def RdCompare():
parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter, description="""RichDEM Dataset Comparison
Parameters:
rda -- A dataset
""")
parser.add_argument('rda1', type=str, help='Elevation model')
parser.add_argument('rda2', type=str, help='Elevation model')
args = parser.parse_args()
ds1 = rd.LoadGDAL(args.rda1)
ds2 = rd.LoadGDAL(args.rda2)
if ds1.no_data!=ds2.no_data:
print("NoData differs")
if ds1.geotransform!=ds2.geotransform:
print("Geotransform differs")
if ds1.projection!=ds2.projection:
print("Projection differs")
if np.any(np.isnan(ds1)):
print("NaN in '{0}'".format(filename))
if np.any(np.isnan(ds2)):
print("NaN in '{0}'".format(filename))
diff = np.array(ds1-ds2)
print("Absolute Max difference: {0:10.6}".format(np.nanmax(np.abs(diff))))
print("Absolute Min difference: {0:10.6}".format(np.nanmin(np.abs(diff))))
print("Absolute Avg difference: {0:10.6}".format(np.mean (np.abs(diff))))
print("RMS difference: {0:10.6}".format(np.sqrt(np.mean(np.square(diff)))))
def FlowAccumulation():
parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter, description="""RichDEM Flow Accumulation
A variety of methods are available.
Method Note Reference
Tarboton Alias for Dinf.
Dinf Alias for Tarboton.
Quinn Holmgren with exponent=1.
Holmgren(E) Generalization of Quinn.
Freeman(E) TODO
FairfieldLeymarie Alias for Rho8.
Rho8 Alias for FairfieldLeymarie.
OCallaghan Alias for D8. 10.1016/S0734-189X(84)80011-0
D8 Alias for OCallaghan. 10.1016/S0734-189X(84)80011-0
Methods marked (E) require the exponent argument.
""")
parser.add_argument('dem', type=str, help='Elevation model')
parser.add_argument('outname', type=str, help='Name of output file')
parser.add_argument('-m', '--method', type=str, required=True, help='Flow accumulation method to use')
parser.add_argument('-e', '--exponent', type=float, help='Some methods require an exponent')
parser.add_argument('-v', '--version', action='version', version=rd._RichDEMVersion())
args = parser.parse_args()
dem = rd.LoadGDAL(args.dem)
rd._AddAnalysis(dem, ' '.join(sys.argv))
accum = rd.FlowAccumulation(dem, method=args.method, exponent=args.exponent)
rd.SaveGDAL(args.outname, accum)
def RichFlow(bassin, zone, root, flowdir, overwrite):
"""
Calcule le plan de drainage en utilisant RichDEM :
FillDepressions + ResolveFlats
"""
raster_template = os.path.join(root, bassin, zone, 'DEM5M.tif')
flow_raster = os.path.join(root, bassin, zone, flowdir)
if os.path.exists(flow_raster) and not overwrite:
click.secho('Output already exists : %s' % flow_raster, fg=WARNING)
return
dem = rd.LoadGDAL(raster_template)
filled = rd.FillDepressions(dem)
rd.ResolveFlats(filled, True)
flow = ta.flowdir(filled, filled.no_data)
ds = rio.open(raster_template)
profile = ds.profile.copy()
profile.update(dtype=np.int16, nodata=-1, compress='deflate')
with rio.open(flow_raster, 'w', **profile) as dst:
dst.write(flow, 1)
click.secho('Saved to %s' % flow_raster, fg=SUCCESS)
def GaussianFilter(in_dem, sigma=1, out_file=None):
"""Applies a Gaussian filter to an image.
Args:
in_dem (str): File path to the input image.
kernel_size (int, optional): The size of the moving window. Defaults to 3.
out_file (str, optional): File path to the output image. Defaults to None.
Returns:
np.array: The numpy array containing the filtered image.
