Detecting Web Entities and Pages

Web Detection detects Web references to an image.

Running Web Detection on a local image

Protocol

Refer to the images:annotate API endpoint for complete details.

To perform Web Detection, make a POST request and provide the appropriate request body:

POST https://vision.googleapis.com/v1/images:annotate?key=YOUR_API_KEY
{
  "requests": [
    {
      "image": {
        "content": "/9j/7QBEUGhvdG9zaG9...base64-encoded-image-content...fXNWzvDEeYxxxzj/Coa6Bax//Z"
      },
      "features": [
        {
          "type": "WEB_DETECTION"
        }
      ]
    }
  ]
}

See the AnnotateImageRequest reference documentation for more information on configuring the request body.

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

{
  "responses": [
    {
      "webDetection": {
        "webEntities": [
          {
            "entityId": "/m/0105pbj4",
            "score": 0.99534,
            "description": "Google Cloud Platform"
          },
        ],
        "partialMatchingImages": [
          {
            "url": "https://example.com/path/img.png",
            "score": 0.01
          },
        ],
        "pagesWithMatchingImages": [
          {
            "url": "https://status.cloud.google.com/",
            "score": 0.87187254
          },
        ],
        "bestGuessLabels": [
          {
            "label": "landmark"
          }
        ]
      }
    }
  ]
}

C#

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

// Load an image from a local file.
var image = Image.FromFile(filePath);
var client = ImageAnnotatorClient.Create();
WebDetection annotation = client.DetectWebInformation(image);
foreach (var matchingImage in annotation.FullMatchingImages)
{
    Console.WriteLine("MatchingImage Score:\t{0}\tUrl:\t{1}",
        matchingImage.Score, matchingImage.Url);
}
foreach (var page in annotation.PagesWithMatchingImages)
{
    Console.WriteLine("PageWithMatchingImage Score:\t{0}\tUrl:\t{1}",
        page.Score, page.Url);
}
foreach (var matchingImage in annotation.PartialMatchingImages)
{
    Console.WriteLine("PartialMatchingImage Score:\t{0}\tUrl:\t{1}",
        matchingImage.Score, matchingImage.Url);
}
foreach (var entity in annotation.WebEntities)
{
    Console.WriteLine("WebEntity Score:\t{0}\tId:\t{1}\tDescription:\t{2}",
        entity.Score, entity.EntityId, entity.Description);
}

Go

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

// 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:")
		for _, entity := range web.WebEntities {
			fmt.Fprintf(w, "\t\t%-12s %s\n", entity.EntityId, 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

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

public static void detectWebDetections(String filePath, PrintStream out) throws Exception,
    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);

  try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
    List<AnnotateImageResponse> responses = response.getResponsesList();

    for (AnnotateImageResponse res : responses) {
      if (res.hasError()) {
        out.printf("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();
      out.println("Entity:Id:Score");
      out.println("===============");
      for (WebEntity entity : annotation.getWebEntitiesList()) {
        out.println(entity.getDescription() + " : " + entity.getEntityId() + " : "
            + entity.getScore());
      }
      for (WebLabel label : annotation.getBestGuessLabelsList()) {
        out.format("\nBest guess label: %s", label.getLabel());
      }
      out.println("\nPages with matching images: Score\n==");
      for (WebPage page : annotation.getPagesWithMatchingImagesList()) {
        out.println(page.getUrl() + " : " + page.getScore());
      }
      out.println("\nPages with partially matching images: Score\n==");
      for (WebImage image : annotation.getPartialMatchingImagesList()) {
        out.println(image.getUrl() + " : " + image.getScore());
      }
      out.println("\nPages with fully matching images: Score\n==");
      for (WebImage image : annotation.getFullMatchingImagesList()) {
        out.println(image.getUrl() + " : " + image.getScore());
      }
      out.println("\nPages with visually similar images: Score\n==");
      for (WebImage image : annotation.getVisuallySimilarImagesList()) {
        out.println(image.getUrl() + " : " + image.getScore());
      }
    }
  }
}

