My implementation for the blog post: https://www.pyimagesearch.com/2014/07/14/3-ways-compare-histograms-using-opencv-python/

my_histo_compare.py

from scipy.spatial import distance as dist
import matplotlib.pyplot as plt
import numpy as np
import argparse
import glob
import cv2

"""
https://www.pyimagesearch.com/2014/07/14/3-ways-compare-histograms-using-opencv-python/

My implementation of hte Blog post above.  I believe since its writing
some of the library APIs have changed.

python: 3.6.6

cv2: 3.4.3


"""

ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help="Path to the directory of images")
args = vars(ap.parse_args())

# initialize the index dictionary to store the image name
# and corresponding histograms and the images dictionary
# to store the images themselves
index = {}
images = {}

# loop over the image paths
for image_path in glob.glob(f"{args['dataset']}/*.png"):
    # extract the image filename, assumed to be unique and
    # load the image, updating the image dictionary
    filename = image_path[image_path.rfind('/') + 1:]
    image = cv2.imread(image_path)
    images[filename] = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # extract a 3D RGB color historgram from the image
    # using 8 bins per channel, normalize, and update the index
    hist = cv2.calcHist([image], [0, 1, 2], None, [8, 8, 8], [0, 256, 0, 256, 0, 256])
    # update: normalize needs the source array AND
    # the dest array
    # returns void
    cv2.normalize(hist, hist).flatten()
    index[filename] = hist

# METHOD #1: UTILIZING OPENCV
# initialize opencv methods for histogram comparison

OPENCV_METHODS = (
    ("Correlation", cv2.HISTCMP_CORREL),
    ("Chi-Squared", cv2.HISTCMP_CHISQR),
    ("Intersection", cv2.HISTCMP_INTERSECT),
    ("Hellinger", cv2.HISTCMP_BHATTACHARYYA)
)

for (methodName, method) in OPENCV_METHODS:
    # initialize the results dictionary and the sort
    # direction
    results = {}
    reverse = False

    # if we are using the correlation or intersection
    # method, then sort the results in reverse order
    if methodName in ("Correlation", "Intersection"):
        reverse = True

    # loop over the index
    for (k, hist) in index.items():
        # compute the distance between the two histograms
        # using the method and update the results dictionary
        d = cv2.compareHist(index["doge.png"], hist, method)
        results[k] = d

    # sort the results
    results = sorted([(v, k) for (k, v) in results.items()], reverse=reverse)

    # show the query image
    fig = plt.figure("Query")
    ax = fig.add_subplot(1, 1, 1)
    ax.imshow(images["doge.png"])
    plt.axis("off")

    # initialize the results figure
    fig = plt.figure("Results: %s" % (methodName))
    fig.suptitle(methodName, fontsize=20)

    # loop over the results
    for (i, (v, k)) in enumerate(results):
        # show the result
        ax = fig.add_subplot(1, len(images), i + 1)
        ax.set_title("%s: %.2f" % (k, v))
        plt.imshow(images[k])
        plt.axis("off")

# show the OpenCV methods
# plt.show()

# METHOD #2: UTILIZING SCIPY
# initialize the scipy methods to compaute distances
SCIPY_METHODS = (
    ("Euclidean", dist.euclidean),
    ("Manhattan", dist.cityblock),
    ("Chebysev", dist.chebyshev))

# loop over the comparison methods
for (methodName, method) in SCIPY_METHODS:
    # initialize the dictionary dictionary
    results = {}

    # loop over the index
    for (k, hist) in index.items():
        # compute the distance between the two histograms
        # using the method and update the results dictionary
        d = method(index["doge.png"].flatten(), hist.flatten())
        results[k] = d

    # sort the results
    results = sorted([(v, k) for (k, v) in results.items()])

    # show the query image
    fig = plt.figure("Query")
    ax = fig.add_subplot(1, 1, 1)
    ax.imshow(images["doge.png"])
    plt.axis("off")

    # initialize the results figure
    fig = plt.figure("Results: %s" % (methodName))
    fig.suptitle(methodName, fontsize=20)

    # loop over the results
    for (i, (v, k)) in enumerate(results):
        # show the result
        ax = fig.add_subplot(1, len(images), i + 1)
        ax.set_title("%s: %.2f" % (k, v))
        plt.imshow(images[k])
        plt.axis("off")

# show the SciPy methods
plt.show()


# METHOD #3: ROLL YOUR OWN
def chi2_distance(histA, histB, eps=1e-10):
    # compute the chi-squared distance
    d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)
                      for (a, b) in zip(histA, histB)])

    # return the chi-squared distance
    return d


# initialize the results dictionary
results = {}

# loop over the index
for (k, hist) in index.items():
    # compute the distance between the two histograms
    # using the custom chi-squared method, then update
    # the results dictionary
    d = chi2_distance(index["doge.png"], hist)
    results[k] = d

# sort the results
results = sorted([(v, k) for (k, v) in results.items()])

# show the query image
fig = plt.figure("Query")
ax = fig.add_subplot(1, 1, 1)
ax.imshow(images["doge.png"])
plt.axis("off")

# initialize the results figure
fig = plt.figure("Results: Custom Chi-Squared")
fig.suptitle("Custom Chi-Squared", fontsize=20)

# loop over the results
for (i, (v, k)) in enumerate(results):
    # show the result
    ax = fig.add_subplot(1, len(images), i + 1)
    ax.set_title("%s: %.2f" % (k, v))
    plt.imshow(images[k])
    plt.axis("off")

# show the custom method
plt.show()