教程：处理来自 Cloud Storage 的图片

设置 gcloud 默认值

要配置您的 Cloud Run 服务的 gcloud 默认值，请执行以下操作：

1. 设置默认项目：

   gcloud config set project PROJECT_ID

   将 PROJECT_ID 替换为您在本教程中创建的项目的名称。

2. 为您选择的区域配置 gcloud：

   gcloud config set run/region REGION

   将 REGION 替换为您选择的受支持的 Cloud Run 区域。

了解操作顺序

本教程中的数据流遵循以下步骤：

1. 用户将图片上传到 Cloud Storage 存储桶。
2. Cloud Storage 将关于新文件的消息发布到 Pub/Sub。
3. Pub/Sub 将消息推送到 Cloud Run 服务。
4. Cloud Run 服务检索 Pub/Sub 消息中引用的图片文件。
5. Cloud Run 服务使用 Cloud Vision API 来分析图片。
6. 如果检测到暴力或成人内容，Cloud Run 服务会使用 ImageMagick 对图片进行模糊处理。
7. Cloud Run 服务将经过模糊处理的图片上传到其他 Cloud Storage 存储桶，以供使用。

后续对经过模糊处理的图片的使用将留给读者自行练习。

创建 Artifact Registry 标准制品库

创建 Artifact Registry 标准制品库以存储您的容器映像：

gcloud artifacts repositories create REPOSITORY \
    --repository-format=docker \
    --location=REGION

您需要将其中的：

• 将 REPOSITORY 替换为制品库的唯一名称。
• 将 REGION 替换为要用于 Artifact Registry 制品库的 Google Cloud 区域。

设置 Cloud Storage 存储桶

resource "random_id" "bucket_suffix" {
  byte_length = 8
}

resource "google_storage_bucket" "imageproc_input" {
  name     = "input-bucket-${random_id.bucket_suffix.hex}"
  location = "us-central1"
}

output "input_bucket_name" {
  value = google_storage_bucket.imageproc_input.name
}

resource "google_storage_bucket" "imageproc_output" {
  name     = "output-bucket-${random_id.bucket_suffix.hex}"
  location = "us-central1"
}

output "blurred_bucket_name" {
  value = google_storage_bucket.imageproc_output.name
}

在以下步骤中，您将创建并部署一项服务，用于处理上传到 INPUT_BUCKET_NAME 的文件通知。请在部署并测试服务后启用通知递送，以避免过早调用新服务。

修改 Pub/Sub 教程示例代码

本教程基于使用 Pub/Sub 教程中汇编的代码构建而成。如果您尚未完成这一基础教程，请立即查看并完成（可跳过清理步骤），然后返回此处来添加图片处理行为。

添加图片处理代码

为了便于阅读和测试，图片处理代码与请求处理是彼此独立的。要添加图片处理代码，请执行以下操作：

1. 切换到 Pub/Sub 教程示例代码的目录。

2. 添加代码以导入图片处理依赖项，包括与 Google Cloud 服务、ImageMagick 和文件系统集成的库。

   Node.js

   在编辑器中打开一个新的 image.js 文件，然后复制以下内容：

   const gm = require('gm').subClass({imageMagick: true});
   const fs = require('fs');
   const {promisify} = require('util');
   const path = require('path');
   const vision = require('@google-cloud/vision');
   
   const {Storage} = require('@google-cloud/storage');
   const storage = new Storage();
   const client = new vision.ImageAnnotatorClient();
   
   const {BLURRED_BUCKET_NAME} = process.env;

   Python

   在编辑器中打开一个新的 image.py 文件，然后复制以下内容：

   import os
   import tempfile
   
   from google.cloud import storage, vision
   from wand.image import Image
   
   storage_client = storage.Client()
   vision_client = vision.ImageAnnotatorClient()

   Go

   在编辑器中打开一个新的 imagemagick/imagemagick.go 文件，然后复制以下内容：

   
   // Package imagemagick contains an example of using ImageMagick to process a
   // file uploaded to Cloud Storage.
   package imagemagick
   
   import (
   	"context"
   	"errors"
   	"fmt"
   	"log"
   	"os"
   	"os/exec"
   
   	"cloud.google.com/go/storage"
   	vision "cloud.google.com/go/vision/apiv1"
   	"cloud.google.com/go/vision/v2/apiv1/visionpb"
   )
   
   // Global API clients used across function invocations.
   var (
   	storageClient *storage.Client
   	visionClient  *vision.ImageAnnotatorClient
   )
   
   func init() {
   	// Declare a separate err variable to avoid shadowing the client variables.
   	var err error
   
   	storageClient, err = storage.NewClient(context.Background())
   	if err != nil {
   		log.Fatalf("storage.NewClient: %v", err)
   	}
   
   	visionClient, err = vision.NewImageAnnotatorClient(context.Background())
   	if err != nil {
   		log.Fatalf("vision.NewAnnotatorClient: %v", err)
   	}
   }
   

