Detecting Faces

Face Detection detects multiple faces within an image along with the associated key facial attributes such as emotional state or wearing headwear. Facial Recognition is not supported.

Detecting Faces in a local image

Protocol

Refer to the images:annotate API endpoint for complete details.

To perform Face Detection, make a POST request and provide the appropriate request body:

POST https://vision.googleapis.com/v1/images:annotate?key=YOUR_API_KEY
{
  "requests": [
    {
      "image": {
        "content": "/9j/7QBEUGhvdG9zaG9...base64-encoded-image-content...fXNWzvDEeYxxxzj/Coa6Bax//Z"
      },
      "features": [
        {
          "type": "FACE_DETECTION"
        }
      ]
    }
  ]
}

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

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 .

// Load an image from a local file.
var image = Image.FromFile(filePath);
var client = ImageAnnotatorClient.Create();
var response = client.DetectFaces(image);
int count = 1;
foreach (var faceAnnotation in response)
{
    Console.WriteLine("Face {0}:", count++);
    Console.WriteLine("  Joy: {0}", faceAnnotation.JoyLikelihood);
    Console.WriteLine("  Anger: {0}", faceAnnotation.AngerLikelihood);
    Console.WriteLine("  Sorrow: {0}", faceAnnotation.SorrowLikelihood);
    Console.WriteLine("  Surprise: {0}", faceAnnotation.SurpriseLikelihood);
}

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 .

// detectFaces gets faces from the Vision API for an image at the given file path.
func detectFaces(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
	}
	annotations, err := client.DetectFaces(ctx, image, nil, 10)
	if err != nil {
		return err
	}
	if len(annotations) == 0 {
		fmt.Fprintln(w, "No faces found.")
	} else {
		fmt.Fprintln(w, "Faces:")
		for i, annotation := range annotations {
			fmt.Fprintln(w, "  Face", i)
			fmt.Fprintln(w, "    Anger:", annotation.AngerLikelihood)
			fmt.Fprintln(w, "    Joy:", annotation.JoyLikelihood)
			fmt.Fprintln(w, "    Surprise:", annotation.SurpriseLikelihood)
		}
	}
	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 .

public static void detectFaces(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.FACE_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
      for (FaceAnnotation annotation : res.getFaceAnnotationsList()) {
        out.printf(
            "anger: %s\njoy: %s\nsurprise: %s\nposition: %s",
            annotation.getAngerLikelihood(),
            annotation.getJoyLikelihood(),
            annotation.getSurpriseLikelihood(),
            annotation.getBoundingPoly());
      }
    }
  }
}

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 .

// Imports the Google Cloud client library
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';

client
  .faceDetection(fileName)
  .then(results => {
    const faces = results[0].faceAnnotations;

    console.log('Faces:');
    faces.forEach((face, i) => {
      console.log(`  Face #${i + 1}:`);
      console.log(`    Joy: ${face.joyLikelihood}`);
      console.log(`    Anger: ${face.angerLikelihood}`);
      console.log(`    Sorrow: ${face.sorrowLikelihood}`);
      console.log(`    Surprise: ${face.surpriseLikelihood}`);
    });
  })
  .catch(err => {
    console.error('ERROR:', err);
  });

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 .

namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;


function detect_face($path, $outFile = null)
{
    $imageAnnotator = new ImageAnnotatorClient();

    # annotate the image
    // $path = 'path/to/your/image.jpg'
    $image = file_get_contents($path);
    $response = $imageAnnotator->faceDetection($image);
    $faces = $response->getFaceAnnotations();

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

    printf("%d faces found:" . PHP_EOL, count($faces));
    foreach ($faces as $face) {
        $anger = $face->getAngerLikelihood();
        printf("Anger: %s" . PHP_EOL, $likelihoodName[$anger]);

        $joy = $face->getJoyLikelihood();
        printf("Joy: %s" . PHP_EOL, $likelihoodName[$joy]);

        $surprise = $face->getSurpriseLikelihood();
        printf("Surprise: %s" . PHP_EOL, $likelihoodName[$surprise]);

        # get bounds
        $vertices = $face->getBoundingPoly()->getVertices();
        $bounds = [];
        foreach ($vertices as $vertex) {
            $bounds[] = sprintf('(%d,%d)', $vertex->getX(), $vertex->getY());
        }
        print('Bounds: ' . join(', ',$bounds) . PHP_EOL);
        print(PHP_EOL);
    }
}

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 .

def detect_faces(path):
    """Detects faces in an image."""
    client = vision.ImageAnnotatorClient()

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

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

    response = client.face_detection(image=image)
    faces = response.face_annotations

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

    for face in faces:
        print('anger: {}'.format(likelihood_name[face.anger_likelihood]))
        print('joy: {}'.format(likelihood_name[face.joy_likelihood]))
        print('surprise: {}'.format(likelihood_name[face.surprise_likelihood]))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in face.bounding_poly.vertices])

        print('face bounds: {}'.format(','.join(vertices)))

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 .