Node.js

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

// 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
client
  .webDetection(fileName)
  .then(results => {
    const webDetection = results[0].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}`);
      });
    }
  })
  .catch(err => {
    console.error('ERROR:', err);
  });

PHP

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;

// $path = 'path/to/your/image.jpg'

function detect_web($path)
{
    $imageAnnotator = new ImageAnnotatorClient();

    # annotate the image
    $image = file_get_contents($path);
    $response = $imageAnnotator->webDetection($image);
    $web = $response->getWebDetection();

    if ($web->getBestGuessLabels()) {
        foreach ($web->getBestGuessLabels() as $label) {
            printf('Best guess label: %s' . PHP_EOL, $label->getLabel());
        }
        print(PHP_EOL);
    }

    if ($web->getPagesWithMatchingImages()) {
        printf('%d pages with matching images found:' . PHP_EOL,
            count($web->getPagesWithMatchingImages()));
        foreach ($web->getPagesWithMatchingImages() as $page) {
            printf('URL: %s' . PHP_EOL, $page->getUrl());
        }
        print(PHP_EOL);
    }

    if ($web->getFullMatchingImages()) {
        printf('%d full matching images found:' . PHP_EOL,
            count($web->getFullMatchingImages()));
        foreach ($web->getFullMatchingImages() as $fullMatchingImage) {
            printf('URL: %s' . PHP_EOL, $fullMatchingImage->getUrl());
        }
        print(PHP_EOL);
    }

    if ($web->getPartialMatchingImages()) {
        printf('%d partial matching images found:' . PHP_EOL,
            count($web->getPartialMatchingImages()));
        foreach ($web->getPartialMatchingImages() as $partialMatchingImage) {
            printf('URL: %s' . PHP_EOL, $partialMatchingImage->getUrl());
        }
        print(PHP_EOL);
    }

    if ($web->getVisuallySimilarImages()) {
        printf('%d visually similar images found:' . PHP_EOL,
            count($web->getVisuallySimilarImages()));
        foreach ($web->getVisuallySimilarImages() as $visuallySimilarImage) {
            printf('URL: %s' . PHP_EOL, $visuallySimilarImage->getUrl());
        }
        print(PHP_EOL);
    }

    if ($web->getWebEntities()) {
        printf('%d web entities found:' . PHP_EOL,
            count($web->getWebEntities()));
        foreach ($web->getWebEntities() as $entity) {
            printf('Description: %s' . PHP_EOL, $entity->getDescription());
            printf('Score: %f' . PHP_EOL, $entity->getScore());
            print(PHP_EOL);
        }
    }
}

Python

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

def detect_web(path):
    """Detects web annotations given an image."""
    client = vision.ImageAnnotatorClient()

    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.types.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('\nBest guess label: {}'.format(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('\n\tPage url   : {}'.format(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('\t\tImage url  : {}'.format(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('\t\tImage url  : {}'.format(image.url))

    if annotations.web_entities:
        print('\n{} Web entities found: '.format(
            len(annotations.web_entities)))

        for entity in annotations.web_entities:
            print('\n\tScore      : {}'.format(entity.score))
            print(u'\tDescription: {}'.format(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('\tImage url    : {}'.format(image.url))

Ruby

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

# project_id = "Your Google Cloud project ID"
# image_path = "Path to local image file, eg. './image.png'"

require "google/cloud/vision"

vision = Google::Cloud::Vision.new project: project_id
image  = vision.image image_path

web = image.web

web.entities.each do |entity|
  puts entity.description
end

web.full_matching_images.each do |image|
  puts image.url
end

Running Web Detection on a remote image

For your convenience, the Vision API can perform Web Detection directly on an image file located in Google Cloud Storage or on the Web without the need to send the contents of the image file in the body of your request.

