def save_tile_by_grid(self):
# query condition: zone of utm + bbox of wgs84
raster = rasterio.open(self.raster_file, 'r')
utm_zone = raster.crs['init'][-2:]
bbox = raster.bounds
utm_extent = [[bbox.left, bbox.top], [bbox.left, bbox.bottom], [bbox.right, bbox.bottom],
[bbox.right, bbox.top],
[bbox.left, bbox.top]]
utm2wgs = GeomTrans(str(raster.crs.wkt), 'EPSG:4326')
wgs_extent = utm2wgs.transform_points(utm_extent)
wgs_boundary = {'coordinates': [wgs_extent], 'type': 'Polygon'}
grid_dicts = self.search_grids(grid_index_name, utm_zone, wgs_boundary)
for grid in grid_dicts:
gridid = grid['_source']['gridid']
utm_geometry = grid['_source']['utm_geometry']
tile_file = self._get_tile(self.dataid, gridid)
tile_path = os.path.dirname(tile_file)
if not os.path.exists(tile_path):
os.makedirs(tile_path)
self.mask_image_by_geometry(utm_geometry, raster, tile_file)
utm_zone = grid['_source']['zone']
wgs_crs = grid['_source']['wgs_crs']
utm_crs = grid['_source']['utm_crs']
wgs_grid = grid['_source']['wgs_geometry']
utm_grid = grid['_source']['utm_geometry']
self.metadb.insert_tilemeta(tile_index_name, "landsat45_tiles", self.dataid, self.bandid, self.ctime,
self.sensor, tile_file, gridid, utm_zone, wgs_crs, utm_crs, wgs_grid, utm_grid)
def main(file):
# dataid for metadata
items = file.split("/")
dataid = items[-1].split(".")[0]
# query condition: zone of utm + bbox of wgs84
raster = rasterio.open(file, 'r')
utm_zone = raster.crs['init'][-2:]
bbox = raster.bounds
utm_extent = [[bbox.left, bbox.top], [bbox.left, bbox.bottom],
[bbox.right, bbox.bottom], [bbox.right, bbox.top],
[bbox.left, bbox.top]]
utm2wgs = GeomTrans(str(raster.crs.wkt), 'EPSG:4326')
wgs_extent = utm2wgs.transform_points(utm_extent)
wgs_boundary = {'coordinates': [wgs_extent], 'type': 'Polygon'}
grid_dicts = query_grids(utm_zone, wgs_boundary)
for grid in grid_dicts:
print(grid['_source']['gridid'])
utm_geometry = grid['_source']['utm_geometry']
tile_path = os.path.join(
'/tmp', dataid + '_' + grid['_source']['gridid'] + '.tif')
mask_image_by_geometry(utm_geometry, raster, tile_path)
def main(file):
# dataid for metadata
items = file.split("/")
dataid = items[-1].split(".")[0]
bandname = 'B0'+dataid[-1:]
time = dataid[14:22]
sensor = dataid[:2]
row = int(dataid[6:9])
path = int(dataid[10:13])
year = int(time[:4])
# query condition: zone of utm + bbox of wgs84
raster = rasterio.open(file, 'r')
utm_zone = raster.crs['init'][-2:]
bbox = raster.bounds
utm_extent = [[bbox.left, bbox.top], [bbox.left, bbox.bottom], [bbox.right, bbox.bottom], [bbox.right, bbox.top],
[bbox.left, bbox.top]]
utm2wgs = GeomTrans(str(raster.crs.wkt), 'EPSG:4326')
wgs_extent = utm2wgs.transform_points(utm_extent)
wgs_boundary = {'coordinates': [wgs_extent], 'type': 'Polygon'}
grid_dicts = query_grids(utm_zone, wgs_boundary)
for grid in grid_dicts:
print(grid['_source']['gridid'])
utm_geometry = grid['_source']['utm_geometry']
# tile_path = os.path.join("/tmp", dataid + '_' + grid['_source']['gridid'] + '.tif')
tile_path = "/LANDSAT/L45TM/%s/%s/%s/%s/%s" % (row, path, year, time, grid['_source']['gridid'])
tile_file = os.path.join(tile_path, dataid + '_' + grid['_source']['gridid'] + '.tif')
mask_image_by_geometry(utm_geometry, raster, tile_file)
# metadata for the tile clipped from landsat by a grid
geojson_l = {}
geojson_l['dataid']=dataid
geojson_l['bandid']=bandname
