 
  
 annotation 
 . 
 getFullMatchingImagesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 image 
 . 
 getUrl 
 () 
  
 + 
  
 " : " 
  
 + 
  
 image 
 . 
 getScore 
 ()); 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "%nPages with visually similar images: Score%n==" 
 ); 
  
 for 
  
 ( 
 WebImage 
  
 image 
  
 : 
  
 annotation 
 . 
 getVisuallySimilarImagesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 image 
 . 
 getUrl 
 () 
  
 + 
  
 " : " 
  
 + 
  
 image 
 . 
 getScore 
 ()); 
  
 } 
  
 } 
  
 } 
  
 } 
 }

    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 
 (); 
 /** 
 * TODO(developer): Uncomment the following line before running the sample. 
 */ 
 // const fileName = 'Local image file, e.g. /path/to/image.png'; 
 // Detect similar images on the web to a local file 
 const 
  
 [ 
 result 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 webDetection 
 ( 
 fileName 
 ); 
 const 
  
 webDetection 
  
 = 
  
 result 
 . 
 webDetection 
 ; 
 if 
  
 ( 
 webDetection 
 . 
 fullMatchingImages 
 . 
 length 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
  
 `Full matches found: 
 ${ 
 webDetection 
 . 
 fullMatchingImages 
 . 
 length 
 } 
 ` 
  
 ); 
  
 webDetection 
 . 
 fullMatchingImages 
 . 
 forEach 
 ( 
 image 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  URL: 
 ${ 
 image 
 . 
 url 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `  Score: 
 ${ 
 image 
 . 
 score 
 } 
 ` 
 ); 
  
 }); 
 } 
 if 
  
 ( 
 webDetection 
 . 
 partialMatchingImages 
 . 
 length 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
  
 `Partial matches found: 
 ${ 
 webDetection 
 . 
 partialMatchingImages 
 . 
 length 
 } 
 ` 
  
 ); 
  
 webDetection 
 . 
 partialMatchingImages 
 . 
 forEach 
 ( 
 image 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  URL: 
 ${ 
 image 
 . 
 url 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `  Score: 
 ${ 
 image 
 . 
 score 
 } 
 ` 
 ); 
  
 }); 
 } 
 if 
  
 ( 
 webDetection 
 . 
 webEntities 
 . 
 length 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
 `Web entities found: 
 ${ 
 webDetection 
 . 
 webEntities 
 . 
 length 
 } 
 ` 
 ); 
  
 webDetection 
 . 
 webEntities 
 . 
 forEach 
 ( 
 webEntity 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  Description: 
 ${ 
 webEntity 
 . 
 description 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `  Score: 
 ${ 
 webEntity 
 . 
 score 
 } 
 ` 
 ); 
  
 }); 
 } 
 if 
  
 ( 
 webDetection 
 . 
 bestGuessLabels 
 . 
 length 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
  
 `Best guess labels found: 
 ${ 
 webDetection 
 . 
 bestGuessLabels 
 . 
 length 
 } 
 ` 
  
 ); 
  
 webDetection 
 . 
 bestGuessLabels 
 . 
 forEach 
 ( 
 label 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  Label: 
 ${ 
 label 
 . 
 label 
 } 
 ` 
 ); 
  
 }); 
 }

    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_web 
 ( 
 path 
 ): 
  
 """Detects web annotations given 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 
 . 
 web_detection 
 ( 
 image 
 = 
 image 
 ) 
 annotations 
 = 
 response 
 . 
 web_detection 
 if 
 annotations 
 . 
 best_guess_labels 
 : 
 for 
 label 
 in 
 annotations 
 . 
 best_guess_labels 
 : 
 print 
 ( 
 f 
 " 
 \n 
 Best guess label: 
 { 
 label 
 . 
 label 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 pages_with_matching_images 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 Pages with matching images found:" 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 pages_with_matching_images 
 ) 
 ) 
 ) 
 for 
 page 
 in 
 annotations 
 . 
 pages_with_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \n\t 
 Page url   : 
 { 
 page 
 . 
 url 
 } 
 " 
 ) 
 if 
 page 
 . 
 full_matching_images 
 : 
 print 
 ( 
 " 
 \t 
 {} 
 Full Matches found: " 
 . 
 format 
 ( 
 len 
 ( 
 page 
 . 
 full_matching_images 
 )) 
 ) 
 for 
 image 
 in 
 page 
 . 
 full_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \t\t 
 Image url  : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 if 
 page 
 . 
 partial_matching_images 
 : 
 print 
 ( 
 " 
 \t 
 {} 
 Partial Matches found: " 
 . 
 format 
 ( 
 len 
 ( 
 page 
 . 
 partial_matching_images 
 ) 
 ) 
 ) 
 for 
 image 
 in 
 page 
 . 
 partial_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \t\t 
 Image url  : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 web_entities 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 Web entities found: " 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 web_entities 
 ))) 
 for 
 entity 
 in 
 annotations 
 . 
 web_entities 
 : 
 print 
 ( 
 f 
 " 
 \n\t 
 Score      : 
 { 
 entity 
 . 
 score 
 } 
 " 
 ) 
 print 
 ( 
 f 
 " 
 \t 
 Description: 
 { 
 entity 
 . 
 description 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 visually_similar_images 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 visually similar images found: 
 \n 
 " 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 visually_similar_images 
 ) 
 ) 
 ) 
 for 
 image 
 in 
 annotations 
 . 
 visually_similar_images 
 : 
 print 
 ( 
 f 
 " 
 \t 
 Image url    : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 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 Web entities with 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/web/carnaval.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": {
          "gcsImageUri": " CLOUD_STORAGE_IMAGE_URI 
"
        }
      },
      "features": [
        {
          "maxResults": RESULTS_INT 
,
          "type": "WEB_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.

