def test_gdal_rasterize_lib_3():
import test_cli_utilities
if test_cli_utilities.get_gdal_contour_path() is None:
pytest.skip()
gdaltest.runexternal(test_cli_utilities.get_gdal_contour_path() + ' ../gdrivers/data/n43.dt0 tmp/n43dt0.shp -i 10 -3d')
with gdaltest.error_handler():
ds = gdal.Rasterize('/vsimem/bogus.tif', 'tmp/n43dt0.shp')
assert ds is None, 'did not expected success'
ds = gdal.Rasterize('', 'tmp/n43dt0.shp', format='MEM', outputType=gdal.GDT_Byte, useZ=True, layers=['n43dt0'], width=121, height=121, noData=0)
ogr.GetDriverByName('ESRI Shapefile').DeleteDataSource('tmp/n43dt0.shp')
ds_ref = gdal.Open('../gdrivers/data/n43.dt0')
assert ds.GetRasterBand(1).GetNoDataValue() == 0.0, \
'did not get expected nodata value'
assert ds.RasterXSize == 121 and ds.RasterYSize == 121, \
'did not get expected dimensions'
gt_ref = ds_ref.GetGeoTransform()
gt = ds.GetGeoTransform()
for i in range(6):
assert abs(gt[i] - gt_ref[i]) <= 1e-6, 'did not get expected geotransform'
wkt = ds.GetProjectionRef()
assert wkt.find("WGS_1984") != -1, 'did not get expected SRS'
def test_gdal_rasterize_lib_102():
target_ds = gdal.GetDriverByName('MEM').Create('', 353, 226)
target_ds.GetRasterBand(1).Fill(255)
md = {
'HEIGHT_OFF': '430',
'HEIGHT_SCALE': '501',
'LAT_OFF': '-0.0734',
'LAT_SCALE': '0.2883',
'LINE_DEN_COEFF': '1 0.0002790015 0.001434672 1.481312e-07 5.866139e-06 1.878347e-07 -7.1677e-08 -1.099383e-05 1.968371e-06 -5.50509e-06 0 -1.227539e-08 0 0 2.40682e-07 -1.144941e-08 0 -1.884406e-08 0 0',
'LINE_NUM_COEFF': '0.002744972 -0.382552 -1.279674 -0.0001147828 0.001140472 1.262068e-07 -1.69402e-07 -0.005830625 -0.001964747 0 -2.006924e-07 3.066144e-07 3.005069e-06 2.103552e-06 -1.981401e-06 -1.636042e-06 7.045145e-06 -5.699422e-08 1.169591e-07 0',
'LINE_OFF': '112.98500331785',
'LINE_SCALE': '113.01499668215',
'LONG_OFF': '-4.498',
'LONG_SCALE': '0.5511',
'SAMP_DEN_COEFF': '1 0.001297913 0.0005878427 -0.0004554233 -7.353773e-05 7.928584e-06 -1.826261e-06 9.516839e-05 5.332457e-07 -4.236274e-05 -1.89316e-08 -1.520878e-06 -8.941367e-07 -7.770314e-07 1.413225e-06 9.681702e-08 -4.724849e-08 -2.244317e-07 1.0665e-08 4.212225e-08',
'SAMP_NUM_COEFF': '0.01819195 1.091934 -0.1976373 0.003166136 0.002648549 0.0003527143 -6.27017e-05 -0.01889831 -0.0005888535 1.729232e-07 3.037208e-06 0.000174218 -3.129558e-05 -4.602708e-05 2.724941e-05 -9.314161e-07 8.328574e-06 -1.240182e-05 -4.652876e-07 -1.322223e-07',
'SAMP_OFF': '176.619681301916',
'SAMP_SCALE': '177.068486184099'
}
target_ds.SetMetadata(md, "RPC")
vector_ds = \
gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
sr = osr.SpatialReference()
sr.ImportFromEPSG(4326)
rast_lyr = vector_ds.CreateLayer('', srs=sr)
# polygon with empty exterior ring
geom = ogr.CreateGeometryFromWkt("""POLYGON ((-3.967081661665 0.0003251483162,-4.976841813513 0.0003251483162,-4.904140485134 0.2151010973779,-4.904124933286 0.2151433982916,-4.904107210626 0.2151848366149,-4.904087364764 0.2152253010321,-4.904065449011 0.2152646828438,-4.904041522241 0.2153028762585,-4.904015648726 0.2153397786774,-4.903987897971 0.2153752909697,-4.903958344523 0.2154093177387,-4.903927067771 0.2154417675784,-4.903894151734 0.2154725533188,-4.903859684834 0.2155015922603,-4.90382375966 0.2155288063955,-4.903786472717 0.2155541226193,-4.903747924169 0.2155774729247,-4.90370821757 0.2155987945858,-4.903667459582 0.2156180303263,-4.903625759694 0.2156351284732,-4.903583229924 0.2156500430959,-4.90353998452 0.2156627341291,-4.903496139651 0.2156731674811,-4.9034518131 0.2156813151247,-4.903407123938 0.2156871551729,-4.903362192216 0.2156906719376,-4.903317138633 0.2156918559717,-4.903272084216 0.2156907040946,-4.903227149995 0.2156872194006,-4.903182456677 0.2156814112505,-3.945022212098 0.0658193544758,-3.94497805444 0.0658112751384,-3.944934372574 0.0658009277184,-3.944891282916 0.0657883397923,-3.944848900303 0.0657735449078,-3.944807337687 0.0657565824944,-3.944766705836 0.0657374977579,-3.944727113036 0.0657163415606,-3.944688664805 0.0656931702851,-3.94465146361 0.0656680456844,-3.944615608594 0.0656410347174,-3.944581195314 0.0656122093701,-3.944548315483 0.065581646464,-3.944517056729 0.0655494274513,-3.944487502358 0.065515638198,-3.944459731134 0.0654803687547,-3.944433817071 0.0654437131168,-3.944409829229 0.0654057689742,-3.94438783154 0.0653666374506,-3.944367882627 0.0653264228341,-3.944350035657 0.0652852322995,-3.944334338192 0.0652431756223,-3.944320832068 0.0652003648864,-3.944309553279 0.0651569141855,-3.944300531884 0.0651129393185,-3.944293791926 0.0650685574815,-3.944289351367 0.0650238869551,-3.944287222041 0.0649790467894,-3.944287409623 0.0649341564864,-3.944289913614 0.0648893356818,-3.94429472734 0.0648447038262,-3.944301837972 0.0648003798665,-3.94431122656 0.064756481929,-3.944322868082 0.0647131270048,-3.944336731513 0.0646704306378,-3.967081661665 0.0003251483162))""")
feat = ogr.Feature(rast_lyr.GetLayerDefn())
feat.SetGeometryDirectly(geom)
rast_lyr.CreateFeature(feat)
ret = gdal.Rasterize(target_ds, vector_ds, burnValues=[0])
