from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K

################################################################################################################
img_width, img_height = 200, 200

train_data_dir = "dataset/train"
validation_data_dir = "dataset/test"
nb_train_samples = 2000
nb_validation_samples = 400
epochs = 50
batch_size = 40 #16

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

train_datagen = ImageDataGenerator(
    rescale = 1. / 255,
    shear_range = 0.2,
    zoom_range= 0.2,
    horizontal_flip= True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size= (img_width, img_height),
    batch_size= batch_size,
    class_mode= 'binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size= (img_width, img_height),
    batch_size= batch_size,
    class_mode= 'binary')

################################################################################################################
model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape = input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size= (2, 2)))
model.summary()

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size= (2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size= (2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size= (2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size= (2, 2)))
model.add(Dropout(0.2))

model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size= (2, 2)))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.summary()

model.compile(loss= 'binary_crossentropy',
    optimizer= 'adam',
    metrics= ['accuracy'] )

model.fit_generator(
    train_generator,
    steps_per_epoch= nb_train_samples // batch_size,
    epochs= epochs,
    validation_data= validation_generator,
    validation_steps= nb_validation_samples // batch_size)
model.save_weights("covid.hdf5")
print("Completed!!")