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utils.py
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utils.py
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"""
ShadeSketch
https://github.com/qyzdao/ShadeSketch
Learning to Shadow Hand-drawn Sketches
Qingyuan Zheng, Zhuoru Li, Adam W. Bargteil
Copyright (C) 2020 The respective authors and Project HAT. All rights reserved.
Licensed under MIT license.
"""
import os
import csv
import scipy.ndimage as ndi
import numpy as np
import cv2
def norm_line(data_dir, model_dir):
import tensorflow
if hasattr(tensorflow.compat, 'v1'):
tf = tensorflow.compat.v1
tf.disable_v2_behavior()
else:
tf = tensorflow
with tf.gfile.FastGFile(model_dir, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='lineNorm')
if not os.path.exists(os.path.join(data_dir, 'norm')):
os.makedirs(os.path.join(data_dir, 'norm'))
with tf.Session() as sess:
for root, dirs, files in os.walk(os.path.join(data_dir, 'line'), topdown=False):
for name in files:
line_path = os.path.join(root, name)
print(line_path)
img = cv2.imread(line_path, cv2.IMREAD_GRAYSCALE)
tensors = np.reshape(img, (1, img.shape[0], img.shape[1], 1)).astype(np.float32) / 255.
tensors = sess.run('lineNorm/conv2d_9/Sigmoid:0', {'lineNorm/input_1:0': tensors})
cv2.imwrite(os.path.join(data_dir, 'norm', name), np.squeeze(tensors) * 255.)
def pack_data(data_dir, output_dir='./data.npy', use_norm=True, norm_model='./models/linenorm.pb'):
linetype = 'norm' if use_norm else 'line'
if use_norm:
assert norm_model is not None, "Please assign a line normalization model."
norm_line(data_dir, norm_model)
with open(os.path.join(data_dir, 'anno.csv'), 'r') as f:
lines = list(csv.reader(f, delimiter=','))
data = []
for l in lines:
file_index = l[0]
# The line in the dataset is not normalized, please normalize it followed the paper
line = cv2.imread(os.path.join(data_dir, linetype, '%s.png' % file_index), cv2.IMREAD_GRAYSCALE)
shade = cv2.imread(os.path.join(data_dir, 'shade', '%s.png' % file_index), cv2.IMREAD_GRAYSCALE)
# Mask is not used
# mask = cv2.imread(os.path.join(data_dir, 'mask', '%s.png' % file_index), cv2.IMREAD_GRAYSCALE)
cond = np.array([int(i) for i in l[1:]], np.int)
data.append([line, cond, shade])
np.save(output_dir, np.array(data))
def load_data(filename):
# Line, Cond(label), Shade
x, c, y = [], [], []
f = np.load(filename, allow_pickle=True)
for e in f:
x.append(np.expand_dims(e[0], axis=-1))
c.append(e[1])
y.append(np.expand_dims(e[2], axis=-1))
# Line, Cond(label), Shade
return np.array(x), np.array(c), np.array(y)
def cond_to_pos(cond):
# Convert the user label lighting direction to position
cond_pos_rel = {
'002': [0, 0, -1],
'110': [0, 1, -1], '210': [1, 1, -1], '310': [1, 0, -1], '410': [1, -1, -1], '510': [0, -1, -1],
'610': [-1, -1, -1], '710': [-1, 0, -1], '810': [-1, 1, -1],
'120': [0, 1, 0], '220': [1, 1, 0], '320': [1, 0, 0], '420': [1, -1, 0], '520': [0, -1, 0],
'620': [-1, -1, 0], '720': [-1, 0, 0], '820': [-1, 1, 0],
'130': [0, 1, 1], '230': [1, 1, 1], '330': [1, 0, 1], '430': [1, -1, 1], '530': [0, -1, 1],
'630': [-1, -1, 1], '730': [-1, 0, 1], '830': [-1, 1, 1],
'001': [0, 0, 1]
}
cond_str = ''.join([str(i) for i in cond])
return cond_pos_rel[cond_str]
def normalize_angle(rg):
rg = rg % 360
if rg < 0:
rg += 360
return rg
def process_data(x_batch, cond_batch, y_batch, seed):
rot = [90, 45, 0, 315, 270, 225, 180, 135]
idx = [1, 2, 3, 4, 5, 6, 7, 8]
r2i = dict(zip(rot, idx))
i2r = dict(zip(idx, rot))
pos_batch = []
for i in range(len(x_batch)):
do_rotation = True
if cond_batch[i][0] == 0:
do_rotation = False
# Data augment
# Rotation -10 ~ 10, Shift -0.2 ~ 0.2, Zoom 0.9 ~ 1.1
x_batch[i] = random_transform(x_batch[i], seed + i)
y_batch[i] = random_transform(y_batch[i], seed + i)
if do_rotation:
angle = i2r[cond_batch[i][0]]
np.random.seed(seed + i)
rot_angle = np.random.randint(0, 7) * 45
if not rot_angle == 0:
x_batch[i] = rotation(x_batch[i], -rot_angle)
y_batch[i] = rotation(y_batch[i], -rot_angle)
cond_batch[i][0] = r2i[normalize_angle(angle + rot_angle)]
pos_batch.append(cond_to_pos(cond_batch[i]))
pos_batch = np.array(pos_batch).astype(np.float32)
cond_batch = np.array(cond_batch).astype(np.float32)
train_x_batch = np.clip(1 - x_batch.astype(np.float32) / 255., 0., 1.)
