further cleanup
This commit is contained in:
parent
f9c86261cb
commit
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@ -1,5 +1,6 @@
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from pathlib import Path
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import os
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import logging
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PARAMETERS_DIR = Path('src/parameters')
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AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
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@ -10,3 +11,9 @@ OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
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cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
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OSM_CACHE_DIR = Path(cache_dir_string)
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logger = logging.getLogger(__name__)
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logging.basicConfig(
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level = logging.INFO,
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format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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@ -1,5 +1,5 @@
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import yaml
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import os
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import logging
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import osmnx as ox
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from shapely.geometry import Point, Polygon, LineString, MultiPolygon
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@ -7,182 +7,145 @@ from structs.landmarks import Landmark, LandmarkType
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from structs.preferences import Preferences, Preference
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import constants
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SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
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NATURE = LandmarkType(landmark_type='nature')
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SHOPPING = LandmarkType(landmark_type='shopping')
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ox.config(cache_folder=constants.OSM_CACHE_DIR)
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# Include the json here
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# Create a list of all things to visit given some preferences and a city. Ready for the optimizer
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def generate_landmarks(preferences: Preferences, center_coordinates: tuple[float, float]) :
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with constants.AMENITY_SELECTORS_PATH.open('r') as f:
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amenity_selectors = yaml.safe_load(f)
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with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
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# even though we don't use the parameters here, we already load them to avoid unnecessary io operations
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parameters = yaml.safe_load(f)
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max_distance = parameters['city_bbox_side']
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L = []
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class LandmarkManager:
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logger = logging.getLogger(__name__)
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# List for sightseeing
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if preferences.sightseeing.score != 0:
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score_func = lambda loc, n_tags: int((count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']) * parameters['church_coeff'])
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L1 = get_landmarks(amenity_selectors['sightseeing'], SIGHTSEEING, center_coordinates, max_distance, score_func)
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correct_score(L1, preferences.sightseeing)
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L += L1
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# List for nature
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if preferences.nature.score != 0:
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score_func = lambda loc, n_tags: int((count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']) * parameters['park_coeff'])
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L2 = get_landmarks(amenity_selectors['nature'], NATURE, center_coordinates, max_distance, score_func)
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correct_score(L2, preferences.nature)
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L += L2
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# List for shopping
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if preferences.shopping.score != 0:
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score_func = lambda loc, n_tags: count_elements_within_radius(loc, parameters['radius_close_to']) + n_tags * parameters['tag_coeff']
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L3 = get_landmarks(amenity_selectors['shopping'], SHOPPING, center_coordinates, max_distance, score_func)
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correct_score(L3, preferences.shopping)
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L += L3
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def __init__(self) -> None:
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ox.config(cache_folder=constants.OSM_CACHE_DIR)
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with constants.AMENITY_SELECTORS_PATH.open('r') as f:
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self.amenity_selectors = yaml.safe_load(f)
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with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
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self.parameters = yaml.safe_load(f)
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# max_distance = parameters['city_bbox_side']
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# remove duplicates
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L = list(set(L))
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print(len(L))
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L_constrained = take_most_important(L, parameters['N_important'])
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print(len(L_constrained))
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return L, L_constrained
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def get_landmark_lists(self, preferences: Preferences, center_coordinates: tuple[float, float]) -> tuple[list[Landmark], list[Landmark]]:
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'''
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Generate a list of landmarks based on the preferences of the user and the center (ie. start) coordinates.
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The list is then used by the pathfinding algorithm to generate a path that goes through the most interesting landmarks.
