def test_mm_rpred_bbox_default_tags(self):
"""
Tests recognition with default tag.
"""
pred = mm_rpred(
defaultdict(lambda: self.model), self.overfit_line, {
'boxes': [[('foobar', [0, 0, 2544, 156])],
[('default', [0, 0, 2544, 156])]],
'text_direction':
'horizontal',
'script_detection':
True
}, True)
record = next(pred)
self.assertEqual(record.prediction, 'ܡ ܘܡ ܗ ܡܕܐ ܐ ܐܐ ܡ ܗܗܐܐܐܕ')
record = next(pred)
self.assertEqual(record.prediction, 'ܡ ܘܡ ܗ ܡܕܐ ܐ ܐܐ ܡ ܗܗܐܐܐܕ')
def recognizer(model, pad, bidi_reordering, base_image, input, output, lines):
try:
im = Image.open(base_image)
except IOError as e:
raise click.BadParameter(str(e))
ctx = click.get_current_context()
scripts = None
st_time = time.time()
if not lines:
lines = input
with open_file(lines, 'r') as fp:
bounds = json.load(fp)
# script detection
if bounds.get('script_detection',False):
scripts = set()
for l in bounds['boxes']:
for t in l:
scripts.add(t[0])
it = rpred.mm_rpred(model, im, bounds, pad, bidi_reordering=bidi_reordering)
else:
it = rpred.rpred(model['default'], im, bounds, pad, bidi_reordering=bidi_reordering)
preds = []
for pred in it:
spin('Processing')
preds.append(pred)
message(u'\b\u2713', fg='green', nl=False)
message('\033[?25h\n', nl=False)
ctx = click.get_current_context()
with open_file(output, 'w', encoding='utf-8') as fp:
message(u'Writing recognition results for {}\t'.format(base_image), nl=False)
logger.info(u'Serializing as {} into {}'.format(ctx.meta['mode'], fp.name))
if ctx.meta['mode'] != 'text':
fp.write(serialization.serialize(preds, base_image,
Image.open(base_image).size, ctx.meta['text_direction'],
scripts, ctx.meta['mode']))
else:
fp.write(u'\n'.join(s.prediction for s in preds))
if not ctx.meta['verbose']:
message(u'\u2713', fg='green')
def recognizer(input_image, model, pad, no_segmentation, bidi_reordering,
script_ignore, mode, text_direction, segments) -> None:
bounds = segments
# Script detection.
if bounds['script_detection']:
for l in bounds['boxes']:
for t in l:
scripts.add(t[0])
it = rpred.mm_rpred(model,
input_image,
bounds,
pad,
bidi_reordering=bidi_reordering,
script_ignore=script_ignore)
else:
it = rpred.rpred(model['default'],
input_image,
bounds,
pad,
bidi_reordering=bidi_reordering)
preds = []
with log.progressbar(it, label='Processing',
length=len(bounds['boxes'])) as bar:
for pred in bar:
preds.append(pred)
#--------------------
print('Recognition results = {}.'.format('\n'.join(s.prediction
for s in preds)))
if False:
with open_file(output, 'w', encoding='utf-8') as fp:
print('Serializing as {} into {}'.format(mode, output))
if mode != 'text':
from kraken import serialization
fp.write(
serialization.serialize(preds, base_image,
Image.open(base_image).size,
text_direction, scripts, mode))
else:
fp.write('\n'.join(s.prediction for s in preds))
def test_mm_rpred_bbox_ignore_tags(self):
"""
Tests mm_rpred recognition with ignore tags.
"""
pred = mm_rpred({'default': self.model},
self.overfit_line, {
'boxes': [[('foobar', [0, 0, 2544, 156])],
[('default', [0, 0, 2544, 156])]],
'text_direction':
'horizontal',
'script_detection':
True
},
True,
tags_ignore=['foobar'])
record = next(pred)
self.assertEqual(record.prediction, '')
record = next(pred)
self.assertEqual(record.prediction, 'ܡ ܘܡ ܗ ܡܕܐ ܐ ܐܐ ܡ ܗܗܐܐܐܕ')
def test_mm_rpred_bl_nobidi(self):
"""
Tests baseline recognition without bidi reordering.
