-
Notifications
You must be signed in to change notification settings - Fork 4
/
fast_geodesic.py
358 lines (333 loc) · 14.6 KB
/
fast_geodesic.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
import numba as nb
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import shortest_path
import svcco
import pyvista as pv
from tqdm import trange, tqdm
import numpy as np
import tetgen
from scipy import stats
import pymeshfix
import pickle
import matplotlib.pyplot as plt
import matplotlib as mpl
from tempfile import TemporaryDirectory
import os
from multiprocessing import Pool
q = 4
resolution = 120
def get_cube():
cu = pv.Cube(x_length=3.72,y_length=3.72,z_length=3.72).triangulate().subdivide(5)
cube = svcco.surface()
cube.set_data(cu.points,cu.point_normals)
cube.solve()
cube.build(q=q,resolution=resolution)
print('cube constructed')
return cube
def get_heart():
heart = svcco.surface()
heart_points = np.genfromtxt('D:\\svcco\\svcco\\implicit\\tests\\heart_points_unique.csv',delimiter=',')
heart_normals = np.genfromtxt('D:\\svcco\\svcco\\implicit\\tests\\heart_normals_unique.csv',delimiter=',')
heart.set_data(heart_points,heart_normals)
heart.solve()
heart.build(q=4,resolution=120,k=2,buffer=5)
print('heart constructed')
return heart
def get_disk():
disk = pv.Disc(inner=2.8,outer=4,r_res=20,c_res=100)
cyl = disk.extrude([0,0,2],capping=True).triangulate()
cyl = svcco.utils.remeshing.remesh.remesh_surface(cyl)
cyl = cyl.subdivide(2)
cyl = svcco.utils.remeshing.remesh.remesh_surface(cyl,hausd=0.005)
cyl = cyl.compute_normals(auto_orient_normals=True,feature_angle=90)
cylinder = svcco.surface()
cylinder.set_data(cyl.points,cyl.point_normals)
cylinder.solve()
cylinder.build(q=q,resolution=resolution)
print('cylinder constructed')
return cylinder
def get_gyrus():
left_gyrus = "D:\\Tree\\Tree_8-0\\brain_testing\\FJ3801_BP58201_FMA72658_Left inferior frontal gyrus.obj"
gyrus_no_scale = pv.read(left_gyrus)
heart = get_heart()
sf = (heart.volume/gyrus_no_scale.volume)**(1/3)
gyrus_scaled = gyrus_no_scale.scale([sf,sf,sf])
left_gyrus_scaled = "left_gyrus_scaled.vtp"
gyrus_scaled.save(left_gyrus_scaled)
gyrus = svcco.surface()
gyrus.load(left_gyrus_scaled)
gyrus.solve()
gyrus.build(q=q,resolution=resolution,buffer=5)
print('gyrus constructed')
return gyrus
def get_all_paths(i,graph):
ds, Pr = shortest_path(csgraph=graph,directed=False,indices=i,return_predecessors=True)
return ds, Pr
def parallel_write(pkg):
start = pkg[0]
stop = pkg[1]
tempdir = pkg[2]
graph = pkg[3]
position = pkg[4]
graph_range = list(range(start,stop))
file_ds = np.memmap(tempdir+os.sep+'ds_{}-{}.dat'.format(start,stop),dtype=np.float64,mode='w+',shape=(len(graph_range),graph.shape[1]))
file_Pr = np.memmap(tempdir+os.sep+'Pr_{}-{}.dat'.format(start,stop),dtype=np.int64,mode='w+',shape=(len(graph_range),graph.shape[1]))
#tqdm(range(len(data['flow'])),desc="Building Vessel Data",position=1,leave=False)
#graph_range = list(range(start,stop))
checks = np.linspace(0,len(graph_range),10,dtype=int)
for i in range(len(graph_range)):
if i in checks:
print("Dijkstra Solve | {} - {}: {}%".format(start,stop,i/len(graph_range)*100))
ds,Pr = get_all_paths(graph_range[i],graph)
file_ds[i,:] = ds
file_Pr[i,:] = Pr
file_ds.flush()
file_Pr.flush()
del ds
del Pr
print("Dijkstra Solve | {} - {}: {}%".format(start,stop,100))
del file_ds
del file_Pr
return
