Searching for Products

Once you have created your product set and the product set has been indexed, you can query the product set using the Cloud Vision API.

You can find similar products to a given image by passing the image's Google Cloud Storage URI, web URL, or base64 encoded string to Vision API Product Search. Refer to the Usage Limits for maximum request size and quota information.

See the Understanding search responses & multi-detection topic for an example of single product detection and multi-detection of products in an image.

Search using a local image

The following samples read a local file and query the API by including inline the raw image bytes (base64 encoded image) in the 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.
  • project-id: Your GCP project ID.
  • location-id: A valid location identifier. Valid location identifiers are: us-west1, us-east1, europe-west1, and asia-east1.
  • product-set-id: The ID for the product set you want to run the operation on.

Field-specific considerations:

  • features.maxResults - The maximum number of results to be returned.
  • imageContext.productCategories - The product category to search in. Currently you can only specify one product category (homegoods, apparel, toys, packaged goods, and general ).
  • imageContext.filter - A key-value filtering expression (or multiple expressions) for product label. Format: "key=value". Filtering key-value pairs can be linked with AND or OR expressions: "color=blue AND style=mens", or "color=blue OR color=black". If using the OR expression all keys in the expression must be the same.

HTTP method and URL:

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

Request JSON body:

{
  "requests": [
    {
      "image": {
        "content": base64-encoded-image
      },
      "features": [
        {
          "type": "PRODUCT_SEARCH",
          "maxResults": 5
        }
      ],
      "imageContext": {
        "productSearchParams": {
          "productSet": "projects/project-id/locations/location-id/productSets/product-set-id",
          "productCategories": [
               "apparel"
          ],
          "filter": "style = womens"
        }
      }
    }
  ]
}

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.

The response JSON includes the two following result types:

  • productSearchResults - Contains a list of matching products for the entire image. In the sample response the matching products are: product_id65, product_id35, product_id34, product_id62, product_id32.
  • productGroupedResults - Contains bounding box coordinates and matching items for each product identified in the image. In the following response there is only one product identified, followed by matching products in the sample product set: product_id65, product_id35, product_id34, product_id93, product_id62.

Note that while there is overlap in the two result types, there may also be differences (for example, product_id32 and product_id93 in the response).

Go


import (
	"context"
	"fmt"
	"io"
	"os"

	vision "cloud.google.com/go/vision/apiv1"
	visionpb "google.golang.org/genproto/googleapis/cloud/vision/v1"
)

// getSimilarProducts searches for products from a product set similar to products in an image file.
func getSimilarProducts(w io.Writer, projectID string, location string, productSetID string, productCategory string, file string, filter string) error {
	ctx := context.Background()
	c, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		return fmt.Errorf("NewImageAnnotatorClient: %v", err)
	}
	defer c.Close()

	f, err := os.Open(file)
	if err != nil {
		return fmt.Errorf("Open: %v", err)
	}
	defer f.Close()

	image, err := vision.NewImageFromReader(f)
	if err != nil {
		return fmt.Errorf("NewImageFromReader: %v", err)
	}

	ictx := &visionpb.ImageContext{
		ProductSearchParams: &visionpb.ProductSearchParams{
			ProductSet:        fmt.Sprintf("projects/%s/locations/%s/productSets/%s", projectID, location, productSetID),
			ProductCategories: []string{productCategory},
			Filter:            filter,
		},
	}

	response, err := c.ProductSearch(ctx, image, ictx)
	if err != nil {
		return fmt.Errorf("ProductSearch: %v", err)
	}

	fmt.Fprintf(w, "Product set index time:\n")
	fmt.Fprintf(w, "seconds: %d\n", response.IndexTime.Seconds)
	fmt.Fprintf(w, "nanos: %d\n", response.IndexTime.Nanos)

	fmt.Fprintf(w, "Search results:\n")
	for _, result := range response.Results {
		fmt.Fprintf(w, "Score(Confidence): %f\n", result.Score)
		fmt.Fprintf(w, "Image name: %s\n", result.Image)

		fmt.Fprintf(w, "Prodcut name: %s\n", result.Product.Name)
		fmt.Fprintf(w, "Product display name: %s\n", result.Product.DisplayName)
		fmt.Fprintf(w, "Product labels: %s\n", result.Product.ProductLabels)
	}

