public EmotionModel()
{
modelPathMap = new Dictionary <string, string>(); // 模型路径信息
modelPathMap.Add("混合模型（5秒）", Environment.CurrentDirectory + "\\models\\data_after_boardline_smote.scale.model");
modelPathMap.Add("混合模型（10秒）", Environment.CurrentDirectory + "\\models\\data_after_boardline_smote_and_pca_with_49.scale.model");
modelPathMap.Add("交互模型（5秒）", Environment.CurrentDirectory + "\\models\\data_after_undersampling.scale.model");
modelPathMap.Add("交互模型（10秒）", Environment.CurrentDirectory + "\\models\\data_after_undersampling_and_pca_with_49.scale.model");
modelPathMap.Add("人像模型", Environment.CurrentDirectory + "\\models\\data_after_undersampling_and_pca_with_49.scale.model");
svmModel = SVM.LoadModel(modelPathMap["混合模型（5秒）"]); // 加载默认模型
// 初始化特征向量
for (int i = 0; i < featureNum; i++)
{
svmFeature[i] = new SVMNode(i, 0);
}
//indexEmotionMap.Add(1, 1);
//indexEmotionMap.Add(4, 2);
//indexEmotionMap.Add(5, 3);
//indexEmotionMap.Add(6, 4);
//indexEmotionMap.Add(7, 5);
//indexEmotionMap.Add(8, 6);
//indexEmotionMap.Add(9, 7);
//indexEmotionMap.Add(10, 8);
//indexEmotionMap.Add(11, 9);
}
//作成した辞書を図でみる
public void Debug_DispPredict()
{
return;
//辞書ファイルのロード
this.libSVM_model = SVM.LoadModel(@"libsvm_model.xml");
using (IplImage retPlot = new IplImage(300, 300, BitDepth.U8, 3))
{
for (int x = 0; x < 300; x++)
{
for (int y = 0; y < 300; y++)
{
float[] sample = { x / 300f, y / 300f };
//問題を作成
SVMNode[] node_array = new SVMNode[2];
node_array[0] = new SVMNode(1, sample[0]);
node_array[1] = new SVMNode(2, sample[1]);
int ret_double = (int)SVM.Predict(libSVM_model, node_array);
int ret_i = (int)ret_double;
CvRect plotRect = new CvRect(x, 300 - y, 1, 1);
if (ret_i == 1)
{
retPlot.Rectangle(plotRect, CvColor.Red);
}
else if (ret_i == 2)
{
retPlot.Rectangle(plotRect, CvColor.GreenYellow);
}
}
}
CvWindow.ShowImages(retPlot);
}
}
private static void TestOne(string prefix)
{
SVMModel model = SVM.LoadModel(MnistDataPath + "model.txt");
SVMProblem testSet = SVMProblemHelper.Load(MnistDataPath + prefix + ".txt");
testSet = testSet.Normalize(SVMNormType.L2);
double[] testResults = testSet.Predict(model);
Console.WriteLine("\nTest result: " + testResults[0].ToString());
}
private void btnLoadModel_Click(object sender, EventArgs e)
{
parameter = load_json_file(parameter_file);
string size = parameter["resize"];
sizes2 = new OpenCvSharp.Size(Convert.ToInt32(size.Split(',')[0]), Convert.ToInt32(size.Split(',')[1]));
model_load = SVM.LoadModel(parameter["path_model"] + comboBox1.Text);
MessageBox.Show("Load model completed");
}
private static void Test(string prefix)
{
SVMModel model = SVM.LoadModel(MnistDataPath + "model.txt");
SVMProblem testSet = SVMProblemHelper.Load(MnistDataPath + prefix + ".txt");
testSet = testSet.Normalize(SVMNormType.L2);
double[] testResults = testSet.Predict(model);
int[,] confusionMatrix;
double testAccuracy = testSet.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
Console.WriteLine("\nTest accuracy: " + testAccuracy);
}
public void LibsvmFirstLook()
{
var prob = new SVMProblem();
prob.Add(new[] { new SVMNode(1, 1), new SVMNode(2, 0), new SVMNode(3, 1) }, 1);
prob.Add(new[] { new SVMNode(1, -1), new SVMNode(2, 0), new SVMNode(3, -1) }, -1);
