Tutorial su ImageMagick (1ª generazione.)

Questo tutorial mostra l'utilizzo delle funzioni Cloud Run, dell'API Cloud Vision e di ImageMagick per rilevare e sfocare le immagini offensive caricate in un bucket Cloud Storage.

Obiettivi

  • Esegui il deployment di una funzione Cloud Run in background attivata da Cloud Storage.
  • Utilizza l'API Cloud Vision per rilevare contenuti violenti o per adulti.
  • Utilizza ImageMagick per sfocare le immagini offensive.
  • Testa la funzione caricando un'immagine di uno zombie mangiacarne.

Costi

In questo documento vengono utilizzati i seguenti componenti fatturabili di Google Cloud:

  • Cloud Run functions
  • Cloud Storage
  • Cloud Vision

Per generare una stima dei costi in base all'utilizzo previsto, utilizza il calcolatore prezzi.

I nuovi utenti di Google Cloud potrebbero avere diritto a una prova senza costi.

Prima di iniziare

  1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

  2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  3. Verify that billing is enabled for your Google Cloud project.

  4. Enable the Cloud Functions, Cloud Build, Cloud Storage, and Cloud Vision APIs.

    Roles required to enable APIs

    To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

    Enable the APIs

  5. Install the Google Cloud CLI.

  6. Se utilizzi un provider di identità (IdP) esterno, devi prima accedere a gcloud CLI con la tua identità federata.

  7. Per inizializzare gcloud CLI, esegui questo comando: 

    gcloud init

  8. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

    Roles required to select or create a project

    • Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
    • Create a project: To create a project, you need the Project Creator (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

    Go to project selector

  9. Verify that billing is enabled for your Google Cloud project.

  10. Enable the Cloud Functions, Cloud Build, Cloud Storage, and Cloud Vision APIs.

    Roles required to enable APIs

    To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains the serviceusage.services.enable permission. Learn how to grant roles.

    Enable the APIs

  11. Install the Google Cloud CLI.

  12. Se utilizzi un provider di identità (IdP) esterno, devi prima accedere a gcloud CLI con la tua identità federata.

  13. Per inizializzare gcloud CLI, esegui questo comando: 

    gcloud init

    14. Se hai già installato gcloud CLI, aggiornala eseguendo il seguente comando:

    gcloud components update
  14. Prepara l'ambiente di sviluppo.

    15. Visualizzare il flusso di dati

    Il flusso di dati nell'applicazione tutorial ImageMagick prevede diversi passaggi:

    1. Un'immagine viene caricata in un bucket Cloud Storage.
    2. La funzione analizza l'immagine utilizzando l'API Vision.
    3. Se vengono rilevati contenuti violenti o per adulti, la funzione utilizza ImageMagick per sfocare l'immagine.
    4. L'immagine sfocata viene caricata in un altro bucket Cloud Storage per l'utilizzo.

    Preparazione della richiesta

    1. Crea un bucket Cloud Storage per caricare le immagini, dove YOUR_INPUT_BUCKET_NAME è un nome di bucket univoco a livello globale:

      gcloud storage buckets create gs://YOUR_INPUT_BUCKET_NAME

    2. Crea un bucket Cloud Storage per ricevere le immagini sfocate, dove YOUR_OUTPUT_BUCKET_NAME è un nome di bucket univoco a livello globale:

      gcloud storage buckets create gs://YOUR_OUTPUT_BUCKET_NAME

    3. Clona il repository dell'app di esempio sulla tua macchina locale:

      Node.js 

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

      In alternativa, puoi scaricare il campione come file ZIP ed estrarlo.

      Python 

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

      In alternativa, puoi scaricare il campione come file ZIP ed estrarlo.

      Vai 

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

      In alternativa, puoi scaricare il campione come file ZIP ed estrarlo.

      Java 

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

      In alternativa, puoi scaricare il campione come file ZIP ed estrarlo.

