random push

faceAnalysis.py

@@ -5,7 +5,9 @@ import cv2
 from utils import *
 from config import emotions, input_shape, modelName
 
-model = keras.models.load_model("models/"+modelName)	#Load our model
+#model = tf.keras.models.load_model("models/"+modelName)	#Load our model
+model = tf.saved_model.load("models/"+modelName)
+
 print('Model used:', modelName)
 
 def detectEmotion(face):

game.py

@@ -3,9 +3,10 @@ import cv2
 import imageProcess as ip
 import faceAnalysis as fa
 import random
+from time import sleep
 from config import emotions
 
-cap = cv2.VideoCapture(0)   #0 means we capture the first camera, your webcam probably
+cap = cv2.VideoCapture(4)   #0 means we capture the first camera, your webcam probably
 score = 0
 N = 15
 
@@ -32,9 +33,12 @@ while cap.isOpened():		 #or while 1. cap.isOpened() is false if there is a probl
         smiley, emotion = smileyRandom(emotion)
 
 
-    cv2.imshow("Caméra", frame)  			#Show you making emotional faces
+    cv2.imshow("Camera", frame)  			#Show you making emotional faces
     cv2.putText(smiley, "Score: "+str(score), (40,40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
     cv2.imshow("Smiley", smiley)            #Show the smiley to mimic
+    #sleep(0.5)
+    
+   
 
     if cv2.waitKey(1) & 0xFF == ord('q'):			#If you press Q, stop the while and so the capture
         break   

utils.py

@@ -18,8 +18,8 @@ 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.