In this project, pixel-wise comparison between two input images is performed and differences are displayed as numerical parameters. This project is basically divided into two parts:
- Pre-processing - In which steps like greyscale conversion, maintaining uniform dimensions are perfomed.
- Compare function - Here the actual comparison between two images takes place which uses skimage's Structual Similarity Index, Mean square Error and Histogramical difference.
This project is created with the help of:
- Numpy
- Scikit Image
- OpenCV
To use it, you require the following:
1. Python3
2. Pip
Once you got the requisites on your machine, for a UNIX based system executing the following command to install the required libraries:
make init
source .venv/bin/activate
OR executing the following command will install all the required libraries for you:
$ pip install -r requirements.txt
To run the project, you can directly run the compare_img.py file and provide the directory path for both the images.
$ python3 compare_img.py DIR_PATH_IMG1 DIR_PATH_IMG2
Example:
$ python3 compare_img.py ./images/img1.jpg ./images/img2.jpg
Note: The image path can be a raw url as well.
Example:
$ python3 compare_img.py https://raw.githubusercontent.com/SiddhanthNB/Automation-scripts/main/compare_img/images/img1.jpg https://raw.githubusercontent.com/SiddhanthNB/Automation-scripts/main/compare_img/images/img2.jpg
After running the script, you will find the output as
SSI value is (some value)
MSE value is (some value)
Histogram difference is (some value)
- Here SSI value ranges from
-1to1, where1implies both images being completely same(which happens when same image is loaded twice). - MSE value is the mean square difference between each pixel loaction in both the both the images, typically for same image loaded twice the value should be
0. - Histogram difference shows the intensity-level difference between the two images, which tends be very small, nevertheless shows the difference.