Mercurial > repos > perssond > coreograph
changeset 2:224e0cf4aaeb draft
planemo upload for repository https://github.com/ohsu-comp-bio/UNetCoreograph commit cb09eb9d2fa0feae993ae994b6beae05972c644b
| author | goeckslab |
|---|---|
| date | Thu, 01 Sep 2022 22:43:42 +0000 |
| parents | 57f1260ca94e |
| children | ee92746d141a |
| files | Dockerfile LICENSE README.md UNet2DtCycifTRAINCoreograph.py UNetCoreograph.py coreograph.xml images/TMA_MAP.jpg images/TMA_MAP.tif images/coreographbanner.png images/coreographbannerv2.png images/coreographbannerv3.png images/coreographbannerv4.png images/coreographbannerv5.png images/probmap.jpg images/probmap.tif images/raw.jpg images/raw.tif macros.xml model/checkpoint model/datasetMean.data model/datasetStDev.data model/hp.data model/model.ckpt.data-00000-of-00001 model/model.ckpt.index model/model.ckpt.meta test-data/coreograph_test.tiff toolbox/PartitionOfImage.py toolbox/__pycache__/PartitionOfImage.cpython-36.pyc toolbox/__pycache__/__init__.cpython-36.pyc toolbox/__pycache__/ftools.cpython-36.pyc toolbox/__pycache__/imtools.cpython-36.pyc toolbox/ftools.py toolbox/imtools.py |
| diffstat | 33 files changed, 70 insertions(+), 2098 deletions(-) [+] |
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--- a/Dockerfile Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,12 +0,0 @@ -FROM tensorflow/tensorflow:1.15.0-py3 - -RUN apt-get update -RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata -RUN apt-get install -y python3-opencv -RUN apt-get install -y libtiff5-dev git - -RUN pip install cython scikit-image==0.14.2 matplotlib tifffile==2020.2.16 scipy==1.1.0 opencv-python==4.3.0.36 - -RUN pip install git+https://github.com/FZJ-INM1-BDA/pytiff.git@0701f28e5862c26024e8daa34201005b16db4c8f - -COPY . /app
--- a/LICENSE Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2020 HMS-IDAC - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE.
--- a/README.md Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,26 +0,0 @@ - - -*Great*....yet **another** TMA dearray program. What does *this* one do? - -Coreograph uses UNet, a deep learning model, to identify complete/incomplete tissue cores on a tissue microarray. It has been trained on 9 TMA slides of different sizes and tissue types. - -<img src="/images/raw.jpg" width="425" height="315" /> <img src="/images/probmap.jpg" width="425" height="315" /> - -Training sets were acquired at 0.2micron/pixel resolution and downsampled 1/32 times to speed up performance. Once the center of each core has been identifed, active contours is used to generate a tissue mask of each core that can aid downstream single cell segmentation. A GPU is not required but will reduce computation time. - -*Coreograph exports these files:** -1. individual cores as tiff stacks with user-selectable channel ranges -2. binary tissue masks (saved in the 'mask' subfolder) -3. a TMA map showing the labels and outlines of each core for quality control purposes - - - -*Instructions for use:** -`python UNetCoreograph.py` -1. `--imagePath` : the path to the image file. Should be tif or ome.tif -2. `--outputPath` : the path to save the above-mentioned files -3. `--downsampleFactor` : how many times to downsample the raw image file. Default is 5 times to match the training data. -4. `--channel` : which is the channel to feed into UNet and generate probabiltiy maps from. This is usually a DAPI channel -5. `--buffer` : the extra space around a core before cropping it. A value of 2 means there is twice the width of the core added as buffer around it. 2 is default -6. `--outputChan` : a range of channels to be exported. -1 is default and will export all channels (takes awhile). Select a single channel or a continuous range. --outputChan 0 10 will export channel 0 up to (and including) channel 10 -
--- a/UNet2DtCycifTRAINCoreograph.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,529 +0,0 @@ -import numpy as np -from scipy import misc -import tensorflow as tf -import shutil -import scipy.io as sio -import os,fnmatch,PIL,glob - -import sys -sys.path.insert(0, 'C:\\Users\\Public\\Documents\\ImageScience') -from toolbox.imtools import * -from toolbox.ftools import * -from toolbox.PartitionOfImage import PI2D - - -def concat3(lst): - return tf.concat(lst,3) - -class UNet2D: - hp = None # hyper-parameters - nn = None # network - tfTraining = None # if training or not (to handle batch norm) - tfData = None # data placeholder - Session = None - DatasetMean = 0 - DatasetStDev = 0 - - def setupWithHP(hp): - UNet2D.setup(hp['imSize'], - hp['nChannels'], - hp['nClasses'], - hp['nOut0'], - hp['featMapsFact'], - hp['downSampFact'], - hp['ks'], - hp['nExtraConvs'], - hp['stdDev0'], - hp['nLayers'], - hp['batchSize']) - - def setup(imSize,nChannels,nClasses,nOut0,featMapsFact,downSampFact,kernelSize,nExtraConvs,stdDev0,nDownSampLayers,batchSize): - UNet2D.hp = {'imSize':imSize, - 'nClasses':nClasses, - 'nChannels':nChannels, - 'nExtraConvs':nExtraConvs, - 'nLayers':nDownSampLayers, - 'featMapsFact':featMapsFact, - 'downSampFact':downSampFact, - 'ks':kernelSize, - 'nOut0':nOut0, - 'stdDev0':stdDev0, - 'batchSize':batchSize} - - nOutX = [UNet2D.hp['nChannels'],UNet2D.hp['nOut0']] - dsfX = [] - for i in range(UNet2D.hp['nLayers']): - nOutX.append(nOutX[-1]*UNet2D.hp['featMapsFact']) - dsfX.append(UNet2D.hp['downSampFact']) - - - # -------------------------------------------------- - # downsampling layer - # -------------------------------------------------- - - with tf.name_scope('placeholders'): - UNet2D.tfTraining = tf.placeholder(tf.bool, name='training') - UNet2D.tfData = tf.placeholder("float", shape=[None,UNet2D.hp['imSize'],UNet2D.hp['imSize'],UNet2D.hp['nChannels']],name='data') - - def down_samp_layer(data,index): - with tf.name_scope('ld%d' % index): - ldXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index], nOutX[index+1]], stddev=stdDev0),name='kernel1') - ldXWeightsExtra = [] - for i in range(nExtraConvs): - 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)) - - c00 = tf.nn.conv2d(data, ldXWeights1, strides=[1, 1, 1, 1], padding='SAME') - for i in range(nExtraConvs): - c00 = tf.nn.conv2d(tf.nn.relu(c00), ldXWeightsExtra[i], strides=[1, 1, 1, 1], padding='SAME') - - ldXWeightsShortcut = tf.Variable(tf.truncated_normal([1, 1, nOutX[index], nOutX[index+1]], stddev=stdDev0),name='shortcutWeights') - shortcut = tf.nn.conv2d(data, ldXWeightsShortcut, strides=[1, 1, 1, 1], padding='SAME') - - bn = tf.layers.batch_normalization(tf.nn.relu(c00+shortcut), training=UNet2D.tfTraining) - - return tf.nn.max_pool(bn, ksize=[1, dsfX[index], dsfX[index], 