How do I equalize an image and plot it to an histogram with openCV and numpy

Question

I am trying to to loop through an nparray which contains pixel data. I want to perform an equalization to each of the pixel values and display them as a histogram.

I already achieved my goal by doing following:

def stratch_contrast(img): 

    hist,bins = np.histogram(img.flatten(),256,[0,256])
    cdf = hist.cumsum()
    cdf_normalized = cdf * hist.max()/ cdf.max()

    cdf_m = np.ma.masked_equal(cdf,0)
    cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
    cdf = np.ma.filled(cdf_m,0).astype('uint8')
    img = cdf[img]

    plt.hist(img.flatten(),256,[0,256], color = 'black')
    plt.xlim([0,256])
    plt.legend(('cdf','histogram'), loc = 'upper left')
    plt.show()

    img = cv2.imread(name,0)
    equ = cv2.equalizeHist(img)
    res = np.hstack((img,equ)) #stacking images side-by-side
    cv2.imwrite('res.png',res)

    return

But I really would like to do this with out using predefined functions for learning purposes.

So I tried following:

 def stratch_contrast(img, darkestValue, whitestValue):

     newImgPixelList = []

     h = img.shape[0] #number of pixels in the hight
     w = img.shape[1] #number of piexels in the weight

     darkestValueStratch = 256 #opposite so it can get darker while loop
     whitestValueStratch = 0 #opposite so it can get lighter while loop

     for y in range(0, w):
         for x in range(0, h):
              newImg[x][y] = (img[x][y]-darkestValue)*256/(whitestValue-darkestValue)
              pxStratch = newImg[x][y]
              newImgPixelList.append(pxStratch)
              if darkestValueStratch > pxStratch:
                  darkestValueStratch = pxStratch
              if whitestValueStratch < pxStratch:
                  whitestValueStratch = pxStratch   

      return newImgPixelList, darkestValueStratch, whitestValueStratch

But when I am then calling my plotting function, like so:

plot(newImgPixelList, int(darkestValueStratch), int(whitestValueStratch))

The plotted histogram is not equalized at all. It looks nearly exactly the same, like my not equalized histogram, so something must be wrong. I would be very gratefull if someone could help me with that!

My complete code:

import matplotlib.pyplot as plt
import numpy as np
import cv2
np.seterr(over='ignore')

name = 'puppy.jpg'

img = cv2.imread(name, cv2.IMREAD_GRAYSCALE) #import image
newImg = np.zeros((img.shape))

def get_histo_scope(img):

    imgPixelList = [] #array which later can save the pixel values of the image

    h = img.shape[0] #number of pixels in the hight
    w = img.shape[1] #number of piexels in the weight

    darkestValue = 256 #opposite so it can get darker while loop
    whitestValue = 0 #opposite so it can get lighter while loop

    for y in range(0, w):
        for x in range(0, h):       
            px = img[x][y] #reads the pixel which is a npndarray [][][]
            imgPixelList.append(px) #saves the pixel data of every pixel we loop so we can use it later to plot the histogram
            if darkestValue > px: #identifies the darkest pixel value
                darkestValue = px
            if whitestValue < px: #identifies the whitest pixel value
                whitestValue = px 

    return darkestValue, whitestValue, imgPixelList

def plot(imgPixelList, darkestValue, whitestValue):
    values = range(darkestValue, whitestValue, 1) #creates and array with all data from whitesValue to darkestValue
    bin_edges = values

    plt.hist(imgPixelList, bins=bin_edges, color='black')
    plt.xlabel('Color Values')
    plt.ylabel('Number of Poxels')
    plt.show()  

    return     

def stratch_contrast(img, darkestValue, whitestValue): 

    #hist,bins = np.histogram(img.flatten(),256,[0,256])
    #cdf = hist.cumsum()
    #cdf_normalized = cdf * hist.max()/ cdf.max()

    #Comment out to remove Equalization 
    #cdf_m = np.ma.masked_equal(cdf,0)
    #cdf_m = (cdf_m - cdf_m.min())*255/(cdf_m.max()-cdf_m.min())
    #cdf = np.ma.filled(cdf_m,0).astype('uint8')
    #img = cdf[img]

    #plt.hist(img.flatten(),256,[0,256], color = 'black')
    #plt.xlim([0,256])
    #plt.legend(('cdf','histogram'), loc = 'upper left')
    #plt.show()

    #img = cv2.imread(name,0)
    #equ = cv2.equalizeHist(img)
    #res = np.hstack((img,equ)) #stacking images side-by-side
    #cv2.imwrite('res.png',res)

    newImgPixelList = []

    h = img.shape[0] #number of pixels in the hight
    w = img.shape[1] #number of piexels in the weight

    darkestValueStratch = 256 #oposite so it can get darker while loop
    whitestValueStratch = 0 #oposite so it can get lighter while loop

    for y in range(0, w):
       for x in range(0, h):
            newImg[x][y] = (img[x][y]-darkestValue)*256/(whitestValue-darkestValue)
            pxStratch = newImg[x][y]
            newImgPixelList.append(pxStratch)
            if darkestValueStratch > pxStratch: #identifies the darkest pixel value
                darkestValueStratch = pxStratch
            if whitestValueStratch < pxStratch: #identifies the whitest pixel value
                whitestValueStratch = pxStratch   

    return newImgPixelList, darkestValueStratch, whitestValueStratch

darkestValue, whitestValue, imgPixelList = get_histo_scope(img) #get scope and pixel values from the img data

plot(imgPixelList, darkestValue, whitestValue) #plot the collected pixel values

newImgPixelList, darkestValueStratch, whitestValueStratch = stratch_contrast(img, darkestValue, whitestValue)

plot(newImgPixelList, int(darkestValueStratch), int(whitestValueStratch))

Answer 1

I think you misunderstood the contrast stretching algorithm.

The goal of the algorithm is to linearly scale the values of the pixels so that your image uses the full dynamic range available, i.e min(I) = 0 and max(I) = 255.

For that, you have to find the current min(I) and max(I) before looping through the pixels and scaling them. Just loop through the whole image while keeping track of the maximum and minimum value for each channel (3 channels for an RGB image). Then use those values to scale your pixels using the formula newValue = 255 * (oldValue - minimum) / (maximum - minimum). Treat each of the R, G and B channels independently.