| 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' |