assert ret == 1
# Check results.
checksum = target_ds.GetRasterBand(1).Checksum()
assert checksum == 1604, 'Did not get expected image checksum'
# Re-try with transformer options
target_ds.GetRasterBand(1).Fill(255)
ret = gdal.Rasterize(target_ds, vector_ds, burnValues=[0],
transformerOptions=['RPC_HEIGHT=1000'])
assert ret == 1
# Check results.
checksum = target_ds.GetRasterBand(1).Checksum()
assert checksum == 2003, 'Did not get expected image checksum'
target_ds = None
def test_gdal_rasterize_lib_3():
import test_cli_utilities
if test_cli_utilities.get_gdal_contour_path() is None:
return 'skip'
gdaltest.runexternal(test_cli_utilities.get_gdal_contour_path() +
' ../gdrivers/data/n43.dt0 tmp/n43dt0.shp -i 10 -3d')
with gdaltest.error_handler():
ds = gdal.Rasterize('/vsimem/bogus.tif', 'tmp/n43dt0.shp')
if ds is not None:
gdaltest.post_reason('did not expected success')
return 'fail'
ds = gdal.Rasterize('',
'tmp/n43dt0.shp',
format='MEM',
outputType=gdal.GDT_Byte,
useZ=True,
layers=['n43dt0'],
width=121,
height=121,
noData=0)
ogr.GetDriverByName('ESRI Shapefile').DeleteDataSource('tmp/n43dt0.shp')
ds_ref = gdal.Open('../gdrivers/data/n43.dt0')
if ds.GetRasterBand(1).GetNoDataValue() != 0.0:
gdaltest.post_reason('did not get expected nodata value')
return 'fail'
if ds.RasterXSize != 121 or ds.RasterYSize != 121:
gdaltest.post_reason('did not get expected dimensions')
return 'fail'
gt_ref = ds_ref.GetGeoTransform()
gt = ds.GetGeoTransform()
for i in range(6):
if (abs(gt[i] - gt_ref[i]) > 1e-6):
gdaltest.post_reason('did not get expected geotransform')
print(gt)
print(gt_ref)
return 'fail'
wkt = ds.GetProjectionRef()
if wkt.find("WGS_1984") == -1:
gdaltest.post_reason('did not get expected SRS')
print(wkt)
return 'fail'
return 'success'
def test_gdal_rasterize_lib_100():
target_ds = gdal.GetDriverByName('MEM').Create('', 100, 100)
# Create a layer to rasterize from.
vector_ds = \
gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
rast_lyr = vector_ds.CreateLayer('rast1')
wkt_geom = 'POLYGON((20 20,20 80,80 80,80 20,20 20))'
feat = ogr.Feature(rast_lyr.GetLayerDefn())
feat.SetGeometryDirectly(ogr.Geometry(wkt=wkt_geom))
rast_lyr.CreateFeature(feat)
ret = gdal.Rasterize(target_ds, vector_ds, burnValues=[255])
if ret != 1:
gdaltest.post_reason('fail')
return 'fail'
# Check results.
expected = 44190
checksum = target_ds.GetRasterBand(1).Checksum()
if checksum != expected:
print(checksum)
gdaltest.post_reason('Did not get expected image checksum')
return 'fail'
target_ds = None
return 'success'
def test_gdal_rasterize_lib_100():
target_ds = gdal.GetDriverByName('MEM').Create('', 100, 100)
# Create a layer to rasterize from.
vector_ds = \
gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
rast_lyr = vector_ds.CreateLayer('rast1')
wkt_geom = 'POLYGON((20 20,20 80,80 80,80 20,20 20))'
feat = ogr.Feature(rast_lyr.GetLayerDefn())
feat.SetGeometryDirectly(ogr.Geometry(wkt=wkt_geom))
rast_lyr.CreateFeature(feat)
ret = gdal.Rasterize(target_ds, vector_ds, burnValues=[255])
assert ret == 1
# Check results.
expected = 44190
checksum = target_ds.GetRasterBand(1).Checksum()
assert checksum == expected, 'Did not get expected image checksum'
target_ds = None
def test_gdal_rasterize_lib_4():
# Setup working spatial reference
# sr_wkt = 'LOCAL_CS["arbitrary"]'
# sr = osr.SpatialReference( sr_wkt )
sr = osr.SpatialReference()
sr.ImportFromEPSG(32631)
sr_wkt = sr.ExportToWkt()
# Create a raster to rasterize into.
for optim in ['RASTER', 'VECTOR', 'AUTO']:
target_ds = gdal.GetDriverByName('MEM').Create('', 100, 100, 3,
gdal.GDT_Byte)
target_ds.SetGeoTransform((1000, 1, 0, 1100, 0, -1))
target_ds.SetProjection(sr_wkt)
# Create a layer to rasterize from.
vector_ds = \
gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
rast_lyr = vector_ds.CreateLayer('rast1', srs=sr)
rast_lyr.GetLayerDefn()
field_defn = ogr.FieldDefn('foo')
rast_lyr.CreateField(field_defn)
# Add a polygon.
wkt_geom = 'POLYGON((1020 1030,1020 1045,1050 1045,1050 1030,1020 1030))'
feat = ogr.Feature(rast_lyr.GetLayerDefn())
feat.SetGeometryDirectly(ogr.Geometry(wkt=wkt_geom))
rast_lyr.CreateFeature(feat)
# Add feature without geometry to test fix for #3310
feat = ogr.Feature(rast_lyr.GetLayerDefn())
rast_lyr.CreateFeature(feat)
