统计每个灰度值对应的个数，计算结果是256行一列的数组
image:使用【】括起来使用
channels: 使用【】括起来使用
- 灰度图像 0
- 彩色图像 0 1 2 分别对应通道B G R
mask:
- 统计整幅图片 None
- 统计图片某一部分的直方图，需要掩码图片。后续介绍。
hietSize:一般256，使用【】括起来使用
ranges:一般【0，255】
accumulate:可以计算一张图片的直方图，也可以计算一组图片的直方图，可以省略不写。
import cv2
import numpy as np
o=cv2.imread('image\\boat.bmp')
hist=cv2.calcHist([o],[0],None,[256],[0,255]) #不要忘记中括号
print(hist)
print(type(hist)) #类型
print(hist.size) #大小
print(hist.shape) #形状
类型 :数组类型
大小:256个数
形状:256行 1列
运算结果详情：
D:\Users\sunny\Anaconda3\python.exe "F:/Pycharm Project/hello world.py"
[[ 1.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.00000000e+00]
[ 1.00000000e+00]
[ 2.00000000e+00]
[ 1.30000000e+01]
[ 7.00000000e+00]
[ 6.00000000e+01]
[ 1.66000000e+02]
[ 4.13000000e+02]
[ 6.96000000e+02]
[ 9.45000000e+02]
[ 9.39000000e+02]
[ 7.78000000e+02]
[ 8.22000000e+02]
[ 8.76000000e+02]
[ 8.35000000e+02]
[ 9.40000000e+02]
[ 1.14600000e+03]
[ 1.25500000e+03]
[ 1.26600000e+03]
[ 1.24800000e+03]
[ 1.35900000e+03]
[ 1.47800000e+03]
[ 1.36500000e+03]
[ 1.38300000e+03]
[ 1.43200000e+03]
[ 1.48100000e+03]
[ 1.30500000e+03]
[ 1.29700000e+03]
[ 1.23400000e+03]
[ 1.34400000e+03]
[ 1.12200000e+03]
[ 1.10600000e+03]
[ 1.13100000e+03]
[ 1.11200000e+03]
[ 9.65000000e+02]
[ 9.77000000e+02]
[ 9.52000000e+02]
[ 9.28000000e+02]
[ 8.33000000e+02]
[ 6.80000000e+02]
[ 6.26000000e+02]
[ 6.04000000e+02]
[ 5.86000000e+02]
[ 5.20000000e+02]
[ 4.80000000e+02]
[ 5.17000000e+02]
[ 5.31000000e+02]
[ 4.47000000e+02]
[ 4.41000000e+02]
[ 4.91000000e+02]
[ 4.97000000e+02]
[ 4.44000000e+02]
[ 5.07000000e+02]
[ 5.25000000e+02]
[ 5.69000000e+02]
[ 5.33000000e+02]
[ 5.04000000e+02]
[ 5.39000000e+02]
[ 5.34000000e+02]
[ 5.15000000e+02]
[ 4.72000000e+02]
[ 4.76000000e+02]
[ 5.07000000e+02]
[ 4.63000000e+02]
[ 4.83000000e+02]
[ 4.92000000e+02]
[ 4.96000000e+02]
[ 4.90000000e+02]
[ 4.52000000e+02]
[ 4.47000000e+02]
[ 4.39000000e+02]
[ 4.63000000e+02]
[ 4.46000000e+02]
[ 4.67000000e+02]
[ 4.65000000e+02]
[ 4.99000000e+02]
[ 4.51000000e+02]
[ 4.82000000e+02]
[ 4.55000000e+02]
[ 4.91000000e+02]
[ 4.91000000e+02]
[ 4.67000000e+02]
[ 5.19000000e+02]
[ 4.81000000e+02]
[ 4.47000000e+02]
[ 5.10000000e+02]
[ 4.93000000e+02]
[ 5.18000000e+02]
[ 5.00000000e+02]
[ 4.90000000e+02]
[ 5.26000000e+02]
[ 5.11000000e+02]
[ 5.47000000e+02]
[ 5.26000000e+02]
[ 5.73000000e+02]
[ 5.98000000e+02]
[ 5.52000000e+02]
