Last Update: 10/12/2017
In [1]:
# Imports
import numpy as np
import scipy.io as sio
from PIL import Image
import ndparse as ndp
import xbrain
import time
import os
import intern
from intern.remote.dvid import DVIDRemote
In [2]:
# Set folder where data is stored
current_dir = os.path.abspath(os.getcwd())
folder = next(os.walk('.'))[1][0]
#print(os.path.exists(folder)) # testing
# ilastik parameters
classifier_file = folder + '/xbrain_vessel_seg_v7.ilp'
#print(classifier_file) # testing
#print(os.path.exists(classifier_file)) # testing
# testing ground truth
image_file = folder + '/V3_imgdata_gt.npy'
#print(image_file) # testing
#print(os.path.exists(image_file)) # testing
# Dictate the processing power
ram_size = 4000 # 4000 MB
no_of_threads = 8 # 8 threads
# Cell detection parameters
cell_probability_threshold = 0.2
stopping_criterion = 0.47
initial_template_size = 18
dilation_size = 8
max_no_cells = 500
# Vessel segmentation parameters
vessel_probability_threshold = .68
dilation_size = 3
minimum_size = 4000
In [18]:
# load data
dvid = DVIDRemote({ "protocol": "http",
"host": "172.19.248.41:8000/",
})
chan = dvid.get_channel('a3afee0bf807466c9b7c3b0bbfd1acbd','grayscale')
print(chan)
input_data = dvid.get_cutout(chan,0,[0,2],[0,64],[390,454])#DVID here -- np.load(image_file)
# plot the 50th slice
#ndp.plot(input_data, 50)
# get the shape of the data
print(input_data.shape) # (200, 200, 200)
eneuro17/grayscale/
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-18-85bcb6837455> in <module>()
6 chan = dvid.get_channel('eneuro17','grayscale')
7 print(chan)
----> 8 input_data = dvid.get_cutout(chan,0,[0,2],[0,64],[390,454])#DVID here -- np.load(image_file)
9
10 # plot the 50th slice
~/.local/lib/python3.6/site-packages/intern-0.9.6-py3.6.egg/intern/remote/dvid/remote.py in get_cutout(self, chan, res, xspan, yspan, zspan)
93 (KeyError): if given invalid version.
94 """
---> 95 return DvidResource.get_cutout(api, chan, res, xspan, yspan, zspan)
96
97 def get_cutoutI(self, chan, res, xspan, yspan, zspan):
~/.local/lib/python3.6/site-packages/intern-0.9.6-py3.6.egg/intern/resource/dvid/resource.py in get_cutout(self, api, chan, res, xspan, yspan, zspan)
108
109 #Specifies the 3 dimensional shape of the numpy array of the size given by the user
--> 110 volumeOut = block.reshape(zpix,ypix,xpix)
111
112 #Returns a 3-dimensional numpy array to the user
ValueError: cannot reshape array of size 127 into shape (64,64,2)
In [87]:
# Compute time required for processing
start = time.time()
# Process the data to probability maps
probability_maps = xbrain.classify_pixel(input_data, classifier_file, threads=no_of_threads, ram=ram_size)
end = time.time()
print("\nElapsed time: %f minutes" % ((end - start)/60))
WARNING default_config.py(243): ResourceWarning: unclosed file <_io.TextIOWrapper name='/opt/conda/ilastik-meta/ilastik/ilastik/ilastik_logging/logging_config.json' mode='r' encoding='UTF-8'>
INFO ilastik_main: Starting ilastik from "/opt/conda".
INFO ilastik_main: Starting ilastik from "/opt/conda".
Starting ilastik from "/opt/conda".
INFO ilastik_main: Resetting lazyflow thread pool with 8 threads.
INFO ilastik_main: Resetting lazyflow thread pool with 8 threads.
INFO ilastik_main: Configuring lazyflow RAM limit to 3.9GiB
INFO ilastik_main: Configuring lazyflow RAM limit to 3.9GiB
INFO lazyflow.utility.memory: Available memory set to 3.9GiB
INFO lazyflow.utility.memory: Available memory set to 3.9GiB
INFO ilastik.shell.projectManager: Opening Project: public_data/xbrain_vessel_seg_v7.ilp
INFO ilastik.shell.projectManager: Opening Project: public_data/xbrain_vessel_seg_v7.ilp
WARNING bind.py(27): DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() or inspect.getfullargspec()
input_data.shape (200, 200, 200)
label_names, label_colors, probability_colors ['Background', 'Blood Vessel', 'Cell Body'] [(255, 0, 0), (0, 255, 0), (0, 0, 255)] [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
INFO ilastik.applets.batchProcessing.batchProcessingApplet: Exporting to in-memory array.
