Detect explicit content (Safe Search)

Safe Search Detection detects explicit content such as adult content or violent content within an image. This feature uses five categories (adult, spoof, medical, violence, and racy) and returns the likelihood that each is present in a given image. See the SafeSearchAnnotation page for details on these fields.

Safe Search detection requests

Set up your GCP project and authentication

If you have not created a Google Cloud Platform (GCP) project and service account credentials, do so now. Expand this section for instructions.

Sign in to your Google Account.
If you don't already have one, sign up for a new account.
Set up a GCP Console project.
Set up a project
Click to:
- Create or select a project.
- Enable the Cloud Vision API for that project.
- Create a service account.
- Download a private key as JSON.
You can view and manage these resources at any time in the GCP Console.

Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the file path of the JSON file that contains your service account key. This variable only applies to your current shell session, so if you open a new session, set the variable again.
Example: Linux or macOS

Replace [PATH] with the file path of the JSON file that contains your service account key.
```
export GOOGLE_APPLICATION_CREDENTIALS="[PATH]"
```
For example:
```
export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/service-account-file.json"
```
Example: Windows

Replace [PATH] with the file path of the JSON file that contains your service account key, and [FILE_NAME] with the filename.

With PowerShell:
```
$env:GOOGLE_APPLICATION_CREDENTIALS="[PATH]"
```
For example:
```
$env:GOOGLE_APPLICATION_CREDENTIALS="C:\Users\username\Downloads\[FILE_NAME].json"
```
With command prompt:
```
set GOOGLE_APPLICATION_CREDENTIALS=[PATH]
```
Install and initialize the Cloud SDK.

Explicit content detection on a local image

The Vision API can perform feature detection on a local image file by sending the contents of the image file as a base64 encoded string in the body of your request.

Command-line

To make a Safe Search detection request using curl from the Linux or MacOS command line, make a POST request to the https://vision.googleapis.com/v1/images:annotate endpoint and specify SAFE_SEARCH_DETECTION as the value of features.type, as shown in the following example:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
https://vision.googleapis.com/v1/images:annotate -d "{
  'requests': [
    {
      'image': {
        'content': '/9j/7QBEUGhvdG9zaG9...base64-encoded-image-content...fXNWzvDEeYxxxzj/Coa6Bax//Z'
      },
      'features': [
        {
          'type': 'SAFE_SEARCH_DETECTION'
        },
      ]
    }
  ]
}"

See the AnnotateImageRequest reference documentation for more information on configuring the request body.

If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format.

Response:

{
  "responses": [
    {
      "safeSearchAnnotation": {
        "adult": "UNLIKELY",
        "spoof": "VERY_UNLIKELY",
        "medical": "VERY_UNLIKELY",
        "violence": "LIKELY",
        "racy": "POSSIBLE"
      }
    }
  ]
}

C#

Before trying this sample, follow the C# setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API C# API reference documentation .

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// Load an image from a local file.
var image = Image.FromFile(filePath);
var client = ImageAnnotatorClient.Create();
var response = client.DetectSafeSearch(image);
Console.WriteLine("Adult: {0}", response.Adult.ToString());
Console.WriteLine("Spoof: {0}", response.Spoof.ToString());
Console.WriteLine("Medical: {0}", response.Medical.ToString());
Console.WriteLine("Violence: {0}", response.Violence.ToString());
Console.WriteLine("Racy: {0}", response.Racy.ToString());

Go

Before trying this sample, follow the Go setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Go API reference documentation .

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// detectSafeSearch gets image properties from the Vision API for an image at the given file path.
func detectSafeSearch(w io.Writer, file string) error {
	ctx := context.Background()

	client, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		return err
	}

	f, err := os.Open(file)
	if err != nil {
		return err
	}
	defer f.Close()

	image, err := vision.NewImageFromReader(f)
	if err != nil {
		return err
	}
	props, err := client.DetectSafeSearch(ctx, image, nil)
	if err != nil {
		return err
	}

	fmt.Fprintln(w, "Safe Search properties:")
	fmt.Fprintln(w, "Adult:", props.Adult)
	fmt.Fprintln(w, "Medical:", props.Medical)
	fmt.Fprintln(w, "Racy:", props.Racy)
	fmt.Fprintln(w, "Spoofed:", props.Spoof)
	fmt.Fprintln(w, "Violence:", props.Violence)

	return nil
}

Java

Before trying this sample, follow the Java setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Java API reference documentation .