   Java

   在编辑器中打开一个新的 src/main/java/com/example/cloudrun/ImageMagick.java 文件，然后复制以下内容：

   import com.google.cloud.storage.Blob;
   import com.google.cloud.storage.BlobId;
   import com.google.cloud.storage.BlobInfo;
   import com.google.cloud.storage.Storage;
   import com.google.cloud.storage.StorageOptions;
   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.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.cloud.vision.v1.ImageSource;
   import com.google.cloud.vision.v1.SafeSearchAnnotation;
   import com.google.gson.JsonObject;
   import java.io.IOException;
   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 ImageMagick {
   
     private static final String BLURRED_BUCKET_NAME = System.getenv("BLURRED_BUCKET_NAME");
     private static Storage storage = StorageOptions.getDefaultInstance().getService();

3. 添加代码以接收作为事件对象的 Pub/Sub 消息并控制图片处理。

   该事件包含最初上传的图片的相关数据。此代码通过查看 Cloud Vision 对暴力或成人内容的分析结果来确定图片是否需要进行模糊处理。

   Node.js

   // Blurs uploaded images that are flagged as Adult or Violence.
   exports.blurOffensiveImages = async event => {
     // This event represents the triggering Cloud Storage object.
     const object = event;
   
     const file = storage.bucket(object.bucket).file(object.name);
     const filePath = `gs://${object.bucket}/${object.name}`;
   
     console.log(`Analyzing ${file.name}.`);
   
     try {
       const [result] = await client.safeSearchDetection(filePath);
       const detections = result.safeSearchAnnotation || {};
   
       if (
         // Levels are defined in https://cloud.google.com/vision/docs/reference/rest/v1/AnnotateImageResponse#likelihood
         detections.adult === 'VERY_LIKELY' ||
         detections.violence === 'VERY_LIKELY'
       ) {
         console.log(`Detected ${file.name} as inappropriate.`);
         return blurImage(file, BLURRED_BUCKET_NAME);
       } else {
         console.log(`Detected ${file.name} as OK.`);
       }
     } catch (err) {
       console.error(`Failed to analyze ${file.name}.`, err);
       throw err;
     }
   };

   Python

   def blur_offensive_images(data):
       """Blurs uploaded images that are flagged as Adult or Violence.
   
       Args:
           data: Pub/Sub message data
       """
       file_data = data
   
       file_name = file_data["name"]
       bucket_name = file_data["bucket"]
   
       blob = storage_client.bucket(bucket_name).get_blob(file_name)
       blob_uri = f"gs://{bucket_name}/{file_name}"
       blob_source = vision.Image(source=vision.ImageSource(image_uri=blob_uri))
   
       # Ignore already-blurred files
       if file_name.startswith("blurred-"):
           print(f"The image {file_name} is already blurred.")
           return
   
       print(f"Analyzing {file_name}.")
   
       result = vision_client.safe_search_detection(image=blob_source)
       detected = result.safe_search_annotation
   
       # Process image
       if detected.adult == 5 or detected.violence == 5:
           print(f"The image {file_name} was detected as inappropriate.")
           return __blur_image(blob)
       else:
           print(f"The image {file_name} was detected as OK.")
   
   

   Go

   
   // GCSEvent is the payload of a GCS event.
   type GCSEvent struct {
   	Bucket string `json:"bucket"`
   	Name   string `json:"name"`
   }
   
   // BlurOffensiveImages blurs offensive images uploaded to GCS.
   func BlurOffensiveImages(ctx context.Context, e GCSEvent) error {
   	outputBucket := os.Getenv("BLURRED_BUCKET_NAME")
   	if outputBucket == "" {
   		return errors.New("BLURRED_BUCKET_NAME must be set")
   	}
   
   	img := vision.NewImageFromURI(fmt.Sprintf("gs://%s/%s", e.Bucket, e.Name))
   
   	resp, err := visionClient.DetectSafeSearch(ctx, img, nil)
   	if err != nil {
   		return fmt.Errorf("AnnotateImage: %w", err)
   	}
   
   	if resp.GetAdult() == visionpb.Likelihood_VERY_LIKELY ||
   		resp.GetViolence() == visionpb.Likelihood_VERY_LIKELY {
   		return blur(ctx, e.Bucket, outputBucket, e.Name)
   	}
   	log.Printf("The image %q was detected as OK.", e.Name)
   	return nil
   }
   

   Java

   // Blurs uploaded images that are flagged as Adult or Violence.
   public static void blurOffensiveImages(JsonObject data) {
     String fileName = data.get("name").getAsString();
     String bucketName = data.get("bucket").getAsString();
     BlobInfo blobInfo = BlobInfo.newBuilder(bucketName, fileName).build();
     // Construct URI to GCS bucket and file.
     String gcsPath = String.format("gs://%s/%s", bucketName, fileName);
     System.out.println(String.format("Analyzing %s", fileName));
   