      Ruby 

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

      In alternativa, puoi scaricare il campione come file ZIP ed estrarlo.

    4. Passa alla directory che contiene il codice campione delle funzioni Cloud Run:

      Node.js 

      cd nodejs-docs-samples/functions/imagemagick/

      Python 

      cd python-docs-samples/functions/imagemagick/

      Vai 

      cd golang-samples/functions/imagemagick/

      Java 

      cd java-docs-samples/functions/imagemagick/

      Ruby 

      cd ruby-docs-samples/functions/imagemagick/

    Nozioni di base sul codice

    Importazione delle dipendenze

    L'applicazione deve importare diverse dipendenze per interagire con i serviziGoogle Cloud , ImageMagick e il file system:

    Node.js 

    const gm = require('gm').subClass({imageMagick: true});
const fs = require('fs').promises;
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 

    import os
import tempfile

from google.cloud import storage, vision
from wand.image import Image

storage_client = storage.Client()
vision_client = vision.ImageAnnotatorClient()

    Go 

    
// 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 

    

import com.google.cloud.functions.BackgroundFunction;
import com.google.cloud.functions.Context;
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 functions.eventpojos.GcsEvent;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.List;
import java.util.logging.Level;
import java.util.logging.Logger;

public class ImageMagick implements BackgroundFunction<GcsEvent> {

  private static Storage storage = StorageOptions.getDefaultInstance().getService();
  private static final String BLURRED_BUCKET_NAME = System.getenv("BLURRED_BUCKET_NAME");
  private static final Logger logger = Logger.getLogger(ImageMagick.class.getName());
}

    Ruby 

    require "functions_framework"

FunctionsFramework.on_startup do
  set_global :storage_client do
    require "google/cloud/storage"
    Google::Cloud::Storage.new
  end

  set_global :vision_client do
    require "google/cloud/vision"
    Google::Cloud::Vision.image_annotator
  end
end

    Analisi delle immagini

    La seguente funzione viene richiamata quando un'immagine viene caricata nel bucket Cloud Storage che hai creato per archiviare le immagini. La funzione utilizza l'API Vision per rilevare contenuti violenti o per adulti nelle immagini caricate.

    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 await 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 

    # Blurs uploaded images that are flagged as Adult or Violence.
def blur_offensive_images(data, context):
    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(gcs_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 

    @Override
// Blurs uploaded images that are flagged as Adult or Violence.
public void accept(GcsEvent event, Context context) {
  // Validate parameters
  if (event.getBucket() == null || event.getName() == null) {
    logger.severe("Error: Malformed GCS event.");
    return;
  }

  BlobInfo blobInfo = BlobInfo.newBuilder(event.getBucket(), event.getName()).build();

  // Construct URI to GCS bucket and file.
  String gcsPath = String.format("gs://%s/%s", event.getBucket(), event.getName());
  logger.info(String.format("Analyzing %s", event.getName()));

  // Construct request.
  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();
  List<AnnotateImageRequest> requests = List.of(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()) {
        logger.info(String.format("Error: %s", res.getError().getMessage()));
        return;
      }
      // Get Safe Search Annotations
      SafeSearchAnnotation annotation = res.getSafeSearchAnnotation();
      if (annotation.getAdultValue() == 5 || annotation.getViolenceValue() == 5) {
        logger.info(String.format("Detected %s as inappropriate.", event.getName()));
        blur(blobInfo);
      } else {
        logger.info(String.format("Detected %s as OK.", event.getName()));
      }
    }
  } catch (IOException e) {
    logger.log(Level.SEVERE, "Error with Vision API: " + e.getMessage(), e);
  }
}

    Ruby 

    # Blurs uploaded images that are flagged as Adult or Violence.
FunctionsFramework.cloud_event "blur_offensive_images" do |event|
  # Event-triggered Ruby functions receive a CloudEvents::Event::V1 object.
  # See https://cloudevents.github.io/sdk-ruby/latest/CloudEvents/Event/V1.html
  # The storage event payload can be obtained from the event data.
  payload = event.data
  file_name = payload["name"]
  bucket_name = payload["bucket"]