	return nil
}

Java

/**
 * Search similar products to image in local file.
 *
 * @param projectId - Id of the project.
 * @param computeRegion - Region name.
 * @param productSetId - Id of the product set.
 * @param productCategory - Category of the product.
 * @param filePath - Local file path of the image to be searched
 * @param filter - Condition to be applied on the labels. Example for filter: (color = red OR
 *     color = blue) AND style = kids It will search on all products with the following labels:
 *     color:red AND style:kids color:blue AND style:kids
 * @throws IOException - on I/O errors.
 */
public static void getSimilarProductsFile(
    String projectId,
    String computeRegion,
    String productSetId,
    String productCategory,
    String filePath,
    String filter)
    throws IOException {
  try (ImageAnnotatorClient queryImageClient = ImageAnnotatorClient.create()) {

    // Get the full path of the product set.
    String productSetPath =
        ProductSearchClient.formatProductSetName(projectId, computeRegion, productSetId);

    // Read the image as a stream of bytes.
    File imgPath = new File(filePath);
    byte[] content = Files.readAllBytes(imgPath.toPath());

    // Create annotate image request along with product search feature.
    Feature featuresElement = Feature.newBuilder().setType(Type.PRODUCT_SEARCH).build();
    // The input image can be a HTTPS link or Raw image bytes.
    // Example:
    // To use HTTP link replace with below code
    //  ImageSource source = ImageSource.newBuilder().setImageUri(imageUri).build();
    //  Image image = Image.newBuilder().setSource(source).build();
    Image image = Image.newBuilder().setContent(ByteString.copyFrom(content)).build();
    ImageContext imageContext =
        ImageContext.newBuilder()
            .setProductSearchParams(
                ProductSearchParams.newBuilder()
                    .setProductSet(productSetPath)
                    .addProductCategories(productCategory)
                    .setFilter(filter))
            .build();

    AnnotateImageRequest annotateImageRequest =
        AnnotateImageRequest.newBuilder()
            .addFeatures(featuresElement)
            .setImage(image)
            .setImageContext(imageContext)
            .build();
    List<AnnotateImageRequest> requests = Arrays.asList(annotateImageRequest);

    // Search products similar to the image.
    BatchAnnotateImagesResponse response = queryImageClient.batchAnnotateImages(requests);

    List<Result> similarProducts =
        response.getResponses(0).getProductSearchResults().getResultsList();
    System.out.println("Similar Products: ");
    for (Result product : similarProducts) {
      System.out.println(String.format("\nProduct name: %s", product.getProduct().getName()));
      System.out.println(
          String.format("Product display name: %s", product.getProduct().getDisplayName()));
      System.out.println(
          String.format("Product description: %s", product.getProduct().getDescription()));
      System.out.println(String.format("Score(Confidence): %s", product.getScore()));
      System.out.println(String.format("Image name: %s", product.getImage()));
    }
  }
}

Node.js

// Imports the Google Cloud client library
const vision = require('@google-cloud/vision');
const fs = require('fs');
// Creates a client
const productSearchClient = new vision.ProductSearchClient();
const imageAnnotatorClient = new vision.ImageAnnotatorClient();

async function getSimilarProductsFile() {
  /**
   * TODO(developer): Uncomment the following line before running the sample.
   */
  // const projectId = 'nodejs-docs-samples';
  // const location = 'us-west1';
  // const productSetId = 'indexed_product_set_id_for_testing';
  // const productCategory = 'apparel';
  // const filePath = './resources/shoes_1.jpg';
  // const filter = '';
  const productSetPath = productSearchClient.productSetPath(
    projectId,
    location,
    productSetId
  );
  const content = fs.readFileSync(filePath, 'base64');
  const request = {
    // The input image can be a GCS link or HTTPS link or Raw image bytes.
    // Example:
    // To use GCS link replace with below code
    // image: {source: {gcsImageUri: filePath}}
    // To use HTTP link replace with below code
    // image: {source: {imageUri: filePath}}
    image: {content: content},
    features: [{type: 'PRODUCT_SEARCH'}],
    imageContext: {
      productSearchParams: {
        productSet: productSetPath,
        productCategories: [productCategory],
        filter: filter,
      },
    },
  };
  const [response] = await imageAnnotatorClient.batchAnnotateImages({
    requests: [request],
  });
  console.log('Search Image:', filePath);
  const results = response['responses'][0]['productSearchResults']['results'];
  console.log('\nSimilar product information:');
  results.forEach(result => {
    console.log('Product id:', result['product'].name.split('/').pop(-1));
    console.log('Product display name:', result['product'].displayName);
    console.log('Product description:', result['product'].description);
    console.log('Product category:', result['product'].productCategory);
  });
}
getSimilarProductsFile();

