def filter_highpass(im, cutoffFrequency, order=1, filterShape=ipcv.IPCV_IDEAL): """ Title: filter_highpass Description: Creates a highpass filter for given image and parameters Attributes: im - 2D ndarray signal to provide shape of filter cutoffFrequency - allow frequencies above this threshold 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(cutoffFrequency) != int or cutoffFrequency < 0: raise ValueError("Given cutoff frequency 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_lowpass(im,cutoffFrequency,order,filterShape) return(H)
def filter_highpass(img,
cutoffFrequency,
order=1,
filterShape=ipcv.IPCV_IDEAL):
"""
:purpose:
generates a highpass filter
:inputs:
img [np.ndarray]
'--> img to generate filter for
cutoffFrequency [np.ndarray]
'--> notch offset
order
'--> order for butterworth filter
filterShape
'--> type of filter to apply
:return:
frequency filter [np.ndarray]
"""
try:
lowPass = ipcv.filter_lowpass(img, cutoffFrequency, order, filterShape)
highPass = 1 - lowPass
return highPass
except Exception as e:
ipcv.debug(e)
def filter_highpass(im, cutoffFrequency, order=1, filterShape=ipcv.IPCV_IDEAL):
'''
title::
filter_highpass
description::
This method creates a highpass filter to be applied to the
centered fourier transform of the input image.
It calls the lowpass method in ipcv and subtracts the resulting
filter from 1.
attributes::
im
Input image of tpye numpy nd array, used to get the dimensions for
the frequency filter.
cutoffFrequency
Frequency of type integer above which to attentuate higher frequencies
(frequencies lower than the cutoff value will be preserved).
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 greater than
the cutoffFrequency to pass. Binary mask.
BUTTERWORTH
Gaussian-like shaped filter that can be tuned based on the order
parameter.
GAUSSIAN
Gaussian-shaped filter.
returns::
highpass filter - numpy array with the same dimensions as the input image
author::
Victoria Scholl
'''
highPass = 1 - ipcv.filter_lowpass(im, cutoffFrequency, order, filterShape)
return highPass.astype(numpy.float64)
if __name__ == '__main__':
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'
im = cv2.imread(filename)
frequencyFilter = ipcv.filter_lowpass(im,
16,
filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_lowpass(im,
16,
order=1,
filterShape=ipcv.IPCV_BUTTERWORTH)
frequencyFilter = ipcv.filter_lowpass(im,
16,
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)
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_lowpass(im, 16, filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_lowpass(im,
16,
order=1,
filterShape=ipcv.IPCV_BUTTERWORTH)
frequencyFilter = ipcv.filter_lowpass(im,
16,
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)
if __name__ == '__main__':
import cv2
import ipcv
import numpy
import os.path
import time
home = os.path.expanduser('~')
filename = home + os.path.sep + 'src/python/examples/data/lenna.tif'
filename = home + os.path.sep + 'src/python/examples/data/giza.jpg'
im = cv2.imread(filename)
frequencyFilter = ipcv.filter_lowpass(im,
16,
filterShape=ipcv.IPCV_GAUSSIAN)
startTime = time.clock()
offset = 0
filteredImage = ipcv.frequency_filter(im, frequencyFilter, delta=offset)
filteredImage = numpy.abs(filteredImage)
filteredImage = filteredImage.astype(dtype=numpy.uint8)
elapsedTime = time.clock() - startTime
print('Elapsed time (frequency_filter)= {0} [s]'.format(elapsedTime))
cv2.namedWindow(filename, cv2.WINDOW_AUTOSIZE)
cv2.imshow(filename, im)
cv2.imshow(filename, ipcv.histogram_enhancement(im))
filterName = 'Filtered (' + filename + ')'
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_lowpass(im,
# 16,
# filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_lowpass(im,
16,
order=4,
filterShape=ipcv.IPCV_BUTTERWORTH)
# frequencyFilter = ipcv.filter_lowpass(im,
# 16,
# 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')
def filter_highpass(im, cutoffFrequency, order=1, filterShape=ipcv.IPCV_IDEAL): lowPass = ipcv.filter_lowpass(im,cutoffFrequency,order,filterShape) highPass = 1 - lowPass return highPass
if __name__ == '__main__':
import cv2
import ipcv
import numpy
import os.path
import time
home = os.path.expanduser('~')
filename = home + os.path.sep + 'src/python/examples/data/lenna.tif'
filename = home + os.path.sep + 'src/python/examples/data/giza.jpg'
im = cv2.imread(filename, cv2.IMREAD_UNCHANGED)
frequencyFilter = ipcv.filter_lowpass(im, 16, filterShape=ipcv.IPCV_IDEAL)
startTime = time.clock()
offset = 0
filteredImage = ipcv.frequency_filter(im, frequencyFilter, delta=offset)
filteredImage = numpy.abs(filteredImage)
filteredImage = filteredImage.astype(dtype=numpy.uint8)
elapsedTime = time.clock() - startTime
print('Elapsed time (frequency_filter)= {0} [s]'.format(elapsedTime))
cv2.namedWindow(filename, cv2.WINDOW_AUTOSIZE)
cv2.imshow(filename, im)
# cv2.imshow(filename, ipcv.histogram_enhancement(im))
filterName = 'Filtered (' + filename + ')'
cv2.namedWindow(filterName, cv2.WINDOW_AUTOSIZE)
return filteredImage.astype(numpy.complex128)
if __name__ == '__main__':
import cv2
import ipcv
import numpy
import time
#filename = '/cis/faculty/cnspci/public_html/courses/common/images/lenna_color.tif'
#filename = 'lena_periodic.jpg'
filename = '/cis/faculty/cnspci/public_html/courses/common/images/giza.jpg'
im = cv2.imread(filename)
frequencyFilter = ipcv.filter_lowpass(im,
32,2,
filterShape=ipcv.IPCV_GAUSSIAN)
#notchCenter = [(32,0)]
#notchRadius = [3]
#frequencyFilter = ipcv.filter_notchreject(im,
# notchCenter,
# notchRadius,
# order=2,
# filterShape=ipcv.IPCV_GAUSSIAN)
startTime = time.clock()
offset = 0
filteredImage = ipcv.frequency_filter(im, frequencyFilter, delta=offset)
filteredImage = numpy.abs(filteredImage)