def show06():
start_image_file = '000238.jpg'
end_image_file = '000193.jpg'
images, _ = data_flow.next()
z_points = vae.encoder.predict(images)
interpolated_images = []
interpolated_images.append(images[0]) # Add the first selected image.
factors = np.arange(0, 1, 0.1)
for factor in factors:
z = z_points[0] * (1 - factor) + z_points[1] * factor
img = vae.decoder.predict(np.array([z]))[0]
interpolated_images.append(img)
interpolated_images.append(images[1]) # Add the second selected image.
num_interpolated_images = len(factors)
viewer = Viewer(num_rows=1, num_cols=num_interpolated_images+2, width=num_interpolated_images+2, height=1)
viewer.add_row(interpolated_images)
viewer.show()
def show03():
viewer = Viewer(num_rows=3, num_cols=10, width=10, height=3)
for i in range(3):
z_points = np.random.normal(size=(10, vae.z_dim))
generated_images = vae.decoder.predict(np.array(z_points))
viewer.add_row(generated_images)
viewer.show()
def show05():
z_avg_pos = np.zeros(shape=vae.z_dim, dtype='float32') # for the attribute with '+1'
z_avg_neg = np.zeros(shape=vae.z_dim, dtype='float32') # for the attribute with '-1'
z_count_pos = 0
z_count_neg = 0
while True:
images, labels = data_flow.next()
z_points = vae.encoder.predict(images)
z_points_pos = z_points[labels== 1]
z_points_neg = z_points[labels==-1]
z_avg_pos += np.sum(z_points_pos, axis=0)
z_avg_neg += np.sum(z_points_neg, axis=0)
z_count_pos += len(z_points_pos)
z_count_neg += len(z_points_neg)
if z_count_pos > 2000: # 2000 points are enough to get the center.
break;
z_avg_pos = z_avg_pos / z_count_pos
z_avg_neg = z_avg_neg / z_count_neg
z_direction = z_avg_pos - z_avg_neg
magnitude = np.linalg.norm(z_direction) # magnitude
z_direction = z_direction / magnitude # normalization
# Select the first five images from the next batch.
images, _ = data_flow.next()[:5]
z_points = vae.encoder.predict(images)
z_scales = [-4, -3, -2, -1, 0, 1, 2, 3, 4]
viewer = Viewer(num_rows=5, num_cols=9, width=9, height=5)
for i in range(5):
images = []
for z_scale in z_scales:
changed_z_point = z_points[i] + z_direction * z_scale
changed_image = vae.decoder.predict(np.array([changed_z_point]))[0]
images.append(changed_image)
viewer.add_row(images)
viewer.show()
def show01():
input_images, _ = data_flow.next() # Get the first batch.
input_images = input_images[:10] # Select the first 10 images from it.
# method 1
#z_points = vae.encoder.predict(input_images)
#reconstructed_images = vae.decoder.predict(z_points)
#print(len(z_points)) = 10: # of inputs
#print(z_points.shape) = (10, 200): (# of inputs, z_dim)
# method 2
output_images = vae.model.predict(input_images)
viewer = Viewer(num_rows=2, num_cols=10, width=10, height=2)
viewer.add_row(input_images)
viewer.add_row(output_images)
viewer.show()
def show04():
viewer = Viewer(num_rows=5, num_cols=10, width=10, height=5)
for i in range(5):
satisfied_images = []
while True:
images, labels = data_flow.next()
for j in range(len(images)):
if labels[j] == 1: # satisfied!
satisfied_images.append(images[j])
if len(satisfied_images) > 10:
break
viewer.add_row(satisfied_images[:10]) # Show only the first 10 images in a column.
viewer.show()
# iteration method 1 #
viewer = Viewer(num_rows=iterations, num_cols=batch_size, width=10, height=10)
for i in range(iterations):
images, labels = [], []
image, label = data_flow.next()
for j in range(batch_size):
images.append(image[j])
labels.append(label[j])
viewer.add_row(images, labels)
viewer.show()
######################
# iteration method 2 #
viewer = Viewer(num_rows=iterations, num_cols=batch_size, width=10, height=10)
for i, (image, label) in enumerate(data_flow):
images, labels = [], []
for j in range(batch_size):
images.append(image[j])
labels.append(label[j])
viewer.add_row(images, labels)