|
|
|
from __future__ import absolute_import
|
|
|
|
|
|
|
|
import os
|
|
|
|
from glob import glob
|
|
|
|
|
|
|
|
import numpy as np
|
|
|
|
from calamari_ocr.ocr import MultiPredictor
|
|
|
|
from calamari_ocr.ocr.voting import voter_from_proto
|
|
|
|
from calamari_ocr.proto import VoterParams
|
|
|
|
from ocrd import Processor
|
|
|
|
from ocrd_modelfactory import page_from_file
|
|
|
|
from ocrd_models.ocrd_page import (
|
|
|
|
LabelType, LabelsType,
|
|
|
|
MetadataItemType,
|
|
|
|
TextEquivType,
|
|
|
|
to_xml
|
|
|
|
)
|
|
|
|
from ocrd_utils import getLogger, concat_padded, MIMETYPE_PAGE
|
|
|
|
|
|
|
|
from ocrd_calamari.config import OCRD_TOOL, TF_CPP_MIN_LOG_LEVEL
|
|
|
|
|
|
|
|
TOOL = 'ocrd-calamari-recognize'
|
|
|
|
log = getLogger('processor.CalamariRecognize')
|
|
|
|
|
|
|
|
|
|
|
|
class CalamariRecognize(Processor):
|
|
|
|
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
|
|
kwargs['ocrd_tool'] = OCRD_TOOL['tools'][TOOL]
|
|
|
|
kwargs['version'] = OCRD_TOOL['version']
|
|
|
|
super(CalamariRecognize, self).__init__(*args, **kwargs)
|
|
|
|
|
|
|
|
def _init_calamari(self):
|
|
|
|
os.environ['TF_CPP_MIN_LOG_LEVEL'] = TF_CPP_MIN_LOG_LEVEL
|
|
|
|
|
|
|
|
checkpoints = glob(self.parameter['checkpoint'])
|
|
|
|
self.predictor = MultiPredictor(checkpoints=checkpoints)
|
|
|
|
|
|
|
|
voter_params = VoterParams()
|
|
|
|
voter_params.type = VoterParams.Type.Value(self.parameter['voter'].upper())
|
|
|
|
self.voter = voter_from_proto(voter_params)
|
|
|
|
|
|
|
|
def _make_file_id(self, input_file, n):
|
|
|
|
file_id = input_file.ID.replace(self.input_file_grp, self.output_file_grp)
|
|
|
|
if file_id == input_file.ID:
|
|
|
|
file_id = concat_padded(self.output_file_grp, n)
|
|
|
|
return file_id
|
|
|
|
|
|
|
|
def process(self):
|
|
|
|
"""
|
|
|
|
Performs the recognition.
|
|
|
|
"""
|
|
|
|
|
|
|
|
self._init_calamari()
|
|
|
|
|
|
|
|
for (n, input_file) in enumerate(self.input_files):
|
|
|
|
page_id = input_file.pageId or input_file.ID
|
|
|
|
log.info("INPUT FILE %i / %s", n, page_id)
|
|
|
|
pcgts = page_from_file(self.workspace.download_file(input_file))
|
|
|
|
|
|
|
|
page = pcgts.get_Page()
|
|
|
|
page_image, page_xywh, page_image_info = self.workspace.image_from_page(page, page_id)
|
|
|
|
|
|
|
|
for region in pcgts.get_Page().get_TextRegion():
|
|
|
|
region_image, region_xywh = self.workspace.image_from_segment(region, page_image, page_xywh)
|
|
|
|
|
|
|
|
textlines = region.get_TextLine()
|
|
|
|
log.info("About to recognize %i lines of region '%s'", len(textlines), region.id)
|
|
|
|
for (line_no, line) in enumerate(textlines):
|
|
|
|
log.debug("Recognizing line '%s' in region '%s'", line_no, region.id)
|
|
|
|
|
|
|
|
line_image, line_xywh = self.workspace.image_from_segment(line, region_image, region_xywh)
|
|
|
|
line_image_np = np.array(line_image, dtype=np.uint8)
|
|
|
|
|
|
|
|
raw_results = list(self.predictor.predict_raw([line_image_np], progress_bar=False))[0]
|
|
|
|
for i, p in enumerate(raw_results):
