def filter_notchpass(img,
notchCenter,
notchRadius,
order=1,
filterShape=ipcv.IPCV_IDEAL):
"""
:purpose:
generates a notch reject filter
:inputs:
img [np.ndarray]
'--> img to generate filter for
notchCenter [tuple]
'--> notch offset
notchRadius
'--> radius of the notch
filterShape
'--> type of filter to apply
:return:
frequency filter [np.ndarray]
"""
notchReject = ipcv.filter_notchreject(img, notchCenter, notchRadius, order,
filterShape)
notchPass = 1 - notchReject
return notchPass
def filter_notchpass(im,
notchCenter,
notchRadius,
order=1,
filterShape=ipcv.IPCV_IDEAL):
notchReject = ipcv.filter_notchreject(im, notchCenter, notchRadius, order,
filterShape)
notchPass = 1 - notchReject
return notchPass
def filter_notchpass(im, notchCenter, notchRadius, order=1, filterShape=ipcv.IPCV_IDEAL):
'''
title::
filter_notchpass
description::
This method creates a notchpass filter to be applied to the
centered fourier transform of the input image.
It calls the notchreject method in ipcv and subtracts the resulting
filter from 1, thereby only letting through the notch frequencies.
attributes::
im
Input image of tpye numpy nd array, used to get the dimensions for
the frequency filter.
notchCenter
List of tuples with coordinates (u,v) corresponding to frequencies
in the fourier transform to be rejected.
notchRadius
The size of the notch to be created at each (u,v) coordinate.
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
specified notch sizes and locations. Binary mask.
BUTTERWORTH
Gaussian-like shaped filter that can be tuned based on the order
parameter.
GAUSSIAN
Gaussian-shaped filter.
returns::
notchpass filter - numpy array with the same dimensions as the input image.
author::
Victoria Scholl
'''
notchPassFilter = 1 - ipcv.filter_notchreject(im, notchCenter, notchRadius, order, filterShape)
return notchPassFilter.astype(numpy.float64)
def filter_notchpass(im, notchCenter, notchRadius, order=1, filterShape=ipcv.IPCV_IDEAL): """ Title: filter_notchpass Description: Creates a notch filter for given image and parameters Attributes: im - 2D ndarray signal to provide shape of filter notchCenter - center of the frequency to be allowed notchRadius - width of notch 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(notchCenter) != tuple or notchCenter[0] < 0 or type(notchCenter[0]) != int \ or notchCenter[1] <0 or type(notchCenter[1]) != int: raise ValueError("Given notchCenter must be tuple of positive integers.") if type(notchRadius) != int or notchRadius < 0: raise ValueError("Given notch radius 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_notchreject(im,notchCenter,notchRadius,order,filterShape) return(H)
if __name__ == '__main__':
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_notchreject(im,
(50,50),
32,
1,
filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_notchreject(im,
(50,50),
32,
1,
filterShape=ipcv.IPCV_BUTTERWORTH)
frequencyFilter = ipcv.filter_notchreject(im,
(50,50),
32,
1,
filterShape=ipcv.IPCV_GAUSSIAN)
# Create a 3D plot and image visualization of the frequency domain filter
rows = im.shape[0]
columns = im.shape[1]
import cv2
import ipcv
import numpy
import matplotlib.pyplot
import matplotlib.cm
import mpl_toolkits.mplot3d
filename = '/cis/faculty/cnspci/public_html/courses/common/images/lenna_color.tif'
filename = 'lena_periodic.jpg'
im = cv2.imread(filename)
notchCenter = [(32,0)]
notchRadius = [3]
frequencyFilter = ipcv.filter_notchreject(im,
notchCenter,
notchRadius,
filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_notchreject(im,
notchCenter,
notchRadius,
order=1,
filterShape=ipcv.IPCV_BUTTERWORTH)
#frequencyFilter = ipcv.filter_notchreject(im,
# notchCenter,
# notchRadius,
# 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)
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_notchreject(im,
#(32,32),
#10,
#filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_notchreject(im,
(32,32),
10,
order=2,
filterShape=ipcv.IPCV_BUTTERWORTH)
#frequencyFilter = ipcv.filter_notchreject(im,
#(32,32),
#10,
#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))