unsure where to go!

src/ModelThread.h

@@ -12,6 +12,10 @@ class ModelThread : public ofThread
     std::vector<types::BoxfWithLandmarks>* detected_faces;
     std::string model_type;
 
+    // emotional recognition model
+    std::vector<ofImage>* croppedFaces;
+    float* emotional_data;
+
 
     ~ModelThread(){
         stop();

src/Onnx.cpp

@@ -52,7 +52,6 @@ void Onnx::Setup(ORTCHAR_T* modelPath, bool isLog, bool useCuda){
 
 // Runs the model, given an image
 std::vector<Ort::Value> Onnx::Run(ofImage &img){
-    std::cout << "hallo" << std::endl;
     auto start = std::chrono::high_resolution_clock::now();
 
     TransformImage(img);

src/Onnx.h

@@ -34,7 +34,7 @@
         void DataToFbo(float* data, size_t width, size_t height, ofFbo& fbo);
         void Softmax(float* data, size_t size);
         void SetPixels(ofFbo& fbo);
-        bool timeStamp = true;
+        bool timeStamp = false;
         bool log = false;
         ofPixels pixels;
     

src/main.cpp

@@ -6,7 +6,7 @@ int main( ){
 
 	//Use ofGLFWWindowSettings for more options like multi-monitor fullscreen
 	ofGLWindowSettings settings;
-	settings.setSize(2000, 1000);
+	settings.setSize(1920, 720);
 	settings.setGLVersion(3, 2);
 	settings.windowMode = OF_WINDOW; //can also be OF_FULLSCREEN
 

src/ofApp.cpp

@@ -30,7 +30,7 @@ void ofApp::setup(){
     yolo.Setup(modelPath2, false, true);
     depth.Setup(modelPath, false, true);
     depth_small.Setup(modelPath4, false, true);
-    emotion.Setup(modelPath3, false, true);
+    //emotion.Setup(modelPath3, false, true);
 
     /* Depth output fbo */
     model_output_fbo.allocate(window_width / 2, window_height, GL_RGB);
@@ -46,6 +46,18 @@ void ofApp::setup(){
     threadMap.setup(&map.fboImage, &model_output_fbo, &depth);
     threadVideo.setup(&img, &model_output_fbo_1, &depth_small);
     threadYolo.setupYolo(&img, &detected_faces, &yolo, &faceDetector);
+
+     /* 
+        Create a dummy initial input of batch_size = 5, as 
+        when initialising the model, it will attempt to create a space in memory for this array.
+        If the batch_size does change it will completely slow down inference, due to how the cudnn_search_algo is set.
+        None of the other search alogithms bar EXHAUSTIVE will work.. no idea why.
+
+        for(int i = 0; i < emotionImageMaxBatchSize; i++){
+            tempImage.setFromPixels(emoteImage.getPixels());
+            croppedFaces.push_back(tempImage);
+        }
+    */
 }
 
 
@@ -78,40 +90,18 @@ void ofApp::update(){
     try{
 
         threadMap.update();
-        depth.SetPixels(model_output_fbo);
-
         threadVideo.update();
-        depth_small.SetPixels(model_output_fbo_1);
-        
         threadYolo.update();
 
+        depth.SetPixels(model_output_fbo);
+        depth_small.SetPixels(model_output_fbo_1);
         faceDetector.ConvertBoxCoordsToOriginalSize(detected_faces, model_output_fbo_1.getWidth(), model_output_fbo_1.getHeight());
 
         /* As no input is generated for the emotion recognition model, run a dummy vector through the model
-            So it can load */
+            So it can load.
-        if(firstRun){
-            /* 
-                Create a dummy initial input of batch_size = 5, as 
-                when initialising the model, it will attempt to create a space in memory for this array.
-                If the batch_size does change it will completely slow down inference, due to how the cudnn_search_algo is set.
-                None of the other search alogithms bar EXHAUSTIVE will work.. no idea why.
-            */
-            // for(int i = 0; i < emotionImageMaxBatchSize; i++){
-            //     tempImage.setFromPixels(emoteImage.getPixels());
-            //     croppedFaces.push_back(tempImage);
-            // }
-
-            // Run model to warmup
-            // auto emotion_output_tensor = emotion.RunBatch(croppedFaces);
-
-        } else {
-            //inferEmotionalState();
-            
-        }
-
-        /*  Run emotion inference */
-        //inferEmotionalState();
 
+            auto emotion_output_tensor = emotion.RunBatch(croppedFaces);
+        */
     } catch (exception e){
 
         std::cout << "Model did not run" << std::endl;
@@ -167,7 +157,6 @@ void ofApp::inferDepthImage(ofFbo& fbo, ofImage& img, Onnx& model){
 
 //--------------------------------------------------------------
 void ofApp::inferEmotionalState(){
-
     /*
         Max faces to process with the model (5)
     */
@@ -192,9 +181,9 @@ void ofApp::inferEmotionalState(){
     auto& output_tensor = emotion_output_tensor.front();
     auto output_shap = output_tensor.GetTensorTypeAndShapeInfo().GetShape();
     size_t batch_size = output_shap[0]; // Number of images in the batch
-    size_t num_classes = output_shap[1]; // Number of emotion classes
+    size_t num_classes = 7; //output_shap[1]; // Number of emotion classes
 
-    float* emotional_data = output_tensor.GetTensorMutableData<float>();
+    emotional_data = output_tensor.GetTensorMutableData<float>();
 
     for (size_t i = 0; i < max_faces_to_process; i++){
 

src/ofApp.h

@@ -55,6 +55,7 @@ class ofApp : public ofBaseApp{
 		
 		Emotef emo;
 		Yolo faceDetector;
+		float* emotional_data;
 		std::vector<types::BoxfWithLandmarks> detected_faces;
 
 		Map map;
@@ -72,4 +73,5 @@ class ofApp : public ofBaseApp{
 		ModelThread threadMap;
 		ModelThread threadVideo;
 		ModelThread threadYolo;
+		ModelThread threadEmotion;
 };