Detecting faces in images

Face detection is currently at the Preview stage.

To detect faces in a photo, use the Face Detection feature.

In the batchAnalyze method, set the type property to FACE_DETECTION.

Examples

Before getting started

To use the examples, install cURL and get the authorization data for your account:

Find faces in an image

1. Prepare an image file that meets the requirements:

   • Supported file formats: JPEG, PNG, PDF.

     You specify the MIME type of the file in the mime_type property. The default is image.

   • Maximum file size: 1 MB.

   • Image size should not exceed 20 MP (length x width).

   Note

   Need an image? Download a sample.

2. Encode the file as Base64:

   UNIX

   Windows

   PowerShell

   Python

   Node.js

   Java

   Go

   $ base64 -i input.jpg > output.txt
   

   C:> Base64.exe -e input.jpg > output.txt
   

   [Convert]::ToBase64String([IO.File]::ReadAllBytes("./input.jpg")) > output.txt
   

   # Импортируйте библиотеку для кодирования в Base64
   import base64
   
   # Создайте функцию, которая кодирует файл и возвращает результат.
   def encode_file(file):
     file_content = file.read()
     return base64.b64encode(file_content)
   

   // Считайте содержимое файла в память.
   var fs = require('fs');
   var file = fs.readFileSync('/path/to/file');
   
   // Получите содержимое файла в формате Base64.
   var encoded = Buffer.from(file).toString('base64');
   

   // Импортируйте библиотеку для кодирования в Base64.
   import org.apache.commons.codec.binary.Base64;
   
   // Получите содержимое файла в формате Base64.
   byte[] fileData = Base64.encodeBase64(yourFile.getBytes());
   

   import (
       "bufio"
       "encoding/base64"
       "io/ioutil"
       "os"
   )
   
   // Откройте файл
   f, _ := os.Open("/path/to/file")
   
   // Прочитайте содержимое файла.
   reader := bufio.NewReader(f)
   content, _ := ioutil.ReadAll(reader)
   
   // Получите содержимое файла в формате Base64.
   base64.StdEncoding.EncodeToString(content)
   

3. Create a file with the request body (for example, body.json). In the content property, specify a Base64-encoded image:

   body.json:

   {
       "folderId": "b1gvmob95yysaplct532",
       "analyze_specs": [{
           "content": "iVBORw0KGgo...",
           "features": [{
               "type": "FACE_DETECTION"
           }]
       }]
   }
   

4. Send a request using the batchAnalyze method and save the response in a file, such as output.json:

   $ export IAM_TOKEN=CggaATEVAgA...
   $ curl -X POST \
       -H "Content-Type: application/json" \
       -H "Authorization: Bearer ${IAM_TOKEN}" \
       -d '@body.json' \
       https://vision.api.cloud.yandex.net/vision/v1/batchAnalyze > output.json
   

Ready-to-use function for sending requests in bash

1. If you don't have the Yandex.Cloud command line interface yet, install and initialize it.

2. Copy the function to the terminal:

   vision_face_detection() {
       curl -H "Authorization: Bearer `yc iam create-token`" \
       "https://vision.api.cloud.yandex.net/vision/v1/batchAnalyze" \
       -d @<(cat << EOF
   {
       "folderId": "`yc config get folder-id`",
       "analyze_specs": [{
           "content": "`base64 -i $1`",
           "features": [{
               "type": "FACE_DETECTION"
           }]
       }]
   }
   EOF
   )
   }
   

   Explanations:

   • yc iam create-token: get an IAM token.
   • -d @<(cat EOF ... EOF): create a request body.
   • yc config get folder-id: get the ID of the default folder selected in the CLI.
   • base64 -i $1: Base64 encoding of the image passed in the function arguments.

3. Now you can call this function by passing the image path in the arguments:

   vision_face_detection path/to/image.jpg