def to_gdal_ds(self):
"""Export raster object to `gdal.DataSource`.
Returns
-------
ds: gdal.DataSource
DataSource converted from the raster object.
"""
ds = tgp.write_gdal_ds(self.data,
geo_transform=self.geo_transform,
projection=self.projection,
gdaldtype=self.gdaldtype,
no_data_value=self.no_data_value)
return ds
def rasterize_layer(src_vector,
rows,
cols,
geo_transform,
use_attribute,
all_touched=False,
no_data_value=0):
"""Rasterize vector data. Get the cell value in defined grid (rows, cols, geo_transform)
from its overlapped polygon.
Parameters
----------
src_vector: Geopandas.GeoDataFrame
Which vector data to be rasterize.
rows: int
Target rasterized image's rows.
cols: int
Target rasterized image's cols.
geo_transform: tuple
Target rasterized image's geo_transform which is the affine parameter.
use_attribute: str
The column to use as rasterized image value.
all_touched: bool, optioonal, default: False
Pixels that touch (not overlap over 50%) the polygon will be assign the use_attribute value of the polygon.
no_data_value: int or float
The pixels not covered by any polygon will be filled no_data_value.
Returns
-------
raster: Raster.
Rasterized result.
Examples
--------
>>> import geopandas as gpd
>>> import TronGisPy as tgp
>>> from TronGisPy import ShapeGrid
>>> from matplotlib import pyplot as plt
>>> ref_raster_fp = tgp.get_testing_fp('satellite_tif') # get the geoinfo from the raster
>>> src_vector_fp = tgp.get_testing_fp('satellite_tif_clipper') # read source shapefile as GeoDataFrame
>>> src_vector = gpd.read_file(src_vector_fp)
>>> src_vector['FEATURE'] = 1 # make the value to fill in the raster cell
>>> rows, cols, geo_transform = tgp.get_raster_info(ref_raster_fp, ['rows', 'cols', 'geo_transform'])
>>> raster = ShapeGrid.rasterize_layer(src_vector, rows, cols, geo_transform, use_attribute='FEATURE', no_data_value=99)
>>> fig, (ax1, ax2) = plt.subplots(1,2) # plot the result
>>> tgp.read_raster(ref_raster_fp).plot(ax=ax1)
>>> src_vector.plot(ax=ax1)
>>> ax1.set_title('polygon with ref_raster')
>>> raster.plot(ax=ax2)
>>> ax2.set_title('rasterized image')
>>> plt.show()
"""
# Open your shapefile
assert type(
src_vector
) is gpd.GeoDataFrame, "src_vector should be GeoDataFrame type."
assert use_attribute in src_vector.columns, "attribute not exists in src_vector."
gdaldtype = tgp.npdtype_to_gdaldtype(src_vector[use_attribute].dtype)
# projection = src_vector.crs.to_wkt() if src_vector.crs is not None else None
projection = pyproj.CRS(
src_vector.crs).to_wkt() if src_vector.crs is not None else None
src_shp_ds = ogr.Open(src_vector.to_json())
src_shp_layer = src_shp_ds.GetLayer()
# Create the destination raster data source
ds = tgp.write_gdal_ds(bands=1,
cols=cols,
rows=rows,
geo_transform=geo_transform,
gdaldtype=gdaldtype,
no_data_value=no_data_value)
# set it to the attribute that contains the relevant unique
options = ["ATTRIBUTE=" + use_attribute]
if all_touched:
options.append('ALL_TOUCHED=TRUE')
gdal.RasterizeLayer(
ds, [1], src_shp_layer, options=options
) # target_ds, band_list, source_layer, options = options
data = ds.GetRasterBand(1).ReadAsArray()
raster = tgp.Raster(data, geo_transform, projection, gdaldtype,
no_data_value)
return raster