# Add a linestring.
wkt_geom = 'LINESTRING(1000 1000, 1100 1050)'
feat = ogr.Feature(rast_lyr.GetLayerDefn())
feat.SetGeometryDirectly(ogr.Geometry(wkt=wkt_geom))
rast_lyr.CreateFeature(feat)
ret = gdal.Rasterize(target_ds, vector_ds, bands=[3, 2, 1], burnValues=[200, 220, 240], layers='rast1', optim=optim)
assert ret == 1
# Check results.
expected = 6452
checksum = target_ds.GetRasterBand(2).Checksum()
if checksum != expected:
print(checksum, optim)
pytest.fail('Did not get expected image checksum')
target_ds = None
def test_gdal_rasterize_lib_101():
target_ds = gdal.GetDriverByName('MEM').Create('', 100, 100)
# Create a layer to rasterize from.
vector_ds = \
gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
rast_lyr = vector_ds.CreateLayer('rast1')
# polygon with empty exterior ring
geom = ogr.CreateGeometryFromJson(
'{ "type": "Polygon", "coordinates": [ [ ] ] }')
feat = ogr.Feature(rast_lyr.GetLayerDefn())
feat.SetGeometryDirectly(geom)
rast_lyr.CreateFeature(feat)
ret = gdal.Rasterize(target_ds, vector_ds, burnValues=[255])
assert ret == 1
# Check results.
checksum = target_ds.GetRasterBand(1).Checksum()
assert checksum == 0, 'Did not get expected image checksum'
target_ds = None
def gdalMask(rasterpath):
vector_layer = "/Data/area.shp"
raster_layer = rasterpath
target_layer = rasterpath + "mask.tif"
# open the raster layer and get its relevant properties
raster_ds = gdal.Open(raster_layer, gdal.GA_ReadOnly)
xSize = raster_ds.RasterXSize
ySize = raster_ds.RasterYSize
geotransform = raster_ds.GetGeoTransform()
projection = raster_ds.GetProjection()
# create the target layer (1 band)
driver = gdal.GetDriverByName('GTiff')
target_ds = driver.Create(target_layer,
xSize,
ySize,
bands=1,
eType=gdal.GDT_Byte,
options=["COMPRESS=DEFLATE"])
target_ds.SetGeoTransform(geotransform)
target_ds.SetProjection(projection)
# rasterize the vector layer into the target one
ds = gdal.Rasterize(target_ds, vector_layer)
target_ds = None
return target_layer
def burn_vector_mask_into_raster(raster_path, vector_path, out_path,
vector_field=None):
"""Create a new raster based on the input raster with vector features
burned into the raster. To be used as a mask for pixels in the vector.
Parameters
----------
raster_path : str
vector_path : str
out_path : str
Path for output raster. Format and Datatype are the same as ``ras``.
vector_field : str or None
Name of a field in the vector to burn values from. If None, all vector
features are burned with a constant value of 1.
Returns
-------
gdal.Dataset
Single band raster with vector geometries burned.
"""
ras = open_raster(raster_path)
vec = open_vector(vector_path)
# Check EPSG are same, if not reproject vector.
if not same_epsg(ras, vec):
raise ValueError(
'Raster and vector are not the same EPSG.\n'
'{} != {}'.format(get_epsg(ras), get_epsg(vec))
)
# Create an empty for GDALRasterize to burn vector values to.
out_ds = _create_empty_raster(ras, out_path, n_bands=1, no_data_value=0)
if vector_field is None:
# Use a constant value for all features.
burn_values = [1]
attribute = None
else:
# Use the values given in the vector field.
burn_values = None
attribute = vector_field
# Options for Rasterize.
# note: burnValues and attribute are exclusive.
rasterize_opts = gdal.RasterizeOptions(
bands=[1],
burnValues=burn_values,
attribute=attribute,
allTouched=True)
_ = gdal.Rasterize(out_ds, vector_path, options=rasterize_opts)
# Explicitly close raster to ensure it is saved.
out_ds.FlushCache()
out_ds = None
return open_raster(out_path)
def test_gdal_rasterize_lib_multipolygon():
sr_wkt = 'LOCAL_CS["arbitrary"]'
sr = osr.SpatialReference(sr_wkt)
# Try rasterizing a multipolygon
vector_ds = gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
layer = vector_ds.CreateLayer('', sr)
feature = ogr.Feature(layer.GetLayerDefn())
feature.SetGeometryDirectly(
ogr.CreateGeometryFromWkt(
'MULTIPOLYGON (((0 0,0 1,1 1,0 0)),((1 1,2 1,2 0,1 1)))'))
layer.CreateFeature(feature)
target_ds = gdal.GetDriverByName('MEM').Create('', 3, 2)
target_ds.SetGeoTransform((-0.5, 1, 0, 1.5, 0, -1))
target_ds.SetSpatialRef(sr)
ret = gdal.Rasterize(target_ds, vector_ds, burnValues=[10])
assert ret == 1
cs1 = target_ds.GetRasterBand(1).Checksum()
# And now each of its parts
vector_ds = gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
layer = vector_ds.CreateLayer('', sr)
feature = ogr.Feature(layer.GetLayerDefn())
feature.SetGeometryDirectly(
ogr.CreateGeometryFromWkt('POLYGON ((0 0,0 1,1 1,0 0))'))
layer.CreateFeature(feature)
feature = ogr.Feature(layer.GetLayerDefn())
feature.SetGeometryDirectly(
ogr.CreateGeometryFromWkt('POLYGON ((1 1,2 1,2 0,1 1))'))
layer.CreateFeature(feature)
target_ds = gdal.GetDriverByName('MEM').Create('', 3, 2)
target_ds.SetGeoTransform((-0.5, 1, 0, 1.5, 0, -1))
target_ds.SetSpatialRef(sr)
ret = gdal.Rasterize(target_ds, vector_ds, burnValues=[10])
assert ret == 1
cs2 = target_ds.GetRasterBand(1).Checksum()
# Check that results are the same
assert cs1 == cs2
def shape_rasterize(shp_path, out_path, tif_path):
"""
统计矢量转换为栅格
:param shp_path: 转换矢量
:param out_path: 输出路径 若设置为空字符串则默认以MEM模式输出
:param tif_path: 为转栅格提供xy向分辨率
:return:
"""
tif_obj = GeoTiffFile(tif_path)
tif_obj.readTif()
x_res = tif_obj.getXRes()
y_res = tif_obj.getYRes()
shp_obj = ShapeFile(shp_path)
shp_obj.readShp()
shp_extend = shp_obj.getExtent()
out_bounds = (shp_extend["xMin"], shp_extend["yMin"],
shp_extend["xMax"], shp_extend["yMax"])
if out_path == '':
options = gdal.RasterizeOptions(outputBounds=out_bounds,
outputType=gdal.GDT_UInt16,
noData=65535,
attribute="obj_id",
useZ=False,
xRes=x_res,
yRes=y_res,
format="MEM")
ds = gdal.Rasterize(out_path, shp_path, options=options)
else:
options = gdal.RasterizeOptions(outputBounds=out_bounds,
outputType=gdal.GDT_UInt16,
