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data_line.py
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data_line.py
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import os
from glob import glob
import threading
import multiprocessing
import signal
import sys
from datetime import datetime
import tensorflow as tf
import numpy as np
import cairosvg
from PIL import Image
import io
import xml.etree.ElementTree as et
import matplotlib.pyplot as plt
from ops import *
SVG_START_TEMPLATE = """<?xml version="1.0" encoding="utf-8"?><!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
<svg width="{w}" height="{h}" viewBox="0 0 64 64" xmlns="http://www.w3.org/2000/svg" version="1.1">
<g fill="none">\n"""
SVG_LINE_TEMPLATE = """<path id="{id}" d="M {x1} {y1} L{x2} {y2}" stroke="rgb({r},{g},{b})" stroke-width="{sw}"/>"""
SVG_CUBIC_BEZIER_TEMPLATE = """<path id="{id}" d="M {sx} {sy} C {cx1} {cy1} {cx2} {cy2} {tx} {ty}" stroke="rgb({r},{g},{b})" stroke-width="{sw}"/>"""
SVG_END_TEMPLATE = """</g>\n</svg>"""
class BatchManager(object):
def __init__(self, config):
self.root = config.data_path
self.rng = np.random.RandomState(config.random_seed)
self.paths = sorted(glob("{}/train/*.{}".format(self.root, 'svg_pre')))
if len(self.paths) == 0:
# create line dataset
data_dir = os.path.join(config.data_dir, config.dataset)
train_dir = os.path.join(data_dir, 'train')
if not os.path.exists(train_dir):
os.makedirs(train_dir)
test_dir = os.path.join(data_dir, 'test')
if not os.path.exists(test_dir):
os.makedirs(test_dir)
self.paths = gen_data(data_dir, config, self.rng,
num_train=45000, num_test=5000)
self.test_paths = sorted(glob("{}/test/*.{}".format(self.root, 'svg_pre')))
assert(len(self.paths) > 0 and len(self.test_paths) > 0)
self.batch_size = config.batch_size
self.height = config.height
self.width = config.width
self.is_pathnet = (config.archi == 'path')
if self.is_pathnet:
feature_dim = [self.height, self.width, 2]
label_dim = [self.height, self.width, 1]
else:
feature_dim = [self.height, self.width, 1]
label_dim = [self.height, self.width, 1]
self.capacity = 10000
self.q = tf.FIFOQueue(self.capacity, [tf.float32, tf.float32], [feature_dim, label_dim])
self.x = tf.placeholder(dtype=tf.float32, shape=feature_dim)
self.y = tf.placeholder(dtype=tf.float32, shape=label_dim)
self.enqueue = self.q.enqueue([self.x, self.y])
self.num_threads = config.num_worker
# np.amin([config.num_worker, multiprocessing.cpu_count(), self.batch_size])
def __del__(self):
try:
self.stop_thread()
except AttributeError:
pass
def start_thread(self, sess):
print('%s: start to enque with %d threads' % (datetime.now(), self.num_threads))
# Main thread: create a coordinator.
self.sess = sess
self.coord = tf.train.Coordinator()
# Create a method for loading and enqueuing
def load_n_enqueue(sess, enqueue, coord, paths, rng,
x, y, w, h, is_pathnet):
with coord.stop_on_exception():
while not coord.should_stop():
id = rng.randint(len(paths))
if is_pathnet:
x_, y_ = preprocess_path(paths[id], w, h, rng)
else:
x_, y_ = preprocess_overlap(paths[id], w, h, rng)
sess.run(enqueue, feed_dict={x: x_, y: y_})
# Create threads that enqueue
self.threads = [threading.Thread(target=load_n_enqueue,
args=(self.sess,
self.enqueue,
self.coord,
self.paths,
self.rng,
self.x,
self.y,
self.width,
self.height,
self.is_pathnet)
) for i in range(self.num_threads)]
# define signal handler
def signal_handler(signum, frame):
#print "stop training, save checkpoint..."