     // Construct request.
     List<AnnotateImageRequest> requests = new ArrayList<>();
     ImageSource imgSource = ImageSource.newBuilder().setImageUri(gcsPath).build();
     Image img = Image.newBuilder().setSource(imgSource).build();
     Feature feature = Feature.newBuilder().setType(Type.SAFE_SEARCH_DETECTION).build();
     AnnotateImageRequest request =
         AnnotateImageRequest.newBuilder().addFeatures(feature).setImage(img).build();
     requests.add(request);
   
     // Send request to the Vision API.
     try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
       BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
       List<AnnotateImageResponse> responses = response.getResponsesList();
       for (AnnotateImageResponse res : responses) {
         if (res.hasError()) {
           System.out.println(String.format("Error: %s\n", res.getError().getMessage()));
           return;
         }
         // Get Safe Search Annotations
         SafeSearchAnnotation annotation = res.getSafeSearchAnnotation();
         if (annotation.getAdultValue() == 5 || annotation.getViolenceValue() == 5) {
           System.out.println(String.format("Detected %s as inappropriate.", fileName));
           blur(blobInfo);
         } else {
           System.out.println(String.format("Detected %s as OK.", fileName));
         }
       }
     } catch (Exception e) {
       System.out.println(String.format("Error with Vision API: %s", e.getMessage()));
     }
   }

4. 从上面创建的 Cloud Storage 输入存储分区检索引用的图片，使用 ImageMagick 转换具有模糊效果的图片，并将结果上传到输出存储分区。

   Node.js

   // Blurs the given file using ImageMagick, and uploads it to another bucket.
   const blurImage = async (file, blurredBucketName) => {
     const tempLocalPath = `/tmp/${path.parse(file.name).base}`;
   
     // Download file from bucket.
     try {
       await file.download({destination: tempLocalPath});
   
       console.log(`Downloaded ${file.name} to ${tempLocalPath}.`);
     } catch (err) {
       throw new Error(`File download failed: ${err}`);
     }
   
     await new Promise((resolve, reject) => {
       gm(tempLocalPath)
         .blur(0, 16)
         .write(tempLocalPath, (err, stdout) => {
           if (err) {
             console.error('Failed to blur image.', err);
             reject(err);
           } else {
             console.log(`Blurred image: ${file.name}`);
             resolve(stdout);
           }
         });
     });
   
     // Upload result to a different bucket, to avoid re-triggering this function.
     const blurredBucket = storage.bucket(blurredBucketName);
   
     // Upload the Blurred image back into the bucket.
     const gcsPath = `gs://${blurredBucketName}/${file.name}`;
     try {
       await blurredBucket.upload(tempLocalPath, {destination: file.name});
       console.log(`Uploaded blurred image to: ${gcsPath}`);
     } catch (err) {
       throw new Error(`Unable to upload blurred image to ${gcsPath}: ${err}`);
     }
   
     // Delete the temporary file.
     const unlink = promisify(fs.unlink);
     return unlink(tempLocalPath);
   };

   Python

   def __blur_image(current_blob):
       """Blurs the given file using ImageMagick.
   
       Args:
           current_blob: a Cloud Storage blob
       """
       file_name = current_blob.name
       _, temp_local_filename = tempfile.mkstemp()
   
       # Download file from bucket.
       current_blob.download_to_filename(temp_local_filename)
       print(f"Image {file_name} was downloaded to {temp_local_filename}.")
   
       # Blur the image using ImageMagick.
       with Image(filename=temp_local_filename) as image:
           image.resize(*image.size, blur=16, filter="hamming")
           image.save(filename=temp_local_filename)
   
       print(f"Image {file_name} was blurred.")
   
       # Upload result to a second bucket, to avoid re-triggering the function.
       # You could instead re-upload it to the same bucket + tell your function
       # to ignore files marked as blurred (e.g. those with a "blurred" prefix)
       blur_bucket_name = os.getenv("BLURRED_BUCKET_NAME")
       blur_bucket = storage_client.bucket(blur_bucket_name)
       new_blob = blur_bucket.blob(file_name)
       new_blob.upload_from_filename(temp_local_filename)
       print(f"Blurred image uploaded to: gs://{blur_bucket_name}/{file_name}")
   
       # Delete the temporary file.
       os.remove(temp_local_filename)
   
   

   Go

   
   // blur blurs the image stored at gs://inputBucket/name and stores the result in
   // gs://outputBucket/name.
   func blur(ctx context.Context, inputBucket, outputBucket, name string) error {
   	inputBlob := storageClient.Bucket(inputBucket).Object(name)
   	r, err := inputBlob.NewReader(ctx)
   	if err != nil {
   		return fmt.Errorf("NewReader: %w", err)
   	}
   
   	outputBlob := storageClient.Bucket(outputBucket).Object(name)
   	w := outputBlob.NewWriter(ctx)
   	defer w.Close()
   
