Refactor OCR backend class to be file object based and use images from document page not the actual file. Use pytesseract instead of calling the CLI directly.

mayan/apps/ocr/classes.py

@@ -0,0 +1,50 @@
+from __future__ import unicode_literals
+
+import logging
+import os
+import tempfile
+
+import sh
+
+from django.utils.module_loading import import_string
+from django.utils.translation import ugettext_lazy as _
+
+from common.settings import TEMPORARY_DIRECTORY
+from common.utils import fs_cleanup
+from converter import converter_class
+from documents.models import DocumentPage
+
+from .exceptions import UnpaperError
+from .literals import (
+    DEFAULT_OCR_FILE_EXTENSION, DEFAULT_OCR_FILE_FORMAT, UNPAPER_FILE_FORMAT
+)
+from .parsers import parse_document_page
+from .parsers.exceptions import ParserError, ParserUnknownFile
+from .settings import UNPAPER_PATH
+
+logger = logging.getLogger(__name__)
+
+
+class OCRBackendBase(object):
+    def process_document_version(self, document_version):
+        logger.info('Starting OCR for document version: %s', document_version)
+        logger.debug('document version: %d', document_version.pk)
+
+        language = document_version.document.language
+
+        for page in document_version.pages.all():
+            image = page.get_image()
+            logger.info('Processing page: %d', page.page_number)
+            page.content = self.execute(file_object=image, language=language)
+            page.save()
+            image.close()
+            logger.info('Finished processing page: %d', page.page_number)
+
+    def execute(self, file_object, language=None, transformations=None):
+        if not transformations:
+            transformations = []
+
+        self.converter = converter_class(file_object=file_object)
+
+        for transformation in transformations:
+            self.converter.transform(transformation=transformation)