我似乎有一个类似于JSON的结构作为API响应。看起来如下。
{
'_links': {
'_self': 'https://api.planet.com/data/v1/item-types/PSScene4Band/items/20190530_183308_100c',
'assets': 'https://api.planet.com/data/v1/item-types/PSScene4Band/items/20190530_183308_100c/assets/',
'thumbnail': 'https://tiles.planet.com/data/v1/item-types/PSScene4Band/items/20190530_183308_100c/thumb'
},
'_permissions': ['assets.basic_analytic_rpc_nitf:download', 'assets.udm2:download', 'assets.analytic_xml:download', 'assets.basic_analytic_dn:download', 'assets.basic_analytic_dn_xml_nitf:download', 'assets.basic_analytic_dn_nitf:download', 'assets.basic_analytic_xml:download', 'assets.basic_analytic_nitf:download', 'assets.basic_analytic_rpc:download', 'assets.analytic_dn:download', 'assets.basic_udm:download', 'assets.basic_analytic_dn_rpc_nitf:download', 'assets.analytic:download', 'assets.analytic_dn_xml:download', 'assets.analytic_sr:download', 'assets.basic_analytic_dn_xml:download', 'assets.basic_udm2:download', 'assets.basic_analytic_dn_rpc:download', 'assets.basic_analytic_xml_nitf:download', 'assets.basic_analytic:download', 'assets.udm:download'],
'geometry': {
'coordinates': [
[
[-122.33099544690265, 37.50055222996912],
[-122.32310279398551, 37.528325690277924],
[-122.32274055258365, 37.5282619001356],
[-122.31970046184915, 37.53940549147412],
[-122.03482745003222, 37.489623639299495],
[-122.0474732844629, 37.44402988056649],
[-122.05518612558149, 37.41692373485298],
[-122.34026781061122, 37.46710050993664],
[-122.33099544690265, 37.50055222996912]
]
],
'type': 'Polygon'
},
'id': '20190530_183308_100c',
'properties': {
'acquired': '2019-05-30T18:33:08.371898Z',
'anomalous_pixels': 0.01,
'clear_confidence_percent': 85,
'clear_percent': 97,
'cloud_cover': 0.03,
'cloud_percent': 1,
'columns': 8996,
'epsg_code': 32610,
'ground_control': True,
'gsd': 3.9,
'heavy_haze_percent': 0,
'instrument': 'PS2',
'item_type': 'PSScene4Band',
'light_haze_percent': 1,
'origin_x': 558339,
'origin_y': 4154931,
'pixel_resolution': 3,
'provider': 'planetscope',
'published': '2019-05-31T02:03:28.763Z',
'quality_category': 'standard',
'rows': 4463,
'satellite_id': '100c',
'shadow_percent': 0,
'snow_ice_percent': 0,
'strip_id': '2405105',
'sun_azimuth': 120.7,
'sun_elevation': 64.2,
'updated': '2019-05-31T03:40:00.992Z',
'usable_data': 0,
'view_angle': 5,
'visible_confidence_percent': 64,
'visible_percent': 99
},
'type': 'Feature'
} {
'_links': {
'_self': 'https://api.planet.com/data/v1/item-types/PSScene4Band/items/20190530_183307_100c',
'assets': 'https://api.planet.com/data/v1/item-types/PSScene4Band/items/20190530_183307_100c/assets/',
'thumbnail': 'https://tiles.planet.com/data/v1/item-types/PSScene4Band/items/20190530_183307_100c/thumb'
},
'_permissions': ['assets.basic_analytic_rpc_nitf:download', 'assets.udm2:download', 'assets.analytic_xml:download', 'assets.basic_analytic_dn:download', 'assets.basic_analytic_dn_xml_nitf:download', 'assets.basic_analytic_dn_nitf:download', 'assets.basic_analytic_xml:download', 'assets.basic_analytic_nitf:download', 'assets.basic_analytic_rpc:download', 'assets.analytic_dn:download', 'assets.basic_udm:download', 'assets.basic_analytic_dn_rpc_nitf:download', 'assets.analytic:download', 'assets.analytic_dn_xml:download', 'assets.analytic_sr:download', 'assets.basic_analytic_dn_xml:download', 'assets.basic_udm2:download', 'assets.basic_analytic_dn_rpc:download', 'assets.basic_analytic_xml_nitf:download', 'assets.basic_analytic:download', 'assets.udm:download'],
'geometry': {
'coordinates': [
[
[-122.3006710736394, 37.605388252597656],
[-122.01624942227079, 37.55570149014945],
[-122.0364127147522, 37.482963540177245],
[-122.32141233388535, 37.53313054639475],
[-122.30471665876881, 37.594329305533236],
[-122.30373184352291, 37.59415601927027],
[-122.3006710736394, 37.605388252597656]
]
],
'type': 'Polygon'
},
'id': '20190530_183307_100c',
'properties': {
'acquired': '2019-05-30T18:33:07.320176Z',
'anomalous_pixels': 0.02,
'clear_confidence_percent': 79,
'clear_percent': 93,
'cloud_cover': 0.07,
'cloud_percent': 7,
'columns': 8980,
'epsg_code': 32610,
'ground_control': True,
'gsd': 3.9,
'heavy_haze_percent': 0,
'instrument': 'PS2',
'item_type': 'PSScene4Band',
'light_haze_percent': 0,
'origin_x': 559953,
'origin_y': 4162266,
'pixel_resolution': 3,
'provider': 'planetscope',
'published': '2019-05-31T02:03:28.727Z',
'quality_category': 'standard',
'rows': 4460,
'satellite_id': '100c',
'shadow_percent': 0,
'snow_ice_percent': 0,
'strip_id': '2405105',
'sun_azimuth': 120.8,
'sun_elevation': 64.2,
'updated': '2019-05-31T03:39:55.970Z',
'usable_data': 0,
'view_angle': 4.9,
'visible_confidence_percent': 56,
'visible_percent': 93
},
'type': 'Feature'
}
每个id是图像ID，geometry包含该特定图像的坐标。我想使用这些坐标进行一些计算并返回类似以下的内容。
img_id area
xxxx nn
yyyy mm
到目前为止，我正在使用类似以下的python字典。
def strip_id_geom(results):
d = {}
for i in results.items_iter(100):
d['geometry_'+ i['id']] = i['geometry']
return d
我将按照以下方式转换数据。
def transform_coordinates(img_geojson):
co = []
if 'coordinates' in img_geojson:
for i in range(len(img_geojson["coordinates"])):
for j in zip(*img_geojson["coordinates"]):
co.append(tuple(*j))
else:
raise Exception('No coordinates exist in this geojson')
我的问题是：这是用于此目的的最佳数据结构吗？我的目标是要快速并针对内存使用进行优化。如果有帮助，则数据来自Plante Labs API。
谢谢
def strip_id_geom(results):
d = {}
for i in results.items_iter(100):
d['geometry_'+ i['id']] = i['geometry']
return d
我将按照以下方式转换数据。
def transform_coordinates(img_geojson):
co = []
if 'coordinates' in img_geojson:
for i in range(len(img_geojson["coordinates"])):
for j in zip(*img_geojson["coordinates"]):
co.append(tuple(*j))
else:
raise Exception('No coordinates exist in this geojson')
img_id area
xxxx nn
yyyy mm