Python - 将图像转换为一串像素值 [英] Python - Convert image to a string of pixel values

问题描述

我有一张从网络摄像头捕获的 .jpg 图像.它是灰度图像.我需要将图像转换为一串像素，如下所示:

"255 232 45 678 56 23....345 76 44 767 433 345"

我该怎么做?

另外，改变图像的大小会改变这些值吗?

解决方案

假设您将图像表示为一个 numpy 数组(因为问题被标记为 OpenCV 相关，那么很可能是这种情况)，然后获取结果如果你愿意，我会采取以下步骤.

• 首先
  <小时>

  更新 3

  从时序来看，变体 1 的一个显着问题是其性能与输入的大小(像素数)不成线性关系，正如人们所期望的那样.相反，它看起来更像 O(n^2).明显的罪魁祸首是字符串相加——由于 Python 中的字符串是不可变的，我们在添加每个像素值时不断复制越来越长的字符串.

  缓解此问题的一种可能方法是使用

  I have a .jpg image captured from a webcam. It is a greyscale image. I need to convert the image to a string of its pixels like so:

  "255 232 45 678 56 23....345 76 44 767 433 345"
  

  How do I go about doing this?

  Also, would changing the size of the image change these values?

  解决方案

  Assuming you have the image represented as a numpy array (since the question is tagged as OpenCV related, then this is likely the case), then to obtain the result you want, I'd take the following steps.

  • First flatten the array to make it linear.
  • Then turn it into a regular python list using tolist
    
  • Convert all the elements into strings using map and str
  • Join all the elements using spaces.

  In steps it would look something like

  # img is our input image represented by a numpy array
  lin_img = img.flatten()
  pixel_list = lin_img.tolist()
  pixel_str_list = map(str, pixel_list)
  img_str = ' '.join(pixel_str_list)
  

  or, put together

  # img is our input image represented by a numpy array
  img_str = ' '.join(map(str,img.flatten().tolist()))
  

  Let's call this Variant 2 for performance testing purposes.

  ---

  Update 1

  Since numpy arrays are themselves iterable, we can skip the second step.

  # img is our input image represented by a numpy array
  img_str = ' '.join(map(str,img.flatten()))
  

  Unfortunately it seems that skipping this step has fairly significant negative effect on performance.

  Let's call this Variant 3 for performance testing purposes.

  ---

  Update 2

  User Manel Fornos (deleted) answer gave me another idea. Although this approach is a bit hackish, it is somewhat faster.

  The gist is to use the existing facilities to get a string represenation of a list, and filter out unwanted characters.

  str_rep = str(img.flatten().tolist())
  img_str = str_rep.strip('[]').replace(',','')
  

  Let's call this Variant 4 for performance testing purposes.

  Variant 1 will be a fixed up version of Liam Lawrence's code:

  pxList = ''
  # The height and width of your Mat
  height = np.size(img, 0)
  width = np.size(img, 1)
  
  # Iterates through the values of your Mat and stores them in pxList
  for i in range(height): 
      for j in range(width):
          pxList = pxList + " " + str(img[i][j])
  
  pxList = pxList[1:] # Drop the first space
  

  I wrote a simple little script to compare the algorithms (the full code is on pastebin). Here are the results:

  # Pixels, Variant 1 (ms), Variant 2 (ms), Variant 3 (ms), Variant 4 (ms)
  (1024, 2.8326225819203277, 0.13493335046772717, 1.5932890912113131, 0.09023493209332506)
  (4096, 13.339841376487794, 0.5257651461289086, 6.325210327010836, 0.3265428986086241)
  (9216, 32.98282323591406, 1.1823080866422975, 14.354809759340927, 0.7088365979475153)
  (16384, 75.67087786296861, 2.1013669335069043, 26.917736751458644, 1.2577715882884644)
  (25600, 137.34306664673863, 3.3527305844737176, 39.52922089259947, 1.9327700867009523)
  (36864, 253.29441311675095, 4.734033934480575, 59.513813906516, 2.9113162427067962)
  (50176, 451.560393848939, 6.5756611524649955, 80.0690276278131, 3.998343364868928)
  (65536, 730.1453117644841, 8.744634443763166, 103.20875278841335, 5.7598277155337385)
  (82944, 1111.2658522242352, 11.029055368769303, 131.75812149309473, 7.009532636131244)
  (102400, 1660.044328259597, 13.671936656754369, 163.50234457172607, 8.832774137495392)
  (123904, 3752.484254283715, 16.593065599119328, 196.8919234148476, 10.672515640955282)
  (147456, 6808.498583618867, 20.05951524565397, 238.21070485215222, 13.339090582743296)
  (173056, 11572.846199726502, 23.518125208653373, 275.5151841924039, 15.51396546209105)
  (200704, 17107.24135330049, 27.29446060882168, 319.9635533287051, 17.9888784747817)
  (230400, 24915.183616213795, 31.83344531218779, 368.9712484407863, 21.44858843792008)
  (262144, 34914.46058437594, 36.754758635524354, 423.5016077462319, 24.536341210961155)
  

  ---

  Update 3

  Looking at the timings, one striking issue with Variant 1 is that its performance doesn't scale linearly with the size of input (number of pixels), as one may expect. Instead it looks more like O(n^2). The obvious culprit is the string addition -- since strings in Python are immutable, we keep copying progressively longer and longer strings as we add each pixel value.

  One possible way to mitigate this problem is to use the cStringIO module.

  output = cStringIO.StringIO()
  # The height and width of your Mat
  height = np.size(img, 0)
  width = np.size(img, 1)
  
  # Iterates through the values of your Mat and stores them in pxList
  for i in range(height): 
      for j in range(width):
          output.write(str(img[i][j]) + " ")
  
  output.truncate(output.tell() - 1)
  img_str = output.getvalue()
  

  Let's call this Variant 5 for performance testing purposes.

  Let's also include Manel Fornos' options, comprehension lists (Variant 6) and generators (Variant 7) for completeness.

  # Number of pixels, variants 1..7 (ms)
  1024, 2.7356, 0.1330, 1.5844, 0.0870, 2.5578, 1.7027, 1.7354
  4096, 13.0483, 0.5250, 6.3810, 0.3227, 10.3566, 6.7979, 6.9346
  9216, 34.9096, 1.1787, 14.2764, 0.7047, 23.0620, 15.1704, 15.3179
  16384, 72.0128, 2.1126, 25.5553, 1.2306, 41.0506, 27.7385, 28.6510
  25600, 142.5863, 3.2655, 40.1804, 1.9044, 64.5345, 42.0542, 42.7847
  36864, 265.1944, 4.7110, 57.3741, 2.9238, 94.8722, 62.3143, 61.8108
  50176, 444.3202, 6.6906, 78.9869, 4.1656, 126.9877, 82.6736, 84.2270
  65536, 739.3482, 8.6936, 101.6483, 5.5619, 163.1796, 110.7537, 111.7517
  82944, 1125.0065, 11.1771, 133.8886, 7.0509, 209.9322, 137.3384, 143.7916
  102400, 1700.3401, 13.8166, 161.2337, 8.7119, 261.8374, 171.3757, 175.0435
  123904, 2304.6573, 16.8627, 196.3455, 10.8982, 314.8287, 205.1966, 210.4597
  147456, 5595.0777, 19.8212, 240.1495, 12.9097, 381.7084, 251.7319, 253.3573
  173056, 10813.7815, 23.5161, 273.9376, 15.6852, 441.5994, 291.8913, 295.0038
  200704, 17561.0637, 27.4871, 322.6305, 17.9567, 517.7028, 340.2233, 342.6525
  230400, 25331.5150, 31.6211, 368.3908, 21.0858, 597.7710, 387.3542, 398.9715
  262144, 34097.1663, 36.3708, 420.1081, 23.9135, 677.7977, 443.1318, 453.0447
  

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