test.py

-#Use this file for test
 
-import tensorflow as tf
-from tensorflow import keras
-from tensorflow.keras import datasets, layers, models, losses
-import tensorflow_datasets as tfds
-#from google.colab import files
-
-from matplotlib import image
-import os
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib
-import random as rd
 import cv2
-import csv
-
-from loadFer2013DS import *
-from loadRavdessDS import *
-from loadExpWDS import *
-from loadAffwild import *
-from utils import *
-
-# X, Y = loadFer2013Data(10)
-# W, Z = loadRavdessData(10)
-# A, B = loadExpWData(10)
-C, D = loadAffwildData(1000)
\ No newline at end of file
+while 1:
+    if cv2.waitKey(0) & 0xFF== ord('q'):			#If you press Q, stop the while and so the capture
+        break
+
+    if cv2.waitKey(1) & 0xFF == ord('p'):			#If you press P, pass the smiley but lower your score
+        score -= 1
+        smiley, emotion = smileyRandom(emotion)
+    print(1)
+    cv2.waitKey(2)
\ No newline at end of file

utils.py

@@ -2,6 +2,8 @@ import numpy as np
 import cv2
 import matplotlib.pyplot as plt
 from config import emotions
+import tensorflow as tf
+
 
 def afficher(image):
     if len(image.shape) == 3:
@@ -16,14 +18,15 @@ def afficher(image):
 
 def predir(modele, image):
     # Return output of image from modele
-    return modele.predict(np.array([image]))[0, :]
-
+    #return modele.predict(np.array([image]))[0, :]
+    return modele(np.array([image]))[0, :]
 
 def normAndResize(image, input_shape):
     # For an array image of shape (a,b,c) or (a,b), transform it into (h,l,p). Also normalize it.
 
     h, l, p = input_shape
-    # resize for h and l                                       #
+    # resize for h and l     
+    # print(image.shape)                                  #
     image = cv2.resize(image, dsize=(h, l), interpolation=cv2.INTER_CUBIC)
     # if we want (h,l,3) -> (h,l,1) , we first transform it in to (h,l) (grey the image)
     if len(image.shape) == 3 and p == 1 and image.shape[2] != 1:
@@ -34,6 +37,50 @@ def normAndResize(image, input_shape):
 
     return image
 
+
 def emotionToNumber(emotion):
-    emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise", "Neutral"]
+    emotions = ["Angry", "Disgust", "Fear",
+                "Happy", "Sad", "Suprise", "Neutral"]
     return emotions.index(emotion)
+
+def stackImages(listOfArrayImage):
+    liste = []
+    for X in listOfArrayImage:
+        liste += X.tolist()
+    return np.stack(liste, axis=0)
+    
+
+def mergeToDatabase(listOfX, listOfY, validation_repart=[0.1, 0.1, 0.1, 0.1]):
+    # This shuffle each X, extract validation data, merge differents X, shuffle again.
+    listOfX_train, listOfY_train = [], []
+    listOfX_test, listOfY_test = [], []
+
+    for X, Y, rate in zip(listOfX, listOfY, validation_repart):
+        N = X.shape[0]
+
+        # Shuffle each X and Y the same way
+        shuffler = np.random.permutation(N)
+        X, Y = X[shuffler], Y[shuffler]
+        # Extract validation data
+        X_train, Y_train = X[:int(N*(1-rate))], Y[:int(N*(1-rate))]
+        X_test, Y_test = X[int(N*(1-rate)):], Y[int(N*(1-rate)):]
+        listOfX_train.append(X_train)
+        listOfY_train.append(Y_train)
+        listOfX_test.append(X_test)
+        listOfY_test.append(Y_test)
+    # Merge
+    BigX_train = stackImages(listOfX_train)
+    BigY_train = stackImages(listOfY_train)
+
+    BigX_test = stackImages(listOfX_test)
+    BigY_test = stackImages(listOfY_test)
+    # Shuffle the whole
+    shuffler = np.random.permutation(len(BigX_train))
+    BigX_train = BigX_train[shuffler]
+    BigY_train = BigY_train[shuffler]
+
+    shuffler = np.random.permutation(len(BigX_test))
+    BigX_test = BigX_test[shuffler]
+    BigY_test = BigY_test[shuffler]
+
+    return BigX_train, BigY_train, BigX_test, BigY_test

videoCapture.py

+
+
+def videoCapture():
+
     #Use your camera for processing the video. Stop by pressing Q
     import cv2
     import imageProcess as ip
@@ -10,7 +14,7 @@ while cap.isOpened():		 #or while 1. cap.isOpened() is false if there is a probl
 
         ip.imageProcess(frame)                          #Process frame
 
-    cv2.imshow("Image traitée", frame)  			#Show processed image in a window
+        cv2.imshow("Image", frame)  			#Show processed image in a window
 
         if cv2.waitKey(1) & 0xFF == ord('q'):			#If you press Q, stop the while and so the capture
             break