import os import sys import cv2 as cv import numpy as np import requests import torch import imageWorking import neuralNetwork import ontologyWorking url = 'http://kb.athene.tech/api/1.0/ontology/' img_path = 'data' img_size = (1280, 720) def analyse_file(uid, image_path): if ontologyWorking.is_ontology_exists(uid, url): raise Exception(f'Онтология с uid {uid} не существует') if not os.path.isfile(image_path): raise Exception(f'Изображение {image_path} не существует') model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) model.names = neuralNetwork.rename_entity(model.names) list_sqwrl = ontologyWorking.get_list_sqwrl(uid, url) results = model(imageWorking.get_image_as_array(image_path)) object_properties = list() data_properties = list() for i, res in enumerate(results.pred): results_ndarray = np.array(res) request = ontologyWorking.get_request_data(model.names, results_ndarray) object_properties += request[0] data_properties += request[1] data = { 'data': { 'objectPropertyAssertions': object_properties, 'dataPropertyAssertions': data_properties } } result = { 'QueryGetNotEmpty': '', 'QueryGetCheck': '', 'QueryGetEmpty': '' } for sqwrl in list_sqwrl['response']: response = requests.post(url + f'{uid}/query/{sqwrl["name"]}', json=data).json() if response['error']: raise Exception(response['error']) result[sqwrl['name']] = [resultSQWRL['name']['value'] for resultSQWRL in response['response']['rows']] print(f'Запрос {sqwrl["name"]} выполнен') print() print('Результаты') if result['QueryGetNotEmpty']: print('Аудитория занята') elif result['QueryGetCheck']: print('Аудиторию необходимо проверить') elif result['QueryGetEmpty']: print('Аудитория пустая') else: print('Неизвестное состояние') # Вывод изображения cv.imshow('result', results.render()[0][:, :, ::-1]) cv.waitKey(0) cv.destroyAllWindows() if __name__ == '__main__': if len(sys.argv) != 3: print(f'Запуск: {sys.argv[0]} ') exit(1) analyse_file(sys.argv[1], sys.argv[2])