Face Detection Tutorial

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using Google.Cloud.Vision.V1;
using System;
using System.Linq;

var client = ImageAnnotatorClient.Create();

Node.js

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// By default, the client will authenticate using the service account file
// specified by the GOOGLE_APPLICATION_CREDENTIALS environment variable and use
// the project specified by the GCLOUD_PROJECT environment variable. See
// https://googlecloudplatform.github.io/gcloud-node/#/docs/google-cloud/latest/guides/authentication
const vision = require('@google-cloud/vision');

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

const fs = require('fs');

PHP

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use Google\Cloud\Vision\V1\ImageAnnotatorClient;

$imageAnnotator = new ImageAnnotatorClient();

Python

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from google.cloud import vision
from google.cloud.vision import types
from PIL import Image, ImageDraw

client = vision.ImageAnnotatorClient()

Ruby

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require "google/cloud/vision"

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

Send a face detection request

To construct a request to the Vision API, first consult the API documentation. In this case, you'll be asking the images resource to annotate your image. A request to this API takes the form of an object with a requests list. Each item in this list contains two bits of information:

The base64-encoded image data
A list of features you'd like annotated about that image.

For this example, you'll simply request FACE_DETECTION annotation on one image, and return the relevant portion of the response:

C#

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var response = client.DetectFaces(Image.FromFile(args[0]));

Node.js

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function detectFaces(inputFile, callback) {
  // Make a call to the Vision API to detect the faces
  const request = {image: {source: {filename: inputFile}}};
  client
    .faceDetection(request)
    .then(results => {
      const faces = results[0].faceAnnotations;
      const numFaces = faces.length;
      console.log('Found ' + numFaces + (numFaces === 1 ? ' face' : ' faces'));
      callback(null, faces);
    })
    .catch(err => {
      console.error('ERROR:', err);
      callback(err);
    });
}

PHP

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

Python

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def detect_face(face_file, max_results=4):
    """Uses the Vision API to detect faces in the given file.

    Args:
        face_file: A file-like object containing an image with faces.

    Returns:
        An array of Face objects with information about the picture.
    """
    client = vision.ImageAnnotatorClient()

    content = face_file.read()
    image = types.Image(content=content)

    return client.face_detection(image=image).face_annotations

Ruby

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image = vision.image path_to_image_file
faces = image.faces

Process the response

Congratulations - you've detected the faces in your image! The response to our face annotation request includes a bunch of metadata about the detected faces, which include coordinates of a polygon encompassing the face. At this point, though, this is only a list of numbers. Let's use them to confirm that you have, in fact, found the faces in your image. We'll draw polygons onto a copy of the image, using the coordinates returned by the Vision API:

C#

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using (var image = System.Drawing.Image.FromFile(args[0]))
using (System.Drawing.Graphics g = System.Drawing.Graphics.FromImage(image))
{
    var cyanPen = new System.Drawing.Pen(System.Drawing.Color.Cyan, 3);
    foreach (var annotation in response)
    {
        g.DrawPolygon(cyanPen, annotation.BoundingPoly.Vertices.Select(
            (vertex) => new System.Drawing.Point(vertex.X, vertex.Y)).ToArray());
        // ...
    }
    // ...
}

Node.js

We use the node-canvas library to draw onto images.

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function highlightFaces(inputFile, faces, outputFile, Canvas, callback) {
  fs.readFile(inputFile, (err, image) => {
    if (err) {
      return callback(err);
    }

    const Image = Canvas.Image;
    // Open the original image into a canvas
    const img = new Image();
    img.src = image;
    const canvas = new Canvas(img.width, img.height);
    const context = canvas.getContext('2d');
    context.drawImage(img, 0, 0, img.width, img.height);

    // Now draw boxes around all the faces
    context.strokeStyle = 'rgba(0,255,0,0.8)';
    context.lineWidth = '5';

    faces.forEach(face => {
      context.beginPath();
      let origX = 0;
      let origY = 0;
      face.boundingPoly.vertices.forEach((bounds, i) => {
        if (i === 0) {
          origX = bounds.x;
          origY = bounds.y;
        }
        context.lineTo(bounds.x, bounds.y);
      });
      context.lineTo(origX, origY);
      context.stroke();
    });

    // Write the result to a file
    console.log('Writing to file ' + outputFile);
    const writeStream = fs.createWriteStream(outputFile);
    const pngStream = canvas.pngStream();

    pngStream.on('data', chunk => {
      writeStream.write(chunk);
    });
    pngStream.on('error', console.log);
    pngStream.on('end', callback);
  });
}

PHP

We use the GD extension to draw onto images.