"""
print("Gaussian filtering ...")
start_time = time.time()
dem = rd.LoadGDAL(in_dem)
no_data = dem.no_data
projection = dem.projection
geotransform = dem.geotransform
gau = ndimage.gaussian_filter(dem, sigma=sigma)
gau = np2rdarray(gau, no_data, projection, geotransform)
print("Run time: {:.4f} seconds".format(time.time() - start_time))
if out_file is not None:
print("Saving dem ...")
rd.SaveGDAL(out_file, gau)
return out_file
return gau
def DelineateMounts(in_dem,
min_size,
min_height,
interval,
out_dir,
bool_shp=False):
if not os.path.exists(out_dir):
os.mkdir(out_dir)
print("Loading data ...")
dem = rd.LoadGDAL(in_dem)
projection = dem.projection
geotransform = dem.geotransform
cell_size = np.round(geotransform[1], decimals=3)
out_dem = os.path.join(out_dir, "dem_flip.tif")
in_dem = FlipDEM(dem, delta=100, out_file=out_dem)
min_elev, max_elev, no_data = get_min_max_nodata(dem)
print("min = {:.2f}, max = {:.2f}, no_data = {}, cell_size = {}".format(
min_elev, max_elev, no_data, cell_size))
sink_path = ExtractSinks(in_dem, min_size, out_dir)
dep_id_path, dep_level_path = DelineateDepressions(sink_path, min_size,
min_height, interval,
out_dir, bool_shp)
return dep_id_path, dep_level_path
def MeanFilter(in_dem, kernel_size=3, out_file=None):
"""Applies a mean filter to an image.
Args:
in_dem (str): File path to the input image.
kernel_size (int, optional): The size of the moving window. Defaults to 3.
out_file (str, optional): File path to the output image. Defaults to None.
Returns:
np.array: The numpy array containing the filtered image.
"""
print("Mean filtering ...")
start_time = time.time()
dem = rd.LoadGDAL(in_dem)
no_data = dem.no_data
projection = dem.projection
geotransform = dem.geotransform
weights = np.full((kernel_size, kernel_size),
1.0 / (kernel_size * kernel_size))
mean = ndimage.filters.convolve(dem, weights)
mean = np2rdarray(mean, no_data, projection, geotransform)
print("Run time: {:.4f} seconds".format(time.time() - start_time))
if out_file is not None:
print("Saving dem ...")
rd.SaveGDAL(out_file, mean)
return out_file
return mean
def calc_topo(dem_path):
"""
Calculates slope and aspect from given DEM and saves output.
The function checks to see whether a slope/aspect file has already been created so as to avoid needless processing.
Parameters:
dem_path (pathlib.PosixPath): The relative or absolute path to an input DEM file.
Dependencies:
richdem module
GDAL binaries
pathlib module
"""
slope_path = Path(
str(dem_path).replace("dem", "slope"))
aspect_path = Path(
str(dem_path).replace("dem", "aspect"))
if ((not slope_path.is_file()) or
(not aspect_path.is_file())):
dem = rd.LoadGDAL(str(dem_path))
if not slope_path.is_file():
slope = rd.TerrainAttribute(
dem, attrib='slope_riserun')
rd.SaveGDAL(str(slope_path), slope)
if not aspect_path.is_file():
aspect = rd.TerrainAttribute(dem, attrib='aspect')
rd.SaveGDAL(str(aspect_path), aspect)
def TerrainAttribute():
parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter, description="""RichDEM Terrain Attribute
A variety of methods are available.
Parameters:
dem -- An elevation model
attrib -- Terrain attribute to calculate. (See below.)
zscale -- How much to scale the z-axis by prior to calculation
Method:
slope_riserun
slope_percentage
slope_degrees
slope_radians
aspect
curvature
planform_curvature
profile_curvature
""")
parser.add_argument('dem', type=str, help='Elevation model')
parser.add_argument('outname', type=str, help='Name of output file')
parser.add_argument('-a', '--attrib', type=str, required=True, help='Terrain attribute to calculate')
parser.add_argument('-z', '--zscale', type=float, default=1.0, help='Scale elevations by this factor prior to calculation')
parser.add_argument('-v', '--version', action='version', version=rd._RichDEMVersion())
args = parser.parse_args()
dem = rd.LoadGDAL(args.dem)
rd._AddAnalysis(dem, ' '.join(sys.argv))
tattrib = rd.TerrainAttribute(dem, attrib=args.attrib, zscale=args.zscale)
rd.SaveGDAL(args.outname, tattrib)
def processDEM(fileloc):
"""
computes hydrologically sound DEM by filling pits
and also computes flow accumulation
