| import numpy as np | |
| from keras.models import * | |
| from keras.layers import * | |
| from keras.applications.vgg16 import VGG16 | |
| from keras.preprocessing.image import ImageDataGenerator | |
| from keras.optimizers import * | |
| from keras.callbacks import ModelCheckpoint | |
| import cv2 | |
| def train_generator(batch_size=32): | |
| data_gen_args = dict(featurewise_center=True, | |
| rotation_range=90., | |
| width_shift_range=0.1, | |
| height_shift_range=0.1, | |
| fill_mode="constant", | |
| cval=255, | |
| horizontal_flip=True, | |
| vertical_flip=True, | |
| zoom_range=0.2) | |
| image_datagen = ImageDataGenerator(**data_gen_args) | |
| mask_datagen = ImageDataGenerator(**data_gen_args) | |
| seed = 1 | |
| image_generator = image_datagen.flow_from_directory( | |
| 'data/train/images', | |
| class_mode=None, | |
| batch_size=batch_size, | |
| color_mode='rgb', | |
| target_size=(512,512), | |
| #save_to_dir='./data/gen/images', | |
| seed=seed) | |
| mask_generator = mask_datagen.flow_from_directory( | |
| 'data/train/masks', | |
| class_mode=None, | |
| color_mode='grayscale', | |
| target_size=(512,512), | |
| batch_size=batch_size, | |
| #save_to_dir='./data/gen/masks', | |
| seed=seed) | |
| # combine generators into one which yields image and masks | |
| train_generator = zip(image_generator, mask_generator) | |
| for (imgs, masks) in train_generator: | |
| imgs = imgs / 255.0 | |
| masks = masks / 255.0 | |
| yield (imgs,masks) | |
| def vgg10_unet(input_shape=(256,256,3), weights='imagenet'): | |
| vgg16_model = VGG16(input_shape=input_shape, weights=weights, include_top=False) | |
| block4_pool = vgg16_model.get_layer('block4_pool').output | |
| block5_conv1 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block4_pool) | |
| block5_conv2 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block5_conv1) | |
| block5_drop = Dropout(0.5)(block5_conv2) | |
| block6_up = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')( | |
| UpSampling2D(size=(2, 2))(block5_drop)) | |
| block6_merge = Concatenate(axis=3)([vgg16_model.get_layer('block4_conv3').output, block6_up]) | |
| block6_conv1 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block6_merge) | |
| block6_conv2 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block6_conv1) | |
| block6_conv3 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block6_conv2) | |
| block7_up = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')( | |
| UpSampling2D(size=(2, 2))(block6_conv3)) | |
| block7_merge = Concatenate(axis=3)([vgg16_model.get_layer('block3_conv3').output, block7_up]) | |
| block7_conv1 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_merge) | |
| block7_conv2 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_conv1) | |
| block7_conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block7_conv2) | |
| block8_up = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')( | |
| UpSampling2D(size=(2, 2))(block7_conv3)) | |
| block8_merge = Concatenate(axis=3)([vgg16_model.get_layer('block2_conv2').output, block8_up]) | |
| block8_conv1 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block8_merge) | |
| block8_conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block8_conv1) | |
| block9_up = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')( | |
| UpSampling2D(size=(2, 2))(block8_conv2)) | |
| block9_merge = Concatenate(axis=3)([vgg16_model.get_layer('block1_conv2').output, block9_up]) | |
| block9_conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(block9_merge) | |
| block9_conv2 = Conv2D(64, 3, activation='relu' |