Detect Labels

The Vision API can detect and extract information about entities in an image, across a broad group of categories.

Labels can identify general objects, locations, activities, animal species, products, and more. If you need targeted custom labels, Cloud AutoML Vision allows you to train a custom machine learning model to classify images.

Labels are returned in English only. The Cloud Translation API can translate English labels into any of a number of other languages.

Setagaya ward street image
Image credit: Alex Knight on Unsplash.

For example, the image above may return the following list of labels:

Description Score
Street 0.872
Snapshot 0.852
Town 0.848
Night 0.804
Alley 0.713

Label detection requests

Set up your GCP project and authentication

Detect Labels in a local image

The Vision API can perform feature detection on a local image file by sending the contents of the image file as a base64 encoded string in the body of your request.

REST & CMD LINE

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.

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": "LABEL_DETECTION"
        }
      ]
    }
  ]
}

To send your request, choose one of these options:

curl

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

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-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 called request.json, and execute the following command: 

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

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 LABEL_DETECTION response includes the detected labels, their score, topicality, and an opaque label ID, where:

  • mid - if present, contains a machine-generated identifier (MID) corresponding to the entity's Google Knowledge Graph entry. Note that mid values remain unique across different languages, so you can use these values to tie entities together from different languages. To inspect MID values, refer to the Google Knowledge Graph API documentation.
  • description - the label description.
  • score - the confidence score, which ranges from 0 (no confidence) to 1 (very high confidence).
  • topicality - The relevancy of the ICA (Image Content Annotation) label to the image. It measures how important/central a label is to the overall context of a page. 
{
  "responses": [
    {
      "labelAnnotations": [
        {
          "mid": "/m/01c8br",
          "description": "Street",
          "score": 0.87294734,
          "topicality": 0.87294734
        },
        {
          "mid": "/m/06pg22",
          "description": "Snapshot",
          "score": 0.8523099,
          "topicality": 0.8523099
        },
        {
          "mid": "/m/0dx1j",
          "description": "Town",
          "score": 0.8481104,
          "topicality": 0.8481104
        },
        {
          "mid": "/m/01d74z",
          "description": "Night",
          "score": 0.80408716,
          "topicality": 0.80408716
        },
        {
          "mid": "/m/01lwf0",
          "description": "Alley",
          "score": 0.7133322,
          "topicality": 0.7133322
        }
      ]
    }
  ]
}

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. 


// detectLabels gets labels from the Vision API for an image at the given file path.
func detectLabels(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
	}
	annotations, err := client.DetectLabels(ctx, image, nil, 10)
	if err != nil {
		return err
	}

	if len(annotations) == 0 {
		fmt.Fprintln(w, "No labels found.")
	} else {
		fmt.Fprintln(w, "Labels:")
		for _, annotation := range annotations {
			fmt.Fprintln(w, annotation.Description)
		}
	}

	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 API 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.EntityAnnotation;
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 DetectLabels {

  public static void detectLabels() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String filePath = "path/to/your/image/file.jpg";
    detectLabels(filePath);
  }

  // Detects labels in the specified local image.
  public static void detectLabels(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.LABEL_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 (EntityAnnotation annotation : res.getLabelAnnotationsList()) {
          annotation
              .getAllFields()
              .forEach((k, v) -> System.out.format("%s : %s%n", k, v.toString()));
        }
      }
    }
  }
}

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. 

// 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';

// Performs label detection on the local file
const [result] = await client.labelDetection(fileName);
const labels = result.labelAnnotations;
console.log('Labels:');
labels.forEach(label => console.log(label.description));

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. 

def detect_labels(path):
    """Detects labels in the file."""
    from google.cloud import vision
    import io
    client = vision.ImageAnnotatorClient()

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

    image = vision.Image(content=content)

    response = client.label_detection(image=image)
    labels = response.label_annotations
    print('Labels:')

    for label in labels:
        print(label.description)

    if response.error.message:
        raise Exception(
            '{}\nFor 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 Labels in a remote image

For your convenience, the Vision API can perform feature 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.