"""
pitremovedDEM = rd.FillDepressions(rd.LoadGDAL(fileloc))
accumDEM = rd.FlowAccumulation(pitremovedDEM, method='Dinf')
return pitremovedDEM, accumDEM
def mosaic_slope(url, Projection, Geotransform, Height, Width, Extent,
Resolution):
tile_id = url.split('/')[7]
name = f'{tile_id}.tif'
urllib.request.urlretrieve(
url, '/Users/jackson/Desktop/Execute/' + tile_id + '.tif')
# We must reproject our DEM tile
gdal.Warp(name, name, dstSRS=Projection)
# Now we run the the BTH over our DEM tile
Tile_GeoTiff = gdal.Open(name, 0)
T = Tile_GeoTiff.GetGeoTransform()
P = Tile_GeoTiff.GetProjection()
Tile_Array = rd.LoadGDAL(name)
Slopes = rd.TerrainAttribute(Tile_Array, attrib='slope_riserun')
filename = name
driver = gdal.GetDriverByName('GTiff')
dataset = driver.Create(filename, Slopes.shape[1], Slopes.shape[0], 1,
gdal.GDT_Float32)
dataset.GetRasterBand(1).WriteArray(Slopes)
dataset.SetGeoTransform(T)
dataset.SetProjection(P)
dataset.FlushCache()
dataset = None
# Preparing to add our new tile to the mosaic
src_files_to_mosaic = []
src = rasterio.open('Topo_Slope.tif')
src_files_to_mosaic.append(src)
src = rasterio.open(name)
src_files_to_mosaic.append(src)
os.remove('/Users/jackson/Desktop/Execute/' + tile_id + '.tif')
# Adding to Mosaic
mosaic, out = merge(src_files_to_mosaic,
method='first',
bounds=Extent,
res=Resolution,
nodata=-9999)
mosaic = np.reshape(mosaic, (Height, Width))
# Using GDAL to write the output raster
filename = 'Topo_Slope.tif' # Tiff holding Black Top Hat
driver = gdal.GetDriverByName('GTiff') # Driver for writing geo-tiffs
dataset = driver.Create(filename, Width, Height, 1,
gdal.GDT_Float32) # Creating our tiff file
dataset.GetRasterBand(1).WriteArray(mosaic) # Writing values to our tiff
dataset.SetGeoTransform(Geotransform) # Setting geo-transform
dataset.SetProjection(Projection) # Setting the projection
dataset.FlushCache() # Saving tiff to disk
dataset = None
def curvature(input_file):
"""Create a curvature layer (combine profile and planform curvature) using richdem."""
arr = rd.LoadGDAL(input_file) #rd.rdarray(input_file,no_data=-9999)
curvature = rd.TerrainAttribute(arr, attrib='curvature')
output_file = input_file[:-9] + '_curvature_temp.tif'
head, tail = os.path.split(output_file)
rd.SaveGDAL(output_file, curvature)
createMetadata(
sys.argv,
head + '/') #just remove the filename so you are left with the path
def elevation_grid(self):
"""
Property storing the grid containing the elevation data.
"""
if not hasattr(self, '_elevation_grid'):
dem_path = os.path.join(os.getcwd(), 'DEM.tif')
elevation.clip(bounds=self.bounds, output=dem_path)
self._elevation_grid = np.array(rd.LoadGDAL(dem_path))
os.remove('DEM.tif')
return self._elevation_grid
def create_slope_aspect(input_file):
arr = rd.LoadGDAL(input_file,
no_data=-9999) #rd.rdarray(input_file,no_data=-9999)
aspect = rd.TerrainAttribute(arr, attrib='aspect')
slope = rd.TerrainAttribute(arr, attrib='slope_radians')
aspect_output = input_file[:-4] + '_aspect.tif'
slope_output = input_file[:-4] + '_slope.tif'
rd.SaveGDAL(aspect_output, aspect)
rd.SaveGDAL(slope_output, slope)
return aspect_output, slope_output
def BreachDepressions():
parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter, description="""RichDEM Depression Breaching""")
parser.add_argument('dem', type=str, help='Elevation model')
parser.add_argument('outname', type=str, help='Name of output file')
parser.add_argument('-v', '--version', action='version', version=rd._RichDEMVersion())
args = parser.parse_args()
dem = rd.LoadGDAL(args.dem)
rd._AddAnalysis(dem, ' '.join(sys.argv))
rd.BreachDepressions(dem)
rd.SaveGDAL(args.outname, dem)
def DepressionFilling():
parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter, description='RichDEM Depression Filling')
parser.add_argument('dem', type=str, help='Elevation model')
parser.add_argument('outname', type=str, help='Name of output file')
parser.add_argument('-g', '--gradient', action='store_true', help='Ensure that all cells are at least an epsilon above their downstream cell. This ensures that each cell has a defined flow direction.')
