tile_overview.py
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# coding=utf-8
#author: 4N
#createtime: 2021/12/20
#email: nheweijun@sina.com
import math
import requests
import numpy
import cv2
from osgeo import gdal,osr
from osgeo.gdal import *
# Math functions taken from https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames #spellok
# TMS 经纬度转
def deg2num( lon_deg,lat_deg, zoom):
lat_rad = math.radians(lat_deg)
n = 2.0 ** zoom
xtile = int((lon_deg + 180.0) / 360.0 * n)
ytile = int((1.0 - math.log(math.tan(lat_rad) + (1 / math.cos(lat_rad))) / math.pi) / 2.0 * n)
return (xtile, ytile)
def num2deg(xtile, ytile, zoom):
n = 2.0 ** zoom
lon_deg = xtile / n * 360.0 - 180.0
lat_rad = math.atan(math.sinh(math.pi * (1 - 2 * ytile / n)))
lat_deg = math.degrees(lat_rad)
return (lat_deg, lon_deg)
def tms_overview(extent,url_format):
lev = 0
r1 = 0
r2 = 0
c1 = 0
c2 = 0
while r1 - r2 < 2:
lev += 1
# 左下角所在行列号
c1, r1 = deg2num( extent[0],extent[2], lev)
# 右上角所在行列号
c2, r2 = deg2num( extent[1],extent[3], lev)
if lev > 20 :
break
print(lev)
# 左下角瓦片的左上角坐标
lat1, lon1 = num2deg(c1, r1, lev)
# 左下角瓦片的右下角坐标
lat2, lon2 = num2deg(c1 + 1, r1 + 1, lev)
# 右上角瓦片的左上角坐标
lat3, lon3 = num2deg(c2, r2, lev)
# 右上角瓦片的右下角坐标
lat4, lon4 = num2deg(c2 + 1, r2 + 1, lev)
# 瓦片拼接底图
dat = numpy.zeros(((r1 - r2 + 1) * 256, (c2 - c1 + 1) * 256, 3), dtype=int) + 255
# x偏移量
offx = (extent[0] - lon1) / ((lon4 - lon1) / ((c2 - c1 + 1) * 256))
# x方向像素量
xnum = (extent[1] - extent[0]) / ((lon4 - lon1) / ((c2 - c1 + 1) * 256))
# y偏移量
offy = (lat3 - extent[3]) / ((lat3 - lat2) / ((r1 - r2 + 1) * 256))
# y方向像素量
ynum = (extent[3] - extent[2]) / ((lat3 - lat2) / ((r1 - r2 + 1) * 256))
# 拼接瓦片
for r in range(r2, r1 + 1):
for c in range(c1, c2 + 1):
url = "{}/{}/{}/{}.png".format(url_format,lev, c, r)
dataset: Dataset = gdal.Open(url, 0)
for b in range(1, 4):
band: Band = dataset.GetRasterBand(b)
dat[(r - r2) * 256:(r - r2 + 1) * 256, (c - c1) * 256: (c - c1 + 1) * 256,
2 - (b - 1)] = band.ReadAsArray(0, 0, 256, 256)
#裁剪
dat2 = dat[int(offy):int(offy + ynum), int(offx):int(offx + xnum), :]
cv2.imwrite("over.jpg", dat2, [cv2.IMWRITE_JPEG_QUALITY, 30])
def get_polygon(parameter,level,row,col):
level=int(level)
row = int(row)
col = int(col)
detaxy = parameter.get(str(level)).get("resolution")* parameter.get("cols")
lon1 = detaxy * col + int(parameter.get("x"))
lat2 = -detaxy * row + int(parameter.get("y"))
lon2 = detaxy + lon1
lat1 = -detaxy + lat2
return [lon1,lat1,lon2,lat2]
def get_rc(parameter,l,lon,lat):
detaxy = parameter.get(str(l)).get("resolution")* parameter.get("cols")
dd = lon - int(parameter.get("x"))
c = math.floor((lon - int(parameter.get("x")))/detaxy)
r = math.floor((int(parameter.get("y"))-lat)/detaxy)
return int(r),int(c)
def wmts_overview(extent,url_format):
# scheme = {"0": {"resolution": 1.4062500000059488, "scale": 590995186.12}, "1": {"resolution": 0.7031250000029744, "scale": 295497593.06}, "2": {"resolution": 0.3515625000014872, "scale": 147748796.53}, "3": {"resolution": 0.1757812500007436, "scale": 73874398.265}, "4": {"resolution": 0.0878906250003718, "scale": 36937199.1325}, "5": {"resolution": 0.0439453125001859, "scale": 18468599.56625}, "6": {"resolution": 0.02197265625009295, "scale": 9234299.783125}, "7": {"resolution": 0.010986328125046475, "scale": 4617149.8915625}, "8": {"resolution": 0.0054931640625232375, "scale": 2308574.94578125}, "9": {"resolution": 0.0027465820312616187, "scale": 1154287.472890625}, "10": {"resolution": 0.0013732910156308094, "scale": 577143.7364453125}, "11": {"resolution": 0.0006866455078154047, "scale": 288571.86822265625}, "12": {"resolution": 0.00034332275390770234, "scale": 144285.93411132813}, "13": {"resolution": 0.00017166137695385117, "scale": 72142.96705566406}, "14": {"resolution": 8.583068847692559e-05, "scale": 36071.48352783203}, "15": {"resolution": 4.291534423846279e-05, "scale": 18035.741763916016}, "16": {"resolution": 2.1457672119231396e-05, "scale": 9017.870881958008}, "17": {"resolution": 1.0728836059615698e-05, "scale": 4508.935440979004}, "18": {"resolution": 5.364418029807849e-06, "scale": 2254.467720489502}, "19": {"resolution": 2.6822090149039246e-06, "scale": 1127.233860244751}, "20": {"resolution": 1.3411045074519623e-06, "scale": 563.6169301223755}, "cols": 256, "rows": 256, "dpi": 96, "wkt": "", "x": -400, "y": 400}
scheme = {"0": {"resolution": 156543.34701231902, "scale": 591658710.9},
