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celeba_gdl.py
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celeba_gdl.py
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# Illustrate latent space embedding and arithmetic for VAE on CelebA faces images
# Code is based on
# https://nbviewer.jupyter.org/github/davidADSP/GDL_code/blob/master/03_06_vae_faces_analysis.ipynb
# Full dataset can be downloaded from https://www.kaggle.com/jessicali9530/celeba-dataset
# In this code, we use a vacuous vae model that does nothing,
# just to check the plumbing. You should redefine vae_encode and
# vae_decode for a real model.
import numpy as np
import matplotlib.pyplot as plt
import os
figdir = "../figures"
def save_fig(fname):
plt.tight_layout()
plt.savefig(os.path.join(figdir, fname))
#import tensorflow as tf
#from tensorflow import keras
#import tensorflow_datasets as tfds
import pandas as pd
#from scipy.stats import norm
from tensorflow.keras.preprocessing.image import ImageDataGenerator
#from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, save_img, img_to_array
#from keras.preprocessing.image import ImageDataGenerator, load_img, save_img, img_to_array
vae = []
latent_dim = 50
INPUT_DIM = (128,128,3)
# Make some dummy functions
def vae_encode(model, x):
#mean, logvar = model.inference_net(x)
#return mean
N = x.shape[0]
return np.zeros((N, latent_dim))
def vae_decode(model, z_points):
#return model.decode(z_points)
N = z_points.shape[0]
return np.zeros((N,128,128,3))
DATA_FOLDER = '/home/murphyk/Data/CelebA/'
IMAGE_FOLDER = '/home/murphyk/Data/CelebA/img_align_celeba/'
att = pd.read_csv(os.path.join(DATA_FOLDER, 'list_attr_celeba.csv'))
att.head()
class ImageLabelLoader():
def __init__(self, image_folder, target_size):
self.image_folder = image_folder
self.target_size = target_size
def build(self, att, batch_size, label = None):
data_gen = ImageDataGenerator(rescale=1./255)
if label:
data_flow = data_gen.flow_from_dataframe(
att
, self.image_folder
, x_col='image_id'
, y_col=label
, target_size=self.target_size
, class_mode='other'
, batch_size=batch_size
, shuffle=True
)
else:
data_flow = data_gen.flow_from_dataframe(
att
, self.image_folder
, x_col='image_id'
, target_size=self.target_size
, class_mode='input'
, batch_size=batch_size
, shuffle=True
)
return data_flow
imageLoader = ImageLabelLoader(IMAGE_FOLDER, INPUT_DIM[:2])
#######
# Reconstructing images
n_to_show = 10
data_flow_generic = imageLoader.build(att, n_to_show)
example_batch = next(data_flow_generic)
example_images = example_batch[0]
fig = plt.figure(figsize=(15, 3))
fig.subplots_adjust(hspace=0.4, wspace=0.4)
for i in range(n_to_show):
img = example_images[i].squeeze()
sub = fig.add_subplot(2, n_to_show, i+1)
sub.axis('off')
sub.imshow(img)
z_points = vae_encode(vae, example_images)
reconst_images = vae_decode(vae, z_points)
for i in range(n_to_show):
img = reconst_images[i].squeeze()
sub = fig.add_subplot(2, n_to_show, i+n_to_show+1)
sub.axis('off')
sub.imshow(img)
###############
# Latent space distribution
# We skip this since it requires predict_generator
###############
# Newly generated faces
n_to_show = 30
znew = np.random.normal(size = (n_to_show, latent_dim))
#reconst = vae.decoder.predict(np.array(znew))
reconst = vae_decode(vae, np.array(znew))
fig = plt.figure(figsize=(18, 5))
fig.subplots_adjust(hspace=0.4, wspace=0.4)
for i in range(n_to_show):
ax = fig.add_subplot(3, 10, i+1)
ax.imshow(reconst[i, :,:,:])
ax.axis('off')
plt.show()
def get_vector_from_label(label, batch_size):
data_flow_label = imageLoader.build(att, batch_size, label = label)
#origin = np.zeros(shape = latent_dim, dtype = 'float32')
current_sum_POS = np.zeros(shape = latent_dim, dtype = 'float32')
current_n_POS = 0
current_mean_POS = np.zeros(shape = latent_dim, dtype = 'float32')
current_sum_NEG = np.zeros(shape = latent_dim, dtype = 'float32')
current_n_NEG = 0
current_mean_NEG = np.zeros(shape = latent_dim, dtype = 'float32')
current_vector = np.zeros(shape = latent_dim, dtype = 'float32')
current_dist = 0
print('label: ' + label)
print('images : POS move : NEG move :distance : 𝛥 distance')
