def transcription(ctx, text_direction, scale, maxcolseps, black_colseps, font,
font_style, prefill, output, images):
st_time = time.time()
ti = transcribe.TranscriptionInterface(font, font_style)
if prefill:
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Loading model {}'.format(time.time() - st_time, prefill))
else:
spin('Loading RNN')
prefill = models.load_any(prefill.encode('utf-8'))
if not ctx.meta['verbose']:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
for fp in images:
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Reading {}'.format(time.time() - st_time, fp.name))
else:
spin('Reading images')
im = Image.open(fp)
if not binarization.is_bitonal(im):
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Binarizing page'.format(time.time() - st_time))
im = binarization.nlbin(im)
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Segmenting page'.format(time.time() - st_time))
res = pageseg.segment(im, text_direction, scale, maxcolseps, black_colseps)
if prefill:
it = rpred.rpred(prefill, im, res)
preds = []
for pred in it:
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] {}'.format(time.time() - st_time, pred.prediction))
else:
spin('Recognizing')
preds.append(pred)
if ctx.meta['verbose'] > 0:
click.echo(u'Execution time: {}s'.format(time.time() - st_time))
else:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
ti.add_page(im, res, records=preds)
else:
ti.add_page(im, res)
fp.close()
if not ctx.meta['verbose']:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
if ctx.meta['verbose'] > 0:
click.echo(u'[{:2.4f}] Writing transcription to {}'.format(time.time() - st_time, output.name))
else:
spin('Writing output')
ti.write(output)
if not ctx.meta['verbose']:
click.secho(u'\b\u2713', fg='green', nl=False)
click.echo('\033[?25h\n', nl=False)
def transcription(ctx, text_direction, scale, maxcolseps, black_colseps, font,
font_style, prefill, output, images, segment_page):
ti = transcribe.TranscriptionInterface(font, font_style)
if prefill:
logger.info('Loading model {}'.format(prefill))
spin('Loading RNN')
prefill = models.load_any(prefill.encode('utf-8'))
message(u'\b\u2713', fg='green', nl=False)
message('\033[?25h\n', nl=False)
for fp in images:
logger.info('Reading {}'.format(fp.name))
spin('Reading images')
im = Image.open(fp)
if not binarization.is_bitonal(im):
logger.info(u'Binarizing page')
im = binarization.nlbin(im)
if segment_page:
logger.info(u'Segmenting page')
res = pageseg.segment(im, text_direction, scale, maxcolseps,
black_colseps)
else:
res = {
'text_direction': 'horizontal-tb',
'boxes': [(0, 0) + im.size]
}
if prefill:
it = rpred.rpred(prefill, im, res)
preds = []
for pred in it:
logger.info('{}'.format(pred.prediction))
spin('Recognizing')
preds.append(pred)
message(u'\b\u2713', fg='green', nl=False)
message('\033[?25h\n', nl=False)
ti.add_page(im, res, records=preds)
else:
ti.add_page(im, res)
fp.close()
message(u'\b\u2713', fg='green', nl=False)
message('\033[?25h\n', nl=False)
logger.info(u'Writing transcription to {}'.format(output.name))
spin('Writing output')
ti.write(output)
message(u'\b\u2713', fg='green', nl=False)
message('\033[?25h\n', nl=False)
def transcribe(book_pages_png: list,
book_path: str = "book",
name_of_transcribed_file="html_transcribe"):
"""
A warper for transcribing book pages
:param book_pages_png: List with all of the pages that need to be transcribed
:param book_path: The path of the pages
:return: None
"""
t_interface = tr.TranscriptionInterface()
t_interface.text_direction = "rl"
for page in book_pages_png:
im = PIL.Image.open(f"{book_path}/{page}").convert(mode='1')
# Create segments with the basic segmentor
segments = segment(im, 'horizontal-rl')
# Manualy fix some errors in the segmentation
boxes = fix_overlapping_boxes(segments['boxes'])
segments['boxes'] = boxes
t_interface.add_page(im, segments)
f = open(f"{name_of_transcribed_file}.html", "wb+")
t_interface.write(f)
f.close()
def transcription(ctx, text_direction, scale, bw, maxcolseps, black_colseps,
font, font_style, prefill, pad, lines, output, images):
"""
Creates transcription environments for ground truth generation.
"""
from PIL import Image
from kraken import rpred
from kraken import pageseg
from kraken import transcribe
from kraken import binarization
from kraken.lib import models
from kraken.lib.util import is_bitonal
ti = transcribe.TranscriptionInterface(font, font_style)
if len(images) > 1 and lines:
raise click.UsageError(
'--lines option is incompatible with multiple image files')
if prefill:
logger.info('Loading model {}'.format(prefill))
message('Loading RNN', nl=False)
prefill = models.load_any(prefill)
message('\u2713', fg='green')
with log.progressbar(images, label='Reading images') as bar:
for fp in bar:
logger.info('Reading {}'.format(fp.name))
im = Image.open(fp)
if im.mode not in ['1', 'L', 'P', 'RGB']:
logger.warning(
'Input {} is in {} color mode. Converting to RGB'.format(
fp.name, im.mode))
im = im.convert('RGB')
logger.info('Binarizing page')
im_bin = binarization.nlbin(im)
im_bin = im_bin.convert('1')
logger.info('Segmenting page')
if not lines:
res = pageseg.segment(im_bin,
text_direction,
scale,
maxcolseps,
black_colseps,
pad=pad)
else:
with open_file(lines, 'r') as fp:
try:
fp = cast(IO[Any], fp)
res = json.load(fp)
except ValueError as e:
raise click.UsageError(
'{} invalid segmentation: {}'.format(
lines, str(e)))
if prefill:
it = rpred.rpred(prefill, im_bin, res)
preds = []
logger.info('Recognizing')
for pred in it:
logger.debug('{}'.format(pred.prediction))
preds.append(pred)
ti.add_page(im, res, records=preds)
else:
ti.add_page(im, res)
fp.close()
logger.info('Writing transcription to {}'.format(output.name))
message('Writing output', nl=False)
ti.write(output)
message('\u2713', fg='green')