#saver.save(sess, "./checkpoints/VDSR_norm_clip_epoch_%03d.ckpt" % epoch ,global_step=global_step)
print('%s: canceled by SIGINT' % datetime.now())
self.coord.request_stop()
self.sess.run(self.q.close(cancel_pending_enqueues=True))
self.coord.join(self.threads)
sys.exit(1)
signal.signal(signal.SIGINT, signal_handler)
# Start the threads and wait for all of them to stop.
for t in self.threads:
t.start()
# dirty way to bypass graph finilization error
g = tf.get_default_graph()
g._finalized = False
qs = 0
while qs < (self.capacity*0.8):
qs = self.sess.run(self.q.size())
print('%s: q size %d' % (datetime.now(), qs))
def stop_thread(self):
# dirty way to bypass graph finilization error
g = tf.get_default_graph()
g._finalized = False
self.coord.request_stop()
self.sess.run(self.q.close(cancel_pending_enqueues=True))
self.coord.join(self.threads)
def test_batch(self):
x_list, y_list = [], []
for i, file_path in enumerate(self.test_paths):
if self.is_pathnet:
x_, y_ = preprocess_path(file_path, self.width, self.height, self.rng)
else:
x_, y_ = preprocess_overlap(file_path, self.width, self.height, self.rng)
x_list.append(x_)
y_list.append(y_)
if i % self.batch_size == self.batch_size-1:
yield np.array(x_list), np.array(y_list)
x_list, y_list = [], []
def batch(self):
return self.q.dequeue_many(self.batch_size)
def sample(self, num):
idx = self.rng.choice(len(self.paths), num).tolist()
return [self.paths[i] for i in idx]
def random_list(self, num):
x_list = []
xs, ys = [], []
file_list = self.sample(num)
for file_path in file_list:
if self.is_pathnet:
x, y = preprocess_path(file_path, self.width, self.height, self.rng)
else:
x, y = preprocess_overlap(file_path, self.width, self.height, self.rng)
x_list.append(x)
if self.is_pathnet:
b_ch = np.zeros([self.height,self.width,1])
xs.append(np.concatenate((x*255, b_ch), axis=-1))
else:
xs.append(x*255)
ys.append(y*255)
return np.array(x_list), np.array(xs), np.array(ys), file_list
def read_svg(self, file_path):
with open(file_path, 'r') as f:
svg = f.read()
svg = svg.format(w=self.width, h=self.height)
img = cairosvg.svg2png(bytestring=svg.encode('utf-8'))
img = Image.open(io.BytesIO(img))
s = np.array(img)[:,:,3].astype(np.float) # / 255.0
max_intensity = np.amax(s)
s = s / max_intensity
path_list = []
svg_xml = et.fromstring(svg)
num_paths = len(svg_xml[0])
for i in range(num_paths):
svg_xml = et.fromstring(svg)
svg_xml[0][0] = svg_xml[0][i]
del svg_xml[0][1:]
svg_one = et.tostring(svg_xml, method='xml')
# leave only one path
y_png = cairosvg.svg2png(bytestring=svg_one)
y_img = Image.open(io.BytesIO(y_png))
path = (np.array(y_img)[:,:,3] > 0)
path_list.append(path)
return s, num_paths, path_list
def draw_line(id, w, h, min_length, max_stroke_width, rng):
stroke_color = rng.randint(240, size=3)
# stroke_width = rng.randint(low=1, high=max_stroke_width+1)
stroke_width = max_stroke_width
while True:
x = rng.randint(w, size=2)
y = rng.randint(h, size=2)
if x[0] - x[1] + y[0] - y[1] < min_length:
continue
break
return SVG_LINE_TEMPLATE.format(
id=id,
x1=x[0], y1=y[0],
x2=x[1], y2=y[1],
r=stroke_color[0], g=stroke_color[1], b=stroke_color[2],
