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

问题描述

我正在使用OpenCV处理一些图像，而我需要执行的第一步就是增加彩色图像上的图像对比度.到目前为止，我找到的最快的方法使用此代码(其中np是numpy导入)进行乘法和加法，如基于C的原始cv1文档:

    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)

在Python中有更快的方法吗?我曾尝试使用numpy的标量乘法，但实际上性能较差.我也尝试使用cv2.convertScaleAbs(OpenCV文档建议使用convertTo，但cv2似乎缺少此功能的接口)，但在测试中，性能再次变差.

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

使用此图像作为示例: http://i.imgur.com/Yjo276D.png

以下是一些类似于亮度/对比度操纵的图像处理:

'''
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()

原始灰度图像:

变亮的图像变亮:

变暗，变亮，对比度更好的深色图像:

如何变换像素强度:

如果使用phi和theta的值，您将获得非常有趣的结果.您也可以对多通道图像数据实施此技巧.

---编辑---

在此youtube视频上查看了级别"和曲线"的概念，其中显示了在photoshop中进行图像编辑.用于线性变换的方程式在每个像素上产生相同的量，即水平"变化.如果您编写了一个可以区分像素类型(例如已经具有一定值的像素)的方程式，则可以根据该方程式描述的曲线"来更改像素.

I'm using OpenCV to process some images, and one of the first steps I need to perform is increasing the image contrast on a color image. The fastest method I've found so far uses this code (where np is the numpy import) to multiply and add as suggested in the original C-based cv1 docs:

    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)

Is there a faster way to do this in Python? I've tried using numpy's scalar multiply instead, but the performance is actually worse. I also tried using cv2.convertScaleAbs (the OpenCV docs suggested using convertTo, but cv2 seems to lack an interface to this function) but again the performance was worse in testing.

解决方案

Simple arithmetic in numpy arrays is the fastest, as Abid Rahaman K commented.

Use this image for example: http://i.imgur.com/Yjo276D.png

Here is a bit of image processing that resembles brightness/contrast manipulation:

'''
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()

Original image in grayscale:

Brightened image that appears to be dilated:

Darkened image that appears to be eroded, sharpened, with better contrast:

How the pixel intensities are being transformed:

If you play with the values of phi and theta you can get really interesting outcomes. You can also implement this trick for multichannel image data.

--- EDIT ---

have a look at the concepts of 'levels' and 'curves' on this youtube video showing image editing in photoshop. The equation for linear transform creates the same amount i.e. 'level' of change on every pixel. If you write an equation which can discriminate between types of pixel (e.g. those which are already of a certain value) then you can change the pixels based on the 'curve' described by that equation.

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