parser.add_argument('-v', '--version', action='version', version=rd._RichDEMVersion())
args = parser.parse_args()
dem = rd.LoadGDAL(args.dem)
rd._AddAnalysis(dem, ' '.join(sys.argv))
rd.FillDepressions(dem, epsilon=args.gradient, in_place=True)
rd.SaveGDAL(args.outname, dem)
def RouteFlow_rd(dempath, flow, method='Dinf'):
"""
Use richdem to route flow
Args:
dempath: path to DEM (.tif)
flow: amount of flow to route
method: richdem routing method (default: 'Dinf')
Returns:
routed flow
"""
rdprop = rd.FlowProportions(rd.LoadGDAL(dempath), method=method)
def overlap_contour_dem(self, contour_path, dem_path):
ct = self.read_contour(contour_path) * 255
dem_data = rd.LoadGDAL(dem_path)
m = np.max(dem_data)
n = np.min(dem_data)
dem_data = (dem_data - n) / float(m) * 255
size = list(dem_data.shape)
size.append(3)
img = np.zeros(size, np.uint8)
img[:, :, 0] = ct
img[:, :, 1] = dem_data
return img
def BreachDepressions():
parser = argparse.ArgumentParser(
formatter_class=RawTextHelpFormatter,
description="""RichDEM Depression Breaching
Modes:
Complete: Breach everything.
Ignore max_path_len, max_path_depth.
There will be no depressions.
There will be no mercy.
Selective: Only breach those depressions that can be breached using the
above criteria.
Constrained: Dig as long a path as necessary, but don't dig it deeper than
max_path_depth.
""")
parser.add_argument('dem', type=str, help='Elevation model')
parser.add_argument('outname', type=str, help='Name of output file')
parser.add_argument('-m',
'--mode',
required=True,
type=str,
help='Breaching mode to use')
parser.add_argument(
'-f',
'--fill',
action='store_true',
help="If depressions can't be breached, should they be filled?")
parser.add_argument('-l',
'--max_path_len',
type=int,
help="Maximum length of breaching path in cells")
parser.add_argument('-d',
'--max_path_depth',
type=float,
help="Maximum depth of breaching path in z-units")
parser.add_argument('-v',
'--version',
action='version',
version=rd._RichDEMVersion())
args = parser.parse_args()
dem = rd.LoadGDAL(args.dem)
rd._AddAnalysis(dem, ' '.join(sys.argv))
rd.BreachDepressions(dem,
mode=args.mode,
fill=args.fill,
max_path_len=args.max_path_len,
max_path_depth=args.max_path_depth,
in_place=True)
rd.SaveGDAL(args.outname, dem)
def drain_area(dem, drain_area_out):
"""
Creates a raster where each pixel represents the contributing
upstream drainage area in km2. DEM should be in a desired projected coordinate system.
PARAMS
:dem: string - path to dem raster file
:drain_area_out: string - path to output drainage area raster file
"""
dem_in = rd.LoadGDAL(dem)
rd.FillDepressions(dem_in, epsilon=True, in_place=True)
accum_d8 = rd.FlowAccumulation(dem_in, method='D8')
da = accum_d8 * (accum_d8.geotransform[1]**2 / 1000000)
rd.SaveGDAL(drain_area_out, da)
return
def pendiente(srcFolder="./", dstFolder="./"):
for archivo in os.listdir(srcFolder):
if archivo.endswith(".tif"):
if srcFolder.endswith("/"):
ruta = srcFolder + archivo
else:
ruta = srcFolder + "/" + archivo
dem = richdem.LoadGDAL(ruta)
slope = richdem.TerrainAttribute(dem, attrib='slope_radians')
archivo = "pendiente_" + archivo
if not os.path.exists(dstFolder):
os.mkdir(dstFolder)
if srcFolder.endswith("/"):
dstRuta = dstFolder + archivo
else:
dstRuta = dstFolder + "/" + archivo
richdem.SaveGDAL(dstRuta, slope)
def RdInfo():
parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter,
description="""RichDEM Dataset Information
Parameters:
rda -- A dataset
""")
parser.add_argument('rda', type=str, help='Elevation model')
parser.add_argument('-s',
'--show',
action='store_true',
help='Show the model')
parser.add_argument('--cmap',
type=str,
default='jet',
help='Colormap (Default: jet)')
args = parser.parse_args()
rda = rd.LoadGDAL(args.rda)
print('File = {0} '.format(args.rda))
print('Data type = {0} '.format(rda.dtype))
print('Width = {0} '.format(rda.shape[1]))
print('Height = {0} '.format(rda.shape[0]))
print('Shape = {0}x{1}'.format(rda.shape[1], rda.shape[0]))
print('Metadata:')
for k, v in rda.metadata.items():
if k == 'PROCESSING_HISTORY':
continue
print('\t{0} = {1}'.format(k, v))
print('Processing History:')
print('-------------------')
if 'PROCESSING_HISTORY' in rda.metadata:
for ph in rda.metadata['PROCESSING_HISTORY'].split('\n'):
ph = ph.strip()
if len(ph) == 0:
continue
print(ph)
print('-------------------')
if args.show:
rd.rdShow(rda, cmap=args.cmap)
def MedianFilter(in_dem, kernel_size=3, out_file=None):
print("Median filtering ...")