def determine_blocks(row_number,block_number):
base = row_number//block_number
remain = row_number%block_number
blocks = []
count = 0
for i in range(block_number):
tmp = [count]
tmp.append(count+base)
if i == block_number - 1:
tmp[-1] += remain
blocks.append(tmp)
count += base
return blocks
def build_work(blocks,tmpdir,graph):
work = []
for i,b in enumerate(blocks):
b.append(tmpdir.name)
b.append(graph)
b.append(i)
work.append(b)
work = tuple(work)
return work
def precompute_graph_predecessors(surface_object,block_num=10,parallel_num=1):
file_dir = 'D:'+os.sep
tmp_dir = TemporaryDirectory(dir=file_dir)
#print(tmp_dir.name)
surface_object.tmp_dir_dijkstra = tmp_dir
#surface_object.graph_predecessor_distance = np.memmap(tmp_dir.name + os.sep + 'ds.dat',dtype='int32',mode='w+',shape=surface_object.graph.shape)
#surface_object.graph_predecessor_id = np.memmap(tmp_dir.name + os.sep + 'Pr.dat',dtype='int32',mode='w+',shape=surface_object.graph.shape)
surface_object.cell_centers = surface_object.tet.grid.cell_centers().points
surface_object.cell_center_closest_point = []
#for i in trange(surface_object.tet.grid.n_points):
# ds,Pr = get_all_paths(i,surface_object.graph)
# surface_object.graph_predecessor_distance[i,:] = ds
# surface_object.graph_predecessor_id[i,:] = Pr
# del ds
# del Pr
blocks = determine_blocks(surface_object.graph.shape[0],block_num)
base = blocks[0][1] - blocks[0][0]
work = build_work(blocks,tmp_dir,surface_object.graph)
p = Pool(parallel_num)
p.map(parallel_write,work)
ds = {'ranges':[],'files':[],'mats':[]}
Pr = {'ranges':[],'files':[],'mats':[]}
for i in range(len(work)):
ds['ranges'].append(work[i][0:2])
ds['files'].append(tmp_dir.name+os.sep+'ds_{}-{}.dat'.format(work[i][0],work[i][1]))
ds['mats'].append(np.memmap(tmp_dir.name+os.sep+'ds_{}-{}.dat'.format(work[i][0],work[i][1]),mode='r',dtype=np.float64,shape=(work[i][1]-work[i][0],surface_object.graph.shape[1])))
Pr['ranges'].append(work[i][0:2])
Pr['files'].append(tmp_dir.name+os.sep+'Pr_{}-{}.dat'.format(work[i][0],work[i][1]))
Pr['mats'].append(np.memmap(tmp_dir.name+os.sep+'Pr_{}-{}.dat'.format(work[i][0],work[i][1]),mode='r',dtype=np.int64,shape=(work[i][1]-work[i][0],surface_object.graph.shape[1])))
surface_object.ds = ds
surface_object.Pr = Pr
tet_vert_map = []
for i in trange(surface_object.tet.grid.n_cells):
surface_object.cell_center_closest_point.append(surface_object.tet.grid.find_closest_point(surface_object.cell_centers[i,:]))
if surface_object.cell_center_closest_point[-1] < blocks[-1][0]:
tmp = [surface_object.cell_center_closest_point[-1]//base,surface_object.cell_center_closest_point[-1]%base]
else:
tmp = [len(blocks)-1,surface_object.cell_center_closest_point[-1] - blocks[-1][0]]
tet_vert_map.append(tmp)
surface_object.tet_vert_map = tet_vert_map
return
@nb.jit(nopython=True)
def get_path(j,ds,Pr):
path = [j]
lengths = []
k = j
while Pr[k] != -9999:
path.append(Pr[k])
lengths.append(ds[k])
k = Pr[k]
path = path[::-1]
lengths = lengths[::-1]
lines = []
for jdx in range(len(path)-1):
lines.append([path[jdx],path[jdx+1]])
return path,lengths,lines
def perfusion_geodesic(tree,perfusion_volume):
terminals = tree.data[tree.data[:,15]<0,:]
terminals = terminals[terminals[:,16]<0,:]
terminal_ids = []
vol = perfusion_volume.tet.grid.compute_cell_sizes().cell_data['Volume']
for idx in range(terminals.shape[0]):
