我写了一个python脚本，以独特的方式为OpenGL着色器组合了图像。问题是我有很多非常大的地图，处理时间很长。有没有一种方法可以更快地编写此代码？
import numpy as np
map_data = {}
image_data = {}
for map_postfix in names:
file_name = inputRoot + '-' + map_postfix + resolution + '.png'
print 'Loading ' + file_name
image_data[map_postfix] = Image.open(file_name, 'r')
map_data[map_postfix] = image_data[map_postfix].load()
color = mapData['ColorOnly']
ambient = mapData['AmbientLight']
shine = mapData['Shininess']
width = imageData['ColorOnly'].size[0]
height = imageData['ColorOnly'].size[1]
arr = np.zeros((height, width, 4), dtype=int)
for i in range(width):
for j in range(height):
ambient_mod = ambient[i,j][0] / 255.0
arr[j, i, :] = [color[i,j][0] * ambient_mod , color[i,j][1] * ambient_mod , color[i,j][2] * ambient_mod , shine[i,j][0]]
print 'Converting Color Map to image'
return Image.fromarray(arr.astype(np.uint8))
这只是大量批处理过程中的一个示例，因此我对是否有一种更快的迭代和修改图像文件的方法感兴趣。几乎所有时间都花在嵌套循环与加载和保存上。
最佳答案
矢量化代码示例-在timeit或zmq.Stopwatch()中测试对您的效果
据说有22.14秒>> 0.1624秒的加速！
虽然您的代码似乎仅在RGBA [x，y]上循环，但让我展示一个代码的“向量化”语法，该语法得益于numpy矩阵操作实用程序（忘记RGB / YUV操作（最初基于OpenCV而不是PIL），但请重新使用向量化语法方法来避免for循环，并使它适应于微积分的有效工作；错误的操作顺序可能会使处理时间增加一倍以上。
并使用测试/优化/重新测试循环来加快速度。
对于测试，如果timeit分辨率足够，请使用标准python [msec]。
如果需要进入zmq.StopWatch()分辨率，请改用[usec]。
# Vectorised-code example, to see the syntax & principles
# do not mind another order of RGB->BRG layers
# it has been OpenCV traditional convention
# it has no other meaning in this demo of VECTORISED code
def get_YUV_U_Cb_Rec709_BRG_frame( brgFRAME ): # For the Rec. 709 primaries used in gamma-corrected sRGB, fast, VECTORISED MUL/ADD CODE
out = numpy.zeros( brgFRAME.shape[0:2] )
out -= 0.09991 / 255 * brgFRAME[:,:,1] # // Red
out -= 0.33601 / 255 * brgFRAME[:,:,2] # // Green
out += 0.436 / 255 * brgFRAME[:,:,0] # // Blue
return out
# normalise to <0.0 - 1.0> before vectorised MUL/ADD, saves [usec] ...
# on 480x640 [px] faster goes about 2.2 [msec] instead of 5.4 [msec]
在您的情况下，使用
dtype = numpy.int猜测，首先通过MUL到ambient[:,:,0]更快，最后通过DIV归一化arr[:,:,:3] /= 255# test if this goes even faster once saving the vectorised overhead on matrix DIV
arr[:,:,0] = color[:,:,0] * ambient[:,:,0] / 255 # MUL remains INT, shall precede DIV
arr[:,:,1] = color[:,:,1] * ambient[:,:,0] / 255 #
arr[:,:,2] = color[:,:,2] * ambient[:,:,0] / 255 #
arr[:,:,3] = shine[:,:,0] # STO alpha
那么它在您的算法中看起来如何？
彼得·杰克逊（Peter Jackson）曾经在新西兰机库中计划，跨越并执行了3年的巨大数字运算，却被一群SGI工作站挤得水泄不通，因为他正在制作《指环王》，数字母版制作流水线，通过逐帧像素操作，立即意识到，大规模生产流水线中的毫秒，微秒甚至是纳秒就很重要。
因此，请深呼吸，然后进行测试和重新测试，以便将实际图像处理性能优化到项目所需的水平。
希望这可以帮助您：
# OPTIONAL for performance testing -------------# ||||||||||||||||||||||||||||||||
from zmq import Stopwatch # _MICROSECOND_ timer
# # timer-resolution step ~ 21 nsec
# # Yes, NANOSECOND-s
# OPTIONAL for performance testing -------------# ||||||||||||||||||||||||||||||||
arr = np.zeros( ( height, width, 4 ), dtype = int )
aStopWatch = zmq.Stopwatch() # ||||||||||||||||||||||||||||||||
# /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\# <<< your original code segment
# aStopWatch.start() # |||||||||||||__.start
# for i in range( width ):
# for j in range( height ):
# ambient_mod = ambient[i,j][0] / 255.0
# arr[j, i, :] = [ color[i,j][0] * ambient_mod, \
# color[i,j][1] * ambient_mod, \
# color[i,j][2] * ambient_mod, \
# shine[i,j][0] \
# ]
# usec_for = aStopWatch.stop() # |||||||||||||__.stop
# print 'Converting Color Map to image'
# print ' FOR processing took ', usec_for, ' [usec]'
# /\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\# <<< proposed alternative
aStopWatch.start() # |||||||||||||__.start
# reduced numpy broadcasting one dimension less # ref. comments below
arr[:,:, 0] = color[:,:,0] * ambient[:,:,0] # MUL ambient[0] * [{R}]
arr[:,:, 1] = color[:,:,1] * ambient[:,:,0] # MUL ambient[0] * [{G}]
arr[:,:, 2] = color[:,:,2] * ambient[:,:,0] # MUL ambient[0] * [{B}]
arr[:,:,:3] /= 255 # DIV 255 to normalise
arr[:,:, 3] = shine[:,:,0] # STO shine[ 0] in [3]
usec_Vector = aStopWatch.stop() # |||||||||||||__.stop
print 'Converting Color Map to image'
print ' Vectorised processing took ', usec_Vector, ' [usec]'
return Image.fromarray( arr.astype( np.uint8 ) )
关于python - Python-PNG的快速批量修改，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/26111329/