Mercurial > repos > perssond > unmicst

comparison batchUNet2DtCycif.py @ 0:6bec4fef6b2e draft

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"planemo upload for repository https://github.com/ohsu-comp-bio/unmicst commit 73e4cae15f2d7cdc86719e77470eb00af4b6ebb7-dirty"
author perssond
date Fri, 12 Mar 2021 00:17:29 +0000
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-1:000000000000 0:6bec4fef6b2e
1 import numpy as np
2 from scipy import misc
3 import tensorflow as tf
4 import shutil
5 import scipy.io as sio
6 import os,fnmatch,glob
7 import skimage.exposure as sk
8
9 import sys
10 sys.path.insert(0, 'C:\\Users\\Clarence\\Documents\\UNet code\\ImageScience')
11 from toolbox.imtools import *
12 from toolbox.ftools import *
13 from toolbox.PartitionOfImage import PI2D
14
15
16 def concat3(lst):
17 return tf.concat(lst,3)
18
19 class UNet2D:
20 hp = None # hyper-parameters
21 nn = None # network
22 tfTraining = None # if training or not (to handle batch norm)
23 tfData = None # data placeholder
24 Session = None
25 DatasetMean = 0
26 DatasetStDev = 0
27
28 def setupWithHP(hp):
29 UNet2D.setup(hp['imSize'],
30 hp['nChannels'],
31 hp['nClasses'],
32 hp['nOut0'],
33 hp['featMapsFact'],
34 hp['downSampFact'],
35 hp['ks'],
36 hp['nExtraConvs'],
37 hp['stdDev0'],
38 hp['nLayers'],
39 hp['batchSize'])
40
41 def setup(imSize,nChannels,nClasses,nOut0,featMapsFact,downSampFact,kernelSize,nExtraConvs,stdDev0,nDownSampLayers,batchSize):
42 UNet2D.hp = {'imSize':imSize,
43 'nClasses':nClasses,
44 'nChannels':nChannels,
45 'nExtraConvs':nExtraConvs,
46 'nLayers':nDownSampLayers,
47 'featMapsFact':featMapsFact,
48 'downSampFact':downSampFact,
49 'ks':kernelSize,
50 'nOut0':nOut0,
51 'stdDev0':stdDev0,
52 'batchSize':batchSize}
53
54 nOutX = [UNet2D.hp['nChannels'],UNet2D.hp['nOut0']]
55 dsfX = []
56 for i in range(UNet2D.hp['nLayers']):
57 nOutX.append(nOutX[-1]*UNet2D.hp['featMapsFact'])
58 dsfX.append(UNet2D.hp['downSampFact'])
59
60
61 # --------------------------------------------------
62 # downsampling layer
63 # --------------------------------------------------
64
65 with tf.name_scope('placeholders'):
66 UNet2D.tfTraining = tf.placeholder(tf.bool, name='training')
67 UNet2D.tfData = tf.placeholder("float", shape=[None,UNet2D.hp['imSize'],UNet2D.hp['imSize'],UNet2D.hp['nChannels']],name='data')
68
69 def down_samp_layer(data,index):
70 with tf.name_scope('ld%d' % index):
71 ldXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index], nOutX[index+1]], stddev=stdDev0),name='kernel1')
72 ldXWeightsExtra = []
73 for i in range(nExtraConvs):
74 ldXWeightsExtra.append(tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name='kernelExtra%d' % i))
75
76 c00 = tf.nn.conv2d(data, ldXWeights1, strides=[1, 1, 1, 1], padding='SAME')
77 for i in range(nExtraConvs):
78 c00 = tf.nn.conv2d(tf.nn.relu(c00), ldXWeightsExtra[i], strides=[1, 1, 1, 1], padding='SAME')
79
80 ldXWeightsShortcut = tf.Variable(tf.truncated_normal([1, 1, nOutX[index], nOutX[index+1]], stddev=stdDev0),name='shortcutWeights')
81 shortcut = tf.nn.conv2d(data, ldXWeightsShortcut, strides=[1, 1, 1, 1], padding='SAME')
82
83 bn = tf.layers.batch_normalization(tf.nn.relu(c00+shortcut), training=UNet2D.tfTraining)
84
85 return tf.nn.max_pool(bn, ksize=[1, dsfX[index], dsfX[index], 1], strides=[1, dsfX[index], dsfX[index], 1], padding='SAME',name='maxpool')
86
87 # --------------------------------------------------
88 # bottom layer
89 # --------------------------------------------------
90
91 with tf.name_scope('lb'):
92 lbWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[UNet2D.hp['nLayers']], nOutX[UNet2D.hp['nLayers']+1]], stddev=stdDev0),name='kernel1')
93 def lb(hidden):
94 return tf.nn.relu(tf.nn.conv2d(hidden, lbWeights1, strides=[1, 1, 1, 1], padding='SAME'),name='conv')
95
96 # --------------------------------------------------
97 # downsampling
98 # --------------------------------------------------
99
100 with tf.name_scope('downsampling'):
101 dsX = []
102 dsX.append(UNet2D.tfData)
103
104 for i in range(UNet2D.hp['nLayers']):
105 dsX.append(down_samp_layer(dsX[i],i))
106
107 b = lb(dsX[UNet2D.hp['nLayers']])
108
109 # --------------------------------------------------
110 # upsampling layer
111 # --------------------------------------------------
112
113 def up_samp_layer(data,index):
114 with tf.name_scope('lu%d' % index):
115 luXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index+1], nOutX[index+2]], stddev=stdDev0),name='kernel1')
116 luXWeights2 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index]+nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name='kernel2')
117 luXWeightsExtra = []
118 for i in range(nExtraConvs):
119 luXWeightsExtra.append(tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name='kernel2Extra%d' % i))
120
121 outSize = UNet2D.hp['imSize']
122 for i in range(index):
123 outSize /= dsfX[i]
124 outSize = int(outSize)
125
126 outputShape = [UNet2D.hp['batchSize'],outSize,outSize,nOutX[index+1]]
127 us = tf.nn.relu(tf.nn.conv2d_transpose(data, luXWeights1, outputShape, strides=[1, dsfX[index], dsfX[index], 1], padding='SAME'),name='conv1')
128 cc = concat3([dsX[index],us])
129 cv = tf.nn.relu(tf.nn.conv2d(cc, luXWeights2, strides=[1, 1, 1, 1], padding='SAME'),name='conv2')
130 for i in range(nExtraConvs):
131 cv = tf.nn.relu(tf.nn.conv2d(cv, luXWeightsExtra[i], strides=[1, 1, 1, 1], padding='SAME'),name='conv2Extra%d' % i)
132 return cv
133
134 # --------------------------------------------------
135 # final (top) layer
136 # --------------------------------------------------
137
138 with tf.name_scope('lt'):
139 ltWeights1 = tf.Variable(tf.truncated_normal([1, 1, nOutX[1], nClasses], stddev=stdDev0),name='kernel')
140 def lt(hidden):
141 return tf.nn.conv2d(hidden, ltWeights1, strides=[1, 1, 1, 1], padding='SAME',name='conv')
142
143
144 # --------------------------------------------------
145 # upsampling
146 # --------------------------------------------------
147
148 with tf.name_scope('upsampling'):
149 usX = []
150 usX.append(b)
151
152 for i in range(UNet2D.hp['nLayers']):
153 usX.append(up_samp_layer(usX[i],UNet2D.hp['nLayers']-1-i))
154
155 t = lt(usX[UNet2D.hp['nLayers']])
156
157
158 sm = tf.nn.softmax(t,-1)
159 UNet2D.nn = sm
160
161
162 def train(imPath,logPath,modelPath,pmPath,nTrain,nValid,nTest,restoreVariables,nSteps,gpuIndex,testPMIndex):
163 os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex
164
165 outLogPath = logPath
166 trainWriterPath = pathjoin(logPath,'Train')
167 validWriterPath = pathjoin(logPath,'Valid')
168 outModelPath = pathjoin(modelPath,'model.ckpt')
169 outPMPath = pmPath
170
171 batchSize = UNet2D.hp['batchSize']
172 imSize = UNet2D.hp['imSize']
173 nChannels = UNet2D.hp['nChannels']
174 nClasses = UNet2D.hp['nClasses']
175
176 # --------------------------------------------------
177 # data
178 # --------------------------------------------------
