CompreFace Laravel SDK makes face recognition into your application even easier.
注意:本sdk是根据compreface-javascript-sdk开发的laravel版本,仅做封装,将js改为laravel的写法,文档后半部分demo代码仍为js语法,参考前面即可
- Requirements
- Installation
- Usage
- Reference
- Contributing
- License info
Before using our SDK make sure you have installed CompreFace and Nodejs on your machine.
- CompreFace (See below compatibility matrix)
- Nodejs (Version 10+)
CompreFace Laravel SDK version | CompreFace 0.4.x | CompreFace 0.5.x | CompreFace 0.6.x | CompreFace 1.0.x |
---|---|---|---|---|
1.0.x | ✘ | 🟡 | 🟡 | ✔ |
Explanation:
- ✔ SDK supports all functionality from CompreFace.
- 🟡 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
To add CompreFace Laravel SDK to your project, run the following command in the project folder:
composer require aoding9/compreface-laravel-sdk
To start using Laravel SDK you need to publish the SDK config file:
php artisan vendor:publish --provider="Aoding9\CompreFace\CompreFaceServiceProvider"
Then you need to set url
and port
in config/compreFace.php
or in .env
file. By default, if you run CompreFace on your local machine, it's
http://localhost` and 8000
respectively.
You can pass optional options
object when creating CompreFace to set default parameters, see reference for more information.
# .env
COMPRE_FACE_SEVER=http://localhost
COMPRE_FACE_PORT=8000
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.
use Aoding9\CompreFace\CompreFace;
$compreFace = app(CompreFace::class);
$recognitionService = $compreFace->initFaceRecognitionService($api_key); // initialize service
$faceCollection = $recognitionService->getFaceCollection(); // use face collection to fill it with known faces
$list = $faceCollection->list();
$subjects = $recognitionService->getSubjects(); // use subjects object to work with subjects directely
$path_to_image = public_path("uploads/images/wujing2.jpg");
$options = [
'limit' => 0,
//'det_prob_threshold' => 0.8,
//'prediction_count' => 1,
//'face_plugins' => "calculator,age,gender,landmarks",
//'status' => "true",
];
$recognize = $recognitionService->recognize($path_to_image, $options);
dd($recognize['result'][0]['subjects'][0]['subject'] ?? $recognize); // 0.98546;
Here is Laravel code example that shows how to add an image to your face collection from your file system:
$path_to_image = public_path("images/boy.jpg");
$name ='Tom';
$faceCollection->add($path_to_image, $name);
This code snippet shows how to recognize unknown face:
$path_to_image = public_path("images/boy.jpg");
$recognitionService->recognize($path_to_image);
NOTE: We provide 3 ways of uploading image to our SDK. They are url, blob and absolute path (from local machine).
Enviroments | from URL | with Blob format | from local machine |
---|---|---|---|
Browser | ✔ | ✔ | ✘ |
Laravel | ✔ | ✔ | ✔ |
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:
new CompreFace(server, 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 |
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:
$server = "http://localhost";
$port = 8000;
$options = [
'limit'=> 0,
'det_prob_threshold'=> 0.8,
'prediction_count'=> 1,
'face_plugins'=> "calculator,age,gender,landmarks",
'status'=> "true"
];
$compreFace = new CompreFace($server, $port, $options);
compreFace.initFaceRecognitionService(api_key)
Inits face recognition service object.
Argument | Type | Required | Notes |
---|---|---|---|
api_key | string | required | Face Recognition Api Key in UUID format |
Example:
let recognitionService = compreFace.initFaceRecognitionService(api_key);
compreFace.initFaceDetectionService(api_key)
Inits face detection service object.
Argument | Type | Required | Notes |
---|---|---|---|
api_key | string | required | Face Detection Api Key in UUID format |
Example:
let detectionService = compreFace.initFaceDetectionService(api_key);
compreFace.initFaceVerificationService(api_key)
Inits face verification service object.
Argument | Type | Required | Notes |
---|---|---|---|
api_key | string | required | Face Verification Api Key in UUID format |
Example:
let verificationService = compreFace.initFaceVerificationService(api_key);
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.
recognitionService.recognize(image_location, options)
Recognizes all faces from the image. The first argument is the image location, it could be a URL or a path on the local machine.
Argument | Type | Required | Notes |
---|---|---|---|
image_location | string | required | URL, image in BLOB format or image from your local machine |
options | string | optional | Object that defines recognition options |
Supported 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 | object | 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 |
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 |
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 |
Example:
$image_location = public_path("images/team.jpg");
$options = {
'limit'=> 0,
'det_prob_threshold'=> 0.8,
'prediction_count'=> 1,
'face_plugins'=> "calculator,age,gender,landmarks",
'status'=> "true"
}
$recognitionService->recognize($image_location, $options)
recognitionService.getFaceCollection()
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:
faceCollection.add(image_location, subject, options)
Adds an image to your face collection.
Argument | Type | Required | Notes |
---|---|---|---|
image_location | string | required | URL, image in BLOB format or image from your local machine |
subject | string | required | Name or any other person ID. It can be just a random string you generate and save for further identification |
options | string | optional | Object that defines adding options |
Supported 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 |
Response:
{
"image_id": "string",
"subject": "string"
}
Field | string | Notes |
---|---|---|
image_id | string | ID of the saved image |
subject | string | Name or any other person ID |
Example:
$image_location = public_path("images/boy.jpg");
$name = 'Tom';
$options = [
'det_prob_threshold'=> 0.8
];
$faceCollection->add($image_location, $name, $options);
faceCollection.list()
Retrieve a list of images saved in a Face Collection
Response:
{
"faces": [
{
"image_id": "string",
"subject": "string"
}
]
}
Field | string | Notes |
---|---|---|
image_id | string | ID of the saved image |
subject | string | Name or any other person ID |
Example:
faceCollection.list()
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(`Oops! There is problem: ${error}`)
})
faceCollection.delete_all_subject(subject)
Removes image(s) according to their given subject.
Argument | Type | Required | Notes |
---|---|---|---|
subject | string | optional | Name or any other person ID. If empty deletes all images in the face collection |
Response:
{
"deleted": <count>
}
Element | Type | Description |
---|---|---|
deleted | integer | Number of deleted faces |
Example:
let subject = "Tom";
faceCollection.delete(subject)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(`Oops! There is problem ${error}`)
})
faceCollection.delete(image_id)
Remove image from face collection.
Argument | Type | Required | Notes |
---|---|---|---|
image_id | string | required | ID of the saved image |
Response:
{
"image_id": "string",
"subject": "string"
}
Field | string | Notes |
---|---|---|
image_id | string | ID of the deleted image |
subject | string | Name or any other person ID |
Example:
let image_id = "79ed78d8-f015-4947-b297-a24306ebbdad";
faceCollection.delete(image_id)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(`Oops! There is problem ${error}`)
})
faceCollection.delete_multiple_images(image_ids)
Remove images from face collection.
Argument | Type | Required | Notes |
---|---|---|---|
image_ids | string[] | required | IDs of the saved images to delete |
Response:
{
"image_id": "string",
"subject": "string"
}
Field | string | Notes |
---|---|---|
image_id | string | ID of the deleted image |
subject | string | Name or any other person ID |
Example:
let image_id = "79ed78d8-f015-4947-b297-a24306ebbdad";
faceCollection.delete(image_id)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(`Oops! There is problem ${error}`)
})
faceCollection.verify(image_path, image_id, options)
Compares similarities of given image with image from your face collection.
Argument | Type | Required | Notes |
---|---|---|---|
image_location | string | required | URL, image in BLOB format or image from your local machine |
options | string | optional | Object that defines recognition options |
Supported 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 | object | 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 |
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 |
let image_location = "../images/team.jpg";
let image_id = "79ed78d8-f015-4947-b297-a24306ebbdad";
let options = {
limit: 0,
det_prob_threshold: 0.8,
prediction_count: 1,
face_plugins: "calculator,age,gender,landmarks,mask",
status: "true"
}
faceCollection.verify(image_location, image_id, options)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(`Oops! There is problem with verifying image ${error}`)
})
recognitionService.getSubjects()
Returns subjects object
Subjects object allows working with subjects directly (not via subject examples).
More information about subjects here
let subjects = recognitionService.getSubjects();
Methods:
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 |
let subjects = recognitionService.getSubjects();
subjects.add("John");
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 |
let subjects = recognitionService.getSubjects();
console.log(subjects.list());
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 |
let subjects = recognitionService.getSubjects();
subjects.add("John");
console.log(subjects.list());
subjects.rename("John", "Jane");
console.log(subjects.list());
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 |
let subjects = recognitionService.getSubjects();
subjects.add("John");
console.log(subjects.list());
subjects.delete("John");
console.log(subjects.list());
Delete all existing subjects and all saved faces.
subjects.deleteAll()
Response:
{
"deleted": "<count>"
}
Element | Type | Description |
---|---|---|
deleted | integer | number of deleted subjects |
let subjects = recognitionService.getSubjects();
subjects.add("John");
subjects.add("Jane");
console.log(subjects.list());
subjects.deleteAll();
console.log(subjects.list());
Face detection service is used for detecting faces in the image.
Methods:
detectionService.detect(image_location, options)
Finds all faces on the image. The first argument is the image location, it could be a URL or a path on the local machine.
Argument | Type | Required | Notes |
---|---|---|---|
image_location | string | required | URL, image in BLOB format or image from your local machine |
options | string | optional | Object that defines detection options |
Supported 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 |
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 |
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 |
Example:
let image_location = "../images/team.jpg";
let options = {
limit: 0,
det_prob_threshold: 0.8,
face_plugins: "calculator,age,gender,landmarks",
status: "true"
}
detectionService.detect(image_location, options)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(`Oops! There is problem with recognizing image ${error}`)
})
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_location, target_image_location, 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. The first two arguments are the image location, it could be a URL or a path on the local machine.
Argument | Type | Required | Notes |
---|---|---|---|
source_image_location | string | required | URL, source image in BLOB format or source image from your local machine |
target_image_location | string | required | URL, target image in BLOB format or target image from your local machine |
options | string | optional | Object that defines detection options |
Supported 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 |
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 |
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 |
Example:
let source_image_location = "../images/boy.jpg";
let target_image_location = "../images/team.jpg";
let options = {
limit: 0,
det_prob_threshold: 0.8,
face_plugins: "calculator,age,gender,landmarks",
status: "true"
}
verificationService.verify(source_image_location, target_image_location, options)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(`Oops! There is problem with recognizing image ${error}`)
})
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.
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
The best way to send us feedback is to file an issue at https://github.com/exadel-inc/compreface-javascript-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.
CompreFace JS SDK is open-source facial recognition SDK released under the Apache 2.0 license.