Python skimage.morphology() Examples

The following are 19 code examples of skimage.morphology(). 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 , or try the search function .
Example #1
Source File: prepro.py    From LapSRN-tensorflow with Apache License 2.0 6 votes vote down vote up 
def dilation(x, radius=3):
    """ Return greyscale morphological dilation of an image,
    see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`_.

    Parameters
    -----------
    x : 2D array image.
    radius : int for the radius of mask.
    """
    from skimage.morphology import disk, dilation
    mask = disk(radius)
    x = dilation(x, selem=mask)
    return x




## Sequence
Example #2
Source File: test_parameters.py    From imgaug with MIT License 6 votes vote down vote up 
def test_different_size_px_arguments(self):
        # different sizes in px
        param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2)
        param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=16)

        seen_components = [0, 0]
        seen_pixels = [0, 0]
        for _ in sm.xrange(100):
            samples1 = param1.draw_samples((16, 16, 1))
            samples2 = param2.draw_samples((16, 16, 1))
            _, num1 = skimage.morphology.label(samples1, connectivity=1,
                                               background=0, return_num=True)
            _, num2 = skimage.morphology.label(samples2, connectivity=1,
                                               background=0, return_num=True)
            seen_components[0] += num1
            seen_components[1] += num2
            seen_pixels[0] += np.sum(samples1 == 1)
            seen_pixels[1] += np.sum(samples2 == 1)

        assert seen_components[0] < seen_components[1]
        assert (
            seen_pixels[0] / seen_components[0]
            > seen_pixels[1] / seen_components[1]
        )
Example #3
Source File: test_parameters.py    From imgaug with MIT License 6 votes vote down vote up 
def test_different_size_px_arguments_with_tuple(self):
        # different sizes in px, one given as tuple (a, b)
        param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2)
        param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=(2, 16))

        seen_components = [0, 0]
        seen_pixels = [0, 0]
        for _ in sm.xrange(400):
            samples1 = param1.draw_samples((16, 16, 1))
            samples2 = param2.draw_samples((16, 16, 1))
            _, num1 = skimage.morphology.label(samples1, connectivity=1,
                                               background=0, return_num=True)
            _, num2 = skimage.morphology.label(samples2, connectivity=1,
                                               background=0, return_num=True)
            seen_components[0] += num1
            seen_components[1] += num2
            seen_pixels[0] += np.sum(samples1 == 1)
            seen_pixels[1] += np.sum(samples2 == 1)

        assert seen_components[0] < seen_components[1]
        assert (
            seen_pixels[0] / seen_components[0]
            > seen_pixels[1] / seen_components[1]
        )
Example #4
Source File: prepro.py    From super-resolution-videos with The Unlicense 6 votes vote down vote up 
def dilation(x, radius=3):
    """ Return greyscale morphological dilation of an image,
    see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`_.

    Parameters
    -----------
    x : 2D array image.
    radius : int for the radius of mask.
    """
    from skimage.morphology import disk, dilation
    mask = disk(radius)
    x = dilation(x, selem=mask)
    return x




## Sequence
Example #5
Source File: test_parameters.py    From imgaug with MIT License 6 votes vote down vote up 
def test_min_size(self):
        # min_size
        param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=2)
        param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_px=1,
                                         min_size=16)

        seen_components = [0, 0]
        seen_pixels = [0, 0]
        for _ in sm.xrange(100):
            samples1 = param1.draw_samples((16, 16, 1))
            samples2 = param2.draw_samples((16, 16, 1))
            _, num1 = skimage.morphology.label(samples1, connectivity=1,
                                               background=0, return_num=True)
            _, num2 = skimage.morphology.label(samples2, connectivity=1,
                                               background=0, return_num=True)
            seen_components[0] += num1
            seen_components[1] += num2
            seen_pixels[0] += np.sum(samples1 == 1)
            seen_pixels[1] += np.sum(samples2 == 1)

        assert seen_components[0] < seen_components[1]
        assert (
            seen_pixels[0] / seen_components[0]
            > seen_pixels[1] / seen_components[1]
        )
Example #6
Source File: test_parameters.py    From imgaug with MIT License 6 votes vote down vote up 
def test_size_percent(self):
        # different sizes in percent
        param1 = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=0.01)
        param2 = iap.FromLowerResolution(iap.Binomial(0.5), size_percent=0.8)

        seen_components = [0, 0]
        seen_pixels = [0, 0]
        for _ in sm.xrange(100):
            samples1 = param1.draw_samples((16, 16, 1))
            samples2 = param2.draw_samples((16, 16, 1))
            _, num1 = skimage.morphology.label(samples1, connectivity=1,
                                               background=0, return_num=True)
            _, num2 = skimage.morphology.label(samples2, connectivity=1,
                                               background=0, return_num=True)
            seen_components[0] += num1
            seen_components[1] += num2
            seen_pixels[0] += np.sum(samples1 == 1)
            seen_pixels[1] += np.sum(samples2 == 1)

