diff --git a/notebooks/NerveSegmentation2D.py b/notebooks/NerveSegmentation2D.py
deleted file mode 100644
index 187e118a5b275ba2de05176a3482d5e13ef47b16..0000000000000000000000000000000000000000
--- a/notebooks/NerveSegmentation2D.py
+++ /dev/null
@@ -1,251 +0,0 @@
-"""
-This script does the exact same as 'NerveSegmenetation2D.ipynb'
-"""
-import numpy as np
-import matplotlib.pyplot as plt
-from skimage.io import imread
-from scipy.ndimage.interpolation import map_coordinates
-from slgbuilder import GraphObject,MaxflowBuilder
-
-def draw_segmentations(data, helper):
- """Draw all segmentations for objects in the helper on top of the data."""
-
- # Create figure.
- plt.figure(figsize=(10, 10))
- plt.imshow(data, cmap='gray')
- plt.xlim([0, data.shape[1]-1])
- plt.ylim([data.shape[0]-1, 0])
-
- # Draw segmentation lines.
- for i, obj in enumerate(helper.objects):
-
- # Get segmentation.
- segment = helper.get_labels(obj)
-
- # Create line.
- line = np.count_nonzero(segment, axis=0)
-
- # Get actual points.
- point_indices = tuple(np.asarray([line - 1, np.arange(len(line))]))
- points = obj.sample_points[point_indices]
- # Close line.
- points = np.append(points, points[:1], axis=0)
-
- # Plot points.
- plt.plot(points[..., 1], points[..., 0])
-
- plt.show()
-
-def unfold_image(img, center, max_dists=None, r_min=1, r_max=20, angles=30, steps=15):
- """ Unfolds around a point in an image with radial sampling """
- # Sampling angles and radii.
- angles = np.linspace(0, 2*np.pi, angles, endpoint=False)
- distances = np.linspace(r_min, r_max, steps, endpoint=True)
-
- if max_dists is not None:
- max_dists.append(np.max(distances))
-
- # Get angles.
- angles_cos = np.cos(angles)
- angles_sin = np.sin(angles)
-
- # Calculate points positions.
- x_pos = center[0] + np.outer(angles_cos, distances)
- y_pos = center[1] + np.outer(angles_sin, distances)
-
- # Create list of sampling points.
- sampling_points = np.array([x_pos, y_pos]).transpose()
- sampling_shape = sampling_points.shape
- sampling_points_flat = sampling_points.reshape((-1, 2))
-
- # Sample from image.
- samples = map_coordinates(img, sampling_points_flat.transpose(), mode='nearest')
- samples = samples.reshape(sampling_shape[:2])
-
- return samples, sampling_points
-#%%
-
-in_dir = 'data/'
-data = imread(in_dir+'nerves2D.png').astype(np.int32)
-
-# Get centers
-path = in_dir+'nerveCenters.png'
-centers = imread(path)[:,:,0]
-
-centers_small = np.transpose(np.where(centers==1))
-centers_large = np.transpose(np.where(centers==2))
-
-
-# Show image with centers.
-plt.imshow(data, cmap='gray')
-plt.scatter(centers_small[..., 1], centers_small[..., 0], color='red', s=6)
-plt.scatter(centers_large[..., 1], centers_large[..., 0], color='blue', s=6)
-plt.show()
-
-print(f'Number of objects: {len(centers_small)+len(centers_large)}')
-
-#%% Example of radial unfolding
-
-
-
-samples, sample_points = unfold_image(data, centers_small[4],r_max=20,angles=30,steps=30)
-
-plt.figure(figsize=(13, 5))
-ax = plt.subplot(1, 3, 1, title='Sample positions in data')
-ax.imshow(data, cmap='gray')
-ax.scatter(sample_points[..., 1], sample_points[..., 0], s=2, color='red')
-ax = plt.subplot(1, 3, 2, title='Sample positions and intensities')
-ax.scatter(sample_points[..., 1], sample_points[..., 0], c=samples, cmap='gray')
-ax = plt.subplot(1, 3, 3, title='Unfolded image')
-ax.imshow(samples, cmap='gray')
-plt.show()
-
-#%% Detect layers in object
-# Create gradient-based objects.