1], strides=[1, dsfX[index], dsfX[index], 1], padding='SAME',name='maxpool') - - # -------------------------------------------------- - # bottom layer - # -------------------------------------------------- - - with tf.name_scope('lb'): - 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') - def lb(hidden): - return tf.nn.relu(tf.nn.conv2d(hidden, lbWeights1, strides=[1, 1, 1, 1], padding='SAME'),name='conv') - - # -------------------------------------------------- - # downsampling - # -------------------------------------------------- - - with tf.name_scope('downsampling'): - dsX = [] - dsX.append(UNet2D.tfData) - - for i in range(UNet2D.hp['nLayers']): - dsX.append(down_samp_layer(dsX[i],i)) - - b = lb(dsX[UNet2D.hp['nLayers']]) - - # -------------------------------------------------- - # upsampling layer - # -------------------------------------------------- - - def up_samp_layer(data,index): - with tf.name_scope('lu%d' % index): - luXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index+1], nOutX[index+2]], stddev=stdDev0),name='kernel1') - luXWeights2 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index]+nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name='kernel2') - luXWeightsExtra = [] - for i in range(nExtraConvs): - 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)) - - outSize = UNet2D.hp['imSize'] - for i in range(index): - outSize /= dsfX[i] - outSize = int(outSize) - - outputShape = [UNet2D.hp['batchSize'],outSize,outSize,nOutX[index+1]] - us = tf.nn.relu(tf.nn.conv2d_transpose(data, luXWeights1, outputShape, strides=[1, dsfX[index], dsfX[index], 1], padding='SAME'),name='conv1') - cc = concat3([dsX[index],us]) - cv = tf.nn.relu(tf.nn.conv2d(cc, luXWeights2, strides=[1, 1, 1, 1], padding='SAME'),name='conv2') - for i in range(nExtraConvs): - cv = tf.nn.relu(tf.nn.conv2d(cv, luXWeightsExtra[i], strides=[1, 1, 1, 1], padding='SAME'),name='conv2Extra%d' % i) - return cv - - # -------------------------------------------------- - # final (top) layer - # -------------------------------------------------- - - with tf.name_scope('lt'): - ltWeights1 = tf.Variable(tf.truncated_normal([1, 1, nOutX[1], nClasses], stddev=stdDev0),name='kernel') - def lt(hidden): - return tf.nn.conv2d(hidden, ltWeights1, strides=[1, 1, 1, 1], padding='SAME',name='conv') - - - # -------------------------------------------------- - # upsampling - # -------------------------------------------------- - - with tf.name_scope('upsampling'): - usX = [] - usX.append(b) - - for i in range(UNet2D.hp['nLayers']): - usX.append(up_samp_layer(usX[i],UNet2D.hp['nLayers']-1-i)) - - t = lt(usX[UNet2D.hp['nLayers']]) - - - sm = tf.nn.softmax(t,-1) - UNet2D.nn = sm - - - def train(imPath,logPath,modelPath,pmPath,nTrain,nValid,nTest,restoreVariables,nSteps,gpuIndex,testPMIndex): - os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex - - outLogPath = logPath - trainWriterPath = pathjoin(logPath,'Train') - validWriterPath = pathjoin(logPath,'Valid') - outModelPath = pathjoin(modelPath,'model.ckpt') - outPMPath = pmPath - - batchSize = UNet2D.hp['batchSize'] - imSize = UNet2D.hp['imSize'] - nChannels = UNet2D.hp['nChannels'] - nClasses = UNet2D.hp['nClasses'] - - # -------------------------------------------------- - # data - # -------------------------------------------------- - - Train = np.zeros((nTrain,imSize,imSize,nChannels)) - Valid = np.zeros((nValid,imSize,imSize,nChannels)) - Test = np.zeros((nTest,imSize,imSize,nChannels)) - LTrain = np.zeros((nTrain,imSize,imSize,nClasses)) - LValid = np.zeros((nValid,imSize,imSize,nClasses)) - LTest = np.zeros((nTest,imSize,imSize,nClasses)) - - print('loading data, computing mean / st dev') - if not os.path.exists(modelPath): - os.makedirs(modelPath) - if restoreVariables: - datasetMean = loadData(pathjoin(modelPath,'datasetMean.data')) - datasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data')) - else: - datasetMean = 0.09 - datasetStDev = 0.09 - #for iSample in range(nTrain+nValid+nTest): - # I = im2double(tifread('%s/I%05d_Img.tif' % (imPath,iSample))) - # datasetMean += np.mean(I) - # datasetStDev += np.std(I) - #datasetMean /= (nTrain+nValid+nTest) - #datasetStDev /= (nTrain+nValid+nTest) - saveData(datasetMean, pathjoin(modelPath,'datasetMean.data')) - saveData(datasetStDev, pathjoin(modelPath,'datasetStDev.data')) - - perm = np.arange(nTrain+nValid+nTest) - np.random.shuffle(perm) - - for iSample in range(0, nTrain): - path = '%s/I%05d_Img.tif' % (imPath,perm[iSample]) - im = im2double(tifread(path)) - #im = im[0, 0, 0, :, :] - Train[iSample,:,:,0] = (im-datasetMean)/datasetStDev - path = '%s/I%05d_Ant.tif' % (imPath,perm[iSample]) - im = tifread(path) - for i in range(nClasses): - LTrain[iSample,:,:,i] = (im == i+1) - - for iSample in range(0, nValid): - path = '%s/I%05d_Img.tif' % (imPath,perm[nTrain+iSample]) - im = im2double(tifread(path)) - #im = im[0, 0, 0, :, :] - Valid[iSample,:,:,0] = (im-datasetMean)/datasetStDev - path = '%s/I%05d_Ant.tif' % (imPath,perm[nTrain+iSample]) - im = tifread(path) - for i in range(nClasses): - LValid[iSample,:,:,i] = (im == i+1) - - for iSample in range(0, nTest): - path = '%s/I%05d_Img.tif' % (imPath,perm[nTrain+nValid+iSample]) - im = im2double(tifread(path)) - #im = im[0, 0, 0, :, :] - Test[iSample,:,:,0] = (im-datasetMean)/datasetStDev - path = '%s/I%05d_Ant.tif' % (imPath,perm[nTrain+nValid+iSample]) - im = tifread(path) - for i in range(nClasses): - LTest[iSample,:,:,i] = (im == i+1) - - # -------------------------------------------------- - # optimization - # -------------------------------------------------- - - tfLabels = tf.placeholder("float", shape=[None,imSize,imSize,nClasses],name='labels') - - globalStep = tf.Variable(0,trainable=False) - learningRate0 = 0.05 - decaySteps = 1000 - decayRate = 0.95 - learningRate = tf.train.exponential_decay(learningRate0,globalStep,decaySteps,decayRate,staircase=True) - - with tf.name_scope('optim'): - loss = tf.reduce_mean(-tf.reduce_sum(tf.multiply(tfLabels,tf.log(UNet2D.nn)),3)) - updateOps = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - # optimizer = tf.train.MomentumOptimizer(1e-3,0.9) - optimizer = tf.train.MomentumOptimizer(learningRate,0.9) - # optimizer = tf.train.GradientDescentOptimizer(learningRate) - with tf.control_dependencies(updateOps): - optOp = optimizer.minimize(loss,global_step=globalStep) - - with tf.name_scope('eval'): - error = [] - for iClass in range(nClasses): - labels0 = tf.reshape(tf.to_int32(tf.slice(tfLabels,[0,0,0,iClass],[-1,-1,-1,1])),[batchSize,imSize,imSize]) - predict0 = tf.reshape(tf.to_int32(tf.equal(tf.argmax(UNet2D.nn,3),iClass)),[batchSize,imSize,imSize]) - correct = tf.multiply(labels0,predict0) - nCorrect0 = tf.reduce_sum(correct) - nLabels0 = tf.reduce_sum(labels0) - error.append(1-tf.to_float(nCorrect0)/tf.to_float(nLabels0)) - errors = tf.tuple(error) - - # -------------------------------------------------- - # inspection - # -------------------------------------------------- - - with tf.name_scope('scalars'): - tf.summary.scalar('avg_cross_entropy', loss) - for iClass in range(nClasses): - tf.summary.scalar('avg_pixel_error_%d' % iClass, error[iClass]) - tf.summary.scalar('learning_rate', learningRate) - with tf.name_scope('images'): - #split0 = tf.slice(UNet2D.nn,[0,0,0,0],[-1,-1,-1,1]) - split0 = tf.slice(UNet2D.nn,[0,0,0,1],[-1,-1,-1,1]) - split1 = tf.slice(tfLabels, [0, 0, 0, 0], [-1, -1, -1, 1]) - if nClasses > 2: - split2 = tf.slice(UNet2D.nn,[0,0,0,2],[-1,-1,-1,1]) - tf.summary.image('pm0',split0) - tf.summary.image('pm1',split1) - if nClasses > 2: - tf.summary.image('pm2',split2) - merged = tf.summary.merge_all() - - - # -------------------------------------------------- - # session - # -------------------------------------------------- - - saver = tf.train.Saver() - sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU - - if os.path.exists(outLogPath): - shutil.rmtree(outLogPath) - trainWriter = tf.summary.FileWriter(trainWriterPath, sess.graph) - validWriter = tf.summary.FileWriter(validWriterPath, sess.graph) - - if restoreVariables: - saver.restore(sess, outModelPath) - print("Model restored.") - else: - sess.run(tf.global_variables_initializer()) - - # -------------------------------------------------- - # train - # -------------------------------------------------- - - batchData = np.zeros((batchSize,imSize,imSize,nChannels)) - batchLabels = np.zeros((batchSize,imSize,imSize,nClasses)) - for i in range(nSteps): - # train - - perm = np.arange(nTrain) - np.random.shuffle(perm) - - for j in range(batchSize): - batchData[j,:,:,:] = Train[perm[j],:,:,:] - batchLabels[j,:,:,:] = LTrain[perm[j],:,:,:] - - summary,_ = sess.run([merged,optOp],feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 1}) - trainWriter.add_summary(summary, i) - - # validation - - perm = np.arange(nValid) - np.random.shuffle(perm) - - for j in range(batchSize): - batchData[j,:,:,:] = Valid[perm[j],:,:,:] - batchLabels[j,:,:,:] = LValid[perm[j],:,:,:] - - summary, es = sess.run([merged, errors],feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 0}) - validWriter.add_summary(summary, i) - - e = np.mean(es) - print('step %05d, e: %f' % (i,e)) - - if i == 0: - if restoreVariables: - lowestError = e - else: - lowestError = np.inf - - if np.mod(i,100) == 0 and e < lowestError: - lowestError = e - print("Model saved in file: %s" % saver.save(sess, outModelPath)) - - - # -------------------------------------------------- - # test - # -------------------------------------------------- - - if not os.path.exists(outPMPath): - os.makedirs(outPMPath) - - for i in range(nTest): - j = np.mod(i,batchSize) - - batchData[j,:,:,:] = Test[i,:,:,:] - batchLabels[j,:,:,:] = LTest[i,:,:,:] - - if j == batchSize-1 or i == nTest-1: - - output = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 0}) - - for k in range(j+1): - pm = output[k,:,:,testPMIndex] - gt = batchLabels[k,:,:,testPMIndex] - im = np.sqrt(normalize(batchData[k,:,:,0])) - imwrite(np.uint8(255*np.concatenate((im,np.concatenate((pm,gt),axis=1)),axis=1)),'%s/I%05d.png' % (outPMPath,i-j+k+1)) - - - # -------------------------------------------------- - # save hyper-parameters, clean-up - # -------------------------------------------------- - - saveData(UNet2D.hp,pathjoin(modelPath,'hp.data')) - - trainWriter.close() - validWriter.close() - sess.close() - - def deploy(imPath,nImages,modelPath,pmPath,gpuIndex,pmIndex): - os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex - - variablesPath = pathjoin(modelPath,'model.ckpt') - outPMPath = pmPath - - hp = loadData(pathjoin(modelPath,'hp.data')) - UNet2D.setupWithHP(hp) - - batchSize = UNet2D.hp['batchSize'] - imSize = UNet2D.hp['imSize'] - nChannels = UNet2D.hp['nChannels'] - nClasses = UNet2D.hp['nClasses'] - - # -------------------------------------------------- - # data - # -------------------------------------------------- - - Data = np.zeros((nImages,imSize,imSize,nChannels)) - - datasetMean = loadData(pathjoin(modelPath,'datasetMean.data')) - datasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data')) - - for iSample in range(0, nImages): - path = '%s/I%05d_Img.tif' % (imPath,iSample) - im = im2double(tifread(path)) - #im = im[0, 0, 0, :, :] - Data[iSample,:,:,0] = (im-datasetMean)/datasetStDev - - # -------------------------------------------------- - # session - # -------------------------------------------------- - - saver = tf.train.Saver() - sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU - - saver.restore(sess, variablesPath) - print("Model restored.") - - # -------------------------------------------------- - # deploy - # -------------------------------------------------- - - batchData = np.zeros((batchSize,imSize,imSize,nChannels)) - - if not os.path.exists(outPMPath): - os.makedirs(outPMPath) - - for i in range(nImages): - print(i,nImages) - - j = np.mod(i,batchSize) - - batchData[j,:,:,:] = Data[i,:,:,:] - - if j == batchSize-1 or i == nImages-1: - - output = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0}) - - for k in range(j+1): - pm = output[k,:,:,pmIndex] - im = np.sqrt(normalize(batchData[k,:,:,0])) - # imwrite(np.uint8(255*np.concatenate((im,pm),axis=1)),'%s/I%05d.png' % (outPMPath,i-j+k+1)) - imwrite(np.uint8(255*im),'%s/I%05d_Im.png' % (outPMPath,i-j+k+1)) - imwrite(np.uint8(255*pm),'%s/I%05d_PM.png' % (outPMPath,i-j+k+1)) - - - # -------------------------------------------------- - # clean-up - # -------------------------------------------------- - - sess.close() - - def singleImageInferenceSetup(modelPath,gpuIndex): - os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex - - variablesPath = pathjoin(modelPath,'model.ckpt') - - hp = loadData(pathjoin(modelPath,'hp.data')) - UNet2D.setupWithHP(hp) - - UNet2D.DatasetMean = loadData(pathjoin(modelPath,'datasetMean.data')) - UNet2D.DatasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data')) - print(UNet2D.DatasetMean) - print(UNet2D.DatasetStDev) - - # -------------------------------------------------- - # session - # -------------------------------------------------- - - saver = tf.train.Saver() - UNet2D.Session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU - - saver.restore(UNet2D.Session, variablesPath) - print("Model restored.") - - def singleImageInferenceCleanup(): - UNet2D.Session.close() - - def singleImageInference(image,mode,pmIndex): - print('Inference...') - - batchSize = UNet2D.hp['batchSize'] - imSize = UNet2D.hp['imSize'] - nChannels = UNet2D.hp['nChannels'] - - PI2D.setup(image,imSize,int(imSize/8),mode) - PI2D.createOutput(nChannels) - - batchData = np.zeros((batchSize,imSize,imSize,nChannels)) - for i in range(PI2D.NumPatches): - j = np.mod(i,batchSize) - batchData[j,:,:,0] = (PI2D.getPatch(i)-UNet2D.DatasetMean)/UNet2D.DatasetStDev - if j == batchSize-1 or i == PI2D.NumPatches-1: - output = UNet2D.Session.