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64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
import numpy as np
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import requests
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def is_ontology_exists(uid: str, url: str) -> bool:
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'''
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Проверяет, существует ли онтология в сервисе.
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@param uid: УИД онтологии.
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@param url: Базовый URL сервиса.
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'''
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list_ontologies = requests.get(url).json()['response']['items']
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for onto in list_ontologies:
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if onto['uid'] == uid:
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return True
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return False
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def rename_entity(list_names: dict) -> dict:
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'''
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Нормализация названий объектов.
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@param list_names: Список названий объектов.
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'''
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temp_list = list()
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for entity in list_names.values():
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entity: str
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temp_list.append(entity.title().replace(' ', ''))
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return temp_list
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def get_entity_square(width: float, height: float) -> float:
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'''
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Получение площади занимаемой области.
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@param width: Ширина области в px.
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@param height: Высота области в px.
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'''
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return abs(width * height)
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def get_request_data(entities: dict, objects: np.ndarray, confs: np.ndarray, boxes: np.ndarray) -> tuple[list, list]:
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'''
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Формирование данных для сервиса онтологий.
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@param entities: Список имён объектов.
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@param results_ndarray: Результат распознавания объектов.
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'''
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classroom = 'classroom'
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entities = rename_entity(entities)
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object_properties = list()
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data_properties = list()
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for entity_idx, entity in enumerate(entities):
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if (entity_idx in objects):
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object_properties.append({'domain': entity, 'property': 'locatedIn', 'range': classroom})
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else:
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object_properties.append({'domain': entity, 'property': 'notLocatedIn', 'range': classroom})
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for object_idx, object in enumerate(objects):
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conf = confs[object_idx]
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box = boxes[object_idx]
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entity = entities[object.item()]
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data_properties.append({'domain': entity, 'property': 'hasArea', 'value': get_entity_square(float(box[2]), float(box[3]))})
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data_properties.append({'domain': entity, 'property': 'hasConfidence', 'value': float(conf)})
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return object_properties, data_properties
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