def test_alternative_image_additions():
pcgts = PcGtsType(pcGtsId="foo")
assert pcgts.pcGtsId == 'foo'
# act
# Page/AlternativeImage
page = PageType()
pcgts.set_Page(page)
page.add_AlternativeImage(AlternativeImageType())
# TextRegion/AlternativeImage
region = TextRegionType()
page.add_TextRegion(region)
region.add_AlternativeImage(AlternativeImageType())
# TextLine/AlternativeImage
line = TextLineType()
region.add_TextLine(line)
line.add_AlternativeImage(AlternativeImageType())
# Word/AlternativeImage
word = WordType()
line.add_Word(word)
word.add_AlternativeImage(AlternativeImageType())
# Glyph/AlternativeImage
glyph = GlyphType()
word.add_Glyph(glyph)
glyph.add_AlternativeImage(AlternativeImageType())
def test_alternativeImage(self):
pcgts = PcGtsType(pcGtsId="foo")
self.assertEqual(pcgts.pcGtsId, 'foo')
# Page/AlternativeImage
page = PageType()
pcgts.set_Page(page)
page.add_AlternativeImage(AlternativeImageType())
# TextRegion/AlternativeImage
region = TextRegionType()
page.add_TextRegion(region)
region.add_AlternativeImage(AlternativeImageType())
# TextLine/AlternativeImage
line = TextLineType()
region.add_TextLine(line)
line.add_AlternativeImage(AlternativeImageType())
# Word/AlternativeImage
word = WordType()
line.add_Word(word)
word.add_AlternativeImage(AlternativeImageType())
# Glyph/AlternativeImage
glyph = GlyphType()
word.add_Glyph(glyph)
glyph.add_AlternativeImage(AlternativeImageType())
def process(self):
"""
Performs the recognition.
"""
assert_file_grp_cardinality(self.input_file_grp, 1)
assert_file_grp_cardinality(self.output_file_grp, 1)
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.id,
region.id)
line_image, line_coords = 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"
# Build line text on our own
#
# Calamari does whitespace post-processing on prediction.sentence, while it does not do the same
# on prediction.positions. Do it on our own to have consistency.
#
# XXX Check Calamari's built-in post-processing on prediction.sentence
def _sort_chars(p):
"""Filter and sort chars of prediction p"""
chars = p.chars
chars = [
c for c in chars if c.char
] # XXX Note that omission probabilities are not normalized?!
chars = [
c for c in chars if c.probability >=
self.parameter['glyph_conf_cutoff']
]
chars = sorted(chars,
key=lambda k: k.probability,
reverse=True)
return chars
def _drop_leading_spaces(positions):
return list(
itertools.dropwhile(
lambda p: _sort_chars(p)[0].char == " ",
positions))
def _drop_trailing_spaces(positions):
return list(
reversed(_drop_leading_spaces(
reversed(positions))))
def _drop_double_spaces(positions):
def _drop_double_spaces_generator(positions):
last_was_space = False
for p in positions:
if p.chars[0].char == " ":
if not last_was_space:
yield p
last_was_space = True
else:
yield p
last_was_space = False
return list(_drop_double_spaces_generator(positions))
positions = prediction.positions
positions = _drop_leading_spaces(positions)
positions = _drop_trailing_spaces(positions)
positions = _drop_double_spaces(positions)
positions = list(positions)
line_text = ''.join(
_sort_chars(p)[0].char for p in positions)
if line_text != prediction.sentence:
log.warning(
"Our own line text is not the same as Calamari's: '%s' != '%s'",
line_text, prediction.sentence)
# Delete existing results
if line.get_TextEquiv():
log.warning("Line '%s' already contained text results",
line.id)
line.set_TextEquiv([])
if line.get_Word():
log.warning(
"Line '%s' already contained word segmentation",
line.id)
line.set_Word([])
# Save line results
line_conf = prediction.avg_char_probability
line.set_TextEquiv(
[TextEquivType(Unicode=line_text, conf=line_conf)])
# Save word results
#
# Calamari OCR does not provide word positions, so we infer word positions from a. text segmentation
# and b. the glyph positions. This is necessary because the PAGE XML format enforces a strict
# hierarchy of lines > words > glyphs.
def _words(s):
"""Split words based on spaces and include spaces as 'words'"""
spaces = None
word = ''
for c in s:
if c == ' ' and spaces is True:
word += c
elif c != ' ' and spaces is False:
word += c
else:
if word:
yield word
word = c
spaces = (c == ' ')
yield word
if self.parameter['textequiv_level'] in ['word', 'glyph']:
word_no = 0
i = 0
for word_text in _words(line_text):
word_length = len(word_text)
if not all(c == ' ' for c in word_text):
word_positions = positions[i:i + word_length]
word_start = word_positions[0].global_start
word_end = word_positions[-1].global_end
polygon = polygon_from_x0y0x1y1([
word_start, 0, word_end, line_image.height
])
points = points_from_polygon(
coordinates_for_segment(
polygon, None, line_coords))
