def save_reference_img(img, direction, pct, fno):
subdname = '{}_{}'.format(direction, pct)
tfi.savejpeg(img, ('{}/{}/overlap_frame_{}.jpeg'.format(
directory, subdname, '%04d' % fno)))
def save_rendered_img(img, direction, pct, fno):
cropped_img = img[margin:-margin, margin:-margin, :]
subdname = '{}_{}'.format(direction, pct)
tfi.savejpeg(
cropped_img,
('{}/{}/frame_{}.jpeg'.format(directory, subdname, '%04d' % fno)))
import tfi
import os
import numpy as np
import PIL.Image
import tensorflow as tf
in_name = 'images/nfrac_1400x840.jpg'
out_name = 'images/start_frame_1400x840.jpeg'
img0 = PIL.Image.open(in_name)
img0 = np.float32(img0)
tfi.reset_graph_and_session()
target = tf.square(tfi.T('mixed4c'))
print('Rendering {}'.format(out_name))
test_img = tfi.render_deepdream(target,
img0,
iter_n=20,
step=0.75,
octave_n=4,
octave_scale=1.5)
tfi.savejpeg(test_img, out_name)
first_ring_offset_b = 120
second_ring_offset_b = 135
clear_offset_b = 150
directory = 'animation_stage_03'
if not os.path.exists(directory):
os.makedirs(directory)
img0 = PIL.Image.open('images/start_frame_1400x840.jpeg')
img0 = np.float32(img0)
tfi.reset_graph_and_session()
current_img = img0
cropped_img = current_img[margin:-margin, margin:-margin, :]
tfi.savejpeg(cropped_img,
('{}/frame_{}.jpeg'.format(directory, '%04d' % start_frame)))
total_zoom = 1.0
total_rot = 0.0
targets = [
'head0_bottleneck_pre_relu',
53, # Feathers
'mixed5a_3x3_bottleneck_pre_relu',
11, # Dog face and circles
'mixed5a_3x3_bottleneck_pre_relu',
119, # Butterfly
'mixed5a_3x3_bottleneck_pre_relu',
33, # Spider monkey brains
'head0_bottleneck_pre_relu',
18, # Trumpets
# Technically this is the end frame, as we're working backwards towards it
img0 = PIL.Image.open('images/start_frame_1400x840.jpeg')
img0 = np.float32(img0)
colour_guides = [
img0,
np.float32(PIL.Image.open('images/colour_guide_a.jpeg')),
np.float32(PIL.Image.open('images/colour_guide_b.jpeg')),
np.float32(PIL.Image.open('images/colour_guide_c.jpeg'))
]
tfi.reset_graph_and_session()
current_img = img0.copy()
cropped_img = current_img[margin:-margin, margin:-margin, :]
tfi.savejpeg(cropped_img,
('{}/frame_{}.jpeg'.format(directory, '%04d' % end_frame)))
slow_zoom = 1.0 / 0.997
slow_rot = 0.2
fast_zoom = 1.0 / 0.994
fast_rot = 0.35
for frame in range(nframes):
fno = end_frame - 1 - frame
section_id = (fno // channel_step)
prev_layer = targets[(section_id - 1) * 2]
prev_channel = targets[(section_id - 1) * 2 + 1]
layer = targets[section_id * 2]
channel = targets[section_id * 2 + 1]
first_ring_offset_b = 120
second_ring_offset_b = 135
clear_offset_b = 150
directory = 'animation_stage_01'
if not os.path.exists(directory):
os.makedirs(directory)
img0 = PIL.Image.open('images/start_frame_1400x840.jpeg')
img0 = np.float32(img0)
tfi.reset_graph_and_session()
current_img = img0
cropped_img = current_img[margin:-margin, margin:-margin, :]
tfi.savejpeg( cropped_img, ('{}/frame_{}.jpeg'.format( directory, '%04d' % 0 ) ) )
total_zoom = 1.0
total_rot = 0.0
transition_start = 930
transition_zoom = 0.997
transition_rot = 0.2
targets = [
'mixed4b_3x3_bottleneck_pre_relu', 105, # girders
'mixed4a_3x3_bottleneck_pre_relu', 8, # molten glass
'mixed4a_3x3_bottleneck_pre_relu', 24, # leopard spots
if not os.path.exists('explore_layers'):
os.makedirs('explore_layers')
for layer in test_layers:
num_channels = tfi.T(layer).get_shape()[3]
directory = 'explore_layers/{}'.format(layer)
if not os.path.exists(directory):
os.makedirs(directory)
print('Rendering {}, all {} channels squared'.format(layer, num_channels))
test_img = tfi.render_deepdream(tf.square(tfi.T(layer)),
img0,
iter_n=iterations,
step=2.0,
octave_n=4,
octave_scale=1.5)
tfi.savejpeg(test_img, ('{}/all_channels_squared.jpeg'.format(directory)))
for channel in range(0, num_channels):
print('Rendering {}, channel {}'.format(layer, channel))
test_img = tfi.render_deepdream(tfi.T(layer)[:, :, :, channel],
img0,
iter_n=iterations,
step=2.0,
octave_n=4,
octave_scale=1.5)
tfi.savejpeg(
test_img,
('{}/channel_{}.jpeg'.format(directory, '%03d' % channel)))