179
180 Train = np.zeros((nTrain,imSize,imSize,nChannels))
181 Valid = np.zeros((nValid,imSize,imSize,nChannels))
182 Test = np.zeros((nTest,imSize,imSize,nChannels))
183 LTrain = np.zeros((nTrain,imSize,imSize,nClasses))
184 LValid = np.zeros((nValid,imSize,imSize,nClasses))
185 LTest = np.zeros((nTest,imSize,imSize,nClasses))
186
187 print('loading data, computing mean / st dev')
188 if not os.path.exists(modelPath):
189 os.makedirs(modelPath)
190 if restoreVariables:
191 datasetMean = loadData(pathjoin(modelPath,'datasetMean.data'))
192 datasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data'))
193 else:
194 datasetMean = 0
195 datasetStDev = 0
196 for iSample in range(nTrain+nValid+nTest):
197 I = im2double(tifread('%s/I%05d_Img.tif' % (imPath,iSample)))
198 datasetMean += np.mean(I)
199 datasetStDev += np.std(I)
200 datasetMean /= (nTrain+nValid+nTest)
201 datasetStDev /= (nTrain+nValid+nTest)
202 saveData(datasetMean, pathjoin(modelPath,'datasetMean.data'))
203 saveData(datasetStDev, pathjoin(modelPath,'datasetStDev.data'))
204
205 perm = np.arange(nTrain+nValid+nTest)
206 np.random.shuffle(perm)
207
208 for iSample in range(0, nTrain):
209 path = '%s/I%05d_Img.tif' % (imPath,perm[iSample])
210 im = im2double(tifread(path))
211 Train[iSample,:,:,0] = (im-datasetMean)/datasetStDev
212 path = '%s/I%05d_Ant.tif' % (imPath,perm[iSample])
213 im = tifread(path)
214 for i in range(nClasses):
215 LTrain[iSample,:,:,i] = (im == i+1)
216
217 for iSample in range(0, nValid):
218 path = '%s/I%05d_Img.tif' % (imPath,perm[nTrain+iSample])
219 im = im2double(tifread(path))
220 Valid[iSample,:,:,0] = (im-datasetMean)/datasetStDev
221 path = '%s/I%05d_Ant.tif' % (imPath,perm[nTrain+iSample])
222 im = tifread(path)
223 for i in range(nClasses):
224 LValid[iSample,:,:,i] = (im == i+1)
225
226 for iSample in range(0, nTest):
227 path = '%s/I%05d_Img.tif' % (imPath,perm[nTrain+nValid+iSample])
228 im = im2double(tifread(path))
229 Test[iSample,:,:,0] = (im-datasetMean)/datasetStDev
230 path = '%s/I%05d_Ant.tif' % (imPath,perm[nTrain+nValid+iSample])
231 im = tifread(path)
232 for i in range(nClasses):
233 LTest[iSample,:,:,i] = (im == i+1)
234
235 # --------------------------------------------------
236 # optimization
237 # --------------------------------------------------
238
239 tfLabels = tf.placeholder("float", shape=[None,imSize,imSize,nClasses],name='labels')
240
241 globalStep = tf.Variable(0,trainable=False)
242 learningRate0 = 0.01
243 decaySteps = 1000
244 decayRate = 0.95
245 learningRate = tf.train.exponential_decay(learningRate0,globalStep,decaySteps,decayRate,staircase=True)
246
247 with tf.name_scope('optim'):
248 loss = tf.reduce_mean(-tf.reduce_sum(tf.multiply(tfLabels,tf.log(UNet2D.nn)),3))
249 updateOps = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
250 # optimizer = tf.train.MomentumOptimizer(1e-3,0.9)
251 optimizer = tf.train.MomentumOptimizer(learningRate,0.9)
252 # optimizer = tf.train.GradientDescentOptimizer(learningRate)
253 with tf.control_dependencies(updateOps):
254 optOp = optimizer.minimize(loss,global_step=globalStep)
255
256 with tf.name_scope('eval'):
257 error = []
258 for iClass in range(nClasses):
259 labels0 = tf.reshape(tf.to_int32(tf.slice(tfLabels,[0,0,0,iClass],[-1,-1,-1,1])),[batchSize,imSize,imSize])
260 predict0 = tf.reshape(tf.to_int32(tf.equal(tf.argmax(UNet2D.nn,3),iClass)),[batchSize,imSize,imSize])
261 correct = tf.multiply(labels0,predict0)