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0}) - for k in range(j+1): - pm = output[k,:,:,pmIndex] - PI2D.patchOutput(i-j+k,pm) - # PI2D.patchOutput(i-j+k,normalize(imgradmag(PI2D.getPatch(i-j+k),1))) - - return PI2D.getValidOutput() - - -if __name__ == '__main__': - logPath = 'D:\\LSP\\UNet\\Coreograph\\TFLogs' - modelPath = 'D:\\LSP\\Coreograph\\model-4layersMaskAug20New' - pmPath = 'D:\\LSP\\UNet\\Coreograph\\TFProbMaps' - - - # ----- test 1 ----- - - # imPath = 'D:\\LSP\\UNet\\tonsil20x1bin1chan\\tonsilAnnotations' - imPath = 'Z:/IDAC/Clarence/LSP/CyCIF/TMA/training data custom unaveraged' - # UNet2D.setup(128,1,2,8,2,2,3,1,0.1,2,8) - # UNet2D.train(imPath,logPath,modelPath,pmPath,500,100,40,True,20000,1,0) - UNet2D.setup(128, 1, 2, 20, 2, 2, 3, 2, 0.03, 4, 32) - UNet2D.train(imPath, logPath, modelPath, pmPath, 2053, 513 , 641, True, 10, 1, 1) - UNet2D.deploy(imPath,100,modelPath,pmPath,1,1) - - - -
--- a/UNetCoreograph.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,800 +0,0 @@ -import numpy as np -from scipy import misc as sm -import shutil -import scipy.io as sio -import os -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' -import logging -logging.getLogger('tensorflow').setLevel(logging.FATAL) -import skimage.exposure as sk -import cv2 -import argparse -import pytiff -import tifffile -import tensorflow as tf -from skimage.morphology import * -from skimage.exposure import rescale_intensity -from skimage.segmentation import chan_vese, find_boundaries, morphological_chan_vese -from skimage.measure import regionprops,label, find_contours -from skimage.transform import resize -from skimage.filters import gaussian, threshold_otsu -from skimage.feature import peak_local_max,blob_log -from skimage.color import gray2rgb as gray2rgb -import skimage.io as skio -from scipy.ndimage.morphology import binary_fill_holes -from skimage import img_as_bool -from skimage.draw import circle_perimeter -from scipy.ndimage.filters import uniform_filter -from scipy.ndimage import gaussian_laplace -from os.path import * -from os import listdir, makedirs, remove - - - -import sys -from typing import Any - -#sys.path.insert(0, 'C:\\Users\\Public\\Documents\\ImageScience') -from toolbox.imtools import * -from toolbox.ftools import * -from toolbox.PartitionOfImage import PI2D - - -def concat3(lst): - return tf.concat(lst,3) - -class UNet2D: - hp = None # hyper-parameters - nn = None # network - tfTraining = None # if training or not (to handle batch norm) - tfData = None # data placeholder - Session = None - DatasetMean = 0 - DatasetStDev = 0 - - def setupWithHP(hp): - UNet2D.setup(hp['imSize'], - hp['nChannels'], - hp['nClasses'], - hp['nOut0'], - hp['featMapsFact'], - hp['downSampFact'], - hp['ks'], - hp['nExtraConvs'], - hp['stdDev0'], - hp['nLayers'], - hp['batchSize']) - - def setup(imSize,nChannels,nClasses,nOut0,featMapsFact,downSampFact,kernelSize,nExtraConvs,stdDev0,nDownSampLayers,batchSize): - UNet2D.hp = {'imSize':imSize, - 'nClasses':nClasses, - 'nChannels':nChannels, - 'nExtraConvs':nExtraConvs, - 'nLayers':nDownSampLayers, - 'featMapsFact':featMapsFact, - 'downSampFact':downSampFact, - 'ks':kernelSize, - 'nOut0':nOut0, - 'stdDev0':stdDev0, - 'batchSize':batchSize} - - nOutX = [UNet2D.hp['nChannels'],UNet2D.hp['nOut0']] - dsfX = [] - for i in range(UNet2D.hp['nLayers']): - nOutX.append(nOutX[-1]*UNet2D.hp['featMapsFact']) - dsfX.append(UNet2D.hp['downSampFact']) - - - # -------------------------------------------------- - # downsampling layer - # -------------------------------------------------- - - with tf.name_scope('placeholders'): - UNet2D.tfTraining = tf.placeholder(tf.bool, name='training') - UNet2D.tfData = tf.placeholder("float", shape=[None,UNet2D.hp['imSize'],UNet2D.hp['imSize'],UNet2D.hp['nChannels']],name='data') - - def down_samp_layer(data,index): - with tf.name_scope('ld%d' % index): - ldXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index], nOutX[index+1]], stddev=stdDev0),name='kernel1') - ldXWeightsExtra = [] - for i in range(nExtraConvs): - 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)) - - c00 = tf.nn.conv2d(data, ldXWeights1, strides=[1, 1, 1, 1], padding='SAME') - for i in range(nExtraConvs): - c00 = tf.nn.conv2d(tf.nn.relu(c00), ldXWeightsExtra[i], strides=[1, 1, 1, 1], padding='SAME') - - ldXWeightsShortcut = tf.Variable(tf.truncated_normal([1, 1, nOutX[index], nOutX[index+1]], stddev=stdDev0),name='shortcutWeights') - shortcut = tf.nn.conv2d(data, ldXWeightsShortcut, strides=[1, 1, 1, 1], padding='SAME') - - bn = tf.layers.batch_normalization(tf.nn.relu(c00+shortcut), training=UNet2D.tfTraining) - - return tf.nn.max_pool(bn, ksize=[1, dsfX[index], dsfX[index], 1], strides=[1, dsfX[index], dsfX[index], 1], padding='SAME',name='maxpool') - - # -------------------------------------------------- - # bottom layer - # -------------------------------------------------- - - with tf.name_scope('lb'): - 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') - def lb(hidden): - return tf.nn.relu(tf.nn.conv2d(hidden, lbWeights1, strides=[1, 1, 1, 1], padding='SAME'),name='conv') - - # -------------------------------------------------- - # downsampling - # -------------------------------------------------- - - with tf.name_scope('downsampling'): - dsX = [] - dsX.append(UNet2D.tfData) - - for i in range(UNet2D.hp['nLayers']): - dsX.append(down_samp_layer(dsX[i],i)) - - b = lb(dsX[UNet2D.hp['nLayers']]) - - # -------------------------------------------------- - # upsampling layer - # -------------------------------------------------- - - def up_samp_layer(data,index): - with tf.name_scope('lu%d' % index): - luXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index+1], nOutX[index+2]], stddev=stdDev0),name='kernel1') - luXWeights2 = tf.Variable(tf.truncated_normal([UNet2D.hp['ks'], UNet2D.hp['ks'], nOutX[index]+nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name='kernel2') - luXWeightsExtra = [] - for i in range(nExtraConvs): - 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)) - - outSize = UNet2D.hp['imSize'] - for i in range(index): - outSize /= dsfX[i] - outSize = int(outSize) - - outputShape = [UNet2D.hp['batchSize'],outSize,outSize,nOutX[index+1]] - us = tf.nn.relu(tf.nn.conv2d_transpose(data, luXWeights1, outputShape, strides=[1, dsfX[index], dsfX[index], 1], padding='SAME'),name='conv1') - cc = concat3([dsX[index],us]) - cv = tf.nn.relu(tf.nn.conv2d(cc, luXWeights2, strides=[1, 1, 1, 1], padding='SAME'),name='conv2') - for i in range(nExtraConvs): - cv = tf.nn.relu(tf.nn.conv2d(cv, luXWeightsExtra[i], strides=[1, 1, 1, 1], padding='SAME'),name='conv2Extra%d' % i) - return cv - - # -------------------------------------------------- - # final (top) layer - # -------------------------------------------------- - - with tf.name_scope('lt'): - ltWeights1 = tf.Variable(tf.truncated_normal([1, 1, nOutX[1], nClasses], stddev=stdDev0),name='kernel') - def lt(hidden): - return tf.nn.conv2d(hidden, ltWeights1, strides=[1, 1, 1, 1], padding='SAME',name='conv') - - - # -------------------------------------------------- - # upsampling - # -------------------------------------------------- - - with tf.name_scope('upsampling'): - usX = [] - usX.append(b) - - for i in range(UNet2D.hp['nLayers']): - usX.append(up_samp_layer(usX[i],UNet2D.hp['nLayers']-1-i)) - - t = lt(usX[UNet2D.hp['nLayers']]) - - - sm = tf.nn.softmax(t,-1) - UNet2D.nn = sm - - - def train(imPath,logPath,modelPath,pmPath,nTrain,nValid,nTest,restoreVariables,nSteps,gpuIndex,testPMIndex): - os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex - - outLogPath = logPath - trainWriterPath = pathjoin(logPath,'Train') - validWriterPath = pathjoin(logPath,'Valid') - outModelPath = pathjoin(modelPath,'model.ckpt') - outPMPath = pmPath - - batchSize = UNet2D.hp['batchSize'] - imSize = UNet2D.hp['imSize'] - nChannels = UNet2D.hp['nChannels'] - nClasses = UNet2D.hp['nClasses'] - - # -------------------------------------------------- - # data - # -------------------------------------------------- - - Train = np.zeros((nTrain,imSize,imSize,nChannels)) - Valid = np.zeros((nValid,imSize,imSize,nChannels)) - Test = np.zeros((nTest,imSize,imSize,nChannels)) - LTrain = np.zeros((nTrain,imSize,imSize,nClasses)) - LValid = np.zeros((nValid,imSize,imSize,nClasses)) - LTest = np.zeros((nTest,imSize,imSize,nClasses)) - - print('loading data, computing mean / st dev') - if not os.path.exists(modelPath): - os.makedirs(modelPath) - if restoreVariables: - datasetMean = loadData(pathjoin(modelPath,'datasetMean.data')) - datasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data')) - else: - datasetMean = 0 - datasetStDev = 0 - for iSample in range(nTrain+nValid+nTest): - I = im2double(tifread('%s/I%05d_Img.tif' % (imPath,iSample))) - datasetMean += np.mean(I) - datasetStDev += np.std(I) - datasetMean /= (nTrain+nValid+nTest) - datasetStDev /= (nTrain+nValid+nTest) - saveData(datasetMean, pathjoin(modelPath,'datasetMean.data')) - saveData(datasetStDev, pathjoin(modelPath,'datasetStDev.data')) - - perm = np.arange(nTrain+nValid+nTest) - np.random.shuffle(perm) - - for iSample in range(0, nTrain): - path = '%s/I%05d_Img.tif' % (imPath,perm[iSample]) - im = im2double(tifread(path)) - Train[iSample,:,:,0] = (im-datasetMean)/datasetStDev - path = '%s/I%05d_Ant.tif' % (imPath,perm[iSample]) - im = tifread(path) - for i in range(nClasses): - LTrain[iSample,:,:,i] = (im == i+1) - - for iSample in range(0, nValid): - path = '%s/I%05d_Img.tif' % (imPath,perm[nTrain+iSample]) - im = im2double(tifread(path)) - Valid[iSample,:,:,0] = (im-datasetMean)/datasetStDev - path = '%s/I%05d_Ant.tif' % (imPath,perm[nTrain+iSample]) - im = tifread(path) - for i in range(nClasses): - LValid[iSample,:,:,i] = (im == i+1) - - for iSample in range(0, nTest): - path = '%s/I%05d_Img.tif' % (imPath,perm[nTrain+nValid+iSample]) - im = im2double(tifread(path)) - Test[iSample,:,:,0] = (im-datasetMean)/datasetStDev - path = '%s/I%05d_Ant.tif' % (imPath,perm[nTrain+nValid+iSample]) - im = tifread(path) - for i in range(nClasses): - LTest[iSample,:,:,i] = (im == i+1) - - # -------------------------------------------------- - # optimization - # -------------------------------------------------- - - tfLabels = tf.placeholder("float", shape=[None,imSize,imSize,nClasses],name='labels') - - globalStep = tf.Variable(0,trainable=False) - learningRate0 = 0.01 - decaySteps = 1000 - decayRate = 0.95 - learningRate = tf.train.exponential_decay(learningRate0,globalStep,decaySteps,decayRate,staircase=True) - - with tf.name_scope('optim'): - loss = tf.reduce_mean(-tf.reduce_sum(tf.multiply(tfLabels,tf.log(UNet2D.nn)),3)) - updateOps = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - # optimizer = tf.train.MomentumOptimizer(1e-3,0.9) - optimizer = tf.train.MomentumOptimizer(learningRate,0.9) - # optimizer = tf.train.GradientDescentOptimizer(learningRate) - with tf.control_dependencies(updateOps): - optOp = optimizer.minimize(loss,global_step=globalStep) - - with tf.name_scope('eval'): - error = [] - for iClass in range(nClasses): - labels0 = tf.reshape(tf.to_int32(tf.slice(tfLabels,[0,0,0,iClass],[-1,-1,-1,1])),[batchSize,imSize,imSize]) - predict0 = tf.reshape(tf.to_int32(tf.equal(tf.argmax(UNet2D.nn,3),iClass)),[batchSize,imSize,imSize]) - correct = tf.multiply(labels0,predict0) - nCorrect0 = tf.reduce_sum(correct) - nLabels0 = tf.reduce_sum(labels0) - error.append(1-tf.to_float(nCorrect0)/tf.to_float(nLabels0)) - errors = tf.tuple(error) - - # -------------------------------------------------- - # inspection - # -------------------------------------------------- - - with tf.name_scope('scalars'): - tf.summary.scalar('avg_cross_entropy', loss) - for iClass in range(nClasses): - tf.summary.scalar('avg_pixel_error_%d' % iClass, error[iClass]) - tf.summary.scalar('learning_rate', learningRate) - with tf.name_scope('images'): - split0 = tf.slice(UNet2D.nn,[0,0,0,0],[-1,-1,-1,1]) - split1 = tf.slice(UNet2D.nn,[0,0,0,1],[-1,-1,-1,1]) - if nClasses > 2: - split2 = tf.slice(UNet2D.nn,[0,0,0,2],[-1,-1,-1,1]) - tf.summary.image('pm0',split0) - tf.summary.image('pm1',split1) - if nClasses > 2: - tf.summary.image('pm2',split2) - merged = tf.summary.merge_all() - - - # -------------------------------------------------- - # session - # -------------------------------------------------- - - saver = tf.train.Saver() - sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU - - if os.path.exists(outLogPath): - shutil.rmtree(outLogPath) - trainWriter = tf.summary.FileWriter(trainWriterPath, sess.graph) - validWriter = tf.summary.FileWriter(validWriterPath, sess.graph) - - if restoreVariables: - saver.restore(sess, outModelPath) - print("Model restored.") - else: - sess.run(tf.global_variables_initializer()) - - # -------------------------------------------------- - # train - # -------------------------------------------------- - - batchData = np.zeros((batchSize,imSize,imSize,nChannels)) - batchLabels = np.zeros((batchSize,imSize,imSize,nClasses)) - for i in range(nSteps): - # train - - perm = np.arange(nTrain) - np.random.shuffle(perm) - - for j in range(batchSize): - batchData[j,:,:,:] = Train[perm[j],:,:,:] - batchLabels[j,:,:,:] = LTrain[perm[j],:,:,:] - - summary,_ = sess.run([merged,optOp],feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 1}) - trainWriter.add_summary(summary, i) - - # validation - - perm = np.arange(nValid) - np.random.shuffle(perm) - - for j in range(batchSize): - batchData[j,:,:,:] = Valid[perm[j],:,:,:] - batchLabels[j,:,:,:] = LValid[perm[j],:,:,:] - - summary, es = sess.run([merged, errors],feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 0}) - validWriter.add_summary(summary, i) - - e = np.mean(es) - print('step %05d, e: %f' % (i,e)) - - if i == 0: - if restoreVariables: - lowestError = e - else: - lowestError = np.inf - - if np.mod(i,100) == 0 and e < lowestError: - lowestError = e - print("Model saved in file: %s" % saver.save(sess, outModelPath)) - - - # -------------------------------------------------- - # test - # -------------------------------------------------- - - if not os.path.exists(outPMPath): - os.makedirs(outPMPath) - - for i in range(nTest): - j = np.mod(i,batchSize) - - batchData[j,:,:,:] = Test[i,:,:,:] - batchLabels[j,:,:,:] = LTest[i,:,:,:] - - if j == batchSize-1 or i == nTest-1: - - output = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, tfLabels: batchLabels, UNet2D.tfTraining: 0}) - - for k in range(j+1): - pm = output[k,:,:,testPMIndex] - gt = batchLabels[k,:,:,testPMIndex] - im = np.sqrt(normalize(batchData[k,:,:,0])) - imwrite(np.uint8(255*np.concatenate((im,np.concatenate((pm,gt),axis=1)),axis=1)),'%s/I%05d.png' % (outPMPath,i-j+k+1)) - - - # -------------------------------------------------- - # save hyper-parameters, clean-up - # -------------------------------------------------- - - saveData(UNet2D.hp,pathjoin(modelPath,'hp.data')) - - trainWriter.close() - validWriter.close() - sess.close() - - def deploy(imPath,nImages,modelPath,pmPath,gpuIndex,pmIndex): - os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex - variablesPath = pathjoin(modelPath,'model.ckpt') - outPMPath = pmPath - - hp = loadData(pathjoin(modelPath,'hp.data')) - UNet2D.setupWithHP(hp) - - batchSize = UNet2D.hp['batchSize'] - imSize = UNet2D.hp['imSize'] - nChannels = UNet2D.hp['nChannels'] - nClasses = UNet2D.hp['nClasses'] - - # -------------------------------------------------- - # data - # -------------------------------------------------- - - Data = np.zeros((nImages,imSize,imSize,nChannels)) - - datasetMean = loadData(pathjoin(modelPath,'datasetMean.data')) - datasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data')) - - for iSample in range(0, nImages): - path = '%s/I%05d_Img.tif' % (imPath,iSample) - im = im2double(tifread(path)) - Data[iSample,:,:,0] = (im-datasetMean)/datasetStDev - - # -------------------------------------------------- - # session - # -------------------------------------------------- - - saver = tf.train.Saver() - sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU - - saver.restore(sess, variablesPath) - print("Model restored.") - - # -------------------------------------------------- - # deploy - # -------------------------------------------------- - - batchData = np.zeros((batchSize,imSize,imSize,nChannels)) - - if not os.path.exists(outPMPath): - os.makedirs(outPMPath) - - for i in range(nImages): - print(i,nImages) - - j = np.mod(i,batchSize) - - batchData[j,:,:,:] = Data[i,:,:,:] - - if j == batchSize-1 or i == nImages-1: - - output = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0}) - - for k in range(j+1): - pm = output[k,:,:,pmIndex] - im = np.sqrt(normalize(batchData[k,:,:,0])) - # imwrite(np.uint8(255*np.concatenate((im,pm),axis=1)),'%s/I%05d.png' % (outPMPath,i-j+k+1)) - imwrite(np.uint8(255*im),'%s/I%05d_Im.png' % (outPMPath,i-j+k+1)) - imwrite(np.uint8(255*pm),'%s/I%05d_PM.png' % (outPMPath,i-j+k+1)) - - - # -------------------------------------------------- - # clean-up - # -------------------------------------------------- - - sess.close() - - def singleImageInferenceSetup(modelPath,gpuIndex): - os.environ['CUDA_VISIBLE_DEVICES']= '%d' % gpuIndex - variablesPath = pathjoin(modelPath,'model.ckpt') - hp = loadData(pathjoin(modelPath,'hp.data')) - UNet2D.setupWithHP(hp) - - UNet2D.DatasetMean =loadData(pathjoin(modelPath,'datasetMean.data')) - UNet2D.DatasetStDev = loadData(pathjoin(modelPath,'datasetStDev.data')) - print(UNet2D.DatasetMean) - print(UNet2D.DatasetStDev) - - # -------------------------------------------------- - # session - # -------------------------------------------------- - - saver = tf.train.Saver() - UNet2D.Session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU - #UNet2D.Session = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0})) - saver.restore(UNet2D.Session, variablesPath) - print("Model restored.") - - def singleImageInferenceCleanup(): - UNet2D.Session.close() - - def singleImageInference(image,mode,pmIndex): - print('Inference...') - - batchSize = UNet2D.hp['batchSize'] - imSize = UNet2D.hp['imSize'] - nChannels = UNet2D.hp['nChannels'] - - PI2D.setup(image,imSize,int(imSize/8),mode) - PI2D.createOutput(nChannels) - - batchData = np.zeros((batchSize,imSize,imSize,nChannels)) - for i in range(PI2D.NumPatches): - j = np.mod(i,batchSize) - batchData[j,:,:,0] = (PI2D.getPatch(i)-UNet2D.DatasetMean)/UNet2D.DatasetStDev - if j == batchSize-1 or i == PI2D.NumPatches-1: - output = UNet2D.Session.