-diff_samples = np.diff(samples, axis=0)
-outer_nerve = GraphObject(255 - diff_samples)
-inner_nerve = GraphObject(diff_samples)
-
-f,ax = plt.subplots(1,2,figsize=(10,5))
-ax[0].imshow(outer_nerve.data, cmap='gray')
-ax[0].set_title('Outer never data')
-ax[1].imshow(inner_nerve.data, cmap='gray')
-ax[1].set_title('Inner never data')
-plt.show()
-
-#%% Segment one sample
-
-helper = MaxflowBuilder()
-helper.add_objects([outer_nerve, inner_nerve])
-helper.add_layered_boundary_cost()
-helper.add_layered_smoothness(delta=2)
-helper.add_layered_containment(outer_nerve, inner_nerve, min_margin=3, max_margin=6)
-
-flow = helper.solve()
-print('Maximum flow/minimum energy:', flow)
-
-segmentations = [helper.get_labels(o).astype(np.int32) for o in helper.objects]
-segmentation_lines = [np.count_nonzero(s, axis=0) - 0.5 for s in segmentations]
-
-
-f,ax = plt.subplots(1,3,figsize = (10,10))
-ax[0].imshow(samples, cmap='gray')
-ax[1].imshow(np.sum(segmentations,axis=0))
-ax[2].imshow(samples, cmap='gray')
-for line in segmentation_lines:
- ax[2].plot(line)
-plt.show()
-
-#%% Multiple object
-# Lists for storing nerve objects.
-nerve_samples = []
-outer_nerves = []
-inner_nerves = []
-
-# For each center, create an inner and outer never.
-for center in centers_small:
- samples, sample_points = unfold_image(data, center,r_max=35,angles=40,steps=30)
- nerve_samples.append(samples)
-
- # Create outer and inner nerve objects.
- diff_samples = np.diff(samples, axis=0)
- diff_sample_points = sample_points[:-1]
-
-
- outer_nerves.append(GraphObject(255 - diff_samples, diff_sample_points))
- inner_nerves.append(GraphObject(diff_samples, diff_sample_points))
-for center in centers_large:
- samples, sample_points = unfold_image(data, center,r_max=60,angles=40,steps=30)
- nerve_samples.append(samples)
-
- # Create outer and inner nerve objects.
- diff_samples = np.diff(samples, axis=0)
- diff_sample_points = sample_points[:-1]
-
-
- outer_nerves.append(GraphObject(255 - diff_samples, diff_sample_points))
- inner_nerves.append(GraphObject(diff_samples, diff_sample_points))
-
-helper = MaxflowBuilder()
-helper.add_objects(outer_nerves + inner_nerves)
-helper.add_layered_boundary_cost()
-helper.add_layered_smoothness(delta=2)
-
-for outer_nerve, inner_nerve in zip(outer_nerves, inner_nerves):
- helper.add_layered_containment(outer_nerve, inner_nerve, min_margin=3, max_margin=6)
-
-flow = helper.solve()
-print('Maximum flow/minimum energy:', flow)
-
-# Get segmentations.
-segmentations = []
-for outer_nerve, inner_nerve in zip(outer_nerves, inner_nerves):
- segmentations.append(helper.get_labels(outer_nerve))
- segmentations.append(helper.get_labels(inner_nerve))
-
-segmentation_lines = [np.count_nonzero(s, axis=0) - 0.5 for s in segmentations]
-
-# Draw segmentations.
-plt.figure(figsize=(15, 5))
-for i, samples in enumerate(nerve_samples):
- ax = plt.subplot(3, len(nerve_samples) // 3 + 1, i + 1)
- ax.imshow(samples, cmap='gray')
-
- ax.plot(segmentation_lines[2*i])
- ax.plot(segmentation_lines[2*i + 1])
-
-plt.show()
-
-draw_segmentations(data, helper)
-
-
-#%% Multi-object exclusion
-from slgbuilder import QPBOBuilder
-
-helper = QPBOBuilder()
-helper.add_objects(outer_nerves + inner_nerves)
-helper.add_layered_boundary_cost()
-helper.add_layered_smoothness(delta=2)
-
-for outer_nerve, inner_nerve in zip(outer_nerves, inner_nerves):
- helper.add_layered_containment(outer_nerve, inner_nerve, min_margin=3, max_margin=6)
-
-twice_flow = helper.solve()
-print('Two times maximum flow/minimum energy:', twice_flow)
-
-if 2*flow == twice_flow:
- print('QPBO flow is exactly twice the Maxflow flow.')
-else:
- print('Something is wrong...')
-
-# Add exclusion constraints between all pairs of outer nerves.
