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,