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0}) - for k in range(j+1): - pm = output[k,:,:,pmIndex] - PI2D.patchOutput(i-j+k,pm) - # PI2D.patchOutput(i-j+k,normalize(imgradmag(PI2D.getPatch(i-j+k),1))) - - return PI2D.getValidOutput() - - -def identifyNumChan(path): - - s = tifffile.TiffFile(path).series[0] - return s.shape[0] if len(s.shape) > 2 else 1 - # shape = tiff.pages[0].shape - # tiff = tifffile.TiffFile(path) - # for i, page in enumerate(tiff.pages): - # print(page.shape) - # if page.shape != shape: - # numChan = i - # return numChan - # break -# else: -# raise Exception("Did not find any pyramid subresolutions") - - -def getProbMaps(I,dsFactor,modelPath): - hsize = int((float(I.shape[0]) * float(0.5))) - vsize = int((float(I.shape[1]) * float(0.5))) - imagesub = cv2.resize(I,(vsize,hsize),cv2.INTER_NEAREST) - - UNet2D.singleImageInferenceSetup(modelPath, 0) - - for iSize in range(dsFactor): - hsize = int((float(I.shape[0]) * float(0.5))) - vsize = int((float(I.shape[1]) * float(0.5))) - I = cv2.resize(I,(vsize,hsize),cv2.INTER_NEAREST) - I = im2double(I) - I = im2double(sk.rescale_intensity(I, in_range=(np.min(I), np.max(I)), out_range=(0, 0.983))) - probMaps = UNet2D.singleImageInference(I,'accumulate',1) - UNet2D.singleImageInferenceCleanup() - return probMaps - -def coreSegmenterOutput(I,initialmask,findCenter): - hsize = int((float(I.shape[0]) * float(0.1))) - vsize = int((float(I.shape[1]) * float(0.1))) - nucGF = cv2.resize(I,(vsize,hsize),cv2.INTER_CUBIC) - #active contours - hsize = int(float(nucGF.shape[0])) - vsize = int(float(nucGF.shape[1])) - initialmask = cv2.resize(initialmask,(vsize,hsize),cv2.INTER_NEAREST) - initialmask = dilation(initialmask,disk(15)) >0 - - nucGF = gaussian(nucGF,0.7) - nucGF=nucGF/np.amax(nucGF) - - nuclearMask = morphological_chan_vese(nucGF, 100, init_level_set=initialmask, smoothing=10,lambda1=1.001, lambda2=1) - - TMAmask = nuclearMask - TMAmask = remove_small_objects(TMAmask>0,round(TMAmask.shape[0])*round(TMAmask.shape[1])*0.005) - TMAlabel = label(TMAmask) -# find object closest to center - if findCenter==True: - - stats= regionprops(TMAlabel) - counter=1 - minDistance =-1 - index =[] - for props in stats: - centroid = props.centroid - distanceFromCenter = np.sqrt((centroid[0]-nucGF.shape[0]/2)**2+(centroid[1]-nucGF.shape[1]/2)**2) - # if distanceFromCenter<0.6/2*np.sqrt(TMAlabel.shape[0]*TMAlabel.shape[1]): - if distanceFromCenter<minDistance or minDistance==-1 : - minDistance =distanceFromCenter - index = counter - counter=counter+1 - # dist = 0.6/2*np.sqrt(TMAlabel.shape[0]*TMAlabel.shape[1]) - TMAmask = morphology.binary_closing(TMAlabel==index,disk(3)) - - return TMAmask - -def overlayOutline(outline,img): - img2 = img.copy() - stacked_img = np.stack((img2,)*3, axis=-1) - stacked_img[outline > 0] = [1, 0, 0] - imshowpair(img2,stacked_img) - -def imshowpair(A,B): - plt.imshow(A,cmap='Purples') - plt.imshow(B,cmap='Greens',alpha=0.5) - plt.show() - - -if __name__ == '__main__': - parser=argparse.ArgumentParser() - parser.add_argument("--imagePath") - parser.add_argument("--outputPath") - parser.add_argument("--maskPath") - parser.add_argument("--tissue", action='store_true') - parser.add_argument("--downsampleFactor", type=int, default = 5) - parser.add_argument("--channel",type = int, default = 0) - parser.add_argument("--buffer",type = float, default = 2) - parser.add_argument("--outputChan", type=int, nargs = '+', default=[-1]) - parser.add_argument("--sensitivity",type = float, default=0.3) - parser.add_argument("--useGrid",action='store_true') - parser.add_argument("--cluster",action='store_true') - args = parser.parse_args() - - outputPath = args.outputPath - imagePath = args.imagePath - sensitivity = args.sensitivity - scriptPath = os.path.dirname(os.path.realpath(__file__)) - modelPath = os.path.join(scriptPath, 'model') - maskOutputPath = os.path.join(outputPath, 'masks') - - -# if not os.path.exists(outputPath): -# os.makedirs(outputPath) -# else: -# shutil.rmtree(outputPath) - if not os.path.exists(maskOutputPath): - os.makedirs(maskOutputPath) - print( - 'WARNING! IF USING FOR TISSUE SPLITTING, IT IS ADVISED TO SET --downsampleFactor TO HIGHER THAN DEFAULT OF 5') - channel = args.channel - dsFactor = 1/(2**args.downsampleFactor) - I = skio.imread(imagePath, img_num=channel) - imagesub = resize(I,(int((float(I.shape[0]) * dsFactor)),int((float(I.shape[1]) * dsFactor)))) - numChan = identifyNumChan(imagePath) - - outputChan = args.outputChan - if len(outputChan)==1: - if outputChan[0]==-1: - outputChan = [0, numChan-1] - else: - outputChan.append(outputChan[0]) - classProbs = getProbMaps(I, args.downsampleFactor, modelPath) - - if not args.tissue: - print('TMA mode selected') - preMask = gaussian(np.uint8(classProbs*255),1)>0.8 - - P = regionprops(label(preMask),cache=False) - area = [ele.area for ele in P] - if len(P) <3: - medArea = np.median(area) - maxArea = np.percentile(area,99) - else: - count=0 - labelpreMask = np.zeros(preMask.shape,dtype=np.uint32) - for props in P: - count += 1 - yi = props.coords[:, 0] - xi = props.coords[:, 1] - labelpreMask[yi, xi] = count - P=regionprops(labelpreMask) - area = [ele.area for ele in P] - medArea = np.median(area) - maxArea = np.percentile(area,99) - preMask = remove_small_objects(preMask,0.2*medArea) - coreRad = round(np.sqrt(medArea/np.pi)) - estCoreDiam = round(np.sqrt(maxArea/np.pi)*1.2*args.buffer) - - #preprocessing - fgFiltered = blob_log(preMask,coreRad*0.6,threshold=sensitivity) - Imax = np.zeros(preMask.shape,dtype=np.uint8) - for iSpot in range(fgFiltered.shape[0]): - yi = np.uint32(round(fgFiltered[iSpot, 0])) - xi = np.uint32(round(fgFiltered[iSpot, 1])) - Imax[yi, xi] = 1 - Imax = Imax*preMask - Idist = distance_transform_edt(1-Imax) - markers = label(Imax) - coreLabel = watershed(Idist,markers,watershed_line=True,mask = preMask) - P = regionprops(coreLabel) - centroids = np.array([ele.centroid for ele in P]) / dsFactor - np.savetxt(outputPath + os.path.sep + 'centroidsY-X.txt', np.asarray(centroids), fmt='%10.5f') - numCores = len(centroids) - print(str(numCores) + ' cores detected!') - estCoreDiamX = np.ones(numCores) * estCoreDiam / dsFactor - estCoreDiamY = np.ones(numCores) * estCoreDiam / dsFactor - else: - print('Tissue mode selected') - imageblur = 5 - Iblur = gaussian(np.uint8(255*classProbs), imageblur) - coreMask = binary_fill_holes(binary_closing(Iblur > threshold_otsu(Iblur), np.ones((imageblur*2,imageblur*2)))) - coreMask = remove_small_objects(coreMask, min_size=0.001 * coreMask.shape[0] * coreMask.shape[1]) - - ## watershed - Idist = distance_transform_edt(coreMask) - markers = peak_local_max(h_maxima(Idist,20),indices=False) - markers = label(markers).astype(np.int8) - coreLabel = watershed(-Idist, markers, watershed_line=True,mask = coreMask) - - P = regionprops(coreLabel) - centroids = np.array([ele.centroid for ele in P]) / dsFactor - np.savetxt(outputPath + os.path.sep + 'centroidsY-X.txt', np.asarray(centroids), fmt='%10.5f') - numCores = len(centroids) - print(str(numCores) + ' tissues detected!') - estCoreDiamX = np.array([(ele.bbox[3]-ele.bbox[1])*1.1 