Python - 将图像转换为像素值字符串

Question

我有一个从网络摄像头捕获的.jpg图像.这是一个灰度图像.我需要将图像转换为像素的字符串,如下所示:

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

我该怎么做呢？

另外,改变图像的大小会改变这些值吗？

Answer 1

假设您将图像表示为numpy数组(因为问题被标记为与OpenCV相关,那么很可能就是这种情况),然后为了获得您想要的结果,我将采取以下步骤.

• 首先flatten使数组成为线性的.
• 然后使用将其转换为常规python列表 tolist
  
• 使用map和将所有元素转换为字符串str
• 使用空格加入所有元素.

在步骤中它看起来像

# 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)

或者,放在一起

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

我们将这个Variant 2称为性能测试目的.

更新1

由于numpy数组本身是可迭代的,我们可以跳过第二步.

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

不幸的是,跳过此步骤似乎对性能有相当大的负面影响.

我们将这个Variant 3称为性能测试目的.

更新2

用户Manel Fornos(删除)回答给了我另一个想法.虽然这种方法有点过时,但速度要快一些.

要点是使用现有设施来获取列表的字符串表示,并过滤掉不需要的字符.

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

我们将此变体4称为性能测试目的.

变体1将是Liam Lawrence代码的固定版本:

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

我写了一个简单的小脚本来比较算法(完整的代码在pastebin上).结果如下:

# 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)

更新3

看看时间,变体1的一个突出问题是它的性能不会像输入(像素数)那样线性地缩放,正如人们所期望的那样.相反,它看起来更像是O(n ^ 2).显而易见的罪魁祸首是字符串的添加 - 因为Python中的字符串是不可变的,我们在添加每个像素值时会逐渐复制更长和更长的字符串.

缓解此问题的一种可能方法是使用该cStringIO模块.

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()

我们将这个Variant 5称为性能测试目的.

让我们还包括Manel Fornos的选项,理解列表(Variant 6)和生成器(Variant 7)的完整性.

# 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