noData=65535,
attribute="obj_id",
useZ=False,
xRes=x_res,
yRes=y_res,
format="GTiff")
ds = gdal.Rasterize(out_path, shp_path, options=options)
return ds
def PolygonToRaster(in_path,
out_path,
x_res,
y_res,
field_name,
out_bounds=None,
no_data=65535):
"""
面矢量转为栅格
:param in_path: str 输入矢量文件路径
:param out_path: str 输出栅格文件路径
:param x_res: float 输出栅格x向分辨率
:param y_res: float 输出栅格y向分辨率
:param field_name: str 栅格赋值的矢量字段名
:param out_bounds: tuple 输出栅格范围 [xmin, ymin, xmax, ymax]
:param no_data: int 无效值大小
:return:
"""
try:
if not out_bounds:
gdal.SetConfigOption("GDAL_FILENAME_IS_UTF8",
"NO") # 为了支持中文路径，请添加
gdal.SetConfigOption("SHAPE_ENCODING",
"GBK") # 为了使属性表字段支持中文，请添加
ogr.RegisterAll() # 注册所有的驱动
layer = ogr.Open(in_path, gdal.GA_ReadOnly).GetLayer(0)
shp_extent = layer.GetExtent()
out_bounds = (shp_extent[0], shp_extent[2], shp_extent[1],
shp_extent[3])
options = gdal.RasterizeOptions(outputBounds=out_bounds,
outputType=gdal.GDT_UInt16,
noData=no_data,
attribute=field_name,
useZ=False,
xRes=x_res,
yRes=y_res,
format="GTiff")
ds = gdal.Rasterize(out_path, in_path, options=options)
if not ds:
return False
del ds
return True
except Exception as e:
print(e)
return False
def rasterize(in_lyr_path, out_raster_path, template_path):
"""Rasterizing an input
Args:
in_lyr_path ([type]): [description]
out_raster_ ([type]): [description]
template_path ([type]): [description]
"""
log = Logger('VBETRasterize')
ds_path, lyr_path = VectorBase.path_sorter(in_lyr_path)
progbar = ProgressBar(100, 50, "Rasterizing ")
with rasterio.open(template_path) as raster:
t = raster.transform
raster_bounds = raster.bounds
def poly_progress(progress, _msg, _data):
progbar.update(int(progress * 100))
# Rasterize the features (roads, rail etc) and calculate a raster of Euclidean distance from these features
progbar.update(0)
# Rasterize the polygon to a temporary file
with TempRaster('vbet_rasterize') as tempfile:
log.debug('Temporary file: {}'.format(tempfile.filepath))
gdal.Rasterize(
tempfile.filepath,
ds_path,
layers=[lyr_path],
xRes=t[0],
yRes=t[4],
burnValues=1,
outputType=gdal.GDT_Int16,
creationOptions=['COMPRESS=LZW'],
# outputBounds --- assigned output bounds: [minx, miny, maxx, maxy]
outputBounds=[
raster_bounds.left, raster_bounds.bottom, raster_bounds.right,
raster_bounds.top
],
callback=poly_progress)
progbar.finish()
# Now mask the output correctly
mask_rasters_nodata(tempfile.filepath, template_path, out_raster_path)
def processAlgorithm(self, parameters, context, feedback):
self.feedback = feedback # Needed for callback function
geojson = json.loads(
self.parameterAsString(parameters, 'INPUT', context))
model_file = self.parameterAsFile(parameters, 'MODEL_FILE', context)
out_file = self.parameterAsFile(parameters, 'OUTPUT', context)
json_file = GetTempFilename('.geojson')
with open(json_file, 'w') as f:
json.dump(geojson, f, separators=(',', ': '))
gdal.UseExceptions()
# Assumes an image with no rotation
gt = gdal.Info(model_file, format='json')['geoTransform']
x_size, y_size = gdal.Info(model_file, format='json')['size']
x_min = min(gt[0], gt[0] + x_size * gt[1])
x_max = max(gt[0], gt[0] + x_size * gt[1])
y_min = min(gt[3], gt[3] + y_size * gt[5])
y_max = max(gt[3], gt[3] + y_size * gt[5])
output_bounds = [x_min, y_min, x_max, y_max]
x_res = gt[1]
y_res = gt[5]
res = gdal.Rasterize(
out_file,
json_file,
format='GTiff',
outputBounds=output_bounds,
initValues=-32767, # Areas that are masked out
burnValues=1, # Areas that are NOT masked out
xRes=x_res,
yRes=y_res,
outputSRS="epsg:4326",
outputType=gdal.GDT_Int16,
creationOptions=['COMPRESS=LZW'],
callback=self.progress_callback)
os.remove(json_file)
if not res or self.feedback.isCanceled():
return {'SUCCESS': False}
else:
return {'SUCCESS': True}
def work(self):
self.toggle_show_progress.emit(True)
self.toggle_show_cancel.emit(True)
json_file = tempfile.NamedTemporaryFile(suffix='.geojson').name
with open(json_file, 'w') as f:
json.dump(self.geojson, f, separators=(',', ': '))
f.close()
gdal.UseExceptions()
if self.model_file:
# Assumes an image with no rotation
gt = gdal.Info(self.model_file, format='json')['geoTransform']
x_size, y_size = gdal.Info(self.model_file, format='json')['size']
x_min = min(gt[0], gt[0] + x_size * gt[1])
x_max = max(gt[0], gt[0] + x_size * gt[1])
y_min = min(gt[3], gt[3] + y_size * gt[5])
y_max = max(gt[3], gt[3] + y_size * gt[5])
output_bounds = [x_min, y_min, x_max, y_max]
x_res = gt[1]
y_res = gt[5]
else:
output_bounds = None
x_res = None
y_res = None
res = gdal.Rasterize(
self.out_file,
json_file,
format='GTiff',
outputBounds=output_bounds,
initValues=-32767, # Areas that are masked out
burnValues=1, # Areas that are NOT masked out
xRes=x_res,
yRes=y_res,
outputSRS="epsg:4326",
outputType=gdal.GDT_Int16,
creationOptions=['COMPRESS=LZW'],
callback=self.progress_callback)
if res:
return True
else:
return None
def shp_rasterize(in_path,
out_path,
xres,
yres,
field_name,
out_bounds=None,
nodataValue=65535):
"""
矢量文件栅格化
:param in_path: str: 输入矢量文件路径
:param out_path: str: 输出栅格文件路径
:param xres: float: x向分辨率
:param yres: float: y向分辨率
:param field_name: str: 栅格化字段
:return:
"""
try:
shp_obj = ShapeFile(in_path)
shp_obj.readShp()
if not out_bounds:
shp_extend = shp_obj.getExtent()
out_bounds = (shp_extend["xMin"], shp_extend["yMin"],
shp_extend["xMax"], shp_extend["yMax"])
# outputBounds [xmin, ymin, xmax, ymax]
options = gdal.RasterizeOptions(outputBounds=out_bounds,
outputType=gdal.GDT_UInt16,
noData=nodataValue,
attribute=field_name,
useZ=False,
xRes=xres,
yRes=yres,
format="GTiff")
ds = gdal.Rasterize(out_path, in_path, options=options)
if not ds:
return False
del ds
return True
except Exception as e:
print(e)
return False
def rasterize(shp, resolution, output_dest):
"""Convert vector to raster."""
