Detect multiple objects

The Vision API can detect and extract multiple objects in an image with Object Localization.

Object localization identifies multiple objects in an image and provides a LocalizedObjectAnnotation for each object in the image. Each LocalizedObjectAnnotation identifies information about the object, the position of the object, and rectangular bounds for the region of the image that contains the object.

Object localization identifies both significant and less-prominent objects in an image.

Object information is returned in English only. The Cloud Translation can translate English labels into any of a number of other languages.

image with bounding boxes
Image credit: Bogdan Dada on Unsplash (annotations added).

For example, the API might return the following information and bounding location data for the objects in the image above:

Name mid Score Bounds
Bicycle wheel /m/01bqk0 0.89648587 (0.32076266, 0.78941387), (0.43812272, 0.78941387), (0.43812272, 0.97331065), (0.32076266, 0.97331065)
Bicycle /m/0199g 0.886761 (0.312, 0.6616471), (0.638353, 0.6616471), (0.638353, 0.9705882), (0.312, 0.9705882)
Bicycle wheel /m/01bqk0 0.6345275 (0.5125398, 0.760708), (0.6256646, 0.760708), (0.6256646, 0.94601655), (0.5125398, 0.94601655)
Picture frame /m/06z37_ 0.6207608 (0.79177403, 0.16160682), (0.97047985, 0.16160682), (0.97047985, 0.31348917), (0.79177403, 0.31348917)
Tire /m/0h9mv 0.55886006 (0.32076266, 0.78941387), (0.43812272, 0.78941387), (0.43812272, 0.97331065), (0.32076266, 0.97331065)
Door /m/02dgv 0.5160098 (0.77569866, 0.37104446), (0.9412425, 0.37104446), (0.9412425, 0.81507325), (0.77569866, 0.81507325)

mid contains a machine-generated identifier (MID) corresponding to a label's Google Knowledge Graph entry. For information on inspecting mid values, see the Google Knowledge Graph Search API documentation.

Object Localization requests

Set up your GCP project and authentication

Detect objects 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 below, 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.

HTTP method and URL: 

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

Request JSON body: 

{
  "requests": [
    {
      "image": {
        "content": "base64-encoded-image"
      },
      "features": [
        {
          "maxResults": 10,
          "type": "OBJECT_LOCALIZATION"
        },
      ]
    }
  ]
}

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.

Response:

C#

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

            var client = ImageAnnotatorClient.Create();
            var response = client.DetectLocalizedObjects(image);

            Console.WriteLine($"Number of objects found {response.Count}");
            foreach (var localizedObject in response)
            {
                Console.Write($"\n{localizedObject.Name}");
                Console.WriteLine($" (confidence: {localizedObject.Score})");
                Console.WriteLine("Normalized bounding polygon vertices: ");

                foreach (var vertex
                        in localizedObject.BoundingPoly.NormalizedVertices)
                {
                    Console.WriteLine($" - ({vertex.X}, {vertex.Y})");
                }
            }

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. 


// localizeObjects gets objects and bounding boxes from the Vision API for an image at the given file path.
func localizeObjects(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.LocalizeObjects(ctx, image, nil)
	if err != nil {
		return err
	}

	if len(annotations) == 0 {
		fmt.Fprintln(w, "No objects found.")
		return nil
	}

	fmt.Fprintln(w, "Objects:")
	for _, annotation := range annotations {
		fmt.Fprintln(w, annotation.Name)
		fmt.Fprintln(w, annotation.Score)

		for _, v := range annotation.BoundingPoly.NormalizedVertices {
			fmt.Fprintf(w, "(%f,%f)\n", v.X, v.Y)
		}
	}

	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.

/**
 * Detects localized objects in the specified local image.
 *
 * @param filePath The path to the file to perform localized object detection on.
 * @throws Exception on errors while closing the client.
 * @throws IOException on Input/Output errors.
 */
public static void detectLocalizedObjects(String filePath) throws IOException {
  List<AnnotateImageRequest> requests = new ArrayList<>();

  ByteString imgBytes = ByteString.readFrom(new FileInputStream(filePath));

  Image img = Image.newBuilder().setContent(imgBytes).build();
  AnnotateImageRequest request =
      AnnotateImageRequest.newBuilder()
          .addFeatures(Feature.newBuilder().setType(Type.OBJECT_LOCALIZATION))
          .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()) {
    // Perform the request
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
    List<AnnotateImageResponse> responses = response.getResponsesList();

