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esenke
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tools/pretraining_data_builder/rsi_process/tile_resample.py
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230
tools/pretraining_data_builder/rsi_process/tile_resample.py
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import numpy as np
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import mercantile
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from pyresample import bilinear, kd_tree, geometry
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TILE_SIZE = 256
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class LngLatTransfer():
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def __init__(self):
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self.x_pi = 3.14159265358979324 * 3000.0 / 180.0
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self.pi = np.pi # π
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self.a = 6378245.0
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self.es = 0.00669342162296594323
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pass
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def GCJ02_to_BD09(self, gcj_lng, gcj_lat):
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"""
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Convert coordinates from GCJ02 to BD09 coordinate system
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:param lng: Longitude in GCJ02 coordinate system
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:param lat: Latitude in GCJ02 coordinate system
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:return: Converted longitude and latitude in BD09
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"""
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z = np.sqrt(gcj_lng * gcj_lng + gcj_lat * gcj_lat) + 0.00002 * np.sin(gcj_lat * self.x_pi)
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theta = np.arctan2(gcj_lat, gcj_lng) + 0.000003 * np.cos(gcj_lng * self.x_pi)
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bd_lng = z * np.cos(theta) + 0.0065
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bd_lat = z * np.sin(theta) + 0.006
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return bd_lng, bd_lat
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def BD09_to_GCJ02(self, bd_lng, bd_lat):
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'''
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Convert coordinates from BD09 to GCJ02 coordinate system
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:param bd_lng: Longitude in BD09 coordinate system
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:param bd_lat: Latitude in BD09 coordinate system
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:return: Converted longitude and latitude in GCJ02
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'''
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x = bd_lng - 0.0065
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y = bd_lat - 0.006
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z = np.sqrt(x * x + y * y) - 0.00002 * np.sin(y * self.x_pi)
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theta = np.arctan2(y, x) - 0.000003 * np.cos(x * self.x_pi)
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gcj_lng = z * np.cos(theta)
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gcj_lat = z * np.sin(theta)
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return gcj_lng, gcj_lat
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def WGS84_to_GCJ02(self, lng, lat):
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'''
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Convert coordinates from WGS84 to GCJ02 coordinate system
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:param lng: Longitude in WGS84 coordinate system
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:param lat: Latitude in WGS84 coordinate system
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:return: Converted longitude and latitude in GCJ02
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'''
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dlat = self._transformlat(lng - 105.0, lat - 35.0)
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dlng = self._transformlng(lng - 105.0, lat - 35.0)
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radlat = lat / 180.0 * self.pi
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magic = np.sin(radlat)
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magic = 1 - self.es * magic * magic
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sqrtmagic = np.sqrt(magic)
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dlat = (dlat * 180.0) / ((self.a * (1 - self.es)) / (magic * sqrtmagic) * self.pi)
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dlng = (dlng * 180.0) / (self.a / sqrtmagic * np.cos(radlat) * self.pi)
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gcj_lng = lng + dlng
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gcj_lat = lat + dlat
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return gcj_lng, gcj_lat
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def GCJ02_to_WGS84(self, gcj_lng, gcj_lat):
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'''
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Convert coordinates from GCJ02 to WGS84 coordinate system
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:param gcj_lng: Longitude in GCJ02 coordinate system
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:param gcj_lat: Latitude in GCJ02 coordinate system
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:return: Converted longitude and latitude in WGS84
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'''
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dlat = self._transformlat(gcj_lng - 105.0, gcj_lat - 35.0)
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dlng = self._transformlng(gcj_lng - 105.0, gcj_lat - 35.0)
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radlat = gcj_lat / 180.0 * self.pi
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magic = np.sin(radlat)
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magic = 1 - self.es * magic * magic
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sqrtmagic = np.sqrt(magic)
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dlat = (dlat * 180.0) / ((self.a * (1 - self.es)) / (magic * sqrtmagic) * self.pi)
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dlng = (dlng * 180.0) / (self.a / sqrtmagic * np.cos(radlat) * self.pi)
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mglat = gcj_lat + dlat
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mglng = gcj_lng + dlng
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lng = gcj_lng * 2 - mglng
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lat = gcj_lat * 2 - mglat
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return lng, lat
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def BD09_to_WGS84(self, bd_lng, bd_lat):
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'''
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Convert coordinates from BD09 to WGS84 coordinate system
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:param bd_lng: Longitude in BD09 coordinate system
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:param bd_lat: Latitude in BD09 coordinate system
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:return: Converted longitude and latitude in WGS84
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'''
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lng, lat = self.BD09_to_GCJ02(bd_lng, bd_lat)
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return self.GCJ02_to_WGS84(lng, lat)
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def WGS84_to_BD09(self, lng, lat):
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'''
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Convert coordinates from WGS84 to BD09 coordinate system
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:param lng: Longitude in WGS84 coordinate system
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:param lat: Latitude in WGS84 coordinate system
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:return: Converted longitude and latitude in BD09
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'''
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lng, lat = self.WGS84_to_GCJ02(lng, lat)
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return self.GCJ02_to_BD09(lng, lat)
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def _transformlat(self, lng, lat):
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ret = -100.0 + 2.0 * lng + 3.0 * lat + 0.2 * lat * lat + \
