// Start training from the collected faces.
// The face recognition algorithm can be one of these and perhaps more, depending on your version of OpenCV, which must be atleast v2.4.1:
// "FaceRecognizer.Eigenfaces": Eigenfaces, also referred to as PCA (Turk and Pentland, 1991).
// "FaceRecognizer.Fisherfaces": Fisherfaces, also referred to as LDA (Belhumeur et al, 1997).
// "FaceRecognizer.LBPH": Local Binary Pattern Histograms (Ahonen et al, 2006).
public static BasicFaceRecognizer LearnCollectedFaces(List <Mat> preprocessedFaces, List <int> faceLabels, string facerecAlgorithm = "FaceRecognizer.Eigenfaces")
{
BasicFaceRecognizer model = null;
Debug.Log("Learning the collected faces using the [" + facerecAlgorithm + "] algorithm ...");
if (facerecAlgorithm == "FaceRecognizer.Fisherfaces")
{
model = FisherFaceRecognizer.create();
}
else if (facerecAlgorithm == "FaceRecognizer.Eigenfaces")
{
model = EigenFaceRecognizer.create();
}
if (model == null)
{
Debug.LogError("ERROR: The FaceRecognizer algorithm [" + facerecAlgorithm + "] is not available in your version of OpenCV. Please update to OpenCV v2.4.1 or newer.");
//exit(1);
}
// Do the actual training from the collected faces. Might take several seconds or minutes depending on input!
MatOfInt labels = new MatOfInt();
labels.fromList(faceLabels);
model.train(preprocessedFaces, labels);
return(model);
}
private void Run()
{
List <Mat> images = new List <Mat> ();
List <int> labelsList = new List <int> ();
MatOfInt labels = new MatOfInt();
images.Add(Imgcodecs.imread(image_0_filepath, 0));
images.Add(Imgcodecs.imread(image_1_filepath, 0));
labelsList.Add(0);
labelsList.Add(1);
labels.fromList(labelsList);
Mat testSampleMat = Imgcodecs.imread(sample_image_filepath, 0);
int testSampleLabel = 0;
// foreach (Mat item in images) {
// Debug.Log ("images.ToString " + item.ToString ());
// }
// foreach (int item in labelsList) {
// Debug.Log ("labels.ToString " + item.ToString ());
// }
int[] predictedLabel = new int[1];
double[] predictedConfidence = new double[1];
BasicFaceRecognizer faceRecognizer = EigenFaceRecognizer.create();
faceRecognizer.train(images, labels);
faceRecognizer.predict(testSampleMat, predictedLabel, predictedConfidence);
Debug.Log("Predicted class: " + predictedLabel [0] + " / " + "Actual class: " + testSampleLabel);
Debug.Log("Confidence: " + predictedConfidence [0]);
Mat predictedMat = images [predictedLabel [0]];
Mat baseMat = new Mat(testSampleMat.rows(), predictedMat.cols() + testSampleMat.cols(), CvType.CV_8UC1);
predictedMat.copyTo(baseMat.submat(new OpenCVForUnity.CoreModule.Rect(0, 0, predictedMat.cols(), predictedMat.rows())));
testSampleMat.copyTo(baseMat.submat(new OpenCVForUnity.CoreModule.Rect(predictedMat.cols(), 0, testSampleMat.cols(), testSampleMat.rows())));
Imgproc.putText(baseMat, "Predicted", new Point(10, 15), Imgproc.FONT_HERSHEY_SIMPLEX, 0.4, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, "Confidence:", new Point(5, 25), Imgproc.FONT_HERSHEY_SIMPLEX, 0.2, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, " " + predictedConfidence [0], new Point(5, 33), Imgproc.FONT_HERSHEY_SIMPLEX, 0.2, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, "TestSample", new Point(predictedMat.cols() + 10, 15), Imgproc.FONT_HERSHEY_SIMPLEX, 0.4, new Scalar(255), 1, Imgproc.LINE_AA, false);
Texture2D texture = new Texture2D(baseMat.cols(), baseMat.rows(), TextureFormat.RGBA32, false);
Utils.matToTexture2D(baseMat, texture);
gameObject.GetComponent <Renderer> ().material.mainTexture = texture;
}
private void Run()
{
List <Mat> images = new List <Mat>();
List <int> labelsList = new List <int>();
MatOfInt labels = new MatOfInt();
