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train_chase.py
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import os
from sklearn.model_selection import train_test_split
from keras.callbacks import TensorBoard, ModelCheckpoint
from util import *
np.random.seed(42)
import scipy.misc as mc
data_location = ''
training_images_loc = data_location + 'Chase/train/image/'
training_label_loc = data_location + 'Chase/train/label/'
validate_images_loc = data_location + 'Chase/validate/images/'
validate_label_loc = data_location + 'Chase/validate/labels/'
train_files = os.listdir(training_images_loc)
train_data = []
train_label = []
validate_files = os.listdir(validate_images_loc)
validate_data = []
validate_label = []
desired_size=1008
for i in train_files:
im = mc.imread(training_images_loc + i)
label = mc.imread(training_label_loc + "Image_" +i.split('_')[1].split(".")[0] +"_1stHO.png" )
old_size = im.shape[:2] # old_size is in (height, width) format
delta_w = desired_size - old_size[1]
delta_h = desired_size - old_size[0]
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
color = [0, 0, 0]
color2 = [0]
new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,
value=color)
new_label = cv2.copyMakeBorder(label, top, bottom, left, right, cv2.BORDER_CONSTANT,
value=color2)
train_data.append(cv2.resize(new_im, (desired_size, desired_size)))
temp = cv2.resize(new_label,
(desired_size, desired_size))
_, temp = cv2.threshold(temp, 127, 255, cv2.THRESH_BINARY)
train_label.append(temp)
for i in validate_files:
im = mc.imread(validate_images_loc + i)
label = mc.imread(validate_label_loc + "Image_" +i.split('_')[1].split(".")[0] +"_1stHO.png" )
old_size = im.shape[:2] # old_size is in (height, width) format
delta_w = desired_size - old_size[1]
delta_h = desired_size - old_size[0]
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
color = [0, 0, 0]
color2 = [0]
new_im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT,
value=color)
new_label = cv2.copyMakeBorder(label, top, bottom, left, right, cv2.BORDER_CONSTANT,
value=color2)
validate_data.append(cv2.resize(new_im, (desired_size, desired_size)))
temp = cv2.resize(new_label,
(desired_size, desired_size))
_, temp = cv2.threshold(temp, 127, 255, cv2.THRESH_BINARY)
validate_label.append(temp)
train_data = np.array(train_data)
train_label = np.array(train_label)
validate_data = np.array(validate_data)
validate_label = np.array(validate_label)
x_train = train_data.astype('float32') / 255.
y_train = train_label.astype('float32') / 255.
x_validate = validate_data.astype('float32') / 255.
y_validate = validate_label.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), desired_size, desired_size, 3)) # adapt this if using `channels_first` image data format
y_train = np.reshape(y_train, (len(y_train), desired_size, desired_size, 1)) # adapt this if using `channels_first` im
x_validate = np.reshape(x_validate, (len(x_validate), desired_size, desired_size, 3)) # adapt this if using `channels_first` image data format
y_validate = np.reshape(y_validate, (len(y_validate), desired_size, desired_size, 1)) # adapt this if using `channels_first` im
TensorBoard(log_dir='./autoencoder', histogram_freq=0,
write_graph=True, write_images=True)
from RSAN import *
model=RSANet(input_size=(desired_size,desired_size,3),start_neurons=16,keep_prob=0.78,lr=1e-3)
weight="Chase/Model/RSAN.h5"
restore=True
if restore and os.path.isfile(weight):
model.load_weights(weight)
model_checkpoint = ModelCheckpoint(weight, monitor='val_accuracy', verbose=1, save_best_only=False)
history=model.fit(x_train, y_train,
epochs=100, #first 100 with lr=1e-3,,and last 50 with lr=1e-4
batch_size=1,
# validation_split=0.1,
validation_data=(x_validate, y_validate),
shuffle=True,
callbacks= [TensorBoard(log_dir='./autoencoder'), model_checkpoint])