if i in gray_key:
num = gray_key.index(i)
gray_result.append(gray_count[num])
else:
gray_result.append(0)
gray_result = np.array(gray_result)
return gray_result
def histogram_equalization(histogram_e, lut_e, image_e):
sum_temp =0
cf =[]
for i in histogram_e:
sum_temp += i
cf.append(sum_temp)
for i, v in enumerate(lut_e):
lut_e[i]=int(255.0*(cf[i]/ sum_temp)+0.5)
equalization_result = lut_e[image_e]
return equalization_result
x =[]
for i in range(0,256):# 横坐标
x.append(i)
image = cv2.imread(r'.\Images\grey.png', cv2.COLOR_BGR2GRAY)# 读取图像
histogram = draw_histogram(image)# 直⽅图转化
plt.bar(x, histogram)# 绘制原图直⽅图
plt.savefig('./equalization_gray/before_histogram.png')
plt.show()
lut = np.zeros(256, dtype=image.dtype)# 创建空的查表，返回image类型的⽤0填充的数组；result = histogram_equalization(histogram, lut, image)# 均衡化处理
cv2.imwrite('./equalization_gray/his_grey.png', result)# 保存均衡化后图⽚
image_equ = cv2.imread(r'.\equalization_gray\his_grey.png', cv2.COLOR_BGR2GRAY)# 读取图像histogram_equ = draw_histogram(image_equ)# 直⽅图转化
plt.bar(x, histogram_equ)# 绘制处理后图像直⽅图
plt.savefig('./equalization_gray/after_histogram.png')
plt.show()
plt.plot(x, lut)# 绘制灰度级变换曲线图
plt.savefig('./equalization_gray/Grayscale_transformation_curve.png')
plt.show()
cv2.imshow('former', image)
cv2.imshow("histogram_equalization", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
彩⾊图像直⽅图均衡化（内含hsi模型和rgb模型互换）
import cv2
import numpy as np
import matplotlib.pyplot as plt
import collections
def rgb_hsi(rgb_image):
# 保存原始图像的⾏列数
rows =int(rgb_image.shape[0])
cols =int(rgb_image.shape[1])
# 图像复制
hsi_image = py()
hsi_image = py()
# 通道拆分
b = rgb_image[:,:,0]
g = rgb_image[:,:,1]
r = rgb_image[:,:,2]
# 归⼀化到[0,1]
b = b /255.0
g = g /255.0
r = r /255.0
for i in range(rows):
for j in range(cols):
num =0.5*((r[i, j]-g[i, j])+(r[i, j]-b[i, j]))
den = np.sqrt((r[i, j]-g[i, j])**2+(r[i, j]-b[i, j])*(g[i, j]-b[i, j]))
if den ==0:
hsi_h =0
else:
theta =float(np.arccos(num / den))
if b[i, j]<= g[i, j]:
hsi_h = theta
else:
hsi_h =2*np.pi - theta
min_RGB =min(min(b[i, j], g[i, j]), r[i, j])
sum= b[i, j]+g[i, j]+r[i, j]
if sum==0:
hsi_s =0
else:
hsi_s =1-3*min_RGB/sum
hsi_h = hsi_h/(2*np.pi)
hsi_i =sum/3.0
# 输出HSI图像，扩充到255以⽅便显⽰，⼀般H分量在[0,2pi]之间，S和I在[0,1]之间            hsi_image[i, j,0]= hsi_h*255
hsi_image[i, j,1]= hsi_s*255
hsi_image[i, j,2]= hsi_i*255
return hsi_image
def hsi_rgb(hsi_image):
# 保存原始图像的⾏列数
rows = np.shape(hsi_image)[0]
cols = np.shape(hsi_image)[1]
# 对原始图像进⾏复制
rgb_image = py()
# 对图像进⾏通道拆分
hsi_h = hsi_image[:,:,0]
hsi_s = hsi_image[:,:,1]
hsi_i = hsi_image[:,:,2]
# 把通道归⼀化到[0,1]
hsi_h = hsi_h /255.0
hsi_s = hsi_s /255.0
hsi_i = hsi_i /255.0
B, G, R = hsi_h, hsi_s, hsi_i
for i in range(rows):
for j in range(cols):
hsi_h[i, j]*=360
if0<= hsi_h[i, j]<120:
B = hsi_i[i, j]*(1- hsi_s[i, j])
R = hsi_i[i, j]*(1+(hsi_s[i, j]* np.cos(hsi_h[i, j]* np.pi /180))/ np.cos( (60- hsi_h[i, j])* np.pi /180))
G =3* hsi_i[i, j]-(R + B)
elif120<= hsi_h[i, j]<240:
hsi_h[i, j]= hsi_h[i, j]-120
R = hsi_i[i, j]*(1- hsi_s[i, j])
G = hsi_i[i, j]*(1+(hsi_s[i, j]* np.cos(hsi_h[i, j]* np.pi /180))/ np.cos(
(60- hsi_h[i, j])* np.pi /180))
(60- hsi_h[i, j])* np.pi /180))
B =3* hsi_i[i, j]-(R + G)
elif240<= hsi_h[i, j]<=300:
hsi_h[i, j]= hsi_h[i, j]-240
G = hsi_i[i, j]*(1- hsi_s[i, j])
B = hsi_i[i, j]*(1+(hsi_s[i, j]* np.cos(hsi_h[i, j]* np.pi /180))/ np.cos(
(60- hsi_h[i, j])* np.pi /180))
R =3* hsi_i[i, j]-(G + B)
rgb_image[i, j,0]= B *255
rgb_image[i, j,1]= G *255
rgb_image[i, j,2]= R *255
return rgb_image
# 计算灰度图的直⽅图
def draw_histogram(grayscale):
# 对图像进⾏通道拆分
hsi_i = grayscale[:,:,2]
color_key =[]
color_count =[]
color_result =[]
histogram_color =list(hsi_i.ravel())# 将多维数组转换成⼀维数组
color =dict(collections.Counter(histogram_color))# 统计图像中每个亮度级出现的次数    color =sorted(color.items(), key=lambda item: item[0])# 根据亮度级⼤⼩排序
for element in color:
key =list(element)[0]
count =list(element)[1]
color_key.append(key)
color_count.append(count)
for i in range(0,256):
if i in color_key:
num = color_key.index(i)
color_result.append(color_count[num])
else:
color_result.append(0)
color_result = np.array(color_result)
return color_result
def histogram_equalization(histogram_e, lut_e, image_e):
sum_temp =0
cf =[]
for i in histogram_e:
sum_temp += i
cf.append(sum_temp)
for i, v in enumerate(lut_e):
lut_e[i]=int(255.0*(cf[i]/ sum_temp)+0.5)局部直方图均衡化
equalization_result = lut_e[image_e]
return equalization_result
x =[]
for i in range(0,256):# 横坐标
x.append(i)
# 原图及其直⽅图
rgb_image = cv2.imread(".\Images\color.jpg")
cv2.imshow('rgb', rgb_image)
histogram = draw_histogram(rgb_image)
plt.bar(x, histogram)# 绘制原图直⽅图
plt.savefig('./equalization_color/before_histogram.png')
plt.show()
# rgb转hsi
hsi_image = rgb_hsi(rgb_image)
cv2.imshow('hsi_image', hsi_image)

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