ImageMagick Tutorial (1st gen)

---

Note: This content applies only to Cloud Run functions (1st gen)—formerly Cloud Functions (1st gen).

For the Cloud Run functions version of this document, see the ImageMagick Tutorial.

This tutorial demonstrates using Cloud Run functions, the Cloud Vision API, and ImageMagick to detect and blur offensive images that get uploaded to a Cloud Storage bucket.

Note: While this tutorial demonstrates blurring images, ImageMagick can do much more than just blur images. You might also consider using the Node.js wrapper for ImageMagick found on npm.

Objectives

• Deploy a storage-triggered Background Cloud Run function.
• Use the Vision API to detect violent or adult content.
• Use ImageMagick to blur offensive images.
• Test the function by uploading an image of a flesh-eating zombie.

Costs

In this document, you use the following billable components of Google Cloud:

• Cloud Run functions
• Cloud Storage
• Cloud Vision

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

Before you begin

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.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

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

   Enable the APIs

5. Install the Google Cloud CLI.

6. To initialize the gcloud CLI, run the following command:

   gcloud init

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

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

8. Make sure that billing is enabled for your Google Cloud project.

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

   Enable the APIs

10. Install the Google Cloud CLI.

11. To initialize the gcloud CLI, run the following command:

    gcloud init

If you already have the gcloud CLI installed, update it by running the following command:

gcloud components update

12. Prepare your development environment.

    Node.js

    Go to the Node.js setup guide

    Python

    Go to the Python setup guide

    Go

    Go to the Go setup guide

    Java

    Go to the Java setup guide

    C#

    Go to the .NET setup guide

    Ruby

    Go to the Ruby setup guide

    PHP

    Go to Using PHP on Google Cloud

Visualizing the flow of data

The flow of data in the ImageMagick tutorial application involves several steps:

1. An image is uploaded to a Cloud Storage bucket.
2. The function analyzes the image using the Vision API.
3. If violent or adult content is detected, the function uses ImageMagick to blur the image.
4. The blurred image is uploaded to another Cloud Storage bucket for use.

Preparing the application

1. Create a Cloud Storage bucket for uploading images, where YOUR_INPUT_BUCKET_NAME is a globally unique bucket name:

   gcloud storage buckets create gs://YOUR_INPUT_BUCKET_NAME
   

2. Create a Cloud Storage bucket to receive blurred images, where YOUR_OUTPUT_BUCKET_NAME is a globally unique bucket name:

   gcloud storage buckets create gs://YOUR_OUTPUT_BUCKET_NAME
   

3. Clone the sample app repository to your local machine:

   Node.js

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

   Alternatively, you can download the sample as a zip file and extract it.

   Python

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

   Alternatively, you can download the sample as a zip file and extract it.

   Go

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

   Alternatively, you can download the sample as a zip file and extract it.

   Java

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

   Alternatively, you can download the sample as a zip file and extract it.

   C#

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

   Alternatively, you can download the sample as a zip file and extract it.

   Ruby

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

   Alternatively, you can download the sample as a zip file and extract it.

   PHP

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

   Alternatively, you can download the sample as a zip file and extract it.

4. Change to the directory that contains the Cloud Run functions sample code:

   Node.js

   cd nodejs-docs-samples/functions/imagemagick/

   Python

   cd python-docs-samples/functions/imagemagick/

   Go

   cd golang-samples/functions/imagemagick/

   Java

   cd java-docs-samples/functions/imagemagick/

   C#

   cd dotnet-docs-samples/functions/imagemagick/

   Ruby

   cd ruby-docs-samples/functions/imagemagick/

   PHP

   cd php-docs-samples/functions/imagemagick/

Understanding the code

Importing dependencies

The application must import several dependencies in order to interact with Google Cloud services, ImageMagick, and the file system:

Note: ImageMagick and its command-line tool convert are included by default within the Cloud Run functions execution environment for most of the runtimes. For PHP, you may need to do some manual configuration. Note that Cloud Run functions does not support installing custom system-level packages.

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());
}

C#

using CloudNative.CloudEvents;
using Google.Cloud.Functions.Framework;
using Google.Cloud.Functions.Hosting;
using Google.Cloud.Storage.V1;
using Google.Cloud.Vision.V1;
using Google.Events.Protobuf.Cloud.Storage.V1;
using Grpc.Core;
using Microsoft.AspNetCore.Hosting;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Logging;
using System;
using System.Diagnostics;
using System.IO;
using System.Threading;
using System.Threading.Tasks;

namespace ImageMagick;

// Dependency injection configuration, executed during server startup.
public class Startup : FunctionsStartup
{
    public override void ConfigureServices(WebHostBuilderContext context, IServiceCollection services) =>
        services
            .AddSingleton(ImageAnnotatorClient.Create())
            .AddSingleton(StorageClient.Create());
}

[FunctionsStartup(typeof(Startup))]
public class Function : ICloudEventFunction<StorageObjectData>
{
    /// <summary>
    /// The bucket to store blurred images in. An alternative to using environment variables here would be to
    /// fetch it from IConfiguration.
    /// </summary>
    private static readonly string s_blurredBucketName = Environment.GetEnvironmentVariable("BLURRED_BUCKET_NAME");

    private readonly ImageAnnotatorClient _visionClient;
    private readonly StorageClient _storageClient;
    private readonly ILogger _logger;

    public Function(ImageAnnotatorClient visionClient, StorageClient storageClient, ILogger<Function> logger) =>
        (_visionClient, _storageClient, _logger) = (visionClient, storageClient, logger);

}

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

PHP

use Google\CloudFunctions\CloudEvent;
use Google\Cloud\Storage\StorageClient;
use Google\Cloud\Vision\V1\ImageAnnotatorClient;
use Google\Cloud\Vision\V1\Likelihood;
use Google\Rpc\Code;

Analyzing images

The following function is invoked when an image is uploaded to the Cloud Storage bucket you created for storing images. The function uses the Vision API to detect violent or adult content in uploaded images.

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);
  }
}

C#

public async Task HandleAsync(CloudEvent cloudEvent, StorageObjectData data, CancellationToken cancellationToken)
{
    // Validate parameters
    if (data.Bucket is null || data.Name is null)
    {
        _logger.LogError("Malformed GCS event.");
        return;
    }

    // Construct URI to GCS bucket and file.
    string gcsUri = $"gs://{data.Bucket}/{data.Name}";
    _logger.LogInformation("Analyzing {uri}", gcsUri);

    // Perform safe search detection using the Vision API.
    Image image = Image.FromUri(gcsUri);
    SafeSearchAnnotation annotation;
    try
    {
        annotation = await _visionClient.DetectSafeSearchAsync(image);
    }
    // If the call to the Vision API fails, log the error but let the function complete normally.
    // If the exceptions weren't caught (and just propagated) the event would be retried.
    // See the "Best Practices" section in the documentation for more details about retry.
    catch (AnnotateImageException e)
    {
        _logger.LogError(e, "Vision API reported an error while performing safe search detection");
        return;
    }
    catch (RpcException e)
    {
        _logger.LogError(e, "Error communicating with the Vision API");
        return;
    }

    if (annotation.Adult == Likelihood.VeryLikely || annotation.Violence == Likelihood.VeryLikely)
    {
        _logger.LogInformation("Detected {uri} as inappropriate.", gcsUri);
        await BlurImageAsync(data, cancellationToken);
    }
    else
    {
        _logger.LogInformation("Detected {uri} as OK.", gcsUri);
    }
}

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

PHP

function blurOffensiveImages(CloudEvent $cloudevent): void
{
    $log = fopen(getenv('LOGGER_OUTPUT') ?: 'php://stderr', 'wb');

    $storage = new StorageClient();
    $data = $cloudevent->getData();

    $file = $storage->bucket($data['bucket'])->object($data['name']);
    $filePath = 'gs://' . $data['bucket'] . '/' . $data['name'];
    fwrite($log, 'Analyzing ' . $filePath . PHP_EOL);

    $annotator = new ImageAnnotatorClient();
    $storage = new StorageClient();

    try {
        $response = $annotator->safeSearchDetection($filePath);

        // Handle error
        if ($response->hasError()) {
            $code = Code::name($response->getError()->getCode());
            $message = $response->getError()->getMessage();
            fwrite($log, sprintf('%s: %s' . PHP_EOL, $code, $message));
            return;
        }

        $annotation = $response->getSafeSearchAnnotation();

        $isInappropriate =
            $annotation->getAdult() === Likelihood::VERY_LIKELY ||
            $annotation->getViolence() === Likelihood::VERY_LIKELY;

        if ($isInappropriate) {
            fwrite($log, 'Detected ' . $data['name'] . ' as inappropriate.' . PHP_EOL);
            $blurredBucketName = getenv('BLURRED_BUCKET_NAME');

            blurImage($log, $file, $blurredBucketName);
        } else {
            fwrite($log, 'Detected ' . $data['name'] . ' as OK.' . PHP_EOL);
        }
    } catch (Exception $e) {
        fwrite($log, 'Failed to analyze ' . $data['name'] . PHP_EOL);
        fwrite($log, $e->getMessage() . PHP_EOL);
    }
}

Blurring images

The following function is called when violent or adult content is detected in an uploaded image. The function downloads the offensive image, uses ImageMagick to blur the image, and then uploads the blurred image over the original image.

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.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.
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);
}

C#

/// <summary>
/// Downloads the Storage object specified by <paramref name="data"/>,
/// blurs it using ImageMagick, and uploads it to the "blurred" bucket.
/// </summary>
private async Task BlurImageAsync(StorageObjectData data, CancellationToken cancellationToken)
{
    // Download image
    string originalImageFile = Path.GetTempFileName();
    using (Stream output = File.Create(originalImageFile))
    {
        await _storageClient.DownloadObjectAsync(data.Bucket, data.Name, output, cancellationToken: cancellationToken);
    }

    // Construct the ImageMagick command
    string blurredImageFile = Path.GetTempFileName();
    // Command-line arguments for ImageMagick.
    // Paths are wrapped in quotes in case they contain spaces.
    string arguments = $"\"{originalImageFile}\" -blur 0x8, \"{blurredImageFile}\"";

    // Run the ImageMagick command line tool ("convert").
    Process process = Process.Start("convert", arguments);
    // Process doesn't expose a way of asynchronously waiting for completion.
    // See https://stackoverflow.com/questions/470256 for examples of how
    // this can be achieved using events, but for the sake of brevity,
    // this sample just waits synchronously.
    process.WaitForExit();

    // If ImageMagick failed, log the error but complete normally to avoid retrying.
    if (process.ExitCode != 0)
    {
        _logger.LogError("ImageMagick exited with code {exitCode}", process.ExitCode);
        return;
    }

    // Upload image to blurred bucket.
    using (Stream input = File.OpenRead(blurredImageFile))
    {
        await _storageClient.UploadObjectAsync(
            s_blurredBucketName, data.Name, data.ContentType, input, cancellationToken: cancellationToken);
    }

    string uri = $"gs://{s_blurredBucketName}/{data.Name}";
    _logger.LogInformation("Blurred image uploaded to: {uri}", uri);

    // Remove images from the file system.
    File.Delete(originalImageFile);
    File.Delete(blurredImageFile);
}

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

PHP

// Blurs the given file using ImageMagick, and uploads it to another bucket.
function blurImage(
    $log,
    Object $file,
    string $blurredBucketName
): void {
    $tempLocalPath = sys_get_temp_dir() . '/' . $file->name();

    // Download file from bucket.
    $image = new Imagick();
    try {
        $image->readImageBlob($file->downloadAsStream());
    } catch (Exception $e) {
        throw new Exception('Streaming download failed: ' . $e);
    }

    // Blur file using ImageMagick
    // (The Imagick class is from the PECL 'imagick' package)
    $image->blurImage(0, 16);

    // Stream blurred image result to a different bucket. // (This avoids re-triggering this function.)
    $storage = new StorageClient();
    $blurredBucket = $storage->bucket($blurredBucketName);

    // Upload the Blurred image back into the bucket.
    $gcsPath = 'gs://' . $blurredBucketName . '/' . $file->name();
    try {
        $blurredBucket->upload($image->getImageBlob(), [
            'name' => $file->name()
        ]);
        fwrite($log, 'Streamed blurred image to: ' . $gcsPath . PHP_EOL);
    } catch (Exception $e) {
        throw new Exception(
            sprintf(
                'Unable to stream blurred image to %s: %s',
                $gcsPath,
                $e->getMessage()
            )
        );
    }
}

Deploying the function

Note: You must deploy the function with a runtime that is based on Ubuntu 18.04. Later runtimes don't include support for ImageMagick.

To deploy your function with a storage trigger, run the following command in the directory that contains the sample code (or in the case of Java, the pom.xml file):

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

Go

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

PHP

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

Replace the following:

• RUNTIME: a runtime that is based on Ubuntu 18.04 (later runtimes don't include support for ImageMagick).
• YOUR_INPUT_BUCKET_NAME: the name of the Cloud Storage bucket for uploading images.
• YOUR_OUTPUT_BUCKET_NAME: the name of the bucket that the blurred images should be saved to.

For this particular example, do not include gs:// as part of the bucket names in the deploy command.

Uploading an image

1. Upload an offensive image, such as this image of a flesh-eating zombie:

   gcloud storage cp zombie.jpg gs://YOUR_INPUT_BUCKET_NAME
   

   where YOUR_INPUT_BUCKET_NAME is the Cloud Storage bucket you created earlier for uploading images.

2. Watch the logs to be sure the executions have completed:

   gcloud functions logs read --limit 100
   

3. You can view the blurred images in the YOUR_OUTPUT_BUCKET_NAME Cloud Storage bucket you created earlier.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Deleting the project

The easiest way to eliminate billing is to delete the project that you created for the tutorial.

To delete the project:

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for the tasks in this document, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple architectures, tutorials, or quickstarts, reusing projects can help you avoid exceeding project quota limits.

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.

Deleting the function

Deleting Cloud Run functions does not remove any resources stored in Cloud Storage.

To delete the function you deployed in this tutorial, run the following command:

Node.js

gcloud functions delete blurOffensiveImages 

Python

gcloud functions delete blur_offensive_images 

Go

gcloud functions delete BlurOffensiveImages 

Java

gcloud functions delete java-blur-function 

C#

gcloud functions delete csharp-blur-function 

Ruby

gcloud functions delete blur_offensive_images 

PHP

gcloud functions delete blurOffensiveImages 

You can also delete Cloud Run functions from the Google Cloud console.