matchFilename = os.path.join(path, 'crowd.jpg')
	matchFilename = os.path.join(path, 'giza.jpg')
	matchFilename = os.path.join(path, 'lenna.tif')

	im = cv2.imread(filename, cv2.IMREAD_UNCHANGED)
	print('Filename = {0}'.format(filename))
	print('Data type = {0}'.format(type(im)))
	print('Image shape = {0}'.format(im.shape))
	print('Image size = {0}'.format(im.size))

	cv2.namedWindow(filename, cv2.WINDOW_AUTOSIZE)
	cv2.imshow(filename, im)

	print('Linear 2% ...')
	startTime = time.time()
	enhancedImage = ipcv.histogram_enhancement(im, etype='linear2')
	print('Elapsed time = {0} [s]'.format(time.time() - startTime))
	cv2.namedWindow(filename + ' (Linear 2%)', cv2.WINDOW_AUTOSIZE)
	cv2.imshow(filename + ' (Linear 2%)', enhancedImage)

	print('Linear 1% ...')
	startTime = time.time()
	enhancedImage = ipcv.histogram_enhancement(im, etype='linear1')
	print('Elapsed time = {0} [s]'.format(time.time() - startTime))
	cv2.namedWindow(filename + ' (Linear 1%)', cv2.WINDOW_AUTOSIZE)
	cv2.imshow(filename + ' (Linear 1%)', enhancedImage)

	#print('Equalized ...')
	#startTime = time.time()
	#enhancedImage = ipcv.histogram_enhancement(im, etype='equalize')
	#print('Elapsed time = {0} [s]'.format(time.time() - startTime))
Esempio n. 2
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    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 + ')'
    cv2.namedWindow(filterName, cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filterName, filteredImage)
    cv2.imshow(filterName, ipcv.histogram_enhancement(filteredImage))

    ipcv.flush()
Esempio n. 3
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    #matchFilename = home + os.path.sep + 'src/python/examples/data/photo1.png'
    #matchFilename = home + os.path.sep + 'src/python/examples/data/gecko.jpg'
    #matchFilename = home + os.path.sep + 'src/python/examples/data/000044290016.jpg'
    matchFilename = home + os.path.sep + 'src/python/examples/data/Some Wack Shit.tif'

    im = cv2.imread(filename, cv2.IMREAD_UNCHANGED)
    print('Filename = {0}'.format(filename))
    print('Data type = {0}'.format(type(im)))
    print('Image shape = {0}'.format(im.shape))
    print('Image size = {0}'.format(im.size))

    cv2.namedWindow(filename, cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filename, im)

    tgtIm = cv2.imread(matchFilename, cv2.IMREAD_UNCHANGED)
    tgtPDF = numpy.ones(256) / 256
    print('Matched (Distribution) ...')
    startTime = time.time()
    enhancedImage = ipcv.histogram_enhancement(im,
                                               etype='equalize',
                                               target=tgtPDF,
                                               userInputs=False,
                                               showHistogram=True)
    print('Elapsed time = {0} [s]'.format(time.time() - startTime))
    cv2.namedWindow(filename + ' (Matched - Distribution)',
                    cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filename + ' (Matched - Distribution)', enhancedImage)
    cv2.imwrite('testVillage.tif', enhancedImage)

    action = ipcv.flush()
Esempio n. 4
0
    matchFilename = home + os.path.sep + 'src/python/examples/data/redhat.ppm'
    matchFilename = home + os.path.sep + 'src/python/examples/data/giza.jpg'
    matchFilename = home + os.path.sep + 'src/python/examples/data/crowd.jpg'

    im = cv2.imread(filename, cv2.IMREAD_UNCHANGED)
    print('Filename = {0}'.format(filename))
    print('Data type = {0}'.format(type(im)))
    print('Image shape = {0}'.format(im.shape))
    print('Image size = {0}'.format(im.size))

    cv2.namedWindow(filename, cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filename, im)

    print('Linear 2% ...')
    startTime = time.time()
    enhancedImage = ipcv.histogram_enhancement(im, etype='linear2')
    print('Elapsed time = {0} [s]'.format(time.time() - startTime))
    cv2.namedWindow(filename + ' (Linear 2%)', cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filename + ' (Linear 2%)', enhancedImage)

    print('Linear 1% ...')
    startTime = time.time()
    enhancedImage = ipcv.histogram_enhancement(im, etype='linear1')
    print('Elapsed time = {0} [s]'.format(time.time() - startTime))
    cv2.namedWindow(filename + ' (Linear 1%)', cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filename + ' (Linear 1%)', enhancedImage)

    print('Equalized ...')
    startTime = time.time()
    enhancedImage = ipcv.histogram_enhancement(im, etype='equalize')
    print('Elapsed time = {0} [s]'.format(time.time() - startTime))
    #matchFilename = home + os.path.sep + 'src/python/examples/data/lenna.tif'
    #matchFilename = home + os.path.sep + 'src/python/examples/data/redhat.ppm'
    #matchFilename = home + os.path.sep + 'src/python/examples/data/crowd.jpg'

    im = cv2.imread(filename, cv2.IMREAD_UNCHANGED)
    print('Filename = {0}'.format(filename))
    print('Data type = {0}'.format(type(im)))
    print('Image shape = {0}'.format(im.shape))
    print('Image size = {0}'.format(im.size))

    cv2.namedWindow(filename, cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filename, im)

    print('Linear 2% ...')
    startTime = time.time()
    enhancedImage = ipcv.histogram_enhancement(im, etype='linear2')
    print('Elapsed time = {0} [s]'.format(time.time() - startTime))
    cv2.namedWindow(filename + ' (Linear 2%)', cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filename + ' (Linear 2%)', enhancedImage)

    print('Linear 1% ...')
    startTime = time.time()
    enhancedImage = ipcv.histogram_enhancement(im, etype='linear1')
    print('Elapsed time = {0} [s]'.format(time.time() - startTime))
    cv2.namedWindow(filename + ' (Linear 1%)', cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filename + ' (Linear 1%)', enhancedImage)

    print('Equalized ...')
    startTime = time.time()
    enhancedImage = ipcv.histogram_enhancement(im, etype='equalize')
    print('Elapsed time = {0} [s]'.format(time.time() - startTime))
    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 + ')'
    cv2.namedWindow(filterName, cv2.WINDOW_AUTOSIZE)
    cv2.imshow(filterName, filteredImage)
    cv2.imshow(filterName, ipcv.histogram_enhancement(filteredImage))

    ipcv.flush()