def load_LAS(las_fname, dtype='float32'):
"""
Load LAS or LAZ file (only coordinates) and return pc_xyz and xy vectors. Converts float64 to float32 by default, unless you set dtype='float64'
"""
from laspy.file import File
inFile = File(las_fname, mode='r')
pc_pc_xyz = np.vstack((inFile.get_x()*inFile.header.scale[0]+inFile.header.offset[0], inFile.get_y()*inFile.header.scale[1]+inFile.header.offset[1], inFile.get_z()*inFile.header.scale[2]+inFile.header.offset[2])).transpose()
#setting datatype to float32 to save memory.
if dtype == 'float32':
pc_pc_xyz = pc_pc_xyz.astype('float32')
return pc_pc_xyz
import numpy as np
import time
from PIL import Image
from laspy.file import File
start_time = time.time() # time the process so we can keep it quick
inFile = File("c:/development/python/sample.las", mode="r")
# Grab all of the points from the file.
point_records = inFile.points
data = np.histogram2d(inFile.get_x(), inFile.get_y(), weights=inFile.get_z())
img = Image.fromarray(data)
img.show()
print("%d" % len(inFile))
print("read duration %.3fs" % (time.time() - start_time)) # time the process
# img.save('my.png')
if os.path.exists(bootstrap_dir) == False:
os.mkdir(bootstrap_dir)
figure_dir = os.path.join(args.outputdir, 'figure')
if os.path.exists(figure_dir) == False:
os.mkdir(figure_dir)
pickle_dir = os.path.join(args.outputdir, 'pickle')
if os.path.exists(pickle_dir) == False:
os.mkdir(pickle_dir)
### Loading data and filtering
print('\nLoading input file: %s'%args.inlas)
inFile = File(args.inlas, mode='r')
pc_xyzic = np.vstack((inFile.get_x()*inFile.header.scale[0]+inFile.header.offset[0], inFile.get_y()*inFile.header.scale[1]+inFile.header.offset[1], inFile.get_z()*inFile.header.scale[2]+inFile.header.offset[2], inFile.get_intensity(), inFile.get_classification())).transpose()
#pc_xyzic is now a point cloud with x, y, z, intensity, and classification
#if args.store_color == True:
# pc_i = inFile.get_intensity().copy()
# pc_blue = inFile.get_blue().copy()
# pc_green = inFile.get_green().copy()
# pc_red = inFile.get_red().copy()
print('Loaded %s points'%"{:,}".format(pc_xyzic.shape[0]))
if args.ground_only == True:
print('\nFiltering points to only work with ground points (class == 2)... ',end='\n')
#get only ground points:
idx_ground = np.where(pc_xyzic[:,4] == 2)[0]
pc_xyzig = pc_xyzic[idx_ground,0:4]
pc_xyzg = pc_xyzic[idx_ground,0:3]
#pc_xyzg is a point cloud with x, y, z, and for class == 2 only