# XXX Crop to line polygon?
word = WordType(id='%s_word%04d' %
(line.id, word_no),
Coords=CoordsType(points))
word.add_TextEquiv(
TextEquivType(Unicode=word_text))
if self.parameter[
'textequiv_level'] == 'glyph':
for glyph_no, p in enumerate(
word_positions):
glyph_start = p.global_start
glyph_end = p.global_end
polygon = polygon_from_x0y0x1y1([
glyph_start, 0, glyph_end,
line_image.height
])
points = points_from_polygon(
coordinates_for_segment(
polygon, None, line_coords))
glyph = GlyphType(
id='%s_glyph%04d' %
(word.id, glyph_no),
Coords=CoordsType(points))
# Add predictions (= TextEquivs)
char_index_start = 1 # Must start with 1, see https://ocr-d.github.io/page#multiple-textequivs
for char_index, char in enumerate(
_sort_chars(p),
start=char_index_start):
glyph.add_TextEquiv(
TextEquivType(
Unicode=char.char,
index=char_index,
conf=char.probability))
word.add_Glyph(glyph)
line.add_Word(word)
word_no += 1
i += word_length
_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 = make_file_id(input_file, self.output_file_grp)
pcgts.set_pcGtsId(file_id)
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))
def process(self):
"""
Perform text recognition with Calamari on the workspace.
If ``texequiv_level`` is ``word`` or ``glyph``, then additionally create word / glyph level segments by
splitting at white space characters / glyph boundaries. In the case of ``glyph``, add all alternative character
hypotheses down to ``glyph_conf_cutoff`` confidence threshold.
"""
log = getLogger('processor.CalamariRecognize')
assert_file_grp_cardinality(self.input_file_grp, 1)
assert_file_grp_cardinality(self.output_file_grp, 1)
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_coords, page_image_info = self.workspace.image_from_page(
page, page_id, feature_selector=self.features)
for region in page.get_AllRegions(classes=['Text']):
region_image, region_coords = self.workspace.image_from_segment(
region,
page_image,
page_coords,
feature_selector=self.features)
textlines = region.get_TextLine()
log.info("About to recognize %i lines of region '%s'",
len(textlines), region.id)
line_images_np = []
line_coordss = []
for line in textlines:
log.debug("Recognizing line '%s' in region '%s'", line.id,
region.id)
line_image, line_coords = self.workspace.image_from_segment(
line,
region_image,
region_coords,
feature_selector=self.features)
if ('binarized' not in line_coords['features']
and 'grayscale_normalized'
not in line_coords['features']
and self.network_input_channels == 1):
# We cannot use a feature selector for this since we don't
# know whether the model expects (has been trained on)
# binarized or grayscale images; but raw images are likely
# always inadequate:
log.warning(
"Using raw image for line '%s' in region '%s'",
line.id, region.id)
line_image = line_image if all(line_image.size) else [[0]]
line_image_np = np.array(line_image, dtype=np.uint8)
line_images_np.append(line_image_np)
line_coordss.append(line_coords)
raw_results_all = self.predictor.predict_raw(
line_images_np, progress_bar=False)
for line, line_coords, raw_results in zip(
textlines, line_coordss, raw_results_all):
for i, p in enumerate(raw_results):
p.prediction.id = "fold_{}".format(i)
prediction = self.voter.vote_prediction_result(raw_results)
prediction.id = "voted"
# Build line text on our own
#
# Calamari does whitespace post-processing on prediction.sentence, while it does not do the same
# on prediction.positions. Do it on our own to have consistency.
#
# XXX Check Calamari's built-in post-processing on prediction.sentence
def _sort_chars(p):
"""Filter and sort chars of prediction p"""
chars = p.chars
chars = [
c for c in chars if c.char
] # XXX Note that omission probabilities are not normalized?!
