Update video_classifier.ipynb, UCF/train_new.csv, PredictionTest.py files

PredictionTest.py

+from glob import glob
+import numpy as np
+from keras.applications.vgg16 import VGG16
+from keras.layers import Dense, Dropout
+from keras.models import Sequential
+from keras.preprocessing import image
+from scipy import stats as s
+
+base_model = VGG16(weights='imagenet', include_top=False)
+
+#defining the model architecture
+model = Sequential()
+model.add(Dense(1024, activation='relu', input_shape=(61440,)))
+model.add(Dropout(0.5))
+model.add(Dense(512, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(256, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(8, activation='softmax'))
+
+# loading the trained weights
+model.load_weights("weight.hdf5")
+
+# compiling the model
+model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
+
+# count = 0
+# # videoFile = test_videos[i]
+# cap = cv2.VideoCapture('UCF/Hello/Hello_1.mp4')   # capturing the video from the given path
+# frameRate = cap.get(5) #frame rate
+# print(frameRate)
+# x=1
+# # removing all other files from the temp folder
+# files = glob('temp/*')
+# for f in files:
+#     os.remove(f)
+# while(cap.isOpened()):
+#     frameId = cap.get(1) #current frame number
+#     ret, frame = cap.read()
+#     if (ret != True):
+#         break
+#     if (frameId % math.floor(frameRate) == 0):
+#         # storing the frames of this particular video in temp folder
+#         filename ='temp/' + "_frame%d.jpg" % count;count+=1
+#         cv2.imwrite(filename, frame)
+# cap.release()
+
+# reading all the frames from temp folder
+images = glob("lb/*.jpg")
+
+prediction_images = []
+for i in range(len(images)):
+    print(images[i])
+    img = image.load_img(images[i], target_size=(480, 272, 3))
+    img = image.img_to_array(img)
+    img = img / 255
+    prediction_images.append(img)
+
+predict = []
+print(len(prediction_images))
+# converting all the frames for a test video into numpy array
+prediction_images = np.array(prediction_images)
+# extracting features using pre-trained model
+prediction_images = base_model.predict(prediction_images)
+
+# converting features in one dimensional array
+print(prediction_images.shape)
+prediction_images = prediction_images.reshape(prediction_images.shape[0], 15*8*512)
+# predicting tags for each array
+prediction = model.predict(prediction_images)
+# appending the mode of predictions in predict list to assign the tag to the video
+print(s.mode(prediction))
+print("Prediction mode")
+print(s.mode(prediction)[0][0])
+print("Prediction mode 1")
+print(np.argmax(s.mode(prediction)[0][0]))
+# predict.append(y.columns.values[s.mode(prediction)[0][0]])
+# appending the actual tag of the video
+# actual.append(videoFile.split('/')[1].split('_')[1])
+yVals = ["He/She","Hello","Here","Job","Me","Name","Teacher","You"]
+ret = yVals[np.argmax(s.mode(prediction)[0][0])]
+print(ret)