input_shp = ogr.Open(shp)
shp_layer = input_shp.GetLayer()
pixel_size = resolution
xmin, xmax, ymin, ymax = shp_layer.GetExtent()
head, tail = os.path.split(shp)
output_raster = output_dest + tail[:-4] + '.tif'
print(output_raster)
ds = gdal.Rasterize(output_raster,
shp,
xRes=pixel_size,
yRes=pixel_size,
burnValues=255,
outputBounds=[xmin, ymin, xmax, ymax],
outputType=gdal.GDT_Byte)
ds = None
return output_raster
def gdal_rasterize(src, dst, options):
"""
a simple wrapper for :osgeo:func:`gdal.Rasterize`
Parameters
----------
src: str or :osgeo:class:`ogr.DataSource`
the input data set
dst: str
the output data set
options: dict
additional parameters passed to :osgeo:func:`gdal.Rasterize`; see :osgeo:func:`gdal.RasterizeOptions`
Returns
-------
"""
out = gdal.Rasterize(dst, src, options=gdal.RasterizeOptions(**options))
out = None
def test_gdal_rasterize_lib_inverse():
sr_wkt = 'LOCAL_CS["arbitrary"]'
sr = osr.SpatialReference(sr_wkt)
# Try rasterizing a multipolygon
vector_ds = gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
layer = vector_ds.CreateLayer('', sr)
feature = ogr.Feature(layer.GetLayerDefn())
feature.SetGeometryDirectly(ogr.CreateGeometryFromWkt('MULTIPOLYGON (((1 1,1 9,4 9,4 1,1 1),(2 2,2 8,3 8,3 2,2 2)))'))
layer.CreateFeature(feature)
feature = ogr.Feature(layer.GetLayerDefn())
feature.SetGeometryDirectly(ogr.CreateGeometryFromWkt('POLYGON ((5 1,5 9,9 9,9 1,5 1))'))
layer.CreateFeature(feature)
# Will not be rasterized
feature = ogr.Feature(layer.GetLayerDefn())
feature.SetGeometryDirectly(ogr.CreateGeometryFromWkt('POINT (5 5)'))
layer.CreateFeature(feature)
target_ds = gdal.GetDriverByName('MEM').Create('', 11, 11)
target_ds.SetGeoTransform((-0.5, 1, 0, 10.5, 0, -1))
target_ds.SetSpatialRef(sr)
with gdaltest.error_handler():
ret = gdal.Rasterize(target_ds, vector_ds, burnValues=[9], inverse=True)
assert ret == 1
expected = (9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 0, 0, 0, 9, 0, 0, 0, 0, 9,
9, 9, 0, 9, 0, 9, 0, 0, 0, 0, 9,
9, 9, 0, 9, 0, 9, 0, 0, 0, 0, 9,
9, 9, 0, 9, 0, 9, 0, 0, 0, 0, 9,
9, 9, 0, 9, 0, 9, 0, 0, 0, 0, 9,
9, 9, 0, 9, 0, 9, 0, 0, 0, 0, 9,
9, 9, 0, 9, 0, 9, 0, 0, 0, 0, 9,
9, 9, 0, 9, 0, 9, 0, 0, 0, 0, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,)
assert struct.unpack('B'* 121, target_ds.ReadRaster()) == expected
def rasterize_feat_shp_ds(shp_ds,
layer_name,
feat_name,
feat_id,
raster_ds,
all_touched=False,
exclude=False):
if not exclude:
str_eq = "='"
else:
str_eq = "!='"
sql_stat = 'SELECT * FROM ' + layer_name + ' WHERE ' + feat_name + str_eq + feat_id + "'"
opts = gdal.RasterizeOptions(burnValues=[1],
bands=[1],
SQLStatement=sql_stat,
allTouched=all_touched)
gdal.Rasterize(raster_ds, shp_ds, options=opts)
def gdal_rasterize(src, dst, options):
"""
a simple wrapper for gdal.Rasterize
Parameters
----------
src: str or ogr.DataSource
the input data set
dst: str
the output data set
options: dict
additional parameters passed to gdal.Rasterize;
see http://gdal.org/python/osgeo.gdal-module.html#RasterizeOptions
Returns
-------
"""
out = gdal.Rasterize(dst, src, options=gdal.RasterizeOptions(**options))
out = None
def rasterize_shp(fn_shp, fn_raster_ref, all_touched=False):
#create memory raster based on reference raster
raster_ds = create_mem_raster_on_ref(fn_raster_ref)
#open .shp with GDAL
shp_ds = gdal.OpenEx(fn_shp, gdal.OF_VECTOR)
#rasterize
opts = gdal.RasterizeOptions(burnValues=[1],
bands=[1],
allTouched=all_touched)
gdal.Rasterize(raster_ds, shp_ds, options=opts)
#get rasterized array
rast_array = raster_ds.GetRasterBand(1).ReadAsArray()
rast_array = rast_array.astype(float)
rast_array[rast_array != 1] = np.nan
#close shp
shp_ds = None
raster_ds = None
return ~np.isnan(rast_array)
def hand_rasterize(in_lyr_path: str, template_dem_path: str,
out_raster_path: str):
log = Logger('hand_rasterize')
ds_path, lyr_path = VectorBase.path_sorter(in_lyr_path)
g = gdal.Open(template_dem_path)
geo_t = g.GetGeoTransform()
width, height = g.RasterXSize, g.RasterYSize
xmin = min(geo_t[0], geo_t[0] + width * geo_t[1])
xmax = max(geo_t[0], geo_t[0] + width * geo_t[1])
ymin = min(geo_t[3], geo_t[3] + geo_t[-1] * height)
ymax = max(geo_t[3], geo_t[3] + geo_t[-1] * height)
# Close our dataset
g = None
progbar = ProgressBar(100, 50, "Rasterizing for HAND")
def poly_progress(progress, _msg, _data):
progbar.update(int(progress * 100))
# https://gdal.org/programs/gdal_rasterize.html
# https://gdal.org/python/osgeo.gdal-module.html#RasterizeOptions
gdal.Rasterize(
out_raster_path,
ds_path,
layers=[lyr_path],
height=height,
width=width,
burnValues=1,
outputType=gdal.GDT_CFloat32,