Python

from google.cloud import vision

def get_similar_products_file(
        project_id, location, product_set_id, product_category,
        file_path, filter):
    """Search similar products to image.
    Args:
        project_id: Id of the project.
        location: A compute region name.
        product_set_id: Id of the product set.
        product_category: Category of the product.
        file_path: Local file path of the image to be searched.
        filter: Condition to be applied on the labels.
        Example for filter: (color = red OR color = blue) AND style = kids
        It will search on all products with the following labels:
        color:red AND style:kids
        color:blue AND style:kids
    """
    # product_search_client is needed only for its helper methods.
    product_search_client = vision.ProductSearchClient()
    image_annotator_client = vision.ImageAnnotatorClient()

    # Read the image as a stream of bytes.
    with open(file_path, 'rb') as image_file:
        content = image_file.read()

    # Create annotate image request along with product search feature.
    image = vision.Image(content=content)

    # product search specific parameters
    product_set_path = product_search_client.product_set_path(
        project=project_id, location=location,
        product_set=product_set_id)
    product_search_params = vision.ProductSearchParams(
        product_set=product_set_path,
        product_categories=[product_category],
        filter=filter)
    image_context = vision.ImageContext(
        product_search_params=product_search_params)

    # Search products similar to the image.
    response = image_annotator_client.product_search(
        image, image_context=image_context)

    index_time = response.product_search_results.index_time
    print('Product set index time: ')
    print(index_time)

    results = response.product_search_results.results

    print('Search results:')
    for result in results:
        product = result.product

        print('Score(Confidence): {}'.format(result.score))
        print('Image name: {}'.format(result.image))

        print('Product name: {}'.format(product.name))
        print('Product display name: {}'.format(
            product.display_name))
        print('Product description: {}\n'.format(product.description))
        print('Product labels: {}\n'.format(product.product_labels))

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the Vision API Product Search reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Vision API Product Search reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Vision API Product Search reference documentation for Ruby.

Search using a remote image

You also have the option of finding similar products to a given image by specifying the Cloud Storage URI to the image.

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:
    • gs://storage-bucket/filename.jpg
  • project-id: Your GCP project ID.
  • location-id: A valid location identifier. Valid location identifiers are: us-west1, us-east1, europe-west1, and asia-east1.
  • product-set-id: The ID for the product set you want to run the operation on.

Field-specific considerations:

  • features.maxResults - The maximum number of results to be returned.
  • imageContext.productCategories - The product category to search in. Currently you can only specify one product category (homegoods, apparel, toys, packaged goods, and general ).
  • imageContext.filter - A key-value filtering expression (or multiple expressions) for product label. Format: "key=value". Filtering key-value pairs can be linked with AND or OR expressions: "color=blue AND style=mens", or "color=blue OR color=black". If using the OR expression all keys in the expression must be the same.

HTTP method and URL:

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

Request JSON body:

{
  "requests": [
    {
      "image": {
        "source": {
          "gcsImageUri": "cloud-storage-image-uri"
        }
      },
      "features": [
        {
          "type": "PRODUCT_SEARCH",
          "maxResults": 5
        }
      ],
      "imageContext": {
        "productSearchParams": {
          "productSet": "projects/project-id/locations/location-id/productSets/product-set-id",
          "productCategories": [
               "apparel"
          ],
          "filter": "style = womens"
        }
      }
    }
  ]
}

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.

The response JSON includes the two following result types:

  • productSearchResults - Contains a list of matching products for the entire image. In the sample response the matching products are: product_id65, product_id35, product_id34, product_id62, product_id32.
  • productGroupedResults - Contains bounding box coordinates and matching items for each product identified in the image. In the following response there is only one product identified, followed by matching products in the sample product set: product_id65, product_id35, product_id34, product_id93, product_id62.

