Files
mayan-edms/mayan/apps/ocr/api.py
Roberto Rosario c51a3be65a Cleanups
2014-07-01 00:26:19 -04:00

131 lines
4.5 KiB
Python

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())