def add_clearing_circle(current_img, pattern_step, offset, size, weight=0.25):
if pattern_step >= offset and pattern_step < offset + size:
ring_id = pattern_step - offset
circle_img = circle_masks[ring_id]
return tfi.masked_mix(current_img, ref_img, circle_img, weight)
elif pattern_step >= offset + size and pattern_step < offset + size + 4:
return tfi.mix_images(current_img, ref_img, 1.0 - weight)
else:
return current_img
def process_image_step(current_img, zoom, rot, mix_ratio, mix_img, target):
current_img = tfi.affine_zoom(current_img, zoom, rot)
current_img = tfi.mix_images(current_img, mix_img, mix_ratio)
return tfi.render_deepdream(target,
current_img,
iter_n=2,
step=1.5,
octave_n=4,
octave_scale=1.5,
direct_objective=True)
# This should line up with 960 being reverse of same thing in stage_01, which we want!
if (fno % 480) < 240:
rot = -rot
if (fno > 970):
if (fno % 240) < 10:
rot *= (fno % 240) / 10.0
if (fno % 240) > 230:
rot *= (240 - (fno % 240)) / 10.0
step_val = 1.25
if (fno > 4600):
step_val = 0.5 + 0.75 * (4800 - fno) / 200.0
current_img = tfi.mix_images(current_img, colour_guides[section_id % 4],
0.997)
current_img = tfi.affine_zoom(current_img, zoom, rot)
current_img = tfi.render_deepdream(target,
current_img,
iter_n=1,
step=step_val,
octave_n=4,
octave_scale=1.5,
direct_objective=True)
cropped_img = current_img[margin:-margin, margin:-margin, :]
tfi.savejpeg(cropped_img,
('{}/frame_{}.jpeg'.format(directory, '%04d' % fno)))
if (fno < 1201):
tfi.savejpeg(
current_img,
layer_1 = targets[section_id * 2]
channel_1 = targets[section_id * 2 + 1]
target = tf.reduce_mean(tfi.T(layer_1)[:, :, :, channel_1])
delta_rot = 0.1
delta_zoom = 1.05
total_rot += delta_rot
total_zoom *= delta_zoom
current_img = tfi.affine_zoom(current_img, delta_zoom, delta_rot)
r = (fno - start_frame) / (end_frame - start_frame)
mix_amount = 0.99 * (1 - r) + 0.96 * r
current_img = tfi.mix_images(current_img, end_colours, mix_amount)
current_img = tfi.render_deepdream(target,
current_img,
iter_n=2,
step=1.5,
octave_n=4,
octave_scale=1.5,
direct_objective=True)
display_img = current_img
# Fade to black
if (fno > 5400):
fade_r = 1.0 - (fno - 5400) / 45.0
display_img = tfi.mix_images(display_img, complete_fade_img, fade_r)
# Fade in credits
target = ri * tf.reduce_mean( tfi.T(layer_1)[:,:,:,channel_1] ) + r * tf.reduce_mean( tfi.T(layer_2)[:,:,:,channel_2] )
else:
target = tf.reduce_mean( tfi.T(layer_2)[:,:,:,channel_2] )
delta_rot = transition_rot
delta_zoom = transition_zoom
if ( fno % 240 ) > 230:
delta_rot *= ( 240 - ( fno % 240 ) ) / 10.0
if fno > 980 and ( fno % 240 ) < 10:
delta_rot *= ( fno % 240 ) / 10.0
total_rot += delta_rot
total_zoom *= delta_zoom
current_img = tfi.affine_zoom( current_img, delta_zoom, delta_rot )
ref_img = tfi.affine_zoom( img0, total_zoom, total_rot )
current_img = tfi.mix_images( current_img, ref_img, 0.998 )
current_img = tfi.mix_images( current_img, end_colours, 0.99 )
current_img = tfi.render_deepdream( target, current_img, iter_n=1, step=1.5, octave_n=4, octave_scale=1.5, direct_objective = True )
tfi.savejpeg( current_img, ('{}/overlap_frame_{}.jpeg'.format( directory, '%04d' % fno ) ) )
cropped_img = current_img[margin:-margin, margin:-margin, :]
tfi.savejpeg( cropped_img, ('{}/frame_{}.jpeg'.format( directory, '%04d' % fno ) ) )
if (frame % 5 == 0):
tfi.reset_graph_and_session()
tfi.close_session()