262 nCorrect0 = tf.reduce_sum(correct)
263 nLabels0 = tf.reduce_sum(labels0)
264 error.append(1-tf.to_float(nCorrect0)/tf.to_float(nLabels0))
265 errors = tf.tuple(error)
266
267 # --------------------------------------------------
268 # inspection
269 # --------------------------------------------------
270
271 with tf.name_scope('scalars'):
272 tf.summary.scalar('avg_cross_entropy', loss)
273 for iClass in range(nClasses):
274 tf.summary.scalar('avg_pixel_error_%d' % iClass, error[iClass])
275 tf.summary.scalar('learning_rate', learningRate)
276 with tf.name_scope('images'):
277 split0 = tf.slice(UNet2D.nn,[0,0,0,0],[-1,-1,-1,1])
278 split1 = tf.slice(UNet2D.nn,[0,0,0,1],[-1,-1,-1,1])
279 if nClasses > 2:
280 split2 = tf.slice(UNet2D.nn,[0,0,0,2],[-1,-1,-1,1])
281 tf.summary.image('pm0',split0)
282 tf.summary.image('pm1',split1)
283 if nClasses > 2:
284 tf.summary.image('pm2',split2)
285 merged = tf.summary.merge_all()
286
287
288 # --------------------------------------------------
289 # session
290 # --------------------------------------------------
291
292 saver = tf.train.Saver()
293 sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU
294
295 if os.path.exists(outLogPath):
296 shutil.rmtree(outLogPath)
297 trainWriter = tf.summary.FileWriter(trainWriterPath, sess.graph)
298 validWriter = tf.summary.FileWriter(validWriterPath, sess.graph)
299
300 if restoreVariables:
301 saver.restore(sess, outModelPath)
302 print("Model restored.")
303 else:
304 sess.run(tf.global_variables_initializer())
305
306 # --------------------------------------------------
307 # train
308 # --------------------------------------------------
309
310 batchData = np.zeros((batchSize,imSize,imSize,nChannels))
311 batchLabels = np.zeros((batchSize,imSize,imSize,nClasses))
312 for i in range(nSteps):
313 # train
314
315 perm = np.arange(nTrain)
316 np.random.shuffle(perm)
317
318 for j in range(batchSize):
319 batchData[j,:,:,:] = Train[perm[j],:,:,:]
320 batchLabels[j,:,:,:] = LTrain[perm[j],:,:,:]
321
322 summary,_ = sess.run([merged,optOp],feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 1})
323 trainWriter.add_summary(summary, i)
324
325 # validation
326
327 perm = np.arange(nValid)
328 np.random.shuffle(perm)
329
330 for j in range(batchSize):
331 batchData[j,:,:,:] = Valid[perm[j],:,:,:]
332 batchLabels[j,:,:,:] = LValid[perm[j],:,:,:]
333
334 summary, es = sess.run([merged, errors],feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 0})
335 validWriter.add_summary(summary, i)
336
337 e = np.mean(es)
338 print('step %05d, e: %f' % (i,e))
339
340 if i == 0:
341 if restoreVariables:
342 lowestError = e
343 else:
344 lowestError = np.inf
345
346 if np.mod(i,100) == 0 and e < lowestError:
347 lowestError = e
348 print("Model saved in file: %s" % saver.save(sess, outModelPath))
349
350
351 # --------------------------------------------------
352 # test
353 # --------------------------------------------------
354
355 if not os.path.exists(outPMPath):
356 os.makedirs(outPMPath)
357
358 for i in range(nTest):
359 j = np.mod(i,batchSize)
360
361 batchData[j,:,:,:] = Test[i,:,:,:]
362 batchLabels[j,:,:,:] = LTest[i,:,:,:]
363
364 if j == batchSize-1 or i == nTest-1:
365
366 output = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 0})
367
368 for k in range(j+1):
369 pm = output[k,:,:,testPMIndex]
370 gt = batchLabels[k,:,:,testPMIndex]
371 im = np.sqrt(normalize(batchData[k,:,:,0]))
