from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import optimizers
from keras import backend as K
import tensorflow
import cv2
import glob
mh=704
mw=160
num_epochs=1000
input_img = Input(shape=(mh, mw, 1)) # adapt this if using `channels_first` image data format
# Encode-----------------------------------------------------------
x = Conv2D(32, (4, 4), strides=2 , activation='relu', padding='same')(input_img)
x = Conv2D(32, (4, 4), strides=2, activation='relu', padding='same')(x)
x = Conv2D(32, (3, 3), strides=1, activation='relu', padding='same')(x)
x = Conv2D(64, (4, 4), strides=2, activation='relu', padding='same')(x)
x = Conv2D(64, (3, 3), strides=1, activation='relu', padding='same')(x)
x = Conv2D(128, (4, 4), strides=2, activation='relu', padding='same')(x)
x = Conv2D(64, (3, 3), strides=1, activation='relu', padding='same')(x)
x = Conv2D(32, (3, 3), strides=1, activation='relu', padding='same')(x)
encoded = Conv2D(1, (8, 8), strides=1, padding='same')(x)
# Decode---------------------------------------------------------------------
x = Conv2D(32, (3, 3), strides=1, activation='relu', padding='same')(encoded)
x = Conv2D(64, (3, 3), strides=1, activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(128, (4, 4), strides=2, activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(64, (3, 3), strides=1, activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(64, (4, 4), strides=2, activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), strides=1, activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (4, 4), strides=2, activation='relu', padding='same')(x)
x = UpSampling2D((4, 4))(x)
x = Conv2D(32, (4, 4), strides=2, activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (8, 8), activation='sigmoid', padding='same')(x)
# ---------------------------------------------------------------------
autoencoder = Model(input_img, decoded)
adam = optimizers.Adam(lr=0.0002, decay=0.00001)
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
autoencoder.summary()
import os, glob
import numpy as np
from sklearn.model_selection import train_test_split
from PIL import Image
# Own Your Image Directory
img_dir = ("./Samples/")
img_files = glob.glob(img_dir + "*.png")
# Setting Image Propertie
width = mw
height = mh
pixels = width * height * 1 # gray scale
# Load Image
# AutoEncoder does not have to label data
x = []
for i, f in enumerate(img_files):
img = Image.open(f)
# img = img.convert("RGB")
img = img.convert("L") # gray sclae
img = img.resize((width,height), 1)
data = np.asarray(img)
x.append(data)
if i % 10 == 0:
print(i, "\n", data)
x = np.array(x)
(x_train, x_test) = train_test_split(x, shuffle=False, train_size=0.7, random_state=1)
img_list = (x_train, x_test)
np.save("./obj.npy", img_list)
print("OK", len(x))
# change to float32
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), mh, mw, 1)) # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), mh, mw, 1)) # adapt this if using `channels_first` image data format
print (x_train.shape)
print (x_test.shape)
from keras.callbacks import TensorBoard
autoencoder.fit(x_train, x_train,
epochs=num_epochs,
batch_size=32,
shuffle=True,
validation_data=(x_test, x_test),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])
####################################################
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'''
import matplotlib.pyplot as plt
decoded_imgs = autoencoder.predict(x_train)
n = 8 # how many digits we will display
plt.figure(figsize=(20, 5), dpi=100)
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_train[i].reshape(mh, mw))
plt.gray()
ax.get_xaxis().set_visible(True)
ax.get_yaxis().set_visible(False)
# SSIM Encode
ax.set_title("Encode_Image")
npImg = x_train[i]
npImg = npImg.reshape((mh, mw))
formatted = (npImg * 255 / np.max(npImg)).astype('uint8')
img = Image.fromarray(formatted)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(mh, mw))
plt.gray()
ax.get_xaxis().set_visible(True)
ax.get_yaxis().set_visible(False)
# SSIM Decoded
npDecoded = decoded_imgs[i]
npDecoded = npDecoded.reshape((mh,mw))
formatted2 = (npDecoded * 255 / np.max(npDecoded)).astype('uint8')
decoded = Image.fromarray(formatted2)
from SSIM_PIL import compare_ssim as ssim
value = ssim(img, decoded)
label = 'SSIM: {:.3f}'
ax.set_title("Decoded_Image")
ax.set_xlabel(label.format(value))
plt.show()
'''
'''
import matplotlib.pyplot as plt
decoded_imgs = autoencoder.predict(x_test)
n = 3 # how many digits we will display
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(mh, mw))
plt.gray()
ax.get_xaxis().set_visible(True)
ax.get_yaxis().set_visible(False)
# SSIM Encode
ax.set_title("Encode_Image")
npImg = x_test[i]
npImg = npImg.reshape((mh, mw))
formatted = (npImg * 255 / np.max(npImg)).astype('uint8')
img = Image.fromarray(formatted)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[i].reshape(mh, mw))
plt.gray()
ax.get_xaxis().set_visible(True)
ax.get_yaxis().set_visible(False)
# SSIM Decoded
npDecoded = decoded_imgs[i]
npDecoded = npDecoded.reshape((mh,mw))
formatted2 = (npDecoded * 255 / np.max(npDecoded)).astype('uint8')
decoded = Image.fromarray(formatted2)
from SSIM_PIL import compare_ssim as ssim
value = ssim(img, decoded)
label = 'SSIM: {:.3f}'
ax.set_title("Decoded_Image")
ax.set_xlabel(label.format(value))
plt.show()
'''
def search_imgs():
path = "./test/*.png"
path_lists = glob.glob(path)
imgs=[]
for p in path_lists:
img=cv2.imread(p,0)
img=cv2.resize(img,(mw,mh))
img=np.expand_dims(img,3)
imgs.append(img.astype('float32') / 255.)
imgs=np.array(imgs)
print("------------------------------------------=",imgs.shape)
#x_test = x_test.astype('float32') / 255.
#x_test = np.reshape(imgs, (len(imgs), mh, mw, 1)) # adapt this if using `channels_first` image data format
return imgs
import matplotlib.pyplot as plt
x_test=search_imgs()
decoded_imgs = autoencoder.predict(x_test)
for i in range(len(x_test)):
# display original
ori_img = x_test[i].reshape(mh, mw)
cv2.imwrite("./xxx/"+str(i)+"_ori_.jpg", (ori_img*255).astype(np.uint8))
# Decoded
npDecoded = decoded_imgs[i]
npDecoded = npDecoded.reshape((mh,mw))
cv2.imwrite("./xxx/"+str(i)+"_npDecoded _.jpg", (npDecoded*255).astype(np.uint8) )
plt.show()
