def get_slope(self):
""" TODO """
beau = rd.rdarray(np.load('imgs/beauford.npz')['beauford'], no_data=-9999)
slope = rd.TerrainAttribute(beau, attrib='slope_riserun')
rd.rdShow(slope, axes=False, cmap='jet', figsize=(8,5.5))
pass
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 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)')
parser.add_argument('--vmin', type=float, default=None, help='Colormap (Default: jet)')
parser.add_argument('--vmax', type=float, default=None, 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, vmin=args.vmin, vmax=args.vmax)
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))
polygonize(out_region, out_vec_file)
# # plot dems
# demfig = rd.rdShow(dem, ignore_colours=[0], axes=False, cmap='jet', figsize=(8, 5.5))
# demfig_filled = rd.rdShow(dem_filled, ignore_colours=[0], axes=False, cmap='jet', vmin=demfig['vmin'],
# vmax=demfig['vmax'], figsize=(8, 5.5))
# demfig_diff = rd.rdShow(dem_diff, ignore_colours=[0], axes=False, cmap='jet', figsize=(8, 5.5))
end_time = time.time()
print("Total run time:\t\t\t {:.4f} s".format(end_time - start_time))
return sink
if __name__ == '__main__':
# ************************ change the following parameters if needed ******************************** #
# set input files
in_dem = "../data/dem.tif"
# parameters for depression filling
min_size = 1000 # minimum number of pixels as a depression
min_depth = 0.3 # minimum depression depth
# set output directory
out_dir = "/home/qiusheng/temp"
# ************************************************************************************************** #
sink = ExtractSinks(in_dem, min_size=min_size, out_dir=out_dir)
dem = rd.LoadGDAL(in_dem)
demfig = rd.rdShow(dem, ignore_colours=[0], axes=False, cmap='jet', figsize=(6, 5.5))
sinkfig = rd.rdShow(sink, ignore_colours=[0], axes=False, cmap='jet', figsize=(6, 5.5))
import richdem as rd
import numpy as np
import matplotlib.pyplot as plt
dem = rd.LoadGDAL("/media/jayanthmouli/54EC-046F/elevation/the_better_one.tif",
no_data=0)
slope = rd.TerrainAttribute(dem, attrib='slope_riserun')
# rd.rdShow(slope, axes=False, cmap='magma', figsize=(8, 5.5))
# plt.show()
aspect = rd.TerrainAttribute(dem, attrib='aspect')
rd.rdShow(aspect, axes=False, cmap='jet', figsize=(8, 5.5))
plt.show()
print aspect.shape
# parameters for identifying sinks and delineating nested depressions
min_size = 1000 # minimum number of pixels as a depression
min_height = 0.3 # minimum depth as a depression
interval = 0.3 # slicing interval for the level-set method
bool_shp = False # output shapefiles for each individual level
mount_id_path, mount_level_path = DelineateMounts(in_dem, min_size,
min_height, interval,
out_dir, bool_shp)
print("Results are saved in: {}".format(out_dir))
dem = rd.LoadGDAL(in_dem)
dem_fig = rd.rdShow(dem,
ignore_colours=[0],
axes=False,
cmap='jet',
figsize=(6, 5.5))
mount_id = rd.LoadGDAL(mount_id_path)
mount_id_fig = rd.rdShow(mount_id,
ignore_colours=[0],
axes=False,
cmap='jet',
figsize=(6, 5.5))
mount_level = rd.LoadGDAL(mount_level_path)
mount_level_fig = rd.rdShow(mount_level,
ignore_colours=[0],
axes=False,
cmap='jet',
figsize=(6, 5.5))
out_tif = "data/ASTERGDEM3/Processed/Elevation_5min.tif"
with rasterio.open(out_tif, "w", **out_meta) as dest:
dest.write(elevation_low)
