Python - 快速批量修改PNG [英] Python - Quick batch modification of PNGs

问题描述

我写了一个python脚本，以独特的方式为OpenGL着色器组合图像。问题是我有大量非常大的地图，需要很长时间才能处理。有没有办法以更快的方式写这个？

I wrote a python script with combines images in unique ways for an OpenGL shader. The problem is that I have a large number of very large maps and it takes a long time to process. Is there a way to write this in a quicker fashion?

    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的一个示例，所以如果有更快的方法，我会更感兴趣迭代并修改图像文件。几乎所有的时间都用在嵌套循环上，而不是加载和保存。

This is just a sample of a large number of batch processes so I am more interested in if there is a faster way to iterate and modify an image file. Almost all the time is being spent on the nested loop vs loading and saving.

推荐答案

矢量化代码示例 - 在 timeit 或 zmq.Stopwatch（）

Vectorised-code example -- test effect on yours in timeit or zmq.Stopwatch()

报告有22.14秒>> 0.1624秒加速！

Reported to have 22.14 seconds >> 0.1624 seconds speedup!

虽然你的代码似乎只是在RGBA [x，y]上循环，让我展示一个 vectorised - 代码的语法，受益于 numpy 矩阵操作实用程序（忘记RGB / YUV操作（最初基于OpenCV而不是PIL），但重新使用矢量化语法方法以避免for循环和使其适应你的微积分。错误的操作顺序可能会使你的处理时间增加一倍以上。

While your code seems to loop just over RGBA[x,y], let me show a "vectorised"-syntax of a code, that benefits from numpy matrix-manipulation utilities ( forget the RGB/YUV manipulation ( originally based on OpenCV rather than PIL ), but re-use the vectorised-syntax approach to avoid for-loops and adapt it to work efficiently for your calculus. Wrong order of operations may more than double yours processing time.

并使用测试/优化/重新测试循环来加速。

And use a test / optimise / re-test loop for speeding up.

测试时，使用标准python timeit 如果 [毫秒] 分辨率足够。

For testing, use standard python timeit if [msec] resolution is enough.

宁愿选择 zmq.StopWatch（ ）如果你需要进入 [usec] 分辨率。

Go rather for zmq.StopWatch() if you need going into [usec] resolution.

# 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]

在你的情况下，使用 dtype = numpy.int ，猜猜它应该更快到 MUL 首先按环境[：，：，0] ，最后 DIV 进行标准化 arr [：,,：，3] / = 255

In your case, using dtype = numpy.int, guess it shall be faster to MUL first by ambient[:,:,0] and finally DIV to normalisearr[:,:,: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

那么你的算法看起来如何？

人们不必拥有彼得杰克逊令人印象深刻的预算和时间一旦计划，跨越和执行超过3年的巨大数字运算一个新西兰机库，由一群SGI工作站挤满了人，因为他正在制作指环王全数字母版装配线，正是通过逐帧像素操作， 意识到大规模生产管道中的毫秒，微秒甚至纳秒只是重要。

One need not have Peter Jackson's impressive budget and time once planned, spanned and executed immense number-crunching over 3 years in a New Zealand hangar, overcrowded by a herd of SGI workstations, as he was producing "The Lord of The Rings" fully-digital mastering assembly-line, right by the frame-by-frame pixel manipulation, to realise that miliseconds and microseconds and even nanoseconds in the mass-production pipe-line simply do matter.

所以，深呼吸一下测试和重新测试，以便将您的真实图像处理性能优化到您的项目所需的水平。

So, take a deep breath and test and re-test so as to optimise your real-world imagery processing performance to levels that your project needs.

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 ) )

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