# project_id = "Your Google Cloud project ID"
# image_path = "Path to local image file, eg. './image.png'"

require "google/cloud/vision"

vision = Google::Cloud::Vision.new project: project_id
image  = vision.image image_path

image.faces.each do |face|
  puts "Joy:      #{face.likelihood.joy?}"
  puts "Anger:    #{face.likelihood.anger?}"
  puts "Sorrow:   #{face.likelihood.sorrow?}"
  puts "Surprise: #{face.likelihood.surprise?}"
end

Detecting Faces in a remote image

For your convenience, the Vision API can perform Face 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.

Protocol

Refer to the images:annotate API endpoint for complete details.

To perform Face Detection, make a POST request and provide the appropriate request body:

POST https://vision.googleapis.com/v1/images:annotate?key=YOUR_API_KEY
{
  "requests": [
    {
      "image": {
        "source": {
          "gcsImageUri": "gs://YOUR_BUCKET_NAME/YOUR_FILE_NAME"
        }
      },
      "features": [
        {
          "type": "FACE_DETECTION"
        }
      ]
    }
  ]
}

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

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 .

// 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.DetectFaces(image);
int count = 1;
foreach (var faceAnnotation in response)
{
    Console.WriteLine("Face {0}:", count++);
    Console.WriteLine("  Joy: {0}", faceAnnotation.JoyLikelihood);
    Console.WriteLine("  Anger: {0}", faceAnnotation.AngerLikelihood);
    Console.WriteLine("  Sorrow: {0}", faceAnnotation.SorrowLikelihood);
    Console.WriteLine("  Surprise: {0}", faceAnnotation.SurpriseLikelihood);
}

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 .

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

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

	image := vision.NewImageFromURI(file)
	annotations, err := client.DetectFaces(ctx, image, nil, 10)
	if err != nil {
		return err
	}
	if len(annotations) == 0 {
		fmt.Fprintln(w, "No faces found.")
	} else {
		fmt.Fprintln(w, "Faces:")
		for i, annotation := range annotations {
			fmt.Fprintln(w, "  Face", i)
			fmt.Fprintln(w, "    Anger:", annotation.AngerLikelihood)
			fmt.Fprintln(w, "    Joy:", annotation.JoyLikelihood)
			fmt.Fprintln(w, "    Surprise:", annotation.SurpriseLikelihood)
		}
	}
	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 .

public static void detectFacesGcs(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.FACE_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
      for (FaceAnnotation annotation : res.getFaceAnnotationsList()) {
        out.printf(
            "anger: %s\njoy: %s\nsurprise: %s\nposition: %s",
            annotation.getAngerLikelihood(),
            annotation.getJoyLikelihood(),
            annotation.getSurpriseLikelihood(),
            annotation.getBoundingPoly());
      }
    }
  }
}

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 .

// 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 face detection on the gcs file
client
  .faceDetection(`gs://${bucketName}/${fileName}`)
  .then(results => {
    const faces = results[0].faceAnnotations;

    console.log('Faces:');
    faces.forEach((face, i) => {
      console.log(`  Face #${i + 1}:`);
      console.log(`    Joy: ${face.joyLikelihood}`);
      console.log(`    Anger: ${face.angerLikelihood}`);
      console.log(`    Sorrow: ${face.sorrowLikelihood}`);
      console.log(`    Surprise: ${face.surpriseLikelihood}`);
    });
  })
  .catch(err => {
    console.error('ERROR:', err);
  });

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 .

namespace Google\Cloud\Samples\Vision;

use Google\Cloud\Vision\V1\ImageAnnotatorClient;

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

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

    # annotate the image
    $response = $imageAnnotator->faceDetection($path);
    $faces = $response->getFaceAnnotations();

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

    printf("%d faces found:" . PHP_EOL, count($faces));
    foreach ($faces as $face) {
        $anger = $face->getAngerLikelihood();
        printf("Anger: %s" . PHP_EOL, $likelihoodName[$anger]);

        $joy = $face->getJoyLikelihood();
        printf("Joy: %s" . PHP_EOL, $likelihoodName[$joy]);

