def text_lexicality(doc, method=u'text_lexicality', language=u'', divert=True):
"""
Calculates the lexicality of text in input documents.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to the output file.
Returns:
(unicode, unicode): Storage tuple of the output document
"""
input_path = storage.get_abs_path(*doc[0])
output_path = storage.insert_suffix(input_path, method,
os.path.basename(input_path))
dictionary = storage.get_abs_path(*nidaba_cfg['lang_dicts'][language]['dictionary'])
with storage.StorageFile(*doc) as fp:
tei = OCRRecord()
tei.load_tei(fp)
cnt = 0
err_cnt = 0
for seg_id, segment in facsimile.segments.iteritems():
tok = alg.sanitize(''.join(x['grapheme'] for x in segment['content'].itervalues()))
tok = regex.sub('[^\w]', '', key)
cnt += 1
if not alg.mmap_bin_search(tok, dictionary, entryparser_fn=alg.key_for_single_word):
err_cnt += 1
if not divert:
storage.write_text(*storage.get_storage_path(output_path),
text=unicode(err_cnt / float(cnt)))
return output_path
else:
return {'edit_ratio': err_cnt / float(cnt), 'ground_truth': '', 'doc': doc}
def setup(*args, **kwargs):
try:
global binarization
global pageseg
global rpred
global models
global mod_db
from kraken import binarization
from kraken import pageseg
from kraken import rpred
from kraken.lib import models
# pronn/clstm models get prioritized over pyrnn ones
mod_db = {k: storage.get_abs_path(*v) for k, v in nidaba_cfg['ocropus_models'].iteritems()}
if kwargs.get('modeldata'):
md = kwargs.get('modeldata')
if isinstance(md, list):
md = storage.get_abs_path(md)
for model in glob.glob(md + '/*/*/DESCRIPTION'):
with open(model) as fp:
meta = json.load(fp)
mod_db[model.split('/')[-2]] = os.path.join(os.path.dirname(model), meta['name'])
ocr_kraken.arg_values['model'] = mod_db.keys()
except ImportError as e:
raise NidabaPluginException(e.message)
def setup(*args, **kwargs):
try:
global binarization
global pageseg
global rpred
global models
global mod_db
from kraken import binarization
from kraken import pageseg
from kraken import rpred
from kraken.lib import models
# pronn/clstm models get prioritized over pyrnn ones
mod_db = {
k: storage.get_abs_path(*v)
for k, v in nidaba_cfg['ocropus_models'].iteritems()
}
if kwargs.get('modeldata'):
md = kwargs.get('modeldata')
if isinstance(md, list):
md = storage.get_abs_path(md)
for model in glob.glob(md + '/*/*/DESCRIPTION'):
with open(model) as fp:
meta = json.load(fp)
mod_db[model.split('/')[-2]] = os.path.join(
os.path.dirname(model), meta['name'])
ocr_kraken.arg_values['model'] = mod_db.keys()
except ImportError as e:
raise NidabaPluginException(e.message)
def nlbin(doc, method=u'nlbin', threshold=0.5, zoom=0.5, escale=1.0,
border=0.1, perc=80, range=20, low=5, high=90):
"""
Binarizes an input document utilizing ocropus'/kraken's nlbin algorithm.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to all output files.
threshold (float):
zoom (float):
escale (float):
border (float)
perc (int):
range (int):
low (int):
high (int):
Returns:
(unicode, unicode): Storage tuple of the output file
Raises:
NidabaInvalidParameterException: Input parameters are outside the valid
range.
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method, unicode(threshold),
unicode(zoom), unicode(escale),
unicode(border), unicode(perc),
unicode(range), unicode(low),
unicode(high))
kraken_nlbin(input_path, output_path, threshold, zoom, escale, border,
perc, range, low, high)
return storage.get_storage_path(output_path)
def status(args):
"""
Implements the status subcommand.
Args:
args (argparse.Namespace): Parsed input object from argparse
"""
batch = Batch(args.jobid)
state = batch.get_state()
print(state)
if state == 'SUCCESS':
ret = batch.get_results()
if ret is None:
print('Something somewhere went wrong.')
print('Please contact your friendly nidaba support technician.')
else:
for doc in ret:
print('\t' + storage.get_abs_path(*doc).encode('utf-8'))
elif state == 'FAILURE':
ret = batch.get_errors()
if ret is None:
print('Something somewhere went wrong.')
else:
for fun in ret:
print(fun[0]['method'].encode('utf-8'),
'failed while operating on',
fun[0]['doc'][1].encode('utf-8'),
'which is based on',
fun[1]['root'][1].encode('utf-8'))
def otsu(doc, method=u'otsu', thresh=100, mincount=50, bgval=255,
smoothx=2, smoothy=2):
"""
Binarizes an input document utilizing a naive implementation of Otsu's
thresholding.
Args:
doc (unicode, unicode): The input document tuple.
id (unicode): The nidaba batch identifier this task is a part of
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
Raises:
NidabaInvalidParameterException: Input parameters are outside the valid
range.
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method, unicode(thresh),
unicode(mincount), unicode(bgval),
unicode(smoothx), unicode(smoothy))
if smoothx < 0 or smoothy < 0 or bgval < 0 or thresh < 0 or mincount < 0:
raise NidabaInvalidParameterException('Parameters (' + unicode(thresh)
+ ',' + unicode(mincount) + ',' +
unicode(bgval) + ',' +
unicode(smoothx) + ',' +
unicode(smoothy) + ',' +
') outside of valid range')
return storage.get_storage_path(leper.otsu_binarize(input_path,
output_path, thresh,
mincount, bgval,
smoothx, smoothy))
def sauvola(doc, method=u'sauvola', whsize=10, factor=0.35):
"""
Binarizes an input document utilizing Sauvola thresholding as described in
[0]. Expects 8bpp grayscale images as input.
[0] Sauvola, Jaakko, and Matti Pietikäinen. "Adaptive document image
binarization." Pattern recognition 33.2 (2000): 225-236.
Args:
doc (unicode): The input document tuple.
method (unicode): The suffix string appended to all output files
whsize (int): The window width and height that local statistics are
calculated on are twice the value of whsize. The minimal
value is 2.
factor (float): The threshold reduction factor due to variance. 0 =<
factor < 1.
Returns:
(unicode, unicode): Storage tuple of the output file
Raises:
NidabaInvalidParameterException: Input parameters are outside the valid
range.
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method, unicode(whsize),
unicode(factor))
lept_sauvola(input_path, output_path, whsize, factor)
return storage.get_storage_path(output_path)
def sauvola(doc, method=u'sauvola', whsize=10, factor=0.35):
"""
Binarizes an input document utilizing Sauvola thresholding as described in
[0]. Expects 8bpp grayscale images as input.
[0] Sauvola, Jaakko, and Matti Pietikäinen. "Adaptive document image
binarization." Pattern recognition 33.2 (2000): 225-236.
Args:
doc (unicode): The input document tuple.
method (unicode): The suffix string appended to all output files
whsize (int): The window width and height that local statistics are
calculated on are twice the value of whsize. The minimal
value is 2.
factor (float): The threshold reduction factor due to variance. 0 =<
factor < 1.
Returns:
(unicode, unicode): Storage tuple of the output file
Raises:
NidabaInvalidParameterException: Input parameters are outside the valid
range.
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method, unicode(whsize),
unicode(factor))
lept_sauvola(input_path, output_path, whsize, factor)
return storage.get_storage_path(output_path)
def ocr_tesseract(doc, method=u'ocr_tesseract', languages=None,
extended=False):
"""
Runs tesseract on an input document.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files
languages (list): A list of tesseract classifier identifiers
extended (bool): Switch to enable extended hOCR generation containing
character cuts and confidences. Has no effect when
direct or legacy implementation is used.
Returns:
(unicode, unicode): Storage tuple for the output file
"""
image_path = storage.get_abs_path(*doc[1])
# rewrite the segmentation file to lines in UZN format
logger.debug('Rewriting TEI ({}) -> UZN ({})'.format(doc[0][1],
splitext(doc[1][1])[0]
+ '.uzn'))
seg = TEIFacsimile()
with storage.StorageFile(*doc[0]) as fp:
seg.read(fp)
with storage.StorageFile(doc[1][0], splitext(doc[1][1])[0] + '.uzn', mode='wb') as fp:
uzn = UZNWriter(fp)
for line in seg.lines:
uzn.writerow(*line[:4])
if isinstance(languages, basestring):
languages = [languages]
output_path = storage.insert_suffix(image_path, method, *languages)
logger.debug('Invoking tesseract with {} call method'.format(implementation))
if implementation == 'legacy':
result_path = output_path + '.html'
ocr_direct(image_path, output_path, languages)
elif implementation == 'direct':
result_path = output_path + '.hocr'
ocr_direct(image_path, output_path, languages)
elif implementation == 'capi':
result_path = output_path + '.xml'
ocr_capi(image_path, result_path, seg, languages, extended)
else:
raise NidabaTesseractException('Invalid implementation selected',
implementation)
if not result_path[-4:] == '.xml':
logger.debug('Converting hOCR ({}) -> TEI ({})'.format(result_path,
output_path +
'.xml'))
tei = TEIFacsimile()
with open(result_path) as fp:
tei.load_hocr(fp)
os.unlink(result_path)
with open(output_path + '.xml', 'wb') as fp:
tei.write(fp)
result_path = output_path + '.xml'
return storage.get_storage_path(result_path)
def any_to_png(doc, method=u'any_to_png'):
"""
Converts an image (color or otherwise) in any format recognized by pillow
to PNG.
The pillow image library relies on external libraries for loading and
saving Image data. To recognize the most common image formats used for
digital archival you'll need:
- libtiff
- zlib
- libjpeg
- openjpeg (version 2.0 +)
- libwebp
To have access to all formats run (on Debian/Ubuntu):
.. code-block:: console
# apt-get -y install libtiff5-dev libjpeg62-turbo-dev zlib1g-dev \
libwebp-dev libopenjp2-dev
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = os.path.splitext(storage.insert_suffix(input_path, method))[0] + '.png'
return storage.get_storage_path(image.any_to_png(input_path, output_path))
def segmentation_kraken(doc, method=u'segment_kraken', black_colseps=False):
"""
Performs page segmentation using kraken's built-in algorithm and writes a
skeleton TEI file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string append to all output files
black_colseps (bool): Assume black column separator instead of white
ones.
Returns:
Two storage tuples with the first one containing the segmentation and
the second one being the file the segmentation was calculated upon.
