Python scipy.ndimage.median_filter() Examples

def MedianFilter(in_dem, kernel_size=3, out_file=None):

    print("Median filtering ...")
    start_time = time.time()
    dem = rd.LoadGDAL(in_dem)
    no_data = dem.no_data
    projection = dem.projection
    geotransform = dem.geotransform

    med = ndimage.median_filter(dem, size=kernel_size)
    med = np2rdarray(med, no_data, projection, geotransform)
    print("Run time: {:.4f} seconds".format(time.time() - start_time))

    if out_file is not None:
        print("Saving dem ...")
        rd.SaveGDAL(out_file, med)
        return out_file

    return med


# Gaussian filter 

def find_jumps2(self,ds,threshold=30000):
        self._prepare_find_jumps()
        ds = self._hf[ds]
        offset=ds[0]
        # first we remove a bit of noise
        #flt = gaussian_filter1d(ds,10)
        flt = median_filter(ds,size=10)
        #flt = ds
        # the sobel filter finds the "jumps" 
        sb=sobel(flt)
        for i in sb:
            self.qps_jpn_hight.append(float(i))
            
        for i in flt: self.qps_jpn_spec.append(float(i))
        """    
        for i in xrange(flt.shape[0]-1):
            if(abs(sb[i])>threshold):
                offset -= sb[i]
                
                self.qps_jpn_spec.append(float(flt[i]-offset))
            else:
                self.qps_jpn_spec.append(float(flt[i]-offset))
        """       

        #for i in sb 

def _background_removal_single_frame_median(frame, footprint=19):
    """Background removal using median filter.

    Parameters
    ----------
    frame : NumPy 2D array
    footprint : float

    Returns
    -------
    background_removed : Numpy 2D array

    Examples
    --------
    >>> import pyxem.utils.dask_tools as dt
    >>> s = pxm.dummy_data.dummy_data.get_cbed_signal()
    >>> s_rem = dt._background_removal_single_frame_median(s.data[0, 0])

    """
    bg_subtracted = frame - ndi.median_filter(frame, size=footprint)
    return bg_subtracted 

def filter(self, size: NumberLike=0.05, kind: str='median'):
        """Filter the profile.

        Parameters
        ----------
        size : float, int
            Size of the median filter to apply.
            If a float, the size is the ratio of the length. Must be in the range 0-1.
            E.g. if size=0.1 for a 1000-element array, the filter will be 100 elements.
            If an int, the filter is the size passed.
        kind : {'median', 'gaussian'}
            The kind of filter to apply. If gaussian, `size` is the sigma value.
        """
        if isinstance(size, float):
            if 0 < size < 1:
                size = int(round(len(self.values)*size))
                size = max(size, 1)
            else:
                raise TypeError("Float was passed but was not between 0 and 1")

        if kind == 'median':
            self.values = ndimage.median_filter(self.values, size=size)
        elif kind == 'gaussian':
            self.values = ndimage.gaussian_filter(self.values, sigma=size) 

def filter(self, size: Union[float, int]=0.05, kind: str='median'):
        """Filter the profile.

        Parameters
        ----------
        size : int, float
            Size of the median filter to apply.
            If a float, the size is the ratio of the length. Must be in the range 0-1.
            E.g. if size=0.1 for a 1000-element array, the filter will be 100 elements.
            If an int, the filter is the size passed.
        kind : {'median', 'gaussian'}
            The kind of filter to apply. If gaussian, *size* is the sigma value.
        """
        if isinstance(size, float):
            if 0 < size < 1:
                size *= len(self.array)
                size = max(size, 1)
            else:
                raise TypeError("Float was passed but was not between 0 and 1")

        if kind == 'median':
            self.array = ndimage.median_filter(self.array, size=size)
        elif kind == 'gaussian':
            self.array = ndimage.gaussian_filter(self.array, sigma=size) 

def test_rank12(self):
        expected = [[3, 3, 2, 4, 4],
                    [3, 5, 2, 5, 1],
                    [5, 5, 8, 3, 5]]
        footprint = [[1, 0, 1], [0, 1, 0]]
        for type_ in self.types:
            array = numpy.array([[3, 2, 5, 1, 4],
                                 [5, 8, 3, 7, 1],
                                 [5, 6, 9, 3, 5]], type_)
            output = ndimage.rank_filter(array, 1, footprint=footprint)
            assert_array_almost_equal(expected, output)
            output = ndimage.percentile_filter(array, 50.0,
                                               footprint=footprint)
            assert_array_almost_equal(expected, output)
            output = ndimage.median_filter(array, footprint=footprint)
            assert_array_almost_equal(expected, output) 

def filter(self, signal):
        """Filter a given signal with a choice of filter type (self.lres_filter).
        """
        signal = signal.copy()
        filter_size = [1, self.downsamp_t*2-1, self.downsamp_xz*2-1, self.downsamp_xz*2-1]

        if self.lres_filter == 'none' or (not self.lres_filter):
            output = signal
        elif self.lres_filter == 'gaussian':
            sigma = [0, int(self.downsamp_t/2), int(self.downsamp_xz/2), int(self.downsamp_xz/2)]
            output = ndimage.gaussian_filter(signal, sigma=sigma)
        elif self.lres_filter == 'uniform':
            output = ndimage.uniform_filter(signal, size=filter_size)
        elif self.lres_filter == 'median':
            output = ndimage.median_filter(signal, size=filter_size)
        elif self.lres_filter == 'maximum':
            output = ndimage.maximum_filter(signal, size=filter_size)
        else:
            raise NotImplementedError(
                "lres_filter must be one of none/gaussian/uniform/median/maximum")
        return output 

def test_rank12(self):
        expected = [[3, 3, 2, 4, 4],
                [3, 5, 2, 5, 1],
                [5, 5, 8, 3, 5]]
        footprint = [[1, 0, 1], [0, 1, 0]]
        for type in self.types:
            array = numpy.array([[3, 2, 5, 1, 4],
                                    [5, 8, 3, 7, 1],
                                    [5, 6, 9, 3, 5]], type)
            output = ndimage.rank_filter(array, 1,
                                                  footprint=footprint)
            assert_array_almost_equal(expected, output)
            output = ndimage.percentile_filter(array, 50.0,
                                                     footprint=footprint)
            assert_array_almost_equal(expected, output)
            output = ndimage.median_filter(array,
                                                    footprint=footprint)
            assert_array_almost_equal(expected, output) 

def prepare_n_mnist(filename, is_filter, num_spikes, step_factor=1):
    """Creates images from the specified n mnist recording
    filename: path to the recording
    is_filter: True if median filtering should be applied to the constructed image
    num_spikes: number of unique spikes per image
    step_factor: proportional amount to shift before generating the next image
        1 would result in no overlapping events between images
        0.6 would result in the next image overlapping with 40% of the previous image
    returns: list of images, where each image is a 2d numpy array (height, width)
    """
    td = ev.read_dataset(filename)
    #td.show_td(100)
    td.data = stabilize(td)
    td.data = td.extract_roi([3, 3], [28, 28], True)
    images = make_td_images(td, num_spikes, step_factor)

    if is_filter:
        images = ndimage.median_filter(images, 3)

    #for image in images:
    #    cv2.imshow('img', image)
    #    cv2.waitKey(70)
    return images 

def prepare_n_mnist_continuous(filename, is_filter, is_normalize=False):
    """Creates image with pixel values indicating probability of a spike
    filename: path to the recording
    is_filter: True if median filtering should be applied to the constructed image
    is_normalize: If True, the probabilities will be normalized to make the image more obvious
    returns: image (2d numpy array (height, width))
    """
    td = ev.read_dataset(filename)
    #td.show_td(100)
    td.data = stabilize(td)
    td.data = td.extract_roi([0, 0], [28, 28], True)
    #td.data = apply_tracking1(td)
    #td.data = apply_tracking2(td)
    #td.data = apply_tracking3(td)
    #td.data = td.extract_roi([3, 3], [28, 28], True)
    image = make_td_probability_image(td, 9, is_normalize)

    if is_filter:
        image = ndimage.median_filter(image, 3)

