用Python和OpenCV提取颜色直方图特征
用OpenCV中自带的cv2.calHist()函数求图像的颜色直方图特征
import cv2
import numpy
image = cv2.imread("D:/lena.jpg", 0)
hist = cv2.calcHist([image], [0], None, [256], [0.0,255.0])
上面程序是以灰度图的方式计算颜色直方图特征，cv2.calcHist()函数的参数
第一个参数[image]，必须带[]， 是读入后的图像
第二个参数[0]，必须带[]，指定通道,若为灰度图则为[0]，若彩色图，则[0]、[1]、[2]分别对应于B、G、R通道
第三个参数是掩膜Mask，指定ROI区域，若对整张图像取特征，则置为None
第四个参数是bins的个数，必须带[]
第五个参数是像素值范围
来看一下hist的内容：
>>> hist
array([[ 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],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 1.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 1.00000000e+00],
[ 2.00000000e+00],
[ 5.00000000e+00],
[ 8.00000000e+00],
[ 1.70000000e+01],
[ 2.40000000e+01],
[ 5.50000000e+01],
[ 8.30000000e+01],
[ 1.12000000e+02],
[ 1.60000000e+02],
[ 2.02000000e+02],
[ 2.81000000e+02],
[ 3.41000000e+02],
[ 4.20000000e+02],
[ 5.28000000e+02],
[ 6.11000000e+02],
[ 7.09000000e+02],
[ 8.85000000e+02],
[ 1.03200000e+03],
[ 1.28200000e+03],
[ 1.44100000e+03],
[ 1.61700000e+03],
[ 1.68600000e+03],
[ 1.90500000e+03],
[ 1.88700000e+03],
[ 2.00000000e+03],
[ 2.00000000e+03],
[ 2.07200000e+03],
[ 2.01000000e+03],
[ 2.02500000e+03],
[ 1.89100000e+03],
[ 1.88400000e+03],
[ 1.76200000e+03],
[ 1.71400000e+03],
[ 1.53300000e+03],
[ 1.44200000e+03],
[ 1.26100000e+03],
[ 1.26800000e+03],
[ 1.18500000e+03],
[ 1.09800000e+03],
[ 9.70000000e+02],
[ 9.78000000e+02],
[ 9.10000000e+02],
[ 8.83000000e+02],
[ 8.23000000e+02],
[ 8.02000000e+02],
[ 7.42000000e+02],
[ 8.03000000e+02],
[ 8.42000000e+02],
[ 8.16000000e+02],
[ 7.85000000e+02],
[ 8.78000000e+02],
[ 8.59000000e+02],
[ 8.70000000e+02],
[ 8.72000000e+02],
[ 8.67000000e+02],
[ 9.57000000e+02],
[ 8.88000000e+02],
[ 9.79000000e+02],
[ 9.06000000e+02],
[ 8.35000000e+02],
[ 9.76000000e+02],
[ 9.40000000e+02],
[ 9.53000000e+02],
[ 9.58000000e+02],
[ 9.96000000e+02],
[ 1.06100000e+03],
[ 1.15800000e+03],
[ 1.14400000e+03],
[ 1.16600000e+03],
[ 1.22200000e+03],
[ 1.25300000e+03],
[ 1.44600000e+03],
[ 1.46600000e+03],
[ 1.59400000e+03],
[ 1.85500000e+03],
[ 1.81000000e+03],
[ 1.93400000e+03],
[ 1.96400000e+03],
[ 1.89900000e+03],
[ 2.00200000e+03],
[ 1.87200000e+03],
[ 1.82300000e+03],
[ 1.68900000e+03],
[ 1.59800000e+03],
[ 1.53900000e+03],
[ 1.39800000e+03],
[ 1.44100000e+03],
[ 1.37500000e+03],
[ 1.33400000e+03],
[ 1.38900000e+03],
[ 1.37600000e+03],
[ 1.38000000e+03],
[ 1.41300000e+03],
[ 1.40200000e+03],
[ 1.45500000e+03],
[ 1.46400000e+03],
[ 1.62700000e+03],
[ 1.62600000e+03],
