How do I increase the contrast of an image in Python OpenCV

I am new to Python OpenCV. I have read some documents and answers here but I am unable to figure out what the following code means:

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)  

I have come to know that Basically, every pixel can be transformed as X = aY + b where a and b are scalars.. Basically, I have understood this. However, I did not understand the code and how to increase contrast with this.

Till now, I have managed to simply read the image using img = cv2.imread('image.jpg',0)

Thanks for your help

I would like to suggest a method using the LAB color space.

LAB color space expresses color variations across three channels. One channel for brightness and two channels for color:

• L-channel: representing lightness in the image
• a-channel: representing change in color between red and green
• b-channel: representing change in color between yellow and blue

In the following I perform adaptive histogram equalization on the L-channel and convert the resulting image back to BGR color space. This enhances the brightness while also limiting contrast sensitivity. I have done the following using OpenCV 3.0.0 and python:

Code:

import cv2
import numpy as np

img = cv2.imread('flower.jpg', 1)
# converting to LAB color space
lab= cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l_channel = lab[:,:,0]

# Applying CLAHE to L-channel
# feel free to try different values for the limit and grid size:
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
cl = clahe.apply(l_channel)

# merge the CLAHE enhanced L-channel with the a and b channel
limg = cv2.merge((cl,a,b))

# Converting image from LAB Color model to BGR color spcae
enhanced_img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)

# Stacking the original image with the enhanced image
result = np.hstack((img, enhanced_img))
cv2.imshow('Result', result)

Result:

The enhanced image is on the right

You can run the code as it is. To know what CLAHE (Contrast Limited Adaptive Histogram Equalization) is about, refer this Wikipedia page

For Python, I haven't found an OpenCV function that provides contrast. As others have suggested, there are some techniques to automatically increase contrast using a very simple formula.

In the official OpenCV docs, it is suggested that this equation can be used to apply both contrast and brightness at the same time:

>new_img = alpha*old_img + beta

where alpha corresponds to a contrast and beta is brightness. Different cases

alpha 1  beta 0      --> no change  
0 < alpha < 1        --> lower contrast  
alpha > 1            --> higher contrast  
-127 < beta < +127   --> good range for brightness values

In C/C++, you can implement this equation using cv::Mat::convertTo, but we don't have access to that part of the library from Python. To do it in Python, I would recommend using the cv::addWeighted function, because it is quick and it automatically forces the output to be in the range 0 to 255 (e.g. for a 24 bit color image, 8 bits per channel). You could also use convertScaleAbs as suggested by @nathancy.

import cv2
img = cv2.imread('input.png')
# call addWeighted function. use beta = 0 to effectively only operate one one image
out = cv2.addWeighted( img, contrast, img, 0, brightness)
output = cv2.addWeighted

The above formula and code is quick to write and will make changes to brightness and contrast. But they yield results that are significantly different than photo editing programs. The rest of this answer will yield a result that will reproduce the behavior in the GIMP and also LibreOffice brightness and contrast. It's more lines of code, but it gives a nice result.

Contrast

In the GIMP, contrast levels go from -127 to +127. I adapted the formulas from here to fit in that range.

>f = 131*(contrast + 127)/(127*(131-contrast))
>new_image = f*(old_image - 127) + 127 = f*(old_image) + 127*(1-f)

To figure out brightness, I figured out the relationship between brightness and levels and used information in this levels post to arrive at a solution.

#pseudo code
if brightness > 0
    shadow = brightness
    highlight = 255
else:
    shadow = 0
    highlight = 255 + brightness
new_img = ((highlight - shadow)/255)*old_img + shadow

brightness and contrast in Python and OpenCV

Putting it all together and adding using the reference "mandrill" image from USC SIPI:

import cv2
import numpy as np

# Open a typical 24 bit color image. For this kind of image there are
# 8 bits (0 to 255) per color channel
img = cv2.imread('mandrill.png')  # mandrill reference image from USC SIPI

s = 128
img = cv2.resize(img, (s,s), 0, 0, cv2.INTER_AREA)

def apply_brightness_contrast(input_img, brightness = 0, contrast = 0):
    
    if brightness != 0:
        if brightness > 0:
            shadow = brightness
            highlight = 255
        else:
            shadow = 0
            highlight = 255 + brightness
        alpha_b = (highlight - shadow)/255
        gamma_b = shadow
        