images.Add(Imgcodecs.imread(facerec_0_bmp_filepath, 0));
images.Add(Imgcodecs.imread(facerec_1_bmp_filepath, 0));
labelsList.Add(0); //积极
labelsList.Add(1); //消极
labels.fromList(labelsList);
Mat testSampleMat = Imgcodecs.imread(facerec_sample_bmp_filepath, 0);
int testSampleLabel = 0;
int[] predictedLabel = new int[1];
double[] predictedConfidence = new double[1];
BasicFaceRecognizer faceRecognizer = EigenFaceRecognizer.create();
//faceRecognizer.train(images, labels); //会清空之前模型
faceRecognizer.update(images, labels); //不会清空load的模型
faceRecognizer.predict(testSampleMat, predictedLabel, predictedConfidence);
//faceRecognizer.save(Application.dataPath + "/Cascades/train_face.txt"); //生成一个txt
Debug.Log("Predicted class: " + predictedLabel[0] + " / " + "Actual class: " + testSampleLabel);
Debug.Log("Confidence: " + predictedConfidence[0]);
Mat predictedMat = images[predictedLabel[0]];
Mat baseMat = new Mat(testSampleMat.rows(), predictedMat.cols() + testSampleMat.cols(), CvType.CV_8UC1);
predictedMat.copyTo(baseMat.submat(new OpenCVForUnity.Rect(0, 0, predictedMat.cols(), predictedMat.rows())));
testSampleMat.copyTo(baseMat.submat(new OpenCVForUnity.Rect(predictedMat.cols(), 0, testSampleMat.cols(), testSampleMat.rows())));
Imgproc.putText(baseMat, "Predicted", new Point(10, 15), Core.FONT_HERSHEY_SIMPLEX, 0.4, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, "Confidence:", new Point(5, 25), Core.FONT_HERSHEY_SIMPLEX, 0.2, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, " " + predictedConfidence[0], new Point(5, 33), Core.FONT_HERSHEY_SIMPLEX, 0.2, new Scalar(255), 1, Imgproc.LINE_AA, false);
Imgproc.putText(baseMat, "TestSample", new Point(predictedMat.cols() + 10, 15), Core.FONT_HERSHEY_SIMPLEX, 0.4, new Scalar(255), 1, Imgproc.LINE_AA, false);
Texture2D t2d = new Texture2D(baseMat.width(), baseMat.height());
Utils.matToTexture2D(baseMat, t2d);
Sprite sp = Sprite.Create(t2d, new UnityEngine.Rect(0, 0, t2d.width, t2d.height), Vector2.zero);
m_srcImage.sprite = sp;
m_srcImage.preserveAspect = true;
}
/// <summary>
/// Raises the load button click event.
/// </summary>
public void LoadModel()
{
string loadDirectoryPath = Path.Combine(Application.persistentDataPath, saveDirectoryName);
if (!Directory.Exists(loadDirectoryPath))
{
Debug.Log("load failure. saved train data file does not exist.");
return;
}
// Restart everything!
dispose();
if (facerecAlgorithm == "FaceRecognizer.Fisherfaces")
{
model = FisherFaceRecognizer.create();
}
else if (facerecAlgorithm == "FaceRecognizer.Eigenfaces")
{
model = EigenFaceRecognizer.create();
}
if (model == null)
{
Debug.LogError("ERROR: The FaceRecognizer algorithm [" + facerecAlgorithm + "] is not available in your version of OpenCV. Please update to OpenCV v2.4.1 or newer.");
m_mode = R_MODES.MODE_DETECTION;
return;
}
// load the train data.
model.read(Path.Combine(loadDirectoryPath, "traindata.yml"));
int maxLabel = (int)Core.minMaxLoc(model.getLabels()).maxVal;
if (maxLabel < 0)
{
Debug.Log("load failure.");
model.Dispose();
return;
}
// Restore the save data.
#if UNITY_WEBGL && !UNITY_EDITOR
string format = "jpg";
#else
string format = "png";
#endif
m_numPersons = maxLabel + 1;
personsNames = new string[m_numPersons];
for (int i = 0; i < m_numPersons; ++i)
{
personsNames[i] = GameManager.instance.personsNames[i];
m_latestFaces.Add(i);
preprocessedFaces.Add(Imgcodecs.imread(Path.Combine(loadDirectoryPath, "preprocessedface" + i + "." + format), 0));
if (preprocessedFaces[i].total() == 0)
{
preprocessedFaces[i] = new Mat(faceHeight, faceWidth, CvType.CV_8UC1, new Scalar(128));
}
faceLabels.Add(i);
}
// go to the recognition mode!
m_mode = R_MODES.MODE_RECOGNITION;
}
