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我写了一个脚本，我在其中阅读了大约 400 万个点和 800.000 个图。该脚本剪辑每个绘图内的点并为每个绘图保存一个新的文本文件。

一段时间后，我的 PC 内存已满。我曾尝试在我的脚本中挖掘，但在每个循环for i in xrange(len(sr)):中，每个对象都被替换，并且剪辑的点保存在一个新的 txt 文件中。

在这种情况下是否有一些策略可以在不降低性能的情况下提高内存使用率（脚本已经很慢）？我是python的初学者，如果问题很简单，我很抱歉。

提前感谢詹尼

inFile ="C://04-las_clip_inside_area//prova//Ku_115_class_Notground_normalize.las"
poly ="C://04-las_clip_inside_area//prova//ku_115_plot_clip.shp"
chunkSize = None
MinPoints = 1

sf = shapefile.Reader(poly) #open shpfile
sr = sf.shapeRecords()
poly_filename, ext = path.splitext(poly)
inFile_filename = os.path.splitext(os.path.basename(inFile))[0]
pbar = ProgressBar(len(sr)) # set progressbar
if chunkSize == None:
    points = [(p.x,p.y) for p in lasfile.File(inFile,None,'r')]
    for i in xrange(len(sr)):
        pbar.update(i+1) # progressbar
        verts = np.array(sr[i].shape.points,float)
        record = sr[i].record[0]
        index = nonzero(points_inside_poly(points, verts))[0]
        if len(index) >= MinPoints:
            file_out = open("{0}_{1}_{2}.txt".format(poly_filename, inFile_filename, record), "w")
            inside_points = [lasfile.File(inFile,None,'r')[l] for l in index]
            for p in inside_points:
                file_out.write("%s %s %s %s %s %s %s %s %s %s %s" % (p.x, p.y, p.z, p.intensity,p.return_number,p.number_of_returns,p.scan_direction,p.flightline_edge,p.classification,p.scan_angle,record)+ "\n")
            file_out.close()

这是原始功能

def LAS2TXTClipSplitbyChunk(inFile,poly,chunkSize=1,MinPoints=1):
    sf = shapefile.Reader(poly) #open shpfile
    sr = sf.shapeRecords()
    poly_filename, ext = path.splitext(poly)
    inFile_filename = os.path.splitext(os.path.basename(inFile))[0]
    pbar = ProgressBar(len(sr)) # set progressbar
    if chunkSize == None:
        points = [(p.x,p.y) for p in lasfile.File(inFile,None,'r')]
        for i in xrange(len(sr)):
            pbar.update(i+1) # progressbar
            verts = np.array(sr[i].shape.points,float)
            record = sr[i].record[0]
            index = nonzero(points_inside_poly(points, verts))[0]
            if len(index) >= MinPoints:
                file_out = open("{0}_{1}_{2}.txt".format(poly_filename, inFile_filename, record), "w")
                inside_points = [lasfile.File(inFile,None,'r')[l] for l in index]
                for p in inside_points:
                    file_out.write("%s %s %s %s %s %s %s %s %s %s %s" % (p.x, p.y, p.z, p.intensity,p.return_number,p.number_of_returns,p.scan_direction,p.flightline_edge,p.classification,p.scan_angle,record)+ "\n")
                file_out.close()
    else:
        for i in xrange(len(sr)):
            pbar.update(i+1) # progressbar
            verts = np.array(sr[i].shape.points,float)
            record = sr[i].record[0]
            f = lasfile.File(inFile,None,'r')
            file_out = open("{0}_{1}_{2}.txt".format(poly_filename, inFile_filename, record), "w")
            TotPoints = 0
            while True:
                chunk = list(islice(f,chunkSize))
                if not chunk:
                    break
                points = [(p.x,p.y) for p in chunk]
                index = nonzero(points_inside_poly(points, verts))[0]
                TotPoints += len(index) #add points to count inside th plot
                chunk = [chunk[l] for l in index]
                for p in chunk:
                    file_out.write("%s %s %s %s %s %s %s %s %s %s %s" % (p.x, p.y, p.z, p.intensity,p.return_number,p.number_of_returns,p.scan_direction,p.flightline_edge,p.classification,p.scan_angle,record)+ "\n")
            if TotPoints >= MinPoints:
                file_out.close()
            else:
                file_out.close()
                os.remove("{0}_{1}_{2}.txt".format(poly_filename, inFile_filename, record))
            f.close()

unutbu 建议的脚本是：

import shapefile
import os
import glob
from os import path
import numpy as np
from numpy import nonzero
from matplotlib.nxutils import points_inside_poly
from itertools import islice
from liblas import file as lasfile
from shapely.geometry import Polygon
from progressbar import ProgressBar
import multiprocessing as mp


inFile ="C://04-las_clip_inside_area//prova//Ku_115_class_Notground_normalize.las"
poly ="C://04-las_clip_inside_area//prova//ku_115_plot_clip.shp"
chunkSize = None
MinPoints = 1

