OpenCV利用python来实现图像的直方图均衡化

import cv2
import numpy as np
def ImageHist(image, type):
  color = (255, 255,255)
  windowName = 'Gray'
  if type == 1:    #判断通道颜色类型 B-G-R
    color = (255, 0, 0)
    windowName = 'B hist'
  elif type == 2:
    color = (0,255,0)
    windowName = 'G hist'
  else:
    color = (0,0,255)
  # 得到直方图
  hist = cv2.c<span>本文来源gaodai#ma#com搞*!代#%^码$网*</span>alcHist([image],[0],None,[256],[0,255])
  # 得到最大值和最小值
  minV,maxV,minL,maxL = cv2.minMaxLoc(hist)
  histImg = np.zeros([256,256,3],np.uint8)
  #直方图归一化
  for h in range(256):
    interNormal = int(hist[h] / maxV * 256)
    cv2.line(histImg, (h, 256), (h, 256 - interNormal), color)
  cv2.imshow(windowName, histImg)
  return histImg
img = cv2.imread('img.jpg', 1)
channels = cv2.split(img) # R-G-B
for i in range(3): 
  ImageHist(channels[i], 1 + i)
cv2.waitKey(0)

import cv2
import numpy as np
img = cv2.imread('img.jpg', 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
dat = cv2.equalizeHist(gray)
cv2.imshow('gray', gray)a
cv2.imshow('dat', dat)
cv2.waitKey(0)

import cv2
import numpy as np
img = cv2.imread('img.jpg', 1)
cv2.imshow('img', img)
(b, g, r) = cv2.split(img)
bH = cv2.equalizeHist(b)
gH = cv2.equalizeHist(g)
rH = cv2.equalizeHist(r)
dat = cv2.merge((bH, gH, rH))
cv2.imshow('dat', dat)
cv2.waitKey(0)