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:param preferences: the preferences specified by the user
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:param center_coordinates: the coordinates of the starting point
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'''
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L = []
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# List for sightseeing
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if preferences.sightseeing.score != 0:
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score_func = lambda loc, n_tags: int((self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']) * self.parameters['church_coeff'])
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L1 = self.fetch_landmarks(self.amenity_selectors['sightseeing'], SIGHTSEEING, center_coordinates, self.parameters['city_bbox_side'], score_func)
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self.correct_score(L1, preferences.sightseeing)
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L += L1
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# List for nature
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if preferences.nature.score != 0:
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score_func = lambda loc, n_tags: int((self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']) * self.parameters['park_coeff'])
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L2 = self.fetch_landmarks(self.amenity_selectors['nature'], NATURE, center_coordinates, self.parameters['city_bbox_side'], score_func)
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self.correct_score(L2, preferences.nature)
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L += L2
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# List for shopping
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if preferences.shopping.score != 0:
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score_func = lambda loc, n_tags: self.count_elements_within_radius(loc, self.parameters['radius_close_to']) + n_tags * self.parameters['tag_coeff']
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L3 = self.fetch_landmarks(self.amenity_selectors['shopping'], SHOPPING, center_coordinates, self.parameters['city_bbox_side'], score_func)
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self.correct_score(L3, preferences.shopping)
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L += L3
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# remove duplicates
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L = list(set(L))
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L_constrained = self.take_most_important(L, self.parameters['N_important'])
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self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
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return L, L_constrained
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# Take the most important landmarks from the list
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def take_most_important(landmarks: list[Landmark], n_max: int) -> list[Landmark]:
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# Take the most important landmarks from the list
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def take_most_important(self, landmarks: list[Landmark], n_max: int) -> list[Landmark]:
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landmarks_sorted = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
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return landmarks_sorted[:n_max]
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landmarks_sorted = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
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return landmarks_sorted[:n_max]
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# Correct the score of a list of landmarks by taking into account preference settings
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def correct_score(L: list[Landmark], preference: Preference) :
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# Correct the score of a list of landmarks by taking into account preference settings
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def correct_score(self, L: list[Landmark], preference: Preference):
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if len(L) == 0 :
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return
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if L[0].type != preference.type :
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raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
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if len(L) == 0 :
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return
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for elem in L :
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elem.attractiveness = int(elem.attractiveness*preference.score/500) # arbitrary computation
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if L[0].type != preference.type :
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raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
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for elem in L :
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elem.attractiveness = int(elem.attractiveness*preference.score/500) # arbitrary computation
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# Function to count elements within a certain radius of a location
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def count_elements_within_radius(point: Point, radius: int) -> int:
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center_coordinates = (point.x, point.y)
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try:
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# Function to count elements within a certain radius of a location
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def count_elements_within_radius(self, point: Point, radius: int) -> int:
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center_coordinates = (point.x, point.y)
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try:
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landmarks = ox.features_from_point(
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center_point = center_coordinates,
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dist = radius,
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tags = {'building': True} # this is a common tag to give an estimation of the number of elements in the area
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)
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return len(landmarks)
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except ox._errors.InsufficientResponseError:
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return 0
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def fetch_landmarks(
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self,
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amenity_selectors: list[dict],
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landmarktype: LandmarkType,
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center_coordinates: tuple[float, float],
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distance: int,
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score_function: callable
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) -> list[Landmark]:
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landmarks = ox.features_from_point(
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center_point = center_coordinates,
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dist = radius,
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tags = {'building': True} # this is a common tag to give an estimation of the number of elements in the area
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dist = distance,
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tags = amenity_selectors
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)
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return len(landmarks)
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except ox._errors.InsufficientResponseError:
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return 0
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# cleanup the list
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# remove rows where name is None
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landmarks = landmarks[landmarks['name'].notna()]
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# TODO: remove rows that are part of another building
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ret_landmarks = []
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for element, description in landmarks.iterrows():
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osm_type = element[0]
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osm_id = element[1]
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location = description['geometry']
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n_tags = len(description['nodes']) if type(description['nodes']) == list else 1
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if type(location) == Point:
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location = location
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elif type(location) == Polygon or type(location) == MultiPolygon:
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location = location.centroid
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elif type(location) == LineString:
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location = location.interpolate(location.length/2)
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def get_landmarks(
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amenity_selectors: list[dict],
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landmarktype: LandmarkType,
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center_coordinates: tuple[float, float],
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distance: int,
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score_function: callable
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) -> list[Landmark]:
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landmarks = ox.features_from_point(
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center_point = center_coordinates,
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dist = distance,
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tags = amenity_selectors
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)
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# cleanup the list
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# remove rows where name is None
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landmarks = landmarks[landmarks['name'].notna()]
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# TODO: remove rows that are part of another building
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ret_landmarks = []
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for element, description in landmarks.iterrows():
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osm_type = element[0]
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osm_id = element[1]
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location = description['geometry']
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n_tags = len(description['nodes']) if type(description['nodes']) == list else 1
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# print(description['nodes'])
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print(description['name'])
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# print(location, type(location))
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if type(location) == Point:
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location = location
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elif type(location) == Polygon or type(location) == MultiPolygon:
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location = location.centroid
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elif type(location) == LineString:
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location = location.interpolate(location.length/2)
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score = score_function(location, n_tags)
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print(score)
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landmark = Landmark(
