fix bug

.gitignore

 data
+config
+test
\ No newline at end of file

faceAnalysis.py

@@ -5,13 +5,14 @@ import cv2
 from utils import *
 from config import emotions, input_shape, modelName
 
+model = keras.models.load_model(modelName)	#Load our model
+print('Model used:', modelName)
+
 def detectEmotion(face):
-	#Return the most likely emotion there is on a 48x48x1 gray face
+	#Return the most likely emotion there is on a face
 	
 	face = normAndResize(face, input_shape)		#Process our image for input of model
 
-	model = keras.models.load_model(modelName)	#Load our model
-
 	emotionVect = predir(model, face)
 	emotionNbr = np.argmax(emotionVect)			 
 	emotion = emotions[emotionNbr]

imageProcess.py

@@ -2,6 +2,7 @@
 import cv2
 import numpy as np
 import faceAnalysis as fa
+import timeit as ti
 
 def imageProcess(image):
     #Objectives : detect faces, identify emotion associated on it, modify the image by framing faces and writing their emotions associated
@@ -35,7 +36,6 @@ def imageProcess(image):
         cv2.putText(image, emotion, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0), 2)
 
 
-
 def selectFace(image):
     #Return a face identified on an colored image
 

utils.py

 import numpy as np
 import cv2
+import matplotlib.pyplot as plt
 
 def afficher(image):
     if len(image.shape) == 3:
@@ -11,19 +12,24 @@ def afficher(image):
     elif len(image.shape) == 2:  # (h,l)
         plt.imshow(image)
 
+
 def predir(modele, image):
     # Return output of image from modele
-  modele.predict(np.array([image]))[0,:]
+    return modele.predict(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
-  image = cv2.resize(image, dsize=(h,l), interpolation=cv2.INTER_CUBIC) #resize for h and l                                       #
-  if len(image.shape) == 3 and p==1 and image.shape[2] != 1 : #if we want (h,l,3) -> (h,l,1) , we first transform it in to (h,l) (grey the image)
+    # resize for h and l                                       #
+    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:
         image = image.mean(2)
     image = np.reshape(image, (h, l, p))  # restore third dimension
     image = image.astype("float32")
     image = image/255  # normalisation
 
     return image
+

videoCapture.py

@@ -4,7 +4,7 @@ import imageProcess as ip
 
 cap = cv2.VideoCapture(0)   #0 means we capture the first camera, your webcam probably
 
-while cap.isOpened():
+while cap.isOpened():		 #or while 1. cap.isOpened() is false if there is a problem
     ret, frame = cap.read()  #Read next video frame, stop if frame not well read
     if not ret: break