sw=stroke_width
)
def draw_cubic_bezier_curve(id, w, h, min_length, max_stroke_width, rng):
stroke_color = rng.randint(240, size=3)
# stroke_width = rng.randint(low=1, high=max_stroke_width+1)
stroke_width = max_stroke_width
x = rng.randint(w, size=4)
y = rng.randint(h, size=4)
return SVG_CUBIC_BEZIER_TEMPLATE.format(
id=id,
sx=x[0], sy=y[0],
cx1=x[1], cy1=y[1],
cx2=x[2], cy2=y[2],
tx=x[3], ty=y[3],
r=stroke_color[0], g=stroke_color[1], b=stroke_color[2],
sw=stroke_width
)
def draw_path(stroke_type, id, w, h, min_length, max_stroke_width, rng):
if stroke_type == 2:
stroke_type = rng.randint(2)
path_selector = {
0: draw_line,
1: draw_cubic_bezier_curve
}
return path_selector[stroke_type](id, w, h,min_length, max_stroke_width, rng)
def gen_data(data_dir, config, rng, num_train, num_test):
file_list = []
num = num_train + num_test
for file_id in range(num):
while True:
svg = SVG_START_TEMPLATE.format(
w=config.width,
h=config.height,
)
svgpre = SVG_START_TEMPLATE
for i in range(config.num_strokes):
path = draw_path(
stroke_type=config.stroke_type,
id=i,
w=config.width,
h=config.height,
min_length=config.min_length,
max_stroke_width=config.max_stroke_width,
rng=rng,
)
svg += path + '\n'
svgpre += path + '\n'
svg += SVG_END_TEMPLATE
svgpre += SVG_END_TEMPLATE
s_png = cairosvg.svg2png(bytestring=svg.encode('utf-8'))
s_img = Image.open(io.BytesIO(s_png))
s = np.array(s_img)[:,:,3].astype(np.float) # / 255.0
max_intensity = np.amax(s)
if max_intensity == 0:
continue
else:
s = s / max_intensity # [0,1]
break
if file_id < num_train:
cat = 'train'
else:
cat = 'test'
# svgpre
svgpre_file_path = os.path.join(data_dir, cat, '%d.svg_pre' % file_id)
print(svgpre_file_path)
with open(svgpre_file_path, 'w') as f:
f.write(svgpre)
# svg and jpg for reference
svg_dir = os.path.join(data_dir, 'svg')
if not os.path.exists(svg_dir):
os.makedirs(svg_dir)
jpg_dir = os.path.join(data_dir, 'jpg')
if not os.path.exists(jpg_dir):
os.makedirs(jpg_dir)
svg_file_path = os.path.join(data_dir, 'svg', '%d.svg' % file_id)
jpg_file_path = os.path.join(data_dir, 'jpg', '%d.jpg' % file_id)
with open(svg_file_path, 'w') as f:
f.write(svg)
s_img.convert('RGB').save(jpg_file_path)
if file_id < num_train:
file_list.append(svgpre_file_path)
return file_list
def preprocess_path(file_path, w, h, rng):
with open(file_path, 'r') as f:
svg = f.read()
svg = svg.format(w=w, h=h)
img = cairosvg.svg2png(bytestring=svg.encode('utf-8'))
img = Image.open(io.BytesIO(img))
s = np.array(img)[:,:,3].astype(np.float) # / 255.0
max_intensity = np.amax(s)
s = s / max_intensity
# while True:
svg_xml = et.fromstring(svg)
path_id = rng.randint(len(svg_xml[0]))
svg_xml[0][0] = svg_xml[0][path_id]
del svg_xml[0][1:]
svg_one = et.tostring(svg_xml, method='xml')
# leave only one path
y_png = cairosvg.svg2png(bytestring=svg_one)
y_img = Image.open(io.BytesIO(y_png))
y = np.array(y_img)[:,:,3].astype(np.float) / max_intensity # [0,1]
pixel_ids = np.nonzero(y)
# if len(pixel_ids[0]) == 0:
# continue
# else:
# break
# select arbitrary marking pixel
point_id = rng.randint(len(pixel_ids[0]))
px, py = pixel_ids[0][point_id], pixel_ids[1][point_id]
