y_train = train_label.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 512, 512, 3)) # adapt this if using `channels_first` image data format
y_train = np.reshape(y_train, (len(y_train), 512, 512, 1)) # adapt this if using `channels_first` im
x_test = test_data.astype('float32') / 255.
y_test = test_label.astype('float32') / 255.
x_test = np.reshape(x_test, (len(x_test), 512, 512, 3)) # adapt this if using `channels_first` image data format
y_test = np.reshape(y_test, (len(y_test), 512, 512, 1)) # adapt this if using `channels_first` im
TensorBoard(log_dir='./autoencoder', histogram_freq=0,
write_graph=True, write_images=True)
from ResUnet import *
model=ResUnet(input_size=(512,512,3),start_neurons=16,keep_prob=0.9,block_size=7)
weight="Model/Luna/ResUnet.h5"
restore=False
if restore and os.path.isfile(weight):
model.load_weights(weight)
model_checkpoint = ModelCheckpoint(weight, monitor='val_acc', verbose=1, save_best_only=True)
model.fit(x_train, y_train,
epochs=300,
batch_size=4,
validation_split=0.12,
# validation_data=(x_test, y_test),
shuffle=True,
callbacks= [TensorBoard(log_dir='./autoencoder'), model_checkpoint]
)
import os
import numpy as np
import cv2
from keras.callbacks import TensorBoard, ModelCheckpoint
from keras.utils.vis_utils import model_to_dot
from keras.utils import plot_model
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/'
testing_images_loc = data_location + 'Chase/test/image/'
testing_label_loc = data_location + 'Chase/test/label/'
train_files = os.listdir(training_images_loc)
train_data = []
train_label = []
desired_size = 1024
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,
y_test = test_label.astype('float32') / 255.
x_test = np.reshape(
x_test, (len(x_test), 512, 512,
3)) # adapt this if using `channels_first` image data format
y_test = np.reshape(
y_test,
(len(y_test), 512, 512, 1)) # adapt this if using `channels_first` im
from ResUnet import *
model = ResUnet(input_size=(512, 512, 3),
start_neurons=16,
keep_prob=1,
block_size=1)
weight = "Model/Luna/ResUnet.h5"
if os.path.isfile(weight): model.load_weights(weight)
model_checkpoint = ModelCheckpoint(weight,
monitor='val_acc',
verbose=1,
save_best_only=True)
y_pred = model.predict(x_test)
y_pred_threshold = []
i = 0
for y in y_pred:
_, temp = cv2.threshold(y, 0.5, 1, cv2.THRESH_BINARY)
y_pred_threshold.append(temp)
y = y * 255
cv2.imwrite('./Luna/test/result/%d.png' % i, y)
import os
import cv2
import numpy as np
from sklearn.metrics import recall_score, roc_auc_score, accuracy_score, confusion_matrix
from keras.callbacks import ModelCheckpoint
from util import *
import scipy.misc as mc
import math
data_location = ''
testing_images_loc = data_location + 'Drive/test/images/'
testing_label_loc = data_location + 'Drive/test/label/'
test_files = os.listdir(testing_images_loc)
test_data = []
test_label = []
desired_size = 592
for i in test_files:
im = mc.imread(testing_images_loc + i)
label = mc.imread(testing_label_loc + i.split('_')[0] + '_manual1.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,