INFO ilastik.applets.batchProcessing.batchProcessingApplet: Exporting to in-memory array.
INFO lazyflow.utility.bigRequestStreamer: Estimated RAM usage per pixel is 216.0B * safety factor (2.0)
INFO lazyflow.utility.bigRequestStreamer: Estimated RAM usage per pixel is 216.0B * safety factor (2.0)
INFO lazyflow.utility.bigRequestStreamer: determining blockshape assuming available_ram is 2.9GiB, split between 8 threads
INFO lazyflow.utility.bigRequestStreamer: determining blockshape assuming available_ram is 2.9GiB, split between 8 threads
INFO lazyflow.utility.bigRequestStreamer: Chose blockshape: (97, 97, 96, 3)
INFO lazyflow.utility.bigRequestStreamer: Chose blockshape: (97, 97, 96, 3)
INFO lazyflow.utility.bigRequestStreamer: Estimated RAM usage per block is 372.1MiB
INFO lazyflow.utility.bigRequestStreamer: Estimated RAM usage per block is 372.1MiB
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DEBUG lazyflow.operators.classifierOperators: Training new classifier: Parallel Vigra Random Forest Factory (100 trees total)
INFO lazyflow.classifiers.parallelVigraRfLazyflowClassifier: Training took, 76.951454 seconds
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DEBUG lazyflow.operators.classifierOperators: Features took 0.462364 seconds, Prediction took 5.166527 seconds for roi: [0, 194, 96, 0] : [97, 200, 192, 3]
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DEBUG lazyflow.operators.classifierOperators: Features took 11.446755 seconds, Prediction took 74.249256 seconds for roi: [0, 0, 96, 0] : [97, 97, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 11.446755 seconds, Prediction took 74.249256 seconds for roi: [0, 0, 96, 0] : [97, 97, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 8.202614 seconds, Prediction took 81.712184 seconds for roi: [0, 0, 0, 0] : [97, 97, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 8.202614 seconds, Prediction took 81.712184 seconds for roi: [0, 0, 0, 0] : [97, 97, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 6.489501 seconds, Prediction took 40.753256 seconds for roi: [97, 0, 96, 0] : [194, 97, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 6.489501 seconds, Prediction took 40.753256 seconds for roi: [97, 0, 96, 0] : [194, 97, 192, 3]
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DEBUG lazyflow.operators.classifierOperators: Features took 1.656944 seconds, Prediction took 3.156047 seconds for roi: [97, 194, 0, 0] : [194, 200, 96, 3]
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DEBUG lazyflow.operators.classifierOperators: Features took 0.130745 seconds, Prediction took 0.180711 seconds for roi: [194, 0, 192, 0] : [200, 97, 200, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 7.668929 seconds, Prediction took 46.631891 seconds for roi: [97, 97, 0, 0] : [194, 194, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 7.668929 seconds, Prediction took 46.631891 seconds for roi: [97, 97, 0, 0] : [194, 194, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.72389 seconds, Prediction took 3.630623 seconds for roi: [194, 97, 0, 0] : [200, 194, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.72389 seconds, Prediction took 3.630623 seconds for roi: [194, 97, 0, 0] : [200, 194, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.204491 seconds, Prediction took 0.285064 seconds for roi: [194, 97, 192, 0] : [200, 194, 200, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.204491 seconds, Prediction took 0.285064 seconds for roi: [194, 97, 192, 0] : [200, 194, 200, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.153668 seconds, Prediction took 0.172545 seconds for roi: [194, 194, 0, 0] : [200, 200, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.153668 seconds, Prediction took 0.172545 seconds for roi: [194, 194, 0, 0] : [200, 200, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 1.3811689999999999 seconds, Prediction took 3.434207 seconds for roi: [194, 97, 96, 0] : [200, 194, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 1.3811689999999999 seconds, Prediction took 3.434207 seconds for roi: [194, 97, 96, 0] : [200, 194, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.066417 seconds, Prediction took 0.023978 seconds for roi: [194, 194, 192, 0] : [200, 200, 200, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.066417 seconds, Prediction took 0.023978 seconds for roi: [194, 194, 192, 0] : [200, 200, 200, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.123421 seconds, Prediction took 0.260477 seconds for roi: [194, 194, 96, 0] : [200, 200, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 0.123421 seconds, Prediction took 0.260477 seconds for roi: [194, 194, 96, 0] : [200, 200, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 45.973719 seconds, Prediction took 80.184277 seconds for roi: [0, 97, 0, 0] : [97, 194, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 45.973719 seconds, Prediction took 80.184277 seconds for roi: [0, 97, 0, 0] : [97, 194, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 121.178586 seconds, Prediction took 2.972022 seconds for roi: [0, 97, 192, 0] : [97, 194, 200, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 121.178586 seconds, Prediction took 2.972022 seconds for roi: [0, 97, 192, 0] : [97, 194, 200, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 120.902669 seconds, Prediction took 2.9984070000000003 seconds for roi: [0, 194, 0, 0] : [97, 200, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 120.902669 seconds, Prediction took 2.9984070000000003 seconds for roi: [0, 194, 0, 0] : [97, 200, 96, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 81.755613 seconds, Prediction took 45.021161 seconds for roi: [0, 97, 96, 0] : [97, 194, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 81.755613 seconds, Prediction took 45.021161 seconds for roi: [0, 97, 96, 0] : [97, 194, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 13.3868 seconds, Prediction took 34.698372 seconds for roi: [97, 97, 96, 0] : [194, 194, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 13.3868 seconds, Prediction took 34.698372 seconds for roi: [97, 97, 96, 0] : [194, 194, 192, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 34.137253 seconds, Prediction took 1.9132479999999998 seconds for roi: [97, 97, 192, 0] : [194, 194, 200, 3]
DEBUG lazyflow.operators.classifierOperators: Features took 34.137253 seconds, Prediction took 1.9132479999999998 seconds for roi: [97, 97, 192, 0] : [194, 194, 200, 3]