    // Display the results
    for (AnnotateImageResponse res : responses) {
      for (LocalizedObjectAnnotation entity : res.getLocalizedObjectAnnotationsList()) {
        System.out.format("Object name: %s%n", entity.getName());
        System.out.format("Confidence: %s%n", entity.getScore());
        System.out.format("Normalized Vertices:%n");
        entity
            .getBoundingPoly()
            .getNormalizedVerticesList()
            .forEach(vertex -> System.out.format("- (%s, %s)%n", vertex.getX(), vertex.getY()));
      }
    }
  }
}

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');
const fs = require('fs');

// Creates a client
const client = new vision.ImageAnnotatorClient();

/**
 * TODO(developer): Uncomment the following line before running the sample.
 */
// const fileName = `/path/to/localImage.png`;
const request = {
  image: {content: fs.readFileSync(fileName)},
};

const [result] = await client.objectLocalization(request);
const objects = result.localizedObjectAnnotations;
objects.forEach(object => {
  console.log(`Name: ${object.name}`);
  console.log(`Confidence: ${object.score}`);
  const vertices = object.boundingPoly.normalizedVertices;
  vertices.forEach(v => console.log(`x: ${v.x}, y:${v.y}`));
});

PHP

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

namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;

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

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

    # annotate the image
    $image = file_get_contents($path);
    $response = $imageAnnotator->objectLocalization($image);
    $objects = $response->getLocalizedObjectAnnotations();

    foreach ($objects as $object) {
        $name = $object->getName();
        $score = $object->getScore();
        $vertices = $object->getBoundingPoly()->getNormalizedVertices();

        printf('%s (confidence %f)):' . PHP_EOL, $name, $score);
        print('normalized bounding polygon vertices: ');
        foreach ($vertices as $vertex) {
            printf(' (%f, %f)', $vertex->getX(), $vertex->getY());
        }
        print(PHP_EOL);
    }

    $imageAnnotator->close();
}

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 localize_objects(path):
    """Localize objects in the local image.

    Args:
    path: The path to the local file.
    """
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()

    with open(path, 'rb') as image_file:
        content = image_file.read()
    image = vision.Image(content=content)

    objects = client.object_localization(
        image=image).localized_object_annotations

    print('Number of objects found: {}'.format(len(objects)))
    for object_ in objects:
        print('\n{} (confidence: {})'.format(object_.name, object_.score))
        print('Normalized bounding polygon vertices: ')
        for vertex in object_.bounding_poly.normalized_vertices:
            print(' - ({}, {})'.format(vertex.x, vertex.y))

Ruby

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

# image_path = "Path to local image file, eg. './image.png'"

require "google/cloud/vision"

image_annotator = Google::Cloud::Vision.image_annotator

response = image_annotator.object_localization_detection image: image_path

response.responses.each do |res|
  res.localized_object_annotations.each do |object|
    puts "#{object.name} (confidence: #{object.score})"
    puts "Normalized bounding polygon vertices:"
    object.bounding_poly.normalized_vertices.each do |vertex|
      puts " - (#{vertex.x}, #{vertex.y})"
    end
  end
end

Detect objects 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 below, 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: 
    • https://cloud.google.com/vision/docs/images/bicycle_example.png

HTTP method and URL: 

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

Request JSON body: 

{
  "requests": [
    {
      "image": {
        "source": {
          "imageUri": "cloud-storage-image-uri"
        }
      },
      "features": [
        {
          "maxResults": 10,
          "type": "OBJECT_LOCALIZATION"
        },
      ]
    }
  ]
}

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.

Response:

C#

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

            var client = ImageAnnotatorClient.Create();
            var response = client.DetectLocalizedObjects(image);

            Console.WriteLine($"Number of objects found {response.Count}");
            foreach (var localizedObject in response)
            {
                Console.Write($"\n{localizedObject.Name}");
                Console.WriteLine($" (confidence: {localizedObject.Score})");
                Console.WriteLine("Normalized bounding polygon vertices: ");

                foreach (var vertex
                        in localizedObject.BoundingPoly.NormalizedVertices)
                {
                    Console.WriteLine($" - ({vertex.X}, {vertex.Y})");
                }
            }

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. 


// localizeObjects gets objects and bounding boxes from the Vision API for an image at the given file path.
func localizeObjectsURI(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.LocalizeObjects(ctx, image, nil)
	if err != nil {
		return err
	}

	if len(annotations) == 0 {
		fmt.Fprintln(w, "No objects found.")
		return nil
	}

	fmt.Fprintln(w, "Objects:")
	for _, annotation := range annotations {
		fmt.Fprintln(w, annotation.Name)
		fmt.Fprintln(w, annotation.Score)

		for _, v := range annotation.BoundingPoly.NormalizedVertices {
			fmt.Fprintf(w, "(%f,%f)\n", v.X, v.Y)
		}
	}

	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.

