我正在處理一個包含 4 個多邊形的 shapefile，我需要在其中提取它們在元組或 numpy 數(shù)組中的坐標(biāo)以供以后操作。雖然 fiona 識別了 4 個多邊形，但使用 shapely 獲取面積只會給我第一個多邊形的值。我想為所有多邊形選擇xy坐標(biāo)并將它們分別放入數(shù)組和元組中進行處理。任何幫助，將不勝感激。import fionaimport pprintfrom shapely.geometry import shape, Polygonc = fiona.open("myShapeFile.shp", 'r')pprint.pprint(len(c))poly = Polygon(next(iter(c))["geometry"]["coordinates"][:])print('Area :: ',poly.area)執(zhí)行給了我以下內(nèi)容：4地區(qū):: 483.6379565269977
1 回答
喵喵時光機
TA貢獻(xiàn)1846條經(jīng)驗 獲得超7個贊
嘗試這個：
import fiona
import numpy as np
c = fiona.open('myShapeFile.shp')
coords = [np.array(poly['geometry']['coordinates'])
for poly in c.values()]
coords是一個 4 項列表，其中包含根據(jù) shapefile 中的多邊形坐標(biāo)構(gòu)建的 numpy 數(shù)組：
[array([[ 3.67739738e+05, 6.35697708e+06, -1.50000000e+03],
[ 3.67709140e+05, 6.35875947e+06, -1.50000000e+03],
[ 3.55633222e+05, 6.35932405e+06, -1.50000000e+03],
[ 3.55340980e+05, 6.35724482e+06, -1.50000000e+03],
[ 3.54012992e+05, 6.35713924e+06, -1.50000000e+03],
[ 3.53169984e+05, 6.33306237e+06, -1.50000000e+03],
[ 3.72936914e+05, 6.33219322e+06, -1.50000000e+03],
[ 3.73638262e+05, 6.34799928e+06, -1.50000000e+03],
[ 3.70536492e+05, 6.34865912e+06, -1.50000000e+03],
[ 3.71030683e+05, 6.35672004e+06, -1.50000000e+03],
[ 3.67739738e+05, 6.35697708e+06, -1.50000000e+03]]),
array([[ 3.60619254e+05, 6.35486104e+06, -1.50000000e+03],
[ 3.61702258e+05, 6.35460318e+06, -1.50000000e+03],
[ 3.62578977e+05, 6.35362332e+06, -1.50000000e+03],
[ 3.61805402e+05, 6.35192145e+06, -1.50000000e+03],
[ 3.60412965e+05, 6.35114787e+06, -1.50000000e+03],
[ 3.59072102e+05, 6.35171516e+06, -1.50000000e+03],
[ 3.59020527e+05, 6.35279817e+06, -1.50000000e+03],
[ 3.60309824e+05, 6.35295288e+06, -1.50000000e+03],
[ 3.60000391e+05, 6.35408746e+06, -1.50000000e+03],
[ 3.60619254e+05, 6.35486104e+06, -1.50000000e+03]]),
array([[ 3.59072102e+05, 6.34439198e+06, -1.50000000e+03],
[ 3.60928684e+05, 6.34537184e+06, -1.50000000e+03],
[ 3.62424262e+05, 6.34480455e+06, -1.50000000e+03],
[ 3.63868270e+05, 6.34361841e+06, -1.50000000e+03],
[ 3.63146266e+05, 6.34155554e+06, -1.50000000e+03],
[ 3.61341258e+05, 6.34036939e+06, -1.50000000e+03],
[ 3.60258250e+05, 6.34109139e+06, -1.50000000e+03],
[ 3.61650688e+05, 6.34227754e+06, -1.50000000e+03],
[ 3.61289688e+05, 6.34356684e+06, -1.50000000e+03],
[ 3.59897250e+05, 6.34294798e+06, -1.50000000e+03],
[ 3.57989094e+05, 6.34346370e+06, -1.50000000e+03],
[ 3.59072102e+05, 6.34439198e+06, -1.50000000e+03]]),
array([[ 3.69489590e+05, 6.34294798e+06, -1.50000000e+03],
[ 3.71397742e+05, 6.34103982e+06, -1.50000000e+03],
[ 3.69438016e+05, 6.33928638e+06, -1.50000000e+03],
[ 3.67581438e+05, 6.34052411e+06, -1.50000000e+03],
[ 3.67787723e+05, 6.34222597e+06, -1.50000000e+03],
[ 3.69489590e+05, 6.34294798e+06, -1.50000000e+03]])]
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