Assignment #2
Due: Thu 11/02/17 11:59 PM
- DFT: (8 Pts.) Write code for computing forward fourier transform, inverse fourier transform, discrete cosine transfrom and magnitude of the fourier transform. The input to your program is a 2D matrix of size 15X15.
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Starter code available in directory DFT/
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DFT/DFT.py: One is required to edit the functions "forward_transform", "inverse_transform", "discrete_cosine_tranform" and "magnitude", you are welcome to add more function.
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For this part of the assignment, please implement your own code for all computations, do not use inbuilt functions like "fft" or "dft" from numpy, opencv or other libraries
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Describe your method and findings in the report.md file
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This part of the assignment can be run using dip_hw2_dft.py (there is no need to edit this file)
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Usage:
./dip_hw1_dft python dip_hw1_dft.py -
Please make sure your code runs when you run the above command from prompt/terminal
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Any output images or files must be saved to "output/" folder (dip_hw1_dft.py automatically does this)
- Frequency Filtering: (15 Pts.) Write Code to perfrom image filtering in the frequency domain by modifying the DFT of images using different Masks. Filter images using six different filters ideal low pass (ideal_l), ideal high pass (ideal_h), butterworth low pass (butterworth_l), butterworth high pass (butterworth_h), gaussian low pass (gaussian_l) and gaussian high pass filter (gaussian_h). The input to your program is an image, name of the mask, cuttoff frequency and order(only for butterworth filter).
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Starter code available in directory DFT/
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DFT/Filtering.py:
- _init_(): Will intialize the required variable for filtering (image, mask function, cutoff, order). There is no need to edit this function
- get_mask_freq_pass_filter(): There are six function definitions one for each of the of the filter. write your code to generate the masks for each filter here.
- filtering(): Write your code to perform image filtering here. The steps can be used as a guideline for filtering. All the variable have already been intialized and can be used as self.image, self.cutoff, etc. The varaible self.filter is a handle to each of the six fitler functions. You can call it using self.filter(shape, cutoff, ...)
- The function returns three images, filtered image, magnitude of the DFT and magnitude of filtered dft
- To be able to display magnitude of the DFT and magnitude of filtered dft, one would have to perform a logrithmic compression and convert the value to uint8
- post_process_image(): After fitlering and computing the inverse DFT, One would typically have to scale the image pixels to view it. You can write code to do a full contrast stretch here and in some cases you would also have to take a negative of the image.
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For this part of the assignment, You can use inbuilt functions to compute the fourier transform
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For example, you are welcome to use fft and dft libraries that are available in numpy and opencv
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Describe your method and findings in the report.md file
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This part of the assignment can be run using dip_hw2_filter.py (there is no need to edit this file)
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Usage:
./dip_hw1_filter -i image -m ideal_l -c 50 python dip_hw1_filter.py -i image -m ideal_l -c 50- Please make sure your code runs when you run the above command from prompt/terminal
- Any output images or files must be saved to "output/" folder (dip_hw1_filter.py automatically does this)
- (2 Pts.) Describe your method and report you findings in report.md for each problem of the assignemnt.
Two images are provided for testing: Lenna.png and Lenna0.jpg
PS. Files not to be changed: requirements.txt and .circleci directory
If you do not like the structure, you are welcome to change the over all code, under two stipulations:
- the first part has to run using command
python dip_hw2_dft.py
and the second part using
python dip_hw2_filtering.py -i image-name -m ideal_l -c 50
- Any output file or image should be written to output/ folder
The TA will only be able to see your results if these two conditions are met
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DFT - 8 Pts.
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Filtering - 15 Pts.
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Report - 2 Pts
Total - 25 Pts.