start_time = time.time()
dem = rd.LoadGDAL(in_dem)
no_data = dem.no_data
projection = dem.projection
geotransform = dem.geotransform
med = ndimage.median_filter(dem, size=kernel_size)
med = np2rdarray(med, no_data, projection, geotransform)
print("Run time: {:.4f} seconds".format(time.time() - start_time))
if out_file is not None:
print("Saving dem ...")
rd.SaveGDAL(out_file, med)
return out_file
return med
def GaussianFilter(in_dem, sigma=1, out_file=None):
print("Gaussian filtering ...")
start_time = time.time()
dem = rd.LoadGDAL(in_dem)
no_data = dem.no_data
projection = dem.projection
geotransform = dem.geotransform
gau = ndimage.gaussian_filter(dem, sigma=sigma)
gau = np2rdarray(gau, no_data, projection, geotransform)
print("Run time: {:.4f} seconds".format(time.time() - start_time))
if out_file is not None:
print("Saving dem ...")
rd.SaveGDAL(out_file, gau)
return out_file
return gau
def DelineateMounts(in_dem,
min_size,
min_height,
interval,
out_dir,
bool_shp=False):
"""Delineates the nested hierarchy of elevated features (i.e., mounts).
Args:
in_dem (str): File path to the input DEM.
min_size (int): The minimum number of pixels to be considered as an object.
min_height (float): The minimum depth of the feature to be considered as an object.
interval (float): The slicing interval.
out_dir (str): The output directory.
bool_shp (bool, optional): Whether to generate shapefiles. Defaults to False.
Returns:
tuple: File paths to the depression ID and level.
"""
if not os.path.exists(out_dir):
os.mkdir(out_dir)
print("Loading data ...")
dem = rd.LoadGDAL(in_dem)
# projection = dem.projection
geotransform = dem.geotransform
cell_size = np.round(geotransform[1], decimals=3)
out_dem = os.path.join(out_dir, "dem_flip.tif")
in_dem = FlipDEM(dem, delta=100, out_file=out_dem)
min_elev, max_elev, no_data = get_min_max_nodata(dem)
print("min = {:.2f}, max = {:.2f}, no_data = {}, cell_size = {}".format(
min_elev, max_elev, no_data, cell_size))
sink_path = ExtractSinks(in_dem, min_size, out_dir)
dep_id_path, dep_level_path = DelineateDepressions(sink_path, min_size,
min_height, interval,
out_dir, bool_shp)
return dep_id_path, dep_level_path
def MeanFilter(in_dem, kernel_size=3, out_file=None):
print("Mean filtering ...")
start_time = time.time()
dem = rd.LoadGDAL(in_dem)
no_data = dem.no_data
projection = dem.projection
geotransform = dem.geotransform
weights = np.full((kernel_size, kernel_size),
1.0 / (kernel_size * kernel_size))
mean = ndimage.filters.convolve(dem, weights)
mean = np2rdarray(mean, no_data, projection, geotransform)
print("Run time: {:.4f} seconds".format(time.time() - start_time))
if out_file is not None:
print("Saving dem ...")
rd.SaveGDAL(out_file, mean)
return out_file
return mean
def add_slope_aspect_curvature(df, file, indexes):
for attr in ['slope_percentage', 'aspect', 'profile_curvature']:
table = None
try:
table = rd.TerrainAttribute(rd.LoadGDAL(file, no_data=-9999),
attrib=attr)
rd.SaveGDAL("./temp.tif", table)
table = None
table = gr.from_file("./temp.tif")
for index in indexes:
try:
row = df.loc[index]
val = table.map_pixel(row['lon'], row['lat'])
df.loc[index, attr] = float(val)
except:
df.loc[index, attr] = np.nan
os.remove("./temp.tif")
except:
for index in indexes:
df.loc[index, attr] = np.nan
return df
def runoff():
# return 788
dem_path = os.path.join(os.getcwd(), 'Konambe_dem_clipped.tif')
village_dem = rd.LoadGDAL(dem_path, no_data=-9999)
rd.FillDepressions(village_dem, epsilon=False, in_place=False)
arr = rd.TerrainAttribute(village_dem,
attrib='slope_percentage',
zscale=1 / 111120)
np.save('out.npy', arr)
demnp = np.load('out.npy')
dem = copy.copy(arr)
dem[np.where((arr > 0) & (arr < 5))] = 1
dem[np.where((arr >= 5) & (arr < 20))] = 2
dem[np.where((arr >= 20))] = 3
c1 = np.count_nonzero(dem == 1)
c2 = np.count_nonzero(dem == 2)
c3 = np.count_nonzero(dem == 3)
area_m2_1 = c1 * 900
area_m2_2 = c2 * 900
area_m2_3 = c3 * 900
area_ha1 = area_m2_1 * 0.0001
area_ha2 = area_m2_2 * 0.0001
area_ha3 = area_m2_3 * 0.0001
#print('area',area_ha1+area_ha2)
worthy_area = area_ha1 + area_ha2
#coeff for rainfall 775mm
runoff1 = area_ha1 * 1.0791
runoff2 = area_ha2 * 1.6186
runoff3 = area_ha3 * 2.1583
#coeff for rainfall 725mm
#runoff1=area_ha1*1.0791
#runoff2=area_ha2*1.3878
#runoff3=area_ha3*1.8496
tot_runoff = runoff1 + runoff2 + runoff3
return tot_runoff, worthy_area
#r=runoff()
def DelineateDepressions(in_sink, min_size, min_depth, interval, out_dir, bool_level_shp=False):
# The following parameters can be used by default
interval = interval * (-1) # convert slicing interval to negative value
out_img_dir = os.path.join(out_dir, "img-level")
out_shp_dir = os.path.join(out_dir, "shp-level")
out_obj_file = os.path.join(out_dir, "depression_id.tif")
out_level_file = os.path.join(out_dir, "depression_level.tif")
out_vec_file = os.path.join(out_dir, "depressions.shp")
out_csv_file = os.path.join(out_dir, "depressions_info.csv")
init_time = time.time()
# delete contents in output folder if existing
if not os.path.exists(out_dir):
os.mkdir(out_dir)
if os.path.exists(out_img_dir):
shutil.rmtree(out_img_dir)
os.mkdir(out_img_dir)
if os.path.exists(out_shp_dir):
shutil.rmtree(out_shp_dir)
os.mkdir(out_shp_dir)
print("Reading data ...")
read_time = time.time()
image = rd.LoadGDAL(in_sink)
no_data_raw, projection, geotransform, resolution = getMetadata(image)
rows_cols = image.shape
print("rows, cols: " + str(rows_cols))
print("Pixel resolution: " + str(resolution))
print("Read data time: {:.4f} seconds".format(time.time() - read_time))
min_elev, max_elev, no_data = get_min_max_nodata(image) # set nodata value to a large value, e.g., 9999
# initialize output image
obj_image = np.zeros(image.shape) # output depression image with unique id for each nested depression
level_image = np.zeros(image.shape) # output depression level image
# nb_labels is the total number of objects. 0 represents background object.
label_objects, nb_labels = regionGroup(image, min_size, no_data)
# regions = measure.regionprops(label_objects, image, coordinates='xy')
regions = measure.regionprops(label_objects, image)
del image # delete the original image to save memory
prep_time = time.time()
print("Data preparation time: {:.4f} seconds".format(prep_time - init_time))
print("Total number of regions: {}".format(nb_labels))
identify_time = time.time()
obj_uid = 0
global_dep_list = []
# loop through regions and identify nested depressions in each region using level-set method
for region in regions: # iterate through each depression region
region_id = region.label
img = region.intensity_image # dem subset for each region
bbox = region.bbox
# save all input parameters needed for level set methods as a dict
image_paras = set_image_paras(no_data, min_size, min_depth, interval, resolution)
# execute level set methods
out_obj, dep_list = levelSet(img, region_id, obj_uid, image_paras)
for dep in dep_list:
global_dep_list.append(dep)
obj_uid += len(dep_list)
level_obj = obj_to_level(out_obj, global_dep_list)
obj_image = writeObject(obj_image, out_obj, bbox) # write region to whole image
level_image = writeObject(level_image, level_obj, bbox)
del out_obj, level_obj, region
del regions, label_objects
print("=========== Run time statistics =========== ")
print("(rows, cols):\t\t\t {0}".format(str(rows_cols)))
print("Pixel resolution:\t\t {0} m".format(str(resolution)))
print("Number of regions:\t\t {0}".format(str(nb_labels)))
print("Data preparation time:\t\t {:.4f} s".format(prep_time - init_time))
print("Identify level time:\t\t {:.4f} s".format(time.time() - identify_time))
write_time = time.time()
# writeRaster(obj_image, out_obj_file, in_sink)
# writeRaster(level_image, out_level_file, in_sink)
# SaveGDAL function can only save data as floating point
level_image = np2rdarray(np.int32(level_image), no_data_raw, projection, geotransform)
rd.SaveGDAL(out_level_file, level_image)
obj_image = np2rdarray(np.int32(obj_image), no_data_raw, projection, geotransform)
rd.SaveGDAL(out_obj_file, obj_image)
print("Write image time:\t\t {:.4f} s".format(time.time() - write_time))
# converting object image to polygon
level_time = time.time()
polygonize(out_obj_file, out_vec_file)
write_dep_csv(global_dep_list, out_csv_file)
print("Polygonize time:\t\t {:.4f} s".format(time.time() - level_time))
# extracting polygons for each individual level
if bool_level_shp:
level_time = time.time()
extract_levels(level_image, obj_image, min_size, no_data, out_img_dir, out_shp_dir, in_sink, False)
print("Extract level time:\t\t {:.4f} s".format(time.time() - level_time))
shutil.rmtree(out_img_dir)
else:
shutil.rmtree(out_shp_dir)
shutil.rmtree(out_img_dir)
del level_image
del obj_image
end_time = time.time()
print("Total run time:\t\t\t {:.4f} s".format(end_time - init_time))
return out_obj_file, out_level_file
del obj_image
end_time = time.time()
print("Total run time:\t\t\t {:.4f} s".format(end_time - init_time))
return out_obj_file, out_level_file
# ##################################### main script
if __name__ == '__main__':
# ************************ change the following parameters if needed ******************************** #
# set input files
in_dem = os.path.join(os.getcwd(), "lidar/data/dem.tif")
in_sink = os.path.join(os.getcwd(), "lidar/data/sink.tif")
# parameters for level set method
min_size = 1000 # minimum number of pixels as a depression
min_depth = 0.3 # minimum depression depth
interval = 0.3 # slicing interval, top-down approach
bool_level_shp = True # whether or not to extract polygons for each individual level
# set output directory
out_dir = os.path.join(os.path.expanduser("~"), "temp") # create a temp folder under user home directory
# **************************************************************************************************#
dep_id_path, dep_level_path = DelineateDepressions(in_sink, min_size, min_depth, interval, out_dir, bool_level_shp)
print("Results are saved in: {}".format(out_dir))
dep_id = rd.LoadGDAL(dep_id_path)
dep_id_fig = rd.rdShow(dep_id, ignore_colours=[0], axes=False, cmap='jet', figsize=(6, 5.5))
dep_level = rd.LoadGDAL(dep_level_path)
dep_level_fig = rd.rdShow(dep_level, ignore_colours=[0], axes=False, cmap='jet', figsize=(6, 5.5))
def gui():
"""An interactive Graphical User Interface (GUI) for the lidar package."""
# identify the sample data directory of the package
package_name = "lidar"
data_dir = pkg_resources.resource_filename(package_name, "data/")
# use the sample dem. Change it to your own dem if needed
in_dem = os.path.join(data_dir, "dem.tif")
# set output directory. By default, use the temp directory under user's home directory
out_dir = os.path.join(os.path.expanduser("~"), "temp")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
with sg.FlexForm("lidar package GUI") as form:
form_rows = [
[
sg.Text(
"Level-set Method for Delineating Topographic Hierarchy",
size=(50, 1),
font=("Arial", 14),
text_color="black",
)
],
[sg.Text("Select DEM:", font=("Arial", 14))],
[sg.InputText(in_dem, size=(60, 1)),
sg.FileBrowse()],
[sg.Text("Delineation Mode:", font=("Arial", 14))],
[
sg.Radio("Depressions", "RADIO1", default=True),
sg.Radio("Mounts", "RADIO1"),
],
[sg.Text("DEM Filtering:", font=("Arial", 14))],
[
sg.Text("Select Filter:"),
sg.InputCombo([
"None", "Mean Filter", "Median Filter", "Gaussian Filter"
]),
sg.Text("Kernel Size: "),
sg.InputText(default_text="3", size=(10, 1)),
],
[sg.Text("Level-set Parameters:", font=("Arial", 14))],
[
sg.Text("Minimum size:"),
sg.InputText(default_text="1000", size=(10, 1)),
sg.Text("Minimum depth:"),
sg.InputText(default_text="1.0", size=(10, 1)),
],
[
sg.Text("Slicing interval:"),
sg.InputText(default_text="0.5", size=(10, 1)),
sg.Text("Output shapefiles:"),
sg.InputCombo(["Yes", "No"], default_value="No"),
],
[sg.Text("Display Results:", font=("Arial", 14))],
[sg.InputCombo(["Yes", "No"], default_value="No")],
[sg.Text("Select Output Directory:", font=("Arial", 14))],
[sg.InputText(out_dir, size=(60, 1)),
sg.FolderBrowse()],
[sg.Submit(), sg.Cancel()],
]
button, (
in_dem,
mode_dep,
mode_mnt,
filter_type,
kernel_szie,
min_size,
min_depth,
interval,
bool_shp,
display,
out_dir,
) = form.LayoutAndRead(form_rows)
if button == "Submit":
kernel_szie = int(kernel_szie)
min_size = int(min_size)
min_depth = float(min_depth)
interval = float(interval)
if bool_shp == "Yes":
bool_shp = True
else:
bool_shp = False
if display == "Yes":
display = True
else:
display = False
if mode_mnt and in_dem == os.path.join(data_dir, "dem.tif"):
in_dem = os.path.join(data_dir, "dsm.tif")
out_dem_name = filter_type.split(" ")[0].lower() + ".tif"
out_dem = os.path.join(out_dir, out_dem_name)
sg.Popup(
"Please Wait!",
"The program is running! You will receive another message when it is done!",
)
if filter_type == "Mean Filter":
in_dem = MeanFilter(in_dem,
kernel_size=kernel_szie,
out_file=out_dem)
elif filter_type == "Median Filter":
in_dem = MedianFilter(in_dem,
kernel_size=kernel_szie,
out_file=out_dem)
elif filter_type == "Gaussian Filter":
in_dem = GaussianFilter(in_dem,
sigma=kernel_szie,
out_file=out_dem)
if mode_dep:
sink_path = ExtractSinks(in_dem, min_size, out_dir)
dep_id_path, dep_level_path = DelineateDepressions(
sink_path, min_size, min_depth, interval, out_dir,
bool_shp)
else:
sink_path = os.path.join(out_dir, "sink.tif")
dep_id_path, dep_level_path = DelineateMounts(
in_dem, min_size, min_depth, interval, out_dir, bool_shp)
if display:
# loading data and results
dem = rd.LoadGDAL(in_dem)
sink = rd.LoadGDAL(sink_path)
dep_id = rd.LoadGDAL(dep_id_path)
dep_level = rd.LoadGDAL(dep_level_path)
# plotting results
dem_fig = rd.rdShow(dem,
ignore_colours=[0],
axes=False,
cmap="jet",
figsize=(6, 5.5))
sink_fig = rd.rdShow(sink,
ignore_colours=[0],
axes=False,
cmap="jet",
figsize=(6, 5.5))
dep_id_fig = rd.rdShow(dep_id,
ignore_colours=[0],
axes=False,
cmap="jet",
figsize=(6, 5.5))
dep_level_path = rd.rdShow(
dep_level,
ignore_colours=[0],
axes=False,
cmap="jet",
figsize=(6, 5.5),
)
del (
dem,
sink,
dep_id,
dep_level,
dem_fig,
sink_fig,
dep_id_fig,
dep_id_path,
)
sg.Popup("Success!",
"The results are saved in: {}".format(out_dir))
