ocrd_calamari/ocrd_calamari/recognize.py

from __future__ import absolute_import

import os
from glob import glob

import numpy as np
import uniseg.wordbreak
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,
        WordType, GlyphType, CoordsType,
        to_xml
)
from ocrd_utils import getLogger, concat_padded, coordinates_for_segment, points_from_polygon, 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_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"

                    line_text = prediction.sentence
                    line_conf = prediction.avg_char_probability

                    # 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.set_TextEquiv([TextEquivType(Unicode=line_text, conf=line_conf)])

                    # Save word results
                    def unwanted(c):
                        return c == " "

                    word_no = 0
                    i = 0
                    for word_text in uniseg.wordbreak.words(prediction.sentence):
                        print(word_text)
                        word_length = len(word_text)
                        do_not_include = all(unwanted(c) for c in word_text)

                        if not do_not_include:
                            word_positions = prediction.positions[i:i+word_length]
                            word_start = word_positions[0].global_start
                            word_end = word_positions[-1].global_end

                            # XXX Maybe use version in ocrd_tesserocr
                            h = line_image.height
                            polygon = [(word_start, 0), (word_end, 0), (word_end, h), (word_start, h)]
                            points = points_from_polygon(coordinates_for_segment(polygon, None, line_coords))

                            word = WordType(id='%s_word%04d' % (line.id, word_no), Coords=CoordsType(points))
                            word.add_TextEquiv(TextEquivType(Unicode=word_text))

                            for glyph_no, p in enumerate(word_positions):
                                glyph_start = p.global_start
                                glyph_end = p.global_end

                                # XXX Maybe use version in ocrd_tesserocr
                                h = line_image.height
                                polygon = [(glyph_start, 0), (glyph_end, 0), (glyph_end, h), (glyph_start, h)]
                                points = points_from_polygon(coordinates_for_segment(polygon, None, line_coords))

                                glyph = GlyphType(id='%s_glyph%04d' % (word.id, glyph_no), Coords=CoordsType(points))

                                chars = sorted(p.chars, key=lambda k: k.probability, reverse=True)
                                for index, char in enumerate(chars):
                                    if char.char:
                                        glyph.add_TextEquiv(TextEquivType(Unicode=char.char, index=index, conf=char.probability))
                                    # XXX Note that omission probabilities are not normalized?!
                                word.add_Glyph(glyph)

                            line.add_Word(word)


                        i += word_length
                        word_no += 1


            _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

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