# =============================
# 3. Calculate slope and aspect
# 3.1. 30`` resolution
# Read elevarion with rd.LoadGDAL
elevation_raw = rd.LoadGDAL("data/ASTERGDEM3/Processed/Elevation_30s.tif",
no_data=-9999)
# Calculate slope
slope = rd.TerrainAttribute(elevation_raw, attrib='slope_riserun')
rd.rdShow(slope, axes=False, cmap='magma', figsize=(8, 5.5))
# Export as tif
rd.SaveGDAL("data/ASTERGDEM3/Processed/Slope_30s.tif", slope)
# Calculate aspect
aspect = rd.TerrainAttribute(elevation_raw, attrib='aspect')
rd.rdShow(aspect, axes=False, cmap='jet', figsize=(8, 5.5))
# Export as tif
rd.SaveGDAL("data/ASTERGDEM3/Processed/Aspect_30s.tif", aspect)
# 3.2. 2.5` resolution
# Read elevarion with rd.LoadGDAL
elevation_raw = rd.LoadGDAL("data/ASTERGDEM3/Processed/Elevation_2p5min.tif",
gaussian_dem = os.path.join(out_dir, "gaussian.tif")
mean_dem = os.path.join(out_dir, "mean.tif")
dem = rd.LoadGDAL(in_dem) # original dem
mean = MeanFilter(in_dem, kernel_size=3)
mean_diff = mean - dem
med = MedianFilter(in_dem, kernel_size=3)
med_diff = med - dem
gau = GaussianFilter(in_dem, sigma=1)
gau_diff = gau - dem
# plotting data
dem_fig = rd.rdShow(dem,
ignore_colours=[0],
axes=False,
cmap="jet",
figsize=(6, 5.5))
mean_fig = rd.rdShow(mean,
ignore_colours=[0],
axes=False,
cmap="jet",
figsize=(6, 5.5))
med_fig = rd.rdShow(med,
ignore_colours=[0],
axes=False,
cmap="jet",
figsize=(6, 5.5))
gau_fig = rd.rdShow(gau,
ignore_colours=[0],
axes=False,
def main():
# 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)
# sg.Popup(button, source_filename)
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))
min_size = 1000 # minimum number of pixels as a depression
min_depth = 0.3 # minimum depth as a depression
interval = 0.3 # slicing interval for the level-set method
bool_shp = False # output shapefiles for each individual level
# extracting sinks based on user-defined minimum depression size
out_dem = os.path.join(out_dir, "median.tif")
in_dem = MedianFilter(in_dem, kernel_size=3, out_file=out_dem)
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
)
print("Results are saved in: {}".format(out_dir))
# 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)
)
def display_slope(self):
self.dem_data = rd.rdarray(self.dem_data, no_data=-9999)
rd.rdShow(self.dem_data, axes=False, cmap='magma', figsize=(8, 5.5))
import richdem as rd
import matplotlib
import matplotlib.pyplot as plt
import os
import time
import PIL
start = time.time()
# Na początku trzeba raster przekonwertować do układu metrycznego, tutaj jest zrobione do UTM34 (gdalwarp)
path = 'C:\\Users\\wojo1\\Desktop\\Doktorat\\Microrelief\\Data\\LIDAR\\NMT'
dem_path = path + '\\nmt_lidar.tif'
dem_plot = path + '\\L_NMT_N-34-138-B-b_utm.tif'
os.chdir("C:\\Users\\wojo1\\Desktop\\Doktorat\\Microrelief\\Data\\TIF")
# string = os.popen("gdalinfo " + dem_plot).read().rstrip()
# min_value = string.splitlines()
# min_float_value = float(min_value[-2][23:])
# print(os.popen('gdalinfo L_NMT_N-34-138-B-b_utm.tif').read().rstrip())
dem = rd.LoadGDAL(dem_plot)
# plt.imshow(dem, interpolation='bilinear')
# plt.clim(vmin=0, vmax=None)
# plt.colorbar(mappable=None, cax=None)
# plt.show()
slope = rd.TerrainAttribute(dem, attrib='slope_degrees')
rd.rdShow(slope, axes=False, cmap='jet', figsize=(10, 8))
plt.show()
# rd.SaveGDAL("nmt_python.tif", slope)
end = time.time()
print('process time: ', end - start)
