Esempio n. 1
0
    def updateImage(self, val):

        """

        Hlavni update metoda.
        Cinny kod pro gaussovske filtrovani, prahovani, binarni uzavreni a otevreni a vraceni nejvetsich nebo oznacenych objektu.

        """

        if (sys.version_info[0] < 3):

            import copy
            self.imgFiltering = copy.copy(self.data)

        else:

            self.imgFiltering = self.data.copy()

        ## Filtrovani

        ## Zjisteni jakou sigmu pouzit
        if(self.firstRun == True and self.inputSigma >= 0):
            sigma = numpy.round(self.inputSigma, 2)
        else:
            sigma = numpy.round(self.ssigma.val, 2)
        sigmaNew = thresholding_functions.calculateSigma(self.voxel, sigma)

        self.imgFiltering = thresholding_functions.gaussFilter(self.imgFiltering, sigmaNew)

        del(sigmaNew)

        ## Prahovani (smin, smax)

        max_threshold = -1
        min_threshold = self.threshold

        if self.interactivity:

            self.smin.val = (numpy.round(self.smin.val, 2))
            self.smin.valtext.set_text('{}'.format(self.smin.val))
            self.smax.val = (numpy.round(self.smax.val, 2))
            self.smax.valtext.set_text('{}'.format(self.smax.val))

            min_threshold = self.smin.val
            max_threshold = self.smax.val

            self.threshold = min_threshold

        if (self.threshold == -1) and self.firstRun:

            min_threshold = thresholding_functions.calculateAutomaticThreshold(self.imgFiltering, self.arrSeed)

        self.imgFiltering = thresholding_functions.thresholding(self.imgFiltering, min_threshold, max_threshold, True, self.interactivity)

        ## Operace binarni otevreni a uzavreni.
        
        ## Nastaveni hodnot slideru.
        if (self.interactivity == True) :

            closeNum = int(numpy.round(self.sclose.val, 0))
            openNum = int(numpy.round(self.sopen.val, 0))
            self.sclose.valtext.set_text('{}'.format(closeNum))
            self.sopen.valtext.set_text('{}'.format(openNum))

        else:

            closeNum = self.ICBinaryClosingIterations
            openNum = self.ICBinaryOpeningIterations

        self.imgFiltering = thresholding_functions.binaryClosingOpening(self.imgFiltering, closeNum, openNum, True)

        ## Zjisteni nejvetsich objektu.
        self.getBiggestObjects()

        ## Vykresleni dat
        if (self.interactivity == True):
            self.drawVisualization()

        ## Nastaveni kontrolnich hodnot
        self.firstRun = False

        garbage.collect()

        self.debugInfo()
Esempio n. 2
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def generate_sample_data(m=1, noise_level=0.005, gauss_sigma=0.1):
    """
    Generate sample vessel system.

    J. Kunes

    | Input:
    |     m - output will be (100*m)^3 numpy array
    |     noise_level - noise power, disable noise with -1
    |     gauss_sigma - gauss filter sigma, disable filter with -1

    | Output:
    |     (100*m)^3 numpy array
    |         voxel size = [1,1,1]
    """
    import thresholding_functions

    data3d = np.zeros((100 * m, 100 * m, 100 * m), dtype=np.int)

    # size 8
    data3d_new = np.ones((100 * m, 100 * m, 100 * m), dtype=np.bool)
    data3d_new[0:30 * m, 20 * m, 20 * m] = 0
    data3d_new[scipy.ndimage.distance_transform_edt(data3d_new) <= 8 * m] = 0
    data3d[data3d_new == 0] = 1
    # size 7
    data3d_new = np.ones((100 * m, 100 * m, 100 * m), dtype=np.bool)
    data3d_new[31 * m:70 * m, 20 * m, 20 * m] = 0
    data3d_new[scipy.ndimage.distance_transform_edt(data3d_new) <= 7 * m] = 0
    data3d[data3d_new == 0] = 1
    # size 6
    data3d_new = np.ones((100 * m, 100 * m, 100 * m), dtype=np.bool)
    data3d_new[70 * m, 20 * m:50 * m, 20 * m] = 0
    data3d_new[31 * m, 20 * m, 20 * m:70 * m] = 0
    data3d_new[scipy.ndimage.distance_transform_edt(data3d_new) <= 6 * m] = 0
    data3d[data3d_new == 0] = 1
    # size 5
    data3d_new = np.ones((100 * m, 100 * m, 100 * m), dtype=np.bool)
    data3d_new[70 * m:95 * m, 20 * m, 20 * m] = 0
    data3d_new[31 * m:60 * m, 20 * m, 70 * m] = 0
    data3d_new[70 * m:90 * m, 50 * m, 20 * m] = 0
    data3d_new[70 * m, 50 * m, 20 * m:50 * m] = 0
    data3d_new[31 * m, 20 * m: 45 * m, 20 * m] = 0
    data3d_new[scipy.ndimage.distance_transform_edt(data3d_new) <= 5*m] = 0
    data3d[data3d_new == 0] = 1
    # size 4
    data3d_new = np.ones((100*m, 100*m, 100*m), dtype=np.bool)
    data3d_new[31*m, 20*m:50*m, 70*m] = 0
    data3d_new[scipy.ndimage.distance_transform_edt(data3d_new) <= 4*m] = 0
    data3d[data3d_new == 0] = 1
    # size 3
    data3d_new = np.ones((100*m, 100*m, 100*m), dtype=np.bool)
    data3d_new[31*m:50*m, 50*m, 70*m] = 0
    data3d_new[31*m:50*m, 45*m, 20*m] = 0
    data3d_new[70*m, 50*m:70*m, 50*m] = 0
    data3d_new[70*m:80*m, 50*m, 50*m] = 0
    data3d_new[scipy.ndimage.distance_transform_edt(data3d_new) <= 3*m] = 0
    data3d[data3d_new == 0] = 1

    data3d = data3d*3030   # 3030+5920 = vessel value
    data3d += 5920         # 5920 = background value

    if gauss_sigma > 0:
        sigma = np.round(gauss_sigma, 2)
        sigmaNew = thresholding_functions.calculateSigma([1, 1, 1], sigma)
        data3d = thresholding_functions.gaussFilter(data3d, sigmaNew)

    if noise_level > 0:
        noise = np.random.normal(1, noise_level, (100*m, 100*m, 100*m))
        data3d = data3d*noise

    return data3d