def tei2txt(doc, method=u'tei2txt'):
"""
Convert a TEI Facsimile to a plain text file.
Args:
doc (unicode, unicode): Storage tuple of the input document
Returns:
(unicode, unicode): Storage tuple of the output document
"""
with storage.StorageFile(*doc) as fp:
tei = TEIFacsimile()
logger.debug('Reading TEI ({}/{})'.format(*doc))
tei.read(fp)
output_path = storage.insert_suffix(doc[1], method)
with storage.StorageFile(doc[0], output_path, 'wb') as fp:
logger.debug('Writing text to {}'.format(fp.abs_path))
tei.write_text(fp)
return (doc[0], output_path)
def tei2txt(doc, method=u'tei2txt'):
"""
Convert a TEI Facsimile to a plain text file.
Args:
doc (unicode, unicode): Storage tuple of the input document
Returns:
(unicode, unicode): Storage tuple of the output document
"""
with storage.StorageFile(*doc) as fp:
tei = TEIFacsimile()
logger.debug('Reading TEI ({}/{})'.format(*doc))
tei.read(fp)
output_path = storage.insert_suffix(doc[1], method)
with storage.StorageFile(doc[0], output_path, 'wb') as fp:
logger.debug('Writing text to {}'.format(fp.abs_path))
tei.write_text(fp)
return (doc[0], 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 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
}
