Files
mayan-edms/apps/converter/api.py
2011-07-16 01:09:36 -04:00

210 lines
7.4 KiB
Python

import os
import subprocess
import hashlib
from django.utils.importlib import import_module
from django.template.defaultfilters import slugify
from common import TEMPORARY_DIRECTORY
from documents.utils import document_save_to_temp_dir
from converter.conf.settings import UNPAPER_PATH
from converter.conf.settings import OCR_OPTIONS
from converter.conf.settings import UNOCONV_PATH
from converter.exceptions import UnpaperError, OfficeConversionError
from converter.literals import DEFAULT_PAGE_NUMBER, \
DEFAULT_OCR_FILE_FORMAT, QUALITY_DEFAULT, DEFAULT_ZOOM_LEVEL, \
DEFAULT_ROTATION, DEFAULT_FILE_FORMAT, QUALITY_PRINT
from converter import backend
from converter.literals import TRANSFORMATION_CHOICES
from converter.literals import TRANSFORMATION_RESIZE, \
TRANSFORMATION_ROTATE, TRANSFORMATION_DENSITY, \
TRANSFORMATION_ZOOM
from converter.literals import DIMENSION_SEPARATOR
HASH_FUNCTION = lambda x: hashlib.sha256(x).hexdigest()
CONVERTER_OFFICE_FILE_EXTENSIONS = [
u'ods', u'docx', u'doc'
]
def cleanup(filename):
"""
Tries to remove the given filename. Ignores non-existent files
"""
try:
os.remove(filename)
except OSError:
pass
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(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())
def execute_unoconv(input_filepath, arguments=''):
"""
Executes the program unoconv using subprocess's Popen
"""
command = []
command.append(UNOCONV_PATH)
command.extend(unicode(arguments).split())
command.append(input_filepath)
proc = subprocess.Popen(command, close_fds=True, stderr=subprocess.PIPE)
return_code = proc.wait()
if return_code != 0:
raise OfficeConversionError(proc.stderr.readline())
def cache_cleanup(input_filepath, *args, **kwargs):
try:
os.remove(create_image_cache_filename(input_filepath, *args, **kwargs))
except OSError:
pass
def create_image_cache_filename(input_filepath, *args, **kwargs):
if input_filepath:
hash_value = HASH_FUNCTION(u''.join([input_filepath, unicode(args), unicode(kwargs)]))
return os.path.join(TEMPORARY_DIRECTORY, hash_value)
else:
return None
def convert_office_document(input_filepath):
if os.path.exists(UNOCONV_PATH):
execute_unoconv(input_filepath, arguments='-f pdf')
return input_filepath + u'.pdf'
return None
def convert_document(document, *args, **kwargs):
document_filepath = create_image_cache_filename(document.checksum, *args, **kwargs)
if os.path.exists(document_filepath):
return document_filepath
return convert(document_save_to_temp_dir(document, document.checksum), *args, **kwargs)
def convert(input_filepath, cleanup_files=True, *args, **kwargs):
size = kwargs.get('size')
file_format = kwargs.get('file_format', DEFAULT_FILE_FORMAT)
zoom = kwargs.get('zoom', DEFAULT_ZOOM_LEVEL)
rotation = kwargs.get('rotation', DEFAULT_ROTATION)
page = kwargs.get('page', DEFAULT_PAGE_NUMBER)
quality = kwargs.get('quality', QUALITY_DEFAULT)
transformations = kwargs.get('transformations', [])
unoconv_output = None
output_filepath = create_image_cache_filename(input_filepath, *args, **kwargs)
if os.path.exists(output_filepath):
return output_filepath
path, extension = os.path.splitext(input_filepath)
if extension[1:].lower() in CONVERTER_OFFICE_FILE_EXTENSIONS:
result = convert_office_document(input_filepath)
if result:
unoconv_output = result
input_filepath = result
extra_options = u''
transformations.append(
{
'transformation': TRANSFORMATION_RESIZE,
'arguments': dict(zip([u'width', u'height'], size.split(DIMENSION_SEPARATOR)))
}
)
if zoom != 100:
transformations.append(
{
'transformation': TRANSFORMATION_ZOOM,
'arguments': {'percent': zoom}
}
)
if rotation != 0 and rotation != 360:
transformations.append(
{
'transformation': TRANSFORMATION_ROTATE,
'arguments': {'degrees': rotation}
}
)
try:
backend.convert_file(input_filepath=input_filepath, output_filepath=output_filepath, quality=quality, transformations=transformations, page=page, file_format=file_format)
finally:
if cleanup_files:
cleanup(input_filepath)
if unoconv_output:
cleanup(unoconv_output)
return output_filepath
def get_page_count(input_filepath):
return backend.get_page_count(input_filepath)
def get_document_dimensions(document, *args, **kwargs):
document_filepath = create_image_cache_filename(document.checksum, *args, **kwargs)
if os.path.exists(document_filepath):
options = [u'-format', u'%w %h']
return [int(dimension) for dimension in backend.identify_file(unicode(document_filepath), options).split()]
else:
return [0, 0]
def convert_document_for_ocr(document, page=DEFAULT_PAGE_NUMBER, file_format=DEFAULT_OCR_FILE_FORMAT):
#Extract document file
input_filepath = document_save_to_temp_dir(document, document.uuid)
#Convert for OCR
temp_filename, separator = os.path.splitext(os.path.basename(input_filepath))
temp_path = os.path.join(TEMPORARY_DIRECTORY, temp_filename)
transformation_output_file = u'%s_trans%s%s%s' % (temp_path, page, os.extsep, file_format)
unpaper_input_file = u'%s_unpaper_in%s%spnm' % (temp_path, page, os.extsep)
unpaper_output_file = u'%s_unpaper_out%s%spnm' % (temp_path, page, os.extsep)
convert_output_file = u'%s_ocr%s%s%s' % (temp_path, page, os.extsep, file_format)
try:
document_page = document.documentpage_set.get(page_number=page)
transformation_string, warnings = document_page.get_transformation_string()
#Apply default transformations
backend.convert_file(input_filepath=input_filepath, page=page, quality=QUALITY_HIGH, arguments=transformation_string, output_filepath=transformation_output_file)
#Do OCR operations
backend.convert_file(input_filepath=transformation_output_file, arguments=OCR_OPTIONS, output_filepath=unpaper_input_file)
# Process by unpaper
execute_unpaper(input_filepath=unpaper_input_file, output_filepath=unpaper_output_file)
# Convert to tif
backend.convert_file(input_filepath=unpaper_output_file, output_filepath=convert_output_file)
finally:
cleanup(transformation_output_file)
cleanup(unpaper_input_file)
cleanup(unpaper_output_file)
return convert_output_file
def get_available_transformations_choices():
result = []
for transformation in backend.get_available_transformations():
transformation_template = u'%s %s' % (TRANSFORMATION_CHOICES[transformation]['label'], u','.join(['<%s>' % argument['name'] if argument['required'] else '[%s]' % argument['name'] for argument in TRANSFORMATION_CHOICES[transformation]['arguments']]))
result.append([transformation, transformation_template])
return result