def get_geo_attribute(self, return_geo_transform=False, crs=None):
"""Get geo_attributes (idx, idx_h, idx_w, geo_transform, geometry)
of all splitted images.
Parameters
----------
return_geo_transform: bool, optional, default: False
Return gdal geo_transform for each geometry in the output GeoDataFrame.
crs: str, optional
The crs for the output GeoDataFrame e.g. 'epsg:4326'.
Returns
-------
df_attribute: gpd.GeoDataFrame
The geo_attributes of all splitted images, which can be output as a shapefile.
"""
rows = []
for i in range(self.n_splitted_images):
idx_h , idx_w = self.convert_order_to_location_index(i)
h_start_inner, h_stop_inner = self.__convert_to_inner_index_h(idx_h, idx_h)
w_start_inner, w_stop_inner = self.__convert_to_inner_index_w(idx_w, idx_w)
left_top_coord = tgp.npidxs_to_coords([(h_start_inner, w_start_inner)], self.src_gt)[0]
left_buttom_coord = tgp.npidxs_to_coords([(h_start_inner, w_stop_inner)], self.src_gt)[0]
right_buttom_coord = tgp.npidxs_to_coords([(h_stop_inner, w_stop_inner)], self.src_gt)[0]
right_top_coord = tgp.npidxs_to_coords([(h_stop_inner, w_start_inner)], self.src_gt)[0]
x_min, y_max = left_top_coord
row = {
"idx":i,
"idx_h":idx_h,
"idx_w":idx_w,
"geo_transform":(x_min, self.src_gt[1], self.src_gt[2], y_max, self.src_gt[4], self.src_gt[5]),
"geometry": Polygon([left_top_coord,
left_buttom_coord,
right_buttom_coord,
right_top_coord,
left_top_coord]),
}
rows.append(row)
df_attribute = gpd.GeoDataFrame(rows, geometry='geometry')
if crs is not None:
df_attribute.crs = crs
elif self.proj is not None:
try:
df_attribute.crs = 'init:' + str(tgp.wkt_to_epsg(self.proj))
except:
df_attribute.crs = self.proj
if not return_geo_transform:
df_attribute.drop('geo_transform', axis=1, inplace=True)
return df_attribute
def __getitem__(self, slice_value):
if type(slice_value) in [int, slice]:
h_start, h_stop = self.__process_slice_value(slice_value)
w_start, w_stop = 0, self.n_steps_w
elif type(slice_value) == tuple:
h_start, h_stop = self.__process_slice_value(slice_value[0])
w_start, w_stop = self.__process_slice_value(slice_value[1])
h_start_inner, h_stop_inner = self.__convert_to_inner_index_h(h_start, h_stop)
w_start_inner, w_stop_inner = self.__convert_to_inner_index_w(w_start, w_stop)
data = self.src_image[h_start_inner:h_stop_inner, w_start_inner:w_stop_inner]
gt = np.array(self.src_gt).copy()
gt[[0, 3]] = tgp.npidxs_to_coords([(h_start_inner, w_start_inner)], self.src_gt)[0]
raster = tgp.Raster(data, gt, self.proj, self.gdaldtype, self.no_data_value)
return raster
def clip_raster_with_polygon(src_raster,
src_poly,
all_touched=False,
no_data_value=0):
"""Clip raster with polygon.
Parameters
----------
src_raster: Raster
Which raster data to be clipped.
src_poly: Geopandas.GeoDataFrame
The clipper(clipping boundary).
Returns
-------
dst_raster: Raster
Clipped result.
Examples
--------
>>> import geopandas as gpd
>>> import TronGisPy as tgp
>>> from TronGisPy import ShapeGrid
>>> from matplotlib import pyplot as plt
>>> src_raster_fp = tgp.get_testing_fp('satellite_tif')
>>> src_poly_fp = tgp.get_testing_fp('satellite_tif_clipper')
>>> src_raster = tgp.read_raster(src_raster_fp)
>>> src_poly = gpd.read_file(src_poly_fp)
>>> dst_raster = ShapeGrid.clip_raster_with_polygon(src_raster, src_poly)
>>> fig, (ax1, ax2) = plt.subplots(1, 2) # plot the result
>>> src_raster.plot(ax=ax1)
>>> src_poly.boundary.plot(ax=ax1)
>>> ax1.set_title('original image and clipper')
>>> dst_raster.plot(ax=ax2)
>>> ax2.set_title('clipped image')
>>> plt.show()
"""
assert src_raster.geo_transform is not None, "src_raster.geo_transform should not be None"
src_poly_copy = src_poly.copy()
src_poly_copy['value'] = 1
src_poly_raster = rasterize_layer_by_ref_raster(src_poly_copy,
src_raster,
use_attribute='value',
all_touched=all_touched,
no_data_value=0)
dst_raster = src_raster.copy()
dst_raster.data[~(src_poly_raster.data[:, :,
0].astype(bool))] = no_data_value
row_idxs, col_idxs, bands_idxs = np.where(src_poly_raster.data != 0)
rmin, rmax, cmin, cmax = np.min(row_idxs), np.max(row_idxs), np.min(
col_idxs), np.max(col_idxs)
dst_raster.data = dst_raster.data[rmin:rmax + 1, cmin:cmax + 1]
coords = tgp.npidxs_to_coords([(rmin, cmin)], src_raster.geo_transform)[0]
geo_transform = np.array(dst_raster.geo_transform)
geo_transform[[0, 3]] = coords
dst_raster.geo_transform = geo_transform
# src_ds = src_raster.to_gdal_ds()
# temp_dir = tgp.create_temp_dir_when_not_exists()
# src_shp_fp = os.path.join(temp_dir, 'src_poly.shp')
# src_poly.to_file(src_shp_fp)
# dst_ds = gdal.Warp('', src_ds, format= 'MEM', cutlineDSName=src_shp_fp, cropToCutline=True)
# dst_raster = tgp.read_gdal_ds(dst_ds)
return dst_raster