   	// Use - as input and output to use stdin and stdout.
   	cmd := exec.Command("convert", "-", "-blur", "0x8", "-")
   	cmd.Stdin = r
   	cmd.Stdout = w
   
   	if err := cmd.Run(); err != nil {
   		return fmt.Errorf("cmd.Run: %w", err)
   	}
   
   	log.Printf("Blurred image uploaded to gs://%s/%s", outputBlob.BucketName(), outputBlob.ObjectName())
   
   	return nil
   }
   

   Java

     // Blurs the file described by blobInfo using ImageMagick,
     // and uploads it to the blurred bucket.
     public static void blur(BlobInfo blobInfo) throws IOException {
       String bucketName = blobInfo.getBucket();
       String fileName = blobInfo.getName();
       // Download image
       Blob blob = storage.get(BlobId.of(bucketName, fileName));
       Path download = Paths.get("/tmp/", fileName);
       blob.downloadTo(download);
   
       // Construct the command.
       List<String> args = new ArrayList<>();
       args.add("convert");
       args.add(download.toString());
       args.add("-blur");
       args.add("0x8");
       Path upload = Paths.get("/tmp/", "blurred-" + fileName);
       args.add(upload.toString());
       try {
         ProcessBuilder pb = new ProcessBuilder(args);
         Process process = pb.start();
         process.waitFor();
       } catch (Exception e) {
         System.out.println(String.format("Error: %s", e.getMessage()));
       }
   
       // Upload image to blurred bucket.
       BlobId blurredBlobId = BlobId.of(BLURRED_BUCKET_NAME, fileName);
       BlobInfo blurredBlobInfo =
           BlobInfo.newBuilder(blurredBlobId).setContentType(blob.getContentType()).build();
       try {
         byte[] blurredFile = Files.readAllBytes(upload);
         Blob blurredBlob = storage.create(blurredBlobInfo, blurredFile);
         System.out.println(
             String.format("Blurred image uploaded to: gs://%s/%s", BLURRED_BUCKET_NAME, fileName));
       } catch (Exception e) {
         System.out.println(String.format("Error in upload: %s", e.getMessage()));
       }
   
       // Remove images from fileSystem
       Files.delete(download);
       Files.delete(upload);
     }
   }

将图片处理集成到 Pub/Sub 示例代码中

要修改现有服务以整合图片处理代码：

1. 为您的服务添加新的依赖项，包括 Cloud Vision 和 Cloud Storage 客户端库：

   Node.js

   npm install --save gm @google-cloud/storage @google-cloud/vision

   Python

   添加必要的客户端库，让您的 requirements.txt 如下所示：

   Flask==3.0.3
   google-cloud-storage==2.12.0
   google-cloud-vision==3.4.5
   gunicorn==22.0.0
   Wand==0.6.13
   Werkzeug==3.0.3
   

   Go

   Go 示例应用使用 go 模块，上述 imagemagick/imagemagick.go import 语句中添加的新依赖项会通过下一个需要它们的命令自动下载。

   Java

   在 pom.xml 中的 <dependencyManagement> 下添加以下依赖项：

   <dependency>
     <groupId>com.google.cloud</groupId>
     <artifactId>spring-cloud-gcp-dependencies</artifactId>
     <version>4.9.2</version>
     <type>pom</type>
     <scope>import</scope>
   </dependency>
   

   在 pom.xml 中的 <dependencies> 下添加以下依赖项：

   <dependency>
     <groupId>com.google.code.gson</groupId>
     <artifactId>gson</artifactId>
     <scope>compile</scope>
   </dependency>
   <dependency>
     <groupId>com.google.cloud</groupId>
     <artifactId>spring-cloud-gcp-starter-vision</artifactId>
   </dependency>
   <dependency>
     <groupId>com.google.cloud</groupId>
     <artifactId>spring-cloud-gcp-starter-storage</artifactId>
   </dependency>
   

2. 通过修改 FROM 语句下的 Dockerfile，将 ImageMagick 系统软件包添加到您的容器中。如果使用的是“多阶段”Dockerfile，请将其置于最后一个阶段。

   Debian/Ubuntu

   
   # Install Imagemagick into the container image.
   # For more on system packages review the system packages tutorial.
   # https://cloud.google.com/run/docs/tutorials/system-packages#dockerfile
   RUN set -ex; \
     apt-get -y update; \
     apt-get -y install imagemagick; \
     rm -rf /var/lib/apt/lists/*
   

   Alpine

   
   # Install Imagemagick into the container image.
   # For more on system packages review the system packages tutorial.
   # https://cloud.google.com/run/docs/tutorials/system-packages#dockerfile
   RUN apk add --no-cache imagemagick
   