  # Ignore already-blurred files
  if file_name.start_with? "blurred-"
    logger.info "The image #{file_name} is already blurred."
    return
  end

  # Get image annotations from the Vision service
  logger.info "Analyzing #{file_name}."
  gs_uri = "gs://#{bucket_name}/#{file_name}"
  result = global(:vision_client).safe_search_detection image: gs_uri
  annotation = result.responses.first.safe_search_annotation

  # Respond to annotations by possibly blurring the image
  if annotation.adult == :VERY_LIKELY || annotation.violence == :VERY_LIKELY
    logger.info "The image #{file_name} was detected as inappropriate."
    blur_image bucket_name, file_name
  else
    logger.info "The image #{file_name} was detected as OK."
  end
end

    Sfocatura delle immagini

    La seguente funzione viene chiamata quando vengono rilevati contenuti violenti o per adulti in un'immagine caricata. La funzione scarica l'immagine offensiva, utilizza ImageMagick per sfocarla e poi la carica sopra l'immagine originale.

    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.
  return fs.unlink(tempLocalPath);
};

    Python 

    # Blurs the given file using ImageMagick.
def __blur_image(current_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.blur(radius=0, sigma=16)
        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.
private 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.
  Path upload = Paths.get("/tmp/", "blurred-" + fileName);
  List<String> args = List.of("convert", download.toString(), "-blur", "0x8", upload.toString());
  try {
    ProcessBuilder pb = new ProcessBuilder(args);
    Process process = pb.start();
    process.waitFor();
  } catch (Exception e) {
    logger.info(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();

  byte[] blurredFile = Files.readAllBytes(upload);
  storage.create(blurredBlobInfo, blurredFile);
  logger.info(
      String.format("Blurred image uploaded to: gs://%s/%s", BLURRED_BUCKET_NAME, fileName));

  // Remove images from fileSystem
  Files.delete(download);
  Files.delete(upload);
}

    Ruby 

    require "tempfile"
require "mini_magick"

# Blurs the given file using ImageMagick.
def blur_image bucket_name, file_name
  tempfile = Tempfile.new
  begin
    # Download the image file
    bucket = global(:storage_client).bucket bucket_name
    file = bucket.file file_name
    file.download tempfile
    tempfile.close

    # Blur the image using ImageMagick
    MiniMagick::Image.new tempfile.path do |image|
      image.blur "0x16"
    end
    logger.info "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 and tell your function
    # to ignore files marked as blurred (e.g. those with a "blurred" prefix.)
    blur_bucket_name = ENV["BLURRED_BUCKET_NAME"]
    blur_bucket = global(:storage_client).bucket blur_bucket_name
    blur_bucket.create_file tempfile.path, file_name
    logger.info "Blurred image uploaded to gs://#{blur_bucket_name}/#{file_name}"
  ensure
    # Ruby will remove the temp file when garbage collecting the object,
    # but it is good practice to remove it explicitly.
    tempfile.unlink
  end
end

    Deployment della funzione

    Per eseguire il deployment della funzione con un trigger di archiviazione, esegui questo comando nella directory che contiene il codice campione (o, nel caso di Java, il file pom.xml):

    Node.js

    gcloud functions deploy blurOffensiveImages \
--no-gen2 \
--runtime=RUNTIME \
--trigger-bucket=YOUR_INPUT_BUCKET_NAME \
--set-env-vars=BLURRED_BUCKET_NAME=YOUR_OUTPUT_BUCKET_NAME

    Python

    gcloud functions deploy blur_offensive_images \
--no-gen2 \
--runtime=RUNTIME \
--trigger-bucket=YOUR_INPUT_BUCKET_NAME \
--set-env-vars=BLURRED_BUCKET_NAME=YOUR_OUTPUT_BUCKET_NAME

    Vai

    gcloud functions deploy BlurOffensiveImages \
--no-gen2 \
--runtime=RUNTIME \
--trigger-bucket=YOUR_INPUT_BUCKET_NAME \
--set-env-vars=BLURRED_BUCKET_NAME=YOUR_OUTPUT_BUCKET_NAME

    Java

    gcloud functions deploy java-blur-function \
--no-gen2 \
--entry-point=functions.ImageMagick \
--runtime=RUNTIME \
--memory 512MB \
--trigger-bucket=YOUR_INPUT_BUCKET_NAME \
--set-env-vars=BLURRED_BUCKET_NAME=YOUR_OUTPUT_BUCKET_NAME

    C#

    gcloud functions deploy csharp-blur-function \
--no-gen2 \
--entry-point=ImageMagick.Function \
--runtime=RUNTIME \
--trigger-bucket=YOUR_INPUT_BUCKET_NAME \
--set-env-vars=BLURRED_BUCKET_NAME=YOUR_OUTPUT_BUCKET_NAME

    Ruby

    gcloud functions deploy blur_offensive_images \
--no-gen2 \
--runtime=RUNTIME \
--trigger-bucket=YOUR_INPUT_BUCKET_NAME \
--set-env-vars=BLURRED_BUCKET_NAME=YOUR_OUTPUT_BUCKET_NAME

    Sostituisci quanto segue:

    • RUNTIME: un runtime basato su Ubuntu 18.04 (i runtime successivi non includono il supporto di ImageMagick).
    • YOUR_INPUT_BUCKET_NAME: il nome del bucket Cloud Storage per il caricamento delle immagini.
    • YOUR_OUTPUT_BUCKET_NAME: il nome del bucket in cui devono essere salvate le immagini sfocate.

    Per questo esempio specifico, non includere gs:// nei nomi dei bucket nel comando deploy.

    Caricamento di un'immagine

    1. Carica un'immagine offensiva, ad esempio questa immagine di uno zombie mangiacarne:

      gcloud storage cp zombie.jpg gs://YOUR_INPUT_BUCKET_NAME

      dove YOUR_INPUT_BUCKET_NAME è il bucket Cloud Storage che hai creato in precedenza per caricare le immagini.

    2. Guarda i log per assicurarti che le esecuzioni siano state completate:

      gcloud functions logs read --limit 100

    3. Puoi visualizzare le immagini sfocate nel bucket YOUR_OUTPUT_BUCKET_NAME Cloud Storage che hai creato in precedenza.

    Esegui la pulizia

    Per evitare che al tuo account Google Cloud vengano addebitati costi relativi alle risorse utilizzate in questo tutorial, elimina il progetto che contiene le risorse oppure mantieni il progetto ed elimina le singole risorse.

    Elimina il progetto

    Il modo più semplice per eliminare la fatturazione è eliminare il progetto creato per il tutorial.

    Per eliminare il progetto:

    1. In the Google Cloud console, go to the Manage resources page.

      Go to Manage resources

    2. In the project list, select the project that you want to delete, and then click Delete.
    3. In the dialog, type the project ID, and then click Shut down to delete the project.

    Eliminazione della funzione

    L'eliminazione delle funzioni Cloud Run non rimuove le risorse archiviate in Cloud Storage.

    Per eliminare la funzione di cui hai eseguito il deployment in questo tutorial, esegui questo comando:

    Node.js 

    gcloud functions delete blurOffensiveImages

    Python 

    gcloud functions delete blur_offensive_images

    Vai 

    gcloud functions delete BlurOffensiveImages

    Java 

    gcloud functions delete java-blur-function

    Ruby 

    gcloud functions delete blur_offensive_images

    Puoi anche eliminare le funzioni Cloud Run dalla consoleGoogle Cloud .