chars = [
c for c in chars if c.probability >=
self.parameter['glyph_conf_cutoff']
]
chars = sorted(chars,
key=lambda k: k.probability,
reverse=True)
return chars
def _drop_leading_spaces(positions):
return list(
itertools.dropwhile(
lambda p: _sort_chars(p)[0].char == " ",
positions))
def _drop_trailing_spaces(positions):
return list(
reversed(_drop_leading_spaces(
reversed(positions))))
def _drop_double_spaces(positions):
def _drop_double_spaces_generator(positions):
last_was_space = False
for p in positions:
if p.chars[0].char == " ":
if not last_was_space:
yield p
last_was_space = True
else:
yield p
last_was_space = False
return list(_drop_double_spaces_generator(positions))
positions = prediction.positions
positions = _drop_leading_spaces(positions)
positions = _drop_trailing_spaces(positions)
positions = _drop_double_spaces(positions)
positions = list(positions)
line_text = ''.join(
_sort_chars(p)[0].char for p in positions)
if line_text != prediction.sentence:
log.warning(
"Our own line text is not the same as Calamari's: '%s' != '%s'",
line_text, prediction.sentence)
# Delete existing results
if line.get_TextEquiv():
log.warning("Line '%s' already contained text results",
line.id)
line.set_TextEquiv([])
if line.get_Word():
log.warning(
"Line '%s' already contained word segmentation",
line.id)
line.set_Word([])
# Save line results
line_conf = prediction.avg_char_probability
line.set_TextEquiv(
[TextEquivType(Unicode=line_text, conf=line_conf)])
# Save word results
#
# Calamari OCR does not provide word positions, so we infer word positions from a. text segmentation
# and b. the glyph positions. This is necessary because the PAGE XML format enforces a strict
# hierarchy of lines > words > glyphs.
def _words(s):
"""Split words based on spaces and include spaces as 'words'"""
spaces = None
word = ''
for c in s:
if c == ' ' and spaces is True:
word += c
elif c != ' ' and spaces is False:
word += c
else:
if word:
yield word
word = c
spaces = (c == ' ')
yield word
if self.parameter['textequiv_level'] in ['word', 'glyph']:
word_no = 0
i = 0
for word_text in _words(line_text):
word_length = len(word_text)
if not all(c == ' ' for c in word_text):
word_positions = positions[i:i + word_length]
word_start = word_positions[0].global_start
word_end = word_positions[-1].global_end
polygon = polygon_from_x0y0x1y1([
word_start, 0, word_end, line_image.height
])
points = points_from_polygon(
coordinates_for_segment(
polygon, None, line_coords))
# XXX Crop to line polygon?
word = WordType(id='%s_word%04d' %
(line.id, word_no),
Coords=CoordsType(points))
word.add_TextEquiv(
TextEquivType(Unicode=word_text))
if self.parameter[
'textequiv_level'] == 'glyph':
for glyph_no, p in enumerate(
word_positions):
glyph_start = p.global_start
glyph_end = p.global_end
polygon = polygon_from_x0y0x1y1([
glyph_start, 0, glyph_end,
line_image.height
])
points = points_from_polygon(
coordinates_for_segment(
polygon, None, line_coords))
glyph = GlyphType(
id='%s_glyph%04d' %
(word.id, glyph_no),
Coords=CoordsType(points))
# Add predictions (= TextEquivs)
char_index_start = 1 # Must start with 1, see https://ocr-d.github.io/page#multiple-textequivs
for char_index, char in enumerate(
_sort_chars(p),
start=char_index_start):
glyph.add_TextEquiv(
TextEquivType(
Unicode=char.char,
index=char_index,
conf=char.probability))
word.add_Glyph(glyph)
line.add_Word(word)
word_no += 1
i += word_length
_page_update_higher_textequiv_levels('line', pcgts)
# Add metadata about this operation and its runtime parameters:
self.add_metadata(pcgts)
file_id = make_file_id(input_file, self.output_file_grp)
pcgts.set_pcGtsId(file_id)
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))
def process_lines(self, textlines, predfiles, fgrp, regionid):
for line in textlines:
for file in predfiles:
if file == '-'.join([fgrp, regionid, line.id]):
self.log.info("Processing text in line '%s'", line.id)
filepath = self.root + '/' + file + '.json'
with open(filepath) as f:
data = json.load(f)
linepred = data['predictions'][0]['sentence']
line_conf = []
line_pos = []
w = ''
word_conf = []
words = []
word_pos = []
positions = data['predictions'][0]['positions']
for i, d in enumerate(positions):
char = d['chars'][0]['char']
char_conf = d['chars'][0]['probability']
char_pos = (d['globalStart'], d['globalEnd'])
if char == ' ':
words.append(w)