    Response:

    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 .

      // detectWeb gets image properties from the Vision API for an image at the given file path. 
 func 
  
 detectWebURI 
 ( 
 w 
  
 io 
 . 
 Writer 
 , 
  
 file 
  
 string 
 ) 
  
 error 
  
 { 
  
 ctx 
  
 := 
  
 context 
 . 
 Background 
 () 
  
 client 
 , 
  
 err 
  
 := 
  
 vision 
 . 
 NewImageAnnotatorClient 
 ( 
 ctx 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 image 
  
 := 
  
 vision 
 . 
 NewImageFromURI 
 ( 
 file 
 ) 
  
 web 
 , 
  
 err 
  
 := 
  
 client 
 . 
 DetectWeb 
 ( 
 ctx 
 , 
  
 image 
 , 
  
 nil 
 ) 
  
 if 
  
 err 
  
 != 
  
 nil 
  
 { 
  
 return 
  
 err 
  
 } 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "Web properties:" 
 ) 
  
 if 
  
 len 
 ( 
 web 
 . 
 FullMatchingImages 
 ) 
  
 != 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\tFull image matches:" 
 ) 
  
 for 
  
 _ 
 , 
  
 full 
  
 := 
  
 range 
  
 web 
 . 
 FullMatchingImages 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\t\t%s\n" 
 , 
  
 full 
 . 
 Url 
 ) 
  
 } 
  
 } 
  
 if 
  
 len 
 ( 
 web 
 . 
 PagesWithMatchingImages 
 ) 
  
 != 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\tPages with this image:" 
 ) 
  
 for 
  
 _ 
 , 
  
 page 
  
 := 
  
 range 
  
 web 
 . 
 PagesWithMatchingImages 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\t\t%s\n" 
 , 
  
 page 
 . 
 Url 
 ) 
  
 } 
  
 } 
  
 if 
  
 len 
 ( 
 web 
 . 
 WebEntities 
 ) 
  
 != 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\tEntities:" 
 ) 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\t\tEntity\t\tScore\tDescription" 
 ) 
  
 for 
  
 _ 
 , 
  
 entity 
  
 := 
  
 range 
  
 web 
 . 
 WebEntities 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\t\t%-14s\t%-2.4f\t%s\n" 
 , 
  
 entity 
 . 
 EntityId 
 , 
  
 entity 
 . 
 Score 
 , 
  
 entity 
 . 
 Description 
 ) 
  
 } 
  
 } 
  
 if 
  
 len 
 ( 
 web 
 . 
 BestGuessLabels 
 ) 
  
 != 
  
 0 
  
 { 
  
 fmt 
 . 
 Fprintln 
 ( 
 w 
 , 
  
 "\tBest guess labels:" 
 ) 
  
 for 
  
 _ 
 , 
  
 label 
  
 := 
  
 range 
  
 web 
 . 
 BestGuessLabels 
  
 { 
  
 fmt 
 . 
 Fprintf 
 ( 
 w 
 , 
  
 "\t\t%s\n" 
 , 
  
 label 
 . 
 Label 
 ) 
  
 } 
  
 } 
  
 return 
  
 nil 
 }

    Java

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

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

      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.Feature 
 ; 
 import 
  
 com.google.cloud.vision.v1.Image 
 ; 
 import 
  
 com.google.cloud.vision.v1.ImageAnnotatorClient 
 ; 
 import 
  
 com.google.cloud.vision.v1.ImageSource 
 ; 
 import 
  
 com.google.cloud.vision.v1.WebDetection 
 ; 
 import 
  
 java.io.IOException 
 ; 
 import 
  
 java.util.ArrayList 
 ; 
 import 
  
 java.util.List 
 ; 
 public 
  
 class 
 DetectWebDetectionsGcs 
  
 { 
  
 public 
  
 static 
  
 void 
  
 detectWebDetectionsGcs 
 () 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 filePath 
  
 = 
  
 "gs://your-gcs-bucket/path/to/image/file.jpg" 
 ; 
  
 detectWebDetectionsGcs 
 ( 
 filePath 
 ); 
  
 } 
  
 // Detects whether the remote image on Google Cloud Storage has features you would want to 
  
 // moderate. 
  
 public 
  
 static 
  
 void 
  
 detectWebDetectionsGcs 
 ( 
 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 
 . 
 WEB_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 
 ; 
  
 } 
  
 // Search the web for usages of the image. You could use these signals later 
  
 // for user input moderation or linking external references. 
  
