keras学习笔记-黑白照片自动着色的神经网络-Beta版

from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D
from keras.layers import Activation, Dense, Dropout, Flatten, InputLayer
from keras.layers.normalization import BatchNormalization
from keras.callbacks import TensorBoard
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from skimage.color import rgb2lab, lab2rgb, rgb2gray
from skimage.io import imsave
import numpy as np
import osimport random
import tensorflow as tf
Using TensorFlow backend.
# Get imagesX = []
for filename in os.listdir('data/color/Train/'):
    X.append(img_to_array(load_img('data/color/Train/'+filename))) X = np.array(X, dtype=float)
# Set up train and test data
split = int(0.95*len(X)) Xtrain = X[:split] Xtrain = 1.0/255*Xtrainmodel = Sequential()
model.add(InputLayer(input_shape=(256, 256, 1))) model.add(Conv2D(64, (3, 3), activation='relu', padding='same')) model.add(Conv2D(64, (3, 3), activation='relu', padding='same', strides=2)) model.add(Conv2D(128, (3, 3), activation='relu', padding='same')) model.add(Conv2D(128, (3, 3), activation='relu', padding='same', strides=2)) model.add(Conv2D(256, (3, 3), activation='relu', padding='same')) model.add(Conv2D(256, (3, 3), activation='relu', padding='same', strides=2)) model.add(Conv2D(512, (3, 3), activation='relu', padding='same')) model.add(Conv2D(256, (3, 3), activation='relu', padding='same')) model.add(Conv2D(128, (3, 3), activation='relu', padding='same')) model.add(UpSampling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu', padding='same')) model.add(UpSampling2D((2, 2))) model.add(Conv2D(32, (3, 3), activation='relu', padding='same')) model.add(Conv2D(2, (3, 3), activation='tanh', padding='same')) model.add(UpSampling2D((2, 2))) model.compile(optimizer='rmsprop', loss='mse')
# Image transformerdatagen = ImageDataGenerator(         shear_range=0.2,         zoom_range=0.2,         rotation_range=20,         horizontal_flip=True)
# Generate training databatch_size = 10def image_a_b_gen(batch_size):     for batch in datagen.flow(Xtrain, batch_size=batch_size):         lab_batch = rgb2lab(batch)         X_batch = lab_batch[:,:,:,0]         Y_batch = lab_batch[:,:,:,1:] / 128         yield (X_batch.reshape(X_batch.shape+(1,)), Y_batch)
# Train model      
tensorboard = TensorBoard(log_dir="data/color/output/first_run") model.fit_generator(image_a_b_gen(batch_size), callbacks=[tensorboard], epochs=1, steps_per_epoch=10)
Epoch 1/1 10/10 [==============================] - 178s - loss: 0.5208      <keras.callbacks.History at 0x1092b5ac8>
# Save modelmodel_json = model.to_json()with open("model.json", "w") as json_file:     json_file.write(model_json) model.save_weights("model.h5")
# Test imagesXtest = rgb2lab(1.0/255*X[split:])[:,:,:,0]
Xtest = Xtest.reshape(Xtest.shape+(1,)) Ytest = rgb2lab(1.0/255*X[split:])[:,:,:,1:] Ytest = Ytest / 128
print(model.evaluate(Xtest, Ytest, batch_size=batch_size))
1/1 [==============================] - 0s 0.00189386657439
color_me = []for filename in os.listdir('data/color/Test/'):     color_me.append(img_to_array(load_img('data/color/Test/'+filename))) color_me = np.array(color_me, dtype=float) color_me = rgb2lab(1.0/255*color_me)[:,:,:,0] color_me = color_me.reshape(color_me.shape+(1,))# Test modeloutput = model.predict(color_me) output = output * 128# Output colorizationsfor i in range(len(output)):     cur = np.zeros((256, 256, 3))     cur[:,:,0] = color_me[i][:,:,0]     cur[:,:,1:] = output[i]     imsave("data/color/output/img1_"+str(i)+".png", lab2rgb(cur))
/usr/local/lib/python3.6/site-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint8   .format(dtypeobj_in, dtypeobj_out))