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Python 3.5 Filtering function.

I need help with two functions.

def post_process_image(self, image):

"""Post process the image to create a full contrast stretch of the image

takes as input:

image: the image obtained from the inverse fourier transform

return an image with full contrast stretch

-----------------------------------------------------

1. Full contrast stretch (fsimage)

2. take negative (255 - fsimage)

"""

return image

def filtering(self):

"""Performs frequency filtering on an input image

returns a filtered image, magnitude of DFT, magnitude of filtered DFT

----------------------------------------------------------

You are allowed to used inbuilt functions to compute fft

There are packages available in numpy as well as in opencv

Steps:

1. Compute the fft of the image

2. shift the fft to center the low frequencies

3. get the mask (write your code in functions provided above) the functions can be called by [url removed, login to view](shape, cutoff, order)

4. filter the image frequency based on the mask (Convolution theorem)

5. compute the inverse shift

6. compute the inverse fourier transform

7. compute the magnitude

8. You will need to do a full contrast stretch on the magnitude and depending on the algorithm you may also need to

take negative of the image to be able to view it (use post_process_image to write this code)

Note: You do not have to do zero padding as discussed in class, the inbuilt functions takes care of that

filtered image, magnitude of DFT, magnitude of filtered DFT: Make sure all images being returned have grey scale full contrast stretch and dtype=uint8

"""

return [[url removed, login to view], [url removed, login to view], [url removed, login to view]]

Skills: C++ Programming, Machine Learning, Matlab and Mathematica, Python, Software Architecture

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Hi. My proposal is to implement (1) the filters masks in freq domain, (2) the function def filtering(self) following the steps in the attached file, and (3) the post_process_image for contrast stretch Relevant Skills More

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