import cv2
import numpy as np
import sys
import matplotlib.pyplot as plt

#计算灰度直方图
def calcGrayHist(image):
  rows,clos = image.shape
  #创建一个矩阵用于存储灰度值
  grahHist = np.zeros([256],np.uint64)
  print('这是初始化矩阵')
  print(grahHist )
  for r in range(rows):
    for c in range(clos):
      #通过图像矩阵的遍历来将灰度值信息放入我们定义的矩阵中
      grahHist[image[r][c]] +=1
  print('这是赋值后的矩阵')
  print(grahHist)
  return grahHist
if __name__=="__main__":
  image = cv2.imread("../img/aa.jpg",cv2.IMREAD_GRAYSCALE)
  grahHist = calcGrayHist(image)
  x_range = range(256)
  plt.plot(x_range,grahHist,'-',linewidth= 3,c='k')
  #设置坐标轴的范围
  y_maxValue = np.max(grahHist)
  plt.axis([0,255,0,y_maxValue])
  #设置标签
  plt.xlabel('gray Level')
  plt.ylabel("number of pixels")
  #显示灰度直方图
  plt.show()

import cv2
import numpy as np
import sys

if __name__=="__main__":
  img = cv2.imread("../img/ae.jpg",cv2.IMREAD_GRAYSCALE)
  a=2
  #线性变换 定义float类型
  O = float(a)*img
  #数据截取 如果大于255 取 255
  O[0>255] = 255
  #数据类型的转换
  O = np.round(O)
  O = O.astype(np.uint8)
  cv2.imshow("img",img)
  cv2.imshow('enhance',O)
  cv2.waitKey(0)
  cv2.destroyAllWindows()

import cv2
import numpy as np
import sys
from enhance.GrayHist import mget
if __name__=="__main__":
  img = cv2.imread("../img/o3.jpg",cv2.IMREAD_GRAYSCALE)
  #求出img 的最大最小值
  Maximg = np.max(img)
  Minimg = np.min(img)
  print(Maximg, Minimg, '-----------')
  #输出最小灰度级和最大灰度级
  Omin,Omax = 0,255
  #求 a, b
  a = float(Omax - Omin)/(Maximg - Minimg)
  b = Omin - a*Minimg
  print(a,b,'-----------')
  #线性变换
  O = a*img + b
  O = O.astype(np.uint8)
  #利用灰度直方图进行比较 mget为GrayHist中的写方法
  mget(img)
  mget(O)


  cv2.imshow('img',img)
  cv2.imshow('enhance',O)
  cv2.waitKey(0)
  cv2.destroyAllWindows()

import cv2
import numpy as np
import sys

# 伽玛变换 power函数实现幂函数

if __name__ == "__main__":
  img = cv2.imread("../img/ae.jpg", cv2.IMREAD_GRAYSCALE)
  # 归1
  Cimg = img / 255
  # 伽玛变换
  gamma = 0.5
  O = np.power(Cimg,gamma)
  #效果
  cv2.imshow('img',img)
  cv2.imshow('O',O)
  cv2.waitKey(0)
  cv2.destroyAllWindows()

import cv2
import numpy as np
from enhance.GrayHist import calcGrayHist

#直方图的均衡化
if __name__ == "__main__":
  image = cv2.imread("../img/ae.jpg", cv2.IMREAD_GRAYSCALE)
  rows,cols = image.shape
  #计算灰度直方图
  grayHist = calcGrayHist(image)
  #计算累加灰度直方图
  zeroCumuMoment = np.zeros([256], np.uint32)
  for p in range(256):
    if p == 0:
      zeroCumuMoment[p] = grayHist[0]
    else:
      zeroCumuMoment[p] = zeroCumuMoment[p-1] + grayHist[p]
  #根据累加的灰度直方图得到输入与输出灰度级之间的映射关系
  output = np.zeros([256],np.uint8)
  cofficient = 256.0/(rows*cols)
  for p in range(256):
    q = cofficient * float(zeroCumuMoment[p])-1
    if q >=0:
      output[p] = np.math.floor(q)
    else:
      output[p] = 0
  #得出均衡化图像
  equalHistimg = np.zeros(image.shape,np.uint8)
  for r in range(rows):
    for c in range(cols):
      equalHistimg[r][c] = output[image[r][c]]
  cv2.imshow('image',image)
  cv2.imshow('histimage',equalHistimg)
  cv2.waitKey(0)
  cv2.destroyAllWindows()