   请阅读使用系统软件包教程，详细了解如何在 Cloud Run 服务中使用系统软件包。

3. 将现有的 Pub/Sub 消息处理代码替换为全新模糊处理逻辑的函数调用。

   Node.js

   app.js 文件定义了 Express.js 应用，并准备了接收到的 Pub/Sub 消息以供使用。进行以下更改：

   • 添加代码以导入新的 image.js 文件
   • 从路由中移除现有的“Hello World”代码
   • 添加代码以进一步验证 Pub/Sub 消息

   • 添加代码以调用新的图片处理函数

     完成后，代码将如下所示：

   
   const express = require('express');
   const app = express();
   
   // This middleware is available in Express v4.16.0 onwards
   app.use(express.json());
   
   const image = require('./image');
   
   app.post('/', async (req, res) => {
     if (!req.body) {
       const msg = 'no Pub/Sub message received';
       console.error(`error: ${msg}`);
       res.status(400).send(`Bad Request: ${msg}`);
       return;
     }
     if (!req.body.message || !req.body.message.data) {
       const msg = 'invalid Pub/Sub message format';
       console.error(`error: ${msg}`);
       res.status(400).send(`Bad Request: ${msg}`);
       return;
     }
   
     // Decode the Pub/Sub message.
     const pubSubMessage = req.body.message;
     let data;
     try {
       data = Buffer.from(pubSubMessage.data, 'base64').toString().trim();
       data = JSON.parse(data);
     } catch (err) {
       const msg =
         'Invalid Pub/Sub message: data property is not valid base64 encoded JSON';
       console.error(`error: ${msg}: ${err}`);
       res.status(400).send(`Bad Request: ${msg}`);
       return;
     }
   
     // Validate the message is a Cloud Storage event.
     if (!data.name || !data.bucket) {
       const msg =
         'invalid Cloud Storage notification: expected name and bucket properties';
       console.error(`error: ${msg}`);
       res.status(400).send(`Bad Request: ${msg}`);
       return;
     }
   
     try {
       await image.blurOffensiveImages(data);
       res.status(204).send();
     } catch (err) {
       console.error(`error: Blurring image: ${err}`);
       res.status(500).send();
     }
   });

   Python

   main.py 文件定义了 Flask 应用，并准备了接收到的 Pub/Sub 消息以供使用。进行以下更改：

   • 添加代码以导入新的 image.py 文件
   • 从路由中移除现有的“Hello World”代码
   • 添加代码以进一步验证 Pub/Sub 消息

   • 添加代码以调用新的图片处理函数

     完成后，代码将如下所示：

   import base64
   import json
   import os
   
   from flask import Flask, request
   
   import image
   
   
   app = Flask(__name__)
   
   
   @app.route("/", methods=["POST"])
   def index():
       """Receive and parse Pub/Sub messages containing Cloud Storage event data."""
       envelope = request.get_json()
       if not envelope:
           msg = "no Pub/Sub message received"
           print(f"error: {msg}")
           return f"Bad Request: {msg}", 400
   
       if not isinstance(envelope, dict) or "message" not in envelope:
           msg = "invalid Pub/Sub message format"
           print(f"error: {msg}")
           return f"Bad Request: {msg}", 400
   
       # Decode the Pub/Sub message.
       pubsub_message = envelope["message"]
   
       if isinstance(pubsub_message, dict) and "data" in pubsub_message:
           try:
               data = json.loads(base64.b64decode(pubsub_message["data"]).decode())
   
           except Exception as e:
               msg = (
                   "Invalid Pub/Sub message: "
                   "data property is not valid base64 encoded JSON"
               )
               print(f"error: {e}")
               return f"Bad Request: {msg}", 400
   
           # Validate the message is a Cloud Storage event.
           if not data["name"] or not data["bucket"]:
               msg = (
                   "Invalid Cloud Storage notification: "
                   "expected name and bucket properties"
               )
               print(f"error: {msg}")
               return f"Bad Request: {msg}", 400
   
           try:
               image.blur_offensive_images(data)
               return ("", 204)
   
           except Exception as e:
               print(f"error: {e}")
               return ("", 500)
   
       return ("", 500)
   

   Go

   main.go 文件定义了 HTTP 服务，并准备了接收到的 Pub/Sub 消息以供使用。进行以下更改：

   • 添加代码以导入新的 imagemagick.go 文件
   • 从处理程序中移除现有的“Hello World”代码
   • 添加代码以进一步验证 Pub/Sub 消息
   • 添加代码以调用新的图片处理函数

   
   // Sample image-processing is a Cloud Run service which performs asynchronous processing on images.
   package main
   
   import (
   	"encoding/json"
   	"io"
   	"log"
   	"net/http"
   	"os"
   
   	"github.com/GoogleCloudPlatform/golang-samples/run/image-processing/imagemagick"
   )
   
   func main() {
   	http.HandleFunc("/", HelloPubSub)
   	// Determine port for HTTP service.
   	port := os.Getenv("PORT")
   	if port == "" {
   		port = "8080"
   	}
   	// Start HTTP server.
   	log.Printf("Listening on port %s", port)
   	if err := http.ListenAndServe(":"+port, nil); err != nil {
   		log.Fatal(err)
   	}
   }
   
