[Python] How do I compare SSIM between one image and many others using python?

Using this fantastic page: https://www.pyimagesearch.com/2014/09/15/python-compare-two-images/ I am able to find the SSIM between three images

# import the necessary packages
from skimage.measure import structural_similarity as ssim
import matplotlib.pyplot as plt
import numpy as np
import cv2 as cv

def mse(imageA, imageB):
# the 'Mean Squared Error' between the two images is the
# sum of the squared difference between the two images;
# NOTE: the two images must have the same dimension
err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
err /= float(imageA.shape[0] * imageA.shape[1])

# return the MSE, the lower the error, the more "similar"
# the two images are
return err

def compare_images(imageA, imageB, title):
# compute the mean squared error and structural similarity
# index for the images
m = mse(imageA, imageB)
s = ssim(imageA, imageB)

# setup the figure
fig = plt.figure(title)
plt.suptitle("MSE: %.2f, SSIM: %.2f" % (m, s))

# show first image
ax = fig.add_subplot(1, 2, 1)
plt.imshow(imageA, cmap = plt.cm.gray)
plt.axis("off")

# show the second image
ax = fig.add_subplot(1, 2, 2)
plt.imshow(imageB, cmap = plt.cm.gray)
plt.axis("off")

# show the images
plt.show()

# load the images -- the original, the original + contrast,
# and the original + photoshop
original = cv.imread("images/jp_gates_original.png")
contrast = cv.imread("images/jp_gates_contrast.png")
shopped = cv.imread("images/jp_gates_photoshopped.png")

# convert the images to grayscale
original = cv.cvtColor(original, cv.COLOR_BGR2GRAY)
contrast = cv.cvtColor(contrast, cv.COLOR_BGR2GRAY)
shopped = cv.cvtColor(shopped, cv.COLOR_BGR2GRAY)

# initialize the figure
fig = plt.figure("Images")
images = ("Original", original), ("Contrast", contrast), ("Photoshopped",
shopped)

# loop over the images
for (i, (name, image)) in enumerate(images):
# show the image
ax = fig.add_subplot(1, 3, i + 1)
ax.set_title(name)
plt.imshow(image, cmap = plt.cm.gray)
plt.axis("off")

# show the figure
plt.show()

# compare the images
compare_images(original, original, "Original vs. Original")
compare_images(original, contrast, "Original vs. Contrast")
compare_images(original, shopped, "Original vs. Photoshopped")


However, I'm not quite sure how to apply this to many images. In particular, how could I take one image (test image) from a folder of hundreds of images and calculate MSE/SSIM between the test images and all other images?

Thank you!

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