 // For a full list of available annotations, see http://g.co/cloud/vision/docs 
  
 WebDetection 
  
 annotation 
  
 = 
  
 res 
 . 
 getWebDetection 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Entity:Id:Score" 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "===============" 
 ); 
  
 for 
  
 ( 
 WebDetection 
 . 
 WebEntity 
  
 entity 
  
 : 
  
 annotation 
 . 
 getWebEntitiesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
  
 entity 
 . 
 getDescription 
 () 
  
 + 
  
 " : " 
  
 + 
  
 entity 
 . 
 getEntityId 
 () 
  
 + 
  
 " : " 
  
 + 
  
 entity 
 . 
 getScore 
 ()); 
  
 } 
  
 for 
  
 ( 
 WebDetection 
 . 
 WebLabel 
  
 label 
  
 : 
  
 annotation 
 . 
 getBestGuessLabelsList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "%nBest guess label: %s" 
 , 
  
 label 
 . 
 getLabel 
 ()); 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "%nPages with matching images: Score%n==" 
 ); 
  
 for 
  
 ( 
 WebDetection 
 . 
 WebPage 
  
 page 
  
 : 
  
 annotation 
 . 
 getPagesWithMatchingImagesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 page 
 . 
 getUrl 
 () 
  
 + 
  
 " : " 
  
 + 
  
 page 
 . 
 getScore 
 ()); 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "%nPages with partially matching images: Score%n==" 
 ); 
  
 for 
  
 ( 
 WebDetection 
 . 
 WebImage 
  
 image 
  
 : 
  
 annotation 
 . 
 getPartialMatchingImagesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 image 
 . 
 getUrl 
 () 
  
 + 
  
 " : " 
  
 + 
  
 image 
 . 
 getScore 
 ()); 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "%nPages with fully matching images: Score%n==" 
 ); 
  
 for 
  
 ( 
 WebDetection 
 . 
 WebImage 
  
 image 
  
 : 
  
 annotation 
 . 
 getFullMatchingImagesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 image 
 . 
 getUrl 
 () 
  
 + 
  
 " : " 
  
 + 
  
 image 
 . 
 getScore 
 ()); 
  
 } 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "%nPages with visually similar images: Score%n==" 
 ); 
  
 for 
  
 ( 
 WebDetection 
 . 
 WebImage 
  
 image 
  
 : 
  
 annotation 
 . 
 getVisuallySimilarImagesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 image 
 . 
 getUrl 
 () 
  
 + 
  
 " : " 
  
 + 
  
 image 
 . 
 getScore 
 ()); 
  
 } 
  
 } 
  
 } 
  
 } 
 }

    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'; 
 // Detect similar images on the web to a remote file 
 const 
  
 [ 
 result 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 webDetection 
 ( 
 `gs:// 
 ${ 
 bucketName 
 } 
 / 
 ${ 
 fileName 
 } 
 ` 
 ); 
 const 
  
 webDetection 
  
 = 
  
 result 
 . 
 webDetection 
 ; 
 if 
  
 ( 
 webDetection 
 . 
 fullMatchingImages 
 . 
 length 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
  
 `Full matches found: 
 ${ 
 webDetection 
 . 
 fullMatchingImages 
 . 
 length 
 } 
 ` 
  
 ); 
  
 webDetection 
 . 
 fullMatchingImages 
 . 
 forEach 
 ( 
 image 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  URL: 
 ${ 
 image 
 . 
 url 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `  Score: 
 ${ 
 image 
 . 
 score 
 } 
 ` 
 ); 
  
 }); 
 } 
 if 
  
 ( 
 webDetection 
 . 
 partialMatchingImages 
 . 
 length 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
  
 `Partial matches found: 
 ${ 
 webDetection 
 . 
 partialMatchingImages 
 . 
 length 
 } 
 ` 
  
 ); 
  
 webDetection 
 . 
 partialMatchingImages 
 . 
 forEach 
 ( 
 image 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  URL: 
 ${ 
 image 
 . 
 url 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `  Score: 
 ${ 
 image 
 . 
 score 
 } 
 ` 
 ); 
  