geojson_l["date_acquired"] = time
geojson_l["sensor"] = sensor
geojson_l['tile_path']=tile_path
geojson_l['gridid'] = grid['_source']['gridid']
geojson_l['zone'] = grid['_source']['zone']
geojson_l['wgs_crs'] = grid['_source']['wgs_crs']
geojson_l['utm_crs'] = grid['_source']['utm_crs']
geojson_l['wgs_grid'] = grid['_source']['wgs_geometry']
print(geojson_l['wgs_grid'])
geojson_l['utm_grid'] = grid['_source']['utm_geometry']
# print(geojson_l)
es.index(index=index_name, doc_type="landsat45_tiles", body=geojson_l)
def __init__(self, geomjson=None, geomproj=None, rsimage=None):
self.geomjson = geomjson
self.geomproj = geomproj
self.raster = rsimage
transform = GeomTrans(str(self.geomproj), str(self.raster.crs.wkt))
if geomjson['type'] == 'Polygon':
geojson_crs_transformed = transform.transform_points(
self.geomjson['coordinates'][0])
elif geomjson['type'] == 'LineString':
geojson_crs_transformed = transform.transform_points(
self.geomjson['coordinates'])
print(geojson_crs_transformed)
geometry = shape({
'coordinates': [geojson_crs_transformed],
'type': 'Polygon'
})
self.geometry = geometry
# json_list = []
# dr = ogr.GetDriverByName("ESRI Shapefile")
# shp_ds = dr.Open(grid_50)
# layer = shp_ds.GetLayer(0)
# sr = layer.GetSpatialRef()
# in_proj = sr.ExportToWkt()
# feat_num = layer.GetFeatureCount()
# for i in range(feat_num):
# feat = layer.GetFeature(i)
# geom = feat.GetGeometryRef()
#
# geojson = geom.ExportToWkt()
# json_list.append(geojson)
wgs_proj = 'EPSG:4326'
utm2wgs = GeomTrans(in_proj, wgs_proj)
china_zone = range(40, 61)
for zone in china_zone:
utm_proj = "EPSG:326%s" % (zone)
print(utm_proj)
wgs2utm = GeomTrans(wgs_proj, utm_proj)
# move geom by mv_degree
mv_degree = (zone - 50) * 6
# create two shps: wgs and utm
# 1 get driver
dr = ogr.GetDriverByName("ESRI Shapefile")
# 2 create shapedata
def mask_image_by_geometry(geomjson, geomproj, raster, tag, name):
print('the %s geometry' % name)
transform = GeomTrans(str(geomproj), str(raster.crs.wkt))
geojson_crs_transformed = transform.transform_points(
geomjson['coordinates'][0])
geometry = shape({
'coordinates': [geojson_crs_transformed],
'type': 'Polygon'
})
bbox = raster.bounds
extent = [[bbox.left, bbox.top], [bbox.left, bbox.bottom],
[bbox.right, bbox.bottom], [bbox.right, bbox.top]]
raster_boundary = shape({'coordinates': [extent], 'type': 'Polygon'})
if not geometry.intersects(raster_boundary):
return
# get pixel coordinates of the geometry's bounding box,
ll = raster.index(
*geometry.bounds[0:2]) # lowerleft bounds[0:2] xmin, ymin
ur = raster.index(
*geometry.bounds[2:4]) # upperright bounds[2:4] xmax, ymax
# # create an affine transform for the subset data
# t = raster.transform
# shifted_affine = Affine(t.a, t.b, t.c + ll[1] * t.a, t.d, t.e, t.f + ur[0] * t.e)
#
# # read the subset of the data into a numpy array
# window = ((ur[0], ll[0] + 1), (ll[1], ur[1] + 1))
# when the shapefile polygon is larger than the raster
row_begin = ur[0] if ur[0] > 0 else 0
row_end = ll[0] + 1 if ll[0] > -1 else 0
col_begin = ll[1] if ll[1] > 0 else 0
col_end = ur[1] + 1 if ur[1] > -1 else 0
window = ((row_begin, row_end), (col_begin, col_end))
# create an affine transform for the subset data
t = raster.transform
shifted_affine = Affine(t.a, t.b, t.c + col_begin * t.a, t.d, t.e,
t.f + row_begin * t.e)
out_data = raster.read(window=window)
