python加快for循环的速度_提⾼通过numpy数组循环的速度-
python
在对地⾯进⾏分类之后，我尝试分割LiDAR点云。我正在使⽤numpy创建点云(pc)的“图像”，并遍历numpy数组。我想加快循环速度，或者⼀起避免循环。我将使⽤图像分割技术，但是⾸先我需要运⾏此代码来创建“图像”，这是需要⼀段时间的部分。有没有办法提⾼这个循环的速度或避免这种循环？
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]j) & (pc[:,1]
# if there are points, give pixel value 1.
if input_point.shape[0]!=0:
img[x,y]=1
print('Image made')
谢谢。
python参考⽅案
这是⼀个⽮量化版本，可在随机测试集上产⽣相同的输出：
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]j) & (pc[:,1]
# 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')
您的代码产⽣5x5像素。那是故意的吗？要重现这⼀点，我必须有点棘⼿。
更新：添加了⼀个使普通像素代替的版本。
样品运⾏：
Shared ops done
Image made
Image remade
took [2.29120255e-04 1.54510736e-01 1.44481659e-04] secs respectively
results equal
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