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import sys
import argparse
import numpy as np
from PIL import Image
import tensorflow as tf
sys.path.append('./')
from utils import get_colors, draw, image_pasting_v3_testing
from model_common_test import DiffPastingV3
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def display_strokes_final(sess, pasting_func, data, init_cursor, image_size, infer_lengths, init_width,
save_base,
cursor_type='next', min_window_size=32, raster_size=128):
"""
: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]
drawn_region = np.zeros_like(canvas)
overlap_region = np.zeros_like(canvas)
canvas_color_with_overlap = np.zeros((image_size, image_size, 3), dtype=np.float32)
canvas_color_wo_overlap = np.zeros((image_size, image_size, 3), dtype=np.float32)
canvas_color_with_moving = np.zeros((image_size, image_size, 3), dtype=np.float32)
cursor_idx = 0
if init_cursor.ndim == 1:
init_cursor = [init_cursor]
stroke_count = len(data)
color_rgb_set = get_colors(stroke_count) # list of (3,) in [0, 255]
color_idx = 0
valid_stroke_count = stroke_count - np.sum(data[:, 0]).astype(np.int32) + len(init_cursor)
valid_color_rgb_set = get_colors(valid_stroke_count) # list of (3,) in [0, 255]
valid_color_idx = -1
# print('Drawn stroke number', valid_stroke_count)
# print(' flag x1\t\t y1\t\t x2\t\t y2\t\t r2\t\t s2')
for round_idx in range(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)
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]
is_overlap = False
if pen_state == 0:
canvas += gt_stroke_img_large # [0.0-BG, 1.0-stroke]
curr_drawn_stroke_region = np.zeros_like(gt_stroke_img_large)
curr_drawn_stroke_region[gt_stroke_img_large > 0.5] = 1
intersection = drawn_region * curr_drawn_stroke_region
# regard stroke with >50% overlap area as overlaped stroke
if np.sum(intersection) / np.sum(curr_drawn_stroke_region) > 0.5:
# enlarge the stroke a bit for better visualization
overlap_region[gt_stroke_img_large > 0] += 1
is_overlap = True
drawn_region[gt_stroke_img_large > 0.5] = 1
color_rgb = color_rgb_set[color_idx] # (3) in [0, 255]
color_idx += 1
color_rgb = np.reshape(color_rgb, (1, 1, 3)).astype(np.float32)
color_stroke = np.expand_dims(gt_stroke_img_large, axis=-1) * (1.0 - color_rgb / 255.0)
canvas_color_with_moving = canvas_color_with_moving * np.expand_dims((1.0 - gt_stroke_img_large),
axis=-1) + color_stroke # (H, W, 3)
if pen_state == 0:
valid_color_idx += 1
if pen_state == 0:
valid_color_rgb = valid_color_rgb_set[valid_color_idx] # (3) in [0, 255]
# valid_color_idx += 1
valid_color_rgb = np.reshape(valid_color_rgb, (1, 1, 3)).astype(np.float32)
valid_color_stroke = np.expand_dims(gt_stroke_img_large, axis=-1) * (1.0 - valid_color_rgb / 255.0)
canvas_color_with_overlap = canvas_color_with_overlap * np.expand_dims((1.0 - gt_stroke_img_large),
axis=-1) + valid_color_stroke # (H, W, 3)
if not is_overlap:
canvas_color_wo_overlap = canvas_color_wo_overlap * np.expand_dims((1.0 - gt_stroke_img_large),
axis=-1) + valid_color_stroke # (H, W, 3)
# 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)
canvas_rgb = np.stack([np.clip(canvas, 0.0, 1.0) for _ in range(3)], axis=-1)
canvas_black = 255 - np.round(canvas_rgb * 255.0).astype(np.uint8)
canvas_color_with_overlap = 255 - np.round(canvas_color_with_overlap * 255.0).astype(np.uint8)
canvas_color_wo_overlap = 255 - np.round(canvas_color_wo_overlap * 255.0).astype(np.uint8)
canvas_color_with_moving = 255 - np.round(canvas_color_with_moving * 255.0).astype(np.uint8)
canvas_black_png = Image.fromarray(canvas_black, 'RGB')
canvas_black_save_path = os.path.join(save_base, 'output_rendered.png')
canvas_black_png.save(canvas_black_save_path, 'PNG')
canvas_color_png = Image.fromarray(canvas_color_with_overlap, 'RGB')
canvas_color_save_path = os.path.join(save_base, 'output_order_with_overlap.png')
canvas_color_png.save(canvas_color_save_path, 'PNG')
canvas_color_wo_png = Image.fromarray(canvas_color_wo_overlap, 'RGB')
canvas_color_wo_save_path = os.path.join(save_base, 'output_order_wo_overlap.png')
canvas_color_wo_png.save(canvas_color_wo_save_path, 'PNG')
canvas_color_m_png = Image.fromarray(canvas_color_with_moving, 'RGB')
canvas_color_m_save_path = os.path.join(save_base, 'output_order_with_moving.png')
canvas_color_m_png.save(canvas_color_m_save_path, 'PNG')
def visualize_drawing(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]
regenerate_base = os.path.join(file_base, file_name)
os.makedirs(regenerate_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)
print('Processing ...')
display_strokes_final(sess, paste_v3_func,
strokes_data, init_cursors, image_size, round_lengths, init_width,
regenerate_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()
visualize_drawing(args.file)
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