while(current_n_POS < 10000):
batch = next(data_flow_label)
im = batch[0]
attribute = batch[1]
#z = vae.encoder.predict(np.array(im))
z = vae_encode(vae, np.array(im))
z_POS = z[attribute==1]
z_NEG = z[attribute==-1]
if len(z_POS) > 0:
current_sum_POS = current_sum_POS + np.sum(z_POS, axis = 0)
current_n_POS += len(z_POS)
new_mean_POS = current_sum_POS / current_n_POS
movement_POS = np.linalg.norm(new_mean_POS-current_mean_POS)
if len(z_NEG) > 0:
current_sum_NEG = current_sum_NEG + np.sum(z_NEG, axis = 0)
current_n_NEG += len(z_NEG)
new_mean_NEG = current_sum_NEG / current_n_NEG
movement_NEG = np.linalg.norm(new_mean_NEG-current_mean_NEG)
current_vector = new_mean_POS-new_mean_NEG
new_dist = np.linalg.norm(current_vector)
dist_change = new_dist - current_dist
print(str(current_n_POS)
+ ' : ' + str(np.round(movement_POS,3))
+ ' : ' + str(np.round(movement_NEG,3))
+ ' : ' + str(np.round(new_dist,3))
+ ' : ' + str(np.round(dist_change,3))
)
current_mean_POS = np.copy(new_mean_POS)
current_mean_NEG = np.copy(new_mean_NEG)
current_dist = np.copy(new_dist)
if np.sum([movement_POS, movement_NEG]) < 0.08:
current_vector = current_vector / current_dist
print('Found the ' + label + ' vector')
break
return current_vector
def add_vector_to_images(feature_vec):
n_to_show = 5
factors = [-4,-3,-2,-1,0,1,2,3,4]
example_batch = next(data_flow_generic)
example_images = example_batch[0]
#example_labels = example_batch[1]
#z_points = vae.encoder.predict(example_images)
z_points = vae_encode(vae, example_images)
fig = plt.figure(figsize=(18, 10))
counter = 1
for i in range(n_to_show):
img = example_images[i].squeeze()
sub = fig.add_subplot(n_to_show, len(factors) + 1, counter)
sub.axis('off')
sub.imshow(img)
counter += 1
for factor in factors:
changed_z_point = z_points[i] + feature_vec * factor
#changed_image = vae.decoder.predict(np.array([changed_z_point]))[0]
changed_image = vae_decode(vae, np.array([changed_z_point]))[0]
img = changed_image.squeeze()
sub = fig.add_subplot(n_to_show, len(factors) + 1, counter)
sub.axis('off')
sub.imshow(img)
counter += 1
plt.show()
BATCH_SIZE = 500
attractive_vec = get_vector_from_label('Attractive', BATCH_SIZE)
mouth_open_vec = get_vector_from_label('Mouth_Slightly_Open', BATCH_SIZE)
smiling_vec = get_vector_from_label('Smiling', BATCH_SIZE)
lipstick_vec = get_vector_from_label('Wearing_Lipstick', BATCH_SIZE)
young_vec = get_vector_from_label('High_Cheekbones', BATCH_SIZE)
male_vec = get_vector_from_label('Male', BATCH_SIZE)
eyeglasses_vec = get_vector_from_label('Eyeglasses', BATCH_SIZE)
blonde_vec = get_vector_from_label('Blond_Hair', BATCH_SIZE)
print('Eyeglasses Vector')
add_vector_to_images(eyeglasses_vec)
##########
# Face morphs
def morph_faces(start_image_file, end_image_file):
factors = np.arange(0,1,0.1)
att_specific = att[att['image_id'].isin([start_image_file, end_image_file])]
att_specific = att_specific.reset_index()
data_flow_label = imageLoader.build(att_specific, 2)
example_batch = next(data_flow_label)
example_images = example_batch[0]
#example_labels = example_batch[1]
#z_points = vae.encoder.predict(example_images)
z_points = vae_encode(vae, example_images)
fig = plt.figure(figsize=(18, 8))
counter = 1
img = example_images[0].squeeze()
sub = fig.add_subplot(1, len(factors)+2, counter)
sub.axis('off')
sub.imshow(img)
counter+=1
for factor in factors:
changed_z_point = z_points[0] * (1-factor) + z_points[1] * factor
#changed_image = vae.decoder.predict(np.array([changed_z_point]))[0]
changed_image = vae_decode(vae, np.array([changed_z_point]))[0]
img = changed_image.squeeze()
sub = fig.add_subplot(1, len(factors)+2, counter)
sub.axis('off')
sub.imshow(img)
counter += 1
img = example_images[1].squeeze()
sub = fig.add_subplot(1, len(factors)+2, counter)
sub.axis('off')
sub.imshow(img)
plt.show()
start_image_file = '000238.jpg'
end_image_file = '000193.jpg' #glasses
morph_faces(start_image_file, end_image_file)
start_image_file = '000112.jpg'
end_image_file = '000258.jpg'
morph_faces(start_image_file, end_image_file)
start_image_file = '000230.jpg'
end_image_file = '000712.jpg'
morph_faces(start_image_file, end_image_file)