def filter_bandpass(img,
radialCenter,
bandwidth,
order=1,
filterShape=ipcv.IPCV_IDEAL):
"""
:purpose:
generates a bandpass filter
:inputs:
img [np.ndarray]
'--> img to generate filter for
radialCenter [np.ndarray]
'--> size of bandpass
bandwidth [np.ndarray]
'--> size of bandpass
order [int]
'--> order for butterworth filter
filterShape [int]
'--> type of filter to apply
:return:
frequency filter [np.ndarray]
"""
bandReject = ipcv.filter_bandreject(img, radialCenter, bandwidth, order,
filterShape)
bandPass = 1 - bandReject
return bandPass
def filter_bandpass(im,
radialCenter,
bandwidth,
order=1,
filterShape=ipcv.IPCV_IDEAL):
bandPassFilter = 1 - ipcv.filter_bandreject(im, radialCenter, bandwidth,
order, filterShape)
return bandPassFilter.astype(np.float64)
def filter_bandpass(im, radialCenter, bandwidth, order=1, filterShape=ipcv.IPCV_IDEAL):
'''
title::
filter_bandpass
description::
This method creates a bandpass filter to be applied to the
centered fourier transform of the input image.
It calls the bandreject method in ipcv and subtracts the resulting
filter from 1, thereby only letting a band of frequencies from the
input image pass through.
attributes::
im
Input image of tpye numpy nd array, used to get the dimensions for
the frequency filter.
radialCenter
Distance (integer) from the center of the fourier transform at which
the frequency range begins.
bandwidth
Thickness of the band of frequencies to be passed, measured
outward from the radialCenter distance.
order
Integer value that influences the shape of the butterworth filter.
The higher the order, the more the butterworth filter resembles an
ideal filter.
filterShape
Options for the shape of the filter, specified in the constants.py
file in the ipcv directory. The different shapes will attenuate
the higher frequencies differently.
IDEAL
Ideal shaped filter STRICTLY allows ALL frequencies within the
range to be passed. Binary mask.
BUTTERWORTH
Gaussian-like shaped filter that can be tuned based on the order
parameter.
GAUSSIAN
Gaussian-shaped filter.
returns::
bandpass filter - numpy array with the same dimensions as the input image.
It looks like a donut! Allows frequencies to pass within the specified
range and attenuates outsiders.
author::
Victoria Scholl
'''
bandPassFilter = 1 - ipcv.filter_bandreject(im, radialCenter, bandwidth, order, filterShape)
return bandPassFilter.astype(numpy.float64)
def filter_bandpass(im,
radialCenter,
bandwidth,
order=1,
filterShape=ipcv.IPCV_IDEAL):
"""
Title:
filter_bandpass
Description:
Creates a bandpass filter for given image and parameters
Attributes:
im - 2D ndarray signal to provide shape of filter
radialCenter - radial center of the band pass annulus
bandwidth - width of band
order - order of the filter (applicable only to Butterworth)
filterShape - shape of frequency filter:
- 0 = Ideal
- 1 = Butterworth
- 2 = Gaussian
Author:
Molly Hill, [email protected]
"""
#error-checking
if type(im) != np.ndarray:
raise TypeError("Source image must be ndarray.")
if type(radialCenter) != int or radialCenter < 0:
raise ValueError("Given radialCenter must be positive integer.")
if type(bandwidth) != int or bandwidth < 0:
raise ValueError("Given bandwidth must be positive integer.")
if type(order) != int or order < 0:
raise ValueError("Given order must be positive integer.")
if type(filterShape) != int or filterShape < 0 or filterShape > 2:
raise ValueError("Filter shape option limited to 0, 1, or 2.")
H = 1 - ipcv.filter_bandreject(im, radialCenter, bandwidth, order,
filterShape)
return (H)
import matplotlib.pyplot
import matplotlib.cm
import mpl_toolkits.mplot3d
import os.path
home = os.path.expanduser('~')
filename = home + os.path.sep + 'src/python/examples/data/lenna.tif'
im = cv2.imread(filename)
# frequencyFilter = ipcv.filter_bandreject(im,
# 32,
# 15,
# filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_bandreject(im,
100,
15,
1,
filterShape=ipcv.IPCV_BUTTERWORTH)
# frequencyFilter = ipcv.filter_bandreject(im,
# 32,
# 15,
# filterShape=ipcv.IPCV_GAUSSIAN)
# Create a 3D plot and image visualization of the frequency domain filter
rows = im.shape[0]
columns = im.shape[1]
u = numpy.arange(-columns/2, columns/2, 1)
v = numpy.arange(-rows/2, rows/2, 1)
u, v = numpy.meshgrid(u, v)
figure = matplotlib.pyplot.figure('Frequency Domain Filter', (14, 6))
home = os.path.expanduser('~')
filename = home + os.path.sep + 'src/python/examples/data/lenna.tif'
im = cv2.imread(filename)
#frequencyFilter = ipcv.filter_bandreject(im,
#32,
#15,
#filterShape=ipcv.IPCV_IDEAL)
#frequencyFilter = ipcv.filter_bandreject(im,
#32,
#15,
#order=1,
#filterShape=ipcv.IPCV_BUTTERWORTH)
frequencyFilter = ipcv.filter_bandreject(im,
32,
15,
filterShape=ipcv.IPCV_GAUSSIAN)
# Create a 3D plot and image visualization of the frequency domain filter
rows = im.shape[0]
columns = im.shape[1]
u = numpy.arange(-columns / 2, columns / 2, 1)
v = numpy.arange(-rows / 2, rows / 2, 1)
u, v = numpy.meshgrid(u, v)
figure = matplotlib.pyplot.figure('Frequency Domain Filter', (14, 6))
p = figure.add_subplot(1, 2, 1, projection='3d')
p.set_xlabel('u')
p.set_xlim3d(-columns / 2, columns / 2)
p.set_ylabel('v')
p.set_ylim3d(-rows / 2, rows / 2)
import cv2
import ipcv
import numpy
import matplotlib.pyplot
import matplotlib.cm
import mpl_toolkits.mplot3d
import os.path
home = os.path.expanduser('~')
filename = home + os.path.sep + 'src/python/examples/data/lenna.tif'
im = cv2.imread(filename)
frequencyFilter = ipcv.filter_bandreject(im,
32,
15,
order=2,
filterShape=ipcv.IPCV_BUTTERWORTH)
frequencyFilter = ipcv.filter_bandreject(im,
32,
15,
filterShape=ipcv.IPCV_GAUSSIAN)
frequencyFilter = ipcv.filter_bandreject(im,
32,
15,
filterShape=ipcv.IPCV_IDEAL)
# Create a 3D plot and image visualization of the frequency domain filter
rows = im.shape[0]
columns = im.shape[1]
u = numpy.arange(-columns/2, columns/2, 1)