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public static void detectSafeSearch(String filePath, PrintStream out) throws Exception,
    IOException {
  List<AnnotateImageRequest> requests = new ArrayList<>();

  ByteString imgBytes = ByteString.readFrom(new FileInputStream(filePath));

  Image img = Image.newBuilder().setContent(imgBytes).build();
  Feature feat = Feature.newBuilder().setType(Type.SAFE_SEARCH_DETECTION).build();
  AnnotateImageRequest request =
      AnnotateImageRequest.newBuilder().addFeatures(feat).setImage(img).build();
  requests.add(request);

  try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
    List<AnnotateImageResponse> responses = response.getResponsesList();

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

      // For full list of available annotations, see http://g.co/cloud/vision/docs
      SafeSearchAnnotation annotation = res.getSafeSearchAnnotation();
      out.printf(
          "adult: %s\nmedical: %s\nspoofed: %s\nviolence: %s\nracy: %s\n",
          annotation.getAdult(),
          annotation.getMedical(),
          annotation.getSpoof(),
          annotation.getViolence(),
          annotation.getRacy());
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Node.js API reference documentation .

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const vision = require('@google-cloud/vision');

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

/**
 * TODO(developer): Uncomment the following line before running the sample.
 */
// const fileName = 'Local image file, e.g. /path/to/image.png';

// Performs safe search detection on the local file
const [result] = await client.safeSearchDetection(fileName);
const detections = result.safeSearchAnnotation;
console.log('Safe search:');
console.log(`Adult: ${detections.adult}`);
console.log(`Medical: ${detections.medical}`);
console.log(`Spoof: ${detections.spoof}`);
console.log(`Violence: ${detections.violence}`);
console.log(`Racy: ${detections.racy}`);

PHP

Before trying this sample, follow the PHP setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API PHP API reference documentation .

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namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;

// $path = 'path/to/your/image.jpg'

function detect_safe_search($path)
{
    $imageAnnotator = new ImageAnnotatorClient();

    # annotate the image
    $image = file_get_contents($path);
    $response = $imageAnnotator->safeSearchDetection($image);
    $safe = $response->getSafeSearchAnnotation();

    $adult = $safe->getAdult();
    $medical = $safe->getMedical();
    $spoof = $safe->getSpoof();
    $violence = $safe->getViolence();
    $racy = $safe->getRacy();

    # names of likelihood from google.cloud.vision.enums
    $likelihoodName = ['UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY',
    'POSSIBLE','LIKELY', 'VERY_LIKELY'];

    printf("Adult: %s" . PHP_EOL, $likelihoodName[$adult]);
    printf("Medical: %s" . PHP_EOL, $likelihoodName[$medical]);
    printf("Spoof: %s" . PHP_EOL, $likelihoodName[$spoof]);
    printf("Violence: %s" . PHP_EOL, $likelihoodName[$violence]);
    printf("Racy: %s" . PHP_EOL, $likelihoodName[$racy]);

    $imageAnnotator->close();
}

Python

Before trying this sample, follow the Python setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Python API reference documentation .

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def detect_safe_search(path):
    """Detects unsafe features in the file."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()

    with io.open(path, 'rb') as image_file:
        content = image_file.read()

    image = vision.types.Image(content=content)

    response = client.safe_search_detection(image=image)
    safe = response.safe_search_annotation

    # Names of likelihood from google.cloud.vision.enums
    likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
                       'LIKELY', 'VERY_LIKELY')
    print('Safe search:')

    print('adult: {}'.format(likelihood_name[safe.adult]))
    print('medical: {}'.format(likelihood_name[safe.medical]))
    print('spoofed: {}'.format(likelihood_name[safe.spoof]))
    print('violence: {}'.format(likelihood_name[safe.violence]))
    print('racy: {}'.format(likelihood_name[safe.racy]))

Ruby

Before trying this sample, follow the Ruby setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Ruby API reference documentation .

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# image_path = "Path to local image file, eg. './image.png'"

require "google/cloud/vision"

image_annotator = Google::Cloud::Vision::ImageAnnotator.new

response = image_annotator.safe_search_detection image: image_path

response.responses.each do |res|
  safe_search = res.safe_search_annotation

  puts "Adult:    #{safe_search.adult}"
  puts "Spoof:    #{safe_search.spoof}"
  puts "Medical:  #{safe_search.medical}"
  puts "Violence: #{safe_search.violence}"
  puts "Racy:     #{safe_search.racy}"
end

Explicit content detection on a remote image

For your convenience, the Vision API can perform feature detection directly on an image file located in Google Cloud Storage or on the Web without the need to send the contents of the image file in the body of your request.

Command-line

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
https://vision.googleapis.com/v1/images:annotate -d "{
  'requests': [
    {
      'image': {
        'source': {
          'gcsImageUri': 'gs://my_bucket/input_file'
        }
      },
      'features': [
        {
          'type': 'SAFE_SEARCH_DETECTION'
        }
      ]
    }
  ]
}"

See the AnnotateImageRequest reference documentation for more information on configuring the request body.

If the request is successful, the server returns a 200 OK HTTP status code and the response in JSON format.

Response:

{
  "responses": [
    {
      "safeSearchAnnotation": {
        "adult": "UNLIKELY",
        "spoof": "VERY_UNLIKELY",
        "medical": "VERY_UNLIKELY",
        "violence": "LIKELY",
        "racy": "POSSIBLE"
      }
    }
  ]
}

C#

Before trying this sample, follow the C# setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API C# API reference documentation .

View on GitHub Feedback

// Specify a Google Cloud Storage uri for the image
// or a publicly accessible HTTP or HTTPS uri.
var image = Image.FromUri(uri);
var client = ImageAnnotatorClient.Create();
var response = client.DetectSafeSearch(image);
Console.WriteLine("Adult: {0}", response.Adult.ToString());
Console.WriteLine("Spoof: {0}", response.Spoof.ToString());
Console.WriteLine("Medical: {0}", response.Medical.ToString());
Console.WriteLine("Violence: {0}", response.Violence.ToString());
Console.WriteLine("Racy: {0}", response.Racy.ToString());

Go

Before trying this sample, follow the Go setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Go API reference documentation .

View on GitHub Feedback

// detectSafeSearch gets image properties from the Vision API for an image at the given file path.
func detectSafeSearchURI(w io.Writer, file string) error {
	ctx := context.Background()

	client, err := vision.NewImageAnnotatorClient(ctx)
	if err != nil {
		return err
	}

	image := vision.NewImageFromURI(file)
	props, err := client.DetectSafeSearch(ctx, image, nil)
	if err != nil {
		return err
	}

	fmt.Fprintln(w, "Safe Search properties:")
	fmt.Fprintln(w, "Adult:", props.Adult)
	fmt.Fprintln(w, "Medical:", props.Medical)
	fmt.Fprintln(w, "Racy:", props.Racy)
	fmt.Fprintln(w, "Spoofed:", props.Spoof)
	fmt.Fprintln(w, "Violence:", props.Violence)

	return nil
}

Java

Before trying this sample, follow the Java setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Java API reference documentation .

View on GitHub Feedback

public static void detectSafeSearchGcs(String gcsPath, PrintStream out) throws Exception,
    IOException {
  List<AnnotateImageRequest> requests = new ArrayList<>();

  ImageSource imgSource = ImageSource.newBuilder().setGcsImageUri(gcsPath).build();
  Image img = Image.newBuilder().setSource(imgSource).build();
  Feature feat = Feature.newBuilder().setType(Type.SAFE_SEARCH_DETECTION).build();
  AnnotateImageRequest request =
      AnnotateImageRequest.newBuilder().addFeatures(feat).setImage(img).build();
  requests.add(request);

  try (ImageAnnotatorClient client = ImageAnnotatorClient.create()) {
    BatchAnnotateImagesResponse response = client.batchAnnotateImages(requests);
    List<AnnotateImageResponse> responses = response.getResponsesList();

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

      // For full list of available annotations, see http://g.co/cloud/vision/docs
      SafeSearchAnnotation annotation = res.getSafeSearchAnnotation();
      out.printf(
          "adult: %s\nmedical: %s\nspoofed: %s\nviolence: %s\nracy: %s\n",
          annotation.getAdult(),
          annotation.getMedical(),
          annotation.getSpoof(),
          annotation.getViolence(),
          annotation.getRacy());
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Node.js API reference documentation .

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// Imports the Google Cloud client libraries
const vision = require('@google-cloud/vision');

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

/**
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const bucketName = 'Bucket where the file resides, e.g. my-bucket';
// const fileName = 'Path to file within bucket, e.g. path/to/image.png';

// Performs safe search property detection on the remote file
const [result] = await client.safeSearchDetection(
  `gs://${bucketName}/${fileName}`
);
const detections = result.safeSearchAnnotation;
console.log(`Adult: ${detections.adult}`);
console.log(`Spoof: ${detections.spoof}`);
console.log(`Medical: ${detections.medical}`);
console.log(`Violence: ${detections.violence}`);

PHP

Before trying this sample, follow the PHP setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API PHP API reference documentation .

View on GitHub Feedback

namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;

// $path = 'gs://path/to/your/image.jpg'

function detect_safe_search_gcs($path)
{
    $imageAnnotator = new ImageAnnotatorClient();

    # annotate the image
    $response = $imageAnnotator->safeSearchDetection($path);
    $safe = $response->getSafeSearchAnnotation();

    if ($safe) {
        $adult = $safe->getAdult();
        $medical = $safe->getMedical();
        $spoof = $safe->getSpoof();
        $violence = $safe->getViolence();
        $racy = $safe->getRacy();

        # names of likelihood from google.cloud.vision.enums
        $likelihoodName = ['UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY',
        'POSSIBLE','LIKELY', 'VERY_LIKELY'];

        printf("Adult: %s" . PHP_EOL, $likelihoodName[$adult]);
        printf("Medical: %s" . PHP_EOL, $likelihoodName[$medical]);
        printf("Spoof: %s" . PHP_EOL, $likelihoodName[$spoof]);
        printf("Violence: %s" . PHP_EOL, $likelihoodName[$violence]);
        printf("Racy: %s" . PHP_EOL, $likelihoodName[$racy]);
    } else {
        print('No Results.' . PHP_EOL);
    }

    $imageAnnotator->close();
}

Python

Before trying this sample, follow the Python setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Python API reference documentation .

View on GitHub Feedback

def detect_safe_search_uri(uri):
    """Detects unsafe features in the file located in Google Cloud Storage or
    on the Web."""
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.types.Image()
    image.source.image_uri = uri

    response = client.safe_search_detection(image=image)
    safe = response.safe_search_annotation

    # Names of likelihood from google.cloud.vision.enums
    likelihood_name = ('UNKNOWN', 'VERY_UNLIKELY', 'UNLIKELY', 'POSSIBLE',
                       'LIKELY', 'VERY_LIKELY')
    print('Safe search:')

    print('adult: {}'.format(likelihood_name[safe.adult]))
    print('medical: {}'.format(likelihood_name[safe.medical]))
    print('spoofed: {}'.format(likelihood_name[safe.spoof]))
    print('violence: {}'.format(likelihood_name[safe.violence]))
    print('racy: {}'.format(likelihood_name[safe.racy]))

Ruby

Before trying this sample, follow the Ruby setup instructions in the Vision API Quickstart Using Client Libraries . For more information, see the Vision API Ruby API reference documentation .

View on GitHub Feedback

# image_path = "Google Cloud Storage URI, eg. 'gs://my-bucket/image.png'"

require "google/cloud/vision"

image_annotator = Google::Cloud::Vision::ImageAnnotator.new

response = image_annotator.safe_search_detection image: image_path

response.responses.each do |res|
  safe_search = res.safe_search_annotation

  puts "Adult:    #{safe_search.adult}"
  puts "Spoof:    #{safe_search.spoof}"
  puts "Medical:  #{safe_search.medical}"
  puts "Violence: #{safe_search.violence}"
  puts "Racy:     #{safe_search.racy}"
end

PowerShell

To make a Safe Search detection request using Windows PowerShell, make a POST request to the https://vision.googleapis.com/v1/images:annotate endpoint and specify SAFE_SEARCH_DETECTION as the value of features.type, as shown in the following example:

$cred = gcloud auth application-default print-access-token
$headers = @{ Authorization = "Bearer $cred" }

Invoke-WebRequest `
  -Method Post `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -Body "{
      'requests': [
        {
          'image': {
            'source': {
              'imageUri': 'gs://my_bucket/input_file'
            }
          },
          'features': [
            {
              'type': 'SAFE_SEARCH_DETECTION'
            }
          ]
        }
      ]
    }" `
  -Uri "https://vision.googleapis.com/v1/images:annotate" | Select-Object -Expand Content

See the AnnotateImageRequest reference documentation for more information on configuring the request body.

GCLOUD COMMAND

To perform Safe Search detection, use the gcloud ml vision detect-safe-search command as shown in the following example:

gcloud ml vision detect-safe-search gs://my_bucket/input_file

Safe Search detection requests

Set up your GCP project and authentication

Explicit content detection on a local image

Command-line

C#

Go

Java

Node.js

PHP

Python

Ruby

Explicit content detection on a remote image

Command-line

C#

Go

Java

Node.js

PHP

Python

Ruby

PowerShell

GCLOUD COMMAND

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