跟我学Python丨图像增强及运算：局部直方图均衡化和自动色彩均衡化处理

# -*- coding: utf- 8  -*- # By:Eastmount

import cv2  

import numpy  as  np  

import matplotlib.pyplot  as  plt

#读取图片

img = cv2.imread( '  lena.bmp  '  )

#灰度转换

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#局部直方图均衡化处理

clahe = cv2.createCLAHE(clipLimit= 2 , tileGridSize=( 10 , 10  ))

#将灰度图像和局部直方图相关联, 把直方图均衡化应用到灰度图 

result = clahe.apply(gray)

#显示图像

plt.subplot(  221  )

plt.imshow(gray, cmap =plt.cm.gray), plt.axis( "  off  " ), plt.title( '  (a)  '  ) 

plt.subplot(  222  )

plt.imshow(result, cmap =plt.cm.gray), plt.axis( "  off  " ), plt.title( '  (b)  '  ) 

plt.subplot(  223  )

plt.hist(img.ravel(),  256 ), plt.title( '  (c)  '  ) 

plt.subplot(  224  )

plt.hist(result.ravel(),  256 ), plt.title( '  (d)  '  ) 

plt.show() 

# -*- coding: utf- 8  -*- # By:Eastmount

# 参考zmshy2128老师文章

import cv2

import numpy  as  np

import math

import matplotlib.pyplot  as  plt

#线性拉伸处理

#去掉最大最小0.  5 %的像素值 线性拉伸至[ 0 , 1  ]

def stretchImage(data, s = 0.005 , bins =  2000  ): 

 ht = np.histogram(data, bins);

    d = np.cumsum(ht[ 0 ])/ float  (data.size)

 lmin =  0 ; lmax=bins- 1 
  while  lmin< bins:  if  d[lmin]>= s:  break  lmin += 1 
  while  lmax>= 0  :  if  d[lmax]<= 1 - s:  break  lmax -= 1 
  return  np.clip((data-ht[ 1 ][lmin])/(ht[ 1 ][lmax]-ht[ 1 ][lmin]),  0 , 1  )

#根据半径计算权重参数矩阵

g_para = {}

def getPara(radius =  5  ):  global  g_para

    m = g_para. get  (radius, None)  if  m  is  not None:  return  m

    size = radius* 2 + 1  m = np.zeros((size, size))  for  h  in  range(-radius, radius+ 1  ):  for  w  in  range(-radius, radius+ 1  ):  if  h== 0  and w== 0  :  continue  m[radius +h, radius+w] =  1.0 /math.sqrt(h** 2 +w** 2  )

    m /= m.sum()

 g_para[radius] = m  return  m

#常规的ACE实现

def zmIce(I, ratio = 4 , radius= 300  ): 

    para = getPara(radius)

 height,width = I.shape

 #Python3报错如下 使用列表append修改

 zh = []

 zw = []

    n =  0 
  while  n < radius:

 zh.append(  0  )

 zw.append(  0  )

        n +=  1 
  for  n  in  range(height):

 zh.append(n)  for  n  in  range(width):

 zw.append(n)

    n =  0 
  while  n < radius:

 zh.append(height - 1  )

 zw.append(width - 1  )

        n +=  1  #print(zh)

 #print(zw)

    Z = I[np.ix_(zh, zw)]

    res = np.zeros(I.shape)  for  h  in  range(radius* 2 + 1  ):  for  w  in  range(radius* 2 + 1  ):  if  para[h][w] ==  0  :  continue  res += (para[h][w] * np.clip((I-Z[h:h+height, w:w+width])*ratio, - 1 ,  1  ))  return  res

#单通道ACE快速增强实现

def zmIceFast(I, ratio, radius):

 print(I)

    height, width = I.shape[: 2  ]  if  min(height, width) <= 2  :  return  np.zeros(I.shape)+ 0.5  Rs = cv2.resize(I, ( int ((width+ 1 )/ 2 ),  int ((height+ 1 )/ 2  )))

    Rf = zmIceFast(Rs, ratio, radius) #递归调用

    Rf = cv2.resize(Rf, (width, height))

    Rs = cv2.resize(Rs, (width, height))  return  Rf+zmIce(I,ratio, radius)- zmIce(Rs,ratio,radius) 

#rgb三通道分别增强 ratio是对比度增强因子 radius是卷积模板半径 

def zmIceColor(I, ratio = 4 , radius= 3  ): 

    res = np.zeros(I.shape)  for  k  in  range( 3  ):

        res[:,:,k] = stretchImage(zmIceFast(I[:,:,k], ratio, radius))  return  res

#主函数  if  __name__ ==  '  __main__  '  :

 img = cv2.imread( '  test01.png  '  )

    res = zmIceColor(img/ 255.0 )* 255  cv2.imwrite(  '  Ice.jpg  ' , res)