1.直方圖

直方圖: (1) 影象中不同畫素等級出現的次數 (2) 影象中具有不同等級的畫素關於總畫素數目的比值。

我們使用cv2.calcHist方法得到直方圖

cv2.calcHist(images,channels,mask,histSize,ranges):

-img: 影象
-channels: 選取影象的哪個通道
-histSize: 直方圖大小
-ranges: 直方圖範圍


cv2.minMaxLoc: 返回直方圖的最大最小值，以及他們的索引

import cv2
import numpy as np
def ImageHist(image,type):
  color = (255,255,255)
  windowName = 'Gray'
  if type == 1:    #判斷通道顏色型別 B-G-R
    color = (255,0)
    windowName = 'B hist'
  elif type == 2:
    color = (0,0)
    windowName = 'G hist'
  else:
    color = (0,255)
  # 得到直方圖
  hist = cv2.calcHist([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)
  #直方圖歸一化
  for h in range(256):
    interNormal = int(hist[h] / maxV * 256)
    cv2.line(histImg,(h,256),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)

2.直方圖均衡化

灰色影象直方圖均衡化

這裡我們直接使用cv2.equalizeHist方法來得到直方圖均衡化之後的影象

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)

原影象：

直方圖均衡化後的影象：

彩色影象直方圖均衡化

彩色影象有3個通道，直方圖是針對單通道上的畫素統計，所以使用cv2.split

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)

D:\Anaconda\lib\site-packages\numpy\_distributor_init.py:32: UserWarning: loaded more than 1 DLL from .libs:
D:\Anaconda\lib\site-packages\numpy\.libs\libopenblas.NOIJJG62EMASZI6NYURL6JBKM4EVBGM7.gfortran-win_amd64.dll
D:\Anaconda\lib\site-packages\numpy\.libs\libopenblas.PYQHXLVVQ7VESDPUVUADXEVJOBGHJPAY.gfortran-win_amd64.dll
stacklevel=1)

原影象：

直方圖均衡化之後的影象：

3.原始碼實現直方圖均衡化

下面我們用原始碼來實現直方圖

橫座標為畫素等級，縱座標為出現的概率

import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('img.jpg',cv2.COLOR_BGR2GRAY)
count = np.zeros(256,np.float)
for i in range(img.shape[0]):
  for j in range(img.shape[1]):
    count[int(gray[i,j])] += 1 # 統計該畫素出現的次數
count = count / (img.shape[0] * img.shape[1]) # 得到概率
x = np.linspace(0,256)
plt.bar(x,count,color = 'b')
plt.show()


# 計算累計概率

for i in range(1,256):
  count[i] += count[i - 1]
# 對映
map1 = count * 255
for i in range(img.shape[0]):
  for j in range(img.shape[1]):
    p = gray[i,j]
    gray[i,j] = map1[p]
cv2.imshow('gray',gray)
cv2.waitKey(0)

直方圖：

直方圖均衡化後的影象：

彩色直方圖原始碼

import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('img.jpg',1)
# R-G-B三種染色直方圖
countb = np.zeros(256,np.float32)
countg = np.zeros(256,np.float32)
countr = np.zeros(256,np.float32)

for i in range(img.shape[0]):
  for j in range(img.shape[1]):
    (b,r) = img[i,j]
    b = int(b)
    g = int(g)
    r = int(r)
    countb[b] += 1 # 統計該畫素出現的次數
    countg[g] += 1
    countr[r] += 1
countb = countb / (img.shape[0] * img.shape[1]) # 得到概率
countg = countg / (img.shape[0] * img.shape[1])
countr = countr / (img.shape[0] * img.shape[1])
x = np.linspace(0,256)
plt.figure()
plt.bar(x,countb,color = 'b')
plt.figure()
plt.bar(x,countg,color = 'g')
plt.figure()
plt.bar(x,countr,color = 'r')
plt.show()


# 計算直方圖累計概率
for i in range(1,256):
  countb[i] += countb[i - 1]
  countg[i] += countg[i - 1]
  countr[i] += countr[i - 1]
#對映表
mapb = countb * 255
mapg = countg * 255
mapr = countr * 255

dat = np.zeros(img.shape,np.uint8)
for i in range(img.shape[0]):
  for j in range(img.shape[1]):
    (b,j]
    dat[i,j] = (mapb[b],mapg[g],mapr[r])
cv2.imshow('dat',dat)
cv2.waitKey(0)

R-G-B 3 種顏色通道的直方圖如下：

影象均衡化之後的結果：

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