在python(cv2)中使用OpenCV增加彩色图像对比度的最快方法是什么？

Question

我正在使用OpenCV处理一些图像,我需要执行的第一步是增加彩色图像上的图像对比度.到目前为止我发现的最快的方法是使用这个代码(其中np是numpy导入)来乘以原始的基于C的cv1文档中的建议:

    if (self.array_alpha is None):
        self.array_alpha = np.array([1.25])
        self.array_beta = np.array([-100.0])

    # add a beta value to every pixel 
    cv2.add(new_img, self.array_beta, new_img)                    

    # multiply every pixel value by alpha
    cv2.multiply(new_img, self.array_alpha, new_img)  

有没有更快的方法在Python中执行此操作？我尝试使用numpy的标量乘法,但性能实际上更糟.我也尝试过使用cv2.convertScaleAbs (OpenCV docs建议使用convertTo,但cv2似乎缺少这个函数的接口)但是再次测试的性能更差.

Answer 1

正如Abid Rahaman K评论的那样,numpy数组中的简单算术是最快的.

使用此图像,例如:http://i.imgur.com/Yjo276D.png

这是一些类似于亮度/对比度操作的图像处理:

'''
Simple and fast image transforms to mimic:
 - brightness
 - contrast
 - erosion 
 - dilation
'''

import cv2
from pylab import array, plot, show, axis, arange, figure, uint8 

# Image data
image = cv2.imread('imgur.png',0) # load as 1-channel 8bit grayscale
cv2.imshow('image',image)
maxIntensity = 255.0 # depends on dtype of image data
x = arange(maxIntensity) 

# Parameters for manipulating image data
phi = 1
theta = 1

# Increase intensity such that
# dark pixels become much brighter, 
# bright pixels become slightly bright
newImage0 = (maxIntensity/phi)*(image/(maxIntensity/theta))**0.5
newImage0 = array(newImage0,dtype=uint8)

cv2.imshow('newImage0',newImage0)
cv2.imwrite('newImage0.jpg',newImage0)

y = (maxIntensity/phi)*(x/(maxIntensity/theta))**0.5

# Decrease intensity such that
# dark pixels become much darker, 
# bright pixels become slightly dark 
newImage1 = (maxIntensity/phi)*(image/(maxIntensity/theta))**2
newImage1 = array(newImage1,dtype=uint8)

cv2.imshow('newImage1',newImage1)

z = (maxIntensity/phi)*(x/(maxIntensity/theta))**2

# Plot the figures
figure()
plot(x,y,'r-') # Increased brightness
plot(x,x,'k:') # Original image
plot(x,z, 'b-') # Decreased brightness
#axis('off')
axis('tight')
show()

# Close figure window and click on other window 
# Then press any keyboard key to close all windows
closeWindow = -1
while closeWindow<0:
    closeWindow = cv2.waitKey(1) 
cv2.destroyAllWindows()

灰度原始图像:

变亮的图像似乎是膨胀的:

变暗的图像似乎被侵蚀,锐化,具有更好的对比度:

像素强度如何变换:

如果您使用的价值发挥phi,并theta可以得到非常有趣的结果.您还可以为多通道图像数据实现此技巧.

---编辑---

看看这个youtube视频中"水平"和"曲线"的概念,在photoshop中显示图像编辑.线性变换的等式产生相同的量,即每个像素的变化"水平".如果您编写一个可以区分像素类型的方程式(例如那些已经具有特定值的方程式),那么您可以根据该方程式描述的"曲线"更改像素.

Answer 2

试试这段代码:

import cv2

img = cv2.imread('sunset.jpg', 1)
cv2.imshow("Original image",img)

# CLAHE (Contrast Limited Adaptive Histogram Equalization)
clahe = cv2.createCLAHE(clipLimit=3., tileGridSize=(8,8))

lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)  # convert from BGR to LAB color space
l, a, b = cv2.split(lab)  # split on 3 different channels

l2 = clahe.apply(l)  # apply CLAHE to the L-channel

lab = cv2.merge((l2,a,b))  # merge channels
img2 = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)  # convert from LAB to BGR
cv2.imshow('Increased contrast', img2)
#cv2.imwrite('sunset_modified.jpg', img2)

cv2.waitKey(0)
cv2.destroyAllWindows()

日落之前: 对比度增加后的日落: