def __init__(self, dirPath, split, config):
self.img_height = config['img_height']
#with open(os.path.join(dirPath,'sets.json')) as f:
with open(os.path.join('data', 'sets.json')) as f:
set_list = json.load(f)[split]
self.authors = defaultdict(list)
self.lineIndex = []
for page_idx, name in enumerate(set_list):
lines, author = parseXML(
os.path.join(dirPath, 'xmls', name + '.xml'))
authorLines = len(self.authors[author])
self.authors[author] += [
(os.path.join(dirPath, 'forms', name + '.png'), ) + l
for l in lines
]
self.lineIndex += [(author, i + authorLines)
for i in range(len(lines))]
char_set_path = config['char_file']
with open(char_set_path) as f:
char_set = json.load(f)
self.char_to_idx = char_set['char_to_idx']
self.augmentation = config[
'augmentation'] if 'augmentation' in config else None
self.normalized_dir = config[
'cache_normalized'] if 'cache_normalized' in config else None
if self.normalized_dir is not None:
ensure_dir(self.normalized_dir)
self.warning = False
#DEBUG
if 'overfit' in config and config['overfit']:
self.lineIndex = self.lineIndex[:10]
self.center = config[
'center_pad'] #if 'center_pad' in config else True
self.add_spaces = config[
'add_spaces'] if 'add_spces' in config else False
def __init__(self, dirPath, split, config):
if 'split' in config:
split = config['split']
self.img_height = config['img_height']
self.a_batch_size = config['a_batch_size']
#with open(os.path.join(dirPath,'sets.json')) as f:
with open(os.path.join('data', 'wordMixed_sets.json')) as f:
set_list = json.load(f)[split]
#self.authors = defaultdict(list)
self.w_authors = defaultdict(list)
allLines = defaultdict(dict)
self.lineIndex = []
for word_idx, word_id in enumerate(set_list):
line_id = word_id[:word_id.rfind('-')]
page_id = line_id[:line_id.rfind('-')]
if page_id not in allLines:
w_lines, lines, author = parseXML(
os.path.join(dirPath, 'xmls', page_id + '.xml'))
for w_line in w_lines:
for bounds, trans, id in w_line:
allLines[page_id][id] = (bounds, trans)
bounds, trans = allLines[page_id][word_id]
self.w_authors[author].append(
(os.path.join(dirPath, 'forms',
page_id + '.png'), bounds, trans, word_id))
#minLines=99999
#for author,lines in self.authors.items():
#print('{} {}'.format(author,len(lines)))
#minLines = min(minLines,len(lines))
#maxCombs = int(nCr(minLines,self.a_batch_size)*1.2)
for author, words in self.w_authors.items():
#if split=='train':
# combs=list(itertools.combinations(list(range(len(lines))),self.a_batch_size))
# np.random.shuffle(combs)
# self.lineIndex += [(author,c) for c in combs[:maxCombs]]
#else:
for i in range(len(words) // self.a_batch_size):
ls = []
for n in range(self.a_batch_size):
ls.append(self.a_batch_size * i + n)
inst = (author, ls)
self.lineIndex.append(inst)
leftover = len(words) % self.a_batch_size
fill = self.a_batch_size - leftover
last = []
for i in range(fill):
last.append(i)
for i in range(leftover):
last.append(len(words) - (1 + i))
self.lineIndex.append((author, last))
#if split=='train':
# ss = set(self.lineIndex)
self.authors = self.w_authors.keys()
char_set_path = config['char_file']
with open(char_set_path) as f:
char_set = json.load(f)
self.char_to_idx = char_set['char_to_idx']
self.augmentation = config[
'augmentation'] if 'augmentation' in config else None
self.warning = False
if 'style_loc' in config:
by_author_styles = defaultdict(list)
by_author_all_ids = defaultdict(set)
style_loc = config['style_loc']
if style_loc[-1] != '*':
style_loc += '*'
all_style_files = glob(style_loc)
assert (len(all_style_files) > 0)
for loc in all_style_files:
#print('loading '+loc)
with open(loc, 'rb') as f:
styles = pickle.load(f)
for i in range(len(styles['authors'])):
by_author_styles[styles['authors'][i]].append(
(styles['styles'][i], styles['ids'][i]))
by_author_all_ids[styles['authors'][i]].update(
styles['ids'][i])
self.styles = defaultdict(lambda: defaultdict(list))
for author in by_author_styles:
for id in by_author_all_ids[author]:
for style, ids in by_author_styles[author]:
if id not in ids:
self.styles[author][id].append(style)
else:
self.styles = None
self.mask_post = config['mask_post'] if 'mask_post' in config else []
#DEBUG
if 'overfit' in config and config['overfit']:
self.lineIndex = self.lineIndex[:10]
self.npr = np.random.RandomState(1234)
def __init__(self, dirPath, split, config):
self.img_height = config['img_height']
self.a_batch_size = config['a_batch_size']
num_style_per_word = config[
'num_style_per_word'] if 'num_style_per_word' in config else 10
#with open(os.path.join(dirPath,'sets.json')) as f:
with open(os.path.join('data', 'sets.json')) as f:
set_list = json.load(f)[split]
#self.authors = defaultdict(list)
self.w_authors = defaultdict(list)
for page_idx, name in enumerate(set_list):
w_lines, lines, author = parseXML(
os.path.join(dirPath, 'xmls', name + '.xml'))
#authorLines = len(self.authors[author])
#self.authors[author] += [(os.path.join(dirPath,'forms',name+'.png'),)+l for l in lines]
for words in w_lines:
self.w_authors[author] += [
(os.path.join(dirPath, 'forms', name + '.png'), ) + w
for w in words
]
#self.lineIndex += [(author,i+authorLines) for i in range(len(lines))]
#minLines=99999
#for author,lines in self.authors.items():
#print('{} {}'.format(author,len(lines)))
#minLines = min(minLines,len(lines))
#maxCombs = int(nCr(minLines,self.a_batch_size)*1.2)
min_words = 999999
for author, words in self.w_authors.items():
min_words = min(min_words, len(words))
self.a_batch_size = min(self.a_batch_size, min_words - 1)
npr = np.random.RandomState(123)
self.index = []
for author, words in self.w_authors.items():
#if split=='train':
# combs=list(itertools.combinations(list(range(len(lines))),self.a_batch_size))
# np.random.shuffle(combs)
# self.lineIndex += [(author,c) for c in combs[:maxCombs]]
#else:
for wi in range(len(words)):
less_words = [wj for wj in range(len(words)) if wj != wi]
choose = scipy.misc.comb(len(less_words), self.a_batch_size)
for i in range(
int(min(1 + (choose - 1) / 2, num_style_per_word))):
indexes = npr.choice(len(less_words), self.a_batch_size)
selection = [less_words[i] for i in indexes]
self.index.append((author, selection))
char_set_path = config['char_file']
with open(char_set_path) as f:
char_set = json.load(f)
self.char_to_idx = char_set['char_to_idx']
self.augmentation = config[
'augmentation'] if 'augmentation' in config else None
self.warning = False
self.mask_post = config['mask_post'] if 'mask_post' in config else []
#DEBUG
if 'overfit' in config and config['overfit']:
self.index = self.index[:10]
def __init__(self, dirPath, split, config):
self.img_height = config['img_height']
self.batch_size = config[
'a_batch_size'] if 'a_batch_size' in config else 1
#assert(config['batch_size']==1)
#with open(os.path.join(dirPath,'sets.json')) as f:
with open(os.path.join('data', 'lineMixed_sets.json')) as f:
set_list = json.load(f)[split]
self.authors = defaultdict(list)
allLines = defaultdict(dict)
self.lineIndex = []
for line_idx, line_id in enumerate(set_list):
page_id = line_id[:line_id.rfind('-')]
if page_id not in allLines:
lines, author = parseXML(
os.path.join(dirPath, 'xmls', page_id + '.xml'))
for bounds, trans, id in lines:
allLines[page_id][id] = (bounds, trans)
bounds, trans = allLines[page_id][line_id]
self.authors[author].append(
(os.path.join(dirPath, 'forms',
page_id + '.png'), bounds, trans))
#self.lineIndex += [(author,i+authorLines) for i in range(len(lines))]
#minLines=99999
#for author,lines in self.authors.items():
#print('{} {}'.format(author,len(lines)))
#minLines = min(minLines,len(lines))
#maxCombs = int(nCr(minLines,self.batch_size)*1.2)
short = config['short'] if 'short' in config else False
for author, lines in self.authors.items():
#if split=='train':
# combs=list(itertools.combinations(list(range(len(lines))),self.batch_size))
# np.random.shuffle(combs)
# self.lineIndex += [(author,c) for c in combs[:maxCombs]]
#else:
for i in range(len(lines) // self.batch_size):
ls = []
for n in range(self.batch_size):
ls.append(self.batch_size * i + n)
inst = (author, ls)
self.lineIndex.append(inst)
if short and i >= short:
break
if short and i >= short:
continue
leftover = len(lines) % self.batch_size
fill = self.batch_size - leftover
last = []
for i in range(fill):
last.append(i)
for i in range(leftover):
last.append(len(lines) - (1 + i))
self.lineIndex.append((author, last))
#if split=='train':
# ss = set(self.lineIndex)
#self.authors = self.authors.keys()
char_set_path = config['char_file']
with open(char_set_path) as f:
char_set = json.load(f)
self.char_to_idx = char_set['char_to_idx']
self.augmentation = config[
'augmentation'] if 'augmentation' in config else None
self.warning = False
#DEBUG
if 'overfit' in config and config['overfit']:
self.lineIndex = self.lineIndex[:10]
self.center = False #config['center_pad'] #if 'center_pad' in config else True
self.mask_post = config['mask_post'] if 'mask_post' in config else []
def __init__(self, dirPath, split, config):
if 'split' in config:
split = config['split']
self.img_height = config['img_height']
self.batch_size = config['a_batch_size']
self.no_spaces = config['no_spaces'] if 'no_spaces' in config else False
self.max_width = config['max_width'] if 'max_width' in config else 3000
#assert(config['batch_size']==1)
self.warning=False
self.triplet = config['triplet'] if 'triplet' in config else False
if self.triplet:
self.triplet_author_size = config['triplet_author_size']
self.triplet_sample_size = config['triplet_sample_size']
only_author = config['only_author'] if 'only_author' in config else None
skip_author = config['skip_author'] if 'skip_author' in config else None
#with open(os.path.join(dirPath,'sets.json')) as f:
with open(os.path.join('data','sets.json')) as f:
set_list = json.load(f)[split]
self.authors = defaultdict(list)
self.lineIndex = []
self.max_char_len=0
self.author_list=set()
for page_idx, name in enumerate(set_list):
lines,author = parseXML(os.path.join(dirPath,'xmls',name+'.xml'))
self.author_list.add(author)
if only_author is not None and type(only_author) is int and page_idx==only_author:
only_author=author
print('Only author: {}'.format(only_author))
if only_author is not None and author!=only_author:
continue
if skip_author is not None and author==skip_author:
continue
self.max_char_len= max([self.max_char_len]+[len(l[1]) for l in lines])
authorLines = len(self.authors[author])
self.authors[author] += [(os.path.join(dirPath,'forms',name+'.png'),)+l for l in lines]
#self.lineIndex += [(author,i+authorLines) for i in range(len(lines))]
self.author_list = list(self.author_list)
self.author_list.sort()
#minLines=99999
#for author,lines in self.authors.items():
#print('{} {}'.format(author,len(lines)))
#minLines = min(minLines,len(lines))
#maxCombs = int(nCr(minLines,self.batch_size)*1.2)
short = config['short'] if 'short' in config else False
for author,lines in self.authors.items():
#if split=='train':
# combs=list(itertools.combinations(list(range(len(lines))),self.batch_size))
# np.random.shuffle(combs)
# self.lineIndex += [(author,c) for c in combs[:maxCombs]]
#else:
for i in range(len(lines)//self.batch_size):
ls=[]
for n in range(self.batch_size):
ls.append(self.batch_size*i+n)
inst = (author,ls)
self.lineIndex.append(inst)
if short and i>=short:
break
if short and i>=short:
continue
leftover = len(lines)%self.batch_size
fill = self.batch_size-leftover
last=[]
for i in range(fill):
last.append(i)
for i in range(leftover):
last.append(len(lines)-(1+i))
self.lineIndex.append((author,last))
self.fg_masks_dir = config['fg_masks_dir'] if 'fg_masks_dir' in config else None
if self.fg_masks_dir is not None:
if self.fg_masks_dir[-1]=='/':
self.fg_masks_dir = self.fg_masks_dir[:-1]
self.fg_masks_dir+='_{}'.format(self.max_width)
