✔ 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)

Argument	Type	Required	Notes
url	string	required	URL with protocol where CompreFace is located. E.g. `http://localhost`
port	string	required	CompreFace port. E.g. `8000`
options	object	optional	Default values for face recognition services. See more here. `AllOptionsDict` object can be used in this method

Possible options:

Option	Type	Notes
det_prob_threshold	string	minimum required confidence that a recognized face is actually a face. Value is between 0.0 and 1.0
limit	integer	maximum number of faces on the image to be recognized. It recognizes the biggest faces first. Value of 0 represents no limit. Default value: 0
prediction_count	integer	maximum number of subject predictions per face. It returns the most similar subjects. Default value: 1
face_plugins	string	comma-separated slugs of face plugins. If empty, no additional information is returned. Learn more
status	boolean	if true includes system information like execution_time and plugin_version fields. Default value is false

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

CompreFace.init_face_recognition(api_key)

Inits face recognition service object.

Argument	Type	Required	Notes
api_key	string	required	Face Recognition Api Key in UUID format

Example:

from compreface.service import RecognitionService

API_KEY: str = 'your_face_recognition_key'

recognition: RecognitionService = compre_face.init_face_recognition(API_KEY)

CompreFace.init_face_detection(api_key)

Inits face detection service object.

Argument	Type	Required	Notes
api_key	string	required	Face Detection Api Key in UUID format

Example:

from compreface.service import DetectionService

DETECTION_API_KEY: str = 'your_face_detection_key'

detection: DetectionService = compre_face.init_face_detection(DETECTION_API_KEY)

CompreFace.init_face_verification(api_key)

Inits face verification service object.

Argument	Type	Required	Notes
api_key	string	required	Face Verification Api Key in UUID format

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

Option	Type	Notes
det_prob_threshold	string	minimum required confidence that a recognized face is actually a face. Value is between 0.0 and 1.0
limit	integer	maximum number of faces on the image to be recognized. It recognizes the biggest faces first. Value of 0 represents no limit. Default value: 0
prediction_count	integer	maximum number of subject predictions per face. It returns the most similar subjects. Default value: 1
face_plugins	string	comma-separated slugs of face plugins. If empty, no additional information is returned. Learn more
status	boolean	if true includes system information like execution_time and plugin_version fields. Default value is false

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)

Argument	Type	Required	Notes
image_path	image	required	Image can pass from url, local path or bytes. Max size is 5Mb
options	object	optional	`AllOptionsDict` object can be used in this method. See more here.

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"
  }
}

Element	Type	Description
age	object	detected age range. Return only if age plugin is enabled
gender	object	detected gender. Return only if gender plugin is enabled
mask	object	detected mask. Return only if face mask plugin is enabled.
embedding	array	face embeddings. Return only if calculator plugin is enabled
box	object	list of parameters of the bounding box for this face
probability	float	probability that a found face is actually a face
x_max, y_max, x_min, y_min	integer	coordinates of the frame containing the face
landmarks	array	list of the coordinates of the frame containing the face-landmarks.
subjects	list	list of similar subjects with size of <prediction_count> order by similarity
similarity	float	similarity that on that image predicted person
subject	string	name of the subject in Face Collection
execution_time	object	execution time of all plugins
plugins_versions	object	contains information about plugin versions

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)

Argument	Type	Required	Notes
image_path	image	required	Image can pass from url, local path or bytes. Max size is 5Mb
subject	string	required	is the name you assign to the image you save
options	object	optional	`DetProbOptionsDict` object can be used in this method. See more here.

Response:

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

Element	Type	Description
image_id	UUID	UUID of uploaded image
subject	string	Subject of the saved image

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>
    },
    ...
  ]
}

Element	Type	Description
image_id	UUID	UUID of the face
subject	string	of the person, whose picture was saved for this api key

Delete All Examples of the Subject by Name

Example

To delete all image examples of the :

face_collection.delete_all(subject)

Argument	Type	Required	Notes
subject	string	optional	is the name you assign to the image you save. If this parameter is absent, all faces in Face Collection will be removed

Response:

{
    "deleted": <count>
}

Element	Type	Description
deleted	integer	Number of deleted faces

Delete an Example of the Subject by ID

Example

To delete an image by ID:

face_collection.delete(image_id)

Argument	Type	Required	Notes
image_id	UUID	required	UUID of the removing face

Response:

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

Element	Type	Description
image_id	UUID	UUID of the removed face
subject	string	of the person, whose picture was saved for this api key

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.

Argument	Type	Required	Notes
image_path	image	required	Image can pass from url, local path or bytes. Max size is 5Mb
image_id	UUID	required	UUID of the verifying face
options	string	Object	`ExpandedOptionsDict` object can be used in this method. See more here.

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"
  }
}

Element	Type	Description
age	object	detected age range. Return only if age plugin is enabled
gender	object	detected gender. Return only if gender plugin is enabled
mask	object	detected mask. Return only if face mask plugin is enabled.
embedding	array	face embeddings. Return only if calculator plugin is enabled
box	object	list of parameters of the bounding box for this face
probability	float	probability that a found face is actually a face
x_max, y_max, x_min, y_min	integer	coordinates of the frame containing the face
landmarks	array	list of the coordinates of the frame containing the face-landmarks. Return only if landmarks plugin is enabled
similarity	float	similarity that on that image predicted person
execution_time	object	execution time of all plugins
plugins_versions	object	contains information about plugin versions

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)

Argument	Type	Required	Notes
subject	string	required	is the name of the subject. It can be any string

Response:

{
  "subject": "subject1"
}

Element	Type	Description
subject	string	is the name of the subject

List Subjects

Example

Returns all subject related to Face Collection.

subjects.list()

Response:

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

Element	Type	Description
subjects	array	the list of subjects in Face Collection

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)

Argument	Type	Required	Notes
subject	string	required	is the name of the subject that will be updated
new_name	string	required	is the name of the subject. It can be any string

Response:

{
  "updated": "true|false"
}

Element	Type	Description
updated	boolean	failed or success

Delete a Subject

Example

Delete existing subject and all saved faces.

subjects.delete(subject)

Argument	Type	Required	Notes
subject	string	required	is the name of the subject.

Response:

{
  "subject": "subject1"
}

Element	Type	Description
subject	string	is the name of the subject

Delete All Subjects

Example

Delete all existing subjects and all saved faces.

subjects.delete_all()

Response:

{
  "deleted": "<count>"
}

Element	Type	Description
deleted	integer	number of deleted subjects

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.

Argument	Type	Required	Notes
image_path	image	required	image where to detect faces. Image can pass from url, local path or bytes. Max size is 5Mb
options	string	Object	`ExpandedOptionsDict` object can be used in this method. See more here.

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"
  }
}

Element	Type	Description
age	object	detected age range. Return only if age plugin is enabled
gender	object	detected gender. Return only if gender plugin is enabled
mask	object	detected mask. Return only if face mask plugin is enabled.
embedding	array	face embeddings. Return only if calculator plugin is enabled
box	object	list of parameters of the bounding box for this face (on processedImage)
probability	float	probability that a found face is actually a face (on processedImage)
x_max, y_max, x_min, y_min	integer	coordinates of the frame containing the face (on processedImage)
landmarks	array	list of the coordinates of the frame containing the face-landmarks. Return only if landmarks plugin is enabled
execution_time	object	execution time of all plugins
plugins_versions	object	contains information about plugin versions

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.

Argument	Type	Required	Notes
image_id	UUID	required	UUID of the verifying face
source_image_path	image	required	file to be verified. Image can pass from url, local path or bytes. Max size is 5Mb
target_image_path	image	required	reference file to check the source file. Image can pass from url, local path or bytes. Max size is 5Mb
options	string	Object	`ExpandedOptionsDict` object can be used in this method. See more here.

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"
    }
  }]
}

Element	Type	Description
source_image_face	object	additional info about source image face
face_matches	array	result of face verification
age	object	detected age range. Return only if age plugin is enabled
gender	object	detected gender. Return only if gender plugin is enabled
mask	object	detected mask. Return only if face mask plugin is enabled.
embedding	array	face embeddings. Return only if calculator plugin is enabled
box	object	list of parameters of the bounding box for this face
probability	float	probability that a found face is actually a face
x_max, y_max, x_min, y_min	integer	coordinates of the frame containing the face
landmarks	array	list of the coordinates of the frame containing the face-landmarks. Return only if landmarks plugin is enabled
similarity	float	similarity between this face and the face on the source image
execution_time	object	execution time of all plugins
plugins_versions	object	contains information about plugin versions

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.

Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
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.

Keywords

FAQs

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Install

compreface-sdk

CompreFace Python SDK

Table of content

Requirements

CompreFace compatibility matrix

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

Keywords

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