I\'m working on a project to measure and visualize image similarity. The images in my dataset come from photographs of images in books, some of which have very high or low expos
I ended up using a numpy implementation of the histogram normalization method @user894763 pointed out. Just save the below as normalize.py then you can call:
python normalize.py cats.jpg
Script:
import numpy as np
from scipy.misc import imsave
from scipy.ndimage import imread
import sys
def get_histogram(img):
'''
calculate the normalized histogram of an image
'''
height, width = img.shape
hist = [0.0] * 256
for i in range(height):
for j in range(width):
hist[img[i, j]]+=1
return np.array(hist)/(height*width)
def get_cumulative_sums(hist):
'''
find the cumulative sum of a numpy array
'''
return [sum(hist[:i+1]) for i in range(len(hist))]
def normalize_histogram(img):
# calculate the image histogram
hist = get_histogram(img)
# get the cumulative distribution function
cdf = np.array(get_cumulative_sums(hist))
# determine the normalization values for each unit of the cdf
sk = np.uint8(255 * cdf)
# normalize the normalization values
height, width = img.shape
Y = np.zeros_like(img)
for i in range(0, height):
for j in range(0, width):
Y[i, j] = sk[img[i, j]]
# optionally, get the new histogram for comparison
new_hist = get_histogram(Y)
# return the transformed image
return Y
img = imread(sys.argv[1])
normalized = normalize_histogram(img)
imsave(sys.argv[1] + '-normalized.jpg', normalized)
Output: