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Thanks for the suggestions.I think it is better to explain the approach i took for any future reference.

1. I've used OpenCV with Numpy
2. Apply a circular convolution for my DFT image after low frequency components are moved to the image center
3. A dictionary is defined with key : angle and values : intensity values of pixels with same angle
4. While iterating through each row(i) and column(j) , calculated pixel(i,j) angle with respect to the image center (i,j) using np.arctan2(i* - i , j* - j) and updated the dictionary

Note : Even though this method is working, you'll notice that angles such as 0, 45, 90, 180 has way more pixel points than others.This is due to square shape of pixels.

Thanks for the suggestions.I think it is better to explain the approach i took for any future reference.

1. I've used OpenCV with Numpy
2. Apply a circular convolution for my DFT image after low frequency components are moved to the image center
3. A dictionary is defined with key : angle and values : intensity values of pixels with same angle
4. While iterating through each row(i) and column(j) , calculated pixel(i,j) angle with respect to the image center (i,j) using np.arctan2(i* - i , j* - j) and updated the dictionary

Note : Even though this method is working, you'll notice that angles such as 0, 45, 90, 180 has way more pixel points than others.This is due to square shape of pixels.

Thanks for the suggestions.I think it is better to explain the approach i took for any future reference.

1. I've used OpenCV with Numpy
2. Apply a circular convolution for my DFT image after low frequency components are moved to the image center
3. A dictionary is defined with key : angle and values : intensity values of pixels with same angle
4. While iterating through each row(i) and column(j) , calculated pixel(i,j) angle with respect to the image center (i,j) (I,J) using np.arctan2(i* np.arctan2(I - i , j* J - j) and updated the dictionary

Note : Even though this method is working, you'll notice that angles such as 0, 45, 90, 180 has way more pixel points than others.This is due to square shape of pixels.

Thanks for the suggestions.I think it is better to explain the approach i took for any future reference.

1. I've used OpenCV with Numpy
2. Apply a circular convolution for my DFT image after low frequency components are moved to the image center
3. A dictionary is defined with key : angle and values : intensity values of pixels with same angle
4. While iterating through each row(i) and column(j) , calculated pixel(i,j) angle with respect to the image center (I,J) using np.arctan2(I - i , J - j) and updated the dictionary

Note : Even though this method is working, you'll notice that angles such as 0, 45, 90, 180 has way more pixel points than others.This is due to square shape of pixels.