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CompreFace Python SDK

CompreFace Python SDK makes face recognition into your application even easier.

Table of content

• Requirements
• Installation
• Usage
  • Initialization
  • Adding faces into a face collection
  • Recognition
  • Webcam demo
• Reference
  • CompreFace Global Object
    • Methods
  • Options structure
  • Face Recognition Service
    • Recognize Faces from a Given Image
    • Get Face Collection
      • Add an Example of a Subject
      • List of All Saved Examples of the Subject
      • Delete All Examples of the Subject by Name
      • Delete an Example of the Subject by ID
      • Verify Faces from a Given Image
    • Get Subjects
      • Add a Subject
      • List Subjects
      • Rename a Subject
      • Delete a Subject
      • Delete All Subjects
    • Face Detection Service
      • Detect
    • Face Verification Service
      • Verify
• Contributing
  • Report Bugs
  • Submit Feedback
• License info

Requirements

Before using our SDK make sure you have installed CompreFace and Python on your machine.

1. CompreFace
2. Python (Version 3.7+)

CompreFace compatibility matrix

Explanation:

• ✔ SDK supports all functionality from CompreFace.
• :yellow_circle: SDK works with this CompreFace version. In case if CompreFace version is newer - SDK won't support new features of CompreFace. In case if CompreFace version is older - new SDK features will fail.
• ✘ There are major backward compatibility issues. It is not recommended to use these versions together

Installation

It can be installed through pip:

pip install compreface-sdk

Usage

All these examples you can find in repository inside examples folder.

Initialization

To start using Python SDK you need to import CompreFace object from 'compreface-sdk' dependency.

Then you need to init it with url and port. By default, if you run CompreFace on your local machine, it's http://localhost and 8000 respectively. You can pass optional options object when call method to set default parameters, see reference for more information.

After you initialized CompreFace object you need to init the service object with the api key of your face service. You can use this service object to recognize faces.

However, before recognizing you need first to add faces into the face collection. To do this, get the face collection object from the service object.

from compreface import CompreFace
from compreface.service import RecognitionService
from compreface.collections import FaceCollection
from compreface.collections.face_collections import Subjects

DOMAIN: str = 'http://localhost'
PORT: str = '8000'
API_KEY: str = 'your_face_recognition_key'

compre_face: CompreFace = CompreFace(DOMAIN, PORT)

recognition: RecognitionService = compre_face.init_face_recognition(API_KEY)

face_collection: FaceCollection = recognition.get_face_collection()

subjects: Subjects = recognition.get_subjects()

Adding faces into a face collection

Here is example that shows how to add an image to your face collection from your file system:

image_path: str = 'examples/common/jonathan-petit-unsplash.jpg'
subject: str = 'Jonathan Petit'

face_collection.add(image_path=image_path, subject=subject)

Recognition

This code snippet shows how to recognize unknown face.

image_path: str = 'examples/common/jonathan-petit-unsplash.jpg'

recognition.recognize(image_path=image_path)

Webcam demo

Webcam demo shows how to use CompreFace Recognition and Detection services using Python SDK. In both cases, age, gender and mask plugins are applied.

Follow this link to see the instructions.

Reference

CompreFace Global Object

Global CompreFace Object is used for initializing connection to CompreFace and setting default values for options. Default values will be used in every service method if applicable. If the option’s value is set in the global object and passed as a function argument then the function argument value will be used.

Constructor:

CompreFace(domain, port, options)

Possible options:

Example:

from compreface import CompreFace

DOMAIN: str = 'http://localhost'
PORT: str = '8000'

compre_face: CompreFace = CompreFace(domain=DOMAIN, port=PORT, options={
    "limit": 0,
    "det_prob_threshold": 0.8,
    "prediction_count": 1,
    "face_plugins": "calculator,age,gender,landmarks",
    "status": "true"
})

Methods

1. CompreFace.init_face_recognition(api_key)

Inits face recognition service object.

Example:

from compreface.service import RecognitionService

API_KEY: str = 'your_face_recognition_key'

recognition: RecognitionService = compre_face.init_face_recognition(API_KEY)

2. CompreFace.init_face_detection(api_key)

Inits face detection service object.

Example:

from compreface.service import DetectionService

DETECTION_API_KEY: str = 'your_face_detection_key'

detection: DetectionService = compre_face.init_face_detection(DETECTION_API_KEY)

3. CompreFace.init_face_verification(api_key)

Inits face verification service object.

Example:

from compreface.service import VerificationService

VERIFICATION_API_KEY: str = 'your_face_verification_key'

verify: VerificationService = compre_face.init_face_verification(VERIFICATION_API_KEY)

Options structure

Options is optional field in every request that contains an image. If the option’s value is set in the global object and passed as a function argument then the function argument value will be used.

class DetProbOptionsDict(TypedDict):
    det_prob_threshold: float


class ExpandedOptionsDict(DetProbOptionsDict):
    limit: int
    status: bool
    face_plugins: str


class AllOptionsDict(ExpandedOptionsDict):
    prediction_count: int

Example of face recognition with object:

recognition.recognize(image_path=image_path, options={
    "limit": 0,
    "det_prob_threshold": 0.8,
    "prediction_count": 1,
    "face_plugins": "calculator,age,gender,landmarks",
    "status": "true"
})

Face Recognition Service

Face recognition service is used for face identification. This means that you first need to upload known faces to face collection and then recognize unknown faces among them. When you upload an unknown face, the service returns the most similar faces to it. Also, face recognition service supports verify endpoint to check if this person from face collection is the correct one. For more information, see CompreFace page.

