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# ------------------------------------------------------------------
# _____ _ _ _
# | ___(_) __| | | ___
# | |_ | |/ _` | |/ _ \
# | _| | | (_| | | __/
# |_| |_|\__,_|_|\___| ImageCallback
# ------------------------------------------------------------------
# Formation Introduction au Deep Learning (FIDLE)
# CNRS/SARI/DEVLOG 2020 - S. Arias, E. Maldonado, JL. Parouty
# ------------------------------------------------------------------
# 2.0 version by JL Parouty, feb 2021
from keras.callbacks import Callback
import numpy as np
import matplotlib.pyplot as plt
from skimage import io
import os
class ImagesCallback(Callback):
'''
Save generated (random mode) or encoded/decoded (z mode) images on epoch end.
params:
x : input images, for z mode (None)
z_dim : size of the latent space, for random mode (None)
nb_images : number of images to save
from_z : save images from z (False)
from_random : save images from random (False)
filename : images filename
run_dir : output directory to save images
'''
def __init__(self, x = None,
z_dim = None,
nb_images = 5,
from_z = False,
from_random = False,
filename = 'image-{epoch:03d}-{i:02d}.jpg',
run_dir = './run'):
# ---- Parameters
#
self.x = None if x is None else x[:nb_images]
self.z_dim = z_dim
self.nb_images = nb_images
self.from_z = from_z
self.from_random = from_random
self.filename_z = run_dir + '/images-z/' + filename
self.filename_random = run_dir + '/images-random/' + filename
if from_z: os.makedirs( run_dir + '/images-z/', mode=0o750, exist_ok=True)
if from_random: os.makedirs( run_dir + '/images-random/', mode=0o750, exist_ok=True)
def save_images(self, images, filename, epoch):
'''Save images as <filename>'''
for i,image in enumerate(images):
image = image.squeeze() # Squeeze it if monochrome : (lx,ly,1) -> (lx,ly)
filenamei = filename.format(epoch=epoch,i=i)
if len(image.shape) == 2:
plt.imsave(filenamei, image, cmap='gray_r')
else:
plt.imsave(filenamei, image)
def on_epoch_end(self, epoch, logs={}):
'''Called at the end of each epoch'''
encoder = self.model.get_layer('encoder')
decoder = self.model.get_layer('decoder')
if self.from_random:
z = np.random.normal( size=(self.nb_images,self.z_dim) )
images = decoder.predict(z)
self.save_images(images, self.filename_random, epoch)
if self.from_z:
z_mean, z_var, z = encoder.predict(self.x)
images = decoder.predict(z)
self.save_images(images, self.filename_z, epoch)
def get_images(self, epochs=None, from_z=True,from_random=True):
'''Read and return saved images. epochs is a range'''
if epochs is None : return
images_z = []
images_r = []
for epoch in list(epochs):
for i in range(self.nb_images):
if from_z:
f = self.filename_z.format(epoch=epoch,i=i)
images_z.append( io.imread(f) )
if from_random:
f = self.filename_random.format(epoch=epoch,i=i)
images_r.append( io.imread(f) )
return images_z, images_r