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name = description['name'],
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type = landmarktype,
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location = (location.x, location.y),
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osm_type = osm_type,
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osm_id = osm_id,
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attractiveness = score,
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must_do = False,
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n_tags = n_tags
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)
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ret_landmarks.append(landmark)
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return ret_landmarks
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# for elem in G.iterrows():
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# print(elem)
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# print(elem.name)
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# print(elem.address)
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# name = elem.tag('name') # Add name
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# location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
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# # skip if unprecise location
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# if name is None or location[0] is None:
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# continue
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# # skip if unused
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# if 'disused:leisure' in elem.tags().keys():
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# continue
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# # skip if part of another building
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# if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
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# continue
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# else :
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# osm_type = elem.type() # Add type : 'way' or 'relation'
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# osm_id = elem.id() # Add OSM id
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# elem_type = landmarktype # Add the landmark type as 'sightseeing
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# n_tags = len(elem.tags().keys()) # Add number of tags
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# # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
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# if amenity == "'amenity'='place_of_worship'" :
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# score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['church_coeff'])
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# elif amenity == "'leisure'='park'" :
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# score = int((count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff'] )*parameters['park_coeff'])
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# else :
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# score = count_elements_within_radius(location, parameters['radius_close_to']) + n_tags*parameters['tag_coeff']
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# if score is not None :
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# # Generate the landmark and append it to the list
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# landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=n_tags)
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# L.append(landmark)
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# return L
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score = score_function(location, n_tags)
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landmark = Landmark(
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name = description['name'],
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type = landmarktype,
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location = (location.x, location.y),
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osm_type = osm_type,
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osm_id = osm_id,
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attractiveness = score,
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must_do = False,
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n_tags = n_tags
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)
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ret_landmarks.append(landmark)
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return ret_landmarks
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@ -1,51 +1,45 @@
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from optimizer import solve_optimization
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from refiner import refine_optimization
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from landmarks_manager import generate_landmarks
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# from refiner import refine_optimization
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from landmarks_manager import LandmarkManager
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from structs.landmarks import Landmark
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from structs.landmarktype import LandmarkType
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from structs.preferences import Preferences, Preference
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from structs.preferences import Preferences
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from fastapi import FastAPI, Query, Body
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from typing import List
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app = FastAPI()
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manager = LandmarkManager()
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# TODO: needs a global variable to store the landmarks accross function calls
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# linked_tour = []
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# Assuming frontend is calling like this :
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#"http://127.0.0.1:8000/process?param1={param1}¶m2={param2}"
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@app.post("/optimizer_coords/{start_lat}/{start_lon}/{finish_lat}/{finish_lon}")
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def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(...), finish_lat: float = None, finish_lon: float = None) -> List[Landmark]:
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@app.post("/route/new")
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def main1(preferences: Preferences, start: tuple[float, float], end: tuple[float, float] = None) -> str:
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'''
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Main function to call the optimizer.
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:param preferences: the preferences specified by the user as the post body
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:param start: the coordinates of the starting point as a tuple of floats (as url query parameters)
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:param end: the coordinates of the finishing point as a tuple of floats (as url query parameters)
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:return: the uuid of the first landmark in the optimized route
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'''
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if preferences is None :
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raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
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if bool(start_lat) ^ bool(start_lon) :
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raise ValueError("Please provide both latitude and longitude for the starting point")
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if bool(finish_lat) ^ bool(finish_lon) :
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raise ValueError("Please provide both latitude and longitude for the finish point")
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if start is None:
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raise ValueError("Please provide the starting coordinates as a tuple of floats.")
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if end is None:
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end = start
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start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start_lat, start_lon), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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start_landmark = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start[0], start[1]), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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end_landmark = Landmark(name='end', type=LandmarkType(landmark_type='end'), location=(end[0], end[1]), osm_type='end', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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if bool(finish_lat) and bool(finish_lon) :
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finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(finish_lat, finish_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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else :
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finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(start_lat, start_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
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# Generate the landmarks from the start location
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landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
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landmarks, landmarks_short = LandmarkManager.get_landmark_lists(preferences=preferences, coordinates=start.location)
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print([l.name for l in landmarks_short])
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# insert start and finish to the landmarks list
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landmarks_short.insert(0, start)
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landmarks_short.append(finish)
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landmarks_short.insert(0, start_landmark)
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landmarks_short.append(end_landmark)
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# TODO use these parameters in another way
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# TODO infer these parameters from the preferences
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max_walking_time = 4 # hours
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detour = 30 # minutes
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@ -53,32 +47,11 @@ def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(..
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base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
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# Second stage optimization
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refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
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# refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
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# TODO: should look something like this
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# # set time to reach and transform into fully functional linked list
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# linked_tour += link(refined_tour)
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# return {
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# 'city_name': 'Paris',
|
||||
# 'n_stops': len(linked_tour),
|
||||
# 'first_landmark_uuid': linked_tour[0].uuid,
|
||||
# }
|
||||
|
||||
return refined_tour
|
||||
|
||||
|
||||
|
||||
|
||||
# input city, country in the form of 'Paris, France'
|
||||
@app.post("/test2/{city_country}")
|
||||
def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landmark]:
|
||||
|
||||
landmarks = generate_landmarks(city_country, preferences)
|
||||
|
||||
max_steps = 9000000
|
||||
|
||||
visiting_order = solve_optimization(landmarks, max_steps, True)