predictions.dtype, predictions.shape float32 (200, 200, 200, 3)
DONE.
Elapsed time: 4.041957 minutes
In [89]:
# pull down the coorisponding matricies
cell_prob_map = probability_maps[:, :, :, 2]
vessel_prob_map = probability_maps[:, :, :, 1]
print("cell_prob_map shape", cell_prob_map.shape)
ndp.plot(cell_prob_map, slice=50, cmap1='jet', alpha=0.5)
print("vessel_prob_map shape", vessel_prob_map.shape)
ndp.plot(vessel_prob_map, slice=50, cmap1='jet')
cell_prob_map shape (200, 200, 200)
vessel_prob_map shape (200, 200, 200)
In [90]:
# reload packages for testing new algorithms
# import importlib
# importlib.reload(xbrain)
# Compute time required for processing
start = time.time()
# cell detection
centroids, cell_map = xbrain.detect_cells(cell_prob_map, cell_probability_threshold, stopping_criterion, initial_template_size, dilation_size, max_no_cells)
print(centroids)
end = time.time()
print("\nElapsed time: %f minutes" % ((end - start)/60))
WARNING _bootstrap.py(205): ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__
Iteration remaining = 499 Correlation = [[ 0.95168388]]
Iteration remaining = 489 Correlation = [[ 0.91369426]]
Iteration remaining = 479 Correlation = [[ 0.88610667]]
Iteration remaining = 469 Correlation = [[ 0.86151671]]
Iteration remaining = 459 Correlation = [[ 0.85078424]]
Iteration remaining = 449 Correlation = [[ 0.84014732]]
Iteration remaining = 439 Correlation = [[ 0.82743627]]
Iteration remaining = 429 Correlation = [[ 0.81597131]]
Iteration remaining = 419 Correlation = [[ 0.80801356]]
Iteration remaining = 409 Correlation = [[ 0.79984474]]
Iteration remaining = 399 Correlation = [[ 0.78171575]]
Iteration remaining = 389 Correlation = [[ 0.76294184]]
Iteration remaining = 379 Correlation = [[ 0.74776268]]
Iteration remaining = 369 Correlation = [[ 0.72482878]]
Iteration remaining = 359 Correlation = [[ 0.70334792]]
Iteration remaining = 349 Correlation = [[ 0.69173962]]
Iteration remaining = 339 Correlation = [[ 0.67132163]]
Iteration remaining = 329 Correlation = [[ 0.6294108]]
Iteration remaining = 319 Correlation = [[ 0.60435903]]
Iteration remaining = 309 Correlation = [[ 0.56057721]]
Iteration remaining = 299 Correlation = [[ 0.54622215]]
Iteration remaining = 289 Correlation = [[ 0.52344346]]
Iteration remaining = 279 Correlation = [[ 0.49780256]]
Iteration remaining = 269 Correlation = [[ 0.47445858]]
Cell Detection is done
[[ 12. 53. 182. 0.95168388]
[ 157. 45. 91. 0.9488734 ]
[ 17. 55. 73. 0.93695861]
[ 75. 64. 147. 0.93604696]
[ 166. 97. 20. 0.93418789]
[ 78. 67. 21. 0.93325639]
[ 138. 17. 16. 0.93160027]
[ 50. 66. 116. 0.92342353]
[ 180. 60. 67. 0.91539407]
[ 86. 147. 148. 0.91476911]
[ 178. 42. 115. 0.91369426]
[ 126. 33. 67. 0.90950239]
[ 168. 117. 103. 0.90849125]
[ 109. 33. 49. 0.9077906 ]
[ 84. 50. 13. 0.90565288]
[ 61. 78. 28. 0.90070063]
[ 129. 174. 162. 0.89882964]
[ 47. 104. 38. 0.89395303]
[ 192. 84. 17. 0.89375401]
[ 107. 79. 116. 0.88733679]
[ 143. 83. 14. 0.88610667]
[ 22. 153. 19. 0.88525873]
[ 65. 186. 89. 0.8842954 ]
[ 87. 83. 92. 0.88342351]
[ 153. 89. 140. 0.88256764]
[ 56. 158. 156. 0.87451035]
[ 184. 126. 187. 0.86917591]
[ 39. 151. 108. 0.86369431]
[ 181. 119. 145. 0.86266714]
