In this guide, we will show you how to integrate your application with the Realeyes Face Verification Library.
The list of dependencies and licensing information for the Face Verification Library is available here
You will need a model file for this library to work.
The latest version of the Face Verification Library is published on demand. To request the package with the library and the model file please visit the Developers Portal SDK page (login required).
Usage
The first step to use the Face Verification Library is to import the library to your project.
After that you can instantiate an FaceVerifier object.
You should provide the model file name and, optionally the maximum number of concurrent calculations in the background in the parameters.
You can call multiple detectFaces(), embedFace() and compareFaces() function calls.
To analyze one image for faces and estimations you can do the followings:
The following example shows the basic usage of the library :
include "faceverifier.h"
include "opencv2/opencv.hpp"
void main()
{
cv::Mat img1 = cv2::imread("1.png");
cv::Mat img2 = cv2::imread("2.png");
fvl::ImageHeader img_hdr1 = {img1.data, img1.cols, img1.rows, static_cast<int>(img1.step), fvl::ImageFormat::BGR};
fvl::ImageHeader img_hdr2 = {img1.data, img1.cols, img1.rows, static_cast<int>(img1.step), fvl::ImageFormat::BGR};
fvl::FaceVerifier fv("model_fv.realZ", 0)
auto faces1 = fv.detectFaces(img_hdr1).get();
auto faces2 = fv.detectFaces(img_hdr2).get();
// let's say we have 1-1 faces in the images
auto emb1 = fv.embedFace(faces1[0]).get();
auto emb2 = fv.embedFace(faces2[0]).get();
float similarity = fv.compareFaces(emb1, emb2);
}
The latest version of the Face Verification Library is published in pypi.org. You can install it with this command: 'pip install realeyes.face_verification'.
You will need a model file for this library to work. To request the model file please visit the Developers Portal SDK page (login required).
Usage
The first step to use the Face Verification Library is to import the realeyes.face_verification module. After that you can instantiate an FaceVerifier object.
You should provide the model file name and the maximum number of concurrent calculations in the background in the parameters.
You can call multiple detect_faces(), embed_face() and compare_faces() function calls.
To analyze one image for faces and estimations you can do the followings:
The following example shows the basic usage of the library:
import cv2
import realeyes.face_verification as fv
img1 = cv2.imread("1.png")
img2 = cv2.imread("2.png")
verifier = fv.FaceVerifier("model_fv.realZ", 0)
faces1 = verifier.detect_faces(img1)
faces2 = verifier.detect_faces(img2)
# let's say we have 1-1 faces in the images
emb1 = verifier.embed_face(faces1[0])
emb2 = verifier.embed_face(faces2[0])
similarity = verifier.compare_faces(emb1, emb2)
The latest version of the Face Verification Library is published in nuget.org. You can simply search for the NuGet package called Realeyes.FaceVerification and add to your project.
You will need a model file for this library to work. To request the model file please visit the Developers Portal SDK page (login required).
Usage
The first step to make sure you imported the FaceVerification namespace in your source file.
Then you can instantiate an FaceVerifier object.
You should provide the model file name in the parameters and the maximum number of concurrent calculations in the background (default: 0, which means automatic).
To analyze an image first you need to call the DetectFaces() method.
After you have detected the faces in the image you can call EmbedFace() on each [Face][face] object. This method will return an the embeddings of the face.
Finally you can compare the embeddings of two faces with the CompareFaces() method.