creationOptions=['COMPRESS=LZW'],
# outputBounds --- assigned output bounds: [minx, miny, maxx, maxy]
outputBounds=[xmin, ymin, xmax, ymax],
callback=poly_progress)
progbar.finish()
# Rasterize the features (roads, rail etc) and calculate a raster of Euclidean distance from these features
progbar.update(0)
def test_gdal_rasterize_lib_int64_attribute():
# Try rasterizing a multipolygon
vector_ds = gdal.GetDriverByName('Memory').Create('', 0, 0, 0)
layer = vector_ds.CreateLayer('')
layer.CreateField(ogr.FieldDefn('val', ogr.OFTInteger64))
feature = ogr.Feature(layer.GetLayerDefn())
feature.SetGeometryDirectly(
ogr.CreateGeometryFromWkt('POLYGON ((0 0,0 1,1 1,1 0,0 0))'))
val = (1 << 63) - 1 # not exactly representable as a double
feature['val'] = val
layer.CreateFeature(feature)
target_ds = gdal.Rasterize('',
vector_ds,
format='MEM',
attribute='val',
width=2,
height=2)
assert target_ds is not None
assert target_ds.GetRasterBand(1).DataType == gdal.GDT_Int64
assert struct.unpack('Q' * 4, target_ds.ReadRaster())[0] == val
def main(in_file, shp_file, nodata, out_file):
# 新建缓存文件夹
sys_str = platform.system()
if (sys_str == 'Windows'):
temp_dir = os.path.join(tempfile.gettempdir(), 'gdal_mask2')
if os.path.exists(temp_dir):
shutil.rmtree(temp_dir)
os.mkdir(temp_dir)
else:
rand_str = ''.join(
random.sample(string.ascii_letters + string.digits, 4))
temp_dir = os.path.join(r'/data6', 'gdal_%s' % rand_str)
if not os.path.exists(temp_dir):
os.mkdir(temp_dir)
# 打开影像
source_dataset = gdal.Open(in_file)
if source_dataset is None:
sys.exit('Problem opening file %s !' % in_file)
# 获取数据基本信息
xsize = source_dataset.RasterXSize
ysize = source_dataset.RasterYSize
num_band = source_dataset.RasterCount
data_type = source_dataset.GetRasterBand(1).DataType
in_geo = source_dataset.GetGeoTransform()
source_proj = source_dataset.GetProjectionRef()
mask_file = os.path.join(
temp_dir,
'%s_mask.tif' % (os.path.splitext(os.path.basename(shp_file))[0]))
out_driver = gdal.GetDriverByName('GTiff')
if os.path.exists(mask_file):
out_driver.Delete(mask_file)
print('shape to mask raster start...')
gdal.Rasterize(mask_file,
shp_file,
burnValues=1,
xRes=abs(in_geo[1] / 2),
yRes=abs(in_geo[5] / 2),
outputType=data_type,
callback=progress)
print('shape to mask raster done.')
print('mask raster start...')
mask_sds = gdal.Open(mask_file)
if mask_sds is None:
sys.exit('Problem opening file %s !' % mask_file)
separate_files = []
for iband in range(num_band):
single_file = os.path.join(
temp_dir, '%s_band%d.tif' %
(os.path.splitext(os.path.basename(in_file))[0], iband + 1))
single_sds = out_driver.Create(single_file, xsize, ysize, 1, data_type)
single_sds.GetRasterBand(1).WriteArray(
source_dataset.GetRasterBand(1 + iband).ReadAsArray(), 0, 0)
single_sds.SetGeoTransform(in_geo)
single_sds.SetProjection(source_proj)
single_sds = None
# gdal.Translate(single_file, in_file, bandList=[iband+1])
separate_files.append(single_file)
progress((1 + iband) / num_band * 0.5)
separate_files.append(mask_file)
vrt_file = os.path.join(
temp_dir,
'%s_vrt.vrt' % (os.path.splitext(os.path.basename(in_file))[0]))
gdal.BuildVRT(vrt_file,
separate_files,
resolution='user',
xRes=abs(in_geo[1]),
yRes=abs(in_geo[5]),
separate=True,
options=['-r', 'near'])
vrt_sds = gdal.Open(vrt_file)
if vrt_sds is None:
sys.exit('Problem opening file %s !' % vrt_file)
out_geotransform = vrt_sds.GetGeoTransform()
out_projection = vrt_sds.GetProjectionRef()
out_xsize = vrt_sds.RasterXSize
out_ysize = vrt_sds.RasterYSize
out_sds = out_driver.Create(out_file, out_xsize, out_ysize, num_band,
data_type)
num_xblock = 10000
for xoffset in range(0, out_xsize, num_xblock):
if xoffset + num_xblock < out_xsize:
num_xsize = num_xblock
else:
num_xsize = out_xsize - xoffset
block_data = vrt_sds.ReadAsArray(xoffset, 0, num_xsize, ysize)
ind = np.where(block_data[num_band, :, :] == 0)
for iband in range(num_band):
block_data[iband, :, :][ind] = nodata
out_sds.GetRasterBand(iband + 1).WriteArray(
block_data[iband, :, :], xoffset, 0)
block_data = None
ind = None
# ind = np.where(out_sds.ReadAsArray()[num_band, :, :].reshape(out_ysize, out_xsize) == 0)
#
# for iband in range(num_band):
# band_data = out_sds.ReadAsArray()[iband, :, :].reshape(out_ysize, out_xsize)
# band_data[ind] = nodata
# out_sds.GetRasterBand(iband + 1).WriteArray(band_data, 0, 0)
# band_data = None
#
progress((xoffset + num_xsize + 1) / out_xsize + 0.5)
#
out_sds.SetGeoTransform(out_geotransform)
out_sds.SetProjection(out_projection)
data = None
mask_data = None
vrt_sds = None
out_sds = None
source_dataset = None
mask_sds = None
print('mask raster done.')