Note that while there is overlap in the two result types, there may also be differences (for example, product_id32 and product_id93 in the response).

Go


import (
	"context"
	"fmt"
	"io"

	vision "cloud.google.com/go/vision/apiv1"
	visionpb "google.golang.org/genproto/googleapis/cloud/vision/v1"
)

// getSimilarProductsURI searches for products from a product set similar to products in an image file on GCS.
func getSimilarProductsURI(w io.Writer, projectID string, location string, productSetID string, productCategory string, imageURI string, filter string) error {
	ctx := context.Background()
	c, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		return fmt.Errorf("NewImageAnnotatorClient: %v", err)
	}
	defer c.Close()

	image := vision.NewImageFromURI(imageURI)

	ictx := &visionpb.ImageContext{
		ProductSearchParams: &visionpb.ProductSearchParams{
			ProductSet:        fmt.Sprintf("projects/%s/locations/%s/productSets/%s", projectID, location, productSetID),
			ProductCategories: []string{productCategory},
			Filter:            filter,
		},
	}

	response, err := c.ProductSearch(ctx, image, ictx)
	if err != nil {
		return fmt.Errorf("ProductSearch: %v", err)
	}

	fmt.Fprintf(w, "Product set index time:\n")
	fmt.Fprintf(w, "seconds: %d\n", response.IndexTime.Seconds)
	fmt.Fprintf(w, "nanos: %d\n", response.IndexTime.Nanos)

	fmt.Fprintf(w, "Search results:\n")
	for _, result := range response.Results {
		fmt.Fprintf(w, "Score(Confidence): %f\n", result.Score)
		fmt.Fprintf(w, "Image name: %s\n", result.Image)

		fmt.Fprintf(w, "Prodcut name: %s\n", result.Product.Name)
		fmt.Fprintf(w, "Product display name: %s\n", result.Product.DisplayName)
		fmt.Fprintf(w, "Product labels: %s\n", result.Product.ProductLabels)
	}

	return nil
}

Java

/**
 * Search similar products to image in Google Cloud Storage.
 *
 * @param projectId - Id of the project.
 * @param computeRegion - Region name.
 * @param productSetId - Id of the product set.
 * @param productCategory - Category of the product.
 * @param gcsUri - GCS file path of the image to be searched
 * @param filter - Condition to be applied on the labels. Example for filter: (color = red OR
 *     color = blue) AND style = kids It will search on all products with the following labels:
 *     color:red AND style:kids color:blue AND style:kids
 * @throws Exception - on errors.
 */
public static void getSimilarProductsGcs(
    String projectId,
    String computeRegion,
    String productSetId,
    String productCategory,
    String gcsUri,
    String filter)
    throws Exception {
  try (ImageAnnotatorClient queryImageClient = ImageAnnotatorClient.create()) {

    // Get the full path of the product set.
    String productSetPath = ProductSetName.of(projectId, computeRegion, productSetId).toString();

    // Get the image from Google Cloud Storage
    ImageSource source = ImageSource.newBuilder().setGcsImageUri(gcsUri).build();

    // Create annotate image request along with product search feature.
    Feature featuresElement = Feature.newBuilder().setType(Type.PRODUCT_SEARCH).build();
    Image image = Image.newBuilder().setSource(source).build();
    ImageContext imageContext =
        ImageContext.newBuilder()
            .setProductSearchParams(
                ProductSearchParams.newBuilder()
                    .setProductSet(productSetPath)
                    .addProductCategories(productCategory)
                    .setFilter(filter))
            .build();

    AnnotateImageRequest annotateImageRequest =
        AnnotateImageRequest.newBuilder()
            .addFeatures(featuresElement)
            .setImage(image)
            .setImageContext(imageContext)
            .build();
    List<AnnotateImageRequest> requests = Arrays.asList(annotateImageRequest);

    // Search products similar to the image.
    BatchAnnotateImagesResponse response = queryImageClient.batchAnnotateImages(requests);

    List<Result> similarProducts =
        response.getResponses(0).getProductSearchResults().getResultsList();
    System.out.println("Similar Products: ");
    for (Result product : similarProducts) {
      System.out.println(String.format("\nProduct name: %s", product.getProduct().getName()));
      System.out.println(
          String.format("Product display name: %s", product.getProduct().getDisplayName()));
      System.out.println(
          String.format("Product description: %s", product.getProduct().getDescription()));
      System.out.println(String.format("Score(Confidence): %s", product.getScore()));
      System.out.println(String.format("Image name: %s", product.getImage()));
    }
  }
}

