I am attempting to segment a LiDAR point cloud after the ground has been classified. I am using numpy to create an "image" of the the point cloud (pc) and am looping through the numpy array. I would like to speed up the loop or avoid it all together. I am going to use image segmentation techniques, but first I need to run this code to create an "image" and this is the part that is taking a while. Is there a way to increase the speed of this loop or a way to avoid it?
import numpy as np
from math import ceil, floor
'''In this case:
pc = point cloud (X,Y,Z values)'''
# point cloud is in the numpy array, pc
minx,maxx,miny,maxy = floor(np.min(pc[:,0]-1)),ceil(np.max(pc[:,0]+1)),floor(np.min(pc[:,1]-1)),ceil(np.max(pc[:,1]+1))# x,y bounding box
# grid x and y direction (resolution: 0.2 meters)
gridx = np.linspace(minx,maxx,int((maxx - minx+0.2)*5),endpoint=True)
gridy = np.linspace(miny,maxy,int((maxy - miny +0.2)*5),endpoint=True)
#shape of the new image with 0.2 meter resolution.
imgx,imgy = int((maxx-minx+0.2)*5),int((maxy - miny +0.2)*5)
# this is what will be created at the end. It will be a binary image.
img = np.zeros((imgx,imgy))
#loop through array to generate image (this is the part that takes a while)
for x,i in enumerate(gridx):
for y,j in enumerate(gridy):
# Test if there any points in this "grid"
input_point = pc[np.where(((pc[:,0]>i) & (pc[:,0]<i+1))& ((pc[:,1]>j) & (pc[:,1]<j+1)))]
# if there are points, give pixel value 1.
if input_point.shape[0]!=0:
img[x,y]=1
print('Image made')
Thank you.
Here is a vectorized version that produces the same output on a random test set:
import numpy as np
from math import ceil, floor
import time
width = 0.2
t = [time.time()]
pc = np.random.uniform(-10, 10, (100, 3))
# point cloud is in the numpy array, pc
minx,maxx,miny,maxy = floor(np.min(pc[:,0]-1)),ceil(np.max(pc[:,0]+1)),floor(np.min(pc[:,1]-1)),ceil(np.max(pc[:,1]+1))# x,y bounding box
# grid x and y direction (resolution: 0.2 meters)
gridx = np.linspace(minx,maxx,int((maxx - minx+0.2)*5),endpoint=True)
gridy = np.linspace(miny,maxy,int((maxy - miny +0.2)*5),endpoint=True)
#shape of the new image with 0.2 meter resolution.
imgx,imgy = int((maxx-minx+0.2)*5),int((maxy - miny +0.2)*5)
print('Shared ops done')
t.append(time.time())
# this is what will be created at the end. It will be a binary image.
img = np.zeros((imgx,imgy))
#loop through array to generate image (this is the part that takes a while)
for x,i in enumerate(gridx):
for y,j in enumerate(gridy):
# Test if there any points in this "grid"
input_point = pc[np.where(((pc[:,0]>i) & (pc[:,0]<i+width))& ((pc[:,1]>j) & (pc[:,1]<j+width)))]
# if there are points, give pixel value 1.
if input_point.shape[0]!=0:
img[x,y]=1
t.append(time.time())
print('Image made')
if width == 0.2:
img2 = np.zeros((imgx, imgy), 'u1')
x2, y2 = (((pc[:, :2] - (minx, miny)) * (5, 5))).astype(int).T
img2[x2, y2] = 1
elif width == 1:
img2 = np.zeros((imgx+4, imgy+4), 'u1')
x2, y2 = (((pc[:, :2] - (minx, miny)) * (5, 5))).astype(int).T
np.lib.stride_tricks.as_strided(img2, (imgx, imgy, 5, 5), 2 * img2.strides)[x2, y2] = 1
img2 = img2[4:, 4:]
t.append(time.time())
print('Image remade')
print('took', np.diff(t), 'secs respectively')
assert((img2==img).all())
print('results equal')
Your code produces 5x5 pixels. Is that intentional? I had to be a bit tricksy to reproduce that.
UPDATE: added a version that makes ordinary pixels instead.
Sample run:
Shared ops done
Image made
Image remade
took [2.29120255e-04 1.54510736e-01 1.44481659e-04] secs respectively
results equal