"""
input_path = storage.get_abs_path(*doc)
output_path, ext = os.path.splitext(
storage.insert_suffix(input_path, method))
logger.debug('Reading image using PIL')
img = Image.open(input_path)
with open(output_path + '.xml', 'w') as fp:
logger.debug('Initializing TEI with {} ({} {})'.format(
doc[1], *img.size))
tei = OCRRecord()
tei.img = storage.get_url(*doc)
tei.dimensions = img.size
tei.title = os.path.basename(doc[1])
tei.add_respstmt('kraken', 'page segmentation')
for seg in pageseg.segment(img, black_colseps=black_colseps)['boxes']:
logger.debug('Found line at {} {} {} {}'.format(*seg))
tei.add_line(seg)
logger.debug('Write segmentation to {}'.format(fp.name))
tei.write_tei(fp)
return storage.get_storage_path(output_path + '.xml')
def any_to_png(doc, method=u'any_to_png'):
"""
Converts an image (color or otherwise) in any format recognized by pillow
to PNG.
The pillow image library relies on external libraries for loading and
saving Image data. To recognize the most common image formats used for
digital archival you'll need:
- libtiff
- zlib
- libjpeg
- openjpeg (version 2.0 +)
- libwebp
To have access to all formats run (on Debian/Ubuntu):
.. code-block:: console
# apt-get -y install libtiff5-dev libjpeg62-turbo-dev zlib1g-dev \
libwebp-dev libopenjp2-dev
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = os.path.splitext(storage.insert_suffix(input_path,
method))[0] + '.png'
return storage.get_storage_path(image.any_to_png(input_path, output_path))
def segmentation_kraken(doc, method=u'segment_kraken', black_colseps=False):
"""
Performs page segmentation using kraken's built-in algorithm and writes a
skeleton TEI file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string append to all output files
black_colseps (bool): Assume black column separator instead of white
ones.
Returns:
Two storage tuples with the first one containing the segmentation and
the second one being the file the segmentation was calculated upon.
"""
input_path = storage.get_abs_path(*doc)
output_path, ext = os.path.splitext(storage.insert_suffix(input_path,
method))
logger.debug('Reading image using PIL')
img = Image.open(input_path)
with open(output_path + '.xml', 'w') as fp:
logger.debug('Initializing TEI with {} ({} {})'.format(doc[1], *img.size))
tei = OCRRecord()
tei.img = storage.get_url(*doc)
tei.dimensions = img.size
tei.title = os.path.basename(doc[1])
tei.add_respstmt('kraken', 'page segmentation')
for seg in pageseg.segment(img, black_colseps=black_colseps)['boxes']:
logger.debug('Found line at {} {} {} {}'.format(*seg))
tei.add_line(seg)
logger.debug('Write segmentation to {}'.format(fp.name))
tei.write_tei(fp)
return storage.get_storage_path(output_path + '.xml')
def ocr_tesseract(doc,
method=u'ocr_tesseract',
languages=None,
extended=False):
"""
Runs tesseract on an input document.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files
languages (list): A list of tesseract classifier identifiers
extended (bool): Switch to enable extended hOCR generation containing
character cuts and confidences. Has no effect when
direct or legacy implementation is used.
Returns:
(unicode, unicode): Storage tuple for the output file
"""
seg = OCRRecord()
with storage.StorageFile(*doc) as fp:
seg.load_tei(fp)
with storage.StorageFile(doc[0], splitext(doc[1])[0] + '.uzn',
mode='wb') as fp:
uzn = UZNWriter(fp)
for line in seg.lines.itervalues():
uzn.writerow(*line['bbox'])
image_path = storage.get_abs_path(*storage.get_storage_path_url(seg.img))
if isinstance(languages, basestring):
languages = [languages]
output_path = storage.insert_suffix(image_path, method, *languages)
logger.debug(
'Invoking tesseract with {} call method'.format(implementation))
if implementation == 'legacy':
result_path = output_path + '.html'
ocr_direct(image_path, output_path, languages)
elif implementation == 'direct':
result_path = output_path + '.hocr'
ocr_direct(image_path, output_path, languages)
elif implementation == 'capi':
result_path = output_path + '.xml'
ocr_capi(image_path, result_path, seg, languages, extended)
else:
raise NidabaTesseractException('Invalid implementation selected',
implementation)
if not result_path[-4:] == '.xml':
logger.debug('Converting hOCR ({}) -> TEI ({})'.format(
result_path, output_path + '.xml'))
tei = OCRRecord()
with open(result_path) as fp:
tei.load_hocr(fp)
os.unlink(result_path)
with open(output_path + '.xml', 'wb') as fp:
tei.write_tei(fp)
result_path = output_path + '.xml'
return storage.get_storage_path(result_path)
def batch(args):
"""
Implements the batch subcommand of the nidaba binary.
Args:
args (argparse.Namespace): Parsed input object from argparse
"""
id = unicode(uuid.uuid4())
batch = Batch(id)
print('Preparing filestore....', end=''),
if storage.prepare_filestore(id) is None:
print('failed.')
exit()
for doc in args.files:
shutil.copy2(doc, storage.get_abs_path(id, os.path.basename(doc)))
batch.add_document((id, os.path.basename(doc)))
print('done.')
print('Building batch...', end='')
batch.add_step()
if not args.grayscale:
batch.add_tick()
batch.add_task('img.rgb_to_gray')
if args.binarize:
batch.add_tick()
for bin in args.binarize:
(alg, _, params) = bin.partition(u':')
for c in params.split(u';'):
kwargs = dict(kwarg.split('=') for kwarg in c.split(",") if len(kwarg.split('=')) == 2)
print(kwargs)
kwargs = {key: int_float_or_str(val)
for key, val in kwargs.items()}
batch.add_task('binarize.' + alg, **kwargs)
if args.ocr:
batch.add_tick()
for ocr in args.ocr:
(engine, _, params) = ocr.partition(u':')
if engine == u'tesseract':
batch.add_task('ocr.tesseract', languages=params.split(u','))
elif engine == u'ocropus':
for model in params.split(u','):
if model not in nidaba_cfg['ocropus_models']:
print('WARNING: ocropus model ' +
model.encode('utf-8') + ' not known.')
else:
batch.add_task('ocr.ocropus', model=model)
else:
print('WARNING: OCR engine ' + engine.encode('utf-8') + ' not\
known.')
if args.willitblend:
batch.add_step()
batch.add_tick()
batch.add_task('util.blend_hocr')
batch.run()
print('done.')
print(id)
def ocr_ocropus(doc, method=u'ocr_ocropus', model=None):
"""
Runs ocropus on an input document.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files
model (unicode): Identifier for the font model to use
Returns:
(unicode, unicode): Storage tuple for the output file
"""
image_path = storage.get_abs_path(*doc[1])
segmentation_path = storage.get_abs_path(*doc[0])
output_path = os.path.splitext(
storage.insert_suffix(image_path, method, model))[0] + '.xml'
model = storage.get_abs_path(*(nidaba_cfg['ocropus_models'][model]))
return storage.get_storage_path(
ocr(image_path, segmentation_path, output_path, model))
def ocr_ocropus(doc, method=u'ocr_ocropus', model=None):
"""
Runs ocropus on an input document.
Args:
doc (unicode, unicode): The input document tuple
id (unicode): The nidaba batch identifier this task is a part of
method (unicode): The suffix string appended to all output files
model (unicode): Identifier for the font model to use
Returns:
(unicode, unicode): Storage tuple for the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = os.path.splitext(storage.insert_suffix(input_path, method,
model))[0] + '.html'
model = storage.get_abs_path(*(nidaba_cfg['ocropus_models'][model]))
return storage.get_storage_path(ocropus.ocr(input_path, output_path,
model))
def spell_check(doc,
method=u'spell_check',
language=u'',
filter_punctuation=False):
"""
Adds spelling suggestions to an TEI XML document.
Alternative spellings for each segment will be included in a choice
tagcontaining a series of corr tags with the original segment appearing
beneath a sic element. Correct words, i.e. words appearing verbatim in the
dictionary, are left untouched.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to the output file.
language (unicode): Identifier defined in the nidaba configuration as a
valid dictionary.
filter_punctuation (bool): Switch to filter punctuation inside
``seg``
Returns:
(unicode, unicode): Storage tuple of the output document
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method, language,
unicode(filter_punctuation))
dictionary = storage.get_abs_path(
*nidaba_cfg['lang_dicts'][language]['dictionary'])
del_dictionary = storage.get_abs_path(
*nidaba_cfg['lang_dicts'][language]['deletion_dictionary'])
with storage.StorageFile(*doc) as fp:
logger.debug('Reading TEI ({})'.format(fp.abs_path))
tei = TEIFacsimile()
tei.read(fp)
logger.debug('Performing spell check')
ret = lex.tei_spellcheck(tei, dictionary, del_dictionary,
filter_punctuation)
with storage.StorageFile(*storage.get_storage_path(output_path),
mode='wb') as fp:
logger.debug('Writing TEI ({})'.format(fp.abs_path))
ret.write(fp)
return storage.get_storage_path(output_path)
def text_lexicality(doc, method=u'text_lexicality', language=u'', divert=True):
"""
Calculates the lexicality of text in input documents.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to the output file.
Returns:
(unicode, unicode): Storage tuple of the output document
"""
input_path = storage.get_abs_path(*doc[0])
output_path = storage.insert_suffix(input_path, method,
os.path.basename(input_path))
dictionary = storage.get_abs_path(
*nidaba_cfg['lang_dicts'][language]['dictionary'])
with storage.StorageFile(*doc) as fp:
tei = OCRRecord()
tei.load_tei(fp)
cnt = 0
err_cnt = 0
for seg_id, segment in facsimile.segments.iteritems():
tok = alg.sanitize(''.join(x['grapheme']
for x in segment['content'].itervalues()))
tok = regex.sub('[^\w]', '', key)
cnt += 1
if not alg.mmap_bin_search(
tok, dictionary, entryparser_fn=alg.key_for_single_word):
err_cnt += 1
if not divert:
storage.write_text(*storage.get_storage_path(output_path),
text=unicode(err_cnt / float(cnt)))
return output_path
else:
return {
'edit_ratio': err_cnt / float(cnt),
'ground_truth': '',
'doc': doc
}
def do_move(batch, src):
if isinstance(batch, NetworkSimpleBatch):
dst = os.path.basename(src)
def callback(monitor):
spin(u'Uploading {}'.format(dst))
batch.add_document(src, callback, auxiliary=True)
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
else:
from nidaba import storage
suffix = uuid.uuid4()
dst = os.path.basename(src) + '_' + unicode(suffix)
shutil.copy2(src, storage.get_abs_path(batch.id, dst))
return (batch.id, dst)
def deskew(doc, method=u'deskew'):
"""
Removes skew (rotational distortion) from an 1bpp input image.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method)
lept_deskew(input_path, output_path)
return storage.get_storage_path(output_path)
def deskew(doc, method=u'deskew'):
"""
Removes skew (rotational distortion) from an 1bpp input image.