Recognize Faces from a Given Image

Example

Recognizes all faces from the image. The first argument is the image location, it can be an url, local path or bytes.

recognition.recognize(image_path, options)

Response:

{
  "result" : [ {
    "age" : {
      "probability": 0.9308982491493225,
      "high": 32,
      "low": 25
    },
    "gender" : {
      "probability": 0.9898611307144165,
      "value": "female"
    },
    "mask" : {
      "probability": 0.9999470710754395,
      "value": "without_mask"
    },
    "embedding" : [ 9.424854069948196E-4, "...", -0.011415496468544006 ],
    "box" : {
      "probability" : 1.0,
      "x_max" : 1420,
      "y_max" : 1368,
      "x_min" : 548,
      "y_min" : 295
    },
    "landmarks" : [ [ 814, 713 ], [ 1104, 829 ], [ 832, 937 ], [ 704, 1030 ], [ 1017, 1133 ] ],
    "subjects" : [ {
      "similarity" : 0.97858,
      "subject" : "subject1"
    } ],
    "execution_time" : {
      "age" : 28.0,
      "gender" : 26.0,
      "detector" : 117.0,
      "calculator" : 45.0,
      "mask": 36.0
    }
  } ],
  "plugins_versions" : {
    "age" : "agegender.AgeDetector",
    "gender" : "agegender.GenderDetector",
    "detector" : "facenet.FaceDetector",
    "calculator" : "facenet.Calculator",
    "mask": "facemask.MaskDetector"
  }
}

Get Face Collection

recognition.get_face_collection()

Returns Face collection object

Face collection could be used to manage known faces, e.g. add, list, or delete them.

Face recognition is performed for the saved known faces in face collection, so before using the recognize method you need to save at least one face into the face collection.

More information about face collection and managing examples here

Methods:

Add an Example of a Subject

Example

This creates an example of the subject by saving images. You can add as many images as you want to train the system. Image should contain only one face.

face_collection.add(image_path, subject, options)

Response:

{
  "image_id": "6b135f5b-a365-4522-b1f1-4c9ac2dd0728",
  "subject": "subject1"
}

List of All Saved Examples of the Subject

To retrieve a list of subjects saved in a Face Collection:

face_collection.list()

Response:

{
  "faces": [
    {
      "image_id": <image_id>,
      "subject": <subject>
    },
    ...
  ]
}

Delete All Examples of the Subject by Name

Example

To delete all image examples of the <subject>:

face_collection.delete_all(subject)

Response:

{
    "deleted": <count>
}

Delete an Example of the Subject by ID

Example

To delete an image by ID:

face_collection.delete(image_id)

Response:

{
  "image_id": <image_id>,
  "subject": <subject>
}

Verify Faces from a Given Image

Example

face_collection.verify(image_path, image_id, options)

Compares similarities of given image with image from your face collection.

Response:

{
  "result" : [ {
    "age" : {
      "probability": 0.9308982491493225,
      "high": 32,
      "low": 25
    },
    "gender" : {
      "probability": 0.9898611307144165,
      "value": "female"
    },
    "mask" : {
      "probability": 0.9999470710754395,
      "value": "without_mask"
    },
    "embedding" : [ 9.424854069948196E-4, "...", -0.011415496468544006 ],
    "box" : {
      "probability" : 1.0,
      "x_max" : 1420,
      "y_max" : 1368,
      "x_min" : 548,
      "y_min" : 295
    },
    "landmarks" : [ [ 814, 713 ], [ 1104, 829 ], [ 832, 937 ], [ 704, 1030 ], [ 1017, 1133 ] ],
    "subjects" : [ {
      "similarity" : 0.97858,
      "subject" : "subject1"
    } ],
    "execution_time" : {
      "age" : 28.0,
      "gender" : 26.0,
      "detector" : 117.0,
      "calculator" : 45.0,
      "mask": 36.0
    }
  } ],
  "plugins_versions" : {
    "age" : "agegender.AgeDetector",
    "gender" : "agegender.GenderDetector",
    "detector" : "facenet.FaceDetector",
    "calculator" : "facenet.Calculator",
    "mask": "facemask.MaskDetector"
  }
}

Get Subjects

recognition.get_subjects()

Returns subjects object

Subjects object allows working with subjects directly (not via subject examples).