[ 138. 91. 95. 0.8620621 ]
[ 119. 154. 29. 0.86151671]
[ 160. 126. 161. 0.85955811]
[ 26. 14. 106. 0.85906452]
[ 28. 177. 30. 0.85816884]
[ 36. 36. 146. 0.85710186]
[ 25. 29. 173. 0.85675555]
[ 55. 142. 86. 0.85406452]
[ 8. 160. 154. 0.85390127]
[ 104. 19. 117. 0.85337579]
[ 136. 186. 46. 0.85263538]
[ 118. 45. 134. 0.85078424]
[ 156. 10. 31. 0.85039014]
[ 173. 99. 147. 0.84972531]
[ 150. 126. 87. 0.8493551 ]
[ 144. 64. 147. 0.84827632]
[ 8. 114. 47. 0.84684718]
[ 152. 96. 121. 0.84479696]
[ 133. 53. 106. 0.84463769]
[ 168. 130. 180. 0.84350318]
[ 125. 99. 46. 0.84016716]
[ 132. 22. 38. 0.84014732]
[ 151. 186. 186. 0.83855498]
[ 85. 59. 72. 0.83664411]
[ 95. 6. 18. 0.8365804 ]
[ 184. 41. 91. 0.83387738]
[ 87. 113. 92. 0.83271897]
[ 38. 78. 160. 0.8298527 ]
[ 135. 125. 31. 0.82984477]
[ 114. 130. 8. 0.82972133]
[ 67. 100. 108. 0.8281011 ]
[ 170. 152. 70. 0.82743627]
[ 12. 8. 168. 0.82501596]
[ 16. 113. 147. 0.82449442]
[ 66. 38. 90. 0.82426351]
[ 18. 9. 20. 0.82404459]
[ 96. 139. 84. 0.82367039]
[ 180. 173. 56. 0.82362258]
[ 139. 124. 125. 0.8234992 ]
[ 8. 176. 50. 0.820207 ]
[ 16. 184. 99. 0.81984073]
[ 69. 51. 170. 0.81597131]
[ 72. 170. 67. 0.81488055]
[ 8. 153. 96. 0.81468952]
[ 96. 49. 183. 0.81421977]
[ 180. 171. 118. 0.81304538]
[ 23. 137. 53. 0.81164008]
[ 43. 63. 8. 0.81097132]
[ 167. 103. 76. 0.8107245 ]
[ 45. 123. 92. 0.80923569]
[ 152. 161. 127. 0.80901277]
[ 81. 172. 36. 0.80801356]
[ 84. 134. 66. 0.80772299]
[ 108. 60. 107. 0.80767912]
[ 108. 116. 188. 0.8063615 ]
[ 87. 96. 128. 0.80574042]
[ 35. 60. 92. 0.80558521]
[ 179. 43. 182. 0.80277067]
[ 188. 192. 158. 0.80152464]
[ 84. 159. 8. 0.80075234]
[ 33. 41. 124. 0.80059314]
[ 56. 192. 159. 0.79984474]
[ 79. 115. 8. 0.7959435 ]
[ 134. 120. 174. 0.79377788]
[ 126. 55. 11. 0.79017913]
[ 7. 15. 57. 0.79006767]
[ 42. 178. 98. 0.78765923]
[ 146. 11. 167. 0.78508359]
[ 20. 48. 151. 0.78486866]
[ 91. 28. 60. 0.78368229]
[ 177. 135. 64. 0.78230888]
[ 137. 66. 191. 0.78171575]
[ 67. 133. 31. 0.78097135]
[ 95. 168. 81. 0.78076029]
[ 61. 117. 178. 0.77951431]
[ 54. 44. 40. 0.77567673]
[ 106. 108. 45. 0.77369428]
[ 78. 83. 62. 0.77232885]
[ 40. 193. 70. 0.76599127]
[ 109. 173. 38. 0.76476508]
[ 157. 70. 35. 0.76447845]
[ 37. 110. 154. 0.76294184]
[ 175. 62. 9. 0.76266319]
[ 180. 66. 146. 0.76122212]
[ 116. 192. 181. 0.7611903 ]
[ 108. 57. 128. 0.75646496]
[ 166. 185. 107. 0.75608283]
[ 32. 72. 37. 0.75450236]
[ 128. 149. 90. 0.75253975]
[ 89. 179. 12. 0.7511664 ]
[ 154. 11. 19. 0.74959791]
[ 65. 145. 122. 0.74776268]
[ 142. 193. 113. 0.74381363]
[ 191. 112. 187. 0.74157643]
[ 194. 10. 86. 0.73810506]
[ 117. 61. 160. 0.73513538]
[ 159. 166. 76. 0.73274684]
[ 52. 194. 92. 0.7319029 ]
[ 131. 87. 191. 0.73132962]
[ 193. 153. 31. 0.72922367]
[ 40. 74. 116. 0.72898883]
[ 175. 131. 87. 0.72482878]
[ 8. 139. 118. 0.72333997]
[ 9. 190. 31. 0.72259951]
[ 35. 129. 158. 0.72048968]
[ 134. 24. 166. 0.71958196]
[ 7. 131. 186. 0.71571654]
[ 148. 186. 170. 0.71541798]
[ 101. 134. 138. 0.71306926]
[ 65. 194. 183. 0.71070462]
[ 44. 141. 138. 0.70396495]
[ 75. 128. 51. 0.70334792]
[ 144. 84. 36. 0.70311701]
[ 42. 92. 140. 0.69875795]
[ 26. 8. 157. 0.69842756]
[ 64. 124. 101. 0.69786227]
[ 148. 10. 193. 0.6965127 ]
[ 97. 171. 108. 0.69570059]
