用OpenCV中自帶的cv2.calHist()函數(shù)求圖像的顏色直方圖特征

import cv2 import numpy image = cv2.imread("D:/lena.jpg", 0) hist = cv2.calcHist([image], [0], None, [256], [0.0,255.0])

上面程序是以灰度圖的方式計(jì)算顏色直方圖特征，cv2.calcHist()函數(shù)的參數(shù)

第一個(gè)參數(shù)[image]，必須帶[]， 是讀入后的圖像

第二個(gè)參數(shù)[0]，必須帶[]，指定通道,若為灰度圖則為[0]，若彩色圖，則[0]、[1]、[2]分別對(duì)應(yīng)于B、G、R通道

第三個(gè)參數(shù)是掩膜Mask，指定ROI區(qū)域，若對(duì)整張圖像取特征，則置為None

第四個(gè)參數(shù)是bins的個(gè)數(shù)，必須帶[]

第五個(gè)參數(shù)是像素值范圍

來看一下hist的內(nèi)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) >>>

總結(jié)

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