Detect logos

Logo Detection detects popular product logos within an image.

Google logo with annotation
Image credit: Robert Scoble (CC BY 2.0, annotation added).

Logo detection requests

Set up your Google Cloud project and authentication

Detect logos in a local image

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

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

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

REST

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

  • BASE64_ENCODED_IMAGE: The base64 representation (ASCII string) of your binary image data. This string should look similar to the following string:
    • /9j/4QAYRXhpZgAA...9tAVx/zDQDlGxn//2Q==
    Visit the base64 encode topic for more information.
  • 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": [
        {
          "type": "LOGO_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: 

{
  "responses": [
    {
      "logoAnnotations": [
        {
          "mid": "/m/045c7b",
          "description": "google",
          "score": 0.980325,
          "boundingPoly": {
            "vertices": [
              {
                "x": 12,
                "y": 42
              },
              {
                "x": 439,
                "y": 42
              },
              {
                "x": 439,
                "y": 285
              },
              {
                "x": 12,
                "y": 285
              }
            ]
          }
        }
      ]
    }
  ]
}

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. 


// detectLogos gets logos from the Vision API for an image at the given file path.
func detectLogos(w io.Writer, file string) error {
	ctx := context.Background()

	client, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		return err
	}

	fileBytes, err := os.ReadFile(file)
	if err != nil {
		return err
	}

	image := &visionpb.Image{
		Content: fileBytes,
	}

	feature := &visionpb.Feature{
		Type:       visionpb.Feature_LOGO_DETECTION,
		MaxResults: 10,
	}

	request := &visionpb.BatchAnnotateImagesRequest{
		Requests: []*visionpb.AnnotateImageRequest{
			{
				Image:    image,
				Features: []*visionpb.Feature{feature},
			},
		},
	}

	response, err := client.BatchAnnotateImages(ctx, request)
	if err != nil {
		return err
	}

	for _, resp := range response.Responses {
		if resp.Error != nil {
			fmt.Fprintf(w, "Error: %v\n", resp.Error)
			continue
		}
		if len(resp.LogoAnnotations) == 0 {
			fmt.Fprintln(w, "No logos found.")
		} else {
			fmt.Fprintln(w, "Logos:")
			for _, annotation := range resp.LogoAnnotations {
				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 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 DetectLogos {
  public static void detectLogos() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String filePath = "path/to/your/image/file.jpg";
    detectLogos(filePath);
  }

  // Detects logos in the specified local image.

  public static void detectLogos(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.LOGO_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()) {