Protocol

Refer to the images:annotate API endpoint for complete details.

To perform Web Detection, make a POST request and provide the appropriate request body:

POST https://vision.googleapis.com/v1/images:annotate?key=YOUR_API_KEY
{
  "requests": [
    {
      "image": {
        "source": {
          "gcsImageUri": "gs://YOUR_BUCKET_NAME/YOUR_FILE_NAME"
        }
      },
      "features": [
        {
          "type": "WEB_DETECTION"
        }
      ]
    }
  ]
}

See the AnnotateImageRequest reference documentation for more information on configuring the request body.

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

{
  "responses": [
    {
      "webDetection": {
        "webEntities": [
          {
            "entityId": "/m/0105pbj4",
            "score": 0.99534,
            "description": "Google Cloud Platform"
          },
        ],
        "partialMatchingImages": [
          {
            "url": "https://example.com/path/img.png",
            "score": 0.01
          },
        ],
        "pagesWithMatchingImages": [
          {
            "url": "https://status.cloud.google.com/",
            "score": 0.87187254
          },
        ],
        "bestGuessLabels": [
          {
            "label": "landmark"
          }
        ]
      }
    }
  ]
}

C#

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

// Specify a Google Cloud Storage uri for the image
// or a publicly accessible HTTP or HTTPS uri.
var image = Image.FromUri(uri);
var client = ImageAnnotatorClient.Create();
WebDetection annotation = client.DetectWebInformation(image);
foreach (var matchingImage in annotation.FullMatchingImages)
{
    Console.WriteLine("MatchingImage Score:\t{0}\tUrl:\t{1}",
        matchingImage.Score, matchingImage.Url);
}
foreach (var page in annotation.PagesWithMatchingImages)
{
    Console.WriteLine("PageWithMatchingImage Score:\t{0}\tUrl:\t{1}",
        page.Score, page.Url);
}
foreach (var matchingImage in annotation.PartialMatchingImages)
{
    Console.WriteLine("PartialMatchingImage Score:\t{0}\tUrl:\t{1}",
        matchingImage.Score, matchingImage.Url);
}
foreach (var entity in annotation.WebEntities)
{
    Console.WriteLine("WebEntity Score:\t{0}\tId:\t{1}\tDescription:\t{2}",
        entity.Score, entity.EntityId, entity.Description);
}

Go

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

// 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:")
		for _, entity := range web.WebEntities {
			fmt.Fprintf(w, "\t\t%-12s %s\n", entity.EntityId, 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

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

public static void detectWebDetectionsGcs(String gcsPath, PrintStream out) throws Exception,
    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(Type.WEB_DETECTION).build();
  AnnotateImageRequest request =
      AnnotateImageRequest.newBuilder().addFeatures(feat).setImage(img).build();
  requests.add(request);

  try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
    List<AnnotateImageResponse> responses = response.getResponsesList();

    for (AnnotateImageResponse res : responses) {
      if (res.hasError()) {
        out.printf("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();
      out.println("Entity:Id:Score");
      out.println("===============");
      for (WebEntity entity : annotation.getWebEntitiesList()) {
        out.println(entity.getDescription() + " : " + entity.getEntityId() + " : "
            + entity.getScore());
      }
      for (WebLabel label : annotation.getBestGuessLabelsList()) {
        out.format("\nBest guess label: %s", label.getLabel());
      }
      out.println("\nPages with matching images: Score\n==");
      for (WebPage page : annotation.getPagesWithMatchingImagesList()) {
        out.println(page.getUrl() + " : " + page.getScore());
      }
      out.println("\nPages with partially matching images: Score\n==");
      for (WebImage image : annotation.getPartialMatchingImagesList()) {
        out.println(image.getUrl() + " : " + image.getScore());
      }
      out.println("\nPages with fully matching images: Score\n==");
      for (WebImage image : annotation.getFullMatchingImagesList()) {
        out.println(image.getUrl() + " : " + image.getScore());
      }
      out.println("\nPages with visually similar images: Score\n==");
      for (WebImage image : annotation.getVisuallySimilarImagesList()) {
        out.println(image.getUrl() + " : " + image.getScore());
      }
    }
  }
}