var param = new SVMParameter();
param.Kernel = SVMKernelType.LINEAR;
param.C = 10;
var m = prob.Train(param);
TestOutput(m.Predict(new [] { new SVMNode(1, 1), new SVMNode(2, 1), new SVMNode(3, 1) }));
m.SaveModel("trainModel");
var ml = SVM.LoadModel("trainModel");
TestOutput(ml.Predict(new[] { new SVMNode(1, 1), new SVMNode(2, 1), new SVMNode(3, 1) }));
}
//SVM判定
public int SVMPredict(FaceFeature.FeatureValue feature)
{
//学習ファイルを読み込んでいなかったらロード
if (this.LoadFlag == false)
{
this.libSVM_model = SVM.LoadModel(@"model_FaceFeature.xml");
this.LoadFlag = true;
}
//スケーリングファイルを読み込む あれば
if (this.LoadScaleFlag == false && JudgeGUII.APPSetting.NORMALIZE_USE)
{
this.LoadScaleFlag = ReadScaleFile(@"out/normalize_scale.csv");
}
double[] feature_array = new double[FEATURE_COUNT];
int answer = 0;
{
SetFeatureToArray(feature, ref feature_array);
//ここでスケーリングのデータを読み込んでいたら使う
if (this.LoadScaleFlag == true && JudgeGUII.APPSetting.NORMALIZE_USE)
{
execNormalize(ref feature_array);
}
//問題を作成
SVMNode[] node_array = new SVMNode[FEATURE_COUNT];
for (int i = 0; i < FEATURE_COUNT; i++)
{
node_array[i] = new SVMNode(i + 1, feature_array[i]);
}
answer = (int)SVM.Predict(libSVM_model, node_array);
return(answer);
}
}
private void pictureBox1_MouseDown(object sender, MouseEventArgs e)
{
if (e.Button == MouseButtons.Left)
{
int selIndex = 0;
if (radioButton1.Checked)
{
selIndex = 0;
}
if (radioButton2.Checked)
{
selIndex = 1;
}
if (radioButton3.Checked)
{
selIndex = 2;
}
if (radioButton4.Checked)
{
selIndex = 3;
}
mList.Add(new DataInfo(selIndex, e.X, e.Y));
Draw();
}
else if (e.Button == MouseButtons.Right)
{
SVMNode[] node = new SVMNode[2];
node[0] = new SVMNode(1, (double)e.X / (double)mWidth);
node[1] = new SVMNode(2, (double)e.Y / (double)mHeight);
SVMModel model = SVM.LoadModel(FILE_MODEL);
double result = SVM.Predict(model, node);
Console.WriteLine("result=" + result);
}
}
public static SVMModel getExistingModel()
{
//MessageBox.Show("Model Exists");
return(SVM.LoadModel(Constants.MODEL_PATH));
}
//학습모델 로드
public static SVMModel SVM_LoadModel(String model)
{
return(SVM.LoadModel(model));
}
static void Main(string[] args)
{
// Load the datasets: In this example I use the same datasets for training and testing which is not suggested
SVMProblem trainingSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_train.txt");
// SVMProblem testSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_test.txt");
SVMProblem testSet1 = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_test1.txt");
// SVMProblem testSet1 = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\result.txt");
// Normalize the datasets if you want: L2 Norm => x / ||x||
trainingSet = trainingSet.Normalize(SVMNormType.L2);
// testSet = testSet.Normalize(SVMNormType.L2);
testSet1 = testSet1.Normalize(SVMNormType.L2);
// Select the parameter set
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 32768.0;
parameter.Gamma = 8.0;
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 5;
// trainingSet1.CrossValidation(parameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
// double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