Args:
doc (unicode, unicode): The input document tuple.
id (unicode): The nidaba batch identifier this task is a part of
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method)
return storage.get_storage_path(leper.deskew(input_path, output_path))
def deskew(doc, method=u'deskew'):
"""
Removes skew (rotational distortion) from an 1bpp input image.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method)
lept_deskew(input_path, output_path)
return storage.get_storage_path(output_path)
def rgb_to_gray(doc, method=u'rgb_to_gray'):
"""
Converts an arbitrary bit depth image to grayscale and writes it back
appending a suffix.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method)
return storage.get_storage_path(image.rgb_to_gray(input_path, output_path))
def rgb_to_gray(doc, method=u'rgb_to_gray'):
"""
Converts an arbitrary bit depth image to grayscale and writes it back
appending a suffix.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method)
return storage.get_storage_path(image.rgb_to_gray(input_path, output_path))
def spell_check(doc, method=u'spell_check', language=u'',
filter_punctuation=False):
"""
Adds spelling suggestions to an TEI XML document.
Alternative spellings for each segment will be included in a choice
tagcontaining a series of corr tags with the original segment appearing
beneath a sic element. Correct words, i.e. words appearing verbatim in the
dictionary, are left untouched.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to the output file.
language (unicode): Identifier defined in the nidaba configuration as a
valid dictionary.
filter_punctuation (bool): Switch to filter punctuation inside
``seg``
Returns:
(unicode, unicode): Storage tuple of the output document
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method, language,
unicode(filter_punctuation))
dictionary = storage.get_abs_path(*nidaba_cfg['lang_dicts'][language]['dictionary'])
del_dictionary = storage.get_abs_path(*nidaba_cfg['lang_dicts'][language]['deletion_dictionary'])
with storage.StorageFile(*doc) as fp:
logger.debug('Reading TEI ({})'.format(fp.abs_path))
tei = OCRRecord()
tei.load_tei(fp)
logger.debug('Performing spell check')
ret = lex.tei_spellcheck(tei, dictionary, del_dictionary,
filter_punctuation)
with storage.StorageFile(*storage.get_storage_path(output_path), mode='wb') as fp:
logger.debug('Writing TEI ({})'.format(fp.abs_path))
ret.write_tei(fp)
return storage.get_storage_path(output_path)
def dewarp(doc, method=u'dewarp'):
"""
Removes perspective distortion (as commonly exhibited by overhead scans)
from an 1bpp input image.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method)
lept_dewarp(input_path, output_path)
return storage.get_storage_path(output_path)
def do_move(batch, src):
if isinstance(batch, NetworkSimpleBatch):
dst = os.path.basename(src)
def callback(monitor):
spin(u'Uploading {}'.format(dst))
batch.add_document(src, callback, auxiliary=True)
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
else:
from nidaba import storage
suffix = uuid.uuid4()
dst = os.path.basename(src) + '_' + unicode(suffix)
shutil.copy2(src, storage.get_abs_path(batch.id, dst))
return (batch.id, dst)
def dewarp(doc, method=u'dewarp'):
"""
Removes perspective distortion (as commonly exhibited by overhead scans)
from an 1bpp input image.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method)
lept_dewarp(input_path, output_path)
return storage.get_storage_path(output_path)
def otsu(doc, method=u'otsu'):
"""
Binarizes an input document utilizing a naive implementation of Otsu's
thresholding.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to all output files.
Returns:
(unicode, unicode): Storage tuple of the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method)
return storage.get_storage_path(image.otsu(input_path, output_path))
def ocr_tesseract(doc, method=u'ocr_tesseract', languages=None):
"""
Runs tesseract on an input document.
Args:
doc (unicode, unicode): The input document tuple
id (unicode): The nidaba batch identifier this task is a part of
method (unicode): The suffix string appended to all output files
languages (list of unicode): A list of languages for
the tesseract language model
Returns:
(unicode, unicode): Storage tuple for the output file
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method, *languages)
return storage.get_storage_path(tesseract.ocr(input_path, output_path,
languages))
def nlbin(doc,
method=u'nlbin',
threshold=0.5,
zoom=0.5,
escale=1.0,
border=0.1,
perc=80,
range=20,
low=5,
high=90):
"""
Binarizes an input document utilizing ocropus'/kraken's nlbin algorithm.
Args:
doc (unicode, unicode): The input document tuple.
method (unicode): The suffix string appended to all output files.
threshold (float):
zoom (float):
escale (float):
border (float)
perc (int):
range (int):
low (int):
high (int):
Returns:
(unicode, unicode): Storage tuple of the output file
Raises:
NidabaInvalidParameterException: Input parameters are outside the valid
range.
"""
input_path = storage.get_abs_path(*doc)
output_path = storage.insert_suffix(input_path, method, unicode(threshold),
unicode(zoom), unicode(escale),
unicode(border), unicode(perc),
unicode(range), unicode(low),
unicode(high))
img = Image.open(input_path)
o_img = binarization.nlbin(img, threshold, zoom, escale, border, perc,
range, low, high)
o_img.save(output_path)
return storage.get_storage_path(output_path)
def blend_hocr(docs, method=u'blend_hocr', language=u''):
"""
Blends multiple hOCR files using the algorithm from Bruce Robertsons
rigaudon. It requires a working spell checking for the input document's
language; otherwise all matched bboxes will be bunched together without any
scoring.
Args:
docs [(id, path), ...]: A list of storage module tupels that will be
merged into a single output document.
id (unicode): The nidaba batch identifier this task is a part of
language (unicode): Language used for spell-checking
based scoring. If not defined no scoring will be used.
method (unicode): The suffix string appended to the output file.
Returns:
(unicode, unicode): Storage tuple of the output document
"""
# create the output document path from the first input document
input_path = storage.get_abs_path(*docs[0])
output_path = storage.insert_suffix(input_path, method)
return merge_hocr.merge(docs, language,
storage.get_storage_path(output_path))
def text_rep_confidence(doc, method=u'text_rep_confidence', divert=True):
"""
Extracts self reported confidence values from input documents.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to the output file.
Returns:
(unicode, unicode): Storage tuple of the output document
"""
input_path = storage.get_abs_path(*doc[0])
output_path = storage.insert_suffix(input_path, method,
os.path.basename(input_path))
with storage.StorageFile(*doc) as fp:
tei = OCRRecord()
tei.load_tei(fp)
edist = numpy.mean([x['confidence'] for x in tei.graphemes.itervalues()])
if not divert:
storage.write_text(*storage.get_storage_path(output_path),
text=unicode(edist))
return output_path
else:
return {'edit_ratio': edist, 'ground_truth': '', 'doc': doc}
def text_rep_confidence(doc, method=u'text_rep_confidence', divert=True):
"""
Extracts self reported confidence values from input documents.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to the output file.
Returns:
(unicode, unicode): Storage tuple of the output document
"""
input_path = storage.get_abs_path(*doc[0])
output_path = storage.insert_suffix(input_path, method,
os.path.basename(input_path))
with storage.StorageFile(*doc) as fp:
tei = OCRRecord()
tei.load_tei(fp)
edist = numpy.mean([x['confidence'] for x in tei.graphemes.itervalues()])
if not divert:
storage.write_text(*storage.get_storage_path(output_path),
text=unicode(edist))
return output_path
else:
return {'edit_ratio': edist, 'ground_truth': '', 'doc': doc}
def batch(files, host, preprocessing, binarize, ocr, segmentation, stats,
postprocessing, output, grayscale, help_tasks):
"""
Add a new job to the pipeline.
"""
if host:
batch = NetworkSimpleBatch(host)
click.echo(u'Preparing filestore\t\t[', nl=False)
try:
batch.create_batch()
except:
click.secho(u'\u2717', fg='red', nl=False)
click.echo(']')
exit()
click.secho(u'\u2713', fg='green', nl=False)
click.echo(']')
for doc in files:
def callback(monitor):
spin(u'Uploading {}'.format(doc))
batch.add_document(doc, callback)
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
else:
from nidaba import storage
click.echo(u'Preparing filestore\t\t[', nl=False)
try:
batch = SimpleBatch()
except:
click.secho(u'\u2717', fg='red', nl=False)
click.echo(']')
exit()
for doc in files:
shutil.copy2(doc, storage.get_abs_path(batch.id, os.path.basename(doc)))
batch.add_document((batch.id, os.path.basename(doc)))
click.secho(u'\u2713', fg='green', nl=False)
click.echo(']')
click.echo(u'Building batch\t\t\t[', nl=False)
if not grayscale:
batch.add_task('img', 'rgb_to_gray')
if preprocessing:
for alg in preprocessing:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('img', alg[0], **kwargs)
if binarize:
for alg in binarize:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('binarize', alg[0], **kwargs)
if segmentation:
for alg in segmentation:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('segmentation', alg[0], **kwargs)
if ocr:
for alg in ocr:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('ocr', alg[0], **kwargs)
if stats:
for alg in stats:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('stats', alg[0], **kwargs)
if postprocessing:
for alg in postprocessing:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('postprocessing', alg[0], **kwargs)
if output:
for alg in output:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('output', alg[0], **kwargs)
batch.run()
click.secho(u'\u2713', fg='green', nl=False)
click.echo(']')
click.echo(batch.id)
def status(verbose, host, job_id):
"""
Diplays the status and results of jobs.
