from __future__ import absolute_import import logging import os import subprocess from django.utils.translation import ugettext as _ from common.conf.settings import TEMPORARY_DIRECTORY from common.utils import fs_cleanup from converter.api import convert from documents.models import DocumentPage from .conf.settings import UNPAPER_PATH, LANGUAGE from .exceptions import UnpaperError from .literals import (DEFAULT_OCR_FILE_FORMAT, UNPAPER_FILE_FORMAT, DEFAULT_OCR_FILE_EXTENSION) from .parsers import parse_document_page from .parsers.exceptions import ParserError, ParserUnknownFile from .runtime import language_backend, ocr_backend logger = logging.getLogger(__name__) def do_document_ocr(queue_document): """ Try first to extract text from document pages using the registered parser, if the parser fails or if there is no parser registered for the document mimetype do a visual OCR by calling the corresponding OCR backend """ for document_page in queue_document.document.pages.all(): try: # Try to extract text by means of a parser parse_document_page(document_page) except (ParserError, ParserUnknownFile): # Fall back to doing visual OCR document_filepath = document_page.document.get_image_cache_name(page=document_page.page_number, version=document_page.document_version.pk) unpaper_output_filename = u'%s_unpaper_out_page_%s%s%s' % (document_page.document.uuid, document_page.page_number, os.extsep, UNPAPER_FILE_FORMAT) unpaper_output_filepath = os.path.join(TEMPORARY_DIRECTORY, unpaper_output_filename) logger.debug('document_filepath: %s' % document_filepath) unpaper_input = convert(document_filepath, file_format=UNPAPER_FILE_FORMAT) logger.debug('unpaper_input: %s' % unpaper_input) execute_unpaper(input_filepath=unpaper_input, output_filepath=unpaper_output_filepath) logger.debug('unpaper_output_filepath: %s' % unpaper_output_filepath) # from PIL import Image, ImageOps # im = Image.open(document_filepath) # #if im.mode=='RGBA': # # im=im.convert('RGB') # #im = im.convert('L') # im = ImageOps.grayscale(im) # im.save(unpaper_output_filepath) # Convert to TIFF pre_ocr_filepath = convert(input_filepath=unpaper_output_filepath, file_format=DEFAULT_OCR_FILE_FORMAT) logger.debug('pre_ocr_filepath: %s' % pre_ocr_filepath) # Tesseract needs an explicit file extension pre_ocr_filepath_w_ext = os.extsep.join([pre_ocr_filepath, DEFAULT_OCR_FILE_EXTENSION]) logger.debug('pre_ocr_filepath_w_ext: %s' % pre_ocr_filepath_w_ext) os.rename(pre_ocr_filepath, pre_ocr_filepath_w_ext) try: ocr_text = ocr_backend.execute(pre_ocr_filepath_w_ext, LANGUAGE) document_page.content = ocr_cleanup(ocr_text) document_page.page_label = _(u'Text from OCR') document_page.save() finally: fs_cleanup(pre_ocr_filepath_w_ext) fs_cleanup(unpaper_input) fs_cleanup(document_filepath) fs_cleanup(unpaper_output_filepath) def ocr_cleanup(text): """ Cleanup the OCR's output passing it thru the selected language's cleanup filter """ output = [] for line in text.splitlines(): line = line.strip() for word in line.split(): if language_backend: result = language_backend.check_word(word) else: result = word if result: output.append(result) output.append(u'\n') return u' '.join(output) def clean_pages(): """ Tool that executes the OCR cleanup code on all of the existing documents """ for page in DocumentPage.objects.all(): if page.content: page.content = ocr_cleanup(page.content) page.save() def execute_unpaper(input_filepath, output_filepath): """ Executes the program unpaper using subprocess's Popen """ command = [] command.append(UNPAPER_PATH) command.append(u'--overwrite') command.append(u'--no-multi-pages') command.append(input_filepath) command.append(output_filepath) proc = subprocess.Popen(command, close_fds=True, stderr=subprocess.PIPE) return_code = proc.wait() if return_code != 0: raise UnpaperError(proc.stderr.readline())