Opencv提取照片hsv分量并绘制hsv直方图_opencv 获取hue分量

原图

hsv各分量提取结果

hsv直方图提取结果（以value为例）

附代码 

# -*- coding:utf-8 -*-
 
import cv2  
import numpy as np
import matplotlib.pyplot as plt
 
def grayHist(img, name):
    h, w = img.shape[:2]
    pixelSequence = img.reshape([h * w, ])
    numberBins = 256
    histogram, bins, patch = plt.hist(pixelSequence, numberBins,
                                      facecolor='black', histtype='bar')
    plt.rcParams['font.sans-serif'] = ['SimHei']  
    plt.rcParams['axes.unicode_minus'] = False
    plt.xlabel("像素值")
    plt.ylabel("像素数量")
    plt.axis([0, 255, 0, np.max(histogram)])
    plt.savefig("E:/save/" + name + ".png")
    plt.show()
 
img = cv2.imread("test.png")  #导入图片，图片放在程序所在目录
cv2.namedWindow("imagshow", 2)   #创建一个窗口
cv2.imshow('imagshow', img)    #显示原始图片
 
#使用cvtColor转换为HSV图
out_img_HSV=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
hsvChannels=cv2.split(out_img_HSV)  
 
cv2.namedWindow("Hue",2)   
cv2.imshow('Hue',hsvChannels[0]) #显示Hue分量
grayHist(hsvChannels[0],"H-Histogram")
cv2.namedWindow("Saturation",2)  
cv2.imshow('Saturation',hsvChannels[1]) #显示Saturation分量
grayHist(hsvChannels[1],"S-Histogram")
cv2.namedWindow("Value",2)   
cv2.imshow('Value',hsvChannels[2]) #显示Value分量
grayHist(hsvChannels[2],"V-Histogarm")
 
cv2.waitKey(0)