# check whether the numpy array is empty or not?
if out_data.size == 0 or np.all(out_data == raster.nodata):
print('the grid does not intersect with the raster')
return
# if len(out_data)==0:
# print('the grid does not intersect with the raster')
# return
with rasterio.open("/tmp/mask_%s.tif" % name,
'w',
driver='GTiff',
width=out_data.shape[2],
height=out_data.shape[1],
crs=raster.crs,
transform=shifted_affine,
dtype=np.uint16,
nodata=256,
count=raster.count,
indexes=raster.indexes) as dst:
# Write the src array into indexed bands of the dataset. If `indexes` is a list, the src must be a 3D array of matching shape. If an int, the src must be a 2D array.
dst.write(out_data.astype(rasterio.uint16), indexes=raster.indexes)
# bands_num = raster.count
# multi_bands_data = []
# for i in range(bands_num):
# # band_name = raster.indexes[i]
# data = raster.read(i + 1, window=window)
# # rasterize the geometry
# mask = rasterio.features.rasterize([(geometry, 0)], out_shape=data.shape, transform=shifted_affine, fill=1,
# all_touched=True, dtype=np.uint8)
#
# # create a masked numpy array
# masked_data = np.ma.array(data=data, mask=mask.astype(bool), dtype=np.float32)
# masked_data.data[masked_data.mask] = np.nan # raster.profile nodata is 256
# out_image = masked_data.data
# multi_bands_data.append(out_image)
# out_data = np.array(multi_bands_data)
# with rasterio.open("/tmp/mask_%s.tif" % name, 'w', driver='GTiff', width=out_data.shape[2],
# height=out_data.shape[1], crs=raster.crs, transform=shifted_affine, dtype=np.uint16, nodata=256,
# count=bands_num, indexes=raster.indexes) as dst:
# dst.write(out_image.astype(rasterio.uint16), 1)
# create a masked label numpy array
out_shape = ((window[0][1] - window[0][0]), (window[1][1] - window[1][0]))
mask = rasterio.features.rasterize([(geometry, 0)],
out_shape=out_shape,
transform=shifted_affine,
fill=1,
all_touched=True,
dtype=np.uint8)
label_array = np.empty(out_shape)
label_array[mask == 0] = tag
with rasterio.open("/tmp/label_%s.tif" % name,
'w',
driver='GTiff',
width=out_shape[1],
height=out_shape[0],
crs=raster.crs,
transform=shifted_affine,
dtype=np.uint16,
nodata=256,
count=1) as dst:
dst.write(label_array.astype(rasterio.uint16), 1)
def mask_image_by_geojson_polygon(geojson_polygon, geoproj, raster):
'''
:param geojson_polygon: the geojson format of a polygon
:param geoproj: the projection coordinate system of the input polygon
:param raster: the raster data after executing the raster = rasterio.open(raster_image_file, 'r')
:return: the data cut out from the raster by the polygon, and its geotransformation
'''
transform = GeomTrans(str(geoproj), str(raster.crs.wkt))
geojson_crs_transformed = transform.transform_points(
geojson_polygon['coordinates'][0])
geometry = shape({
'coordinates': [geojson_crs_transformed],
'type': 'Polygon'
})
bbox = raster.bounds
extent = [[bbox.left, bbox.top], [bbox.left, bbox.bottom],
[bbox.right, bbox.bottom], [bbox.right, bbox.top]]
raster_boundary = shape({'coordinates': [extent], 'type': 'Polygon'})
# if not geometry.intersects(raster_boundary):
# return
if not geometry.within(raster_boundary):
print('the geometry is not within the raster image')
return
# get pixel coordinates of the geometry's bounding box,
ll = raster.index(
*geometry.bounds[0:2]) # lowerleft bounds[0:2] xmin, ymin
ur = raster.index(
*geometry.bounds[2:4]) # upperright bounds[2:4] xmax, ymax
# create an affine transform for the subset data
t = raster.transform