/**
 * Detects localized objects in a remote image on Google Cloud Storage.
 *
 * @param gcsPath The path to the remote file on Google Cloud Storage to detect localized objects
 *     on.
 * @throws Exception on errors while closing the client.
 * @throws IOException on Input/Output errors.
 */
public static void detectLocalizedObjectsGcs(String gcsPath) throws IOException {
  List<AnnotateImageRequest> requests = new ArrayList<>();

  ImageSource imgSource = ImageSource.newBuilder().setGcsImageUri(gcsPath).build();
  Image img = Image.newBuilder().setSource(imgSource).build();

  AnnotateImageRequest request =
      AnnotateImageRequest.newBuilder()
          .addFeatures(Feature.newBuilder().setType(Type.OBJECT_LOCALIZATION))
          .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()) {
    // Perform the request
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
    List<AnnotateImageResponse> responses = response.getResponsesList();
    client.close();
    // Display the results
    for (AnnotateImageResponse res : responses) {
      for (LocalizedObjectAnnotation entity : res.getLocalizedObjectAnnotationsList()) {
        System.out.format("Object name: %s%n", entity.getName());
        System.out.format("Confidence: %s%n", entity.getScore());
        System.out.format("Normalized Vertices:%n");
        entity
            .getBoundingPoly()
            .getNormalizedVerticesList()
            .forEach(vertex -> System.out.format("- (%s, %s)%n", vertex.getX(), vertex.getY()));
      }
    }
  }
}

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 line before running the sample.
 */
// const gcsUri = `gs://bucket/bucketImage.png`;

const [result] = await client.objectLocalization(gcsUri);
const objects = result.localizedObjectAnnotations;
objects.forEach(object => {
  console.log(`Name: ${object.name}`);
  console.log(`Confidence: ${object.score}`);
  const veritices = object.boundingPoly.normalizedVertices;
  veritices.forEach(v => console.log(`x: ${v.x}, y:${v.y}`));
});

PHP

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

namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;

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

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

    # annotate the image
    $response = $imageAnnotator->objectLocalization($path);
    $objects = $response->getLocalizedObjectAnnotations();

    foreach ($objects as $object) {
        $name = $object->getName();
        $score = $object->getScore();
        $vertices = $object->getBoundingPoly()->getNormalizedVertices();

        printf('%s (confidence %d)):' . PHP_EOL, $name, $score);
        print('normalized bounding polygon vertices: ');
        foreach ($vertices as $vertex) {
            printf(' (%d, %d)', $vertex->getX(), $vertex->getY());
        }
        print(PHP_EOL);
    }

    $imageAnnotator->close();
}

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 localize_objects_uri(uri):
    """Localize objects in the image on Google Cloud Storage

    Args:
    uri: The path to the file in Google Cloud Storage (gs://...)
    """
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()

    image = vision.Image()
    image.source.image_uri = uri

    objects = client.object_localization(
        image=image).localized_object_annotations

    print('Number of objects found: {}'.format(len(objects)))
    for object_ in objects:
        print('\n{} (confidence: {})'.format(object_.name, object_.score))
        print('Normalized bounding polygon vertices: ')
        for vertex in object_.bounding_poly.normalized_vertices:
            print(' - ({}, {})'.format(vertex.x, vertex.y))

Ruby

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

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

require "google/cloud/vision"

image_annotator = Google::Cloud::Vision.image_annotator

response = image_annotator.object_localization_detection image: image_path

response.responses.each do |res|
  res.localized_object_annotations.each do |object|
    puts "#{object.name} (confidence: #{object.score})"
    puts "Normalized bounding polygon vertices:"
    object.bounding_poly.normalized_vertices.each do |vertex|
      puts " - (#{vertex.x}, #{vertex.y})"
    end
  end
end
# image_path = "URI, eg. 'https://site.tld/image.png'"

require "google/cloud/vision"

image_annotator = Google::Cloud::Vision.image_annotator

response = image_annotator.object_localization_detection image: image_path

response.responses.each do |res|
  res.localized_object_annotations.each do |object|
    puts "#{object.name} (confidence: #{object.score})"
    puts "Normalized bounding polygon vertices:"
    object.bounding_poly.normalized_vertices.each do |vertex|
      puts " - (#{vertex.x}, #{vertex.y})"
    end
  end
end

gcloud

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

gcloud ml vision detect-objects https://cloud.google.com/vision/docs/images/bicycle_example.png

Try it

Try object detection and localization below. You can use the image specified already (https://cloud.google.com/vision/docs/images/bicycle_example.png) or specify your own image in its place. Send the request by selecting Execute.

image without bounding boxes
Image credit: Bogdan Dada on Unsplash.