Get products similar to a local image file

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

Explore further

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

Code sample

Go

To learn how to install and use the client library for Vision API Product Search, see Vision API Product Search client libraries. For more information, see the Vision API Product Search Go API reference documentation.

To authenticate to Vision API Product Search, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


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

	vision "cloud.google.com/go/vision/apiv1"
	"cloud.google.com/go/vision/v2/apiv1/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: %w", err)
	}
	defer c.Close()

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

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

To authenticate to Vision API Product Search, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * 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 = ProductSetName.format(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

To authenticate to Vision API Product Search, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

// 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

To authenticate to Vision API Product Search, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import vision

def get_similar_products_file(
    project_id,
    location,
    product_set_id,
    product_category,
    file_path,
    filter,
    max_results,
):
    """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
        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 = 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, max_results=max_results
    )

    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(f"Score(Confidence): {result.score}")
        print(f"Image name: {result.image}")

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

What's next

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