372 imwrite(np.uint8(255*np.concatenate((im,np.concatenate((pm,gt),axis=1)),axis=1)),'%s/I%05d.png' % (outPMPath,i-j+k+1))
373
374
375 # --------------------------------------------------
376 # save hyper-parameters, clean-up
377 # --------------------------------------------------
378
379 saveData(UNet2D.hp,pathjoin(modelPath,'hp.data'))
380
381 trainWriter.close()
382 validWriter.close()
383 sess.close()
384
385 def deploy(imPath,nImages,modelPath,pmPath,gpuIndex,pmIndex):
386 os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex
387
388 variablesPath = pathjoin(modelPath,'model.ckpt')
389 outPMPath = pmPath
390
391 hp = loadData(pathjoin(modelPath,'hp.data'))
392 UNet2D.setupWithHP(hp)
393
394 batchSize = UNet2D.hp['batchSize']
395 imSize = UNet2D.hp['imSize']
396 nChannels = UNet2D.hp['nChannels']
397 nClasses = UNet2D.hp['nClasses']
398
399 # --------------------------------------------------
400 # data
401 # --------------------------------------------------
402
403 Data = np.zeros((nImages,imSize,imSize,nChannels))
404
405 datasetMean = loadData(pathjoin(modelPath,'datasetMean.data'))
406 datasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data'))
407
408 for iSample in range(0, nImages):
409 path = '%s/I%05d_Img.tif' % (imPath,iSample)
410 im = im2double(tifread(path))
411 Data[iSample,:,:,0] = (im-datasetMean)/datasetStDev
412
413 # --------------------------------------------------
414 # session
415 # --------------------------------------------------
416
417 saver = tf.train.Saver()
418 sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU
419
420 saver.restore(sess, variablesPath)
421 print("Model restored.")
422
423 # --------------------------------------------------
424 # deploy
425 # --------------------------------------------------
426
427 batchData = np.zeros((batchSize,imSize,imSize,nChannels))
428
429 if not os.path.exists(outPMPath):
430 os.makedirs(outPMPath)
431
432 for i in range(nImages):
433 print(i,nImages)
434
435 j = np.mod(i,batchSize)
436
437 batchData[j,:,:,:] = Data[i,:,:,:]
438
439 if j == batchSize-1 or i == nImages-1:
440
441 output = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0})
442
443 for k in range(j+1):
444 pm = output[k,:,:,pmIndex]
445 im = np.sqrt(normalize(batchData[k,:,:,0]))
446 # imwrite(np.uint8(255*np.concatenate((im,pm),axis=1)),'%s/I%05d.png' % (outPMPath,i-j+k+1))
447 imwrite(np.uint8(255*im),'%s/I%05d_Im.png' % (outPMPath,i-j+k+1))
448 imwrite(np.uint8(255*pm),'%s/I%05d_PM.png' % (outPMPath,i-j+k+1))
449
450
451 # --------------------------------------------------
452 # clean-up
453 # --------------------------------------------------
454
455 sess.close()
456
457 def singleImageInferenceSetup(modelPath,gpuIndex):
458 #os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex
459
460 variablesPath = pathjoin(modelPath,'model.ckpt')
461
462 hp = loadData(pathjoin(modelPath,'hp.data'))
463 UNet2D.setupWithHP(hp)
464
465 UNet2D.DatasetMean = loadData(pathjoin(modelPath,'datasetMean.data'))
466 UNet2D.DatasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data'))
467 print(UNet2D.DatasetMean)
468 print(UNet2D.DatasetStDev)
469
470 # --------------------------------------------------
471 # session
472 # --------------------------------------------------
473
474 saver = tf.train.Saver()
475 UNet2D.Session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU
476
477 saver.restore(UNet2D.Session, variablesPath)
478 print("Model restored.")
479
480 def singleImageInferenceCleanup():
481 UNet2D.Session.close()
482
483 def singleImageInference(image,mode,pmIndex):
484 print('Inference...')
485
486 batchSize = UNet2D.hp['batchSize']
487 imSize = UNet2D.hp['imSize']
488 nChannels = UNet2D.hp['nChannels']
489
490 PI2D.setup(image,imSize,int(imSize/8),mode)
491 PI2D.createOutput(nChannels)
492
493 batchData = np.zeros((batchSize,imSize,imSize,nChannels))
494 for i in range(PI2D.NumPatches):
495 j = np.mod(i,batchSize)
496 batchData[j,:,:,0] = (PI2D.getPatch(i)-UNet2D.DatasetMean)/UNet2D.DatasetStDev
497 if j == batchSize-1 or i == PI2D.NumPatches-1:
498 output = UNet2D.Session.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0})
499 for k in range(j+1):
500 pm = output[k,:,:,pmIndex]
501 PI2D.patchOutput(i-j+k,pm)
502 # PI2D.patchOutput(i-j+k,normalize(imgradmag(PI2D.getPatch(i-j+k),1)))
503
504 return PI2D.getValidOutput()
505
506
507 if __name__ == '__main__':
508 logPath = 'C://Users//Clarence//Documents//UNet code//TFLogs'
509 modelPath = 'D:\\LSP\\UNet\\tonsil20x1bin1chan\\TFModel - 3class 16 kernels 5ks 2 layers'
510 pmPath = 'C://Users//Clarence//Documents//UNet code//TFProbMaps'
511
512
513
514 UNet2D.singleImageInferenceSetup(modelPath, 0)
515 imagePath = 'D:\\LSP\\cycif\\testsets'
516 sampleList = glob.glob(imagePath + '//exemplar-001*')
517 dapiChannel = 0
518 dsFactor = 1
519 for iSample in sampleList:
520 fileList = glob.glob(iSample + '//registration//*.tif')
521 print(fileList)
522 for iFile in fileList:
523 fileName = os.path.basename(iFile)
524 fileNamePrefix = fileName.split(os.extsep, 1)
525 I = tifffile.imread(iFile, key=dapiChannel)
526 rawI = I
527 hsize = int((float(I.shape[0])*float(dsFactor)))
528 vsize = int((float(I.shape[1])*float(dsFactor)))
529 I = resize(I,(hsize,vsize))
530 I = im2double(sk.rescale_intensity(I, in_range=(np.min(I), np.max(I)), out_range=(0, 0.983)))
531 rawI = im2double(rawI)/np.max(im2double(rawI))
532 outputPath = iSample + '//prob_maps'
533 if not os.path.exists(outputPath):
534 os.makedirs(outputPath)
535 K = np.zeros((2,rawI.shape[0],rawI.shape[1]))
536 contours = UNet2D.singleImageInference(I,'accumulate',1)
537 hsize = int((float(I.shape[0]) * float(1/dsFactor)))
538 vsize = int((float(I.shape[1]) * float(1/dsFactor)))
539 contours = resize(contours, (rawI.shape[0], rawI.shape[1]))
540 K[1,:,:] = rawI
541 K[0,:,:] = contours
542 tifwrite(np.uint8(255 * K),
543 outputPath + '//' + fileNamePrefix[0] + '_ContoursPM_' + str(dapiChannel + 1) + '.tif')
544 del K
545 K = np.zeros((1, rawI.shape[0], rawI.shape[1]))
546 nuclei = UNet2D.singleImageInference(I,'accumulate',2)
547 nuclei = resize(nuclei, (rawI.shape[0], rawI.shape[1]))
548 K[0, :, :] = nuclei
549 tifwrite(np.uint8(255 * K),
550 outputPath + '//' + fileNamePrefix[0] + '_NucleiPM_' + str(dapiChannel + 1) + '.tif')
551 del K
552 UNet2D.singleImageInferenceCleanup()
553