        assert seen_components[0] < seen_components[1]
        assert (
            seen_pixels[0] / seen_components[0]
            > seen_pixels[1] / seen_components[1]
        )
Example #7
Source File: segmentation_test.py    From DRFNS with MIT License 5 votes vote down vote up 
def morpho_rec2(self, img, size=10):
        # internal gradient of the cells: 
        se = morphology.diamond(size)
        dil = morphology.dilation(img, se)
        rec = morphology.reconstruction(dil, img, method='erosion').astype(np.dtype('uint8'))
                
        return rec
Example #8
Source File: prepro.py    From super-resolution-videos with The Unlicense 5 votes vote down vote up 
def binary_dilation(x, radius=3):
    """ Return fast binary morphological dilation of an image.
    see `skimage.morphology.binary_dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation>`_.

    Parameters
    -----------
    x : 2D array image.
    radius : int for the radius of mask.
    """
    from skimage.morphology import disk, binary_dilation
    mask = disk(radius)
    x = binary_dilation(image, selem=mask)
    return x
Example #9
Source File: segmentation_test.py    From DRFNS with MIT License 5 votes vote down vote up 
def h_minima(self, img, h):
        img_shift = img.copy()
        img_shift[img_shift >= 255 - h] = 255-h
        img_shift = img_shift + h
        rec = morphology.reconstruction(img_shift, img, method='erosion').astype(np.dtype('uint8'))
        diff = rec - img
        return diff
Example #10
Source File: segmentation_test.py    From DRFNS with MIT License 5 votes vote down vote up 
def prefilter_new(self, img, rec_size=20, se_size=3):
    
        img_cc = ccore.numpy_to_image(img, copy=True)        
        im1 = ccore.diameter_open(img_cc, rec_size, 8)        
        im2 = ccore.diameter_close(im1, int(rec_size / 2), 8)        

        #im1 = self.morpho_rec(img, rec_size)
        #im2 = self.morpho_rec2(im1, int(rec_size / 2))

        se = morphology.disk(se_size)        
        im3 = morphology.closing(im2.toArray(), se)
        
        return im3
Example #11
Source File: segmentation_test.py    From DRFNS with MIT License 5 votes vote down vote up 
def get_rough_detection(self, img, bigsize=40.0, smallsize=4.0, thresh = 0):
        diff = self.difference_of_gaussian(-img, bigsize, smallsize)
        diff[diff>thresh] = 1
        
        se = morphology.square(4)
        ero = morphology.erosion(diff, se)
        
        labimage = label(ero)
        #rec = morphology.reconstruction(ero, img, method='dilation').astype(np.dtype('uint8'))
        
        # connectivity=1 corresponds to 4-connectivity.
        morphology.remove_small_objects(labimage, min_size=600, connectivity=1, in_place=True)
        #res = np.zeros(img.shape)
        ero[labimage==0] = 0
        ero = 1 - ero
        labimage = label(ero)
        morphology.remove_small_objects(labimage, min_size=400, connectivity=1, in_place=True)
        ero[labimage==0] = 0
        res = 1 - ero
        res[res>0] = 255
        
        #temp = 255 - temp
        #temp = morphology.remove_small_objects(temp, min_size=400, connectivity=1, in_place=True)
        #res = 255 - temp
        
        return res
Example #12
Source File: segmentation_test.py    From DRFNS with MIT License 5 votes vote down vote up 
def test_morpho2(self, bigsize=20.0, smallsize=3.0, threshold=5.0):
    
        img = self.read_H_image()

        pref = self.morpho_rec(img, 10)
        filename = os.path.join(test_out_folder, 'morpho_00_rec_%s.png' % self.image_name)
        skimage.io.imsave(filename, pref)

        res = self.difference_of_gaussian(pref, bigsize, smallsize)
        filename = os.path.join(test_out_folder, 'morpho_01_diff_%s_%i_%i.png' % (self.image_name, int(bigsize), int(smallsize)))
        skimage.io.imsave(filename, res)
        
        #res = self.morpho_rec2(diff, 15)
        #filename = os.path.join(test_out_folder, 'morpho_02_rec_%s.png' % self.image_name)
        #skimage.io.imsave(filename, res)

        res[res>threshold] = 255
        filename = os.path.join(test_out_folder, 'morpho_03_res_%s_%i.png' % (self.image_name, threshold))
        skimage.io.imsave(filename, res)
        
        se = morphology.diamond(3)
        ero = morphology.erosion(res, se)
        filename = os.path.join(test_out_folder, 'morpho_03_ero_%s_%i.png' % (self.image_name, threshold))
        skimage.io.imsave(filename, ero)
        res[ero>0] = 0
        
        overlay_img = self.overlay(img, res)
        filename = os.path.join(test_out_folder, 'morpho_04_overlay_%s_%i.png' % (self.image_name, int(threshold)))
        skimage.io.imsave(filename, overlay_img)
        
        return
Example #13
Source File: prepro.py    From LapSRN-tensorflow with Apache License 2.0 5 votes vote down vote up 
def binary_dilation(x, radius=3):
    """ Return fast binary morphological dilation of an image.
    see `skimage.morphology.binary_dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation>`_.

