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