Python skimage.filters.threshold_local() Examples

The following are 4 code examples of skimage.filters.threshold_local(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module skimage.filters , or try the search function .
Example #1
Source File: eyes.py    From stytra with GNU General Public License v3.0 6 votes vote down vote up 
def _local_thresholding(im, padding=2, block_size=17, offset=70):
    """Local thresholding

    Parameters
    ----------
    im :
        The camera frame with the eyes
    padding :
        padding of the camera frame (Default value = 2)
    block_size :
        param offset: (Default value = 17)
    offset :
         (Default value = 70)

    Returns
    -------
    type
        thresholded image

    """
    padded = _pad(im, padding, im.min())
    return padded > threshold_local(padded, block_size=block_size, offset=offset)
Example #2
Source File: sct_maths.py    From spinalcordtoolbox with MIT License 5 votes vote down vote up 
def adap(data, block_size, offset):
    from skimage.filters import threshold_local
    mask = data
    for iz in range(data.shape[2]):
        adaptive_thresh = threshold_local(data[:, :, iz], block_size, method='gaussian', offset=offset)
        mask[:, :, iz] = mask[:, :, iz] > adaptive_thresh
    return mask
Example #3
Source File: SPM.py    From pySPM with Apache License 2.0 5 votes vote down vote up 
def get_bin_threshold(self, percent, high=True, adaptive=False, binary=True, img=False):
        """
        Threshold the image into binary values
        
        Parameters
        ----------
        percent : float
            The percentage where the thresholding is made
        high : bool
            If high a value of 1 is returned for values > percent
        adaptive : bool
            If True, performs an adaptive thresholding (see skimage.filters.threshold_adaptive)
        binary : bool
            If True return bool data (True/False) otherwise  numeric (0/1)
        img : bool
            If True return a SPM_image otherwise a numpy array
        """
        if adaptive:
            if binary:
                return self.pixels > threshold_local(self.pixels, percent)
            return threshold_local(self.pixels, percent)
        mi = np.min(self.pixels)
        norm = (self.pixels-mi)/(np.max(self.pixels)-mi)
        if high:
            r = norm > percent
        else:
            r = norm < percent
        if not img:
            if binary:
                return r
            return np.ones(self.pixels.shape)*r
        else:
            I = copy.deepcopy(self)
            I.channel = "Threshold from "+I.channel
            if binary:
                I.pixels = r
            else:
                I.pixels = np.ones(self.pixels.shape)*r
            return I
Example #4
Source File: rbm_faces_sampling.py    From neupy with MIT License 5 votes vote down vote up 
def binarize_images(data):
    binarized_data = []
    for image in data:
        image = image.reshape((62, 47))
        image_threshold = threshold_local(image, block_size=15)
        binary_adaptive_image = image > image_threshold
        binarized_data.append(binary_adaptive_image.ravel())
    return asfloat(binarized_data)