def analyzeDocumentWithTemplate(self, cr, uid, documentId, templateId, context):
# Whether templateId is valid or not
# Remove previous properties
ids = self.pool.get('nan.document.property').search( cr, uid, [('document_id','=',documentId)], context=context )
self.pool.get('nan.document.property').unlink( cr, uid, ids, context )
if templateId:
template = self.pool.get('nan.template').getTemplateFromId( cr, uid, templateId, context )
documents = self.read(cr, uid, [documentId], context=context)
if not documents:
return
document = documents[0]
fp, image = tempfile.mkstemp()
fp = os.fdopen( fp, 'wb+' )
try:
fp.write( base64.decodestring( document['datas'] ) )
finally:
fp.close()
recognizer = Recognizer()
recognizer.recognize( QImage( image ) )
doc = recognizer.extractWithTemplate( image, template )
for box in doc.boxes:
obj.create(cr, uid, {
'name': box.templateBox.name,
'value': box.text,
'document_id': document['id'],
'template_box_id': box.templateBox.id
}, context)
self.executeAttachs( cr, uid, [documentId], context )
self.executeActions( cr, uid, [documentId], True, context )
def scanDocumentWithTemplate(self, cr, uid, documentId, templateId):
# Whether templateId is valid or not
# Remove previous properties
obj = self.pool.get('nan.document.property')
ids = obj.search(cr, uid, [('document', '=', documentId)])
obj.unlink(cr, uid, ids)
if templateId:
# Initialize Ocr System (Gamera)
initOcrSystem()
template = self.pool.get('nan.template').getTemplateFromId(
cr, uid, templateId)
documents = self.read(cr, uid, [documentId])
if not documents:
return
document = documents[0]
fp, image = tempfile.mkstemp()
fp = os.fdopen(fp, 'wb+')
fp.write(base64.decodestring(document['datas']))
fp.close()
recognizer = Recognizer()
recognizer.recognize(QImage(image))
doc = recognizer.extractWithTemplate(image, template)
for box in doc.boxes:
obj.create(
cr, uid, {
'name': box.templateBox.name,
'value': box.text,
'document': document['id'],
'template_box': box.templateBox.id
})
self.executeAttachs(cr, uid, [documentId])
self.executeActions(cr, uid, [documentId], True)
cr.commit()
def scan_document(self, cr, uid, imageIds, notify=False):
print "Scan_documentcalled"
# Load templates into 'templates' list
templates = self.pool.get('nan.template').getAllTemplates(cr, uid)
# Initialize Ocr System (Gamera)
initOcrSystem()
recognizer = Recognizer()
# Iterate over all images and try to find the most similar template
for document in self.browse(cr, uid, imageIds):
if document.state not in ('pending', 'scanning'):
continue
fp, image = tempfile.mkstemp()
fp = os.fdopen(fp, 'wb+')
fp.write(base64.decodestring(document.datas))
fp.close()
recognizer.recognize(QImage(image))
result = recognizer.findMatchingTemplateByOffset(templates)
template = result['template']
doc = result['document']
if not template:
print "No template found for document %s." % document.name
else:
print "The best template found for document %s is %s." % (
document.name, template.name)
if template:
template_id = template.id
else:
template_id = False
self.write(cr, uid, [document.id], {
'template': template_id,
'state': 'scanned'
})
if doc:
obj = self.pool.get('nan.document.property')
for box in doc.boxes:
obj.create(
cr, uid, {
'name': box.templateBox.name,
'value': box.text,
'document': document.id,
'template_box': box.templateBox.id
})
if notify:
self.pool.get('res.request').create(
cr, uid, {
'act_from': uid,
'act_to': uid,
'name': 'Finished scanning document',
'body':
'The auto_attach system has finished scanning the document you requested. A reference to the document can be found in field Document Ref 1.',
'ref_doc1': 'nan.document,%d' % document.id,
})
self.executeAttachs(cr, uid, imageIds)
self.executeActions(cr, uid, imageIds, True)
cr.commit()
def analyze_document(self, cr, uid, imageIds, context=None):
# Load templates into 'templates' list
templates = self.pool.get('nan.template').getAllTemplates( cr, uid, context )
templatesWithAnalysis = [x for x in templates if x.analysisFunction]
templatesWithoutAnalysis = [x for x in templates if not x.analysisFunction]
# Search what recognizers are used so we do not execute unnecessary processes.
recognizers = set()
for template in templates:
for box in template.boxes:
recognizers.add( box.recognizer )
recognizers = list(recognizers)
recognizer = Recognizer()
# Iterate over all images and try to find the most similar template
for document in self.browse(cr, uid, imageIds, context):
if document.state not in ('pending','analyzing'):
continue
if not document.datas:
continue
fp, image = tempfile.mkstemp()
fp = os.fdopen( fp, 'wb+' )
try:
fp.write( base64.decodestring(document.datas) )
finally:
fp.close()
recognizer.recognize( QImage( image ), recognizers )
template = False
doc = False
for template in templatesWithAnalysis:
function = re.sub( ' *', '', template.analysisFunction )
if function.endswith('()'):
function = function[:-2]
doc = eval( 'self.%s(cr, uid, document, template, recognizer, context)' % function )
if doc:
break
if not doc:
result = recognizer.findMatchingTemplateByOffset( templatesWithoutAnalysis )
template = result['template']
doc = result['document']
if not template:
print("No template found for document %s." % document.name)
else:
print("The best template found for document %s is %s." % (document.name, template.name))
if template:
template_id = template.id
else:
template_id = False
self.write(cr, uid, [document.id], {
'template_id': template_id,
'state': 'analyzed'
}, context=context)
if doc:
for box in doc.boxes:
self.pool.get('nan.document.property').create(cr, uid, {
'name': box.name,
'value': box.text,
'document_id': document.id,
'template_box_id': box.templateBox and box.templateBox.id or False
}, context)
if document.state == 'analyzing':
self.pool.get('res.request').create( cr, uid, {
'act_from': uid,
'act_to': uid,
'name': 'Finished analyzing document',
'body': 'The auto_attach system has finished analyzing the document you requested. A reference to the document can be found in field Document Ref 1.',
'ref_doc1': 'nan.document,%d' % document.id,
}, context)
self.executeAttachs( cr, uid, imageIds, context )
self.executeActions( cr, uid, imageIds, True, context )