ensure_dir(self.fg_masks_dir)
for author,lines in self.lineIndex:
for line in lines:
img_path, lb, gt = self.authors[author][line]
fg_path = os.path.join(self.fg_masks_dir,'{}_{}.png'.format(author,line))
if not os.path.exists(fg_path):
img = cv2.imread(img_path,0)[lb[0]:lb[1],lb[2]:lb[3]] #read as grayscale, crop line
if img.shape[0] != self.img_height:
if img.shape[0] < self.img_height and not self.warning:
self.warning = True
print("WARNING: upsampling image to fit size")
percent = float(self.img_height) / img.shape[0]
if img.shape[1]*percent > self.max_width:
percent = self.max_width/img.shape[1]
img = cv2.resize(img, (0,0), fx=percent, fy=percent, interpolation = cv2.INTER_CUBIC)
if img.shape[0]<self.img_height:
diff = self.img_height-img.shape[0]
img = np.pad(img,((diff//2,diff//2+diff%2),(0,0)),'constant',constant_values=255)
th,binarized = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
binarized = 255-binarized
ele = cv2.getStructuringElement( cv2.MORPH_ELLIPSE, (9,9) )
binarized = cv2.dilate(binarized,ele)
cv2.imwrite(fg_path,binarized)
print('saved fg mask: {}'.format(fg_path))
#test_path = os.path.join(fg_masks_dir,'{}_{}_test.png'.format(author,line))
##print(img.shape)
#img = np.stack((img,img,img),axis=2)
#img[:,:,0]=binarized
#cv2.imwrite(test_path,img)
#print('saved fg mask: {}'.format(fg_path))
#if split=='train':
# ss = set(self.lineIndex)
#self.authors = self.authors.keys()
char_set_path = config['char_file']
with open(char_set_path) as f:
char_set = json.load(f)
self.char_to_idx = char_set['char_to_idx']
self.augmentation = config['augmentation'] if 'augmentation' in config else None
self.normalized_dir = config['cache_normalized'] if 'cache_normalized' in config else None
if self.normalized_dir is not None:
ensure_dir(self.normalized_dir)
self.max_strech=0.4
self.max_rot_rad= 45/180 * math.pi
self.remove_bg = config['remove_bg'] if 'remove_bg' in config else False
self.include_stroke_aug = config['include_stroke_aug'] if 'include_stroke_aug' in config else False
#DEBUG
if 'overfit' in config and config['overfit']:
self.lineIndex = self.lineIndex[:10]
self.center = False #config['center_pad'] #if 'center_pad' in config else True
if 'style_loc' in config:
by_author_styles=defaultdict(list)
by_author_all_ids=defaultdict(set)
style_loc = config['style_loc']
if style_loc[-1]!='*':
style_loc+='*'
all_style_files = glob(style_loc)
assert( len(all_style_files)>0)
for loc in all_style_files:
#print('loading '+loc)
with open(loc,'rb') as f:
styles = pickle.load(f)
for i in range(len(styles['authors'])):
by_author_styles[styles['authors'][i]].append((styles['styles'][i],styles['ids'][i]))
by_author_all_ids[styles['authors'][i]].update(styles['ids'][i])
self.styles = defaultdict(lambda: defaultdict(list))
for author in by_author_styles:
for id in by_author_all_ids[author]:
for style, ids in by_author_styles[author]:
if id not in ids:
self.styles[author][id].append(style)
for author in self.authors:
assert(author in self.styles)
else:
self.styles=None
if 'spaced_loc' in config:
with open(config['spaced_loc'],'rb') as f:
self.spaced_by_name = pickle.load(f)
#for name,v in spaced_by_name.items():
# author, id = name.split('_')
else:
self.spaced_by_name = None
self.identity_spaced = config['no_spacing_for_spaced'] if 'no_spacing_for_spaced' in config else False
self.mask_post = config['mask_post'] if 'mask_post' in config else []
self.mask_random = config['mask_random'] if 'mask_random' in config else False
import os, sys, json
from utils.parseIAM import getLineBoundaries as parseXML
dirPath = sys.argv[1]
outPath = sys.argv[2]
with open(os.path.join('data', 'sets.json')) as f:
set_list = json.load(f)['test']
texts = []
for page_idx, name in enumerate(set_list):
lines, author = parseXML(os.path.join(dirPath, 'xmls', name + '.xml'))
texts += [t for b, t in lines]
with open(outPath, 'w') as out:
for t in texts:
out.write('{}\n'.format(t))