[ 5.78000000e+02]
[ 5.66000000e+02]
[ 5.77000000e+02]
[ 6.04000000e+02]
[ 5.47000000e+02]
[ 6.11000000e+02]
[ 6.66000000e+02]
[ 6.08000000e+02]
[ 6.17000000e+02]
[ 6.05000000e+02]
[ 6.08000000e+02]
[ 6.46000000e+02]
[ 6.59000000e+02]
[ 6.14000000e+02]
[ 7.25000000e+02]
[ 7.35000000e+02]
[ 7.11000000e+02]
[ 6.65000000e+02]
[ 7.34000000e+02]
[ 7.84000000e+02]
[ 8.06000000e+02]
[ 7.86000000e+02]
[ 8.71000000e+02]
[ 9.10000000e+02]
[ 9.21000000e+02]
[ 1.02900000e+03]
[ 9.63000000e+02]
[ 1.12000000e+03]
[ 1.13100000e+03]
[ 1.13300000e+03]
[ 1.23200000e+03]
[ 1.46200000e+03]
[ 1.46600000e+03]
[ 1.37200000e+03]
[ 1.49500000e+03]
[ 1.68600000e+03]
[ 1.69900000e+03]
[ 1.64200000e+03]
[ 1.62100000e+03]
[ 1.79800000e+03]
[ 1.84000000e+03]
[ 1.81700000e+03]
[ 1.82700000e+03]
[ 1.89000000e+03]
[ 2.06400000e+03]
[ 1.99600000e+03]
[ 2.17400000e+03]
[ 2.32700000e+03]
[ 2.64700000e+03]
[ 2.77400000e+03]
[ 3.39400000e+03]
[ 4.17800000e+03]
[ 5.15200000e+03]
[ 4.75500000e+03]
[ 4.56700000e+03]
[ 4.26800000e+03]
[ 4.46500000e+03]
[ 3.97100000e+03]
[ 4.03400000e+03]
[ 4.09600000e+03]
[ 4.74500000e+03]
[ 4.55500000e+03]
[ 4.80800000e+03]
[ 4.78200000e+03]
[ 5.39400000e+03]
[ 4.92700000e+03]
[ 4.66400000e+03]
[ 4.32200000e+03]
[ 4.75600000e+03]
[ 4.53500000e+03]
[ 4.37400000e+03]
[ 4.16300000e+03]
[ 4.36700000e+03]
[ 3.75900000e+03]
[ 3.18400000e+03]
[ 2.76400000e+03]
[ 2.42300000e+03]
[ 2.18300000e+03]
[ 1.85100000e+03]
[ 1.77500000e+03]
[ 1.72400000e+03]
[ 1.50700000e+03]
[ 1.22500000e+03]
[ 9.16000000e+02]
[ 7.45000000e+02]
[ 6.53000000e+02]
[ 5.31000000e+02]
[ 4.25000000e+02]
[ 8.24000000e+02]
[ 0.00000000e+00]
[ 3.44000000e+02]
[ 3.44000000e+02]
[ 3.34000000e+02]
[ 3.72000000e+02]
[ 3.15000000e+02]
[ 3.29000000e+02]
[ 6.66000000e+02]
[ 0.00000000e+00]
[ 6.76000000e+02]
[ 0.00000000e+00]
[ 3.53000000e+02]
[ 3.85000000e+02]
[ 3.91000000e+02]
[ 3.78000000e+02]
[ 3.98000000e+02]
[ 4.39000000e+02]
[ 4.26000000e+02]
[ 3.74000000e+02]
[ 3.62000000e+02]
[ 3.35000000e+02]
[ 3.03000000e+02]
[ 2.39000000e+02]
[ 1.84000000e+02]
[ 1.52000000e+02]
[ 1.00000000e+02]
[ 7.10000000e+01]
[ 5.30000000e+01]
[ 6.10000000e+01]
[ 4.40000000e+01]
[ 3.40000000e+01]
[ 2.60000000e+01]
[ 2.20000000e+01]
[ 1.00000000e+01]
[ 7.00000000e+00]
[ 3.00000000e+00]
[ 2.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]]
<class 'numpy.ndarray'>
256
(256, 1)