    Parameters
    -----------
    x : 2D array image.
    radius : int for the radius of mask.
    """
    from skimage.morphology import disk, binary_dilation
    mask = disk(radius)
    x = binary_dilation(image, selem=mask)
    return x
Example #14
Source File: segmentation_test.py    From DRFNS with MIT License 5 votes vote down vote up 
def morpho_rec(self, img, size=10):
        # internal gradient of the cells: 
        se = morphology.diamond(size)
        ero = morphology.erosion(img, se)
        rec = morphology.reconstruction(ero, img, method='dilation').astype(np.dtype('uint8'))
                
        return rec
Example #15
Source File: segmentation_test.py    From DRFNS with MIT License 5 votes vote down vote up 
def overlay(self, img, imbin, contour=False):
        colim = color.gray2rgb(img)
        colorvalue = (0, 100, 200)
        if contour:
            se = morphology.diamond(2)
            ero = morphology.erosion(imbin, se)
            grad = imbin - ero
            colim[grad > 0] = colorvalue
        else:
            colim[imbin>0] = colorvalue
            
        return colim
Example #16
Source File: test_parameters.py    From imgaug with MIT License 5 votes vote down vote up 
def test_different_size_px_argument_with_stochastic_parameters(self):
        # different sizes in px, given as StochasticParameter
        param1 = iap.FromLowerResolution(iap.Binomial(0.5),
                                         size_px=iap.Deterministic(1))
        param2 = iap.FromLowerResolution(iap.Binomial(0.5),
                                         size_px=iap.Choice([8, 16]))

        seen_components = [0, 0]
        seen_pixels = [0, 0]
        for _ in sm.xrange(100):
            samples1 = param1.draw_samples((16, 16, 1))
            samples2 = param2.draw_samples((16, 16, 1))
            _, num1 = skimage.morphology.label(samples1, connectivity=1,
                                               background=0, return_num=True)
            _, num2 = skimage.morphology.label(samples2, connectivity=1,
                                               background=0, return_num=True)
            seen_components[0] += num1
            seen_components[1] += num2
            seen_pixels[0] += np.sum(samples1 == 1)
            seen_pixels[1] += np.sum(samples2 == 1)

        assert seen_components[0] < seen_components[1]
        assert (
            seen_pixels[0] / seen_components[0]
            > seen_pixels[1] / seen_components[1]
        )
Example #17
Source File: dsbowl_preprocess_2d.py    From Kaggle-DSB with MIT License 4 votes vote down vote up 
def get_segmented_lungs(image):
    #Creation of the markers as shown above:
    marker_internal, marker_external, marker_watershed = generate_markers(image)
    
    #Creation of the Sobel-Gradient
    sobel_filtered_dx = ndimage.sobel(image, 1)
    sobel_filtered_dy = ndimage.sobel(image, 0)
    sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
    sobel_gradient *= 255.0 / np.max(sobel_gradient)
    
    #Watershed algorithm
    watershed = morphology.watershed(sobel_gradient, marker_watershed)
    
    #Reducing the image created by the Watershed algorithm to its outline
    outline = ndimage.morphological_gradient(watershed, size=(3,3))
    outline = outline.astype(bool)
    
    #Performing Black-Tophat Morphology for reinclusion
    #Creation of the disk-kernel and increasing its size a bit
    blackhat_struct = [[0, 0, 1, 1, 1, 0, 0],
                       [0, 1, 1, 1, 1, 1, 0],
                       [1, 1, 1, 1, 1, 1, 1],
                       [1, 1, 1, 1, 1, 1, 1],
                       [1, 1, 1, 1, 1, 1, 1],
                       [0, 1, 1, 1, 1, 1, 0],
                       [0, 0, 1, 1, 1, 0, 0]]
    #blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
    blackhat_struct = ndimage.iterate_structure(blackhat_struct, 14) # <- retains more of the area, 12 works well. Changed to 14, 12 still excluded some parts.
    #Perform the Black-Hat
    outline += ndimage.black_tophat(outline, structure=blackhat_struct)
    
    #Use the internal marker and the Outline that was just created to generate the lungfilter
    lungfilter = np.bitwise_or(marker_internal, outline)
    #Close holes in the lungfilter
    #fill_holes is not used here, since in some slices the heart would be reincluded by accident
    lungfilter = ndimage.morphology.binary_closing(lungfilter, structure=np.ones((5,5)), iterations=3)
    
