video_classification/video_classifier.py file

video_classification/video_classifier.py

+import keras
+from keras.models import Sequential
+from keras.applications.vgg16 import VGG16
+from keras.layers import Dense, InputLayer, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D
+from keras.preprocessing import image
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from sklearn.model_selection import train_test_split
+
+train = pd.read_csv('UCF/train_new.csv')
+train.head()
+
+# creating an empty list
+train_image = []
+
+# for loop to read and store frames
+for i in tqdm(range(train.shape[0])):
+    # loading the image and keeping the target size as (224,224,3)
+    img = image.load_img('UCF/' + train['image'][i], target_size=(480, 272, 3))
+    # converting it to array
+    img = image.img_to_array(img)
+    # normalizing the pixel value
+    img = img / 255
+    # appending the image to the train_image list
+    train_image.append(img)
+
+# converting the list to numpy array
+X = np.array(train_image)
+
+# separating the target
+y = train['class']
+
+# creating the training and validation set
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2, stratify = y)
+
+# separating the target
+y = train['class']
+
+# creating the training and validation set
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2, stratify = y)
+
+# creating dummies of target variable for train and validation set
+y_train = pd.get_dummies(y_train)
+y_test = pd.get_dummies(y_test)
+
+# creating the base model of pre-trained VGG16 model
+base_model = VGG16(weights='imagenet', include_top=False)
+
+# extracting features for training frames
+X_train = base_model.predict(X_train)
+X_train.shape
+
+# extracting features for validation frames
+X_test = base_model.predict(X_test)
+
+# reshaping the training as well as validation frames in single dimension
+X_train = X_train.reshape(522, 15*8*512)
+X_test = X_test.reshape(131, 15*8*512)
+
+# normalizing the pixel values
+max = X_train.max()
+X_train = X_train/max
+X_test = X_test/max
+
+#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'))
+
+# defining a function to save the weights of best model
+from keras.callbacks import ModelCheckpoint
+mcp_save = ModelCheckpoint('weight.hdf5', save_best_only=True, monitor='val_loss', mode='min')
+
+# compiling the model
+model.compile(loss='categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])
+
+# training the model
+model.fit(X_train, y_train, epochs=200, validation_data=(X_test, y_test), callbacks=[mcp_save], batch_size=128)
+
+model.save_weights('model_saved.h5')
+