   // PubSubMessage is the payload of a Pub/Sub event.
   // See the documentation for more details:
   // https://cloud.google.com/pubsub/docs/reference/rest/v1/PubsubMessage
   type PubSubMessage struct {
   	Message struct {
   		Data []byte `json:"data,omitempty"`
   		ID   string `json:"id"`
   	} `json:"message"`
   	Subscription string `json:"subscription"`
   }
   
   // HelloPubSub receives and processes a Pub/Sub push message.
   func HelloPubSub(w http.ResponseWriter, r *http.Request) {
   	var m PubSubMessage
   	body, err := io.ReadAll(r.Body)
   	if err != nil {
   		log.Printf("ioutil.ReadAll: %v", err)
   		http.Error(w, "Bad Request", http.StatusBadRequest)
   		return
   	}
   	if err := json.Unmarshal(body, &m); err != nil {
   		log.Printf("json.Unmarshal: %v", err)
   		http.Error(w, "Bad Request", http.StatusBadRequest)
   		return
   	}
   
   	var e imagemagick.GCSEvent
   	if err := json.Unmarshal(m.Message.Data, &e); err != nil {
   		log.Printf("json.Unmarshal: %v", err)
   		http.Error(w, "Bad Request", http.StatusBadRequest)
   		return
   	}
   
   	if e.Name == "" || e.Bucket == "" {
   		log.Printf("invalid GCSEvent: expected name and bucket")
   		http.Error(w, "Bad Request", http.StatusBadRequest)
   		return
   	}
   
   	if err := imagemagick.BlurOffensiveImages(r.Context(), e); err != nil {
   		log.Printf("imagemagick.BlurOffensiveImages: %v", err)
   		http.Error(w, "Internal Server Error", http.StatusInternalServerError)
   	}
   }
   

   Java

   PubSubController.java 文件定义了处理 HTTP 请求的控制器，并准备了接收到的 Pub/Sub 消息以供使用。进行以下更改：

   • 添加新的导入
   • 从控制器中移除现有的“Hello World”代码
   • 添加代码以进一步验证 Pub/Sub 消息
   • 添加代码以调用新的图片处理函数

   import com.google.gson.JsonObject;
   import com.google.gson.JsonParser;
   import java.util.Base64;
   import org.springframework.http.HttpStatus;
   import org.springframework.http.ResponseEntity;
   import org.springframework.web.bind.annotation.RequestBody;
   import org.springframework.web.bind.annotation.RequestMapping;
   import org.springframework.web.bind.annotation.RequestMethod;
   import org.springframework.web.bind.annotation.RestController;
   
   // PubsubController consumes a Pub/Sub message.
   @RestController
   public class PubSubController {
     @RequestMapping(value = "/", method = RequestMethod.POST)
     public ResponseEntity<String> receiveMessage(@RequestBody Body body) {
       // Get PubSub message from request body.
       Body.Message message = body.getMessage();
       if (message == null) {
         String msg = "Bad Request: invalid Pub/Sub message format";
         System.out.println(msg);
         return new ResponseEntity<>(msg, HttpStatus.BAD_REQUEST);
       }
   
       // Decode the Pub/Sub message.
       String pubSubMessage = message.getData();
       JsonObject data;
       try {
         String decodedMessage = new String(Base64.getDecoder().decode(pubSubMessage));
         data = JsonParser.parseString(decodedMessage).getAsJsonObject();
       } catch (Exception e) {
         String msg = "Error: Invalid Pub/Sub message: data property is not valid base64 encoded JSON";
         System.out.println(msg);
         return new ResponseEntity<>(msg, HttpStatus.BAD_REQUEST);
       }
   
       // Validate the message is a Cloud Storage event.
       if (data.get("name") == null || data.get("bucket") == null) {
         String msg = "Error: Invalid Cloud Storage notification: expected name and bucket properties";
         System.out.println(msg);
         return new ResponseEntity<>(msg, HttpStatus.BAD_REQUEST);
       }
   
       try {
         ImageMagick.blurOffensiveImages(data);
       } catch (Exception e) {
         String msg = String.format("Error: Blurring image: %s", e.getMessage());
         System.out.println(msg);
         return new ResponseEntity<>(msg, HttpStatus.INTERNAL_SERVER_ERROR);
       }
       return new ResponseEntity<>(HttpStatus.OK);
     }
   }

