Python filter_bandpass 예제들

예제 #1

파일: filter_bandpass.py 프로젝트: ktp-forked-repos/digital-image-processing

    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)

예제 #2

파일: filter_bandpass.py 프로젝트: mollymh/frequency_filter

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)

예제 #3

파일: filter_bandpass.py 프로젝트: trevorbrashich/Digital-image-processing

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)

예제 #4

    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 + ')'

예제 #5

파일: filter_bandpass.py 프로젝트: victoriascholl/ipcv

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)

예제 #6

    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))

예제 #7

파일: frequency_filter.py 프로젝트: JRJurman/digital-image-processing

    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 + ')'