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"""
This script does the exact same as 'NerveSegmenetation3D.ipynb'
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.image import imsave
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)