terminal_ids.append(perfusion_volume.tet.grid.find_closest_point(terminals[idx,3:6]))
territory_id = []
territory_volumes = np.zeros(terminals.shape[0])
for idx in tqdm(range(perfusion_volume.tet.grid.n_cells),desc='Calculating Perfusion Territories'):
tmp_geodesic_lengths = []
linear_distances = []
center = perfusion_volume.cell_centers[idx,:] #perfusion_volume.tet.grid.cell_centers().points[idx,:]
cell_id = perfusion_volume.cell_center_closest_point[idx] #perfusion_volume.tet.grid.find_closest_point(center)
for jdx in range(len(terminal_ids)):
#tmp_ds = perfusion_volume.graph_predecessor_distan[cell_id]
#tmp_Pr = perfusion_volume.graph_predecessor_id[cell_id]
tmp_ds = np.array(perfusion_volume.ds['mats'][perfusion_volume.tet_vert_map[idx][0]][perfusion_volume.tet_vert_map[idx][1],:])
tmp_Pr = np.array(perfusion_volume.Pr['mats'][perfusion_volume.tet_vert_map[idx][0]][perfusion_volume.tet_vert_map[idx][1],:])
_,L,_ = get_path(terminal_ids[jdx],tmp_ds,tmp_Pr)
L = sum(L)
tmp_geodesic_lengths.append(L)
linear_distances.append(np.linalg.norm(terminals[jdx,3:6] - center))
tmp_geodesic_lengths = np.array(tmp_geodesic_lengths)
min_geodesic_value = np.min(tmp_geodesic_lengths)
min_geodesic_instances = np.where(tmp_geodesic_lengths==min_geodesic_value)
linear_distances = np.array(linear_distances)
if len(min_geodesic_instances) == 1:
territory_id.append(min_geodesic_instances[0])
territory_volumes[min_geodesic_instances[0]] += vol[idx]
else:
absolute_min = np.argwhere(linear_distances[min_geodesic_instances])
absolute_min = min_geodesic_instances[absolute_min]
territory_id.append(absolute_min)
territory_volumes[absolute_min] += vol[idx]
territory_id = np.array(territory_id)
perfusion_volume.tet.grid['perfusion_territory_id'] = territory_id
return territory_id,territory_volumes/np.sum(vol),perfusion_volume.tet
def test(surf_object,size=50,restarts=50,name='default'):
t = svcco.tree()
t.set_boundary(surf_object)
perfusion_folder = os.getcwd()+os.sep+name+os.sep
delaunay = surf_object.pv_polydata.delaunay_3d()
convexity = surf_object.volume/delaunay.volume
if convexity > 0.95:
t.convex = True
t.set_root()
add_amount = size//restarts
VOLS = []
COUNT = []
_,vols,_ = perfusion_geodesic(t,surf_object)
for i in range(restarts):
t.n_add(add_amount)
if i == 0:
_,vols,tet = perfusion_geodesic(t,surf_object)
np.save(perfusion_folder + 'perfusion_volumes_num_terminals_{}.npy'.format(i*add_amount+1),vols)
else:
_,vols,tet = perfusion_geodesic(t,surf_object)
np.save(perfusion_folder + 'perfusion_volumes_num_terminals_{}.npy'.format(i*add_amount+1),vols)
VOLS.append(vols)
if i == 0:
COUNT.append(add_amount)
else:
COUNT.append(COUNT[-1]+add_amount)
return VOLS,COUNT
def results(test_list,size=10,restarts=10,bins=50,repeat=1):
fig,ax = plt.subplots(nrows=2,ncols=2)
#ax = ax.flatten()
#cw = plt.get_cmap('coolwarm')
norm = mpl.colors.Normalize(vmin=0, vmax=size+1)
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.coolwarm)
cmap.set_array([])
DATA = {'volumes':[],'tree_size':[]}
for i in range(len(test_list)):
vols,sizes = test(test_list[i],size=size,restarts=restarts)
DATA['volumes'].append(vols)
DATA['tree_size'].append(sizes)
DATA['repeat'] = repeat
idx = i//2
jdx = i%2
for j in range(len(vols)):
#color_scale = (sizes[j]/size)*255
x_min = min(vols[-1])
x_max = max(vols[-1])
x_range = np.linspace(x_min,x_max)
clean_vols = vols[j][vols[j]>0]