shifted_affine = Affine(t.a, t.b, t.c + ll[1] * t.a, t.d, t.e,
t.f + ur[0] * t.e)
# read the subset of the data into a numpy array
window = ((ur[0], ll[0] + 1), (ll[1], ur[1] + 1))
bands_num = raster.count
multi_bands_data = []
for i in range(bands_num):
# band_name = raster.indexes[i]
data = raster.read(i + 1, window=window)
# rasterize the geometry
mask = rasterio.features.rasterize([(geometry, 0)],
out_shape=data.shape,
transform=shifted_affine,
fill=1,
all_touched=True,
dtype=np.uint8)
# create a masked numpy array
masked_data = np.ma.array(data=data,
mask=mask.astype(bool),
dtype=np.float32)
masked_data.data[
masked_data.mask] = np.nan # raster.profile nodata is 256
out_image = masked_data.data
multi_bands_data.append(out_image)
out_data = np.array(multi_bands_data)
return out_data, shifted_affine
def mask_image(geomjson, geomproj, raster, tag, name, size=(128, 128)):
print('the %s geometry' % name)
transform = GeomTrans(str(geomproj), str(raster.crs.wkt))
geojson_crs_transformed = transform.transform_points(
geomjson['coordinates'][0])
geometry = shape({
'coordinates': [geojson_crs_transformed],
'type': 'Polygon'
})
bbox = raster.bounds
extent = [[bbox.left, bbox.top], [bbox.left, bbox.bottom],
[bbox.right, bbox.bottom], [bbox.right, bbox.top]]
raster_boundary = shape({'coordinates': [extent], 'type': 'Polygon'})
if not geometry.intersects(raster_boundary):
return
# get pixel coordinates of the geometry's bounding box,
ll = raster.index(
*geometry.bounds[0:2]) # lowerleft bounds[0:2] xmin, ymin
ur = raster.index(
*geometry.bounds[2:4]) # upperright bounds[2:4] xmax, ymax
# create an affine transform for the subset data
t = raster.transform
shifted_affine = Affine(t.a, t.b, t.c + ll[1] * t.a, t.d, t.e,
t.f + ur[0] * t.e)
# read the subset of the data into a numpy array
window = ((ur[0], ll[0] + 1), (ll[1], ur[1] + 1)
) # rows, cols of ((xmax, xmin+1),(ymin, ymax+1))
window_shape = (
(window[0][1] - window[0][0]), (window[1][1] - window[1][0])
) # or data = raster.read(1,window=window); out_shape = data.shape;
# to generate 512*512 size mask
if window_shape[0] <= size[0] and window_shape[1] <= size[1]:
mask = rasterio.features.rasterize([(geometry, 0)],
out_shape=window_shape,
transform=shifted_affine,
fill=1,
all_touched=True,
dtype=np.uint8)
out_shape = np.zeros((size[0], size[1]))
# expand the window of rasterized polygon into specified size
mask_expanded = np.pad(mask, ((0, size[0] - window_shape[0]),
(0, size[1] - window_shape[1])),
mode='constant',
constant_values=1)
# generate label array
label_array = np.empty_like(mask_expanded, dtype=np.float)
label_array[mask_expanded == 0] = tag
with rasterio.open("/tmp/label_%s.tif" % name,
'w',
driver='GTiff',
width=size[1],
height=size[0],
crs=raster.crs,
transform=shifted_affine,
dtype=np.uint16,
nodata=256,
count=1) as dst:
dst.write(label_array.astype(rasterio.uint16), 1)
# read the original rs image into specified size
window_expanded = ((window[0][0], window[0][0] + size[0]),
(window[1][0], window[1][0] + size[1]))
out_data = raster.read(window=window_expanded)
with rasterio.open("/tmp/mask_%s2.tif" % name,
'w',
driver='GTiff',
width=size[1],
height=size[0],
crs=raster.crs,
transform=shifted_affine,
dtype=np.uint16,
nodata=256,
count=raster.count,
indexes=raster.indexes) as dst:
# Write the src array into indexed bands of the dataset. If `indexes` is a list, the src must be a 3D array of matching shape. If an int, the src must be a 2D array.
dst.write(out_data.astype(rasterio.uint16), indexes=raster.indexes)