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_bandpass(im,
32,
15,
order=2,
filterShape=ipcv.IPCV_BUTTERWORTH)
frequencyFilter = ipcv.filter_bandpass(im,
32,
15,
filterShape=ipcv.IPCV_GAUSSIAN)
frequencyFilter = ipcv.filter_bandpass(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)
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_bandpass(im,
32,
15,
filterShape=ipcv.IPCV_IDEAL)
#frequencyFilter = ipcv.filter_bandpass(im,
#32,
#15,
#order=1,
#filterShape=ipcv.IPCV_BUTTERWORTH)
#frequencyFilter = ipcv.filter_bandpass(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)
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_bandpass(im,
16,
2,
filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_bandpass(im,
32,
1,
order=2,
filterShape=ipcv.IPCV_BUTTERWORTH)
#frequencyFilter = ipcv.filter_bandpass(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)
import cv2
import ipcv
import numpy
import os.path
import time
home = os.path.expanduser('~')
filename = home + os.path.sep + 'src/python/examples/data/giza.jpg'
filename = home + os.path.sep + 'src/python/examples/data/checkerboard.tif'
filename = home + os.path.sep + 'src/python/examples/data/lenna.tif'
im = cv2.imread(filename)
frequencyFilter = ipcv.filter_bandpass(im,
32,
10,
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 + ')'
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_bandpass(im,
16,
2,
filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_bandpass(im,
32,
1,
order=2,
filterShape=ipcv.IPCV_BUTTERWORTH)
#frequencyFilter = ipcv.filter_bandpass(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)
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_bandpass(im,
# 32,
# 15,
# filterShape=ipcv.IPCV_IDEAL)
frequencyFilter = ipcv.filter_bandpass(im,
100,
15,
1,
filterShape=ipcv.IPCV_BUTTERWORTH)
# frequencyFilter = ipcv.filter_bandpass(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))
import cv2
import ipcv
import numpy
import os.path
import time
home = os.path.expanduser('~')
filename = home + os.path.sep + 'src/python/examples/data/giza.jpg'
filename = home + os.path.sep + 'src/python/examples/data/checkerboard.tif'
filename = home + os.path.sep + 'src/python/examples/data/lenna.tif'
im = cv2.imread(filename)
frequencyFilter = ipcv.filter_bandpass(im,
32,
10,
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 + ')'