76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
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import os
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import osmnx as ox
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from shapely.geometry import Point
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from shapely.ops import nearest_points
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from geopy import distance
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from tqdm import tqdm
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import pickle as pkl
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import pandas as pd
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import geopandas as gpd
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import multiprocessing
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import numpy as np
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from util import constants as C
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def get_buildings(city, city_tag):
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tags = tags = {'building': True}
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building_path = f"/share/data/camera/shape/building/{city}.pkl"
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if False:#os.path.exists(building_path):
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with open(building_path, "rb") as f:
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gdf = pkl.load(f)
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else:
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gdf = ox.geometries_from_place(city_tag, tags)
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with open(building_path, "wb") as f:
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pkl.dump(gdf, f)
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rows = []
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for rid, row in tqdm(gdf.iterrows(), total=len(gdf)):
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if isinstance(row['geometry'], Point):
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continue
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row['centroid_lat'] = row['geometry'].centroid.y
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row['centroid_lon'] = row['geometry'].centroid.x
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rows.append(row)
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buildings = gpd.GeoDataFrame(rows)
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return buildings
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def get_coverage(lat, lon, buildings, t=0.005, default=50):
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dist = default
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try:
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near_buildings = buildings.query(f"{lat-t} < centroid_lat < {lat+t} and \
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{lon-t} < centroid_lon < {lon+t}")
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for rid, row in near_buildings.iterrows():
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building = row['geometry']
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p = nearest_points(building, Point(lon, lat))[0]
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_lat, _lon = p.y, p.x
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_dist = distance.distance((lat, lon), (_lat, _lon)).m
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dist = min(dist, _dist)
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except Exception as e:
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print(str(e))
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pass
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return 2 * dist
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def get_coverage_df(rtuple):
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global buildings
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rid, row = rtuple
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lat, lon = row['lat'], row['lon']
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row['coverage'] = get_coverage(lat, lon, buildings)
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return row
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def calculate_coverage(meta_path="/share/data/camera/deployment/verified_0425.csv"):
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df = pd.read_csv(meta_path)
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dfs = []
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for city, place in list(C.CITIES.items())[:10]:
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print(f"Load building footprint [{place}]..")
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buildings = get_buildings(city, place)
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pano = df.query(f"city == '{city}'")
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print(f"Start coverage calculation ..")
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with multiprocessing.Pool(50) as p:
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rows = list(tqdm(p.imap(get_coverage_df, pano.iterrows()),
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total=len(pano),
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smoothing=0.1))
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pano = pd.DataFrame(rows)
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dfs.append(pano)
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pd.concat(dfs).to_csv("/share/data/camera/deployment/verified_0425_coverage.csv", index=False)
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