下载完整示例

要检索完整的图片处理代码示例以供使用，请执行以下操作：

1. 将示例应用代码库克隆到本地机器：

   Node.js

   git clone https://github.com/GoogleCloudPlatform/nodejs-docs-samples.git

   或者，您也可以下载该示例的 zip 文件并将其解压缩。

   Python

   git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git

   或者，您也可以下载该示例的 zip 文件并将其解压缩。

   Go

   git clone https://github.com/GoogleCloudPlatform/golang-samples.git

   或者，您也可以下载该示例的 zip 文件并将其解压缩。

   Java

   git clone https://github.com/GoogleCloudPlatform/java-docs-samples.git

   或者，您也可以下载该示例的 zip 文件并将其解压缩。

2. 切换到包含 Cloud Run 示例代码的目录：

   Node.js

   cd nodejs-docs-samples/run/image-processing/

   Python

   cd python-docs-samples/run/image-processing/

   Go

   cd golang-samples/run/image-processing/

   Java

   cd java-docs-samples/run/image-processing/

交付代码

交付代码包括三个步骤：使用 Cloud Build 构建容器映像、将容器映像上传到 Artifact Registry，以及将容器映像部署到 Cloud Run。

如需交付代码，请执行以下操作：

1. 构建容器并将其发布到 Artifact Registry 上：

   Node.js

   gcloud builds submit --tag REGION-docker.pkg.dev/PROJECT_ID
   /REPOSITORY/pubsub

   其中，pubsub 是服务的名称。

   您需要将其中的：

   • 将 PROJECT_ID 替换为您的 Google Cloud 项目 ID
   • 将 REPOSITORY 替换为 Artifact Registry 制品库的名称。
   • 将 REGION 替换为要用于 Artifact Registry 制品库的 Google Cloud 区域。

   成功完成后，您将看到一条包含 ID、创建时间和映像名称的 SUCCESS 消息。 该映像存储在 Artifact Registry 中，并可根据需要重复使用。

   Python

   gcloud builds submit --tag REGION-docker.pkg.dev/PROJECT_ID
   /REPOSITORY/pubsub

   其中，pubsub 是服务的名称。

   您需要将其中的：

   • 将 PROJECT_ID 替换为您的 Google Cloud 项目 ID
   • 将 REPOSITORY 替换为 Artifact Registry 制品库的名称。
   • 将 REGION 替换为要用于 Artifact Registry 制品库的 Google Cloud 区域。

   成功完成后，您将看到一条包含 ID、创建时间和映像名称的 SUCCESS 消息。 该映像存储在 Artifact Registry 中，并可根据需要重复使用。

   Go

   gcloud builds submit --tag REGION-docker.pkg.dev/PROJECT_ID
   /REPOSITORY/pubsub

   其中，pubsub 是服务的名称。

   您需要将其中的：

   • 将 PROJECT_ID 替换为您的 Google Cloud 项目 ID
   • 将 REPOSITORY 替换为 Artifact Registry 制品库的名称。
   • 将 REGION 替换为要用于 Artifact Registry 制品库的 Google Cloud 区域。

   成功完成后，您将看到一条包含 ID、创建时间和映像名称的 SUCCESS 消息。 该映像存储在 Artifact Registry 中，并可根据需要重复使用。

   Java

   此示例使用 Jib 利用常见 Java 工具构建 Docker 映像。无需编写 Dockerfile 或安装 Docker，Jib 便可以优化容器构建。详细了解如何使用 Jib 构建 Java 容器。

   1. 使用 Dockerfile，配置并构建已安装系统软件包的基本映像，以覆盖 Jib 的默认基本映像：

      # Use eclipse-temurin for base image.
      # It's important to use JDK 8u191 or above that has container support enabled.
      # https://hub.docker.com/_/eclipse-temurin/
      # https://docs.docker.com/develop/develop-images/multistage-build/#use-multi-stage-builds
      FROM eclipse-temurin:17.0.12_7-jre
      
      # Install Imagemagick into the container image.
      # For more on system packages review the system packages tutorial.
      # https://cloud.google.com/run/docs/tutorials/system-packages#dockerfile
      RUN set -ex; \
        apt-get -y update; \
        apt-get -y install imagemagick; \
        rm -rf /var/lib/apt/lists/*

      gcloud builds submit --tag REGION-docker.pkg.dev/PROJECT_ID
      /REPOSITORY/imagemagick

      您需要进行如下替换：

      • 将 PROJECT_ID 替换为您的 Google Cloud 项目 ID
      • 将 REPOSITORY 替换为 Artifact Registry 制品库的名称。
      • 将 REGION 替换为要用于 Artifact Registry 制品库的 Google Cloud 区域。

   2. 使用 gcloud 凭据帮助程序，授权 Docker 推送到您的 Artifact Registry。

      gcloud auth configure-docker

   3. 使用 Jib 构建最终容器并在 Artifact Registry 上发布：

      <plugin>
        <groupId>com.google.cloud.tools</groupId>
        <artifactId>jib-maven-plugin</artifactId>
        <version>3.4.0</version>
        <configuration>
          <from>
            <image>gcr.io/PROJECT_ID/imagemagick</image>
          </from>
          <to>
            <image>gcr.io/PROJECT_ID/pubsub</image>
          </to>
        </configuration>
      </plugin>
      

      mvn compile jib:build \
        -Dimage=REGION-docker.pkg.dev/PROJECT_ID
      /REPOSITORY/pubsub \
        -Djib.from.image=REGION-docker.pkg.dev/PROJECT_ID
      /REPOSITORY/imagemagick