"1": {"resolution": 78271.67350615951, "scale": 295829355.45},
"2": {"resolution": 39135.83675440268 , "scale": 147914677.73} ,
"3": {"resolution": 19567.91837587842, "scale": 73957338.86},
"4": {"resolution": 9783.95918793921, "scale": 36978669.43},
"5": {"resolution": 4891.979593969605, "scale": 18489334.715},
"6": {"resolution": 2445.9897969848025, "scale": 9244667.3575},
"7": {"resolution": 1222.9948984924013 , "scale": 4622333.67875},
"8": {"resolution": 611.4974492462006, "scale": 2311166.839375},
"9": {"resolution": 305.7487246231003, "scale": 1155583.4196875},
"10": {"resolution": 152.87436231155016, "scale": 577791.70984375},
"11": {"resolution": 76.43718115577508, "scale": 288895.854921875},
"12": {"resolution": 38.21859057788754, "scale": 144447.9274609375},
"13": {"resolution": 0.00017166137695385117, "scale": 72142.96705566406},
"14": {"resolution": 8.583068847692559e-05, "scale": 36071.48352783203},
"15": {"resolution": 4.291534423846279e-05, "scale": 18035.741763916016},
"16": {"resolution": 2.1457672119231396e-05, "scale": 9017.870881958008},
"17": {"resolution": 1.0728836059615698e-05, "scale": 4508.935440979004},
"18": {"resolution": 5.364418029807849e-06, "scale": 2254.467720489502},
"19": {"resolution": 2.6822090149039246e-06, "scale": 1127.233860244751},
"20": {"resolution": 1.3411045074519623e-06, "scale": 563.6169301223755},
"cols": 256, "rows": 256, "dpi": 96, "wkt": "", "x":-4923200, "y":10002100}
lev = 0
r1 = 0
r2 = 0
c1 = 0
c2 = 0
while r1 - r2 <1:
lev += 1
# 左下角所在瓦片
r1,c1 = get_rc(scheme,lev, extent[0],extent[2])
# 右上角所在瓦片
r2,c2 = get_rc(scheme,lev, extent[1],extent[3])
if lev > 20 :
break
print(lev)
# 左下角瓦片的范围
[lon1, lat1, lon2, lat2] = get_polygon(scheme, lev, r1, c1)
# 右上角角瓦片的范围
[lon12, lat12, lon22, lat22] = get_polygon(scheme, lev, r2, c2)
# 瓦片拼接底图
dat = numpy.zeros(((r1 - r2 + 1) * 256, (c2 - c1 + 1) * 256, 3), dtype=int)
# x偏移量
offx = (extent[0] - lon1) / ((lon22 - lon1) / ((c2 - c1 + 1) * 256))
# x方向像素量
xnum = (extent[1] - extent[0]) / ((lon22 - lon1) / ((c2 - c1 + 1) * 256))
# y偏移量
offy = (lat22 - extent[3]) / ((lat22 - lat1) / ((r1 - r2 + 1) * 256))
# y方向像素量
ynum = (extent[3] - extent[2]) / ((lat22 - lat1) / ((r1 - r2 + 1) * 256))
# 拼接瓦片
for r in range(r2, r1 + 1):
for c in range(c1, c2 + 1):
url = url_format.format(lev, c, r)
print(url)
dataset: Dataset = gdal.Open(url, 0)
if dataset.RasterCount==1:
continue
for b in range(1, 4):
band: Band = dataset.GetRasterBand(b)
dat[(r - r2) * 256:(r - r2 + 1) * 256, (c - c1) * 256: (c - c1 + 1) * 256,
2 - (b - 1)] = band.ReadAsArray(0, 0, 256, 256)
cv2.imwrite("overwmtsori2.jpg", dat, [cv2.IMWRITE_JPEG_QUALITY, 30])
dat2 = dat[int(offy):int(offy + ynum), int(offx):int(offx + xnum), :]
#裁剪
cv2.imwrite("overwmts2.jpg", dat2, [cv2.IMWRITE_JPEG_QUALITY, 30])
if __name__ == '__main__':
# tms_overview([107.78981494180618, 120.43553603935675, 18.870480519260582, 25.999868757737033],"http://172.26.60.101:8820/DMap/Services/GDMap/TileServer/TMSService"
# )
# wmts_url = "http://172.26.60.101:8820/DMap/Services/GDMap/TileServer/WMTSService?layer=&style=default&tilematrixset=nativeTileMatrixSet&Service=WMTS&Request=GetTile&Version=1.0.0&Format=image%2Fpng&TileMatrix={}&TileCol={}&TileRow={}"
# wmts_overview([107.78981494180618, 120.43553603935675, 18.870480519260582, 25.999868757737033],wmts_url)
wmts_url = "http://172.26.60.101:8820/DMap/Services/SDMap/TileServer/WMTSService?layer=&style=default&tilematrixset=nativeTileMatrixSet&Service=WMTS&Request=GetTile&Version=1.0.0&Format=image%2Fpng&TileMatrix={}&TileCol={}&TileRow={}"
wmts_overview([699385.63699999999,740828.85219999996,2507679.2525999998,2546752.4369999999],wmts_url)