shutil.rmtree(temp_dir)
print()
print('all done')
def process_rain(adate,TC_Rain):
"""process rainfall data"""
## VRT template to read the csv
vrt_template="""<OGRVRTDataSource>
<OGRVRTLayer name='{}'>
<SrcDataSource>{}</SrcDataSource>
<GeometryType>wkbPoint</GeometryType>
<GeometryField encoding="PointFromColumns" x="lon" y="lat" z="Z"/>
</OGRVRTLayer>
</OGRVRTDataSource>"""
## Read each text file and create the separate tiff file
for i in TC_Rain:
with open(i,'r') as f:
variable=csv.reader(f, delimiter=' ')
row_count=1
for row in variable:
if row_count == 1:
while ('' in row):
row.remove('')
XLC=float(row[0])
XRC=float(row[1])
YBC=float(row[2])
YTC=float(row[3])
res=float(row[4])
nrows=float(row[5])
ncol=float(row[6])
row_count = row_count + 1
df = (pd.read_table(i, skiprows=1, delim_whitespace=True, names=('lat', 'lon', 'Z'))).fillna(-999)
df.sort_values(by=["lat","lon"], ascending=[False, True])
df=df[['lon','lat','Z']]
df = df[df.lon >= XLC]
df = df[df.lon <= XRC]
df = df[df.lat >= YBC]
df = df[df.lat <= YTC]
df = df[df.Z > 0]
df.to_csv(i.replace(".ascii",".csv"),index=False, sep=" ")
with open(i.replace(".ascii",".vrt"),"w") as g:
g.write(vrt_template.format(i.replace(".ascii",""),i.replace(".ascii",".csv")))
g.close()
r=gdal.Rasterize(i.replace(".ascii",".tiff"),i.replace(".ascii",".vrt"),outputSRS="EPSG:4326",xRes=res, yRes=res,attribute="Z", noData=-999)
r=None
os.remove(i.replace(".ascii",".csv"))
##merge all tiffs file als
# nd delete the individual tiff, vrt and ascii file
TC_Rain_tiff=[]
for i in TC_Rain:
TC_Rain_tiff.append(i.replace(".ascii",".tiff"))
filename="hwrf."+ adate +"rainfall.vrt"
raintiff = filename.replace(".vrt",".tiff")
vrt = gdal.BuildVRT(filename, TC_Rain_tiff)
gdal.Translate(raintiff, vrt)
vrt=None
# no need
#gdalcmd = "gdal_translate -of GTiff " + filename + " " + raintiff
#subprocess.call(gdalcmd, shell=True)
# create a zipfile
zip_file="hwrf."+ adate +"rainfall.zip"
with zipfile.ZipFile(zip_file, 'w',zipfile.ZIP_DEFLATED) as zipObj:
for i in TC_Rain_tiff:
asfile = i.replace(".tiff",".ascii")
zipObj.write(asfile)
for i in TC_Rain_tiff:
os.remove(i)
os.remove(i.replace(".tiff",".ascii"))
os.remove(i.replace(".tiff",".vrt"))
return raintiff
def cellneigh_ctry(raster=None, region=None, vector=None, csize=10, rank=1):
"""
Compute number of spatial cells and neighbours.
:param raster: Path to raster file to compute region.
:param vector: Path to vector file with country borders.
:param region: List/tuple of region coordinates (east, west, south, north).
:param csize: Spatial cell size (in km).
:param rank: Rank of the neighborhood (1 for chess king's move).
:return: List of length 2 with number of neighbours for each cell \
and adjacent cells.
