Vision 客户端库

本页面介绍如何开始使用 Vision API 的 Cloud 客户端库。通过客户端库，您可以更轻松地使用支持的语言访问 Google Cloud API。虽然您可以通过向服务器发出原始请求来直接使用 Google Cloud API，但客户端库可实现简化，从而显著减少您需要编写的代码量。

请参阅客户端库说明，详细了解 Cloud 客户端库和旧版 Google API 客户端库。

安装客户端库

C++

如需详细了解此客户端库的要求和安装依赖项，请参阅设置 C++ 开发环境。

C#

如果您使用的是 Visual Studio 2017 或更高版本，请打开 nuget 软件包管理器窗口并输入以下内容：

Install-Package Google.Apis

如果您使用 .NET Core 命令行界面工具来安装依赖项，请运行以下命令：

dotnet add package Google.Apis

如需了解详情，请参阅设置 C# 开发环境。

Go

go get cloud.google.com/go/vision/apiv1

如需了解详情，请参阅设置 Go 开发环境。

Java

If you are using Maven, add the following to your pom.xml file. For more information about BOMs, see The Google Cloud Platform Libraries BOM.

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>com.google.cloud</groupId>
      <artifactId>libraries-bom</artifactId>
      <version>26.39.0</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

<dependencies>
  <dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-vision</artifactId>
  </dependency>

If you are using Gradle, add the following to your dependencies:

implementation 'com.google.cloud:google-cloud-vision:3.42.0'

If you are using sbt, add the following to your dependencies:

libraryDependencies += "com.google.cloud" % "google-cloud-vision" % "3.42.0"

If you're using Visual Studio Code, IntelliJ, or Eclipse, you can add client libraries to your project using the following IDE plugins:

The plugins provide additional functionality, such as key management for service accounts. Refer to each plugin's documentation for details.

如需了解详情，请参阅设置 Java 开发环境。

Node.js

npm install --save @google-cloud/vision

如需了解详情，请参阅设置 Node.js 开发环境。

PHP

composer require google/apiclient

如需了解详情，请参阅在 Google Cloud 上使用 PHP。

Python

pip install --upgrade google-cloud-vision

如需了解详情，请参阅设置 Python 开发环境。

Ruby

gem install google-api-client

如需了解详情，请参阅设置 Ruby 开发环境。

设置身份验证

为了对 Google Cloud API 的调用进行身份验证，客户端库支持应用默认凭据 (ADC)；这些库会在一组指定的位置查找凭据，并使用这些凭据对发送到 API 的请求进行身份验证。借助 ADC，您可以在各种环境（例如本地开发或生产环境）中为您的应用提供凭据，而无需修改应用代码。

对于生产环境，设置 ADC 的方式取决于服务和上下文。如需了解详情，请参阅设置应用默认凭据。

对于本地开发环境，您可以使用与您的 Google 账号关联的凭据设置 ADC：

  1. Install the Google Cloud CLI, then initialize it by running the following command: 

    gcloud init

  2. If you're using a local shell, then create local authentication credentials for your user account: 

    gcloud auth application-default login

    You don't need to do this if you're using Cloud Shell.

    登录屏幕随即出现。在您登录后，您的凭据会存储在 ADC 使用的本地凭据文件中。

使用客户端库

以下示例展示了如何使用客户端库。

C++


#include "google/cloud/vision/v1/image_annotator_client.h"
#include <iostream>

int main(int argc, char* argv[]) try {
  auto constexpr kDefaultUri =
      "gs://cloud-samples-data/vision/label/wakeupcat.jpg";
  if (argc > 2) {
    std::cerr << "Usage: " << argv[0] << " [gcs-uri]\n"
              << "  The gcs-uri must be in gs://... format. It defaults to "
              << kDefaultUri << "\n";
    return 1;
  }
  auto uri = std::string{argc == 2 ? argv[1] : kDefaultUri};

  namespace vision = ::google::cloud::vision_v1;
  auto client =
      vision::ImageAnnotatorClient(vision::MakeImageAnnotatorConnection());

  // Define the image we want to annotate
  google::cloud::vision::v1::Image image;
  image.mutable_source()->set_image_uri(uri);
  // Create a request to annotate this image with Request text annotations for a
  // file stored in GCS.
  google::cloud::vision::v1::AnnotateImageRequest request;
  *request.mutable_image() = std::move(image);
  request.add_features()->set_type(
      google::cloud::vision::v1::Feature::TEXT_DETECTION);

  google::cloud::vision::v1::BatchAnnotateImagesRequest batch_request;
  *batch_request.add_requests() = std::move(request);
  auto batch = client.BatchAnnotateImages(batch_request);
  if (!batch) throw std::move(batch).status();

  // Find the longest annotation and print it
  auto result = std::string{};
  for (auto const& response : batch->responses()) {
    for (auto const& annotation : response.text_annotations()) {
      if (result.size() < annotation.description().size()) {
        result = annotation.description();
      }
    }
  }
  std::cout << "The image contains this text: " << result << "\n";

  return 0;
} catch (google::cloud::Status const& status) {
  std::cerr << "google::cloud::Status thrown: " << status << "\n";
  return 1;
}

Go


// Sample vision-quickstart uses the Google Cloud Vision API to label an image.
package main

import (
	"context"
	"fmt"
	"log"
	"os"

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

func main() {
	ctx := context.Background()

	// Creates a client.
	client, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		log.Fatalf("Failed to create client: %v", err)
	}
	defer client.Close()

	// Sets the name of the image file to annotate.
	filename := "../testdata/cat.jpg"

	file, err := os.Open(filename)
	if err != nil {
		log.Fatalf("Failed to read file: %v", err)
	}
	defer file.Close()
	image, err := vision.NewImageFromReader(file)
	if err != nil {
		log.Fatalf("Failed to create image: %v", err)
	}

	labels, err := client.DetectLabels(ctx, image, nil, 10)
	if err != nil {
		log.Fatalf("Failed to detect labels: %v", err)
	}

	fmt.Println("Labels:")
	for _, label := range labels {
		fmt.Println(label.Description)
	}
}

Java

// Imports the Google Cloud client library

import com.google.cloud.vision.v1.AnnotateImageRequest;
import com.google.cloud.vision.v1.AnnotateImageResponse;
import com.google.cloud.vision.v1.BatchAnnotateImagesResponse;
import com.google.cloud.vision.v1.EntityAnnotation;
import com.google.cloud.vision.v1.Feature;
import com.google.cloud.vision.v1.Feature.Type;
import com.google.cloud.vision.v1.Image;
import com.google.cloud.vision.v1.ImageAnnotatorClient;
import com.google.protobuf.ByteString;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;

public class QuickstartSample {
  public static void main(String... args) throws Exception {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (ImageAnnotatorClient vision = ImageAnnotatorClient.create()) {

      // The path to the image file to annotate
      String fileName = "./resources/wakeupcat.jpg";

      // Reads the image file into memory
      Path path = Paths.get(fileName);
      byte[] data = Files.readAllBytes(path);
      ByteString imgBytes = ByteString.copyFrom(data);

      // Builds the image annotation request
      List<AnnotateImageRequest> requests = new ArrayList<>();
      Image img = Image.newBuilder().setContent(imgBytes).build();
      Feature feat = Feature.newBuilder().setType(Type.LABEL_DETECTION).build();
      AnnotateImageRequest request =
          AnnotateImageRequest.newBuilder().addFeatures(feat).setImage(img).build();
      requests.add(request);

      // Performs label detection on the image file
      BatchAnnotateImagesResponse response = vision.batchAnnotateImages(requests);
      List<AnnotateImageResponse> responses = response.getResponsesList();

      for (AnnotateImageResponse res : responses) {
        if (res.hasError()) {
          System.out.format("Error: %s%n", res.getError().getMessage());
          return;
        }

        for (EntityAnnotation annotation : res.getLabelAnnotationsList()) {
          annotation
              .getAllFields()
              .forEach((k, v) -> System.out.format("%s : %s%n", k, v.toString()));
        }
      }
    }
  }
}

Node.js

async function quickstart() {
  // Imports the Google Cloud client library
  const vision = require('@google-cloud/vision');

  // Creates a client
  const client = new vision.ImageAnnotatorClient();

  // Performs label detection on the image file
  const [result] = await client.labelDetection('./resources/wakeupcat.jpg');
  const labels = result.labelAnnotations;
  console.log('Labels:');
  labels.forEach(label => console.log(label.description));
}
quickstart();

Python


# Imports the Google Cloud client library
from google.cloud import vision



def run_quickstart() -> vision.EntityAnnotation:
    """Provides a quick start example for Cloud Vision."""

    # Instantiates a client
    client = vision.ImageAnnotatorClient()

    # The URI of the image file to annotate
    file_uri = "gs://cloud-samples-data/vision/label/wakeupcat.jpg"

    image = vision.Image()
    image.source.image_uri = file_uri

    # Performs label detection on the image file
    response = client.label_detection(image=image)
    labels = response.label_annotations

    print("Labels:")
    for label in labels:
        print(label.description)

    return labels

其他资源

C++

以下列表包含与 C++ 版客户端库相关的更多资源的链接：

C#

以下列表包含与 C# 版客户端库相关的更多资源的链接：

Go

以下列表包含与 Go 版客户端库相关的更多资源的链接：

Java

以下列表包含与 Java 版客户端库相关的更多资源的链接：

Node.js

以下列表包含与 Node.js 版客户端库相关的更多资源的链接：

PHP

以下列表包含与 PHP 版客户端库相关的更多资源的链接：

Python

以下列表包含与 Python 版客户端库相关的更多资源的链接：

Ruby

以下列表包含与 Ruby 版客户端库相关的更多资源的链接：

其他客户端库

除了上面显示的库外，Spring Cloud Google Cloud 可用于 Java 应用。Spring Vision API 可帮助您在通过 Spring 框架构建的任何应用中使用 Cloud Vision。

如需开始使用，请了解如何将 Spring Cloud Vision 添加到应用。

自行试用

如果您是 Google Cloud 新手，请创建一个账号来评估 Cloud Vision API 在实际场景中的表现。新客户还可获享 $300 赠金，用于运行、测试和部署工作负载。

免费试用 Cloud Vision API