train_y_batch = 2 * np.clip(1 - y_batch.astype(np.float32) / 255., 0., 1.) - 1
return train_x_batch, cond_batch, pos_batch, train_y_batch
"""
Implementation of data augment based on Keras's image preprocessing module
Keras
MIT License
COPYRIGHT
All contributions by François Chollet:
Copyright (c) 2015, François Chollet.
All rights reserved.
All contributions by Google:
Copyright (c) 2015, Google, Inc.
All rights reserved.
All other contributions:
Copyright (c) 2015, the respective contributors.
All rights reserved.
Each contributor holds copyright over their respective contributions.
The project versioning (Git) records all such contribution source information.
https://github.com/keras-team/keras/blob/keras-2/keras/preprocessing/image.py
"""
def random_transform(x, seed=None):
"""Randomly augment a single image tensor.
# Arguments
x: 3D tensor, single image.
seed: random seed.
# Returns
A randomly transformed version of the input (same shape).
"""
np.random.seed(seed)
img_row_axis = 0
img_col_axis = 1
img_channel_axis = 2
rotation_range = 10
theta = np.deg2rad(np.random.uniform(-rotation_range, rotation_range))
height_shift_range = width_shift_range = 0.2
if height_shift_range:
try: # 1-D array-like or int
tx = np.random.choice(height_shift_range)
tx *= np.random.choice([-1, 1])
except ValueError: # floating point
tx = np.random.uniform(-height_shift_range,
height_shift_range)
if np.max(height_shift_range) < 1:
tx *= x.shape[img_row_axis]
else:
tx = 0
if width_shift_range:
try: # 1-D array-like or int
ty = np.random.choice(width_shift_range)
ty *= np.random.choice([-1, 1])
except ValueError: # floating point
ty = np.random.uniform(-width_shift_range,
width_shift_range)
if np.max(width_shift_range) < 1:
ty *= x.shape[img_col_axis]
else:
ty = 0
zoom_range = (0.9, 1.1)
if zoom_range[0] == 1 and zoom_range[1] == 1:
zx, zy = 1, 1
else:
zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2)
transform_matrix = None
if theta != 0:
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
transform_matrix = rotation_matrix
if tx != 0 or ty != 0:
shift_matrix = np.array([[1, 0, tx],
[0, 1, ty],
[0, 0, 1]])
transform_matrix = shift_matrix if transform_matrix is None else np.dot(transform_matrix, shift_matrix)
if zx != 1 or zy != 1:
zoom_matrix = np.array([[zx, 0, 0],
[0, zy, 0],
[0, 0, 1]])
transform_matrix = zoom_matrix if transform_matrix is None else np.dot(transform_matrix, zoom_matrix)
if transform_matrix is not None:
h, w = x.shape[img_row_axis], x.shape[img_col_axis]
transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)
x = apply_transform(x, transform_matrix, img_channel_axis, fill_mode='nearest')
return x
def transform_matrix_offset_center(matrix, x, y):
o_x = float(x) / 2 + 0.5
o_y = float(y) / 2 + 0.5
offset_matrix = np.array([[1, 0, o_x], [0, 1, o_y], [0, 0, 1]])
reset_matrix = np.array([[1, 0, -o_x], [0, 1, -o_y], [0, 0, 1]])
transform_matrix = np.dot(np.dot(offset_matrix, matrix), reset_matrix)
return transform_matrix
def apply_transform(x, transform_matrix, channel_index=0, fill_mode='nearest', cval=0.):
x = np.rollaxis(x, channel_index, 0)
final_affine_matrix = transform_matrix[:2, :2]
final_offset = transform_matrix[:2, 2]
channel_images = [ndi.interpolation.affine_transform(x_channel, final_affine_matrix,
final_offset, order=0, mode=fill_mode, cval=cval) for x_channel
in x]
x = np.stack(channel_images, axis=0)
x = np.rollaxis(x, 0, channel_index + 1)
return x
def rotation(x, rg):
theta = np.deg2rad(rg)
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0],
[np.sin(theta), np.cos(theta), 0],
[0, 0, 1]])
h, w = x.shape[0], x.shape[1]
transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w)
x = apply_transform(x, transform_matrix, 2, fill_mode='constant', cval=255)
return x
if __name__ == '__main__':
pack_data('./ShadeSketchDataset')