|
|
|
|
p.prediction.id = "fold_{}".format(i)
|
|
|
|
|
|
|
|
prediction = self.voter.vote_prediction_result(raw_results)
|
|
|
|
prediction.id = "voted"
|
|
|
|
|
|
|
|
line_text = prediction.sentence
|
|
|
|
line_conf = prediction.avg_char_probability
|
|
|
|
|
|
|
|
if line.get_TextEquiv():
|
|
|
|
log.warning("Line '%s' already contained text results", line.id)
|
|
|
|
line.set_TextEquiv([TextEquivType(Unicode=line_text, conf=line_conf)])
|
|
|
|
|
|
|
|
if line.get_Word():
|
|
|
|
log.warning("Line '%s' already contained word segmentation", line.id)
|
|
|
|
line.set_Word([])
|
|
|
|
|
|
|
|
_page_update_higher_textequiv_levels('line', pcgts)
|
|
|
|
|
|
|
|
|
|
|
|
# Add metadata about this operation and its runtime parameters:
|
|
|
|
metadata = pcgts.get_Metadata() # ensured by from_file()
|
|
|
|
metadata.add_MetadataItem(
|
|
|
|
MetadataItemType(type_="processingStep",
|
|
|
|
name=self.ocrd_tool['steps'][0],
|
|
|
|
value=TOOL,
|
|
|
|
Labels=[LabelsType(
|
|
|
|
externalModel="ocrd-tool",
|
|
|
|
externalId="parameters",
|
|
|
|
Label=[LabelType(type_=name, value=self.parameter[name])
|
|
|
|
for name in self.parameter.keys()])]))
|
|
|
|
|
|
|
|
|
|
|
|
file_id = self._make_file_id(input_file, n)
|
|
|
|
self.workspace.add_file(
|
|
|
|
ID=file_id,
|
|
|
|
file_grp=self.output_file_grp,
|
|
|
|
pageId=input_file.pageId,
|
|
|
|
mimetype=MIMETYPE_PAGE,
|
|
|
|
local_filename=os.path.join(self.output_file_grp, file_id + '.xml'),
|
|
|
|
content=to_xml(pcgts))
|
|
|
|
|
|
|
|
|
|
|
|
# TODO: This is a copy of ocrd_tesserocr's function, and should probably be moved to a ocrd lib
|
|
|
|
def _page_update_higher_textequiv_levels(level, pcgts):
|
|
|
|
"""Update the TextEquivs of all PAGE-XML hierarchy levels above `level` for consistency.
|
|
|
|
|
|
|
|
Starting with the hierarchy level chosen for processing,
|
|
|
|
join all first TextEquiv (by the rules governing the respective level)
|
|
|
|
into TextEquiv of the next higher level, replacing them.
|
|
|
|
"""
|
|
|
|
regions = pcgts.get_Page().get_TextRegion()
|
|
|
|
if level != 'region':
|
|
|
|
for region in regions:
|
|
|
|
lines = region.get_TextLine()
|
|
|
|
if level != 'line':
|
|
|
|
for line in lines:
|
|
|
|
words = line.get_Word()
|
|
|
|
if level != 'word':
|
|
|
|
for word in words:
|
|
|
|
glyphs = word.get_Glyph()
|
|
|
|
word_unicode = u''.join(glyph.get_TextEquiv()[0].Unicode
|
|
|
|
if glyph.get_TextEquiv()
|
|
|
|
else u'' for glyph in glyphs)
|
|
|
|
word.set_TextEquiv(
|
|
|
|
[TextEquivType(Unicode=word_unicode)]) # remove old
|
|
|
|
line_unicode = u' '.join(word.get_TextEquiv()[0].Unicode
|
|
|
|
if word.get_TextEquiv()
|
|
|
|
else u'' for word in words)
|
|
|
|
line.set_TextEquiv(
|
|
|
|
[TextEquivType(Unicode=line_unicode)]) # remove old
|
|
|
|
region_unicode = u'\n'.join(line.get_TextEquiv()[0].Unicode
|
|
|
|
if line.get_TextEquiv()
|
|
|
|
else u'' for line in lines)
|
|
|
|
region.set_TextEquiv(
|
|
|
|
[TextEquivType(Unicode=region_unicode)]) # remove old
|