w = ''
line_conf.append(word_conf)
word_conf = []
line_pos.append(word_pos)
word_pos = []
else:
w += char
word_conf.append(char_conf)
word_pos.append(char_pos)
if i == len(positions) - 1:
words.append(w)
line_conf.append(word_conf)
line_pos.append(word_pos)
wconfs = [(min(conf) + max(conf)) / 2
for conf in line_conf]
lineconf = (min(wconfs) + max(wconfs)) / 2
line.replace_TextEquiv_at(
0,
TextEquivType(Unicode=linepred,
conf=str(lineconf)))
if self.maxlevel == 'word' or 'glyph':
box = bounding_box(line.get_Coords().points)
line.Word = []
for w_no, w in enumerate(words):
# Coords of word
wordbounding = (line_pos[w_no][0][0],
line_pos[w_no][-1][-1])
word_bbox = [
box[0] + wordbounding[0], box[1],
box[2] + wordbounding[1], box[3]
]
word_id = '%s_word%04d' % (line.id, w_no)
word = WordType(
id=word_id,
Coords=CoordsType(
points_from_x0y0x1y1(word_bbox)))
line.add_Word(word)
word.add_TextEquiv(
TextEquivType(Unicode=w,
conf=str(wconfs[w_no])))
if self.maxlevel == 'glyph':
for glyph_no, g in enumerate(w):
glyphbounding = (
line_pos[w_no][glyph_no][0],
line_pos[w_no][glyph_no][-1])
glyph_bbox = [
box[0] + glyphbounding[0], box[1],
box[2] + glyphbounding[1], box[3]
]
glyph_id = '%s_glyph%04d' % (word.id,
glyph_no)
glyph = GlyphType(
id=glyph_id,
Coords=CoordsType(
points_from_x0y0x1y1(
glyph_bbox)))
word.add_Glyph(glyph)
glyph.add_TextEquiv(
TextEquivType(
Unicode=g,
conf=str(line_conf[w_no]
[glyph_no])))
def process_lines(self, textlines, maxlevel, region_image, region_coords):
edits = 0
lengs = 0
for line in textlines:
line_image, line_coords = self.workspace.image_from_segment(
line, region_image, region_coords)
self.logger.info("Recognizing text in line '%s'", line.id)
if line.get_TextEquiv():
linegt = line.TextEquiv[0].Unicode
else:
linegt = ''
self.logger.debug("GT '%s': '%s'", line.id, linegt)
# remove existing annotation below line level:
line.set_TextEquiv([])
line.set_Word([])
if line_image.size[1] < 16:
self.logger.debug(
"ERROR: bounding box is too narrow at line %s", line.id)
continue
# resize image to 48 pixel height
final_img, scale = resize_keep_ratio(line_image)
# process ocropy:
try:
linepred, clist, rlist, confidlist = recognize(final_img,
self.pad,
self.network,
check=True)
except Exception as err:
self.logger.debug('error processing line "%s": %s', line.id,
err)
continue
self.logger.debug("OCR '%s': '%s'", line.id, linepred)
edits += Levenshtein.distance(linepred, linegt)
lengs += len(linegt)
words = [x.strip() for x in linepred.split(' ') if x.strip()]
word_r_list = [[0]] # r-positions of every glyph in every word
word_conf_list = [[]] # confidences of every glyph in every word
if words != []:
w_no = 0
found_char = False
for i, c in enumerate(clist):
if c != ' ':
found_char = True
word_conf_list[w_no].append(confidlist[i])
word_r_list[w_no].append(rlist[i])
if c == ' ' and found_char:
if i == 0:
word_r_list[0][0] = rlist[i]
elif i + 1 <= len(clist) - 1 and clist[i + 1] != ' ':
word_conf_list.append([])
word_r_list.append([rlist[i]])
w_no += 1
else:
word_conf_list = [[0]]
word_r_list = [[0, line_image.width]]
# conf for each word
wordsconf = [(min(x) + max(x)) / 2 for x in word_conf_list]
# conf for the line
line_conf = (min(wordsconf) + max(wordsconf)) / 2
# line text
line.add_TextEquiv(TextEquivType(Unicode=linepred, conf=line_conf))
if maxlevel in ['word', 'glyph']:
for word_no, word_str in enumerate(words):
word_points = points_from_polygon(
coordinates_for_segment(
np.array(
polygon_from_bbox(
word_r_list[word_no][0] / scale, 0,
word_r_list[word_no][-1] / scale,
0 + line_image.height)), line_image,
line_coords))
word_id = '%s_word%04d' % (line.id, word_no)
word = WordType(id=word_id, Coords=CoordsType(word_points))
line.add_Word(word)
word.add_TextEquiv(
TextEquivType(Unicode=word_str,
conf=wordsconf[word_no]))
if maxlevel == 'glyph':
for glyph_no, glyph_str in enumerate(word_str):
glyph_points = points_from_polygon(
coordinates_for_segment(
np.array(
polygon_from_bbox(
word_r_list[word_no][glyph_no] /
scale, 0,
word_r_list[word_no][glyph_no + 1]
/ scale, 0 + line_image.height)),
line_image, line_coords))
glyph_id = '%s_glyph%04d' % (word.id, glyph_no)
glyph = GlyphType(id=glyph_id,
Coords=CoordsType(glyph_points))
word.add_Glyph(glyph)
glyph.add_TextEquiv(
TextEquivType(
Unicode=glyph_str,
conf=word_conf_list[word_no][glyph_no]))
return edits, lengs