More information about subjects here

Methods:

Add a Subject

Example

Create a new subject in Face Collection.

subjects.add(subject)

Response:

{
  "subject": "subject1"
}

List Subjects

Example

Returns all subject related to Face Collection.

subjects.list()

Response:

{
  "subjects": [
    "<subject_name1>",
    "<subject_name2>"
  ]
}

Rename a Subject

Example

Rename existing subject. If a new subject name already exists, subjects are merged - all faces from the old subject name are reassigned to the subject with the new name, old subject removed.

subjects.rename(subject, new_name)

Response:

{
  "updated": "true|false"
}

Delete a Subject

Example

Delete existing subject and all saved faces.

subjects.delete(subject)

Response:

{
  "subject": "subject1"
}

Delete All Subjects

Example

Delete all existing subjects and all saved faces.

subjects.delete_all()

Response:

{
  "deleted": "<count>"
}

Face Detection Service

Face detection service is used for detecting faces in the image.

Methods:

Detect

Example

detection.detect(image_path, options)

Finds all faces on the image.

Response:

{
  "result" : [ {
    "age" : {
      "probability": 0.9308982491493225,
      "high": 32,
      "low": 25
    },
    "gender" : {
      "probability": 0.9898611307144165,
      "value": "female"
    },
    "mask" : {
      "probability": 0.9999470710754395,
      "value": "without_mask"
    },
    "embedding" : [ -0.03027934394776821, "...", -0.05117142200469971 ],
    "box" : {
      "probability" : 0.9987509250640869,
      "x_max" : 376,
      "y_max" : 479,
      "x_min" : 68,
      "y_min" : 77
    },
    "landmarks" : [ [ 156, 245 ], [ 277, 253 ], [ 202, 311 ], [ 148, 358 ], [ 274, 365 ] ],
    "execution_time" : {
      "age" : 30.0,
      "gender" : 26.0,
      "detector" : 130.0,
      "calculator" : 49.0,
      "mask": 36.0
    }
  } ],
  "plugins_versions" : {
    "age" : "agegender.AgeDetector",
    "gender" : "agegender.GenderDetector",
    "detector" : "facenet.FaceDetector",
    "calculator" : "facenet.Calculator",
    "mask": "facemask.MaskDetector"
  }
}

Face Verification Service

Example

Face verification service is used for comparing two images. A source image should contain only one face which will be compared to all faces on the target image.

Methods:

Verify

verify.verify(source_image_path, target_image_path, options)

Compares two images provided in arguments. Source image should contain only one face, it will be compared to all faces in the target image.

Response:

{
  "result" : [{
    "source_image_face" : {
      "age" : {
        "probability": 0.9308982491493225,
        "high": 32,
        "low": 25
      },
      "gender" : {
        "probability": 0.9898611307144165,
        "value": "female"
      },
      "mask" : {
        "probability": 0.9999470710754395,
        "value": "without_mask"
      },
      "embedding" : [ -0.0010271212086081505, "...", -0.008746841922402382 ],
      "box" : {
        "probability" : 0.9997453093528748,
        "x_max" : 205,
        "y_max" : 167,
        "x_min" : 48,
        "y_min" : 0
      },
      "landmarks" : [ [ 92, 44 ], [ 130, 68 ], [ 71, 76 ], [ 60, 104 ], [ 95, 125 ] ],
      "execution_time" : {
        "age" : 85.0,
        "gender" : 51.0,
        "detector" : 67.0,
        "calculator" : 116.0,
        "mask": 36.0
      }
    },
    "face_matches": [
      {
        "age" : {
          "probability": 0.9308982491493225,
          "high": 32,
          "low": 25
        },
        "gender" : {
          "probability": 0.9898611307144165,
          "value": "female"
        },
        "mask" : {
          "probability": 0.9999470710754395,
          "value": "without_mask"
        },
        "embedding" : [ -0.049007344990968704, "...", -0.01753818802535534 ],
        "box" : {
          "probability" : 0.99975,
          "x_max" : 308,
          "y_max" : 180,
          "x_min" : 235,
          "y_min" : 98
        },
        "landmarks" : [ [ 260, 129 ], [ 273, 127 ], [ 258, 136 ], [ 257, 150 ], [ 269, 148 ] ],
        "similarity" : 0.97858,
        "execution_time" : {
          "age" : 59.0,
          "gender" : 30.0,
          "detector" : 177.0,
          "calculator" : 70.0,
          "mask": 36.0
        }
      }],
    "plugins_versions" : {
      "age" : "agegender.AgeDetector",
      "gender" : "agegender.GenderDetector",
      "detector" : "facenet.FaceDetector",
      "calculator" : "facenet.Calculator",
      "mask": "facemask.MaskDetector"
    }
  }]
}

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

After creating your first contributing pull request, you will receive a request to sign our Contributor License Agreement by commenting your pull request with a special message.

Report Bugs

Please report any bugs here.

If you are reporting a bug, please specify:

• Your operating system name and version
• Any details about your local setup that might be helpful in troubleshooting
• Detailed steps to reproduce the bug

Submit Feedback

The best way to send us feedback is to file an issue at https://github.com/exadel-inc/compreface-python-sdk/issues.

If you are proposing a feature, please:

• Explain in detail how it should work.
• Keep the scope as narrow as possible to make it easier to implement.

License info

CompreFace Python SDK is open-source facial recognition SDK released under the Apache 2.0 license.