Node.js

// Imports the Google Cloud client library
const vision = require('@google-cloud/vision');
// Creates a client
const productSearchClient = new vision.ProductSearchClient();
const imageAnnotatorClient = new vision.ImageAnnotatorClient();

async function getSimilarProductsGcs(
  projectId,
  location,
  productSetId,
  productCategory,
  filePath,
  filter
) {
  /**
   * TODO(developer): Uncomment the following line before running the sample.
   */
  // const projectId = 'Your Google Cloud project Id';
  // const location = 'A compute region name';
  // const productSetId = 'Id of the product set';
  // const productCategory = 'Category of the product';
  // const filePath = 'Local file path of the image to be searched';
  // const filter = 'Condition to be applied on the labels';
  const productSetPath = productSearchClient.productSetPath(
    projectId,
    location,
    productSetId
  );

  const request = {
    // The input image can be a GCS link or HTTPS link or Raw image bytes.
    // Example:
    // To use GCS link replace with below code
    // image: {source: {gcsImageUri: filePath}}
    // To use HTTP link replace with below code
    // image: {source: {imageUri: filePath}}
    image: {source: {gcsImageUri: filePath}},
    features: [{type: 'PRODUCT_SEARCH'}],
    imageContext: {
      productSearchParams: {
        productSet: productSetPath,
        productCategories: [productCategory],
        filter: filter,
      },
    },
  };
  console.log(request.image);

  const [response] = await imageAnnotatorClient.batchAnnotateImages({
    requests: [request],
  });
  console.log('Search Image:', filePath);
  console.log('\nSimilar product information:');

  const results = response['responses'][0]['productSearchResults']['results'];
  results.forEach(result => {
    console.log('Product id:', result['product'].name.split('/').pop(-1));
    console.log('Product display name:', result['product'].displayName);
    console.log('Product description:', result['product'].description);
    console.log('Product category:', result['product'].productCategory);
  });
}
getSimilarProductsGcs();

Python

from google.cloud import vision

def get_similar_products_uri(
        project_id, location, product_set_id, product_category,
        image_uri, filter):
    """Search similar products to image.
    Args:
        project_id: Id of the project.
        location: A compute region name.
        product_set_id: Id of the product set.
        product_category: Category of the product.
        image_uri: Cloud Storage location of image to be searched.
        filter: Condition to be applied on the labels.
        Example for filter: (color = red OR color = blue) AND style = kids
        It will search on all products with the following labels:
        color:red AND style:kids
        color:blue AND style:kids
    """
    # product_search_client is needed only for its helper methods.
    product_search_client = vision.ProductSearchClient()
    image_annotator_client = vision.ImageAnnotatorClient()

    # Create annotate image request along with product search feature.
    image_source = vision.ImageSource(image_uri=image_uri)
    image = vision.Image(source=image_source)

    # product search specific parameters
    product_set_path = product_search_client.product_set_path(
        project=project_id, location=location,
        product_set=product_set_id)
    product_search_params = vision.ProductSearchParams(
        product_set=product_set_path,
        product_categories=[product_category],
        filter=filter)
    image_context = vision.ImageContext(
        product_search_params=product_search_params)

    # Search products similar to the image.
    response = image_annotator_client.product_search(
        image, image_context=image_context)

    index_time = response.product_search_results.index_time
    print('Product set index time: ')
    print(index_time)

    results = response.product_search_results.results

    print('Search results:')
    for result in results:
        product = result.product

        print('Score(Confidence): {}'.format(result.score))
        print('Image name: {}'.format(result.image))

        print('Product name: {}'.format(product.name))
        print('Product display name: {}'.format(
            product.display_name))
        print('Product description: {}\n'.format(product.description))
        print('Product labels: {}\n'.format(product.product_labels))

Additional languages

C#: Please follow the C# setup instructions on the client libraries page and then visit the Vision API Product Search reference documentation for .NET.

PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Vision API Product Search reference documentation for PHP.

Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Vision API Product Search reference documentation for Ruby.