from PIL import Image
from PIL import ImageEnhance
import numpy as np
# image = Image.open('file:///C:/Users/25764/Desktop/新建位图图像.bmp')
#image.show()
def BrightnessEnhancement(brightness):
# '''
# #亮度增强 :brightness在(0-1)之间,新图像较原图暗,在(1-~)新图像较原图亮 ,
# ##brightness=1,保持原图像不变;可自定义参数范围
# '''
image = Image.open(filepath)
enh_bri = ImageEnhance.Brightness(image)
# brightness =1.5
image_brightened = enh_bri.enhance(brightness)
image_brightened.show()
def ContrastEnhancement(contrast):
# '''
# #对比度增强: 可自定义参数contrast范围,contrast=1,保持原图像不变
# '''
image = Image.open(filepath)
enh_con = ImageEnhance.Contrast(image)
# contrast =1.5
image_contrasted = enh_con.enhance(contrast)
image_contrasted.show()
def ColorEnhancement(color):
# '''
# #色度增强 : 饱和度 color=1,保持原图像不变
# '''
image = Image.open(filepath)
enh_col = ImageEnhance.Color(image)
# color =0.8
image_colored = enh_col.enhance(color)
image_colored.show()
def SharpnessEnhancement(sharpness):
# '''
# #锐度增强: 清晰度 sharpness=1,保持原图像不变
# '''
image = Image.open(filepath)
enh_sha = ImageEnhance.Sharpness(image)
# sharpness = 2
image_sharped = enh_sha.enhance(sharpness)
image_sharped.show()
def Filter(image):
# """
# 色彩窗的半径
# 图像将呈现类似于磨皮的效果
# """
#image:输入图像,可以是Mat类型,
# 图像必须是8位或浮点型单通道、三通道的图像
#0:表示在过滤过程中每个像素邻域的直径范围,一般为0
#后面两个数字:空间高斯函数标准差,灰度值相似性标准差
import cv2
image =cv2.imread(filepath)
Remove=cv2.bilateralFilter(image,0,0,10)
cv2.imshow('filter',Remove)
cv2.waitKey(0)
cv2.destroyAllWindows()
# res = np.uint8(np.clip((1.2 * image + 10), 0, 255))
# tmp = np.hstack((dst, res))
# cv2.imshow('bai',res)
def WhiteBeauty(image,whi):
# '''
# 美白
# '''
import cv2
image =cv2.imread(filepath)
white = np.uint8(np.clip((whi * image + 10), 0, 255))
cv2.imshow('bai',white)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ =="__main__":
filepath = 'C:/Users/25764/Pictures/Saved Pictures/timg.jpg'
brightness = 1.5
contrast = 0.2
color=1.9
sharpness=0.1
BrightnessEnhancement(brightness)
ContrastEnhancement(contrast)
ColorEnhancement(color)
SharpnessEnhancement(sharpness)
whi = 1.2
image =cv2.imread('C:/Users/25764/Pictures/Saved Pictures/timg.jpg')
Filter(image)
WhiteBeauty(image,whi)
![](https://img2020.cnblogs.com/blog/1966851/202005/1966851-20200506191452703-1502852134.png)
![](https://img2020.cnblogs.com/blog/1966851/202005/1966851-20200506191345073-1860424429.jpg)
![](https://img2020.cnblogs.com/blog/1966851/202005/1966851-20200506191815457-1340360528.png)
![](https://img2020.cnblogs.com/blog/1966851/202005/1966851-20200506200651217-457161929.png)
![](https://img2020.cnblogs.com/blog/1966851/202005/1966851-20200506200755726-198938246.png)
![](https://img2020.cnblogs.com/blog/1966851/202005/1966851-20200506191517127-16907836.png)
![](https://img2020.cnblogs.com/blog/1966851/202005/1966851-20200506191539630-1401861565.png)