add model

faceAnalysis.py

 #Objective of this file is to analyse a face
+import keras
+import numpy as np
+import cv2
+from utils import *
+emotions = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Suprise", "Neutral"]
+input_shape = (48,48,1)
 
 def detectEmotion(face):
-	return "Happy?" 
\ No newline at end of file
+	#Return the most likely emotion there is on a 48x48x1 gray face
+	#input = 48
+	
+	face = normAndResize(face, input_shape)
+
+	model = keras.models.load_model('firstModel')	#Load our model
+	emotionVect = predir(model, face)	
+	emotionNbr = np.argmax(emotionVect)			 
+	emotion = emotions[emotionNbr]
+	return emotion
\ No newline at end of file

imageProcess.py

 #File to process images
 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
@@ -31,6 +33,7 @@ def imageProcess(image):
 
         #Write emotion on the image
         emotion = fa.detectEmotion(face_color)
+        print(emotion)
         cv2.putText(image, emotion, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0), 2)
 
 
@@ -51,3 +54,10 @@ def selectFace(image):
         x,y,w,h = faces[0]
         face = image[y:y+h, x:x+w]
         return face
+
+image = cv2.imread("cagnol.jpg", 1)  #Load Cagnol colored image
+imageProcess(image)
+cv2.imshow("Cagnol", image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+

utils.py

+import numpy as np
+import cv2
+
 def afficher(image):
   if len(image.shape) == 3:
     if image.shape[2] == 3: # (h,l,3)
@@ -8,12 +11,14 @@ def afficher(image):
   elif len(image.shape)== 2:  # (h,l)
     plt.imshow(image)
 
-def predir():
-  pass
+def predir(modele, image):
+  #Return output of image from modele
+  modele.predict(np.array([image]))[0,:]
 
-def normAndResize(image):
+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)
     image = image.mean(2)

videoCapture.py

@@ -2,7 +2,7 @@
 import cv2
 import imageProcess as ip
 
-cap = cv2.VideoCapture(-1)   #0 means we capture the first camera, your webcam probably
+cap = cv2.VideoCapture(0)   #0 means we capture the first camera, your webcam probably
 
 while cap.isOpened():
     ret, frame = cap.read()  #Read next video frame, stop if frame not well read