y = np.reshape(y, [h, w, 1])
x = np.zeros([h, w, 2])
x[:,:,0] = s
x[px,py,1] = 1.0
# # debug
# plt.figure()
# plt.subplot(221)
# plt.imshow(img)
# plt.subplot(222)
# plt.imshow(s, cmap=plt.cm.gray)
# plt.subplot(223)
# plt.imshow(np.concatenate((x, np.zeros([h, w, 1])), axis=-1))
# plt.subplot(224)
# plt.imshow(y[:,:,0], cmap=plt.cm.gray)
# plt.show()
return x, y
def preprocess_overlap(file_path, w, h, rng):
with open(file_path, 'r') as f:
svg = f.read()
svg = svg.format(w=w, h=h)
img = cairosvg.svg2png(bytestring=svg.encode('utf-8'))
img = Image.open(io.BytesIO(img))
s = np.array(img)[:,:,3].astype(np.float) # / 255.0
max_intensity = np.amax(s)
s = s / max_intensity
# while True:
path_list = []
svg_xml = et.fromstring(svg)
num_paths = len(svg_xml[0])
for i in range(num_paths):
svg_xml = et.fromstring(svg)
svg_xml[0][0] = svg_xml[0][i]
del svg_xml[0][1:]
svg_one = et.tostring(svg_xml, method='xml')
# leave only one path
y_png = cairosvg.svg2png(bytestring=svg_one)
y_img = Image.open(io.BytesIO(y_png))
path = (np.array(y_img)[:,:,3] > 0)
path_list.append(path)
y = np.zeros([h, w], dtype=np.int)
for i in range(num_paths-1):
for j in range(i+1, num_paths):
intersect = np.logical_and(path_list[i], path_list[j])
y = np.logical_or(intersect, y)
x = np.expand_dims(s, axis=-1)
y = np.expand_dims(y, axis=-1)
# # debug
# plt.figure()
# plt.subplot(131)
# plt.imshow(img)
# plt.subplot(132)
# plt.imshow(s, cmap=plt.cm.gray)
# plt.subplot(133)
# plt.imshow(y[:,:,0], cmap=plt.cm.gray)
# plt.show()
return x, y
def main(config):
prepare_dirs_and_logger(config)
batch_manager = BatchManager(config)
# preprocess_path('data/line/train/0.svg_pre', 64, 64, batch_manager.rng)
# preprocess_overlap('data/line/train/0.svg_pre', 64, 64, batch_manager.rng)
# thread test
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
sess_config.allow_soft_placement = True
sess_config.log_device_placement = False
sess = tf.Session(config=sess_config)
batch_manager.start_thread(sess)
x, y = batch_manager.batch()
if config.data_format == 'NCHW':
x = nhwc_to_nchw(x)
x_, y_ = sess.run([x, y])
batch_manager.stop_thread()
if config.data_format == 'NCHW':
x_ = x_.transpose([0, 2, 3, 1])
if config.archi == 'path':
b_ch = np.zeros([config.batch_size,config.height,config.width,1])
x_ = np.concatenate((x_*255, b_ch), axis=-1)
else:
x_ = x_*255
y_ = y_*255
save_image(x_, '{}/x_fixed.png'.format(config.model_dir))
save_image(y_, '{}/y_fixed.png'.format(config.model_dir))
# random pick from parameter space
x_samples, x_gt, y_gt, sample_list = batch_manager.random_list(8)
save_image(x_gt, '{}/x_gt.png'.format(config.model_dir))
save_image(y_gt, '{}/y_gt.png'.format(config.model_dir))
with open('{}/sample_list.txt'.format(config.model_dir), 'w') as f:
for sample in sample_list:
f.write(sample+'\n')
print('batch manager test done')
if __name__ == "__main__":
from config import get_config
from utils import prepare_dirs_and_logger, save_config, save_image
config, unparsed = get_config()
setattr(config, 'archi', 'path') # overlap
setattr(config, 'dataset', 'line')
setattr(config, 'width', 64)
setattr(config, 'height', 64)
main(config)