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main.py
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# from model import *
from preProcess import *
import preProcess
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from keras.models import Model, load_model
import cv2
import numpy as np
from PIL import ImageOps
import matplotlib.pyplot as plt
from tensorflow.keras.utils import load_img
from tensorflow.keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
save_to_dir=None
data_gen_args = dict(rotation_range=0.2,
width_shift_range=0.05,
height_shift_range=0.05,
shear_range=0.05,
zoom_range=0.05,
horizontal_flip=True,
fill_mode='nearest')
myRoot = preProcess.trainData(2,"/root_data/",'image','label2',data_gen_args,save_to_dir = save_to_dir)
def unetModel(pretrained_weights=None, input_size=(512,512, 1)):
inputs = tf.keras.layers.Input(input_size)
conv1 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
conv1 = tf.keras.layers.BatchNormalization()(conv1)
pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
conv2 = tf.keras.layers.BatchNormalization()(conv2)
pool2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
conv3 = tf.keras.layers.BatchNormalization()(conv3)
pool3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
conv4 = tf.keras.layers.BatchNormalization()(conv4)
drop4 = tf.keras.layers.Dropout(0.5)(conv4)
pool4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = tf.keras.layers.Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = tf.keras.layers.Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
conv5 = tf.keras.layers.BatchNormalization()(conv5)
drop5 = tf.keras.layers.Dropout(0.5)(conv5)
up6 = tf.keras.layers.Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
tf.keras.layers.UpSampling2D(size=(2, 2))(drop5))
merge6 = tf.keras.layers.concatenate([drop4, up6], axis=3)
conv6 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = tf.keras.layers.BatchNormalization()(conv6)
conv6 = tf.keras.layers.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
conv6 = tf.keras.layers.BatchNormalization()(conv6)
up7 = tf.keras.layers.Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
tf.keras.layers.UpSampling2D(size=(2, 2))(conv6))
merge7 = tf.keras.layers.concatenate([conv3, up7], axis=3)
conv7 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = tf.keras.layers.BatchNormalization()(conv7)
conv7 = tf.keras.layers.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
conv7 = tf.keras.layers.BatchNormalization()(conv7)
up8 = tf.keras.layers.Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
tf.keras.layers.UpSampling2D(size=(2, 2))(conv7))
merge8 = tf.keras.layers.concatenate([conv2, up8], axis=3)
conv8 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = tf.keras.layers.BatchNormalization()(conv8)
conv8 = tf.keras.layers.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv8 = tf.keras.layers.BatchNormalization()(conv8)
up9 = tf.keras.layers.Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
tf.keras.layers.UpSampling2D(size=(2, 2))(conv8))
merge9 = tf.keras.layers.concatenate([conv1, up9], axis=3)
conv9 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = tf.keras.layers.BatchNormalization()(conv9)
conv9 = tf.keras.layers.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = tf.keras.layers.BatchNormalization()(conv9)
conv10 = tf.keras.layers.Conv2D(1, 1, activation='sigmoid')(conv9)
model = Model(inputs=[inputs], outputs=conv10)
model.compile(optimizer=Adam(lr = 1e-4), loss='binary_crossentropy', metrics=['accuracy'])
model.summary()
return model
model = unetModel()
model_checkpoint = ModelCheckpoint('unet_membrane.hdf5', monitor='loss',verbose=1, save_best_only=True)
# model.fit_generator(myGene,steps_per_epoch=300,epochs=1,callbacks=[model_checkpoint])
model.fit(myRoot,steps_per_epoch=300,epochs=5,callbacks=[model_checkpoint])
model.save('model_root_test.h5')
testRoot = preProcess.testData("/root_data2/test3/")
# results = model.predict_generator(testGene,30,verbose=1)
results = model.predict(testRoot,5,verbose=1)
plt.imshow(results[0],aspect="auto",cmap='gray')