I have been using Structural Similarity Index (through tensorflow) for comparing images, however it takes too long. I was wondering if there is an alternative technique that doesn't take so much time. It is also okay if someone could point out a more efficient implementation of SSIM than tensorflow in Python.
My intention for using SSIM, is that given a reference image (A) and a set of images (B), I need to understand which image in B is the most similar to the reference image A.
UPDATE 01-02-2021
I decided to explore some other Python modules that could be used for Image comparison. I also wanted to use concurrent.futures, which I hadn't used before.
I created two GitGub Gists with the code that I wrote.
ImageHash aHash image comparison
The ImageHash module was able to compare 100 images in 0.29 of a second and the skimage module took 1.2 seconds for the same task.
ORIGINAL POST
I haven't tested the speed of the code in this answer, because I have only used the code in some image testing that I posted to GitHub:
The code below will produce a similarity score between reference image (A) and set of images (B).
The complete code is located in my GitHub repository
import os
from os import walk
import numpy as np
from PIL import Image
from math import *
def get_image_files(directory_of_images):
"""
This function is designed to traverse a directory tree and extract all
the image names contained in the directory.
:param directory_of_images: the name of the target directory containing
the images to be trained on.
:return: list of images to be processed.
"""
images_to_process = []
for (dirpath, dirnames, filenames) in walk(directory_of_images):
for filename in filenames:
accepted_extensions = ('.bmp', '.gif', '.jpg', '.jpeg', '.png', '.svg', '.tiff')
if filename.endswith(accepted_extensions):
images_to_process.append(os.path.join(dirpath, filename))
return images_to_process
def pre_process_images(image_one, image_two, additional_resize=False, max_image_size=1000):
"""
This function is designed to resize the images using the Pillow module.
:param image_one: primary image to evaluate against a secondary image
:param image_two: secondary image to evaluate against the primary image
:param additional_resize:
:param max_image_size: maximum allowable image size in pixels
:return: resized images
"""
lower_boundary_size = (min(image_one.size[0], image_two.size[0]), min(image_one.size[1], image_two.size[1]))
# reference: https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.resize
# reference: https://pillow.readthedocs.io/en/stable/handbook/concepts.html#PIL.Image.LANCZOS
image_one = image_one.resize(lower_boundary_size, resample=Image.LANCZOS)
image_two = image_two.resize(lower_boundary_size, resample=Image.LANCZOS)
if max(image_one.size) > max_image_size and additional_resize:
resize_factor = max_image_size / max(image_one.size)
image_one = image_one.resize((int(lower_boundary_size[0] * resize_factor),
int(lower_boundary_size[1] * resize_factor)), resample=Image.LANCZOS)
image_two = image_two.resize((int(lower_boundary_size[0] * resize_factor),
int(lower_boundary_size[1] * resize_factor)), resample=Image.LANCZOS)
return image_one, image_two
def get_ssim_similarity(image_one_name, image_two_name, window_size=7, dynamic_range=255):
"""
The Structural Similarity Index (SSIM) is a method for measuring the similarity between two images.
The SSIM index can be viewed as a quality measure of one of the images being compared, provided the
other image is regarded as of perfect quality.
:param image_one_name: primary image to evaluate against a secondary image
:param image_two_name: secondary image to evaluate against the primary image
:param window_size: The side-length of the sliding window used in comparison. Must be an odd value.
:param dynamic_range: Dynamic range of the input image, specified as a positive scalar.
The default dynamic range is 255 for images of data type uint8.
:return: computational score and image names
"""
image_one = Image.open(image_one_name)
image_two = Image.open(image_two_name)
if min(list(image_one.size) + list(image_two.size)) < 7:
raise Exception("One of the images was too small to process using the SSIM approach")
image_one, image_two = pre_process_images(image_one, image_two, True)
image_one, image_two = image_one.convert('I'), image_two.convert('I')
c1 = (dynamic_range * 0.01) ** 2
c2 = (dynamic_range * 0.03) ** 2
pixel_length = window_size ** 2
ssim = 0.0
adjusted_width = image_one.size[0] // window_size * window_size
adjusted_height = image_one.size[1] // window_size * window_size
for i in range(0, adjusted_width, window_size):
for j in range(0, adjusted_height, window_size):
crop_box = (i, j, i + window_size, j + window_size)
crop_box_one = image_one.crop(crop_box)
crop_box_two = image_two.crop(crop_box)
np_array_one, np_array_two = np.array(crop_box_one).flatten(), np.array(crop_box_two).flatten()
np_variable_one, np_variable_two = np.var(np_array_one), np.var(np_array_two)
np_average_one, np_average_two = np.average(np_array_one), np.average(np_array_two)
cov = (np.sum(np_array_one * np_array_two) - (np.sum(np_array_one) *
np.sum(crop_box_two) / pixel_length)) / pixel_length
ssim += ((2.0 * np_average_one * np_average_two + c1) * (2.0 * cov + c2)) / \
((np_average_one ** 2 + np_average_two ** 2 + c1) * (np_variable_one + np_variable_two + c2))
similarity_percent = (ssim * pixel_length / (adjusted_height * adjusted_width)) * 100
return round(similarity_percent, 2)
target_image = 'a.jpg'
image_directory = 'b_images'
images = get_image_files(image_directory)
for image in images:
ssim_result = get_ssim_similarity(target_image, image)
I would also recommend looking at the Python module ImageHash. I have multiple code examples and test cases published here.