Updated videoAnalyzing(expressions).py

videoAnalyzing(expressions).py

-# from skimage import io
-import cv2
-import imutils
-import numpy as np
 import tensorflow as tf
-from tensorflow import keras
 from keras.preprocessing import image
-from keras.models import Sequential, load_model
-from keras.preprocessing.image import load_img
-from keras.preprocessing.image import img_to_array
 import matplotlib.pyplot as plt
-from skimage import io
-import os
 import cv2
 import numpy as np
 
@@ -19,24 +9,7 @@ Savedmodel.summary()
 
 objects = ('Angry', 'Happy', 'Sad', 'Neutral')
 vid = cv2.VideoCapture(0)
-#
-# def run():
-#     while True:
-#
-#         _, frame = vid.read()
-#         frame = imutils.resize(frame, width=500)
-#
-#         # result = api(frame)
-#
-#         cv2.imshow("frame",frame)
-#         # getPrediction(frame)
-#
-#         # cv.waitKey(0)
-#         if cv2.waitKey(20) & 0XFF == ord('q'):
-#             break
-#
-#     vid.release()
-#     cv2.destroyAllWindows()
+
 def emotion_analysis(emotions):
     objects = ['Angry', 'Happy', 'Sad', 'Neutral']
     y_pos = np.arange(len(objects))
@@ -47,35 +20,7 @@ def emotion_analysis(emotions):
     plt.title('emotion')
 
 
-
-
-# def getPrediction(img):
-#
-#     x = image.img_to_array(img)
-#     x = np.expand_dims(x, axis=0)
-#
-#     x /= 255
-#
-#     custom = Savedmodel.predict(x)
-#     # print(custom[0])
-#     emotion_analysis(custom[0])
-#
-#     x = np.array(x, 'float32')
-#     x = x.reshape([48, 48]);
-#
-#     plt.gray()
-#     plt.show()
-#
-#     m = 0.000000000000000000001
-#     a = custom[0]
-#     for i in range(0, len(a)):
-#         if a[i] > m:
-#             m = a[i]
-#             ind = i
-#
-#     print('Expression Prediction:', objects[ind])
-
-imgdir='./speechVideo'
+videoDir = './speechVideo'
 cap = cv2.VideoCapture('./speechVideo/speech.mp4')
 
 while(cap.isOpened()):
@@ -108,7 +53,6 @@ while(cap.isOpened()):
   if cv2.waitKey(20) & 0XFF == ord('q'):
     break
 
-
 cap.release()
 cv2.destroyAllWindows()