compreface-sdk 0.1.0

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

CompreFace Python SDK

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

Table of content

• Requirements
• Installation
• Usage
  • Initialization
  • Example Add an Example of a Subject
  • Example Recognize Faces from a Given Image
  • Example List of All Saved Subjects
  • Example Delete All Examples of the Subject by Name
  • Example Delete an Example of the Subject by ID
  • Example Compare Faces from a Given Image
  • Example Detect Faces from a Given Image
  • Example Verify Face from a Given Images
• Reference
  • CompreFace Global Object
  • Options structure
  • Face Recognition Service
    • Add an Example of a Subject
    • Recognize Faces from a Given Image
    • List of All Saved Subjects
    • Delete All Examples of the Subject by Name
    • Delete an Example of the Subject by ID
    • Verify Faces from a Given Image
  • Face Detection Service
  • Face Verification Service

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

CompreFace Python SDK version	CompreFace 0.5.x
0.1.0	✔

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

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()

Example. Add an Example of a Subject

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)

Example. Recognize Faces from a Given Image

This code snippet shows how to recognize unknown face.

image_path: "str or bytes"

recognition.recognize(image_path=image_path)

Example. List of All Saved Subjects

This code shows how to retrieve a list of subject examples saved in a Face Collection:

face_collection.list()

Example. Delete All Examples of the Subject by Name

This code shows how to delete all image examples of the subject:

subject: str = 'Jonathan Petit'

print(face_collection.delete_all(subject))

Example. Delete an Example of the Subject by ID

This example to delete an image by ID:

faces: list = face_collection.list().get('faces')

if(len(faces) != 0):
    last_face: dict = faces[len(faces) - 1]
    print(face_collection.delete(last_face.get('image_id')))
else:
    print('No subject found')

Example. Compare Faces from a Given Image

This example shows how to compare unknown face with existing face in face collection:

image_path: "str or bytes"

face: dict = next(item for item in face_collection.list().get('faces') if item['subject'] ==
                  'Jonathan Petit')

image_id = face.get('image_id')

face_collection.verify(image_path=image_path, image_id=image_id)

Example. Detect Faces from a Given Image

Here is example to detect faces from a given image.

from compreface import CompreFace
from compreface.service import DetectionService

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

compre_face: CompreFace = CompreFace(DOMAIN, PORT)

detection: DetectionService = compre_face.init_face_detection(DETECTION_API_KEY)

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

detection.detect(image_path=image_path)

Example. Verify Face from a Given Images

Here is example to verify face from a given images.

from compreface import CompreFace
from compreface.service import VerificationService

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

compre_face: CompreFace = CompreFace(DOMAIN, PORT)

verify: VerificationService = compre_face.init_face_verification(VERIFICATION_API_KEY)

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

verify.verify(source_image_path=image_path, target_image_path=image_path)

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:

1. 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)

2. 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)

3. 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.

Add an Example of a Subject

This creates an example of the subject by saving images. You can add as many images as you want to train the system.

FaceCollection.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 body on success:

{
  "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

Recognize Faces from a Given Image

Recognizes faces from the uploaded image.

RecognitionService.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 body on success:

{
  "result": [
    {
      "age": [25, 32],
      "gender": "female",
      "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
      }
    }
  ],
  "plugins_versions": {
    "age": "agegender.AgeDetector",
    "gender": "agegender.GenderDetector",
    "detector": "facenet.FaceDetector",
    "calculator": "facenet.Calculator"
  }
}

Element	Type	Description
age	array	detected age range. Return only if age plugin is enabled
gender	string	detected gender. Return only if gender 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

List of All Saved Subjects

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

FaceCollection.list()

Response body on success:

{
  "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

To delete all image examples of the :

FaceCollection.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 body on success:

[
  {
    "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

Delete an Example of the Subject by ID

To delete an image by ID:

FaceCollection.delete(image_id)

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

Response body on success:

{
  "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

FaceCollection.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 body on success:

{
  "result": [
    {
      "box": {
        "probability": <probability>,
        "x_max": <integer>,
        "y_max": <integer>,
        "x_min": <integer>,
        "y_min": <integer>
      },
      "similarity": <similarity1>
    },
    ...
  ]
}

Element	Type	Description
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
similarity	float	similarity that on that image predicted person
subject	string	name of the subject in Face Collection

Face Detection Service

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

Methods:

Detect

DetectionService.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 body on success:

{
  "result": [
    {
      "age": [25, 32],
      "gender": "female",
      "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
      }
    }
  ],
  "plugins_versions": {
    "age": "agegender.AgeDetector",
    "gender": "agegender.GenderDetector",
    "detector": "facenet.FaceDetector",
    "calculator": "facenet.Calculator"
  }
}

Face Verification Service

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:

VerificationService.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 body on success:

{
  "source_image_face" : {
    "age" : [ 25, 32 ],
    "gender" : "female",
    "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" : {},
  "face_matches": [
    {
      "age" : [ 25, 32 ],
      "gender" : "female",
      "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
      }
    }],
  "plugins_versions" : {
    "age" : "agegender.AgeDetector",
    "gender" : "agegender.GenderDetector",
    "detector" : "facenet.FaceDetector",
    "calculator" : "facenet.Calculator"
  }
 }
}

Element	Type	Description
source_image_face	object	additional info about source image face
face_matches	array	result of face verification
age	array	detected age range.
gender	string	detected gender. Return only if
embedding	array	face embeddings. Return only if
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
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.

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.