// Train the model, If your parameter set gives good result on cross validation
// SVMModel model = trainingSet.Train(parameter);
// Save the model
// SVM.SaveModel(model, @"Model\activity_recognition.txt");
SVMModel model = SVM.LoadModel(@"Model\activity_recognition.txt");
int p, q, w, e, r, ok = 0;
double sum;
q = 0;
w = 0;
e = 0;
r = 0;
// Predict the instances in the test set
double[] testResults = testSet1.Predict(model);
while (ok < testSet1.Length)
{
var resut = model.Predict(testSet1.X[ok]);
// Console.WriteLine("resut111:" + resut);
p = Convert.ToInt16(resut);
switch (p)
{
case 1:
q++;
break;
case 2:
w++;
break;
case 3:
e++;
break;
case 4:
r++;
break;
}
ok++;
}
sum = q + w + e + r;
Console.WriteLine("result:" + Math.Round(q / sum, 2) + "," + Math.Round(w / sum, 2) + "," + Math.Round(e / sum, 2) + "," + Math.Round(r / sum, 2));
// Evaluate the test results
int[,] confusionMatrix;
double testAccuracy = testSet1.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
// Print the resutls
// Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
Console.WriteLine("\nTest accuracy: " + testAccuracy);
Console.WriteLine("\nConfusion matrix:\n");
// Print formatted confusion matrix
Console.Write(String.Format("{0,6}", ""));
for (int i = 0; i < model.Labels.Length; i++)
{
Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
}
Console.WriteLine();
for (int i = 0; i < confusionMatrix.GetLength(0); i++)
{
Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
for (int j = 0; j < confusionMatrix.GetLength(1); j++)
{
Console.Write(String.Format("{0,5}", confusionMatrix[i, j]));
}
Console.WriteLine();
}
Console.WriteLine("\n\nPress any key to quit...");
Console.ReadLine();
}
static void Main(string[] args)
{
SVMProblem testSet = SVMProblemHelper.Load(@"Dataset\wine.txt"); // Same as the training set
SVMModel model = SVM.LoadModel(@"Model\wine_model.txt");
Console.WriteLine("Feature count in one instance: " + model.SV[0].Length + "\n\n");
// Test 1: Predict instances with SVMProblem's Predict extension method.
sw.Start();
double[] target = testSet.Predict(model);
sw.Stop();
double elapsedTimeInTest1 = (double)sw.ElapsedMilliseconds / (double)testSet.Length;
Console.WriteLine("> Test 1: \nPredict instances with SVMProblem's Predict extension method.\n");
Console.WriteLine("\tAverage elapsed time of one prediction: " + elapsedTimeInTest1 + " ms\n");
// Test 2: Predict instances with RapidPreditor class which is an explicit implementation of the method used in Test 1.
using (RapidPredictor predictor = new RapidPredictor(model)) // It needs to be Disposed
{
sw.Start();
target = new double[testSet.Length];
for (int i = 0; i < testSet.Length; i++)
{
target[i] = predictor.Predict(testSet.X[i]);
}
sw.Stop();
}
double elapsedTimeInTest2 = (double)sw.ElapsedMilliseconds / (double)testSet.Length;
Console.WriteLine("> Test 2: \nPredict instances with RapidPreditor class which is an explicit implementation of the method used in Test 1.\n");
Console.WriteLine("\tAverage elapsed time of one prediction: " + elapsedTimeInTest2 + " ms\n");
// Test 3: Predict instances with standard SVM.Predict method or SVMNode[]'s predict extension method.