"""
pred = mm_rpred(
defaultdict(lambda: self.model),
self.overfit_line, {
'lines': [{
'tags': {
'type': 'default'
},
'baseline': [[0, 10], [2543, 10]],
'boundary': [[0, 0], [2543, 0], [2543, 155], [0, 155]]
}],
'script_detection':
True,
'type':
'baselines'
},
bidi_reordering=False)
record = next(pred)
self.assertEqual(record.prediction,
'ܕܗ .ܣܐܗܗ.ܐ ܗܣ ܕ ܗܣ ܗ.ܗܝܣܗ ܣ ܗܢ ܪܗܗܕ ܐ ܗܠ')
def recognizer(model, pad, no_segmentation, bidi_reordering, script_ignore,
input, output) -> None:
import json
from kraken import rpred
ctx = click.get_current_context()
bounds = None
if 'base_image' not in ctx.meta:
ctx.meta['base_image'] = input
if ctx.meta['first_process']:
if ctx.meta['input_format_type'] != 'image':
doc = get_input_parser(ctx.meta['input_format_type'])(input)
ctx.meta['base_image'] = doc['image']
doc['text_direction'] = 'horizontal-lr'
bounds = doc
try:
im = Image.open(ctx.meta['base_image'])
except IOError as e:
raise click.BadParameter(str(e))
if not bounds and ctx.meta['base_image'] != input:
with open_file(input, 'r') as fp:
try:
fp = cast(IO[Any], fp)
bounds = json.load(fp)
except ValueError as e:
raise click.UsageError(
f'{input} invalid segmentation: {str(e)}')
elif not bounds:
if no_segmentation:
bounds = {
'script_detection': False,
'text_direction': 'horizontal-lr',
'boxes': [(0, 0) + im.size]
}
else:
raise click.UsageError(
'No line segmentation given. Add one with the input or run `segment` first.'
)
elif no_segmentation:
logger.warning(
'no_segmentation mode enabled but segmentation defined. Ignoring --no-segmentation option.'
)
scripts = set()
# script detection
if 'script_detection' in bounds and bounds['script_detection']:
it = rpred.mm_rpred(model,
im,
bounds,
pad,
bidi_reordering=bidi_reordering,
script_ignore=script_ignore)
else:
it = rpred.rpred(model['default'],
im,
bounds,
pad,
bidi_reordering=bidi_reordering)
preds = []
with log.progressbar(it, label='Processing') as bar:
for pred in bar:
preds.append(pred)
ctx = click.get_current_context()
with open_file(output, 'w', encoding='utf-8') as fp:
fp = cast(IO[Any], fp)
message(f'Writing recognition results for {ctx.meta["orig_file"]}\t',
nl=False)
logger.info('Serializing as {} into {}'.format(ctx.meta['output_mode'],
output))
if ctx.meta['output_mode'] != 'native':
from kraken import serialization
fp.write(
serialization.serialize(
preds, ctx.meta['base_image'],
Image.open(ctx.meta['base_image']).size,
ctx.meta['text_direction'], scripts,
bounds['regions'] if 'regions' in bounds else None,
ctx.meta['output_mode']))
else:
fp.write('\n'.join(s.prediction for s in preds))
message('\u2713', fg='green')
def recognizer(model, pad, no_segmentation, bidi_reordering, script_ignore,
base_image, input, output, lines) -> None:
import json
import tempfile
from kraken import rpred
try:
im = Image.open(base_image)
except IOError as e:
raise click.BadParameter(str(e))
ctx = click.get_current_context()
# input may either be output from the segmenter then it is a JSON file or
# be an image file when running the OCR subcommand alone. might still come
# from some other subcommand though.
scripts = set()
if not lines and base_image != input:
lines = input
if not lines:
if no_segmentation:
lines = tempfile.NamedTemporaryFile(mode='w', delete=False)
logger.info(
'Running in no_segmentation mode. Creating temporary segmentation {}.'
.format(lines.name))
json.dump(
{
'script_detection': False,
'text_direction': 'horizontal-lr',
'boxes': [(0, 0) + im.size]
}, lines)
lines.close()
lines = lines.name
else:
raise click.UsageError(
'No line segmentation given. Add one with `-l` or run `segment` first.'
