example on a face

config.py

+#Name of model used
+modelName = 'firstModel'
+
+#Emotions provided by the dataset
+emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise", "Neutral"]
+
+#Shape of input of the model
+input_shape = (48,48,1)
\ No newline at end of file

faceAnalysis.py

@@ -3,8 +3,7 @@ import keras
 import numpy as np
 import cv2
 from utils import *
-emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise", "Neutral"]
-input_shape = (48,48,1)
+from config import emotions, input_shape
 
 def detectEmotion(face):
 	#Return the most likely emotion there is on a 48x48x1 gray face

imageProcess.py

@@ -2,7 +2,6 @@
 import cv2
 import numpy as np
 import faceAnalysis as fa
-input_shape = (48,48,1)
 
 def imageProcess(image):
     #Objectives : detect faces, identify emotion associated on it, modify the image by framing faces and writing their emotions associated

loadFer2013ds.py

@@ -9,14 +9,6 @@ nbrImages = 35887
 maxNbrImages = nbrImages
 emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise", "Neutral"]
 
-def traitement(a,b,c):  #For testing
-	pass
-	# arr = strToArray(b)
-	# print(a)
-	# plt.imshow(arr)
-	# plt.show()
-	# pass
-
 def strToArray(string):  #Fer2013 provides images as string so it needs to be transformed
 	A = []
 	lenght = len(string)