"""
click.secho('Status:', underline=True, nl=False)
if host:
batch = NetworkSimpleBatch(host, job_id)
else:
try:
batch = Batch(job_id)
except NidabaInputException:
click.echo(' UNKNOWN')
return
state = batch.get_extended_state()
if not state:
click.echo(' UNKNOWN')
return
bs = 'success'
done = 0
running = 0
pending = 0
failed = 0
results = []
errors = []
expected = len(state)
for task_id, subtask in state.iteritems():
if subtask['state'] == 'SUCCESS':
done += 1
elif subtask['state'] == 'RUNNING':
running += 1
if bs == 'success':
bs = 'pending'
elif subtask['state'] == 'PENDING':
pending += 1
if bs == 'success':
bs = 'pending'
elif subtask['state'] == 'FAILURE':
errors.append(subtask)
bs = 'failed'
# leaf nodes/result extraction
if len(subtask['children']) == 0 and subtask['result'] is not None:
# try to find statistics results
parents = [task_id] + subtask['parents']
misc = None
for parent in parents:
parents.extend(state[parent]['parents'])
if 'misc' in state[parent]:
misc = state[parent]['misc']
break
# archival tasks bunch everything together. do a sort-based matching of input and output tasks
if isinstance(subtask['result'][0],
list) or host and len(subtask['result']) > 1:
for res, rd in zip(sorted(subtask['result']),
sorted(subtask['root_documents'])):
if host:
res = [res]
results.append((res, [rd], misc))
else:
results.append(
(subtask['result'], subtask['root_documents'], misc))
final = '(final)' if expected - done == 0 else ''
click.echo(' {} {}\n'.format(bs, final))
click.echo('{}/{} tasks completed. {} running.\n'.format(
done, expected, running))
# render results
click.secho('Output files:\n', underline=True)
results = sorted(results,
key=lambda x: x[0][0][1]
if isinstance(x[0], list) else x[0][1])
if results:
for doc in results:
if host:
output = ', '.join(doc[0])
input = ', '.join(doc[1])
else:
from nidaba import storage
if isinstance(doc[0][0], list):
for d in doc:
output = ', '.join(
click.format_filename(storage.get_abs_path(*d)))
else:
output = click.format_filename(
storage.get_abs_path(*doc[0]))
input = ', '.join(d[1] for d in doc[1])
if doc[2] is not None:
click.echo(u'{} \u2192 {} ({:.1f}% / {})'.format(
input, output, 100 * doc[2]['edit_ratio'],
doc[2]['ground_truth'][1]))
else:
click.echo(u'{} \u2192 {}'.format(input, output))
# render errors
if errors:
click.secho('\nErrors:\n', underline=True)
for task in errors:
tb = ''
args = ''
if verbose > 0:
tb = task['errors'][2]
if verbose > 1:
task['errors'][0].pop('method')
args = ', ' + str(task['errors'][0])
if host:
rd = ', '.join(
os.path.basename(x) for x in task['root_documents'])
else:
rd = ', '.join(
os.path.basename(x[1]) for x in task['root_documents'])
click.echo('{}.{} ({}{}): {}{}'.format(task['task'][0],
task['task'][1], rd, args,
tb, task['errors'][1]))
def merge(docs, lang, output):
"""
Merges multiple hOCR documents into a single one.
First bboxes from all documents are roughly matched, then all matching
bboxes are scored using a spell checker. If no spell checker is available
all matches will be merged without ranking.
The matching is naive, i.e. we just grab the first input document and
assume that all other documents have similar segmentation results. Issues
like high variance in segmentation, especially word boundaries are not
accounted for.
Args:
docs (iterable): A list of storage tuples of input documents
lang (unicode): A language identifier for the spell checker
output (tuple): Storage tuple for the result
Returns:
tuple: The output storage tuple. Should be the same as ```output```.
"""
parser = etree.HTMLParser()
tree1 = etree.parse(storage.get_abs_path(docs[0][0], docs[0][1]), parser)
lines_1, words_1 = get_hocr_lines_for_tree(tree1)
sort_words_bbox(words_1)
other_words = []
for doc in docs[1:]:
try:
tree2 = etree.parse(storage.get_abs_path(doc[0], doc[1]), parser)
lines_2, words_2 = get_hocr_lines_for_tree(tree2)
other_words = other_words + words_2
except Exception as e:
print(e)
sort_words_bbox(other_words)
positional_lists = []
positional_list = []
x = 0
# Make a list of positional_lists, that is alternatives for a given
# position, skipping duplicate position-words
while x < len(other_words):
try:
if len(positional_list) == 0:
positional_list.append(other_words[x])
else:
if close_enough(other_words[x - 1].bbox, other_words[x].bbox):
# skip if the text is the same, so that we just get unique
# texts for this position
if not other_words[x - 1].text == other_words[x].text:
positional_list.append(other_words[x])
else:
if not x == 0:
positional_lists.append(positional_list)
positional_list = []
except IndexError:
pass
x = x + 1
# we now have a list of list of unique words for each position
# let's select from each the first one that passes spellcheck
replacement_words = []
# make a 'replacement_words' list with all of the best, non-zero-scoring
# suggestions for each place
for positional_list in positional_lists:
for word in positional_list:
word.score = score_word(lang, word.text)
positional_list.sort(key=attrgetter('score'), reverse=True)
if positional_list[0].score > 0:
replacement_words.append(positional_list[0])
# now replace the originals
for word in words_1:
for replacement_word in replacement_words:
word.score = score_word(lang, word.text)
if close_enough(word.bbox, replacement_word.bbox) and (
word.score < replacement_word.score):
word.element.text = replacement_word.text
for positional_list in positional_lists:
print("##")
for word in positional_list:
print(word.bbox, word.text)
storage.write_text(*output,
text=etree.tostring(tree1.getroot(),
encoding='unicode'))
return output
def segmentation_tesseract(doc, method=u'segment_tesseract'):
"""
Performs page segmentation using tesseract's built-in algorithm and writes
a TEI XML segmentation file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files.
Returns:
Two storage tuples with the first one containing the segmentation and
the second one being the file the segmentation was calculated upon.
"""
input_path = storage.get_abs_path(*doc)
output_path = splitext(storage.insert_suffix(input_path,
method))[0] + '.xml'
ver = tesseract.TessVersion()
if int(ver.split('.')[0]) < 3 or int(ver.split('.')[1]) < 2:
raise NidabaTesseractException('libtesseract version is too old. Set '
'implementation to direct.')
# tesseract has a tendency to crash arbitrarily on some inputs
# necessitating execution in a separate process to ensure the worker
# doesn't just die. We use fork as the multiprocessing module thinks
# programmers are too stupid to reap their children.
logger.info('Forking before entering unstable ctypes code')
pid = os.fork()
if pid != 0:
try:
logger.info('Waiting for child to complete')
_, status = os.waitpid(pid, 0)
except OSError as e:
if e.errno not in (errno.EINTR, errno.ECHILD):
raise
return storage.get_storage_path(output_path)
if os.WIFSIGNALED(status):
raise NidabaTesseractException(
'Tesseract killed by signal: {0}'.format(os.WTERMSIG(status)))
return storage.get_storage_path(output_path)
api = tesseract.TessBaseAPICreate()
rc = tesseract.TessBaseAPIInit3(api, tessdata.encode('utf-8'), None)
if (rc):
tesseract.TessBaseAPIDelete(api)
raise NidabaTesseractException('Tesseract initialization failed.')
# only do segmentation and script detection
logger.debug('Setting page set mode to 2')
tesseract.TessBaseAPISetPageSegMode(api, 2)
logger.debug('Reading {} using leptonica'.format(input_path))
pix = leptonica.pixRead(input_path.encode('utf-8'))
logger.debug('Setting PIX as input image')
tesseract.TessBaseAPISetImage2(api, pix)
logger.debug('Analyzing page layout')
it = tesseract.TessBaseAPIAnalyseLayout(api)
logger.debug('Destroying PIX')
leptonica.pixDestroy(ctypes.byref(pix))
x0, y0, x1, y1 = (ctypes.c_int(), ctypes.c_int(), ctypes.c_int(),
ctypes.c_int())
w, h = Image.open(input_path).size
logger.info('Initializing TEI XML file with {}x{} {}/{}'.format(
w, h, *doc))
tei = OCRRecord()
tei.dimensions = (w, h)
tei.img = storage.get_url(*doc)
tei.title = os.path.basename(doc[1])
tei.add_respstmt('tesseract', 'page segmentation')
while True:
if tesseract.TessPageIteratorIsAtBeginningOf(it, RIL_TEXTLINE):
tesseract.TessPageIteratorBoundingBox(it, RIL_TEXTLINE,
ctypes.byref(x0),
ctypes.byref(y0),
ctypes.byref(x1),
ctypes.byref(y1))
tei.add_line((x0.value, y0.value, x1.value, y1.value))
logger.debug('Segmenter found new line at {} {} {} {}'.format(
x0.value, y0.value, x1.value, y1.value))
if tesseract.TessPageIteratorIsAtBeginningOf(it, RIL_WORD):
tesseract.TessPageIteratorBoundingBox(it, RIL_WORD,
ctypes.byref(x0),
ctypes.byref(y0),
ctypes.byref(x1),
ctypes.byref(y1))
tei.add_segment((x0.value, y0.value, x1.value, y1.value))
logger.debug('Segmenter found new word at {} {} {} {}'.format(
x0.value, y0.value, x1.value, y1.value))
tesseract.TessPageIteratorBoundingBox(it, RIL_SYMBOL, ctypes.byref(x0),
ctypes.byref(y0),
ctypes.byref(x1),
ctypes.byref(y1))
tei.add_graphemes([{
'grapheme': '',
'bbox': (x0.value, y0.value, x1.value, y1.value)
}])
logger.debug('Segmenter found new symbol at {} {} {} {}'.format(
x0.value, y0.value, x1.value, y1.value))
if not tesseract.TessPageIteratorNext(it, RIL_SYMBOL):
logger.debug('No more elements on page')
break
logger.debug('Deleting page iterator and base API')
tesseract.TessPageIteratorDelete(it)
tesseract.TessBaseAPIEnd(api)
tesseract.TessBaseAPIDelete(api)
logger.info('Writing segmentation to {}'.format(output_path))
with open(output_path, 'w') as fp:
tei.write_tei(fp)
logger.info('Quitting child process')
os._exit(os.EX_OK)
return storage.get_storage_path(output_path)
def text_diff_ratio(doc, method=u'text_diff_ratio', ground_truth=None,
xml_in=True, gt_format=u'tei', clean_in=True, clean_gt=True,
divert=True):
"""
Calculates the similarity of the input documents and a given ground truth
using the algorithm of python's difflib SequenceMatcher. The result is a
value between 0.0 (no commonality) and 1.0 (identical strings).
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to the output file.
ground_truth (unicode): Ground truth location tuple or a list of ground
truths to choose from. When more than one is
given, the file sharing the longest prefix with
the input document is chosen.
xml_in (bool): Switch to treat input as an TEI-XML document.
gt_format (unicode): Switch to select ground truth format. Valid values
are 'tei', 'hocr', and 'text'.
clean_in (bool): Normalize to NFD and strip input data. (DO NOT DISABLE!)
clean_gt (bool): Normalize to NFD and strip ground truth. (DO NOT DISABLE!)
divert (bool): Switch selecting output diversion. If enabled the output
will be added to the tracking arguments and the input
document will be returned as the result of the task. Use
this to insert a statistical measure into a chain
without affecting the results.
Returns:
(unicode, unicode): Storage tuple of the output document
"""
input_path = storage.get_abs_path(*doc[0])
output_path = storage.insert_suffix(input_path, method,
os.path.basename(input_path))
if not isinstance(ground_truth[0], basestring):
ground_truth = find_matching(doc, ground_truth)
with storage.StorageFile(*ground_truth) as fp:
if gt_format == 'tei':
tei = TEIFacsimile()
tei.read(fp)
t = StringIO.StringIO()
tei.write_text(t)
gt = t.getvalue()
elif gt_format == 'hocr':
gt = html.parse(fp).text_content()
elif gt_format == 'text':
gt = fp.read()
else:
raise NidabaInvalidParameterException('Input format ' + gt_format + ' unknown.')
with storage.StorageFile(*doc) as fp:
if xml_in:
tei = TEIFacsimile()
tei.read(fp)
t = StringIO.StringIO()
tei.write_text(t)
text = t.getvalue()
else:
text = fp.read()
if clean_in:
text = cleanup(text)
if clean_gt:
gt = cleanup(gt)
logger.debug('Recognition result: \n{}'.format(text))
logger.debug('Ground truth: \n{}'.format(gt))
sm = difflib.SequenceMatcher()
sm.set_seqs(text, gt)
logger.debug('Accuracy: {}'.format(sm.ratio()))
if not divert:
storage.write_text(*storage.get_storage_path(output_path),
text=unicode(sm.ratio()))
return output_path
else:
return {'diff_ratio': sm.ratio(), 'ground_truth': ground_truth, 'doc': doc}
def setup(*args, **kwargs):
if kwargs.get(u'implementation'):
global implementation
implementation = kwargs.get(u'implementation')
if kwargs.get(u'tessdata'):
global tessdata
if isinstance(kwargs.get(u'tessdata'), list):
tessdata = storage.get_abs_path(*kwargs.get(u'tessdata'))
else:
tessdata = kwargs.get(u'tessdata')
if implementation == 'direct' and not spawn.find_executable('tesseract'):
raise NidabaPluginException('No tesseract executable found')
if implementation == 'capi':
try:
global tesseract, leptonica
tesseract = ctypes.cdll.LoadLibrary('libtesseract.so.3')
leptonica = ctypes.cdll.LoadLibrary('liblept.so')
except:
raise NidabaPluginException(
'Loading libtesseract/leptonica failed.')
tesseract.TessBaseAPICreate.restype = POINTER(TessBaseAPI)
tesseract.TessBaseAPIEnd.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIEnd.restype = None
tesseract.TessBaseAPIDelete.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIDelete.restype = None
tesseract.TessBaseAPIInit3.argtypes = [
POINTER(TessBaseAPI), ctypes.c_char_p, ctypes.c_char_p
]
tesseract.TessBaseAPIInit3.restype = ctypes.c_int
tesseract.TessBaseAPISetImage2.restype = None
tesseract.TessBaseAPISetImage2.argtypes = [
POINTER(TessBaseAPI), POINTER(Pix)
]
tesseract.TessBaseAPIRecognize.argtypes = [
POINTER(TessBaseAPI), POINTER(TessBaseAPI)
]
tesseract.TessBaseAPIRecognize.restype = ctypes.c_int
tesseract.TessResultIteratorGetUTF8Text.restype = ctypes.c_char_p
tesseract.TessResultIteratorGetUTF8Text.argtypes = [
POINTER(TessResultIterator), ctypes.c_int
]
tesseract.TessResultIteratorConfidence.argtypes = [
POINTER(TessResultIterator), ctypes.c_int
]
tesseract.TessResultIteratorConfidence.restype = ctypes.c_float
tesseract.TessResultIteratorWordRecognitionLanguage.argtypes = [
POINTER(TessResultIterator)
]
tesseract.TessResultIteratorWordRecognitionLanguage.restype = ctypes.c_char_p
tesseract.TessVersion.restype = ctypes.c_char_p
tesseract.TessBaseAPISetPageSegMode.argtypes = [
POINTER(TessBaseAPI), ctypes.c_int
]
tesseract.TessBaseAPISetPageSegMode.restype = None
tesseract.TessBaseAPIProcessPages.argtypes = [
POINTER(TessBaseAPI), ctypes.c_char_p, ctypes.c_char_p,
ctypes.c_int,
POINTER(TessResultRenderer)
]
tesseract.TessBaseAPIProcessPages.restype = ctypes.c_int
tesseract.TessBaseAPIAnalyseLayout.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIAnalyseLayout.restype = POINTER(TessPageIterator)
tesseract.TessPageIteratorIsAtBeginningOf.argtypes = [
POINTER(TessPageIterator), ctypes.c_int
]
tesseract.TessPageIteratorIsAtBeginningOf.restype = ctypes.c_int
tesseract.TessPageIteratorBoundingBox.argtypes = [
POINTER(TessPageIterator), ctypes.c_int,
POINTER(ctypes.c_int),
POINTER(ctypes.c_int),
POINTER(ctypes.c_int),
POINTER(ctypes.c_int)
]
tesseract.TessPageIteratorBoundingBox.restype = ctypes.c_int
tesseract.TessBaseAPIGetIterator.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIGetIterator.restype = POINTER(TessResultIterator)
tesseract.TessResultIteratorGetPageIterator.argtypes = [
POINTER(TessResultIterator)
]
tesseract.TessResultIteratorGetPageIterator.restype = POINTER(
TessPageIterator)
tesseract.TessResultIteratorNext.argtypes = [
POINTER(TessResultIterator), ctypes.c_int
]
tesseract.TessResultIteratorNext.restype = ctypes.c_int
tesseract.TessResultIteratorDelete.argtypes = [
POINTER(TessResultIterator)
]
tesseract.TessResultIteratorDelete.restype = None
tesseract.TessPageIteratorDelete.argtypes = [POINTER(TessPageIterator)]
tesseract.TessPageIteratorDelete.restype = None
tesseract.TessDeleteText.argtypes = [POINTER(ctypes.c_char)]
tesseract.TessDeleteText.restype = None
leptonica.pixRead.argtypes = [ctypes.c_char_p]
leptonica.pixRead.restype = POINTER(Pix)
leptonica.pixDestroy.argtypes = [POINTER(POINTER(Pix))]
leptonica.pixDestroy.restype = None
tesseract.TessBaseAPIGetHOCRText.argtypes = [
POINTER(TessBaseAPI), ctypes.c_int
]
tesseract.TessBaseAPIGetHOCRText.restype = POINTER(ctypes.c_char)
tesseract.TessBaseAPIGetAvailableLanguagesAsVector.argtypes = [
POINTER(TessBaseAPI)
]
tesseract.TessBaseAPIGetAvailableLanguagesAsVector.restype = POINTER(
ctypes.c_char_p)
# fill in available tesseract classifiers as they are only determinable
# after setting the tessdata directory.
ocr_tesseract.arg_values['languages'] = _get_available_classifiers()
def ocr_kraken(doc, method=u'ocr_kraken', model=None):
"""
Runs kraken on an input document and writes a TEI file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string append to all output files
model (unicode): Identifier for the font model to use
Returns:
(unicode, unicode): Storage tuple for the output file
"""
input_path = storage.get_abs_path(*doc[1])
output_path = (doc[1][0], os.path.splitext(storage.insert_suffix(doc[1][1],
method,
model))[0]
+ '.xml')
logger.debug('Searching for model {}'.format(model))
if model in nidaba_cfg['kraken_models']:
model = storage.get_abs_path(*(nidaba_cfg['kraken_models'][model]))
elif model in nidaba_cfg['ocropus_models']:
model = storage.get_abs_path(*(nidaba_cfg['ocropus_models'][model]))
else:
raise NidabaInvalidParameterException('Model not defined in '
'configuration')
img = Image.open(input_path)
logger.debug('Reading TEI segmentation from {}'.format(doc[1]))
tei = OCRRecord()
with storage.StorageFile(*doc[0]) as seg:
tei.load_tei(seg)
logger.debug('Clearing out word/grapheme boxes')
# kraken is a line recognizer
tei.clear_graphemes()
tei.clear_segments()
# add and scope new responsibility statement
tei.add_respstmt('kraken', 'character recognition')
lines = tei.lines
logger.debug('Loading model {}'.format(model))
rnn = models.load_any(model)
i = 0
logger.debug('Start recognizing characters')
for line_id, rec in zip(lines, rpred.rpred(rnn, img, [x['bbox'] for x in lines.itervalues()])):
# scope the current line and add all graphemes recognized by kraken to
# it.
logger.debug('Scoping line {}'.format(line_id))
tei.scope_line(line_id)
i += 1
splits = regex.split(u'(\s+)', rec.prediction)
line_offset = 0
for segment, whitespace in izip_longest(splits[0::2], splits[1::2]):
if len(segment):
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset + len(segment)])
logger.debug('Creating new segment at {} {} {} {}'.format(*seg_bbox))
tei.add_segment(seg_bbox)
logger.debug('Adding graphemes (segment): {}'.format(rec.prediction[line_offset:line_offset+len(segment)]))
tei.add_graphemes([{'grapheme': x[0],
'bbox': x[1],
'confidence': int(x[2] * 100)} for x in rec[line_offset:line_offset+len(segment)]])
line_offset += len(segment)
if whitespace:
logger.debug('Adding graphemes (whitespace): {}'.format(rec.prediction[line_offset:line_offset+len(whitespace)]))
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset + len(whitespace)])
tei.add_segment(seg_bbox)
tei.add_graphemes([{'grapheme': x[0],
'bbox': x[1],
'confidence': int(x[2] * 100)} for x in rec[line_offset:line_offset+len(whitespace)]])
line_offset += len(whitespace)
with storage.StorageFile(*output_path, mode='wb') as fp:
logger.debug('Writing TEI to {}'.format(fp.abs_path))
tei.write_tei(fp)
return output_path
def text_diff_ratio(doc,
method=u'text_diff_ratio',
ground_truth=None,
xml_in=True,
gt_format=u'tei',
clean_in=True,
clean_gt=True,
divert=True):
"""
Calculates the similarity of the input documents and a given ground truth
using the algorithm of python's difflib SequenceMatcher. The result is a
value between 0.0 (no commonality) and 1.0 (identical strings).
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to the output file.
ground_truth (unicode): Ground truth location tuple or a list of ground
truths to choose from. When more than one is
given, the file sharing the longest prefix with
the input document is chosen.
xml_in (bool): Switch to treat input as an TEI-XML document.
gt_format (unicode): Switch to select ground truth format. Valid values
are 'tei', 'hocr', and 'text'.
clean_in (bool): Normalize to NFD and strip input data. (DO NOT DISABLE!)
clean_gt (bool): Normalize to NFD and strip ground truth. (DO NOT DISABLE!)
divert (bool): Switch selecting output diversion. If enabled the output
will be added to the tracking arguments and the input
document will be returned as the result of the task. Use
this to insert a statistical measure into a chain
without affecting the results.
Returns:
(unicode, unicode): Storage tuple of the output document
"""
input_path = storage.get_abs_path(*doc[0])
output_path = storage.insert_suffix(input_path, method,
os.path.basename(input_path))
if not isinstance(ground_truth[0], basestring):
ground_truth = find_matching(doc, ground_truth)
with storage.StorageFile(*ground_truth) as fp:
if gt_format == 'tei':
tei = TEIFacsimile()
tei.read(fp)
t = StringIO.StringIO()
tei.write_text(t)
gt = t.getvalue()
elif gt_format == 'hocr':
gt = html.parse(fp).text_content()
elif gt_format == 'text':
gt = fp.read()
else:
raise NidabaInvalidParameterException('Input format ' + gt_format +
' unknown.')
with storage.StorageFile(*doc) as fp:
if xml_in:
tei = TEIFacsimile()
tei.read(fp)
t = StringIO.StringIO()
tei.write_text(t)
text = t.getvalue()
else:
text = fp.read()
if clean_in:
text = cleanup(text)
if clean_gt:
gt = cleanup(gt)
logger.debug('Recognition result: \n{}'.format(text))
logger.debug('Ground truth: \n{}'.format(gt))
sm = difflib.SequenceMatcher()
sm.set_seqs(text, gt)
logger.debug('Accuracy: {}'.format(sm.ratio()))
if not divert:
storage.write_text(*storage.get_storage_path(output_path),
text=unicode(sm.ratio()))
return output_path
else:
return {
'diff_ratio': sm.ratio(),
'ground_truth': ground_truth,
'doc': doc
}
def batch(files, host, preprocessing, binarize, ocr, segmentation, stats,
postprocessing, output, grayscale, help_tasks):
"""
Add a new job to the pipeline.
"""
if host:
batch = NetworkSimpleBatch(host)
click.echo(u'Preparing filestore\t\t[', nl=False)
try:
batch.create_batch()
except:
click.secho(u'\u2717', fg='red', nl=False)
click.echo(']')
exit()
click.secho(u'\u2713', fg='green', nl=False)
click.echo(']')
for doc in files:
def callback(monitor):
spin(u'Uploading {}'.format(doc))
batch.add_document(doc, callback)
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
else:
from nidaba import storage
click.echo(u'Preparing filestore\t\t[', nl=False)
try:
batch = SimpleBatch()
except:
click.secho(u'\u2717', fg='red', nl=False)
click.echo(']')
exit()
for doc in files:
shutil.copy2(doc,
storage.get_abs_path(batch.id, os.path.basename(doc)))
batch.add_document((batch.id, os.path.basename(doc)))
click.secho(u'\u2713', fg='green', nl=False)
click.echo(']')
click.echo(u'Building batch\t\t\t[', nl=False)
if not grayscale:
batch.add_task('img', 'rgb_to_gray')
if preprocessing:
for alg in preprocessing:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('img', alg[0], **kwargs)
if binarize:
for alg in binarize:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('binarize', alg[0], **kwargs)
if segmentation:
for alg in segmentation:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('segmentation', alg[0], **kwargs)
if ocr:
for alg in ocr:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('ocr', alg[0], **kwargs)
if stats:
for alg in stats:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('stats', alg[0], **kwargs)
if postprocessing:
for alg in postprocessing:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('postprocessing', alg[0], **kwargs)
if output:
for alg in output:
for kwargs in alg[1]:
kwargs = move_to_storage(batch, kwargs)
batch.add_task('output', alg[0], **kwargs)
batch.run()
click.secho(u'\u2713', fg='green', nl=False)
click.echo(']')
click.echo(batch.id)
def segmentation_tesseract(doc, method=u'segment_tesseract'):
"""
Performs page segmentation using tesseract's built-in algorithm and writes
a TEI XML segmentation file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string appended to all output files.
Returns:
Two storage tuples with the first one containing the segmentation and
the second one being the file the segmentation was calculated upon.
"""
input_path = storage.get_abs_path(*doc)
output_path = splitext(storage.insert_suffix(input_path, method))[0] + '.xml'
ver = tesseract.TessVersion()
if int(ver.split('.')[0]) < 3 or int(ver.split('.')[1]) < 2:
raise NidabaTesseractException('libtesseract version is too old. Set '
'implementation to direct.')
# tesseract has a tendency to crash arbitrarily on some inputs
# necessitating execution in a separate process to ensure the worker
# doesn't just die. We use fork as the multiprocessing module thinks
# programmers are too stupid to reap their children.
logger.info('Forking before entering unstable ctypes code')
pid = os.fork()
if pid != 0:
try:
logger.info('Waiting for child to complete')
_, status = os.waitpid(pid, 0)
except OSError as e:
if e.errno not in (errno.EINTR, errno.ECHILD):
raise
return storage.get_storage_path(output_path), doc
if os.WIFSIGNALED(status):
raise NidabaTesseractException('Tesseract killed by signal: {0}'.format(os.WTERMSIG(status)))
return storage.get_storage_path(output_path), doc
api = tesseract.TessBaseAPICreate()
rc = tesseract.TessBaseAPIInit3(api, tessdata.encode('utf-8'), None)
if (rc):
tesseract.TessBaseAPIDelete(api)
raise NidabaTesseractException('Tesseract initialization failed.')
# only do segmentation and script detection
logger.debug('Setting page set mode to 2')
tesseract.TessBaseAPISetPageSegMode(api, 2)
logger.debug('Reading {} using leptonica'.format(input_path))
pix = leptonica.pixRead(input_path.encode('utf-8'))
logger.debug('Setting PIX as input image')
tesseract.TessBaseAPISetImage2(api, pix)
logger.debug('Analyzing page layout')
it = tesseract.TessBaseAPIAnalyseLayout(api)
logger.debug('Destroying PIX')
leptonica.pixDestroy(ctypes.byref(pix))
x0, y0, x1, y1 = (ctypes.c_int(), ctypes.c_int(), ctypes.c_int(),
ctypes.c_int())
w, h = Image.open(input_path).size
logger.info('Initializing TEI XML file with {}x{} {}/{}'.format(w, h, *doc))
tei = TEIFacsimile()
tei.document((w, h), os.path.join(*doc))
tei.title = os.path.basename(doc[1])
tei.add_respstmt('tesseract', 'page segmentation')
while True:
if tesseract.TessPageIteratorIsAtBeginningOf(it, RIL_TEXTLINE):
tesseract.TessPageIteratorBoundingBox(it,
RIL_TEXTLINE,
ctypes.byref(x0),
ctypes.byref(y0),
ctypes.byref(x1),
ctypes.byref(y1))
tei.add_line((x0.value, y0.value, x1.value, y1.value))
logger.debug('Segmenter found new line at {} {} {} {}'.format(x0.value,
y0.value,
x1.value,
y1.value))
if tesseract.TessPageIteratorIsAtBeginningOf(it, RIL_WORD):
tesseract.TessPageIteratorBoundingBox(it,
RIL_WORD,
ctypes.byref(x0),
ctypes.byref(y0),
ctypes.byref(x1),
ctypes.byref(y1))
tei.add_segment((x0.value, y0.value, x1.value, y1.value))
logger.debug('Segmenter found new word at {} {} {} {}'.format(x0.value,
y0.value,
x1.value,
y1.value))
tesseract.TessPageIteratorBoundingBox(it,
RIL_SYMBOL,
ctypes.byref(x0),
ctypes.byref(y0),
ctypes.byref(x1),
ctypes.byref(y1))
tei.add_graphemes([(None, (x0.value, y0.value, x1.value, y1.value))])
logger.debug('Segmenter found new symbol at {} {} {} {}'.format(x0.value,
y0.value,
x1.value,
y1.value))
if not tesseract.TessPageIteratorNext(it, RIL_SYMBOL):
logger.debug('No more elements on page')
break
logger.debug('Deleting page iterator and base API')
tesseract.TessPageIteratorDelete(it)
tesseract.TessBaseAPIEnd(api)
tesseract.TessBaseAPIDelete(api)
logger.info('Writing segmentation to {}'.format(output_path))
with open(output_path, 'w') as fp:
tei.write(fp)
logger.info('Quitting child process')
os._exit(os.EX_OK)
return storage.get_storage_path(output_path), doc
def setup(*args, **kwargs):
if kwargs.get(u'implementation'):
global implementation
implementation = kwargs.get(u'implementation')
if kwargs.get(u'tessdata'):
global tessdata
if isinstance(kwargs.get(u'tessdata'), list):
tessdata = storage.get_abs_path(*kwargs.get(u'tessdata'))
else:
tessdata = kwargs.get(u'tessdata')
if implementation == 'direct' and not spawn.find_executable('tesseract'):
raise NidabaPluginException('No tesseract executable found')
if implementation == 'capi':
try:
global tesseract, leptonica
tesseract = ctypes.cdll.LoadLibrary('libtesseract.so.3')
leptonica = ctypes.cdll.LoadLibrary('liblept.so')
except:
raise NidabaPluginException('Loading libtesseract/leptonica failed.')