)
elif no_segmentation:
logger.warning(
'no_segmentation mode enabled but segmentation defined. Ignoring --no-segmentation option.'
)
with open_file(lines, 'r') as fp:
try:
fp = cast(IO[Any], fp)
bounds = json.load(fp)
except ValueError as e:
raise click.UsageError('{} invalid segmentation: {}'.format(
lines, str(e)))
# script detection
if bounds['script_detection']:
for l in bounds['boxes']:
for t in l:
scripts.add(t[0])
it = rpred.mm_rpred(model,
im,
bounds,
pad,
bidi_reordering=bidi_reordering,
script_ignore=script_ignore)
else:
it = rpred.rpred(model['default'],
im,
bounds,
pad,
bidi_reordering=bidi_reordering)
if not lines and no_segmentation:
logger.debug('Removing temporary segmentation file.')
os.unlink(lines.name)
preds = []
with log.progressbar(it, label='Processing',
length=len(bounds['boxes'])) as bar:
for pred in bar:
preds.append(pred)
ctx = click.get_current_context()
with open_file(output, 'w', encoding='utf-8') as fp:
fp = cast(IO[Any], fp)
message('Writing recognition results for {}\t'.format(base_image),
nl=False)
logger.info('Serializing as {} into {}'.format(ctx.meta['mode'],
output))
if ctx.meta['mode'] != 'text':
from kraken import serialization
fp.write(
serialization.serialize(preds, base_image,
Image.open(base_image).size,
ctx.meta['text_direction'], scripts,
ctx.meta['mode']))
else:
fp.write('\n'.join(s.prediction for s in preds))
message('\u2713', fg='green')
def recognizer(model, pad, no_segmentation, bidi_reordering, script_ignore, base_image, input, output, lines) -> None:
import json
import tempfile
from kraken import rpred
try:
im = Image.open(base_image)
except IOError as e:
raise click.BadParameter(str(e))
ctx = click.get_current_context()
# input may either be output from the segmenter then it is a JSON file or
# be an image file when running the OCR subcommand alone. might still come
# from some other subcommand though.
scripts = set()
if not lines and base_image != input:
lines = input
if not lines:
if no_segmentation:
lines = tempfile.NamedTemporaryFile(mode='w', delete=False)
logger.info('Running in no_segmentation mode. Creating temporary segmentation {}.'.format(lines.name))
json.dump({'script_detection': False,
'text_direction': 'horizontal-lr',
'boxes': [(0, 0) + im.size]}, lines)
lines.close()
lines = lines.name
else:
raise click.UsageError('No line segmentation given. Add one with `-l` or run `segment` first.')
elif no_segmentation:
logger.warning('no_segmentation mode enabled but segmentation defined. Ignoring --no-segmentation option.')
with open_file(lines, 'r') as fp:
try:
fp = cast(IO[Any], fp)
bounds = json.load(fp)
except ValueError as e:
raise click.UsageError('{} invalid segmentation: {}'.format(lines, str(e)))
# script detection
if bounds['script_detection']:
for l in bounds['boxes']:
for t in l:
scripts.add(t[0])
it = rpred.mm_rpred(model, im, bounds, pad,
bidi_reordering=bidi_reordering,
script_ignore=script_ignore)
else:
it = rpred.rpred(model['default'], im, bounds, pad,
bidi_reordering=bidi_reordering)
if not lines and no_segmentation:
logger.debug('Removing temporary segmentation file.')
os.unlink(lines.name)
preds = []
with log.progressbar(it, label='Processing', length=len(bounds['boxes'])) as bar:
for pred in bar:
preds.append(pred)
ctx = click.get_current_context()
with open_file(output, 'w', encoding='utf-8') as fp:
fp = cast(IO[Any], fp)
message('Writing recognition results for {}\t'.format(base_image), nl=False)
logger.info('Serializing as {} into {}'.format(ctx.meta['mode'], output))
if ctx.meta['mode'] != 'text':
from kraken import serialization
fp.write(serialization.serialize(preds, base_image,
Image.open(base_image).size,
ctx.meta['text_direction'],
scripts,
ctx.meta['mode']))
else:
fp.write('\n'.join(s.prediction for s in preds))
message('\u2713', fg='green')
