IMAGE SOUND MAPPER

Credits

Heavily inspired by the algorithm used in 2FA made by Samip Regmi, Raju Bhetwal, and Nayan Nembang

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IMAGE TO SOUND

Following is the algorithm I used to convert image to sound:

1. RESIZE

• Resizes the source image to specified image size

2. GRAYSCALE

• Converts the resized image to grayscale whose pixel value can be mapped from 0-255

3. PIXEL TO FREQUENCY

• Each pixel from 0-255 is mapped to frequency from 200-1000 using linear mapping

4. AUDIO FILE IS SAVED

• Those mapped frequencies are saved in each 0.01 second of the audio file, holding 441 samples each
• with total frequency data being size of resized image
• default: 50 x 50 = 2500

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SOUND TO IMAGE

1. AUDIO CHUNK DETECTION

• As duration per frequency is known, we extract all the chunks of data.
• Each chunk holds 441 samples, in total 2500 chunks.

2. CHUNKS TO FREQUENCY

• We then use librosa to find the frequency of each chunk

3. FREQUENCY TO PIXEL

• Using linear mapping we convert the frequencies back to pixels

4. PIXEL TO IMAGE

• Finally, all the pixel data is saved back into the image

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2025-09-22.23-27-28.mp4

CLONING

• clone the repo using git clone <remote-url> u can use ssh or https
• after cloning install required dependencies using pip install -r requirements.txt
• add and correct the required paths in main.py
• run the program using python3 main.py