Lidar colab

In [1]:

import subprocess

try:
    import lidar
except ImportError:
    print("Installing lidar ...")
    subprocess.check_call(["python", "-m", "pip", "install", "lidar"])

import subprocess try: import lidar except ImportError: print("Installing lidar ...") subprocess.check_call(["python", "-m", "pip", "install", "lidar"])

In [2]:

import os
import pkg_resources
from lidar import *

# 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 the output directory
out_dir = os.getcwd()

# parameters for identifying sinks and delineating nested depressions
min_size = 1000  # minimum number of pixels as a depression
min_depth = 0.5  # minimum depth as a depression
interval = 0.3  # slicing interval for the level-set method
bool_shp = True  # 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))

import os import pkg_resources from lidar import * # 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 the output directory out_dir = os.getcwd() # parameters for identifying sinks and delineating nested depressions min_size = 1000 # minimum number of pixels as a depression min_depth = 0.5 # minimum depth as a depression interval = 0.3 # slicing interval for the level-set method bool_shp = True # 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))

Median filtering ...
Run time: 0.0599 seconds
Saving dem ...
Loading data ...
min = 379.70, max = 410.72, no_data = -3.402823e+38, cell_size = 1.0
Depression filling ...
Saving filled dem ...
Region grouping ...
Computing properties ...
Saving sink dem ...
Saving refined dem ...
Converting raster to vector ...
Total run time:			 0.0972 s

Reading data ...
rows, cols: (400, 400)
Pixel resolution: 1.0
Read data time: 0.0029 seconds
Data preparation time: 0.0092 seconds
Total number of regions: 1
Processing Region # 1 ...
=========== Run time statistics =========== 
(rows, cols):			 (400, 400)
Pixel resolution:		 1.0 m
Number of regions:		 1
Data preparation time:		 0.0092 s
Identify level time:		 0.2358 s
Write image time:		 0.0026 s
Polygonize time:		 0.0098 s
Extract level time:		 0.0570 s
Total run time:			 0.3150 s
Results are saved in: /home/qiusheng/temp