freq,edges = np.histogram(clean_vols,range=(0,1),bins=bins)
centers = 0.5*(edges[1:]+edges[:-1])
width = edges[1]-edges[0]
#if j == 0:
# ax[idx][jdx].bar(centers,freq/len(vols[j]),width=width,color='blue')
#else:
ax[idx][jdx].bar(centers,freq/len(clean_vols),width=width,alpha=0.4,color=cmap.to_rgba(len(vols[j])))
#ax[idx][jdx].hist(vols[j],bins=50,density=True,alpha=0.25,color=cw(color_scale),range = (x_min,x_max))
#kde = stats.gaussian_kde(vols[j])
#ax[idx][jdx].plot(x_range,kde(x_range)/10,color=cw(color_scale),alpha=0.75)
#sm = plt.cm.ScalarMappable(cmap='coolwarm')
#sm.set_array(range(0,size))
if repeat > 1:
for j in range(len(test_list)):
for i in range(len(DATA['volumes'][j])):
DATA['volumes'][j][i] = DATA['volumes'][j][i].tolist()
for j in range(repeat-1):
for i in range(len(test_list)):
vols,sizes = test(test_list[i],size=size,restarts=restarts)
for k in range(len(vols)):
DATA['volumes'][i][k].extend(vols[k])
fig.colorbar(cmap,ax=ax.ravel().tolist(),label="Number of Terminals")
fig.savefig('perfusion-{}_num_vessels-{}_restarts-{}_num_bins-{}.svg'.format(len(test_list),size,restarts,bins),format='svg')
return fig,ax,DATA
"""
q = 4
resolution = 20 #120
cu = pv.Cube(x_length=3.72,y_length=3.72,z_length=3.72).triangulate().subdivide(5)
cube = svcco.surface()
cube.set_data(cu.points,cu.point_normals)
cube.solve(quiet=False)
cube.build(q=q,resolution=resolution,verbose=True)
print('preprocessing cells')
blocks = determine_blocks(cube.graph.shape[0],10)
tmpdir = TemporaryDirectory(dir='D:'+os.sep)
work = build_work(blocks,tmpdir,cube.graph)
precompute_graph_predecessors(cube)
print('cube constructed')
heart = svcco.surface()
heart_points = np.genfromtxt('D:\\svcco\\svcco\\implicit\\tests\\heart_points_unique.csv',delimiter=',')
heart_normals = np.genfromtxt('D:\\svcco\\svcco\\implicit\\tests\\heart_normals_unique.csv',delimiter=',')
heart.set_data(heart_points,heart_normals)
heart.solve(quiet=False)
heart.build(q=4,resolution=120,k=2,buffer=5,verbose=True)
print('preprocessing cells')
precompute_graph_predecessors(heart)
print('heart constructed')
disk = pv.Disc(inner=2.8,outer=4,r_res=20,c_res=100)
cyl = disk.extrude([0,0,2],capping=True).triangulate()
cyl = svcco.utils.remeshing.remesh.remesh_surface(cyl)
cyl = cyl.subdivide(2)
cyl = svcco.utils.remeshing.remesh.remesh_surface(cyl,hausd=0.005)
cyl = cyl.compute_normals(auto_orient_normals=True,feature_angle=90)
cylinder = svcco.surface()
cylinder.set_data(cyl.points,cyl.point_normals)
cylinder.solve(quiet=False)
cylinder.build(q=q,resolution=resolution,verbose=True)
print('preprocessing cells')
precompute_graph_predecessors(cylinder)
print('cylinder constructed')
left_gyrus = "D:\\Tree\\Tree_8-0\\brain_testing\\FJ3801_BP58201_FMA72658_Left inferior frontal gyrus.obj"
gyrus_no_scale = pv.read(left_gyrus)
sf = (heart.volume/gyrus_no_scale.volume)**(1/3)
gyrus_scaled = gyrus_no_scale.scale([sf,sf,sf])
left_gyrus_scaled = "left_gyrus_scaled.vtp"
gyrus_scaled.save(left_gyrus_scaled)
gyrus = svcco.surface()
gyrus.load(left_gyrus_scaled)
gyrus.solve(quiet=False)
gyrus.build(q=q,resolution=resolution,buffer=5,verbose=True)
print('preprocessing cells')
precompute_graph_predecessors(gyrus)
print('gyrus constructed')
f,a,data = results([cube,cylinder,heart,gyrus],size=50,restarts=50,repeat=10)
file = open('perfusion_fast_data.pkl','wb+')
pickle.dump(data,file)
file.close()
"""