improutils.filtration package

Submodules

improutils.filtration.filtration module

improutils.filtration.filtration.apply_fft(image)

Apply FFT on the image.

Parameters:

image (2D array) – Image to perform FFT on.

Returns:

• mag_spec (2D array) – Normalized magnitude spectrum.

• fftcls_shift (2D array) – Centered product of FFT.

improutils.filtration.filtration.create_filter_mask(size, rows, columns)

Create a filter mask specified by rows and columns.

Specified rows and columns are set to 255, others 0.

Parameters:

• size (tuple of int) – Tuple of two integers (height, width) defining the dimensions of the output mask.

• rows (array-like of int) – Indices of rows to set to 255.

• columns (array-like of int) – Indices of columns to set to 255.

Returns:

filter_mask – 2D array mask containing 255 and 0 values.

Return type:

2D array

Raises:

TypeError – If size is not a tuple with 2 int values.

improutils.filtration.filtration.filter_mag_spec(mag_spec, filter_mask)

Filter input spectrum using filter_mask image.

Parameters:

• mag_spec (2D ndarray) – Image with magnitude spectrum.

• filter_mask (2D array) – Filter binary mask image containing values to keep (255) and filter out (0).

Returns:

result – Vizualization of spectrum after filtering.

Return type:

2D ndarray

improutils.filtration.filtration.filtration_box(img, filter_size)

Filter image noise using box blur algorithm.

Parameters:

• img (numpy.ndarray) – Input image.

• filter_size (int) – Size of box blur filter.

Return type:

Output image.

improutils.filtration.filtration.filtration_gauss(img, filter_size, sigma_x)

Filter image noise using Gaussian blur algorithm.

Parameters:

• img (numpy.ndarray) – Input image.

• filter_size (int) – Size of Gaussian filter.

• sigma_x (float) – Gaussian kernel standard deviation in the X direction.

Return type:

Output image.

improutils.filtration.filtration.filtration_median(img, filter_size)

Filter image noise using median blur algorithm.

Parameters:

• img (numpy.ndarray) – Input image.

• filter_size (int) – Odd number - size of median filter.

Return type:

Output image.

improutils.filtration.filtration.inverse_fft(fft_shift, filter_mask=None)

Apply inverse FFT.

Parameters:

• fft_shift (2D array) – Shifted computed FFT

• filter_mask (2D array) – 2D array mask containing 255 and 0 values.

Returns:

img_back – Image made by inverse FFT.

Return type:

2D array

Module contents

improutils.filtration.apply_fft(image)

Apply FFT on the image.

Parameters:

image (2D array) – Image to perform FFT on.

Returns:

• mag_spec (2D array) – Normalized magnitude spectrum.

• fftcls_shift (2D array) – Centered product of FFT.

improutils.filtration.create_filter_mask(size, rows, columns)

Create a filter mask specified by rows and columns.

Specified rows and columns are set to 255, others 0.

Parameters:

• size (tuple of int) – Tuple of two integers (height, width) defining the dimensions of the output mask.

• rows (array-like of int) – Indices of rows to set to 255.

• columns (array-like of int) – Indices of columns to set to 255.

Returns:

filter_mask – 2D array mask containing 255 and 0 values.

Return type:

2D array

Raises:

TypeError – If size is not a tuple with 2 int values.

improutils.filtration.filter_mag_spec(mag_spec, filter_mask)

Filter input spectrum using filter_mask image.

Parameters:

• mag_spec (2D ndarray) – Image with magnitude spectrum.

• filter_mask (2D array) – Filter binary mask image containing values to keep (255) and filter out (0).

Returns:

result – Vizualization of spectrum after filtering.

Return type:

2D ndarray

improutils.filtration.filtration_box(img, filter_size)

Filter image noise using box blur algorithm.

Parameters:

• img (numpy.ndarray) – Input image.

• filter_size (int) – Size of box blur filter.

Return type:

Output image.

improutils.filtration.filtration_gauss(img, filter_size, sigma_x)

Filter image noise using Gaussian blur algorithm.

Parameters:

• img (numpy.ndarray) – Input image.

• filter_size (int) – Size of Gaussian filter.

• sigma_x (float) – Gaussian kernel standard deviation in the X direction.

Return type:

Output image.

improutils.filtration.filtration_median(img, filter_size)

Filter image noise using median blur algorithm.

Parameters:

• img (numpy.ndarray) – Input image.

• filter_size (int) – Odd number - size of median filter.

Return type:

Output image.

improutils.filtration.inverse_fft(fft_shift, filter_mask=None)

Apply inverse FFT.

Parameters:

• fft_shift (2D array) – Shifted computed FFT

• filter_mask (2D array) – 2D array mask containing 255 and 0 values.

Returns:

img_back – Image made by inverse FFT.

Return type:

2D array