# Task1
from dataloader import gen_list, shuffle_split, read_data
from Unet import get_UNet
from plot import plot_learning_curve
from metrics import dice_coef_loss, dice_coef
# Read the data
path = '/Lab1/Lab3/X_ray/'
img_h, img_w = 256, 256
Mask = gen_list(path, 'Mask')
Img = gen_list(path,'Image')
Mask_train, Mask_val, Img_train, Img_val = shuffle_split(Mask, Img, 0.8)
Mask_train = read_data(path+'Mask/', Mask_train, img_h, img_w)
Mask_val = read_data(path+'Mask/', Mask_val, img_h, img_w)
Img_train = read_data(path+'Image/', Img_train, img_h, img_w)
Img_val = read_data(path+'Image/', Img_val, img_h, img_w)
# Train the model
model = get_UNet(img_shape=(256,256,1), Base=16, depth=4, inc_rate=2,
activation='relu', drop=0.5, batchnorm=True)
model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy',
metrics=[dice_coef])
History = model.fit(Img_train, Mask_train, batch_size=8, epochs=150, verbose=2,
validation_data=(Img_val, Mask_val))
# Plot the learning curve
plot_learning_curve(History, 'Task1a')
# Train the model
metrics=['accuracy'])
History = model.fit_generator(MyBatchGenerator(X_train, y_train, batch_size=1),
epochs=50,
validation_data=MyBatchGenerator(X_val,
y_val,
batch_size=1),
validation_steps=len(X_val))
plot_learning_curve(History, 'Task2_units={0}_loss'.format(units))
plot_validation_metric_2(History, 'Task2_units={0}_metrics'.format(units))
''' Task 3 '''
path = '/Lab1/Lab3/MRI/'
img_h, img_w = 240, 240
Mask = sorted(gen_list(path, 'Mask'))
Img = sorted(gen_list(path, 'Image'))
Mask_train, Mask_val, Img_train, Img_val = shuffle_split(Mask, Img, 0.8)
Mask_train = read_data(path + 'Mask/', Mask_train, img_h, img_w)
Mask_val = read_data(path + 'Mask/', Mask_val, img_h, img_w)
Img_train = read_data(path + 'Image/', Img_train, img_h, img_w)
Img_val = read_data(path + 'Image/', Img_val, img_h, img_w)
data_gen_args = dict(rotation_range=5,
width_shift_range=0.1,
height_shift_range=0.1,
validation_split=0.2)
image_train_datagen = ImageDataGenerator(**data_gen_args)
mask_train_datagen = ImageDataGenerator(**data_gen_args)
image_val_datagen = ImageDataGenerator(**data_gen_args)