        $surprise = $face->getSurpriseLikelihood();
        printf("Surprise: %s" . PHP_EOL, $likelihoodName[$surprise]);

        # get bounds
        $vertices = $face->getBoundingPoly()->getVertices();
        $bounds = [];
        foreach ($vertices as $vertex) {
            $bounds[] = sprintf('(%d,%d)', $vertex->getX(), $vertex->getY());
        }
        print('Bounds: ' . join(', ',$bounds) . PHP_EOL);
        print(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 .

def detect_faces_uri(uri):
    """Detects faces in the file located in Google Cloud Storage or the web."""
    client = vision.ImageAnnotatorClient()
    image = vision.types.Image()
    image.source.image_uri = uri

    response = client.face_detection(image=image)
    faces = response.face_annotations

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

    for face in faces:
        print('anger: {}'.format(likelihood_name[face.anger_likelihood]))
        print('joy: {}'.format(likelihood_name[face.joy_likelihood]))
        print('surprise: {}'.format(likelihood_name[face.surprise_likelihood]))

        vertices = (['({},{})'.format(vertex.x, vertex.y)
                    for vertex in face.bounding_poly.vertices])

        print('face bounds: {}'.format(','.join(vertices)))

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 .

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

require "google/cloud/vision"

vision = Google::Cloud::Vision.new project: project_id
image  = vision.image image_path

image.faces.each do |face|
  puts "Joy:      #{face.likelihood.joy?}"
  puts "Anger:    #{face.likelihood.anger?}"
  puts "Sorrow:   #{face.likelihood.sorrow?}"
  puts "Surprise: #{face.likelihood.surprise?}"
end

FACE_DETECTION Responses

A successful FACE_DETECTION request produces a response containing a set of faceAnnotations of type FaceAnnotation.

The code example contains a sample face detection response for the photo shown below:

{
  "responses": [
    {
      "faceAnnotations": [
        {
          "boundingPoly": {
            "vertices": [
              {
                "x": 932,
                "y": 313
              },
              {
                "x": 1028,
                "y": 313
              },
              {
                "x": 1028,
                "y": 425
              },
              {
                "x": 932,
                "y": 425
              }
            ]
          },
          "fdBoundingPoly": {
            "vertices": [
              {
                "x": 936,
                "y": 333
              },
              {
                "x": 1017,
                "y": 333
              },
              {
                "x": 1017,
                "y": 413
              },
              {
                "x": 936,
                "y": 413
              }
            ]
          },
          "landmarks": [
            {
              "type": "LEFT_EYE",
              "position": {
                "x": 959.60065,
                "y": 355.98782,
                "z": -0.00016746686
              }
            },
            {
              "type": "RIGHT_EYE",
              "position": {
                "x": 984.92914,
                "y": 362.48074,
                "z": -14.466843
              }
            },
            {
              "type": "LEFT_OF_LEFT_EYEBROW",
              "position": {
                "x": 954.3997,
                "y": 348.13577,
                "z": 7.6285343
              }
            },
            {
              "type": "RIGHT_OF_LEFT_EYEBROW",
              "position": {
                "x": 965.15735,
                "y": 349.91434,
                "z": -7.9691405
              }
            },
            {
              "type": "LEFT_OF_RIGHT_EYEBROW",
              "position": {
                "x": 976.60974,
                "y": 352.59775,
                "z": -14.814832
              }
            },
            {
              "type": "RIGHT_OF_RIGHT_EYEBROW",
              "position": {
                "x": 995.2661,
                "y": 359.14246,
                "z": -15.962653
              }
            },
            {
              "type": "MIDPOINT_BETWEEN_EYES",
              "position": {
                "x": 968.7824,
                "y": 356.87964,
                "z": -12.243763
              }
            },
            {
              "type": "NOSE_TIP",
              "position": {
                "x": 959.8401,
                "y": 371.1136,
                "z": -21.012028
              }
            },
            {
              "type": "UPPER_LIP",
              "position": {
                "x": 960.88947,
                "y": 382.35114,
                "z": -15.794773
              }
            },
            {
              "type": "LOWER_LIP",
              "position": {
                "x": 959.00604,
                "y": 392.5613,
                "z": -14.722658
              }
            },
            {
              "type": "MOUTH_LEFT",
              "position": {
                "x": 953.558,
                "y": 385.6143,
                "z": -3.7360961
              }
            },
            {
              "type": "MOUTH_RIGHT",
              "position": {
                "x": 975.03265,
                "y": 390.148,
                "z": -14.706936
              }
            },
            {
              "type": "MOUTH_CENTER",
              "position": {
                "x": 960.53326,
                "y": 387.2606,
                "z": -14.258573
              }
            },
            {
              "type": "NOSE_BOTTOM_RIGHT",
              "position": {
                "x": 971.79193,
                "y": 376.1076,
                "z": -15.152608
              }
            },
            {
              "type": "NOSE_BOTTOM_LEFT",
              "position": {
                "x": 957.7185,
                "y": 373.1746,
                "z": -7.1614866
              }
            },
            {
              "type": "NOSE_BOTTOM_CENTER",
              "position": {
                "x": 962.575,
                "y": 376.29388,
                "z": -15.418351
              }
            },
            {
              "type": "LEFT_EYE_TOP_BOUNDARY",
              "position": {
                "x": 959.1816,
                "y": 353.80328,
                "z": -1.5174211
              }
            },
            {
              "type": "LEFT_EYE_RIGHT_CORNER",
              "position": {
                "x": 964.36786,
                "y": 357.51196,
                "z": -2.7060971
              }
            },
            {
              "type": "LEFT_EYE_BOTTOM_BOUNDARY",
              "position": {
                "x": 958.7769,
                "y": 358.01065,
                "z": -0.3359541
              }
            },
            {
              "type": "LEFT_EYE_LEFT_CORNER",
              "position": {
                "x": 955.79767,
                "y": 355.51834,
                "z": 5.151253
              }
            },
            {
              "type": "LEFT_EYE_PUPIL",
              "position": {
                "x": 958.7773,
                "y": 355.84012,
                "z": -0.38514262
              }
            },
            {
              "type": "RIGHT_EYE_TOP_BOUNDARY",
              "position": {
                "x": 983.61804,
                "y": 359.85156,
                "z": -15.601014
              }
            },
            {
              "type": "RIGHT_EYE_RIGHT_CORNER",
              "position": {
                "x": 990.0031,
                "y": 364.02197,
                "z": -14.567666
              }
            },
            {
              "type": "RIGHT_EYE_BOTTOM_BOUNDARY",
              "position": {
                "x": 983.9871,
                "y": 364.64563,
                "z": -14.510015
              }
            },
            {
              "type": "RIGHT_EYE_LEFT_CORNER",
              "position": {
                "x": 978.7498,
                "y": 361.2154,
                "z": -11.121486
              }
            },
            {
              "type": "RIGHT_EYE_PUPIL",
              "position": {
                "x": 983.81213,
                "y": 362.04236,
                "z": -14.877491
              }
            },
            {
              "type": "LEFT_EYEBROW_UPPER_MIDPOINT",
              "position": {
                "x": 959.80444,
                "y": 345.24878,
                "z": -1.5490607
              }
            },
            {
              "type": "RIGHT_EYEBROW_UPPER_MIDPOINT",
              "position": {
                "x": 986.2949,
                "y": 351.80408,
                "z": -16.823978
              }
            },
            {
              "type": "LEFT_EAR_TRAGION",
              "position": {
                "x": 956.0783,
                "y": 372.93738,
                "z": 39.021652
              }
            },
            {
              "type": "RIGHT_EAR_TRAGION",
              "position": {
                "x": 1012.669,
                "y": 387.13126,
                "z": 7.191323
              }
            },
            {
              "type": "FOREHEAD_GLABELLA",
              "position": {
                "x": 970.6526,
                "y": 350.78348,
                "z": -12.321499
              }
            },
            {
              "type": "CHIN_GNATHION",
              "position": {
                "x": 956.40735,
                "y": 406.87085,
                "z": -12.346105
              }
            },
            {
              "type": "CHIN_LEFT_GONION",
              "position": {
                "x": 948.2937,
                "y": 388.85358,
                "z": 25.902096
              }
            },
            {
              "type": "CHIN_RIGHT_GONION",
              "position": {
                "x": 998.49835,
                "y": 401.61972,
                "z": -3.1576655
              }
            }
          ],
          "rollAngle": 16.379295,
          "panAngle": -29.333826,
          "tiltAngle": 4.458676,
          "detectionConfidence": 0.980691,
          "landmarkingConfidence": 0.57905465,
          "joyLikelihood": "VERY_LIKELY",
          "sorrowLikelihood": "VERY_UNLIKELY",
          "angerLikelihood": "VERY_UNLIKELY",
          "surpriseLikelihood": "VERY_UNLIKELY",
          "underExposedLikelihood": "VERY_UNLIKELY",
          "blurredLikelihood": "VERY_UNLIKELY",
          "headwearLikelihood": "VERY_UNLIKELY"
        }
      ]
    }
  ]
}
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Cloud Vision API Documentation
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