Python:使用多处理模块作为可能的解决方案来提高我的功能的速度

Question

我在 Python 2.7(在 Window OS 64 位)中编写了一个函数，以计算 ESRI shapefile format 中引用多边形 (Ref) 和一个或多个分段 (Seg) 多边形的交叉区域的平均值.代码非常慢，因为我有超过 2000 个引用多边形，并且对于每个 Ref_polygon，函数每次都运行所有 Seg 多边形(超过 7000 个)。很抱歉，该功能是一个原型(prototype)。

我想知道 multiprocessing 是否可以帮助我提高循环速度或者是否有更多性能解决方案。如果多处理可以成为一个可能的解决方案，我希望知道优化我的以下功能的最佳方法

import numpy as np
import ogr
import osr,gdal
from shapely.geometry import Polygon
from shapely.geometry import Point
import osgeo.gdal
import osgeo.gdal as gdal

def AreaInter(reference,segmented,outFile):
     # open shapefile
     ref = osgeo.ogr.Open(reference)
     if ref is None:
          raise SystemExit('Unable to open %s' % reference)
     seg = osgeo.ogr.Open(segmented)
     if seg is None:
          raise SystemExit('Unable to open %s' % segmented)
     ref_layer = ref.GetLayer()
     seg_layer = seg.GetLayer()
     # create outfile
     if not os.path.split(outFile)[0]:
          file_path, file_name_ext = os.path.split(os.path.abspath(reference))
          outFile_filename = os.path.splitext(os.path.basename(outFile))[0]
          file_out = open(os.path.abspath("{0}\\{1}.txt".format(file_path, outFile_filename)), "w")
     else:
          file_path_name, file_ext = os.path.splitext(outFile)
          file_out = open(os.path.abspath("{0}.txt".format(file_path_name)), "w")
     # For each reference objects-i
     for index in xrange(ref_layer.GetFeatureCount()):
          ref_feature = ref_layer.GetFeature(index)
          # get FID (=Feature ID)
          FID = str(ref_feature.GetFID())
          ref_geometry = ref_feature.GetGeometryRef()
          pts = ref_geometry.GetGeometryRef(0)
          points = []
          for p in xrange(pts.GetPointCount()):
               points.append((pts.GetX(p), pts.GetY(p)))
          # convert in a shapely polygon
          ref_polygon = Polygon(points)
          # get the area
          ref_Area = ref_polygon.area
          # create an empty list               
          Area_seg, Area_intersect = ([] for _ in range(2))
          # For each segmented objects-j
          for segment in xrange(seg_layer.GetFeatureCount()):
               seg_feature = seg_layer.GetFeature(segment)
               seg_geometry = seg_feature.GetGeometryRef()
               pts = seg_geometry.GetGeometryRef(0)
               points = []
               for p in xrange(pts.GetPointCount()):
                    points.append((pts.GetX(p), pts.GetY(p)))
               seg_polygon = Polygon(points)
               seg_Area.append = seg_polygon.area
               # intersection (overlap) of reference object with the segmented object
               intersect_polygon = ref_polygon.intersection(seg_polygon)
               # area of intersection (= 0, No intersection)
               intersect_Area.append = intersect_polygon.area
          # Avarage for all segmented objects (because 1 or more segmented polygons can  intersect with reference polygon)
          seg_Area_average = numpy.average(seg_Area)
          intersect_Area_average = numpy.average(intersect_Area)
          file_out.write(" ".join(["%s" %i for i in [FID, ref_Area,seg_Area_average,intersect_Area_average]])+ "\n")
     file_out.close()

Answer 1

您可以使用 multiprocessing包，尤其是 Pool 类。首先创建一个函数，它可以在 for 循环中完成所有您想做的事情，并且仅将索引作为参数:

def process_reference_object(index):
      ref_feature = ref_layer.GetFeature(index)
      # all your code goes here
      return (" ".join(["%s" %i for i in [FID, ref_Area,seg_Area_average,intersect_Area_average]])+ "\n")

请注意 这不会写入文件本身 - 这会很困惑，因为您会有多个进程同时写入同一个文件。相反，它返回需要写入的字符串。另请注意，此函数中有一些对象，如 ref_layer 或 ref_geometry 需要以某种方式到达它 - 这取决于你如何去做(你可以把 process_reference_object 作为用它们初始化的类中的方法，或者它可能像全局定义它们一样丑陋。

然后，您创建一个进程资源池，并使用 Pool.imap_unordered 运行所有索引(它本身会根据需要将每个索引分配给不同的进程):

from multiprocessing import Pool
p = Pool()  # run multiple processes
for l in p.imap_unordered(process_reference_object, range(ref_layer.GetFeatureCount())):
    file_out.write(l)

这将跨多个进程并行独立处理您的引用对象，并将它们写入文件(以任意顺序，注意)。