    #Apply the lungfilter (note the filtered areas being assigned threshold_min HU)
    segmented = np.where(lungfilter == 1, image, threshold_min*np.ones(image.shape))
    
    #return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed
    return segmented
Example #18
Source File: LUNA_3d_merge_preproc.py    From Kaggle-DSB with MIT License 4 votes vote down vote up 
def get_segmented_lungs(image):
    #Creation of the markers as shown above:
    marker_internal, marker_external, marker_watershed = generate_markers(image)
    
    #Creation of the Sobel-Gradient
    sobel_filtered_dx = ndimage.sobel(image, 1)
    sobel_filtered_dy = ndimage.sobel(image, 0)
    sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
    sobel_gradient *= 255.0 / np.max(sobel_gradient)
    
    #Watershed algorithm
    watershed = morphology.watershed(sobel_gradient, marker_watershed)
    
    #Reducing the image created by the Watershed algorithm to its outline
    outline = ndimage.morphological_gradient(watershed, size=(3,3))
    outline = outline.astype(bool)
    
    #Performing Black-Tophat Morphology for reinclusion
    #Creation of the disk-kernel and increasing its size a bit
    blackhat_struct = [[0, 0, 1, 1, 1, 0, 0],
                       [0, 1, 1, 1, 1, 1, 0],
                       [1, 1, 1, 1, 1, 1, 1],
                       [1, 1, 1, 1, 1, 1, 1],
                       [1, 1, 1, 1, 1, 1, 1],
                       [0, 1, 1, 1, 1, 1, 0],
                       [0, 0, 1, 1, 1, 0, 0]]
    #blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
    blackhat_struct = ndimage.iterate_structure(blackhat_struct, 14) # <- retains more of the area, 12 works well. Changed to 14, 12 still excluded some parts.
    #Perform the Black-Hat
    outline += ndimage.black_tophat(outline, structure=blackhat_struct)
    
    #Use the internal marker and the Outline that was just created to generate the lungfilter
    lungfilter = np.bitwise_or(marker_internal, outline)
    #Close holes in the lungfilter
    #fill_holes is not used here, since in some slices the heart would be reincluded by accident
    lungfilter = ndimage.morphology.binary_closing(lungfilter, structure=np.ones((5,5)), iterations=3)
    
    #Apply the lungfilter (note the filtered areas being assigned threshold_min HU)
    segmented = np.where(lungfilter == 1, image, threshold_min*np.ones(image.shape))
    
    #return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed
    return segmented
Example #19
Source File: preproc_utils.py    From Kaggle-DSB with MIT License 4 votes vote down vote up 
def get_segmented_lungs(image):
    #Creation of the markers as shown above:
    marker_internal, marker_external, marker_watershed = generate_markers(image)
    
    #Creation of the Sobel-Gradient
    sobel_filtered_dx = ndimage.sobel(image, 1)
    sobel_filtered_dy = ndimage.sobel(image, 0)
    sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
    sobel_gradient *= 255.0 / np.max(sobel_gradient)
    
    #Watershed algorithm
    watershed = morphology.watershed(sobel_gradient, marker_watershed)
    
    #Reducing the image created by the Watershed algorithm to its outline
    outline = ndimage.morphological_gradient(watershed, size=(3,3))
    outline = outline.astype(bool)
    
    #Performing Black-Tophat Morphology for reinclusion
    #Creation of the disk-kernel and increasing its size a bit
    blackhat_struct = [[0, 0, 1, 1, 1, 0, 0],
                       [0, 1, 1, 1, 1, 1, 0],
                       [1, 1, 1, 1, 1, 1, 1],
                       [1, 1, 1, 1, 1, 1, 1],
                       [1, 1, 1, 1, 1, 1, 1],
                       [0, 1, 1, 1, 1, 1, 0],
                       [0, 0, 1, 1, 1, 0, 0]]
    #blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
    blackhat_struct = ndimage.iterate_structure(blackhat_struct, 14) # <- retains more of the area, 12 works well. Changed to 14, 12 still excluded some parts.
    #Perform the Black-Hat
    outline += ndimage.black_tophat(outline, structure=blackhat_struct)
    
    #Use the internal marker and the Outline that was just created to generate the lungfilter
    lungfilter = np.bitwise_or(marker_internal, outline)
    #Close holes in the lungfilter
    #fill_holes is not used here, since in some slices the heart would be reincluded by accident
    lungfilter = ndimage.morphology.binary_closing(lungfilter, structure=np.ones((5,5)), iterations=3)
    
    #Apply the lungfilter (note the filtered areas being assigned threshold_min HU)
    segmented = np.where(lungfilter == 1, image, threshold_min*np.ones(image.shape))
    
    #return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed
    return segmented