 }); 
 } 
 if 
  
 ( 
 webDetection 
 . 
 webEntities 
 . 
 length 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
 `Web entities found: 
 ${ 
 webDetection 
 . 
 webEntities 
 . 
 length 
 } 
 ` 
 ); 
  
 webDetection 
 . 
 webEntities 
 . 
 forEach 
 ( 
 webEntity 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  Description: 
 ${ 
 webEntity 
 . 
 description 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 `  Score: 
 ${ 
 webEntity 
 . 
 score 
 } 
 ` 
 ); 
  
 }); 
 } 
 if 
  
 ( 
 webDetection 
 . 
 bestGuessLabels 
 . 
 length 
 ) 
  
 { 
  
 console 
 . 
 log 
 ( 
  
 `Best guess labels found: 
 ${ 
 webDetection 
 . 
 bestGuessLabels 
 . 
 length 
 } 
 ` 
  
 ); 
  
 webDetection 
 . 
 bestGuessLabels 
 . 
 forEach 
 ( 
 label 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `  Label: 
 ${ 
 label 
 . 
 label 
 } 
 ` 
 ); 
  
 }); 
 }

    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_web_uri 
 ( 
 uri 
 ): 
  
 """Detects web annotations in the file located in Google Cloud Storage.""" 
 from 
  
 google.cloud 
  
 import 
 vision 
 client 
 = 
 vision 
 . 
 ImageAnnotatorClient 
 () 
 image 
 = 
 vision 
 . 
 Image 
 () 
 image 
 . 
 source 
 . 
 image_uri 
 = 
 uri 
 response 
 = 
 client 
 . 
 web_detection 
 ( 
 image 
 = 
 image 
 ) 
 annotations 
 = 
 response 
 . 
 web_detection 
 if 
 annotations 
 . 
 best_guess_labels 
 : 
 for 
 label 
 in 
 annotations 
 . 
 best_guess_labels 
 : 
 print 
 ( 
 f 
 " 
 \n 
 Best guess label: 
 { 
 label 
 . 
 label 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 pages_with_matching_images 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 Pages with matching images found:" 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 pages_with_matching_images 
 ) 
 ) 
 ) 
 for 
 page 
 in 
 annotations 
 . 
 pages_with_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \n\t 
 Page url   : 
 { 
 page 
 . 
 url 
 } 
 " 
 ) 
 if 
 page 
 . 
 full_matching_images 
 : 
 print 
 ( 
 " 
 \t 
 {} 
 Full Matches found: " 
 . 
 format 
 ( 
 len 
 ( 
 page 
 . 
 full_matching_images 
 )) 
 ) 
 for 
 image 
 in 
 page 
 . 
 full_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \t\t 
 Image url  : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 if 
 page 
 . 
 partial_matching_images 
 : 
 print 
 ( 
 " 
 \t 
 {} 
 Partial Matches found: " 
 . 
 format 
 ( 
 len 
 ( 
 page 
 . 
 partial_matching_images 
 ) 
 ) 
 ) 
 for 
 image 
 in 
 page 
 . 
 partial_matching_images 
 : 
 print 
 ( 
 f 
 " 
 \t\t 
 Image url  : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 web_entities 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 Web entities found: " 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 web_entities 
 ))) 
 for 
 entity 
 in 
 annotations 
 . 
 web_entities 
 : 
 print 
 ( 
 f 
 " 
 \n\t 
 Score      : 
 { 
 entity 
 . 
 score 
 } 
 " 
 ) 
 print 
 ( 
 f 
 " 
 \t 
 Description: 
 { 
 entity 
 . 
 description 
 } 
 " 
 ) 
 if 
 annotations 
 . 
 visually_similar_images 
 : 
 print 
 ( 
 " 
 \n 
 {} 
 visually similar images found: 
 \n 
 " 
 . 
 format 
 ( 
 len 
 ( 
 annotations 
 . 
 visually_similar_images 
 ) 
 ) 
 ) 
 for 
 image 
 in 
 annotations 
 . 
 visually_similar_images 
 : 
 print 
 ( 
 f 
 " 
 \t 
 Image url    : 
 { 
 image 
 . 
 url 
 } 
 " 
 ) 
 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 Web detection, use the gcloud ml vision detect-web command as shown in the following example:

    gcloud ml vision detect-web gs://cloud-samples-data/vision/web/carnaval.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 Web entities detection below. You can use the image specified already ( gs://cloud-samples-data/vision/web/carnaval.jpeg ) or specify your own image in its place. Send the request by selecting Execute.

    Carnaval image
    Image credit : Quinten de Graaf on Unsplash .

    Request body:

    {
  "requests": [
    {
      "features": [
        {
          "type": "WEB_DETECTION"
        }
      ],
      "image": {
        "source": {
          "gcsImageUri": "gs://cloud-samples-data/vision/web/carnaval.jpeg"
        }
      }
    }
  ]
}
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