        buf = cv2.addWeighted(input_img, alpha_b, input_img, 0, gamma_b)
    else:
        buf = input_img.copy()
    
    if contrast != 0:
        f = 131*(contrast + 127)/(127*(131-contrast))
        alpha_c = f
        gamma_c = 127*(1-f)
        
        buf = cv2.addWeighted(buf, alpha_c, buf, 0, gamma_c)

    return buf


font = cv2.FONT_HERSHEY_SIMPLEX
fcolor = (0,0,0)

blist = [0, -127, 127,   0,  0, 64] # list of brightness values
clist = [0,    0,   0, -64, 64, 64] # list of contrast values


out = np.zeros((s*2, s*3, 3), dtype = np.uint8)

for i, b in enumerate(blist):
    c = clist[i]
    print('b, c:  ', b,', ',c)
    row = s*int(i/3)
    col = s*(i%3)
    
    print('row, col:   ', row, ', ', col)
    
    out[row:row+s, col:col+s] = apply_brightness_contrast(img, b, c)
    msg = 'b %d' % b
    cv2.putText(out,msg,(col,row+s-22), font, .7, fcolor,1,cv2.LINE_AA)
    msg = 'c %d' % c
    cv2.putText(out,msg,(col,row+s-4), font, .7, fcolor,1,cv2.LINE_AA)
    
    cv2.putText(out, 'OpenCV',(260,30), font, 1.0, fcolor,2,cv2.LINE_AA)

cv2.imwrite('out.png', out)

I manually processed the images in the GIMP and added text tags in Python/OpenCV:

Note: @UtkarshBhardwaj has suggested that Python 2.x users must cast the contrast correction calculation code into float for getting floating result, like so:

...
if contrast != 0:
        f = float(131*(contrast + 127))/(127*(131-contrast))
...

Contrast and brightness can be adjusted using alpha (α) and beta (β), respectively. These variables are often called the gain and bias parameters. The expression can be written as

OpenCV already implements this as cv2.convertScaleAbs(), just provide user defined alpha and beta values

import cv2

image = cv2.imread('1.jpg')

alpha = 1.5 # Contrast control (1.0-3.0)
beta = 0 # Brightness control (0-100)

adjusted = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)

cv2.imshow('original', image)
cv2.imshow('adjusted', adjusted)
cv2.waitKey()

Before -> After

Note: For automatic brightness/contrast adjustment take a look at automatic contrast and brightness adjustment of a color photo

Best explanation for X = aY + b (in fact it f(x) = ax + b)) is provided at https://math.stackexchange.com/a/906280/357701

A Simpler one by just adjusting lightness/luma/brightness for contrast as is below:

import cv2

img = cv2.imread('test.jpg')
cv2.imshow('test', img)
cv2.waitKey(1000)
imghsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)


imghsv[:,:,2] = [[max(pixel - 25, 0) if pixel < 190 else min(pixel + 25, 255) for pixel in row] for row in imghsv[:,:,2]]
cv2.imshow('contrast', cv2.cvtColor(imghsv, cv2.COLOR_HSV2BGR))
cv2.waitKey(1000)
raw_input()

img = cv2.imread("/x2.jpeg")

image = cv2.resize(img, (1800, 1800))

alpha=1.5
beta=20

new_image=cv2.addWeighted(image,alpha,np.zeros(image.shape, image.dtype),0,beta)

cv2.imshow("new",new_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

There are quite a few answers here ranging from simple to complex. I want to add another on the simpler side that seems a little more practical for actual contrast and brightness adjustments.

def adjust_contrast_brightness(img, contrast:float=1.0, brightness:int=0):
    """
    Adjusts contrast and brightness of an uint8 image.
    contrast:   (0.0,  inf) with 1.0 leaving the contrast as is
    brightness: [-255, 255] with 0 leaving the brightness as is
    """
    brightness += int(round(255*(1-contrast)/2))
    return cv2.addWeighted(img, contrast, img, 0, brightness)

We do the a*x+b adjustment through the addWeighted() function. However, to change the contrast without also modifying the brightness, the data needs to be zero centered. That's not the case with OpenCVs default uint8 datatype. So we also need to adjust the brightness according to how the distribution is shifted.