The following example shows the basic usage of the library:
using FaceVerification;
using System.Threading;
string png1_file = "1.png";
string png2_file = "2.png";
Image<Rgb24> img1 = SixLabors.ImageSharp.Image.Load<Rgb24>(png1_file);
byte[] bytes1 = new byte[img1.Width * img1.Height * Unsafe.SizeOf<Rgb24>()];
img1.CopyPixelDataTo(bytes1);
ImageHeader img1_hdr = new ImageHeader(bytes1, img1.Width, img1.Height,
img1.Width * Unsafe.SizeOf<Rgb24>(), ImageFormat.RGB);
Image<Rgb24> img2 = SixLabors.ImageSharp.Image.Load<Rgb24>(png2_file);
byte[] bytes2 = new byte[img2.Width * img2.Height * Unsafe.SizeOf<Rgb24>()];
img2.CopyPixelDataTo(bytes2);
ImageHeader img2_hdr = new ImageHeader(bytes2, img2.Width, img2.Height,
img2.Width * Unsafe.SizeOf<Rgb24>(), ImageFormat.RGB);
FaceVerifier verifier = new FaceVerifier("model_de.realZ", 0);
Faces faces1 = await verifier.DetectFaces(img1_hdr)).Results;
Faces faces2 = await verifier.DetectFaces(img2_hdr)).Results;
// let's say we have 1-1 faces in the images
Face face1 = faces1.GetFace(0);
Face face2 = faces2.GetFace(0);
float[] emb1 = await verifier.EmbedFace(face1)).Results;
float[] emb2 = await verifier.EmbedFace(face2)).Results;
float similarity = verifier.CompareFaces(emb1, emb2);
faces1.Dispose();
faces2.Dispose();
verifier.Dispose();
The latest version of the Face Verification Plugin is published in Unity Assets Store. You can simply search for the package called Realeyes.FaceVerification and add to your project.
You will need a model file for this library to work. To request the model file please visit the Developers Portal SDK page (login required).
Usage
The first step to make sure you imported the FaceVerification namespace in your source file.
Then you can instantiate an FaceVerifier object.
You should provide the model file name in the parameters and the maximum number of concurrent calculations in the background (default: 0, which means automatic).
To analyze an image first you need to call the DetectFaces() method.
After you have detected the faces in the image you can call EmbedFace() on each [Face][face] object. This method will return an the embeddings of the face.
Finally you can compare the embeddings of two faces with the CompareFaces() method.
The following example shows the basic usage of the library:
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using FaceVerification;
public class Main : MonoBehaviour
{
public string deviceName;
WebCamTexture wct;
FaceVerifier fv;
public float[] mainEmbeddings;
// Start is called before the first frame update
void Start()
{
fv = new FaceVerifier("./model_fv.realZ", 0);
WebCamDevice[] devices = WebCamTexture.devices;
deviceName = devices[0].name;
wct = new WebCamTexture(deviceName, 640, 480, 12);
Renderer renderer = GetComponent<Renderer>();
renderer.material.mainTexture = wct;
renderer.enabled = true;
wct.Play();
}
// Update is called once per frame
void Update()
{
GetComponent<Renderer>().material.mainTexture = wct;
}
void OnGUI()
{
if (GUI.Button(new Rect(10, 70, 150, 30), "Set Main Face"))
TakeSnapshot();
if (GUI.Button(new Rect(10, 110, 150, 30), "Check Face"))
CheckFace();
if (GUI.Button(new Rect(10, 200, 150, 30), "Exit"))
Application.Quit();
}
void TakeSnapshot()
{
Texture2D snap = new Texture2D(wct.width, wct.height);
snap.SetPixels(wct.GetPixels());
snap.Apply();
ImageHeader img = new ImageHeader(snap.GetRawTextureData(), 640, 480, 640*4, ImageFormat.RGBA);
var task = fv.DetectFaces(img);
task.Wait();
Faces faces = task.Result.Results;
if (faces.Count() >= 1)
{
var task_embed = fv.EmbedFace(faces.GetFace(0));
task_embed.Wait();
mainEmbeddings = task_embed.Result.Results;
Camera.main.backgroundColor = new Color32(137, 85, 131, 0);
}
}
public bool isSame = false;
public float similarity = 0.0f;
void CheckFace()
{
isSame = false;
Texture2D snap = new Texture2D(wct.width, wct.height);
snap.SetPixels(wct.GetPixels());
snap.Apply();
ImageHeader img = new ImageHeader(snap.GetRawTextureData(), snap.width, snap.height, snap.width * 4, ImageFormat.RGBA);
var task = fv.DetectFaces(img);
task.Wait();
Faces faces = task.Result.Results;
if (faces.Count() >= 1)
{
var task_embed = fv.EmbedFace(faces.GetFace(0));
task_embed.Wait();
float[] embeddings = task_embed.Result.Results;
similarity = fv.CompareFaces(mainEmbeddings, embeddings);
if (similarity > 0.6f)
isSame = true;
if (isSame)
Camera.main.backgroundColor = new Color32(0, 143, 0, 0);
else
Camera.main.backgroundColor = new Color32(143, 0, 0, 0);
}
else
Camera.main.backgroundColor = new Color32(128, 128, 128, 0);
}
}