Compare similarity of images using OpenCV with Python

Question

I\'m trying to compare a image to a list of other images and return a selection of images (like Google search images) of this list with up to 70% of similarity.

I ge

Answer 1

For a simpler implementation of Earth Mover's Distance (aka Wasserstein Distance) in Python, you could use Scipy:

from scipy.stats import wasserstein_distance
from scipy.ndimage import imread
import numpy as np

def get_histogram(img):
  '''
  Get the histogram of an image. For an 8-bit, grayscale image, the
  histogram will be a 256 unit vector in which the nth value indicates
  the percent of the pixels in the image with the given darkness level.
  The histogram's values sum to 1.
  '''
  h, w = img.shape
  hist = [0.0] * 256
  for i in range(h):
    for j in range(w):
      hist[img[i, j]] += 1
  return np.array(hist) / (h * w)

a = imread('a.jpg', mode='L')
b = imread('b.jpg', mode='L')
a_hist = get_histogram(a)
b_hist = get_histogram(b)
dist = wasserstein_distance(a_hist, b_hist)
print(dist)