Get products similar to a local image file

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Search for products that are similar to a locally stored image.

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For detailed documentation that includes this code sample, see the following:

Code sample


import (

	vision ""
	visionpb ""

// 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


 * 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 =

    AnnotateImageRequest annotateImageRequest =
    List<AnnotateImageRequest> requests = Arrays.asList(annotateImageRequest);

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

    List<Result> similarProducts =
    System.out.println("Similar Products: ");
    for (Result product : similarProducts) {
      System.out.println(String.format("\nProduct name: %s", product.getProduct().getName()));
          String.format("Product display name: %s", product.getProduct().getDisplayName()));
          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()));


// 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(
  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);


from import vision

def get_similar_products_file(
    """Search similar products to image.
        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
        max_results: The maximum number of results (matches) to return. If omitted, all results are returned.
    # 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 =

    # 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_search_params = vision.ProductSearchParams(
    image_context = vision.ImageContext(

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

    index_time = response.product_search_results.index_time
    print('Product set 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(
        print('Product display name: {}'.format(
        print('Product description: {}\n'.format(product.description))
        print('Product labels: {}\n'.format(product.product_labels))

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