A simple tool to find dynamic range, signal-to-noise ratio and more for your photos

I introduce my simple to use tool (downloadable below) to find the following parameters for your photos: dynamic range, signal-to-noise ratio, min pixel value, max pixel value, average pixel value, median pixel value. Dynamic range is calculated using 3 different algorithms. The tool will output all these parameters in a tabular form. The tool is in the form of a Python script that you can run by placing it in the same folder with your photos and executing it via terminal. The script runs on Windows, Mac and Linux. The video shows how to run the script (dynamic_range.py) and what it can do. For trouble-shooting please refer to the links below or write me a note under the video.
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LINKS:
* Download the SCRIPT (dynamic range_region.py) here: drive.google.c...
* How to install Python: • A quick method for mea...
(Watch until 7:05)
* Running a Python script on Mac or Linux: • A quick method for mea...
(Watch until time 4:04)
* Running a Python script on Windows: • A quick method for mea...
(Watch until time 5:58)
* Troubleshooting id script won’t run: • A quick method for mea...
(Watch until 7:24)
* A fast script to measure photo sharpness: • A quick method for mea...
* A script to find the sharpest region in your photo: • Find the sharpest spot...
PR_137

[@albertobelfast]

Thank you for this material. This is super interesting! I look forward to you comparing photos and cameras in the next video to understand more how the output can be used

[@tom_photo]

Hi Alberto! I'm glad to year you consider my script interesting. I hope it will work well for you. Kind regards!

[@IvoPavlik]

Quite useful. Thank you!

[@tom_photo]

Hi Ivo! I'm glad you found my video useful. Kind regards!

[@igor822605]

Don't understand that number. What does it mean dynamic range 196? How to convert it into stops?

[@tom_photo]

Hi! Thank you for asking. My provides a simple solution that simply allows you to compare different photos. The methods I implemented here reflect dynamic range indirectly and you cannot directly get the DR stops out of it. You can get a very rough estimate when you do this: 1) Determine the range of pixel values representing the darkest and brightest areas of the image, 2) Calculate the ratio of the brightest to darkest pixel value, 3) Convert this ratio into stops using the logarithmic scale (usually ln base-2, I think). For accurate DR stops estimation you will probably want to use more sophisticated solutions (that will also require more experimentation than taking a single photo). Kind regards!