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>26.42.0</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:3.44.0'

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

libraryDependencies += "com.google.cloud" % "google-cloud-vision" % "3.44.0"

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