JavaScript SDK for CompreFace - free and open-source face recognition system from Exadel
npm install test3-cf-sdknpm i @exadel/compreface-js-sdk`
Usage
Initialization
To start using JavaScript SDK you need to import CompreFace object from 'compreface-js-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 creating CompreFace 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.
`javascript
import { CompreFace } from 'compreface-js-sdk';
let api_key = "your_key";
let url = "http://localhost";
let port = 8000;
let compreFace = new CompreFace(url, port); // set CompreFace url and port
let recognitionService = compreFace.initFaceRecognitionService(api_key); // initialize service
let faceCollection = recognitionService.getFaceCollection(); // use face collection to fill it with known faces
let subjects = recognitionService.getSubjects(); // use subjects object to work with subjects directely
`
Adding faces into a face collection
Here is JavaScript code example that shows how to add an image to your face collection from your file system:
`javascript
let path_to_image = "../images/boy.jpg";
let name = encodeURIComponent('Tom');
faceCollection.add(path_to_image, name)
.then(response => {
// your code
})
.catch(error => {
console.log(Oops! There is problem in uploading image ${error})
})
`
Recognition
This code snippet shows how to recognize unknown face:
`javascript
let path_to_image = "../images/team.jpg";
recognitionService.recognize(path_to_image)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(Oops! There is problem with recognizing image ${error})
})
`
Environments
NOTE: We provide 3 ways of uploading image to our SDK. They are url, blob and relative path (from local machine).
| Enviroments | from URL | with Blob format | from local machine|
| ------------|--------- | ---------------- | ---------------- |
| Browser | ✔ | ✔ | ✘ |
| Nodejs | ✔ | ✔ | ✔ |
Webcam demo
Documentation is here
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:
`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:
`js
let server = "http://localhost";
let port = 8000;
let options = {
limit: 0,
det_prob_threshold: 0.8,
prediction_count: 1,
face_plugins: "calculator,age,gender,landmarks",
status: "true"
}
let compreFace = new CompreFace(server, port, options);
`
$3
1. `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:
`js
let recognitionService = compreFace.initFaceRecognitionService(api_key);
`
2. `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:
`js
let detectionService = compreFace.initFaceDetectionService(api_key);
`
3. `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:
`js
let verificationService = compreFace.initFaceVerificationService(api_key);
`
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.
$3
`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:
`json
{
"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 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:
`javascript
let image_location = "../images/team.jpg";
let options = {
limit: 0,
det_prob_threshold: 0.8,
prediction_count: 1,
face_plugins: "calculator,age,gender,landmarks",
status: "true"
}
recognitionService.recognize(image_location, options)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(Oops! There is problem with recognizing image ${error})
})
`
$3
`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:
#### Add an Example of a Subject
`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:
`json
{
"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:
`javascript
let image_location = "../images/boy.jpg";
let name = encodeURIComponent('Tom');
let options = {
det_prob_threshold: 0.8
}
faceCollection.add(image_location, name, options)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(Oops! There is problem in uploading image ${error})
})
`
#### List of All Saved Examples of the Subject
`faceCollection.list()`
Retrieve a list of images saved in a Face Collection
Response:
`json
{
"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:
`javascript
faceCollection.list()
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(Oops! There is problem: ${error})
})
`
#### Delete All Examples of the Subject by Name
`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":
}
`
| Element | Type | Description |
| -------- | ------- | ------------------------ |
| deleted | integer | Number of deleted faces |
Example:
`javascript
let subject = "Tom";
faceCollection.delete(subject)
.then(response => {
console.log(JSON.stringify(response));
})
.catch(error => {
console.log(Oops! There is problem ${error})
})
`
#### Delete an Example of the Subject by ID
`faceCollection.delete(image_id)`
Remove image from face collection.
| Argument | Type | Required | Notes |
| --------------- | ------ | -------- | ----------------------------------------- |
| image_id | string | required | ID of the saved image |
Response:
`json
{
"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:
`javascript
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})
})
`
#### 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_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:
`json
{
"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 |
`javascript
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})
})
`
$3
`javascript
recognitionService.getSubjects()
`
Returns subjects object
Subjects object allows working with subjects directly (not via subject examples).
More information about subjects here
`javascript
let subjects = recognitionService.getSubjects();
`
Methods:
#### Add a Subject
Create a new subject in Face Collection.
`javascript
subjects.add(subject)
`
| Argument | Type | Required | Notes |
| ------------------ | ------ | -------- | ------------------------------------------------------------------------|
| subject | string | required | is the name of the subject. It can be any string |
Response:
`json
{
"subject": "subject1"
}
`
| Element | Type | Description |
| -------- | ------ | -------------------------- |
| subject | string | is the name of the subject |
`javascript
let subjects = recognitionService.getSubjects();
subjects.add("John");
`
#### List Subjects
Returns all subject related to Face Collection.
`javascript
subjects.list()
`
Response:
`json
{
"subjects": [
"",
""
]
}
`
| Element | Type | Description |
| -------- | ------ | -------------------------- |
| subjects | array | the list of subjects in Face Collection |
`javascript
let subjects = recognitionService.getSubjects();
console.log(subjects.list());
`
#### Rename a Subject
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.
`javascript
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:
`json
{
"updated": "true|false"
}
`
| Element | Type | Description |
| -------- | ------ | -------------------------- |
| updated | boolean | failed or success |
`javascript
let subjects = recognitionService.getSubjects();
subjects.add("John");
console.log(subjects.list());
subjects.rename("John", "Jane");
console.log(subjects.list());
`
#### Delete a Subject
Delete existing subject and all saved faces.
`javascript
subjects.delete(subject)
`
| Argument | Type | Required | Notes |
| ------------------ | ------ | -------- | ------------------------------------------------------------------------|
| subject | string | required | is the name of the subject. |
Response:
`json
{
"subject": "subject1"
}
`
| Element | Type | Description |
| -------- | ------ | -------------------------- |
| subject | string | is the name of the subject |
`javascript
let subjects = recognitionService.getSubjects();
subjects.add("John");
console.log(subjects.list());
subjects.delete("John");
console.log(subjects.list());
`
#### Delete All Subjects
Delete all existing subjects and all saved faces.
`javascript
subjects.deleteAll()
`
Response:
`json
{
"deleted": ""
}
`
| Element | Type | Description |
| -------- | ------ | -------------------------- |
| deleted | integer | number of deleted subjects |
`javascript
let subjects = recognitionService.getSubjects();
subjects.add("John");
subjects.add("Jane");
console.log(subjects.list());
subjects.deleteAll();
console.log(subjects.list());
`
Face Detection Service
Face detection service is used for detecting faces in the image.
Methods:
$3
`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:
`json
{
"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:
`javascript
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
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:
$3
`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:
`json
{
"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:
`javascript
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})
})
`
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`)