|
||||
# linked_tour = ...
|
||||
# return linked_tour[0].uuid
|
||||
return base_tour[0].uuid
|
||||
|
||||
|
||||
|
||||
@ -86,5 +59,3 @@ def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landm
|
||||
def get_landmark(landmark_uuid: str) -> Landmark:
|
||||
#cherche dans linked_tour et retourne le landmark correspondant
|
||||
pass
|
||||
|
||||
|
||||
|
||||
@ -1,11 +1,11 @@
|
||||
import pandas as pd
|
||||
|
||||
from typing import List
|
||||
from landmarks_manager import generate_landmarks
|
||||
from landmarks_manager import LandmarkManager
|
||||
from fastapi.encoders import jsonable_encoder
|
||||
|
||||
from optimizer import solve_optimization
|
||||
from refiner import refine_optimization
|
||||
# from refiner import refine_optimization
|
||||
from structs.landmarks import Landmark
|
||||
from structs.landmarktype import LandmarkType
|
||||
from structs.preferences import Preferences, Preference
|
||||
@ -23,74 +23,37 @@ def write_data(L: List[Landmark], file_name: str):
|
||||
|
||||
data.to_json(file_name, indent = 2, force_ascii=False)
|
||||
|
||||
def test3(city_country: str) -> List[Landmark]:
|
||||
def main(coordinates: tuple[float, float]) -> List[Landmark]:
|
||||
|
||||
manager = LandmarkManager()
|
||||
|
||||
preferences = Preferences(
|
||||
sightseeing=Preference(
|
||||
name='sightseeing',
|
||||
type=LandmarkType(landmark_type='sightseeing'),
|
||||
score = 5),
|
||||
nature=Preference(
|
||||
name='nature',
|
||||
type=LandmarkType(landmark_type='nature'),
|
||||
score = 0),
|
||||
shopping=Preference(
|
||||
name='shopping',
|
||||
type=LandmarkType(landmark_type='shopping'),
|
||||
score = 5))
|
||||
|
||||
coordinates = None
|
||||
|
||||
landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
|
||||
|
||||
#write_data(landmarks)
|
||||
|
||||
start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.2044576, 16.3870242), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.2044576, 16.3870242), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
|
||||
test = landmarks_short
|
||||
|
||||
test.insert(0, start)
|
||||
test.append(finish)
|
||||
|
||||
max_walking_time = 2 # hours
|
||||
|
||||
visiting_list = solve_optimization(test, max_walking_time*60, True)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def test4(coordinates: tuple[float, float]) -> List[Landmark]:
|
||||
|
||||
|
||||
preferences = Preferences(
|
||||
sightseeing=Preference(
|
||||
name='sightseeing',
|
||||
type=LandmarkType(landmark_type='sightseeing'),
|
||||
score = 5),
|
||||
nature=Preference(
|
||||
name='nature',
|
||||
type=LandmarkType(landmark_type='nature'),
|
||||
score = 5),
|
||||
shopping=Preference(
|
||||
name='shopping',
|
||||
type=LandmarkType(landmark_type='shopping'),
|
||||
score = 5))
|
||||
sightseeing=Preference(
|
||||
name='sightseeing',
|
||||
type=LandmarkType(landmark_type='sightseeing'),
|
||||
score = 5
|
||||
),
|
||||
nature=Preference(
|
||||
name='nature',
|
||||
type=LandmarkType(landmark_type='nature'),
|
||||
score = 5
|
||||
),
|
||||
shopping=Preference(
|
||||
name='shopping',
|
||||
type=LandmarkType(landmark_type='shopping'),
|
||||
score = 5
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
# Create start and finish
|
||||
start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
# Generate the landmarks from the start location
|
||||
landmarks, landmarks_short = generate_landmarks(preferences=preferences, center_coordinates=start.location)
|
||||
landmarks, landmarks_short = manager.get_landmark_lists(preferences=preferences, center_coordinates=start.location)
|
||||
print([l.name for l in landmarks_short])
|
||||
|
||||
#write_data(landmarks, "landmarks.txt")
|
||||
|
||||
# Insert start and finish to the landmarks list
|
||||
@ -105,12 +68,13 @@ def test4(coordinates: tuple[float, float]) -> List[Landmark]:
|
||||
base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
|
||||
|
||||
# Second stage optimization
|
||||
refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
|
||||
# refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
|
||||
|
||||
return refined_tour
|
||||
return base_tour
|
||||
|
||||
|
||||
test4(tuple((48.8344400, 2.3220540))) # Café Chez César
|
||||
#test4(tuple((48.8375946, 2.2949904))) # Point random
|
||||
#test4(tuple((47.377859, 8.540585))) # Zurich HB
|
||||
#test3('Vienna, Austria')
|
||||
|
||||
if __name__ == '__main__':
|
||||
start = (48.847132, 2.312359) # Café Chez César
|
||||
# start = (47.377859, 8.540585) # Zurich HB
|
||||
main(start)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user