sw.Start();
target = new double[testSet.Length];
for (int i = 0; i < testSet.Length; i++)
{
target[i] = SVM.Predict(model, testSet.X[i]);
}
sw.Stop();
double elapsedTimeInTest3 = (double)sw.ElapsedMilliseconds / (double)testSet.Length;
Console.WriteLine("> Test 3: \nPredict instances with standard SVM.Predict method or SVMNode[]'s Predict extension method.\n");
Console.WriteLine("\tAverage elapsed time of one prediction: " + elapsedTimeInTest3 + " ms\n");
// Print the results
Console.WriteLine("\nExplanation:\n");
Console.WriteLine(
"In standard SVM.Predict method, the SVMModel object is allocated and deallocated every time when the method called. " +
"Also the SVMNode[]'s Predict extension methods directly calls the SVM.Predict. " +
"However, the model is allocated once and is used to predict whole instances with its pointer in SVMProblem's " +
"Predict extension method as implemented in the RapidPredictor class. You can take or modify this class in order " +
"to use in your applications, if you have performance considerations. " +
"I am not suggesting that SVMProblem's Predict extension method is used in real-time, because the model is allocated" +
"in every method call.");
Console.WriteLine("\n\nPress any key to quit...");
Console.ReadLine();
}
public void loadModel(string key)
{
svmModel = SVM.LoadModel(modelPathMap[key]);
}
private async Task PerformAnalysis(String path, Rectangle rectangle)
{
UShortArrayAsImage image = null;
double[] pcaComponents = null;
int tasksComplete = 0;
UpdateStatus(path, startingImageStatusStr);
List <Task> tasks = new List <Task>()
{
new Task(() =>
{
var file = db.FileStorage.FindById($"images/{path}");
var ms = new MemoryStream();
file.CopyTo(ms);
ms.Seek(0, 0);
image = DicomFile.Open(ms).GetUshortImageInfo();
UpdateStatus(path, loadedImageStatusStr);
}),
new Task(() =>
{
image = Normalization.GetNormalizedImage(image, rectangle,
int.Parse(Configuration.Get("sizeImageToAnalyze")));
db.FileStorage.Upload($"images/{path}-cropped", $"{path}-cropped",
image.GetPngAsMemoryStream());
UpdateStatus(path, croppedImageStatusStr);
}),
new Task(() =>
{
image = Contrast.ApplyHistogramEqualization(image);
db.FileStorage.Upload($"images/{path}-croppedContrast", $"{path}-croppedContrast",
image.GetPngAsMemoryStream());
UpdateStatus(path, contrastImageStatusStr);
}),
new Task(() =>
{
//PCA
PCA pca = PCA.LoadModelFromFile(Configuration.Get("PcaModelLocation"));
if (!int.TryParse(Configuration.Get("componentsToUse"), out int components))
{
components = pca.Eigenvalues.Length;
}
pcaComponents = pca.GetComponentsFromImage(image, components);
UpdateStatus(path, pcaImageStatusStr);
}),
new Task(() =>
{
//SVM
SVMProblem svmProblem = new SVMProblem();
// add all the components to an SVMNode[]
SVMNode[] nodes = new SVMNode[pcaComponents.Length];
for (int i = 0; i < pcaComponents.Length; i++)
{
nodes[i] = new SVMNode(i + 1, pcaComponents[i]);
}
svmProblem.Add(nodes, 0);
svmProblem = svmProblem.Normalize(SVMNormType.L2);
SVMModel svmModel = SVM.LoadModel(Configuration.Get("ModelLocation"));
double[] results = svmProblem.PredictProbability(svmModel, out var probabilities);
var analysis = db.GetCollection <Analysis>("analysis");
Analysis currentAnalysis = analysis.FindOne(x => x.Id.ToString().Equals(path));
currentAnalysis.Certainty = results[0] == 0 ? probabilities[0][1] * 100 : probabilities[0][0] * 100;
currentAnalysis.Diagnosis = results[0] == 0
? DdsmImage.Pathologies.Benign
: DdsmImage.Pathologies
.Malignant;
analysis.Update(currentAnalysis);
UpdateStatus(path, svmImageStatusStr);
})
};
foreach (Task task in tasks)
{
task.Start();
await task;
// lets set percentage done:
var analysis = db.GetCollection <Analysis>("analysis");
Analysis currentAnalysis = analysis.FindOne(x => x.Id.ToString().Equals(path));
currentAnalysis.PercentageDone = (++tasksComplete * 100) / tasks.Count;
analysis.Update(currentAnalysis);
}
UpdateStatus(path, doneStatusStr);
}
public void SVM_LoadModel_FilenameDoesNotExist_ReturnsNull()
{
SVM.LoadModel(Contants.WRONG_MODEL_PATH_TO_BE_LOADED);
}
public void SVM_LoadModel_FilenameIsInvalid_ReturnsNull()
{
SVM.LoadModel("");
}
