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gif_making.py
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gif_making.py
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import os
import sys
import argparse
import numpy as np
from PIL import Image
import tensorflow as tf
sys.path.append('./')
from utils import draw, image_pasting_v3_testing
from model_common_test import DiffPastingV3
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def add_scaling_visualization(canvas_images, cursor, window_size, image_size):
"""
:param canvas_images: (N, H, W, 3)
:param cursor:
:param window_size:
:param image_size:
:return:
"""
cursor_pos = cursor * float(image_size)
cursor_x, cursor_y = int(round(cursor_pos[0])), int(round(cursor_pos[1])) # in large size
vis_color = [255, 0, 0]
cursor_width = 3
box_width = 2
canvas_imgs = 255 - np.round(canvas_images * 255.0).astype(np.uint8)
# add cursor visualization
canvas_imgs[:, cursor_y - cursor_width: cursor_y + cursor_width, cursor_x - cursor_width: cursor_x + cursor_width, :] = vis_color
# add box visualization
up = max(0, cursor_y - window_size // 2)
down = min(image_size, cursor_y + window_size // 2)
left = max(0, cursor_x - window_size // 2)
right = min(image_size, cursor_x + window_size // 2)
# up = cursor_y - window_size // 2
# down = cursor_y + window_size // 2
# left = cursor_x - window_size // 2
# right = cursor_x + window_size // 2
if up > 0:
canvas_imgs[:, up: up + box_width, left: right, :] = vis_color
if down < image_size:
canvas_imgs[:, down - box_width: down, left: right, :] = vis_color
if left > 0:
canvas_imgs[:, up: down, left: left + box_width, :] = vis_color
if right < image_size:
canvas_imgs[:, up: down, right - box_width: right, :] = vis_color
return canvas_imgs
def make_gif(sess, pasting_func, data, init_cursor, image_size, infer_lengths, init_width,
save_base,
cursor_type='next', min_window_size=32, raster_size=128, add_box=True):
"""
:param data: (N_strokes, 9): flag, x0, y0, x1, y1, x2, y2, r0, r2
:return:
"""
canvas = np.zeros((image_size, image_size), dtype=np.float32) # [0.0-BG, 1.0-stroke]
gif_frames = []
cursor_idx = 0
if init_cursor.ndim == 1:
init_cursor = [init_cursor]
for round_idx in range(len(infer_lengths)):
print('Making progress', round_idx + 1, '/', len(infer_lengths))
round_length = infer_lengths[round_idx]
cursor_pos = init_cursor[cursor_idx] # (2)
cursor_idx += 1
prev_width = init_width
prev_scaling = 1.0
prev_window_size = float(raster_size) # (1)
for round_inner_i in range(round_length):
stroke_idx = np.sum(infer_lengths[:round_idx]).astype(np.int32) + round_inner_i
curr_window_size_raw = prev_scaling * prev_window_size
curr_window_size_raw = np.maximum(curr_window_size_raw, min_window_size)
curr_window_size_raw = np.minimum(curr_window_size_raw, image_size)
curr_window_size = int(round(curr_window_size_raw)) # ()
pen_state = data[stroke_idx, 0]
stroke_params = data[stroke_idx, 1:] # (8)
x1y1, x2y2, width2, scaling2 = stroke_params[0:2], stroke_params[2:4], stroke_params[4], stroke_params[5]
x0y0 = np.zeros_like(x2y2) # (2), [-1.0, 1.0]
x0y0 = np.divide(np.add(x0y0, 1.0), 2.0) # (2), [0.0, 1.0]
x2y2 = np.divide(np.add(x2y2, 1.0), 2.0) # (2), [0.0, 1.0]
widths = np.stack([prev_width, width2], axis=0) # (2)
stroke_params_proc = np.concatenate([x0y0, x1y1, x2y2, widths], axis=-1) # (8)
next_width = stroke_params[4]
next_scaling = stroke_params[5]
next_window_size = next_scaling * curr_window_size_raw
next_window_size = np.maximum(next_window_size, min_window_size)
next_window_size = np.minimum(next_window_size, image_size)
prev_width = next_width * curr_window_size_raw / next_window_size
prev_scaling = next_scaling
prev_window_size = curr_window_size_raw
f = stroke_params_proc.tolist() # (8)
f += [1.0, 1.0]
gt_stroke_img = draw(f) # (H, W), [0.0-stroke, 1.0-BG]
gt_stroke_img_large = image_pasting_v3_testing(1.0 - gt_stroke_img, cursor_pos,
image_size,
curr_window_size_raw,
pasting_func, sess) # [0.0-BG, 1.0-stroke]
if pen_state == 0:
canvas += gt_stroke_img_large # [0.0-BG, 1.0-stroke]
canvas_rgb = np.stack([np.clip(canvas, 0.0, 1.0) for _ in range(3)], axis=-1)
if add_box:
vis_inputs = np.expand_dims(canvas_rgb, axis=0)
vis_outputs = add_scaling_visualization(vis_inputs, cursor_pos, curr_window_size, image_size)
canvas_vis = vis_outputs[0]
else:
canvas_vis = canvas_rgb
canvas_vis_png = Image.fromarray(canvas_vis, 'RGB')
gif_frames.append(canvas_vis_png)
# update cursor_pos based on hps.cursor_type
new_cursor_offsets = stroke_params[2:4] * (float(curr_window_size_raw) / 2.0) # (1, 6), patch-level
new_cursor_offset_next = new_cursor_offsets
# important!!!
new_cursor_offset_next = np.concatenate([new_cursor_offset_next[1:2], new_cursor_offset_next[0:1]], axis=-1)
cursor_pos_large = cursor_pos * float(image_size)
stroke_position_next = cursor_pos_large + new_cursor_offset_next # (2), large-level
if cursor_type == 'next':
cursor_pos_large = stroke_position_next # (2), large-level
else:
raise Exception('Unknown cursor_type')
cursor_pos_large = np.minimum(np.maximum(cursor_pos_large, 0.0), float(image_size - 1)) # (2), large-level
cursor_pos = cursor_pos_large / float(image_size)
print('Saving to GIF ...')
save_path = os.path.join(save_base, 'dynamic.gif')
first_frame = gif_frames[0]
first_frame.save(save_path, save_all=True, append_images=gif_frames, loop=0, duration=0.01)
def gif_making(npz_path):
assert npz_path != ''
min_window_size = 32
raster_size = 128
split_idx = npz_path.rfind('/')
if split_idx == -1:
file_base = './'
file_name = npz_path[:-4]
else:
file_base = npz_path[:npz_path.rfind('/')]
file_name = npz_path[npz_path.rfind('/') + 1: -4]
gif_base = os.path.join(file_base, file_name)
os.makedirs(gif_base, exist_ok=True)
# differentiable pasting graph
paste_v3_func = DiffPastingV3(raster_size)
tfconfig = tf.ConfigProto()
tfconfig.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=tfconfig)
sess.run(tf.global_variables_initializer())
data = np.load(npz_path, encoding='latin1', allow_pickle=True)
strokes_data = data['strokes_data']
init_cursors = data['init_cursors']
image_size = data['image_size']
round_length = data['round_length']
init_width = data['init_width']
if round_length.ndim == 0:
round_lengths = [round_length]
else:
round_lengths = round_length
# print('round_lengths', round_lengths)
make_gif(sess, paste_v3_func,
strokes_data, init_cursors, image_size, round_lengths, init_width,
gif_base,
min_window_size=min_window_size, raster_size=raster_size)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--file', '-f', type=str, default='', help="define a npz path")
args = parser.parse_args()
gif_making(args.file)