Node.js

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

// 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
client
  .webDetection(`gs://${bucketName}/${fileName}`)
  .then(results => {
    const webDetection = results[0].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}`);
      });
    }
  })
  .catch(err => {
    console.error('ERROR:', err);
  });

PHP

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;

// $path = 'gs://path/to/your/image.jpg'

function detect_web_gcs($path)
{
    $imageAnnotator = new ImageAnnotatorClient();

    # annotate the image
    $response = $imageAnnotator->webDetection($path);
    $web = $response->getWebDetection();

    if ($web->getBestGuessLabels()) {
        foreach ($web->getBestGuessLabels() as $label) {
            printf('Best guess label: %s' . PHP_EOL, $label->getLabel());
        }
        print(PHP_EOL);
    }

    if ($web->getPagesWithMatchingImages()) {
        printf('%d pages with matching images found:' . PHP_EOL,
            count($web->getPagesWithMatchingImages()));
        foreach ($web->getPagesWithMatchingImages() as $page) {
            printf('URL: %s' . PHP_EOL, $page->getUrl());
        }
        print(PHP_EOL);
    }

    if ($web->getFullMatchingImages()) {
        printf('%d full matching images found:' . PHP_EOL,
            count($web->getFullMatchingImages()));
        foreach ($web->getFullMatchingImages() as $fullMatchingImage) {
            printf('URL: %s' . PHP_EOL, $fullMatchingImage->getUrl());
        }
        print(PHP_EOL);
    }

    if ($web->getPartialMatchingImages()) {
        printf('%d partial matching images found:' . PHP_EOL,
            count($web->getPartialMatchingImages()));
        foreach ($web->getPartialMatchingImages() as $partialMatchingImage) {
            printf('URL: %s' . PHP_EOL, $partialMatchingImage->getUrl());
        }
        print(PHP_EOL);
    }

    if ($web->getVisuallySimilarImages()) {
        printf('%d visually similar images found:' . PHP_EOL,
            count($web->getVisuallySimilarImages()));
        foreach ($web->getVisuallySimilarImages() as $visuallySimilarImage) {
            printf('URL: %s' . PHP_EOL, $visuallySimilarImage->getUrl());
        }
        print(PHP_EOL);
    }

    if ($web->getWebEntities()) {
        printf('%d web entities found:' . PHP_EOL,
            count($web->getWebEntities()));
        foreach ($web->getWebEntities() as $entity) {
            printf('Description: %s' . PHP_EOL, $entity->getDescription());
            printf('Score: %f' . PHP_EOL, $entity->getScore());
            print(PHP_EOL);
        }
    }
}

Python

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

def detect_web_uri(uri):
    """Detects web annotations in the file located in Google Cloud Storage."""
    client = vision.ImageAnnotatorClient()
    image = vision.types.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('\nBest guess label: {}'.format(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('\n\tPage url   : {}'.format(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('\t\tImage url  : {}'.format(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('\t\tImage url  : {}'.format(image.url))

    if annotations.web_entities:
        print('\n{} Web entities found: '.format(
            len(annotations.web_entities)))

        for entity in annotations.web_entities:
            print('\n\tScore      : {}'.format(entity.score))
            print(u'\tDescription: {}'.format(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('\tImage url    : {}'.format(image.url))

Ruby

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

# project_id = "Your Google Cloud project ID"
# image_path = "Google Cloud Storage URI, eg. 'gs://my-bucket/image.png'"

require "google/cloud/vision"

vision = Google::Cloud::Vision.new project: project_id
image  = vision.image image_path

web = image.web

web.entities.each do |entity|
  entity.description
end

web.full_matching_images.each do |image|
  puts image.url
end

Geographic metadata

The Vision API can access geotag metadata in your image files to return more relevant web entities and pages. To allow geotag usage, specify "include_geo_results": true in your request.