      您需要进行如下替换：

      • 将 PROJECT_ID 替换为您的 Google Cloud 项目 ID
      • 将 REPOSITORY 替换为 Artifact Registry 制品库的名称。
      • 将 REGION 替换为要用于 Artifact Registry 制品库的 Google Cloud 区域。

2. 使用您在使用 Cloud Pub/Sub 教程中所用的同一服务名称，运行以下命令来部署您的服务：

   Node.js

   gcloud run deploy pubsub-tutorial --image REGION-docker.pkg.dev/PROJECT_ID
   /REPOSITORY/pubsub --set-env-vars=BLURRED_BUCKET_NAME=BLURRED_BUCKET_NAME --no-allow-unauthenticated

   Python

   gcloud run deploy pubsub-tutorial --image REGION-docker.pkg.dev/PROJECT_ID
   /REPOSITORY/pubsub --set-env-vars=BLURRED_BUCKET_NAME=BLURRED_BUCKET_NAME --no-allow-unauthenticated

   Go

   gcloud run deploy pubsub-tutorial --image REGION-docker.pkg.dev/PROJECT_ID
   /REPOSITORY/pubsub --set-env-vars=BLURRED_BUCKET_NAME=BLURRED_BUCKET_NAME --no-allow-unauthenticated

   Java

   gcloud run deploy pubsub-tutorial --image REGION-docker.pkg.dev/PROJECT_ID
   /REPOSITORY/pubsub --set-env-vars=BLURRED_BUCKET_NAME=BLURRED_BUCKET_NAME --memory 512M --no-allow-unauthenticated

   其中，pubsub 是容器名称，pubsub-tutorial 是服务名称。请注意，容器映像会部署到您之前在设置 gcloud 默认值下配置的服务和区域 (Cloud Run)。您需要将其中的：

   • 将 PROJECT_ID 替换为您的 Google Cloud 项目 ID
   • 将 REPOSITORY 替换为 Artifact Registry 制品库的名称。
   • 将 REGION 替换为要用于 Artifact Registry 制品库的 Google Cloud 区域。
   • 将 BLURRED_BUCKET_NAME 替换为您之前创建来接收经过模糊处理的图片的 Cloud Storage 存储桶，以设置环境变量。

   --no-allow-unauthenticated 标志限制对服务未经身份验证的访问。通过将该服务保留为不公开状态，您可以依赖 Cloud Run 的自动 Pub/Sub 集成功能对请求进行身份验证。如需详细了解如何进行此项配置，请参阅与 Pub/Sub 集成。如需详细了解基于 IAM 的身份验证，请参阅管理访问权限。

   等待部署完成，这可能需要半分钟左右的时间。 成功完成时，命令行会显示服务网址。

开启 Cloud Storage 通知

配置 Cloud Storage，以便在上传或更改文件（称为对象）时将消息发布到 Pub/Sub 主题。将通知发送到之前创建的主题，以便上传任何新文件都会调用该服务。

gcloud storage service-agent --project=PROJECT_ID
gcloud storage buckets notifications create gs://INPUT_BUCKET_NAME --topic=myRunTopic --payload-format=json

data "google_storage_project_service_account" "gcs_account" {}

resource "google_pubsub_topic_iam_binding" "binding" {
  topic   = google_pubsub_topic.default.name
  role    = "roles/pubsub.publisher"
  members = ["serviceAccount:${data.google_storage_project_service_account.gcs_account.email_address}"]
}

resource "google_storage_notification" "notification" {
  bucket         = google_storage_bucket.imageproc_input.name
  payload_format = "JSON_API_V1"
  topic          = google_pubsub_topic.default.id
  depends_on     = [google_pubsub_topic_iam_binding.binding]
}

试试看

1. 上传一张令人反感的图片，比如这张肉食僵尸图片：

   curl -o zombie.jpg https://cdn.pixabay.com/photo/2015/09/21/14/24/zombie-949916_960_720.jpg
   gcloud storage cp zombie.jpg gs://INPUT_BUCKET_NAME

   其中，INPUT_BUCKET_NAME 是您之前为了上传图片而创建的 Cloud Storage 存储桶。

2. 进入服务日志：

   1. 前往 Google Cloud 控制台中的 Cloud Run 页面
   2. 点击 pubsub-tutorial 服务。
   3. 选择日志标签页。您可能需要等待一些时间才能看到日志。 如果您没有立即看到日志，请稍等片刻再检查一次。

3. 查找 Blurred image: zombie.png 消息。

4. 您可以在之前创建的 BLURRED_BUCKET_NAME Cloud Storage 存储桶中查看经过模糊处理的图片：在 Google Cloud 控制台中的 Cloud Storage 页面找到该存储桶