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# draw box around faces
if ($faces && $outFile) {
    $imageCreateFunc = [
        'png' => 'imagecreatefrompng',
        'gd' => 'imagecreatefromgd',
        'gif' => 'imagecreatefromgif',
        'jpg' => 'imagecreatefromjpeg',
        'jpeg' => 'imagecreatefromjpeg',
    ];
    $imageWriteFunc = [
        'png' => 'imagepng',
        'gd' => 'imagegd',
        'gif' => 'imagegif',
        'jpg' => 'imagejpeg',
        'jpeg' => 'imagejpeg',
    ];

    copy($path, $outFile);
    $ext = strtolower(pathinfo($path, PATHINFO_EXTENSION));
    if (!array_key_exists($ext, $imageCreateFunc)) {
        throw new \Exception('Unsupported image extension');
    }
    $outputImage = call_user_func($imageCreateFunc[$ext], $outFile);

    foreach ($faces as $face) {
        $vertices = $face->getBoundingPoly()->getVertices();
        if ($vertices) {
            $x1 = $vertices[0]->getX();
            $y1 = $vertices[0]->getY();
            $x2 = $vertices[2]->getX();
            $y2 = $vertices[2]->getY();
            imagerectangle($outputImage, $x1, $y1, $x2, $y2, 0x00ff00);
        }
    }

Python

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def highlight_faces(image, faces, output_filename):
    """Draws a polygon around the faces, then saves to output_filename.

    Args:
      image: a file containing the image with the faces.
      faces: a list of faces found in the file. This should be in the format
          returned by the Vision API.
      output_filename: the name of the image file to be created, where the
          faces have polygons drawn around them.
    """
    im = Image.open(image)
    draw = ImageDraw.Draw(im)

    for face in faces:
        box = [(vertex.x, vertex.y)
               for vertex in face.bounding_poly.vertices]
        draw.line(box + [box[0]], width=5, fill='#00ff00')

    im.save(output_filename)

Ruby

We use the rmagick gem to draw onto images.

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require "rmagick"
  image = Magick::Image.read(path_to_image_file).first

  faces.each do |face|
    puts "Face bounds:"
    face.bounds.face.each do |vector|
      puts "(#{vector.x}, #{vector.y})"
    end

    draw = Magick::Draw.new
    draw.stroke = "green"
    draw.stroke_width 5
    draw.fill_opacity 0

    x1 = face.bounds.face[0].x.to_i
    y1 = face.bounds.face[0].y.to_i
    x2 = face.bounds.face[2].x.to_i
    y2 = face.bounds.face[2].y.to_i

    draw.rectangle x1, y1, x2, y2
    draw.draw image
  end

  image.write path_to_output_file

  puts "Output file: #{path_to_output_file}"

Put it all together

C#

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static readonly string s_usage = @"dotnet run image-file

Use the Google Cloud Vision API to detect faces in the image.
Writes an output file called image-file.faces.
";
public static void Main(string[] args)
{
    if (args.Length < 1)
    {
        Console.WriteLine(s_usage);
        return;
    }

    var client = ImageAnnotatorClient.Create();
    var response = client.DetectFaces(Image.FromFile(args[0]));

    int numberOfFacesFound = 0;
    using (var image = System.Drawing.Image.FromFile(args[0]))
    using (System.Drawing.Graphics g = System.Drawing.Graphics.FromImage(image))
    {
        var cyanPen = new System.Drawing.Pen(System.Drawing.Color.Cyan, 3);
        foreach (var annotation in response)
        {
            g.DrawPolygon(cyanPen, annotation.BoundingPoly.Vertices.Select(
                (vertex) => new System.Drawing.Point(vertex.X, vertex.Y)).ToArray());
            // ...
        }
        // ...
    }
    // ...
}

To build the sample, open the Solution file dotnet-doc-samples/vision/api/Vision.sln in Visual Studio and build the solution.

To run the sample:

C:\...\> cd dotnet-docs-samples\vision\api\DetectFaces\bin\Debug
C:\...\bin\Debug> DetectFaces ..\..\..\VisionTest\data\face.png

Node.js

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function main(inputFile, outputFile, Canvas, callback) {
  outputFile = outputFile || 'out.png';
  detectFaces(inputFile, (err, faces) => {
    if (err) {
      return callback(err);
    }

    console.log('Highlighting...');
    highlightFaces(inputFile, faces, outputFile, Canvas, err => {
      if (err) {
        return callback(err);
      }
      console.log('Finished!');
      callback(null, faces);
    });
  });
}

To run the sample, run the following command from the sample code directory:

node faceDetection face.png

PHP

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call_user_func($imageWriteFunc[$ext], $outputImage, $outFile);
printf('Output image written to %s' . PHP_EOL, $outFile);

To run the sample, run the following command from the sample code directory:

composer install
php vision.php face images/face.png output-image.png

Python

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def main(input_filename, output_filename, max_results):
    with open(input_filename, 'rb') as image:
        faces = detect_face(image, max_results)
        print('Found {} face{}'.format(
            len(faces), '' if len(faces) == 1 else 's'))

        print('Writing to file {}'.format(output_filename))
        # Reset the file pointer, so we can read the file again
        image.seek(0)
        highlight_faces(image, faces, output_filename)

Ruby