    #cv2.imshow('img', image)
    #cv2.waitKey(1)
    return image 

def show_active_img_and_save_denoise_filter2(name,img,path):
    mat = img.astype(np.float)
    mat[mat<0.1] = 0
    mat = - mat + 1
    mat = mat * 255.0
    mat[mat < 0] = 0
    mat[mat > 255] = 255
    mat=mat.astype(np.uint8)
    mat = ndimage.median_filter(mat, 1)
    cv2.imshow(name,mat)
    cv2.imwrite(path,mat)
    return 

def show_active_img_and_save_denoise_filter(name,img,path):
    mat = img.astype(np.float)
    mat[mat<0.18] = 0
    mat = - mat + 1
    mat = mat * 255.0
    mat[mat < 0] = 0
    mat[mat > 255] = 255
    mat=mat.astype(np.uint8)
    mat = ndimage.median_filter(mat, 1)
    cv2.imshow(name,mat)
    cv2.imwrite(path,mat)
    return 

def show_active_img_and_save_denoise(name,img,path):
    mat = img.astype(np.float)
    mat = - mat + 1
    mat = mat * 255.0
    mat[mat < 0] = 0
    mat[mat > 255] = 255
    mat=mat.astype(np.uint8)
    mat = ndimage.median_filter(mat, 1)
    cv2.imshow(name,mat)
    cv2.imwrite(path,mat)
    return 

def denoise_mat(img,i):
    return ndimage.median_filter(img, i) 

def filter_noise(self, ):
        self.imgarr = nd.median_filter(self.imgarr, size=3) 

def filter_grads(grads):
    return ndimage.median_filter(grads, 10) 

def to_keras_enhanced(img):
    mat = img.astype(np.float)
    mat[mat<0.1] = 0
    mat = - mat + 1
    mat = mat * 255.0
    mat[mat < 0] = 0
    mat[mat > 255] = 255
    mat=mat.astype(np.uint8)
    mat = ndimage.median_filter(mat, 1)

    return mat 

def display_img(i,x,style,is_val=False):
    # save current generated image
    img = x #deprocess_image(x)
    if is_val:
        #img = ndimage.median_filter(img, 3)

        fname = 'images/output/%s_%d_val.png' % (style,i)
    else:
        fname = 'images/output/%s_%d.png' % (style,i)
    imsave(fname, img)
    print('Image saved as', fname) 

def split_traces(self,ds,threshold=30000):
        self._prepare_find_jumps()
        ds = self._hf[ds]
        # first we remove a bit of noise, size is the number of averages
        #flt = gaussian_filter1d(ds,10)
        flt = median_filter(ds,size=3)
        #flt = ds
        # the sobel filter finds the "jumps" 
        sb=sobel(flt)
        for i in sb:
            self.qps_jpn_hight.append(float(i))
            
        #for i in flt: self.qps_jpn_spec.append(float(i))
        offset=ds[0]
        tr_num = 0
        tr_name = "qps_tr_"+str(tr_num)
        tr_obj =  self._hf.add_value_vector(tr_name, 
                                            folder = 'analysis', 
                                            x = self._x_co, 
                                            unit = 'Hz')
        keepout = 4
        for i,tr in enumerate(flt):
            keepout += 1
            if abs(sb[i])>threshold and keepout>3:
                keepout = 0
                # new trace
                tr_num +=1
                tr_name = "qps_tr_"+str(tr_num)
                tr_obj =  self._hf.add_value_vector(tr_name, 
                                                    folder = 'analysis', 
                                                    x =  self._x_co, 
                                                    unit = 'Hz')
                print tr , i
                #tr_obj.append(float(tr))
            else:
                if keepout>2:
                    tr_obj.append(float(tr-offset)) 

def median_filter(img_arr, size=3):
    return _median_filter(img_arr, size=size) 

def homomorphic_median(image, sigma=10):
    log_img = np.log(image)
    return np.exp(log_img - median_filter(log_img, int(sigma))) 

