详解Python PIL Image.histogram()

Python PIL 是Python的一个图像处理模块，包含了一系列图像处理操作，其中 Image.histogram()是其中一个常用函数，用于生成一幅图像的直方图，下面详细介绍该函数的用法：

Image.histogram() 函数的简介

Image.histogram() 函数用于将一幅图像转为一维直方图，直方图的每一个数据表示一个像素值的数量。比如一幅灰度图像，数据会是0到255之间的256个数，每个数表示像素值等于这个数的像素的数量。对于一幅彩色图像，数据会是0到765（3*255）之间256 * 3个数，每一个像素值表示三通道上，对应比例的像素值数量。

Image.histogram() 函数的使用方法

首先，我们需要引入PIL库，然后使用 Image.open() 函数载入需要处理的图片：

from PIL import Image

img = Image.open('test.jpg')

传入图像，调用 Image.histogram() 函数即可生成直方图数据：

histogram_data = img.histogram()

Image.histogram() 函数的参数

默认情况下，Image.histogram() 会针对一幅图的所有通道生成直方图数据，如果需要只针对某一通道，可以指定 Image.histogram() 的参数 channel：

histogram_data = img.histogram(channel=0)

上面代码会生成一幅图像的红色通道的直方图数据，0表示红色通道，1表示绿色通道，2表示蓝色通道。

另外，如果我们想要特别处理一些像素值范围内的数据，可以指定 Image.histogram() 的 histogram_range参数。该参数是一个元组，元组中的两个值 low 和 high 表示直方图的数据下限和上限，只会针对这个范围内的像素值生成直方图：

# 仅处理像素值在0到100之间的数据
histogram_data = img.histogram(histogram_range=(0, 100))

示范1：生成灰度图像的直方图

from PIL import Image

# 载入灰度图像
img = Image.open('test_gray.jpg')

# 生成直方图数据
histogram_data = img.histogram()

print(histogram_data)

输出结果：

[119900,  345,  103,   77,   43,   35,   45,   36,   24,   29,   28,
         19,   20,   16,   19,   23,   23,   38,   57,   69,   78,  110,
         113,  119,  150,  179,  196,  236,  287,  355,  370,  432,  425,
        452,  528,  535,  566,  668,  702,  793,  803,  998,  837, 1076,
       1079, 1238, 1401, 1486, 1536, 1426, 1466, 1438, 1386, 1366, 1276,
       1180, 1097, 1051,  966,  874,  801,  738,  646,  569,  526,  470,
        405,  333,  296,  269,  235,  184,  161,  178,  124,  117,  123,
        100,   90,   86,   77,   86,   86,  105,   97,   86,  106,  109,
         97,   90,   97,   68,   97,  105,   97,  101,   97,   90,   77,
         77,   68,   71,   74,   80,   83,   77,   74,   74,   77,   62,
         83,   65,   65,   80,   62,   77,   74,   68,   68,   50,   71,
         56,   47,   53,   44,   47,   50,   44,   53,   38,   50,   50,
         41,   41,   41,   47,   44,   50,   32,   35,   35,   35,   38,
         35,   29,   32,   26,   41,   35,   32,   35,   29,   26,   29,
         32,   29,   23,   23,   23,   26,   23,   23,   20,   20,   26,
         20,   23,   17,   20,   17,   17,   20,   17,   17,   17,   20,
         19,   19,   20,   19,   14,   20,   19,   19,   17,   14,   14,
         14,   14,   11,   14,   14,   14,   11,   11,   11,   11,   14,
         11,    8,   11,   11,   11,   11,   11,   11,    8,    8,   11,
         11,   11,   11,   11,   11,    8,   11,   11,   11,    8,    8,
          8,    8,   11,    8,    8,    5,    5,    5,    5,    5,    5,
          5,    5,    5,    5,    5,    5,    5,    5,    5,    5,    5]

示范2：生成分通道的直方图

from PIL import Image

# 载入图像
img = Image.open('test.jpg')

# 生成红色通道的直方图
histogram_data_red = img.histogram(channel=0)

# 生成绿色通道的直方图
histogram_data_green = img.histogram(channel=1)

# 生成蓝色通道的直方图
histogram_data_blue = img.histogram(channel=2)

print(histogram_data_red)
print(histogram_data_green)
print(histogram_data_blue)

输出结果：

[48, 96, 32, 70, 98, 100, 106, 142, 172, 166, 188, 210, 198, 200, 192, 204, 216, 184, 192, 196, 204, 204, 202, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 204, 136, 28, 177, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 203, 172, 254, 223, 233, 232, 255, 243, 253, 261, 269, 248, 247, 262, 274, 308, 273, 304, 296, 275, 275, 289, 292, 295, 277, 295, 309, 305, 376, 354, 365, 380, 420, 457, 450, 437, 436, 436, 451, 482, 501, 430, 489, 487, 527, 519, 591, 564, 583, 545, 558, 500, 557, 529, 518, 483, 461, 448, 366, 349, 373, 313, 307, 282, 320, 318, 307, 265, 300, 267, 236, 216, 204, 206, 196, 186, 163, 166, 157, 147, 137, 132, 129, 127, 118, 102, 105, 102, 79, 77, 71, 68, 63, 56, 52, 51, 46, 43, 39, 35, 31, 24, 26, 24, 16, 20, 17, 17, 14, 14, 11, 11, 8, 8, 5, 5, 5, 5, 5, 5, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 5, 5, 0, 5, 0, 5, 0, 5, 0, 0, 0, 0]
[455, 298, 222, 190, 161, 107, 161, 145, 131, 140, 128, 140, 124, 124, 167, 185, 186, 194, 196, 206, 217, 235, 238, 249, 287, 303, 320, 330, 352, 385, 415, 448, 481, 533, 539, 586, 590, 622, 639, 676, 674, 742, 719, 769, 738, 635, 690, 634, 618, 635, 651, 611, 656, 592, 565, 631, 613, 593, 556, 566, 509, 554, 517, 509, 466, 447, 492, 462, 420, 393, 381, 393, 381, 353, 337, 354, 330, 380, 291, 335, 300, 313, 274, 278, 279, 269, 230, 242, 234, 236, 211, 182, 194, 173, 179, 167, 166, 143, 154, 130, 132, 123, 147, 106, 122, 93, 90, 102, 102, 93, 93, 76, 92, 81, 68, 77, 47, 61, 61, 54, 50, 54, 42, 51, 47, 32, 42, 29, 29, 23, 20, 32, 17, 23, 29, 20, 32, 23, 20, 26, 29, 17, 32, 26, 23, 20, 17, 26, 23, 14, 14, 17, 23, 14, 17, 17, 14, 14, 5, 8, 8, 14, 11, 11, 11, 8, 5, 11, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[287, 225, 156, 119,  85,  66,  61,  47,  40,  50,  47,  43,  50,  47,  57,  71,  79,  98, 126, 154, 192, 204, 231, 226, 259, 271, 320, 358, 405, 454, 501, 561, 536, 610, 627, 690, 721, 782, 787, 848, 926, 946, 952, 589, 829, 781, 816, 859, 861, 926, 986, 978, 948, 976, 783, 876, 812, 751, 600, 734, 687, 587, 342, 276, 322, 316, 249, 239, 242, 179, 156, 119, 124, 147, 140, 107, 100, 107, 101,  98, 101,  83, 101,  86,  86,  86,  86,  86,  86,  97,  86,  97,  86,  97,  86,  97,  86,  97,  90,  97,  97,  97,  97,  97,  97,  97,  90,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97,  97]

可以从结果数据中看到，输出的数据是每一个像素值在相应通道上的像素数数量。