[ 1.60400000e+03],
[ 1.80800000e+03],
[ 1.82700000e+03],
[ 2.03400000e+03],
[ 2.09700000e+03],
[ 2.21300000e+03],
[ 2.35200000e+03],
[ 2.43300000e+03],
[ 2.36800000e+03],
[ 2.46700000e+03],
[ 2.30400000e+03],
[ 2.27600000e+03],
[ 2.05000000e+03],
[ 1.96000000e+03],
[ 1.91000000e+03],
[ 1.88900000e+03],
[ 1.92500000e+03],
[ 2.05800000e+03],
[ 2.04300000e+03],
[ 2.33100000e+03],
[ 2.30200000e+03],
[ 2.34000000e+03],
[ 2.39100000e+03],
[ 2.47500000e+03],
[ 2.43100000e+03],
[ 2.25300000e+03],
[ 2.27100000e+03],
[ 2.23300000e+03],
[ 2.19300000e+03],
[ 2.27900000e+03],
[ 2.30300000e+03],
[ 2.42600000e+03],
[ 2.67100000e+03],
[ 2.64700000e+03],
[ 2.71900000e+03],
[ 2.73300000e+03],
[ 2.58300000e+03],
[ 2.43700000e+03],
[ 2.25600000e+03],
[ 2.07600000e+03],
[ 1.91100000e+03],
[ 1.74400000e+03],
[ 1.64400000e+03],
[ 1.47100000e+03],
[ 1.43000000e+03],
[ 1.39500000e+03],
[ 1.28800000e+03],
[ 1.23000000e+03],
[ 1.19300000e+03],
[ 1.17000000e+03],
[ 1.24400000e+03],
[ 1.26800000e+03],
[ 1.22900000e+03],
[ 1.23700000e+03],
[ 1.26300000e+03],
[ 1.24200000e+03],
[ 1.16400000e+03],
[ 1.11500000e+03],
[ 1.03900000e+03],
[ 9.53000000e+02],
[ 8.19000000e+02],
[ 7.48000000e+02],
[ 6.62000000e+02],
[ 6.37000000e+02],
[ 6.41000000e+02],
[ 5.97000000e+02],
[ 6.63000000e+02],
[ 6.25000000e+02],
[ 7.11000000e+02],
[ 7.87000000e+02],
[ 7.77000000e+02],
[ 8.10000000e+02],
[ 8.73000000e+02],
[ 9.09000000e+02],
[ 9.61000000e+02],
[ 9.53000000e+02],
[ 8.37000000e+02],
[ 8.52000000e+02],
[ 8.67000000e+02],
[ 8.39000000e+02],
[ 9.10000000e+02],
[ 8.33000000e+02],
[ 9.02000000e+02],
[ 9.20000000e+02],
[ 9.46000000e+02],
[ 9.68000000e+02],
[ 1.01000000e+03],
[ 1.09300000e+03],
[ 1.08000000e+03],
[ 9.57000000e+02],
[ 9.67000000e+02],
[ 1.02200000e+03],
[ 8.74000000e+02],
[ 7.03000000e+02],
[ 5.66000000e+02],
[ 4.62000000e+02],
[ 3.97000000e+02],
[ 3.65000000e+02],
[ 3.35000000e+02],
[ 2.54000000e+02],
[ 2.07000000e+02],
[ 2.13000000e+02],
[ 1.76000000e+02],
[ 1.20000000e+02],
[ 1.06000000e+02],
[ 7.70000000e+01],
[ 6.50000000e+01],
[ 3.60000000e+01],
[ 3.50000000e+01],
[ 2.50000000e+01],
[ 1.80000000e+01],
[ 1.00000000e+01],
[ 9.00000000e+00],
[ 3.00000000e+00],
[ 4.00000000e+00],
[ 1.00000000e+00],
[ 2.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 1.00000000e+00],
[ 0.00000000e+00],
[ 0.00000000e+00],
[ 1.00000000e+00],
[ 0.00000000e+00],
[ 1.00000000e+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]], dtype=float32)
>>>