def pointinside(record):
    verts = np.array(record.shape.points, float)
    record = record.record[0]
    index = nonzero(points_inside_poly(points, verts))[0]
    if len(index) >= MinPoints:
        outfile = "{0}_{1}_{2}.txt".format(poly_filename, inFile_filename, record)
        with open(outfile, "w") as file_out:
            inside_points = [lasfile.File(inFile, None, 'r')[l] for l in index]
            for p in inside_points:
                fields = (p.x, p.y, p.z, p.intensity, p.return_number,
                          p.number_of_returns, p.scan_direction, p.flightline_edge,
                          p.classification, p.scan_angle, record)
                file_out.write(' '.join(map(str, fields)) + "\n")

sf = shapefile.Reader(poly) #open shpfile
sr = sf.shapeRecords()
poly_filename, ext = path.splitext(poly)
inFile_filename = os.path.splitext(os.path.basename(inFile))[0]
pbar = ProgressBar(len(sr)) # set progressbar
if chunkSize == None:
    points = [(p.x,p.y) for p in lasfile.File(inFile,None,'r')]
    for i in xrange(len(sr)):
        pbar.update(i+1) # progressbar
        proc = mp.Process(target = pointinside, args = (sr[i], ))
        proc.start()
        proc.join()
4

1 回答 1

5

释放用于临时计算的内存的唯一可靠方法是在子进程中运行该计算。当子进程结束时，内存将被释放。

如果您将外循环中的代码移动到一个函数中（我们称之为它work），那么您可以使用该模块work在子进程中运行：multiprocessing

import sys
import os
import time
import itertools
import multiprocessing as mp
import numpy as np
import matplotlib.nxutils as nx
import liblas
import shapefile

clock = time.clock if sys.platform == 'win32' else time.time

def LAS2TXTClipSplitbyChunk(inFile, poly, chunkSize = 1, MinPoints = 1):
    sf = shapefile.Reader(poly) #open shpfile
    sr = sf.shapeRecords()
    poly_filename, ext = os.path.splitext(poly)
    for record in sr:
        inFile_filename = os.path.splitext(os.path.basename(inFile))[0]
        record_num = record.record[0]        
        out_filename = '{0}_{1}_{2}.txt'.format(
            poly_filename, inFile_filename, record_num)
        pool.apply_async(pointinside,
                         args = (record, out_filename, inFile, chunkSize, MinPoints),
                         callback = update)

def pointinside(record, out_filename, inFile, chunkSize, MinPoints):
    start = clock()
    record_num = record.record[0]   
    verts = np.array(record.shape.points, float)
    f = iter(liblas.file.File(inFile, None, 'rb'))
    result = []
    worth_writing = False
    for chunk in iter(lambda: list(itertools.islice(f, chunkSize)), []):
        points = [(p.x, p.y) for p in chunk]
        index = nx.points_inside_poly(points, verts)
        chunk = [p for inside, p in itertools.izip(index,chunk) if inside]
        for p in chunk:
            fields = (p.x, p.y, p.z, p.intensity, p.return_number,
                      p.number_of_returns, p.scan_direction, p.flightline_edge,
                      p.classification, p.scan_angle, record_num)
            result.append(' '.join(map(str, fields)))
        if len(result) >= bufferSize:
            # Writing to disk is slow. Doing it once for every iteration is
            # inefficient.  So instead build up bufferSize number of lines
            # before writing them all to disk.
            worth_writing = True
            with open(out_filename, 'a') as file_out:
                file_out.write('\n'.join(result)+'\n')
            result = []
    # In case there were some results (less than bufferSize lines), we
    # dump them to disk here.
    if (len(result) >= MinPoints) or worth_writing:
        with open(out_filename, 'a') as file_out:
            file_out.write('\n'.join(result)+'\n')
    f.close()                    
    end = clock()
    return end-start

def update(result):
    with open(debug_filename, 'a') as f:
        f.write('{r}\n'.format(r = result))

if __name__ == '__main__':
    workdir = 'C://04-las_clip_inside_area//prova//'
    # workdir = os.path.expanduser('~/tmp/tmp')
    os.chdir(workdir)
    inFile = 'Ku_115_class_Notground_normalize.las'
    poly = 'ku_115_plot_clip.shp'
    debug_filename = 'debug.dat'
    chunkSize = None
    MinPoints = 1
    bufferSize = max(MinPoints, 100)

    pool = mp.Pool()
    LAS2TXTClipSplitbyChunk(inFile, poly, chunkSize, MinPoints)
    pool.close()
    pool.join()

以下是每项任务完成所需时间的图表：

In [129]: import matplotlib.pyplot as plt

In [130]: import numpy as np

In [131]: x = np.genfromtxt('debug.dat')

In [132]: plt.plot(x)
Out[132]: [<matplotlib.lines.Line2D object at 0xe309b4c>]

In [133]: plt.show()

在此处输入图像描述

我没有看到任何渐进式的放缓。也许试试这个代码。

于 2012-10-13T11:30:17.893 回答