Mendapatkan produk yang mirip dengan file gambar Cloud Storage

Telusuri produk yang mirip dengan gambar yang disimpan sebagai file di Cloud Storage.

Jelajahi lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

Contoh kode

Go

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Vision API Product Search, lihat library klien Vision API Product Search. Untuk informasi selengkapnya, lihat dokumentasi referensi API Go Product Search Vision API.

Untuk mengautentikasi ke Product Search Vision API, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.


import (
	"context"
	"fmt"
	"io"

	vision "cloud.google.com/go/vision/apiv1"
	"cloud.google.com/go/vision/v2/apiv1/visionpb"
)

// 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: %w", 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: %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

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Vision API Product Search, lihat library klien Vision API Product Search. Untuk informasi selengkapnya, lihat dokumentasi referensi API Java Product Search Vision API.

Untuk mengautentikasi ke Product Search Vision API, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

/**
 * 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

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Vision API Product Search, lihat library klien Vision API Product Search. Untuk informasi selengkapnya, lihat dokumentasi referensi API Node.js Product Search Vision API.

Untuk mengautentikasi ke Product Search Vision API, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

// 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

Untuk mempelajari cara menginstal dan menggunakan library klien untuk Vision API Product Search, lihat library klien Vision API Product Search. Untuk informasi selengkapnya, lihat dokumentasi referensi API Python Product Search Vision API.

Untuk mengautentikasi ke Product Search Vision API, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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(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")

Langkah selanjutnya

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