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

Question

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

User · Answer

I would like to suggest a method using the LAB color channel. Wikipedia has enough information regarding what the LAB color channel is about.

I have done the following using OpenCV 3.0.0 and python:

import cv2

#-----Reading the image-----------------------------------------------------
img = cv2.imread('Dog.jpg', 1)
cv2.imshow("img",img) 

#-----Converting image to LAB Color model----------------------------------- 
lab= cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
cv2.imshow("lab",lab)

#-----Splitting the LAB image to different channels-------------------------
l, a, b = cv2.split(lab)
cv2.imshow('l_channel', l)
cv2.imshow('a_channel', a)
cv2.imshow('b_channel', b)

#-----Applying CLAHE to L-channel-------------------------------------------
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
cl = clahe.apply(l)
cv2.imshow('CLAHE output', cl)

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

#-----Converting image from LAB Color model to RGB model--------------------
final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
cv2.imshow('final', final)

#_____END_____#

You can run the code as it is. To know what CLAHE (Contrast Limited Adaptive Histogram Equalization)is about, you can again check Wikipedia.

User · Answer

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  lt  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

User · Answer

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

User · Answer

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      -- gt  no change   0  lt  alpha  lt  1        -- gt  lower contrast   alpha  gt  1            -- gt  higher contrast   -127  lt  beta  lt   127   -- gt  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  gt  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  quot mandrill quot  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  gt  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

User · Answer

Brightness and contrast can be adjusted using alpha  a  and beta       respectively  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 - gt  After     Note  For automatic brightness contrast adjustment take a look at automatic contrast and brightness adjustment of a color photo

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

Examples related to python

Examples related to image

Examples related to opencv

Examples related to image-processing

Examples related to computer-vision