[ 142. 191. 89. 0.69550955]
[ 97. 8. 47. 0.69536221]
[ 175. 165. 41. 0.69189882]
[ 94. 113. 30. 0.69173962]
[ 149. 132. 139. 0.69089568]
[ 40. 10. 5. 0.69009155]
[ 51. 99. 174. 0.68993628]
[ 137. 110. 58. 0.68693864]
[ 161. 152. 134. 0.68484873]
[ 195. 141. 100. 0.68157244]
[ 170. 26. 143. 0.68133754]
[ 146. 84. 151. 0.67826033]
[ 8. 150. 48. 0.67352706]
[ 142. 160. 67. 0.67132163]
[ 95. 195. 125. 0.66791004]
[ 94. 134. 123. 0.66669184]
[ 13. 174. 133. 0.66459394]
[ 69. 91. 82. 0.65728104]
[ 194. 120. 162. 0.64690286]
[ 8. 103. 173. 0.64678347]
[ 64. 19. 143. 0.64238054]
[ 127. 134. 92. 0.64077234]
[ 79. 160. 60. 0.64071256]
[ 126. 9. 168. 0.6294108 ]
[ 31. 106. 106. 0.62874603]
[ 160. 31. 4. 0.62644506]
[ 182. 195. 87. 0.61454618]
[ 43. 134. 105. 0.61292595]
[ 187. 151. 6. 0.61162019]
[ 158. 147. 58. 0.61100316]
[ 87. 140. 112. 0.60947454]
[ 77. 76. 194. 0.6084156 ]
[ 75. 196. 119. 0.60578424]
[ 108. 102. 22. 0.60435903]
[ 25. 128. 6. 0.60410827]
[ 96. 195. 148. 0.60203421]
[ 66. 51. 6. 0.600824 ]
[ 175. 194. 34. 0.59742433]
[ 18. 163. 56. 0.59701431]
[ 89. 25. 50. 0.58889729]
[ 21. 68. 194. 0.5750199 ]
[ 123. 67. 102. 0.57133359]
[ 80. 175. 5. 0.56263936]
[ 42. 16. 196. 0.56057721]
[ 189. 194. 13. 0.55930734]
[ 4. 114. 108. 0.55701834]
[ 55. 104. 75. 0.55648887]
[ 5. 71. 52. 0.55586779]
[ 56. 107. 104. 0.55505574]
[ 6. 147. 11. 0.55483282]
[ 128. 23. 16. 0.55356288]
[ 63. 91. 177. 0.55053741]
[ 192. 5. 107. 0.54811704]
[ 64. 101. 197. 0.54622215]
[ 139. 124. 193. 0.54526275]
[ 22. 83. 188. 0.54464573]
[ 115. 196. 44. 0.54359472]
[ 92. 186. 176. 0.54035825]
[ 129. 139. 196. 0.54022288]
[ 5. 117. 148. 0.53437102]
[ 175. 47. 174. 0.52721733]
[ 102. 196. 92. 0.52522296]
[ 41. 192. 51. 0.52455413]
[ 50. 180. 4. 0.52344346]
[ 85. 94. 121. 0.52134156]
[ 184. 4. 133. 0.51175159]
[ 30. 29. 4. 0.51149285]
[ 162. 173. 159. 0.50619823]
[ 159. 192. 6. 0.50583994]
[ 136. 88. 106. 0.505418 ]
[ 142. 101. 122. 0.50068468]
[ 26. 175. 23. 0.49946257]
[ 160. 118. 101. 0.49912024]
[ 74. 195. 51. 0.49780256]
[ 132. 195. 82. 0.49447054]
[ 84. 165. 32. 0.48992836]
[ 20. 182. 111. 0.48929536]
[ 33. 143. 145. 0.48859078]
[ 184. 113. 179. 0.48837975]
[ 111. 58. 14. 0.48816481]
[ 71. 110. 177. 0.48660827]
[ 134. 188. 38. 0.48623806]
[ 97. 127. 91. 0.47779459]
[ 123. 109. 158. 0.47445858]
[ 18. 8. 32. 0.4731887 ]
[ 137. 13. 11. 0.47169983]
[ 27. 62. 102. 0.47125399]]
In [91]:
# find vessels
vessel_map = xbrain.segment_vessels(vessel_prob_map, vessel_probability_threshold, dilation_size, minimum_size)
In [95]:
# show results
print("Vessel Segmentation")
ndp.plot(input_data, vessel_map, slice = 50, alpha = 0.5)
print("Cell Segmentation")
ndp.plot(input_data, cell_map, slice = 50, alpha = 0.5)
WARNING assess.py(299): MatplotlibDeprecationWarning: pyplot.hold is deprecated.
Future behavior will be consistent with the long-time default:
plot commands add elements without first clearing the
Axes and/or Figure.
WARNING __init__.py(917): UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.
WARNING rcsetup.py(152): UserWarning: axes.hold is deprecated, will be removed in 3.0
Vessel Segmentation
Cell Segmentation
In [ ]:
Content source: nerdslab/xbrain
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