Assessing image quality

Image classification is currently in the Preview stage.

To evaluate the quality of an image, use the Image Classification feature.

In the batchAnalyze method, set the type property to Classification, and in the configuration, specify the quality model.

Examples

Before getting started

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

Apply the model to assess quality

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": "CLASSIFICATION",
               "classificationConfig": {
                   "model": "quality"
               }
           }]
       }]
   }
   

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
   

   The response will contain the properties and the probability of matching them. You can use these properties to moderate the image:

   {
     "results": [{
       "results": [{
         "faceDetection": {
           "properties": [{
               "name": "low",
               "probability": 0.001466292142868043
             },
             {
               "name": "medium",
               "probability": 0.003421348333358767
             },
             {
               "name": "high",
               "probability": 0.99511235952377319
             }
           ]
         }
       }]
     }]
   }
   

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_quality() {
       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": "CLASSIFICATION",
               "classificationConfig": {
                   "model": "quality"
               }
           }]
       }]
   }
   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_quality path/to/image.jpg