tomjerry.py

import cv2     # for capturing videos
import math   # for mathematical operations
import matplotlib.pyplot as plt    # for plotting the images %matplotlib inline
import pandas as pd
from keras.preprocessing import image   # for preprocessing the images
import numpy as np    # for mathematical operations
from keras.utils import np_utils
from skimage.transform import resize   # for resizing images

count = 0
videoFile = "Tom and jerry.mp4"
cap = cv2.VideoCapture(videoFile)   # capturing the video from the given path
frameRate = cap.get(5) #frame rate
x=1
while(cap.isOpened()):
    frameId = cap.get(1) #current frame number
    ret, frame = cap.read()
    if (ret != True):
        break
    if (frameId % math.floor(frameRate) == 0):
        filename ="frame%d.jpg" % count;count+=1
        cv2.imwrite(filename, frame)
cap.release()
print ("Done!")

img = plt.imread('frame0.jpg')   # reading image using its name
plt.imshow(img)

data = pd.read_csv('mapping.csv')     # reading the csv file
data.head()      # printing first five rows of the file

X = [ ]     # creating an empty array
for img_name in data.Image_ID:
    img = plt.imread('' + img_name)
    X.append(img)  # storing each image in array X
X = np.array(X)    # converting list to array

y = data.Class
dummy_y = np_utils.to_categorical(y)    # one hot encoding Classes

image = []
for i in range(0,X.shape[0]):
    a = resize(X[i], preserve_range=True, output_shape=(224,224)).astype(int)      # reshaping to 224*224*3
    image.append(a)
X = np.array(image)

from sklearn.model_selection import train_test_split
X_train, X_valid, y_train, y_valid = train_test_split(X, dummy_y, test_size=0.3, random_state=42)    # preparing the validation set



from keras.models import Sequential
from keras.applications.vgg16 import VGG16
from keras.layers import Dense, InputLayer, Dropout

base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))    # include_top=False to remove the top layer

X_train = base_model.predict(X_train)
X_valid = base_model.predict(X_valid)
X_train.shape, X_valid.shape

X_train = X_train.reshape(208, 7*7*512)      # converting to 1-D
X_valid = X_valid.reshape(90, 7*7*512)

train = X_train/X_train.max()      # centering the data
X_valid = X_valid/X_train.max()

# i. Building the model
model = Sequential()
model.add(InputLayer((7*7*512,)))    # input layer
model.add(Dense(units=1024, activation='sigmoid')) # hidden layer
model.add(Dense(3, activation='softmax'))    # output layer

model.summary()

# ii. Compiling the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# iii. Training the model
model.fit(train, y_train, epochs=100, validation_data=(X_valid, y_valid))

count = 0
videoFile = "Tom and Jerry 3.mp4"
cap = cv2.VideoCapture(videoFile)
frameRate = cap.get(5) #frame rate
x=1
while(cap.isOpened()):
    frameId = cap.get(1) #current frame number
    ret, frame = cap.read()
    if (ret != True):
        break
    if (frameId % math.floor(frameRate) == 0):
        filename ="test%d.jpg" % count;count+=1
        cv2.imwrite(filename, frame)
cap.release()
print ("Done!")

test = pd.read_csv('test.csv')

test_image = []
for img_name in test.Image_ID:
    img = plt.imread('' + img_name)
    test_image.append(img)
test_img = np.array(test_image)

test_image = []
for i in range(0,test_img.shape[0]):
    a = resize(test_img[i], preserve_range=True, output_shape=(224,224)).astype(int)
    test_image.append(a)
test_image = np.array(test_image)

from keras.applications.vgg16 import preprocess_input
# preprocessing the images
test_image = preprocess_input(test_image, mode='tf')

# extracting features from the images using pretrained model
test_image = base_model.predict(test_image)

# converting the images to 1-D form
test_image = test_image.reshape(186, 7*7*512)

# zero centered images
test_image = test_image/test_image.max()

predictions = model.predict_classes(test_image)

print("The screen time of JERRY is", predictions[predictions==1].shape[0], "seconds")
print("The screen time of TOM is", predictions[predictions==2].shape[0], "seconds")