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0.1 * lng * lat + 0.2 * np.sqrt(np.fabs(lng))
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ret += (20.0 * np.sin(6.0 * lng * self.pi) + 20.0 *
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np.sin(2.0 * lng * self.pi)) * 2.0 / 3.0
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ret += (20.0 * np.sin(lat * self.pi) + 40.0 *
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np.sin(lat / 3.0 * self.pi)) * 2.0 / 3.0
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ret += (160.0 * np.sin(lat / 12.0 * self.pi) + 320 *
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np.sin(lat * self.pi / 30.0)) * 2.0 / 3.0
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return ret
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def _transformlng(self, lng, lat):
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ret = 300.0 + lng + 2.0 * lat + 0.1 * lng * lng + \
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0.1 * lng * lat + 0.1 * np.sqrt(np.fabs(lng))
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ret += (20.0 * np.sin(6.0 * lng * self.pi) + 20.0 *
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np.sin(2.0 * lng * self.pi)) * 2.0 / 3.0
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ret += (20.0 * np.sin(lng * self.pi) + 40.0 *
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np.sin(lng / 3.0 * self.pi)) * 2.0 / 3.0
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ret += (150.0 * np.sin(lng / 12.0 * self.pi) + 300.0 *
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np.sin(lng / 30.0 * self.pi)) * 2.0 / 3.0
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return ret
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def WGS84_to_WebMercator(self, lng, lat):
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'''
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Convert coordinates from WGS84 to Web Mercator
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:param lng: Longitude in WGS84
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:param lat: Latitude in WGS84
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:return: Converted Web Mercator coordinates
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'''
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x = lng * 20037508.342789 / 180
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y = np.log(np.tan((90 + lat) * self.pi / 360)) / (self.pi / 180)
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y = y * 20037508.34789 / 180
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return x, y
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def WebMercator_to_WGS84(self, x, y):
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'''
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Convert coordinates from Web Mercator to WGS84
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:param x: Web Mercator x coordinate
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:param y: Web Mercator y coordinate
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:return: Converted longitude and latitude in WGS84
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'''
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lng = x / 20037508.34 * 180
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lat = y / 20037508.34 * 180
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lat = 180 / self.pi * (2 * np.arctan(np.exp(lat * self.pi / 180)) - self.pi / 2)
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return lng, lat
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transfer = LngLatTransfer()
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def get_tile_array(x, y, z, method='nearest', func_source=None, radius=2, fill_value=0, use_gc02=True):
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"""Resample source image data to map tile
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Args:
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x, y, z: Tile coordinates
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method: Resampling method ('nearest' or 'bilinear')
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func_source: Function to get source image data
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radius: Search radius in pixels
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fill_value: Value for no data areas
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gc02: Whether the coordinates are in GCJ02 system (True) or WGS84 (False)
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Returns:
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ndarray: Resampled tile data
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"""
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bounds = mercantile.bounds(x, y, z)
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if use_gc02:
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# Convert coordinates from GCJ02 to WGS84
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wgs84_lngs, wgs84_lats = transfer.GCJ02_to_WGS84(
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gcj_lng=np.array([bounds.west, bounds.west, bounds.east, bounds.east]),
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gcj_lat=np.array([bounds.north, bounds.south, bounds.south, bounds.north])
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)
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boundary = list(zip(wgs84_lngs, wgs84_lats))
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else:
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boundary = list(zip(
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[bounds.west, bounds.west, bounds.east, bounds.east],
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[bounds.north, bounds.south, bounds.south, bounds.north]
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))
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source_data = func_source(boundary)
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if source_data is None:
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return None
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arr_image, arr_lngs, arr_lats = source_data
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if use_gc02:
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gcj02_lngs, gcj02_lats = transfer.WGS84_to_GCJ02(arr_lngs, arr_lats)
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else:
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gcj02_lngs, gcj02_lats = arr_lngs, arr_lats
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# Define source and target geometries
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source_def = geometry.SwathDefinition(lons=gcj02_lngs, lats=gcj02_lats)
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xy_bounds = mercantile.xy_bounds(x, y, z)
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target_def = geometry.AreaDefinition(
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'tile', 'tile', 'tile',
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'EPSG:3857',
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TILE_SIZE, TILE_SIZE,
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(xy_bounds.left, xy_bounds.bottom, xy_bounds.right, xy_bounds.top)
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)
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# Resample
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pixel_size = mercantile.CE / 2 ** z / TILE_SIZE
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if method == 'nearest':
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result = kd_tree.resample_nearest(
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source_def, arr_image, target_def,
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radius_of_influence=radius * pixel_size,
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fill_value=fill_value
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)
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elif method == 'bilinear':
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resampler = bilinear.NumpyBilinearResampler(
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source_def, target_def,
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radius_of_influence=radius * pixel_size,
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neighbours=8
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)
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result = resampler.resample(arr_image).astype(arr_image.dtype)
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else:
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raise ValueError(f'Unknown resampling method: {method}')
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return result
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