About

Developed with Python utilizing the OpenCV library, this program compares two images of identical sizes, visually highlighting their differences by drawing red rectangles. Offering flexibility for various automation Quality Assurance (QA) tests, especially visual regression testing. Selenium-Reference

Key Features:

• Utilizes standard Python language and specific modules for implementation.
• Generates an output comprising copies of the 'actual' images, with discrepancies delineated by red rectangles.
• This tool serves as a valuable asset for automated visual regression testing, facilitating precise visual comparisons to ensure the integrity and accuracy of image-based applications.

Usage

Installation

pip install visual-comparison

Configuration

All these methods can be combined based on your requirements.

Method	Description
read_image	Function to read image from the specified path. This can load both expected and actual images that need to be compared.
compare_images	Function to compare two images. This function takes three arguments: expected_image, actual_image, and result_destination. It highlights the differences between the images with red rectangles.
check_match	Function to check if two images match. This function takes two arguments: expected_image and actual_image. It returns true if both images are identical.
check_mismatch	Function to check if two images do not match. This function takes two arguments: expected_image and actual_image. It returns true if the images are different.

To compare two images through visual-comparison module

1. Sample Code to get Similarity Index:

Get Similarity Index

    # Using ImageComparisonUtil to get similarity index and save output image as result.png
    # Load images to be compared
    expected_image = ImageComparisonUtil.read_image("expected.png")
    actual_image = ImageComparisonUtil.read_image("actual.png")
    
    # Provide the path to save output image
    result_destination = "result.png"
    
    # Compare the images, print the similarity index and save it as result.png
    similarity_index = ImageComparisonUtil.compare_images(expected_image, actual_image, result_destination)
    print("Similarity Index:", similarity_index)

2. Sample Code to assert match/mismatch:

Assert Match/Mismatch

    # Using ImageComparisonUtil
    # Load images to be compared
    expected_image = ImageComparisonUtil.read_image("expected.png")
    actual_image = ImageComparisonUtil.read_image("actual.png")
    
    # Asserting both images
    match_result = ImageComparisonUtil.check_match(expected_image, actual_image)
    assert match_result

Demo

1. Demo shows how basic image comparison works.

Expected Image

Actual Image

Result

2. Demo shows how colour comparison works.

Expected Image

Actual Image

Result

Support and Contributions

If you find this project useful, please consider giving it a star! ⭐ Your support is greatly appreciated. If you have any ideas for improvements or would like to contribute, we welcome your input and collaboration. Feel free to open an issue or submit a pull request. Thanks for your support!