Skip to content

The FFT is an optimized algorithm for the implementation of the "Discrete Fourier Transformation" (DFT). A signal is sampled over a period of time and divided into its frequency components. These components are single sinusoidal oscillations at distinct frequencies each with their own amplitude and phase. This transformation is illustrated in th…

License

Notifications You must be signed in to change notification settings

sandeepyadav1478/Fast_Fourier_Transformation

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Fast Fourier TransformX

GitHub Logo

Requirements :

            1) Numpy (pip install numpy)
            2) cv2 (pip install opencv-python)
            3) json
            4) tqdm
            5) pygame
            6) keyboard
            7) json
            8) Pillow
            9) opencv-python

Main GUI :

GitHub Logo

Here, Int. means Integral

Loading co-ordinations for ARM design :

GitHub Logo

Help :

  1. [Optional] You can change in 'user editable area' in main.py file

  2. first recoord coordinates with "Draw" button or load image with "Image" button.

  3. Put reference image in file location and if file exists then, It will detect edge of objects in image and return cords.

  4. Or Draw something on board by dragging mouse.

  5. You can Undo with "X" for single point undo or "Z" for redo , "S" is for save and "R" to stop rotation

  6. After input we have to arrange cords in pattern for drawing, Also have to remove repeated cords and minimize large size. So select any "Keep" or "Remove" and type needed percentage, press ">" button.

  7. You can undo cords alterations using "Undo" button. It only works for once.

  8. Lastly press "fftx" button for arms draw.

Example Print :

GitHub Logo

Thanks for checking

About

The FFT is an optimized algorithm for the implementation of the "Discrete Fourier Transformation" (DFT). A signal is sampled over a period of time and divided into its frequency components. These components are single sinusoidal oscillations at distinct frequencies each with their own amplitude and phase. This transformation is illustrated in th…

Topics

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages