egor43 / pyimagecomparsion

Comparing images

PyImageComparsion

Version in another language: Русский 🇷🇺

PyImageComparsion is a package that provides a simple API and a set of tools for determining the degree of similarity of images.

Principle of operation

Two approaches are used to assess the degree of similarity of images: comparison of hashes and comparison of ORB descriptors. Since working with ORB descriptors is quite an expensive process, it is used only in case of image mismatch based on hash match assessment. The conceptual comparison can be broken down into the following steps:

1. Get images;
2. Get image metrics (hashes and ORB descriptors);
3. Estimate the degree of hash match;
4. If it was not possible to determine the hash match, estimate the degree of ORB descriptors match;
5. Return the result of identity assessment.

Main components

The PyImageComparsion package consists of the following main components:

• compare_api - image comparison API;
• image_opener - capture/open an image;
• image_metrick - calculating image metrics;
• compare_tools - an extended set of image comparison tools.

Installation

You can copy the package folder and set dependencies manually, or install the package using "setup.py". Below you will find several options for installing and running testing.

Installation from source code

$ python setup.py install

Test launches

$ python setup.py test

Launch in docker

At the moment, there are several docker image assemblies. By default, when the container starts, testing of the main functions of the package is started, at the end of which the container finishes its work.

Launching the latest version of the package

$ docker run egorrich/py_image_comparsion

Launch a specific version

You can run the required version of the package. To do this, execute the command below, replacing VERSION with the version of the desired release.

$ docker run egorrich/py_image_comparsion:release-VERSION

Examples:

$ docker run egorrich/py_image_comparsion:release-0.3

$ docker run egorrich/py_image_comparsion:release-0.1

Launching the latest developer build

$ docker run egorrich/py_image_comparsion:dev

Main usage options

The following uses imply that you have PyImageComparsion installed.

Quick comparison of two images

This comparison is based only on evaluating the hash match of the two images.

from image_comparsion import compare_api

img_1_path = "./tests/files/2.png"
img_2_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/3.png"

is_similar = compare_api.fast_image_compare(img_1_path, img_2_url)
print(is_similar)

Complete comparison of two images

Full image comparison evaluates not only the degree of image hash matching, but also the degree of ORB descriptors matching. The descriptors are only compared if the comparison of hashes has not yielded a positive result.

from image_comparsion import compare_api

img_1_path = "./tests/files/2.png"
img_2_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/3.png"

is_similar = compare_api.full_image_compare(img_1_path, img_2_url)
print(is_similar)

Fast grouping of similar images

Quick grouping allows you to group similar images based on an estimate of the degree of match of hash images.

from image_comparsion import compare_api

img_1_path = "./tests/files/2.png"
img_2_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/3.png"
img_3_path = "./tests/files/4.png"
img_4_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/5.png"

img_paths = [img_1_path, img_2_url, img_3_path, img_4_url]

for group in compare_api.fast_grouping_similar_images(img_paths):
    for img in group:
        print(id(img))
    print('===========')

Full grouping of similar images

Full grouping allows you to group similar images based on an estimate of the degree of match of hash images and an estimate of ORB descriptors match. Due to the comparison of descriptors, this function works much longer than fast grouping, but has higher accuracy.

from image_comparsion import compare_api

img_1_path = "./tests/files/2.png"
img_2_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/3.png"
img_3_path = "./tests/files/4.png"
img_4_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/5.png"

img_paths = [img_1_path, img_2_url, img_3_path, img_4_url]

for group in compare_api.full_grouping_similar_images(img_paths):
    for img in group:
        print(id(img))
    print('===========')

Specifying a match threshold

Some image comparison functions allow you to specify the matching threshold value at which the images will be considered the same.

Setting the hash match threshold = 50%

from image_comparsion import compare_api

img_1_path = "./tests/files/2.png"
img_2_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/3.png"

is_similar = compare_api.fast_image_compare(img_1_path, img_2_url, match_threshold_hash_percent=50)
print(is_similar)

Setting the ORB descriptor match threshold = 15%

from image_comparsion import compare_api

img_1_path = "./tests/files/2.png"
img_2_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/3.png"

is_similar = compare_api.full_image_compare(img_1_path, img_2_url, match_threshold_orb_percent=15)
print(is_similar)

Setting hash match threshold = 50% and ORB descriptors = 15%

from image_comparsion import compare_api

img_1_path = "./tests/files/2.png"
img_2_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/3.png"
img_3_path = "./tests/files/4.png"
img_4_url = "https://raw.githubusercontent.com/egor43/PyImageComparsion/master/tests/files/5.png"

img_paths = [img_1_path, img_2_url, img_3_path, img_4_url]

for group in compare_api.full_grouping_similar_images(img_paths, match_threshold_hash_percent=50, match_threshold_orb_percent=15):
    for img in group:
        print(id(img))
    print('===========')

Other use cases

Other uses can be studied in the attached tests or by studying the modules provided in the package in detail.