搜索与本地存储的图片类似的商品。
深入探索
如需查看包含此代码示例的详细文档，请参阅以下内容：
代码示例
Go
如需了解如何安装和使用 Vision API Product Search 客户端库，请参阅 Vision API Product Search 客户端库。如需了解详情，请参阅 Vision API Product Search Go API 参考文档。
如需向 Vision API Product Search 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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
如需了解如何安装和使用 Vision API Product Search 客户端库，请参阅 Vision API Product Search 客户端库。如需了解详情，请参阅 Vision API Product Search Java API 参考文档。
如需向 Vision API Product Search 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* 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
如需了解如何安装和使用 Vision API Product Search 客户端库，请参阅 Vision API Product Search 客户端库。如需了解详情，请参阅 Vision API Product Search Node.js API 参考文档。
如需向 Vision API Product Search 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
// 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
如需了解如何安装和使用 Vision API Product Search 客户端库，请参阅 Vision API Product Search 客户端库。如需了解详情，请参阅 Vision API Product Search Python API 参考文档。
如需向 Vision API Product Search 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
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")
后续步骤
如需搜索和过滤其他 Google Cloud 产品的代码示例，请参阅 Google Cloud 示例浏览器。