Protocol

This code uses a sample image stored in Google Cloud Storage. You can execute the code using this sample image, provide your own, or modify the code to accept an inline image (see the web detection samples page.)

Try setting includeGeoResults to false to test the impact of geographic hints on the results.

Refer to the images:annotate API endpoint for complete details.

To perform web entities detection, make a POST request and provide the appropriate request body:

POST https://vision.googleapis.com/v1/images:annotate?key=YOUR_API_KEY
{
  "requests": [
    {
      "features": [
        {
          "type": "WEB_DETECTION"
        }
      ],
      "image": {
        "source": {
          "gcsImageUri": "gs://bucket-name-123/geotagged.jpg"
        }
      },
      "imageContext": {
        "webDetectionParams": {
          "includeGeoResults": true
          }
        }
    }
  ]
}

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

{
  "responses": [
    {
      "webDetection": {
        "webEntities": [
          {
            "entityId": "/m/040sd3",
            "score": 40.5952,
            "description": "Luna Park Sydney"
          },
          {
            "entityId": "/m/0701q",
            "score": 22.4608,
            "description": "Sydney Harbour Bridge"
          },
          {
            "entityId": "/m/0d6_f6",
            "score": 9.904,
            "description": "Sea Life Sydney Aquarium"
          },
          {
            "entityId": "/g/11bxfg6b1k",
            "score": 8.384,
            "description": "Bradfield Park"
          },
          {
            "entityId": "/m/02q0lr",
            "score": 5.5,
            "description": "The Rocks"
          },
          ...
        ],
        "partialMatchingImages": [
          ...
        ],
        "pagesWithMatchingImages": [
          ...
        ],
        "visuallySimilarImages": [
          ...
        ],
        "bestGuessLabels": [
          {
            "label": "landmark"
          }
        ]
      }
    }
  ]
}

Go

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

// detectWebGeo detects geographic metadata from the Vision API for an image at the given file path.
func detectWebGeo(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
	}
	imageContext := &visionpb.ImageContext{
		WebDetectionParams: &visionpb.WebDetectionParams{
			IncludeGeoResults: true,
		},
	}
	web, err := client.DetectWeb(ctx, image, imageContext)
	if err != nil {
		return err
	}

	if len(web.WebEntities) != 0 {
		fmt.Fprintln(w, "Entities:")
		for _, entity := range web.WebEntities {
			fmt.Fprintf(w, "\t%-12s %s\n", entity.EntityId, entity.Description)
		}
	}

	return nil
}

Java

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

/**
 * Find web entities given the remote image on Google Cloud Storage.
 * @param gcsPath The path to the remote file on Google Cloud Storage to detect web entities with
 *                geo results.
 * @param out A {@link PrintStream} to write the results to.
 * @throws Exception on errors while closing the client.
 * @throws IOException on Input/Output errors.
 */
public static void detectWebEntitiesIncludeGeoResultsGcs(String gcsPath, PrintStream out) throws
    Exception, IOException {
  // Instantiates a client
  try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
    // Set the image source to the given gs uri
    ImageSource imageSource = ImageSource.newBuilder()
        .setGcsImageUri(gcsPath)
        .build();
    // Build the image
    Image image = Image.newBuilder().setSource(imageSource).build();

    // Enable `IncludeGeoResults`
    WebDetectionParams webDetectionParams = WebDetectionParams.newBuilder()
        .setIncludeGeoResults(true)
        .build();

    // Set the parameters for the image
    ImageContext imageContext = ImageContext.newBuilder()
        .setWebDetectionParams(webDetectionParams)
        .build();

