#!/usr/bin/env python3 # -*-coding:UTF-8 -* """ The OcrExtractor Module ====================== """ ################################## # Import External packages ################################## import os import sys sys.path.append(os.environ['AIL_BIN']) ################################## # Import Project packages ################################## from modules.abstract_module import AbstractModule from lib.ConfigLoader import ConfigLoader from lib import chats_viewer from lib.objects import Messages from lib.objects import Ocrs # Default to eng def get_model_languages(obj, add_en=True): if add_en: model_languages = {'en'} else: model_languages = set() ob = obj.get_first_correlation('message') if ob: message = Messages.Message(ob.split(':', 2)[-1]) lang = message.get_language() if lang: model_languages.add(lang) return model_languages ob = obj.get_first_correlation('chat-subchannel') if ob: ob = chats_viewer.get_obj_chat_from_global_id(ob) lang = ob.get_main_language() if lang: model_languages.add(lang) return model_languages ob = obj.get_first_correlation('chat') if ob: ob = chats_viewer.get_obj_chat_from_global_id(ob) lang = ob.get_main_language() if lang: model_languages.add(lang) return model_languages return model_languages # TODO thread class OcrExtractor(AbstractModule): """ OcrExtractor for AIL framework """ def __init__(self): super(OcrExtractor, self).__init__() # Waiting time in seconds between to message processed self.pending_seconds = 1 config_loader = ConfigLoader() self.r_cache = config_loader.get_redis_conn("Redis_Cache") # Send module state to logs self.logger.info(f'Module {self.module_name} initialized') def is_cached(self): return self.r_cache.exists(f'ocr:no:{self.obj.id}') def add_to_cache(self): self.r_cache.setex(f'ocr:no:{self.obj.id}', 86400, 0) def compute(self, message): image = self.get_obj() print(image.id) ocr = Ocrs.Ocr(image.id) if self.is_cached(): return None if not ocr.exists(): path = image.get_filepath() languages = get_model_languages(image) print(languages) texts = Ocrs.extract_text(path, languages) if texts: print('create') ocr = Ocrs.create(image.id, texts) self.add_message_to_queue(ocr) # Save in cache else: print('no text detected') self.add_to_cache() else: print('update correlation') ocr.update_correlation() if __name__ == '__main__': module = OcrExtractor() module.run()