Python skimage.morphology.diamond() Examples

def get_pos_dst_transform(label_unmodified, img, instance, old_label=None, dt_method="edt"):
  label = np.where(label_unmodified == instance, 1, 0)

  # If an old label is available, then sample positive clicks on the difference between the two.
  if old_label is not None:
    # The difference should be taken only if there is atleast one object pixel in the difference.
    label = np.max(0, label - old_label) if np.any((label - old_label) == 1) else label

  # Leave a margin around the object boundary
  img_area = morphology.binary_erosion(label, morphology.diamond(D_MARGIN))
  img_area = img_area if len(np.where(img_area == 1)[0]) > 0 else np.copy(label)

  # Set of ground truth pixels.
  O = np.where(img_area == 1)
  # Randomly sample the number of positive clicks and negative clicks to use.
  num_clicks_pos = 0 if len(O) == 0 else random.sample(list(range(1, Npos + 1)), 1)
  # num_clicks_pos = random.sample(range(1, Npos + 1), 1)
  pts = get_sampled_locations(O, img_area, num_clicks_pos)
  u1 = get_distance_transform(pts, img_area, img=img, dt_method=dt_method)

  return u1, pts 

def get_image_area_to_sample(img):
  """
  calculate set g_c, which has two properties
  1) They represent background pixels 
  2) They are within a certain distance to the object
  :param img: Image that represents the object instance
  """

  #TODO: In the paper 'Deep Interactive Object Selection', they calculate g_c first based on the original object instead
  # of the dilated one.

  # Dilate the object by d_margin pixels to extend the object boundary
  img_area = np.copy(img)
  img_area = morphology.binary_dilation(img_area, morphology.diamond(D_MARGIN)).astype(np.uint8)

  g_c = np.logical_not(img_area).astype(int)
  g_c[np.where(distance_transform_edt(g_c) > D)] = 0

  return g_c 

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 

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 

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 

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 

def postprocessing(prediction, threshold=0.75, dataset='G'):
    if dataset[0] == 'D':
        prediction = prediction.numpy()
        prediction_copy = np.copy(prediction)
        disc_mask = prediction[1]
        cup_mask = prediction[0]
        disc_mask = (disc_mask > 0.5)  # return binary mask
        cup_mask = (cup_mask > 0.1)  # return binary mask
        disc_mask = disc_mask.astype(np.uint8)
        cup_mask = cup_mask.astype(np.uint8)
        for i in range(5):
            disc_mask = scipy.signal.medfilt2d(disc_mask, 7)
            cup_mask = scipy.signal.medfilt2d(cup_mask, 7)
        disc_mask = morphology.binary_erosion(disc_mask, morphology.diamond(7)).astype(np.uint8)  # return 0,1
        cup_mask = morphology.binary_erosion(cup_mask, morphology.diamond(7)).astype(np.uint8)  # return 0,1
        disc_mask = get_largest_fillhole(disc_mask).astype(np.uint8)  # return 0,1
        cup_mask = get_largest_fillhole(cup_mask).astype(np.uint8)
        prediction_copy[0] = cup_mask
        prediction_copy[1] = disc_mask
        return prediction_copy
    else:
        prediction = prediction.numpy()
        prediction = (prediction > threshold)  # return binary mask
        prediction = prediction.astype(np.uint8)
        prediction_copy = np.copy(prediction)
        disc_mask = prediction[1]
        cup_mask = prediction[0]
        for i in range(5):
            disc_mask = scipy.signal.medfilt2d(disc_mask, 7)
            cup_mask = scipy.signal.medfilt2d(cup_mask, 7)
        disc_mask = morphology.binary_erosion(disc_mask, morphology.diamond(7)).astype(np.uint8)  # return 0,1
        cup_mask = morphology.binary_erosion(cup_mask, morphology.diamond(7)).astype(np.uint8)  # return 0,1
        disc_mask = get_largest_fillhole(disc_mask).astype(np.uint8)  # return 0,1
        cup_mask = get_largest_fillhole(cup_mask).astype(np.uint8)
        prediction_copy[0] = cup_mask
        prediction_copy[1] = disc_mask
        return prediction_copy 

def _get_axial_shifts(ndim=2, include_diagonals=False):
    r'''
    Helper function to generate the axial shifts that will be performed on
    the image to identify bordering pixels/voxels
    '''
    if ndim == 2:
        if include_diagonals:
            neighbors = square(3)
        else:
            neighbors = diamond(1)
        neighbors[1, 1] = 0
        x, y = np.where(neighbors)
        x -= 1
        y -= 1
        return np.vstack((x, y)).T
    else:
        if include_diagonals:
            neighbors = cube(3)
        else:
            neighbors = octahedron(1)
        neighbors[1, 1, 1] = 0
        x, y, z = np.where(neighbors)
        x -= 1
        y -= 1
        z -= 1
        return np.vstack((x, y, z)).T