计算机视觉：彩色图片的直方图和图片的均衡化

Posted 2021-12-18 il_持之以恒_li

1. 彩色图片的直方图

import cv2
import numpy as np


def ImageHist(image,type):
    color = (255,255,255)
    windowName = 'gray'
    if type == 31:
        color = (255,0,0)
        windowName = 'blue'
    elif type ==32:
        color = (0,255,0)
        windowName = 'green'
    elif type == 33:
        color = (0,0,255)
        windowName = 'red'
    hist = cv2.calcHist([image],[0],None,[256],[0.0,255.0])
    # 像素的范围
    # 大小
    minV,maxV,minL,maxL = cv2.minMaxLoc(hist)
    histImg = np.zeros([256,256,3],np.uint8)
    for h in range(256):
        intenNormal = int(hist[h]*256/maxV)
        cv2.line(histImg,(h,256),(h,256-intenNormal),color)
    cv2.imshow(windowName,histImg)
    return histImg

img = cv2.imread(filename='../anqila.jpg',flags=1)
channels = cv2.split(img)
for i in range(0,3):
    ImageHist(channels[i],31+i)
cv2.waitKey(0)

2. 图片的均衡化

2.1 灰度图片的均衡化

import cv2
# 灰度 直方图均衡化

img = cv2.imread(filename='anqila21.jpg',flags=1)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow('img',gray)
dst = cv2.equalizeHist(gray)
cv2.imshow('dst',dst)
cv2.waitKey(0)

2.2 彩色图片的均衡化

import cv2

# 彩色图片的均衡化
img = cv2.imread(filename='anqila21.jpg',flags=1)
cv2.imshow('img',img)
(b,g,r) = cv2.split(img)   # 通道分解
bH = cv2.equalizeHist(b)
gH = cv2.equalizeHist(g)
rH = cv2.equalizeHist(r)
result = cv2.merge((bH,gH,rH))
cv2.imshow('result',result)
cv2.waitKey(0)

2.3 YUV 直方图均衡化

import cv2

# yuv 直方图均衡化
img = cv2.imread(filename='anqila21.jpg',flags=1)
cv2.imshow('img',img)
imgYUV = cv2.cvtColor(img,cv2.COLOR_BGR2YCrCb)
channelYUV = cv2.split(imgYUV)
channelYUV[0] = cv2.equalizeHist(channelYUV[0])
channels = cv2.merge(channelYUV)
result = cv2.cvtColor(channels,cv2.COLOR_YCrCb2BGR)
cv2.imshow('result',result)
cv2.waitKey(0)