    // Create the request with the image, imageContext, and the specified feature: web detection
    AnnotateImageRequest request = AnnotateImageRequest.newBuilder()
        .addFeatures(Feature.newBuilder().setType(Type.WEB_DETECTION))
        .setImage(image)
        .setImageContext(imageContext)
        .build();

    // Perform the request
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(Arrays.asList(request));

    // Display the results
    response.getResponsesList().stream()
        .forEach(r -> r.getWebDetection().getWebEntitiesList().stream()
            .forEach(entity -> {
              out.format("Description: %s\n", entity.getDescription());
              out.format("Score: %f\n", entity.getScore());
            }));
  }
}

Node.js

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

// 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';

const request = {
  image: {
    source: {
      filename: fileName,
    },
  },
  imageContext: {
    webDetectionParams: {
      includeGeoResults: true,
    },
  },
};

// Detect similar images on the web to a local file
client
  .webDetection(request)
  .then(results => {
    const webDetection = results[0].webDetection;

    webDetection.webEntities.forEach(entity => {
      console.log(`Score: ${entity.score}`);
      console.log(`Description: ${entity.description}`);
    });
  })
  .catch(err => {
    console.error('ERROR:', err);
  });
// 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 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';

const request = {
  image: {
    source: {
      imageUri: `gs://${bucketName}/${fileName}`,
    },
  },
  imageContext: {
    webDetectionParams: {
      includeGeoResults: true,
    },
  },
};

// Detect similar images on the web to a remote file
client
  .webDetection(request)
  .then(results => {
    const webDetection = results[0].webDetection;

    webDetection.webEntities.forEach(entity => {
      console.log(`Score: ${entity.score}`);
      console.log(`Description: ${entity.description}`);
    });
  })
  .catch(err => {
    console.error('ERROR:', err);
  });

PHP

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;
use Google\Cloud\Vision\V1\ImageContext;
use Google\Cloud\Vision\V1\WebDetectionParams;

// $path = 'gs://path/to/your/image.jpg'

/**
 * Detect web entities on an image and include the image's geo metadata
 * to improve the quality of the detection.
 */
function detect_web_with_geo_metadata_gcs($path)
{
    $imageAnnotator = new ImageAnnotatorClient();

    # enable include geo results
    $params = new WebDetectionParams();
    $params->setIncludeGeoResults(true);
    $imageContext = new ImageContext();
    $imageContext-> setWebDetectionParams($params);

    # annotate the image
    $response = $imageAnnotator->webDetection($path, ['imageContext' => $imageContext]);
    $web = $response->getWebDetection();

    if ($web->getWebEntities()) {
        printf('%d web entities found:' . PHP_EOL,
            count($web->getWebEntities()));
        foreach ($web->getWebEntities() as $entity) {
            printf('Description: %s ' . PHP_EOL, $entity->getDescription());
            printf('Score: %f' . PHP_EOL, $entity->getScore());
            print(PHP_EOL);
        }
    }
}

Python

For more on installing and creating a Vision API client, refer to Vision API Client Libraries.

def web_entities_include_geo_results(path):
    """Detects web annotations given an image, using the geotag metadata
    in the iamge to detect web entities."""
    client = vision.ImageAnnotatorClient()

    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.types.Image(content=content)

    web_detection_params = vision.types.WebDetectionParams(
        include_geo_results=True)
    image_context = vision.types.ImageContext(
        web_detection_params=web_detection_params)

    response = client.web_detection(image=image, image_context=image_context)

    for entity in response.web_detection.web_entities:
        print('\n\tScore      : {}'.format(entity.score))
        print(u'\tDescription: {}'.format(entity.description))

Try it

Try web entities detection below. You can use the image specified already (gs://bucket-name-123/geotagged.jpg) by clicking Execute, or you can specify your own image in its place.

Try repeating the request with includeGeoResults set to false.

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Cloud Vision API Documentation