tesseract.TessBaseAPICreate.restype = POINTER(TessBaseAPI)
tesseract.TessBaseAPIEnd.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIEnd.restype = None
tesseract.TessBaseAPIDelete.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIDelete.restype = None
tesseract.TessBaseAPIInit3.argtypes = [POINTER(TessBaseAPI),
ctypes.c_char_p,
ctypes.c_char_p]
tesseract.TessBaseAPIInit3.restype = ctypes.c_int
tesseract.TessBaseAPISetImage2.restype = None
tesseract.TessBaseAPISetImage2.argtypes = [POINTER(TessBaseAPI),
POINTER(Pix)]
tesseract.TessBaseAPIRecognize.argtypes = [POINTER(TessBaseAPI), POINTER(TessBaseAPI)]
tesseract.TessBaseAPIRecognize.restype = ctypes.c_int
tesseract.TessResultIteratorGetUTF8Text.restype = ctypes.c_char_p
tesseract.TessResultIteratorGetUTF8Text.argtypes = [POINTER(TessResultIterator),
ctypes.c_int]
tesseract.TessResultIteratorConfidence.argtypes = [POINTER(TessResultIterator),
ctypes.c_int]
tesseract.TessResultIteratorConfidence.restype = ctypes.c_float
tesseract.TessResultIteratorWordRecognitionLanguage.argtypes = [POINTER(TessResultIterator)]
tesseract.TessResultIteratorWordRecognitionLanguage.restype = ctypes.c_char_p
tesseract.TessVersion.restype = ctypes.c_char_p
tesseract.TessBaseAPISetPageSegMode.argtypes = [POINTER(TessBaseAPI),
ctypes.c_int]
tesseract.TessBaseAPISetPageSegMode.restype = None
tesseract.TessBaseAPIProcessPages.argtypes = [POINTER(TessBaseAPI),
ctypes.c_char_p,
ctypes.c_char_p,
ctypes.c_int,
POINTER(TessResultRenderer)]
tesseract.TessBaseAPIProcessPages.restype = ctypes.c_int
tesseract.TessBaseAPIAnalyseLayout.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIAnalyseLayout.restype = POINTER(TessPageIterator)
tesseract.TessPageIteratorIsAtBeginningOf.argtypes = [POINTER(TessPageIterator),
ctypes.c_int]
tesseract.TessPageIteratorIsAtBeginningOf.restype = ctypes.c_int
tesseract.TessPageIteratorBoundingBox.argtypes = [POINTER(TessPageIterator),
ctypes.c_int,
POINTER(ctypes.c_int),
POINTER(ctypes.c_int),
POINTER(ctypes.c_int),
POINTER(ctypes.c_int)]
tesseract.TessPageIteratorBoundingBox.restype = ctypes.c_int
tesseract.TessBaseAPIGetIterator.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIGetIterator.restype = POINTER(TessResultIterator)
tesseract.TessResultIteratorGetPageIterator.argtypes = [POINTER(TessResultIterator)]
tesseract.TessResultIteratorGetPageIterator.restype = POINTER(TessPageIterator)
tesseract.TessResultIteratorNext.argtypes = [POINTER(TessResultIterator), ctypes.c_int]
tesseract.TessResultIteratorNext.restype = ctypes.c_int
tesseract.TessResultIteratorDelete.argtypes = [POINTER(TessResultIterator)]
tesseract.TessResultIteratorDelete.restype = None
tesseract.TessPageIteratorDelete.argtypes = [POINTER(TessPageIterator)]
tesseract.TessPageIteratorDelete.restype = None
leptonica.pixRead.argtypes = [ctypes.c_char_p]
leptonica.pixRead.restype = POINTER(Pix)
leptonica.pixDestroy.argtypes = [POINTER(POINTER(Pix))]
leptonica.pixDestroy.restype = None
tesseract.TessBaseAPIGetHOCRText.argtypes = [POINTER(TessBaseAPI), ctypes.c_int]
tesseract.TessBaseAPIGetHOCRText.restype = ctypes.c_char_p
tesseract.TessBaseAPIGetAvailableLanguagesAsVector.argtypes = [POINTER(TessBaseAPI)]
tesseract.TessBaseAPIGetAvailableLanguagesAsVector.restype = POINTER(ctypes.c_char_p)
# fill in available tesseract classifiers as they are only determinable
# after setting the tessdata directory.
ocr_tesseract.arg_values['languages'] = _get_available_classifiers()
def status(verbose, host, job_id):
"""
Diplays the status and results of jobs.
"""
if host:
batch = NetworkSimpleBatch(host, job_id)
else:
batch = SimpleBatch(job_id)
state = batch.get_extended_state()
click.secho('Status:', underline=True, nl=False)
if not state:
click.echo(' UNKNOWN')
return
bs = 'success'
done = 0
running = 0
pending = 0
failed = 0
results = []
errors = []
expected = len(state)
failed_children = set()
for task_id, subtask in state.iteritems():
if subtask['state'] == 'SUCCESS':
done += 1
elif subtask['state'] == 'RUNNING':
running += 1
if bs == 'success':
bs = 'pending'
elif subtask['state'] == 'PENDING':
pending += 1
if bs == 'success':
bs = 'pending'
elif subtask['state'] == 'FAILURE':
failed += 1
children = []
if not isinstance(subtask['children'], list):
subtask['children'] = [subtask['children']]
for child in subtask['children']:
if not isinstance(state[child]['children'], list):
state[child]['children'] = [state[child]['children']]
children.extend(state[child]['children'])
failed_children.add(child)
errors.append(subtask)
bs = 'failed'
if len(subtask['children']) == 0 and not subtask['housekeeping'] and subtask['result'] is not None:
# try to find statistics results
parents = [task_id] + subtask['parents']
misc = None
for parent in parents:
parents.extend(state[parent]['parents'])
if 'misc' in state[parent]:
misc = state[parent]['misc']
break
results.append((subtask['result'], subtask['root_document'], misc))
final = '(final)' if not expected - failed - done - len(failed_children) else ''
click.echo(' {} {}\n'.format(bs, final))
click.echo('{}/{} tasks completed. {} running.\n'.format(done, len(state), running))
click.secho('Output files:\n', underline=True)
results = sorted(results, key=lambda x: x[0][1])
if results and host:
for doc in results:
if doc[2] is not None:
click.echo(u'{} \u2192 {} ({:.1f}% / {})'.format(doc[1],
doc[0],
100 *
doc[2]['edit_ratio'],
doc[2]['ground_truth'][1]))
else:
click.echo(u'{} \u2192 {}'.format(doc[1], doc[0]))
elif results:
from nidaba import storage
for doc in results:
output = click.format_filename(storage.get_abs_path(*doc[0]))
if doc[2] is not None:
click.echo(u'{} \u2192 {} ({:.1f}% / {})'.format(doc[1][1],
output,
100 *
doc[2]['edit_ratio'],
doc[2]['ground_truth'][1]))
else:
click.echo(u'{} \u2192 {}'.format(doc[1][1], output))
if errors:
click.secho('\nErrors:\n', underline=True)
for task in errors:
tb = ''
args = ''
if verbose > 0:
tb = task['errors'][2]
if verbose > 1:
task['errors'][0].pop('method')
args = ', ' + str(task['errors'][0])
click.echo('{0} ({1}{2}): {3}{4}'.format(task['task'][0],
task['root_document'][1],
args,
tb,
task['errors'][1]))
def status(verbose, host, job_id):
"""
Diplays the status and results of jobs.
"""
if host:
batch = NetworkSimpleBatch(host, job_id)
else:
batch = SimpleBatch(job_id)
state = batch.get_extended_state()
click.secho('Status:', underline=True, nl=False)
if not state:
click.echo(' UNKNOWN')
return
bs = 'success'
done = 0
running = 0
pending = 0
failed = 0
results = []
errors = []
expected = len(state)
failed_children = set()
for task_id, subtask in state.iteritems():
if subtask['state'] == 'SUCCESS':
done += 1
elif subtask['state'] == 'RUNNING':
running += 1
if bs == 'success':
bs = 'pending'
elif subtask['state'] == 'PENDING':
pending += 1
if bs == 'success':
bs = 'pending'
elif subtask['state'] == 'FAILURE':
failed += 1
children = []
if not isinstance(subtask['children'], list):
subtask['children'] = [subtask['children']]
for child in subtask['children']:
if not isinstance(state[child]['children'], list):
state[child]['children'] = [state[child]['children']]
children.extend(state[child]['children'])
failed_children.add(child)
errors.append(subtask)
bs = 'failed'
if len(subtask['children']) == 0 and not subtask[
'housekeeping'] and subtask['result'] is not None:
# try to find statistics results
parents = [task_id] + subtask['parents']
misc = None
for parent in parents:
parents.extend(state[parent]['parents'])
if 'misc' in state[parent]:
misc = state[parent]['misc']
break
results.append((subtask['result'], subtask['root_document'], misc))
final = '(final)' if not expected - failed - done - len(
failed_children) else ''
click.echo(' {} {}\n'.format(bs, final))
click.echo('{}/{} tasks completed. {} running.\n'.format(
done, len(state), running))
click.secho('Output files:\n', underline=True)
results = sorted(results, key=lambda x: x[0][1])
if results and host:
for doc in results:
if doc[2] is not None:
click.echo(u'{} \u2192 {} ({:.1f}% / {})'.format(
doc[1], doc[0], 100 * doc[2]['edit_ratio'],
doc[2]['ground_truth'][1]))
else:
click.echo(u'{} \u2192 {}'.format(doc[1], doc[0]))
elif results:
from nidaba import storage
for doc in results:
output = click.format_filename(storage.get_abs_path(*doc[0]))
if doc[2] is not None:
click.echo(u'{} \u2192 {} ({:.1f}% / {})'.format(
doc[1][1], output, 100 * doc[2]['edit_ratio'],
doc[2]['ground_truth'][1]))
else:
click.echo(u'{} \u2192 {}'.format(doc[1][1], output))
if errors:
click.secho('\nErrors:\n', underline=True)
for task in errors:
tb = ''
args = ''
if verbose > 0:
tb = task['errors'][2]
if verbose > 1:
task['errors'][0].pop('method')
args = ', ' + str(task['errors'][0])
click.echo('{0} ({1}{2}): {3}{4}'.format(task['task'][0],
task['root_document'][1],
args, tb,
task['errors'][1]))
def ocr_kraken(doc, method=u'ocr_kraken', model=None):
"""
Runs kraken on an input document and writes a TEI file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string append to all output files
model (unicode): Identifier for the font model to use
Returns:
(unicode, unicode): Storage tuple for the output file
"""
output_path = (
doc[0],
os.path.splitext(storage.insert_suffix(doc[1], method, model))[0] +
'.xml')
logger.debug('Loading model {}'.format(model))
try:
rnn = models.load_any(mod_db[model])
except Exception as e:
raise NidabaInvalidParameterException(str(e))
logger.debug('Reading TEI segmentation from {}'.format(doc))
tei = OCRRecord()
with storage.StorageFile(*doc) as seg:
tei.load_tei(seg)
img = Image.open(
storage.get_abs_path(*storage.get_storage_path_url(tei.img)))
if is_bitonal(img):
img = img.convert('1')
else:
raise NidabaInvalidParameterException('Input image is not bitonal')
logger.debug('Clearing out word/grapheme boxes')
# kraken is a line recognizer
tei.clear_graphemes()
tei.clear_segments()
# add and scope new responsibility statement
tei.add_respstmt('kraken', 'character recognition')
lines = tei.lines
i = 0
rnn = models.load_any(mod_db[model])
logger.debug('Start recognizing characters')
for line_id, rec in izip(
lines,
rpred.rpred(
rnn, img, {
'text_direction': 'horizontal-tb',
'boxes': [list(x['bbox']) for x in lines.itervalues()]
})):
# scope the current line and add all graphemes recognized by kraken to
# it.
logger.debug('Scoping line {}'.format(line_id))
tei.scope_line(line_id)
i += 1
splits = regex.split(u'(\s+)', rec.prediction)
line_offset = 0
for segment, whitespace in izip_longest(splits[0::2], splits[1::2]):
if len(segment):
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset +
len(segment)])
logger.debug(
'Creating new segment at {} {} {} {}'.format(*seg_bbox))
tei.add_segment(seg_bbox)
logger.debug('Adding graphemes (segment): {}'.format(
rec.prediction[line_offset:line_offset + len(segment)]))
tei.add_graphemes([{
'grapheme': x[0],
'bbox': x[1],
'confidence': int(x[2] * 100)
} for x in rec[line_offset:line_offset + len(segment)]])
line_offset += len(segment)
if whitespace:
logger.debug('Adding graphemes (whitespace): {}'.format(
rec.prediction[line_offset:line_offset + len(whitespace)]))
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset +
len(whitespace)])
tei.add_segment(seg_bbox)
tei.add_graphemes([{
'grapheme': x[0],
'bbox': x[1],
'confidence': int(x[2] * 100)
} for x in rec[line_offset:line_offset + len(whitespace)]])
line_offset += len(whitespace)
with storage.StorageFile(*output_path, mode='wb') as fp:
logger.debug('Writing TEI to {}'.format(fp.abs_path))
tei.write_tei(fp)
return output_path
def ocr_kraken(doc, method=u'ocr_kraken', model=None):
"""
Runs kraken on an input document and writes a TEI file.
Args:
doc (unicode, unicode): The input document tuple
method (unicode): The suffix string append to all output files
model (unicode): Identifier for the font model to use
Returns:
(unicode, unicode): Storage tuple for the output file
"""
input_path = storage.get_abs_path(*doc[1])
output_path = (
doc[1][0],
os.path.splitext(storage.insert_suffix(doc[1][1], method, model))[0] +
'.xml')
logger.debug('Searching for model {}'.format(model))
if model in nidaba_cfg['kraken_models']:
model = storage.get_abs_path(*(nidaba_cfg['kraken_models'][model]))
elif model in nidaba_cfg['ocropus_models']:
model = storage.get_abs_path(*(nidaba_cfg['ocropus_models'][model]))
else:
raise NidabaInvalidParameterException('Model not defined in '
'configuration')
img = Image.open(input_path)
logger.debug('Reading TEI segmentation from {}'.format(doc[1]))
tei = TEIFacsimile()
with storage.StorageFile(*doc[0]) as seg:
tei.read(seg)
logger.debug('Clearing out word/grapheme boxes')
# kraken is a line recognizer
tei.clear_graphemes()
tei.clear_segments()
# add and scope new responsibility statement
tei.add_respstmt('kraken', 'character recognition')
lines = tei.lines
logger.debug('Loading model {}'.format(model))
rnn = models.load_any(model)
i = 0
logger.debug('Start recognizing characters')
for rec in rpred.rpred(rnn, img,
[(int(x[0]), int(x[1]), int(x[2]), int(x[3]))
for x in lines]):
# scope the current line and add all graphemes recognized by kraken to
# it.
logger.debug('Scoping line {}'.format(lines[i][4]))
tei.scope_line(lines[i][4])
i += 1
splits = regex.split(u'(\s+)', rec.prediction)
line_offset = 0
for segment, whitespace in izip_longest(splits[0::2], splits[1::2]):
if len(segment):
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset +
len(segment)])
logger.debug(
'Creating new segment at {} {} {} {}'.format(*seg_bbox))
tei.add_segment(seg_bbox)
logger.debug('Adding graphemes (segment): {}'.format(
rec.prediction[line_offset:line_offset + len(segment)]))
tei.add_graphemes([
(x[0], x[1], int(x[2] * 100))
for x in rec[line_offset:line_offset + len(segment)]
])
line_offset += len(segment)
if whitespace:
logger.debug('Adding graphemes (whitespace): {}'.format(
rec.prediction[line_offset:line_offset + len(whitespace)]))
seg_bbox = max_bbox(rec.cuts[line_offset:line_offset +
len(whitespace)])
tei.add_segment(seg_bbox)
tei.add_graphemes([
(x[0], x[1], int(x[2] * 100))
for x in rec[line_offset:line_offset + len(whitespace)]
])
line_offset += len(whitespace)
with storage.StorageFile(*output_path, mode='wb') as fp:
logger.debug('Writing TEI to {}'.format(fp.abs_path))
tei.write(fp)
return output_path
def status(verbose, host, job_id):
"""
Diplays the status and results of jobs.
"""
click.secho('Status:', underline=True, nl=False)
if host:
batch = NetworkSimpleBatch(host, job_id)
else:
try:
batch = Batch(job_id)
except NidabaInputException:
click.echo(' UNKNOWN')
return
state = batch.get_extended_state()
if not state:
click.echo(' UNKNOWN')
return
bs = 'success'
done = 0
running = 0
pending = 0
failed = 0
results = []
errors = []
expected = len(state)
for task_id, subtask in state.iteritems():
if subtask['state'] == 'SUCCESS':
done += 1
elif subtask['state'] == 'RUNNING':
running += 1
if bs == 'success':
bs = 'pending'
elif subtask['state'] == 'PENDING':
pending += 1
if bs == 'success':
bs = 'pending'
elif subtask['state'] == 'FAILURE':
errors.append(subtask)
bs = 'failed'
# leaf nodes/result extraction
if len(subtask['children']) == 0 and subtask['result'] is not None:
# try to find statistics results
parents = [task_id] + subtask['parents']
misc = None
for parent in parents:
parents.extend(state[parent]['parents'])
if 'misc' in state[parent]:
misc = state[parent]['misc']
break
# archival tasks bunch everything together. do a sort-based matching of input and output tasks
if isinstance(subtask['result'][0], list) or (host and isinstance(subtask['result'], list)):
for res, rd in zip(sorted(subtask['result']), sorted(subtask['root_documents'])):
if host:
res = [res]
results.append((res, [rd], misc))
else:
if host:
subtask['result'] = [subtask['result']]
results.append((subtask['result'], subtask['root_documents'], misc))
final = '(final)' if expected - done == 0 else ''
click.echo(' {} {}\n'.format(bs, final))
click.echo('{}/{} tasks completed. {} running.\n'.format(done, expected, running))
# render results
click.secho('Output files:\n', underline=True)
results = sorted(results, key=lambda x: x[0][0][1] if isinstance(x[0], list) else x[0][1])
if results:
for doc in results:
if host:
output = ', '.join(doc[0])
input = ', '.join(doc[1])
else:
from nidaba import storage
if isinstance(doc[0][0], list):
for d in doc:
output = ', '.join(click.format_filename(storage.get_abs_path(*d)))
else:
output = click.format_filename(storage.get_abs_path(*doc[0]))
input = ', '.join(d[1] for d in doc[1])
if doc[2] is not None:
click.echo(u'{} \u2192 {} ({:.1f}% / {})'.format(input,
output,
100 *
doc[2]['edit_ratio'],
doc[2]['ground_truth'][1]))
else:
click.echo(u'{} \u2192 {}'.format(input, output))
# render errors
if errors:
click.secho('\nErrors:\n', underline=True)
for task in errors:
tb = ''
args = ''
if verbose > 0:
tb = task['errors'][2]
if verbose > 1:
task['errors'][0].pop('method')
args = ', ' + str(task['errors'][0])
if host:
rd = ', '.join(os.path.basename(x) for x in task['root_documents'])
else:
rd = ', '.join(os.path.basename(x[1]) for x in task['root_documents'])
click.echo('{}.{} ({}{}): {}{}'.format(task['task'][0],
task['task'][1],
rd,
args,
tb,
task['errors'][1]))
def merge(docs, lang, output):
"""
Merges multiple hOCR documents into a single one.
First bboxes from all documents are roughly matched, then all matching
bboxes are scored using a spell checker. If no spell checker is available
all matches will be merged without ranking.
The matching is naive, i.e. we just grab the first input document and
assume that all other documents have similar segmentation results. Issues
like high variance in segmentation, especially word boundaries are not
accounted for.
Args:
docs (iterable): A list of storage tuples of input documents
lang (unicode): A language identifier for the spell checker
output (tuple): Storage tuple for the result
Returns:
tuple: The output storage tuple. Should be the same as ```output```.
"""
tree1 = etree.parse(storage.get_abs_path(docs[0][0], docs[0][1]))
lines_1, words_1 = get_hocr_lines_for_tree(tree1)
sort_words_bbox(words_1)
other_words = []
for doc in docs[1:]:
try:
tree2 = etree.parse(storage.get_abs_path(doc[0], doc[1]))
lines_2, words_2 = get_hocr_lines_for_tree(tree2)
other_words = other_words + words_2
except Exception as e:
print e
sort_words_bbox(other_words)
positional_lists = []
positional_list = []
x = 0
# Make a list of positional_lists, that is alternatives for a given
# position, skipping duplicate position-words
while x < len(other_words):
try:
if len(positional_list) == 0:
positional_list.append(other_words[x])
else:
if close_enough(other_words[x - 1].bbox, other_words[x].bbox):
# skip if the text is the same, so that we just get unique
# texts for this position
if not other_words[x - 1].text == other_words[x].text:
positional_list.append(other_words[x])
else:
if not x == 0:
positional_lists.append(positional_list)
positional_list = []
except IndexError:
pass
x = x + 1
# we now have a list of list of unique words for each position
# let's select from each the first one that passes spellcheck
replacement_words = []
# make a 'replacement_words' list with all of the best, non-zero-scoring
# suggestions for each place
for positional_list in positional_lists:
for word in positional_list:
word.score = score_word(lang, word.text)
positional_list.sort(key=attrgetter('score'), reverse=True)
if positional_list[0].score > 0:
replacement_words.append(positional_list[0])
# now replace the originals
for word in words_1:
for replacement_word in replacement_words:
word.score = score_word(lang, word.text)
if close_enough(word.bbox, replacement_word.bbox) and (
word.score < replacement_word.score):
word.element.text = replacement_word.text
for positional_list in positional_lists:
print "##"
for word in positional_list:
print word.bbox, word.text
storage.write_text(*output, text=etree.tostring(tree1.getroot(),
encoding='unicode'))
return output
