added Video analyzing .py file

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
+
+Savedmodel = tf.keras.models.load_model('./new model8.h5')
+Savedmodel.summary()
+objects = ('Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', '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 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])
+
+
+def emotion_analysis(emotions):
+    objects = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+    y_pos = np.arange(len(objects))
+    plt.bar(y_pos, emotions, align='center', alpha=0.9)
+    plt.tick_params(axis='x', which='both', pad=10, width=4, length=10)
+    plt.xticks(y_pos, objects)
+    plt.ylabel('percentage')
+    plt.title('emotion')
+
+run()
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