REST & CMD LINE

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/label/setagaya.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.

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": "LABEL_DETECTION"
        },
      ]
    }
  ]
}

To send your request, choose one of these options:

curl

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

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-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 called request.json, and execute the following command: 

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

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 LABEL_DETECTION response includes the detected labels, their score, topicality, and an opaque label ID, where:

  • mid - if present, contains a machine-generated identifier (MID) corresponding to the entity's Google Knowledge Graph entry. Note that mid values remain unique across different languages, so you can use these values to tie entities together from different languages. To inspect MID values, refer to the Google Knowledge Graph API documentation.
  • description - the label description.
  • score - the confidence score, which ranges from 0 (no confidence) to 1 (very high confidence).
  • topicality - The relevancy of the Image Content Annotation (ICA) label to the image. It measures how important/central a label is to the overall context of a page. 
{
  "responses": [
    {
      "labelAnnotations": [
        {
          "mid": "/m/01c8br",
          "description": "Street",
          "score": 0.87294734,
          "topicality": 0.87294734
        },
        {
          "mid": "/m/06pg22",
          "description": "Snapshot",
          "score": 0.8523099,
          "topicality": 0.8523099
        },
        {
          "mid": "/m/0dx1j",
          "description": "Town",
          "score": 0.8481104,
          "topicality": 0.8481104
        },
        {
          "mid": "/m/01d74z",
          "description": "Night",
          "score": 0.80408716,
          "topicality": 0.80408716
        },
        {
          "mid": "/m/01lwf0",
          "description": "Alley",
          "score": 0.7133322,
          "topicality": 0.7133322
        }
      ]
    }
  ]
}

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. 


// detectLabels gets labels from the Vision API for an image at the given file path.
func detectLabelsURI(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.DetectLabels(ctx, image, nil, 10)
	if err != nil {
		return err
	}

	if len(annotations) == 0 {
		fmt.Fprintln(w, "No labels found.")
	} else {
		fmt.Fprintln(w, "Labels:")
		for _, annotation := range annotations {
			fmt.Fprintln(w, annotation.Description)
		}
	}

	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 API 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.EntityAnnotation;
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 DetectLabelsGcs {

  public static void detectLabelsGcs() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String filePath = "gs://your-gcs-bucket/path/to/image/file.jpg";
    detectLabelsGcs(filePath);
  }

  // Detects labels in the specified remote image on Google Cloud Storage.
  public static void detectLabelsGcs(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.LABEL_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 (EntityAnnotation annotation : res.getLabelAnnotationsList()) {
          annotation
              .getAllFields()
              .forEach((k, v) -> System.out.format("%s : %s%n", k, v.toString()));
        }
      }
    }
  }
}

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. 

// 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 label detection on the gcs file
const [result] = await client.labelDetection(
  `gs://${bucketName}/${fileName}`
);
const labels = result.labelAnnotations;
console.log('Labels:');
labels.forEach(label => console.log(label.description));

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. 

def detect_labels_uri(uri):
    """Detects labels in the file located in Google Cloud Storage or on the
    Web."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image()
    image.source.image_uri = uri

    response = client.label_detection(image=image)
    labels = response.label_annotations
    print('Labels:')

    for label in labels:
        print(label.description)

    if response.error.message:
        raise Exception(
            '{}\nFor more info on error messages, check: '
            'https://cloud.google.com/apis/design/errors'.format(
                response.error.message))

gcloud

To detect labels in an image, use the gcloud ml vision detect-labels command as shown in the following example:

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

Setagaya ward street image
Image credit: Alex Knight on Unsplash.

Request body:

{
  "requests": [
    {
      "features": [
        {
          "maxResults": 5,
          "type": "LABEL_DETECTION"
        }
      ],
      "image": {
        "source": {
          "imageUri": "gs://cloud-samples-data/vision/label/setagaya.jpeg"
        }
      }
    }
  ]
}