"""
# Region
if raster is not None:
r = gdal.Open(raster)
ncol_r = r.RasterXSize
nrow_r = r.RasterYSize
gt = r.GetGeoTransform()
Xmin = gt[0]
Xmax = gt[0] + gt[1] * ncol_r
Ymin = gt[3] + gt[5] * nrow_r
Ymax = gt[3]
elif region is not None:
Xmin = region[0]
Xmax = region[1]
Ymin = region[2]
Ymax = region[3]
else:
print("raster or region must be specified")
sys.exit(1)
# Cell number from region
print("Compute number of {} x {} km spatial cells".format(csize, csize))
csize = csize * 1000 # Transform km in m
ncol = np.int(np.ceil((Xmax - Xmin) / csize))
nrow = np.int(np.ceil((Ymax - Ymin) / csize))
Xmax_new = Xmin + ncol * csize
Ymin_new = Ymax + nrow * (-csize)
ncell = ncol * nrow
print("... {} cells ({} x {})".format(ncell, nrow, ncol))
# Cells within country borders (rasterizing method)
cb_ds = gdal.OpenEx(vector, gdal.OF_VECTOR)
rOptions = gdal.RasterizeOptions(
xRes=csize,
yRes=-csize,
allTouched=True,
outputBounds=[Xmin, Ymin_new, Xmax_new, Ymax],
burnValues=1,
noData=0)
outfile = "/vsimem/tmpfile"
ds = gdal.Rasterize(outfile, cb_ds, options=rOptions)
mask = ds.ReadAsArray()
ds = None
gdal.Unlink(outfile)
y_in, x_in = np.where(mask == 1)
cell_in = y_in * ncol + x_in
# Adjacent cells and number of neighbors
print("Identify adjacent cells and compute number of neighbors")
nneigh = []
adj = []
adj_sort = []
around = np.arange(-rank, rank + 1)
for i in range(nrow):
for j in range(ncol):
if mask[i, j] == 1:
I = i + around
Iprim = I[(I >= 0) & (I < nrow)]
J = j + around
Jprim = J[(J >= 0) & (J < ncol)]
# Loop on potential neighbors
nneighbors = 0
for cy in Iprim:
for cx in Jprim:
if (not (cy == i and cx == j)) and (mask[cy, cx] == 1):
adj.append(cy * ncol + cx)
nneighbors += 1
nneigh.append(nneighbors)
nneigh = np.array(nneigh)
adj = np.array(adj)
for i in adj:
adj_sort.append(np.flatnonzero(cell_in == i)[0])
adj_sort = np.array(adj_sort)
return (nneigh, adj_sort, cell_in, ncell)
if not os.path.exists(output_folder):
os.makedirs(output_folder)
match_fn = f"data/nc/MODIS/MCD64A1/{region.get('name')}/MCD64A1_500m.nc"
match_ds = xarray.open_dataset(match_fn)
stack = []
for feature in ACCESSIBILITY_FEATURES:
# Create temporal raster
rasterize_options = gdal.RasterizeOptions(
xRes=xres, yRes=yres, allTouched=True, burnValues=[1]
)
temp1 = gdal.Rasterize(
"/vsimem/temp", feature.get("path"), options=rasterize_options
)
temp1_band = temp1.GetRasterBand(1)
# Create temporal proximity raster
driver = gdal.GetDriverByName("MEM")
temp2 = driver.Create("temp2", temp1.RasterXSize, temp1.RasterYSize)
temp2.SetGeoTransform(temp1.GetGeoTransform())
temp2.SetProjection(temp1.GetProjection())
temp2_band = temp2.GetRasterBand(1)
gdal.ComputeProximity(temp1_band, temp2_band, ["VALUES=1"])
temp2_band.FlushCache()
# Clip to region area
warp_options = gdal.WarpOptions(
outputBounds=match_ds.rio.bounds(),
def predict_and_save(self, datas, fn):
geo = self.geo
prj = self.prj
xsize = self.xsize
ysize = self.ysize
obsdate = self.obsdate
obswkt = self.obswkt
username = self.username
password = self.password
translations = self.translations
resolutions = self.resolutions
todate = obsdate + timedelta(days=14)
fromdate = obsdate - timedelta(days=14)
ptsfn = get_hotspots(obswkt,
fromdate=fromdate,
todate=todate,
t_prj=prj,
username=username,
password=password)
fd = ogr.Open(ptsfn)
layer = fd.GetLayer()
sensors = set()
for ftr in layer:
sensors.add(ftr.GetField('sensor'))
driver = ogr.GetDriverByName('GeoJSON')
tsr = osr.SpatialReference()
tsr.ImportFromProj4(prj)
datas = []
for sensor in sensors:
layer.SetAttributeFilter(f"sensor='{sensor}'")
count = layer.GetFeatureCount()
ds = driver.CreateDataSource(f"/vsimem/{sensor}.json")
olayer = ds.CreateLayer(sensor, tsr, geom_type=ogr.wkbMultiPolygon)
ldef = layer.GetLayerDefn()
for i in range(ldef.GetFieldCount()):
fdef = ldef.GetFieldDefn(i)
olayer.CreateField(fdef)
oldef = olayer.GetLayerDefn()
for ftr in layer:
geom = ftr.GetGeometryRef()
dx, dy = translations[sensor]
if dx != 0 or dy != 0:
x, y = geom.GetPoint_2D()
geom.SetPoint(0, x + dx, y + dy)
geom = geom.Buffer(resolutions[sensor])
oftr = ogr.Feature(oldef)
oftr.SetGeometry(geom)
for i in range(oldef.GetFieldCount()):
oftr.SetField(
oldef.GetFieldDefn(i).GetNameRef(), ftr.GetField(i))
olayer.CreateFeature(oftr)
olayer.SyncToDisk()
ds = None
geo = self.geo
bounds = [
geo[0], geo[3], geo[0] + xsize * geo[1],
geo[3] + geo[5] * ysize
]
ds = gdal.Rasterize(f"{sensor}.tif",
f"/vsimem/{sensor}.json",
outputBounds=bounds,
width=xsize,
height=ysize,
attribute="temp_kelvin")
geo = ds.GetGeoTransform()
prj = ds.GetProjection()
data = ds.ReadAsArray()
#pl, pu = np.percentile(data, PROBPERCCLIP)
#if np.abs(pu - pl) > 0:
# data -= pl
# data /= (pu - pl)
# np.clip(data, 0, 1, out=data)
#log(f"{sensor}\tpoints: {count} pl: {pl} pu: {pu}")
#data /= 100.
#data = morphology.convex_hull_object(data > 0, connectivity=2)
filters.gaussian(data,
sigma=25,
preserve_range=True,
truncate=3,
output=data)
mask = data > 0
morphology.binary_erosion(mask, morphology.disk(10), out=mask)
data[mask == 0] = np.nan
rb = ds.GetRasterBand(1)
rb.WriteArray(data)
rb.SetNoDataValue(np.nan)
datas.append(data)
data = np.dstack(datas)
#data = np.nanmean(data, axis=-1)
#data = data[:,:,np.newaxis]
#sensors = ['squashed']
driver = gdal.GetDriverByName("GTiff")
ds = driver.Create(fn, data.shape[1], data.shape[0], data.shape[2],
gdal.GDT_Float32)
ds.SetGeoTransform(geo)
ds.SetProjection(prj)
for i, sensor in zip(range(data.shape[2]), sensors):
rb = ds.GetRasterBand(i + 1)
rb.WriteArray(data[:, :, i].astype(np.float32))
rb.SetNoDataValue(0.0)
rb.SetDescription(sensor)
del ds
from osgeo import ogr, gdal
shape_file = 'test.shp'
output_raster = 'test.tiff'
input_shp = ogr.Open(shape_file)
shp_layer = input_shp.GetLayer()
pixel_size = 0.01
xmin, xmax, ymin, ymax = shp_layer.GetExtent()
ds = gdal.Rasterize(output_raster,
shape_file,
xRes=pixel_size,
yRes=pixel_size,
burnValues=255,
outputBounds=[xmin, ymin, xmax, ymax],
outputType=gdal.GDT_Byte)
ds = None