def test_median_filter(self):
        d = np.random.randn(500, 500)
        os = np.empty([4] + list(d.shape))
        ot = np.empty_like(os)
        self.check_func_serial(4, sndi.median_filter, (d, 3), os)
        self.check_func_thread(4, sndi.median_filter, (d, 3), ot)
        assert_array_equal(os, ot) 

def test_rank08(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                             [5, 8, 3, 7, 1],
                             [5, 6, 9, 3, 5]])
        expected = [[3, 3, 2, 4, 4],
                    [5, 5, 5, 4, 4],
                    [5, 6, 7, 5, 5]]
        output = ndimage.percentile_filter(array, 50.0, size=(2, 3))
        assert_array_almost_equal(expected, output)
        output = ndimage.rank_filter(array, 3, size=(2, 3))
        assert_array_almost_equal(expected, output)
        output = ndimage.median_filter(array, size=(2, 3))
        assert_array_almost_equal(expected, output) 

def test_rank04(self):
        array = numpy.array([3, 2, 5, 1, 4])
        expected = [3, 3, 2, 4, 4]
        output = ndimage.rank_filter(array, 1, size=3)
        assert_array_almost_equal(expected, output)
        output = ndimage.percentile_filter(array, 50, size=3)
        assert_array_almost_equal(expected, output)
        output = ndimage.median_filter(array, size=3)
        assert_array_almost_equal(expected, output) 

def test_rank02(self):
        array = numpy.array([1, 2, 3, 4, 5])
        output = ndimage.rank_filter(array, 1, size=[3])
        assert_array_almost_equal(array, output)
        output = ndimage.percentile_filter(array, 50, size=3)
        assert_array_almost_equal(array, output)
        output = ndimage.median_filter(array, (3,))
        assert_array_almost_equal(array, output) 

def test_rank01(self):
        array = numpy.array([1, 2, 3, 4, 5])
        output = ndimage.rank_filter(array, 1, size=2)
        assert_array_almost_equal(array, output)
        output = ndimage.percentile_filter(array, 100, size=2)
        assert_array_almost_equal(array, output)
        output = ndimage.median_filter(array, 2)
        assert_array_almost_equal(array, output) 

def test_rank08(self):
        array = numpy.array([[3, 2, 5, 1, 4],
                                [5, 8, 3, 7, 1],
                                [5, 6, 9, 3, 5]])
        expected = [[3, 3, 2, 4, 4],
                [5, 5, 5, 4, 4],
                [5, 6, 7, 5, 5]]
        output = ndimage.percentile_filter(array, 50.0,
                                                    size=(2, 3))
        assert_array_almost_equal(expected, output)
        output = ndimage.rank_filter(array, 3, size=(2, 3))
        assert_array_almost_equal(expected, output)
        output = ndimage.median_filter(array, size=(2, 3))
        assert_array_almost_equal(expected, output) 

def test_rank04(self):
        array = numpy.array([3, 2, 5, 1, 4])
        expected = [3, 3, 2, 4, 4]
        output = ndimage.rank_filter(array, 1, size=3)
        assert_array_almost_equal(expected, output)
        output = ndimage.percentile_filter(array, 50, size=3)
        assert_array_almost_equal(expected, output)
        output = ndimage.median_filter(array, size=3)
        assert_array_almost_equal(expected, output) 

def test_rank02(self):
        array = numpy.array([1, 2, 3, 4, 5])
        output = ndimage.rank_filter(array, 1, size=[3])
        assert_array_almost_equal(array, output)
        output = ndimage.percentile_filter(array, 50, size=3)
        assert_array_almost_equal(array, output)
        output = ndimage.median_filter(array, (3,))
        assert_array_almost_equal(array, output) 

def test_rank01(self):
        array = numpy.array([1, 2, 3, 4, 5])
        output = ndimage.rank_filter(array, 1, size=2)
        assert_array_almost_equal(array, output)
        output = ndimage.percentile_filter(array, 100, size=2)
        assert_array_almost_equal(array, output)
        output = ndimage.median_filter(array, 2)
        assert_array_almost_equal(array, output)