-for i in range(len(outer_nerves)):
- for j in range(i + 1, len(outer_nerves)):
- helper.add_layered_exclusion(outer_nerves[i], outer_nerves[j], margin=3)
-
-twice_flow = helper.solve()
-print('Two times maximum flow/minimum energy:', twice_flow)
-
-draw_segmentations(data, helper)
-
-
-#%% Region cost
-
-# mu_inside = 90
-# mu_ring = 70
-# mu_outside = 90
-# beta = 0.1
-
-# for samples, outer_nerve, inner_nerve in zip(nerve_samples, outer_nerves, inner_nerves):
-# samples = samples[:-1]
-
-# inside_cost = np.abs(samples - mu_inside) * beta
-# ring_cost = np.abs(samples - mu_ring) * beta
-# outside_cost = np.abs(samples - mu_outside) * beta
-
-# helper.add_layered_region_cost(inner_nerve, ring_cost, inside_cost)
-# helper.add_layered_region_cost(outer_nerve, outside_cost, ring_cost)
-
-# twice_flow = helper.solve()
-# print('Two times maximum flow/minimum energy:', twice_flow)
-# draw_segmentations(data, helper)
diff --git a/notebooks/NerveSegmentation3D.py b/notebooks/NerveSegmentation3D.py
deleted file mode 100644
index 1ae8d7281e23279d3d3f59bd6d64e1fb06939fce..0000000000000000000000000000000000000000
--- a/notebooks/NerveSegmentation3D.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""
-This script does the exact same as 'NerveSegmenetation3D.ipynb'
-"""
-import os
-import numpy as np
-import matplotlib.pyplot as plt
-from skimage.io import imread
-from scipy.ndimage.interpolation import map_coordinates
-
-from qimtools import visualization, inspection, io
-from slgbuilder import GraphObject,MaxflowBuilder
-
-def draw_segmentations(data, helper,layer=0):
- """Draw all segmentations for objects in the helper on top of the data."""
-
- if data.ndim != 3:
- raise ValueError('Data should be in three dimensions')
- K = (len(helper.objects)//vol.shape[-1])//2
-
- # Create figure.
- plt.figure(figsize=(10, 10))
- plt.imshow(data[...,layer], cmap='gray')
- plt.xlim([0, data[...,layer].shape[1]-1])
- plt.ylim([data[...,layer].shape[0]-1, 0])
-
- # Draw segmentation lines.
- for i, obj in enumerate(helper.objects):
- if (i >= layer*K and i < (layer+1)*K) or (i >= (vol.shape[-1]+layer)*K and i< (vol.shape[-1]+layer+1)*K):
- # Get segmentation.
- segment = helper.get_labels(obj)
-
- # Create line.
- line = np.count_nonzero(segment, axis=0)
-
- # Get actual points.
- point_indices = tuple(np.asarray([line - 1, np.arange(len(line))]))
- points = obj.sample_points[point_indices]
- # Close line.
- points = np.append(points, points[:1], axis=0)
-
- # Plot points.
- plt.plot(points[..., 1], points[..., 0])
-
- plt.show()
-
-def unfold_image(img, center, max_dists=None, r_min=1, r_max=20, angles=30, steps=15):
-
- # Sampling angles and radii.
- angles = np.linspace(0, 2*np.pi, angles, endpoint=False)
- distances = np.linspace(r_min, r_max, steps, endpoint=True)
-
- if max_dists is not None:
- max_dists.append(np.max(distances))
-
- # Get angles.
- angles_cos = np.cos(angles)
- angles_sin = np.sin(angles)
-
- # Calculate points positions.
- x_pos = center[0] + np.outer(angles_cos, distances)
- y_pos = center[1] + np.outer(angles_sin, distances)
-
- # Create list of sampling points.
- sampling_points = np.array([x_pos, y_pos]).transpose()
- sampling_shape = sampling_points.shape
- sampling_points_flat = sampling_points.reshape((-1, 2))
-
- # Sample from image.
- samples = map_coordinates(img, sampling_points_flat.transpose(), mode='nearest')
- samples = samples.reshape(sampling_shape[:2])
-
- return samples, sampling_points
-
-#%%
-
-in_dir = 'data/'
-Vol_path = os.path.join(in_dir, 'nerves3D.tiff')
-
-# Load the data
-vol = io.Volume( Vol_path )
-# Convert the stack of 2D slices to a 3D volume
-vol = vol.concatenate().astype(np.int32)
-vol = np.transpose(vol,(1,2,0))
-
-# visualization.show_vol( vol, axis=2 )
-
-#%%
-# Get centers
-path = in_dir+'nerveCenters.png'
-centers = imread(path)[:,:,0]
-
-centers_small = np.transpose(np.where(centers==1))
-centers_large = np.transpose(np.where(centers==2))
-
-
-# Show volume slice with centers.
-plt.imshow(vol[...,0], cmap='gray')
-plt.scatter(centers_small[..., 1], centers_small[..., 0], color='red', s=6)
-plt.scatter(centers_large[..., 1], centers_large[..., 0], color='blue', s=6)
-plt.show()
-
-print(f'Number of objects: {len(centers_small)+len(centers_large)}')
-
-#%%
-
-
-nerve_samples = []
-outer_nerves = []
-inner_nerves = []
-
-layers = []
-
-# For each center, create an inner and outer never.
-for i in range(vol.shape[-1]):
- for center in centers_small:
- samples, sample_points = unfold_image(vol[...,i], center,r_max=40,angles=40,steps=30)
- nerve_samples.append(samples)
-
- # Create outer and inner nerve objects.
- diff_samples = np.diff(samples, axis=0)
- diff_sample_points = sample_points[:-1]
-
-
- outer_nerves.append(GraphObject(255 - diff_samples, diff_sample_points))
- inner_nerves.append(GraphObject(diff_samples, diff_sample_points))
- for center in centers_large:
- samples, sample_points = unfold_image(vol[...,i], center,r_max=60,angles=40,steps=30)
- nerve_samples.append(samples)
-
- # Create outer and inner nerve objects.
- diff_samples = np.diff(samples, axis=0)
- diff_sample_points = sample_points[:-1]
-
-
- outer_nerves.append(GraphObject(255 - diff_samples, diff_sample_points))
- inner_nerves.append(GraphObject(diff_samples, diff_sample_points))
-
-helper = MaxflowBuilder()
-helper.add_objects(outer_nerves + inner_nerves)
-helper.add_layered_boundary_cost()
-helper.add_layered_smoothness(delta=2)
-
-for outer_nerve, inner_nerve in zip(outer_nerves, inner_nerves):
- helper.add_layered_containment(outer_nerve, inner_nerve, min_margin=3, max_margin=6)
-
-flow = helper.solve()
-print('Maximum flow/minimum energy:', flow)
-
-# Get segmentations.
-segmentations = []
-
-# for i in range(vol.shape[0]):
-for outer_nerve, inner_nerve in zip(outer_nerves, inner_nerves):
- segmentations.append(helper.get_labels(outer_nerve))
- segmentations.append(helper.get_labels(inner_nerve))
-
-segmentation_lines = [np.count_nonzero(s, axis=0) - 0.5 for s in segmentations]
-
-
-
-
-
-# plt.figure(figsize=(15, 5))
-# for i, samples in enumerate(nerve_samples):
-# ax = plt.subplot(3, len(nerve_samples) // 3 + 1, i + 1)
-# ax.imshow(samples, cmap='gray')
-
-# ax.plot(segmentation_lines[2*i])
-# ax.plot(segmentation_lines[2*i + 1])
-
-# plt.show()
-
-
-
-draw_segmentations(vol, helper,layer=0)
-
-
-#%% Multi-object exclusion
-from slgbuilder import QPBOBuilder
-
-helper = QPBOBuilder()
-helper.add_objects(outer_nerves + inner_nerves)
-helper.add_layered_boundary_cost()
-helper.add_layered_smoothness(delta=2)
-
-for outer_nerve, inner_nerve in zip(outer_nerves, inner_nerves):
- helper.add_layered_containment(outer_nerve, inner_nerve, min_margin=3, max_margin=6)
-
-twice_flow = helper.solve()
-print('Two times maximum flow/minimum energy:', twice_flow)
-
-if 2*flow == twice_flow:
- print('QPBO flow is exactly twice the Maxflow flow.')
-else:
- print('Something is wrong...')
-
-# Add exclusion constraints between all pairs of outer nerves.
-interval = len(outer_nerves)//vol.shape[-1]
-for i in range(vol.shape[-1]):
- for j in range(len(outer_nerves)//vol.shape[-1]):
- for k in range(j + 1, len(outer_nerves)//vol.shape[-1]):
- helper.add_layered_exclusion(outer_nerves[interval*i+j],
- outer_nerves[interval*i+k], margin=3)
-
-
-twice_flow = helper.solve()
-print('Two times maximum flow/minimum energy:', twice_flow)
-
-draw_segmentations(vol, helper,layer=9)
\ No newline at end of file