for ele in P]) / dsFactor - estCoreDiamY = np.array([(ele.bbox[2]-ele.bbox[0])*1.1 for ele in P]) / dsFactor - - if numCores ==0 & args.cluster: - print('No cores detected. Try adjusting the downsample factor') - sys.exit(255) - - singleMaskTMA = np.zeros(imagesub.shape) - maskTMA = np.zeros(imagesub.shape) - bbox = [None] * numCores - imagesub = imagesub/np.percentile(imagesub,99.9) - imagesub = (imagesub * 255).round().astype(np.uint8) - imagesub = gray2rgb(imagesub) - x=np.zeros(numCores) - xLim=np.zeros(numCores) - y=np.zeros(numCores) - yLim=np.zeros(numCores) - -# segmenting each core - ####################### - for iCore in range(numCores): - x[iCore] = centroids[iCore,1] - estCoreDiamX[iCore]/2 - xLim[iCore] = x[iCore]+estCoreDiamX[iCore] - if xLim[iCore] > I.shape[1]: - xLim[iCore] = I.shape[1] - if x[iCore]<1: - x[iCore]=1 - - y[iCore] = centroids[iCore,0] - estCoreDiamY[iCore]/2 - yLim[iCore] = y[iCore] + estCoreDiamY[iCore] - if yLim[iCore] > I.shape[0]: - yLim[iCore] = I.shape[0] - if y[iCore]<1: - y[iCore]=1 - - bbox[iCore] = [round(x[iCore]), round(y[iCore]), round(xLim[iCore]), round(yLim[iCore])] - coreStack = np.zeros((outputChan[1]-outputChan[0]+1,np.int(round(yLim[iCore])-round(y[iCore])-1),np.int(round(xLim[iCore])-round(x[iCore])-1)),dtype='uint16') - - for iChan in range(outputChan[0],outputChan[1]+1): - with pytiff.Tiff(imagePath, "r", encoding='utf-8') as handle: - handle.set_page(iChan) - coreStack[iChan,:,:] =handle[np.uint32(bbox[iCore][1]):np.uint32(bbox[iCore][3]-1), np.uint32(bbox[iCore][0]):np.uint32(bbox[iCore][2]-1)] - - skio.imsave(outputPath + os.path.sep + str(iCore+1) + '.tif',np.uint16(coreStack),imagej=True,bigtiff=True) - with pytiff.Tiff(imagePath, "r", encoding='utf-8') as handle: - handle.set_page(args.channel) - coreSlice= handle[np.uint32(bbox[iCore][1]):np.uint32(bbox[iCore][3]-1), np.uint32(bbox[iCore][0]):np.uint32(bbox[iCore][2]-1)] - - core = (coreLabel ==(iCore+1)) - initialmask = core[np.uint32(y[iCore] * dsFactor):np.uint32(yLim[iCore] * dsFactor), - np.uint32(x[iCore] * dsFactor):np.uint32(xLim[iCore] * dsFactor)] - if not args.tissue: - initialmask = resize(initialmask,size(coreSlice),cv2.INTER_NEAREST) - - singleProbMap = classProbs[np.uint32(y[iCore]*dsFactor):np.uint32(yLim[iCore]*dsFactor),np.uint32(x[iCore]*dsFactor):np.uint32(xLim[iCore]*dsFactor)] - singleProbMap = resize(np.uint8(255*singleProbMap),size(coreSlice),cv2.INTER_NEAREST) - TMAmask = coreSegmenterOutput(coreSlice,initialmask,False) - else: - Irs = resize(coreSlice,(int((float(coreSlice.shape[0]) * 0.25)),int((float(coreSlice.shape[1]) * 0.25)))) - TMAmask = coreSegmenterOutput(Irs, np.uint8(initialmask), False) - - if np.sum(TMAmask)==0: - TMAmask = np.ones(TMAmask.shape) - vsize = int(float(coreSlice.shape[0])) - hsize = int(float(coreSlice.shape[1])) - masksub = resize(resize(TMAmask,(vsize,hsize),cv2.INTER_NEAREST),(int((float(coreSlice.shape[0])*dsFactor)),int((float(coreSlice.shape[1])*dsFactor))),cv2.INTER_NEAREST) - singleMaskTMA[int(y[iCore]*dsFactor):int(y[iCore]*dsFactor)+masksub.shape[0],int(x[iCore]*dsFactor):int(x[iCore]*dsFactor)+masksub.shape[1]]=masksub - maskTMA = maskTMA + resize(singleMaskTMA,maskTMA.shape,cv2.INTER_NEAREST) - - cv2.putText(imagesub, str(iCore+1), (int(P[iCore].centroid[1]),int(P[iCore].centroid[0])), 0, 0.5, (0,255,0), 1, cv2.LINE_AA) - - skio.imsave(maskOutputPath + os.path.sep + str(iCore+1) + '_mask.tif',np.uint8(TMAmask)) - print('Segmented core/tissue ' + str(iCore+1)) - - boundaries = find_boundaries(maskTMA) - imagesub[boundaries==1] = 255 - skio.imsave(outputPath + os.path.sep + 'TMA_MAP.tif' ,imagesub) - print('Segmented all cores/tissues!') - -#restore GPU to 0 - #image load using tifffile
--- a/coreograph.xml Fri Mar 11 23:40:51 2022 +0000 +++ b/coreograph.xml Thu Sep 01 22:43:42 2022 +0000 @@ -1,51 +1,39 @@ -<tool id="unet_coreograph" name="UNetCoreograph" version="@VERSION@.3" profile="17.09"> - <description>Coreograph uses UNet, a deep learning model, to identify complete/incomplete tissue cores on a tissue microarray. It has been trained on 9 TMA slides of different sizes and tissue types.</description> +<tool id="unet_coreograph" name="UNetCoreograph" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="19.01"> + <description>TMA core detection and dearraying</description> <macros> <import>macros.xml</import> </macros> <expand macro="requirements"/> - @VERSION_CMD@ + <expand macro="version_cmd"/> <command detect_errors="exit_code"><![CDATA[ - #set $type_corrected = str($source_image)[:-3]+'ome.tif' - ln -s $source_image `basename $type_corrected`; - + #set $type_corrected = 'image.' + str($source_image.file_ext) + ln -s '$source_image' '$type_corrected' && + @CMD_BEGIN@ python \$UNET_PATH - --imagePath `basename $type_corrected` + --imagePath '$type_corrected' --downsampleFactor $downsamplefactor --channel $channel --buffer $buffer --sensitivity $sensitivity - - ##if $usegrid - ##--useGrid - ##end if - - #if $cluster - --cluster - #end if - - #if $tissue - --tissue - #end if - - --outputPath .; + $cluster + $tissue + --outputPath '.' ]]></command> <inputs> - <param name="source_image" type="data" format="tiff" label="Registered TIFF"/> + <param name="source_image" type="data" format="tiff,ome.tiff" label="Registered TIFF"/> <param name="downsamplefactor" type="integer" value="5" label="Down Sample Factor"/> <param name="channel" type="integer" value="0" label="Channel"/> <param name="buffer" type="float" value="2.0" label="Buffer"/> <param name="sensitivity" type="float" value="0.3" label="Sensitivity"/> - <!--<param name="usegrid" type="boolean" label="Use Grid"/>--> - <param name="cluster" type="boolean" checked="false" label="Cluster"/> - <param name="tissue" type="boolean" checked="false" label="Tissue"/> + <param name="cluster" type="boolean" truevalue="--cluster" falsevalue="" checked="false" label="Cluster"/> + <param name="tissue" type="boolean" truevalue="--tissue" falsevalue="" checked="false" label="Tissue"/> </inputs> <outputs> @@ -57,7 +45,56 @@ </collection> <data name="TMA_MAP" format="tiff" label="${tool.name} on ${on_string}: TMA Map" from_work_dir="TMA_MAP.tif"/> </outputs> + <tests> + <test> + <param name="source_image" value="coreograph_test.tiff" /> + <output_collection name="tma_sections" type="list"> + <element name="1" ftype="tiff"> + <assert_contents> + <has_size value="18000" delta="1000" /> + </assert_contents> + </element> + <element name="2" ftype="tiff"> + <assert_contents> + <has_size value="18000" delta="1000" /> + </assert_contents> + </element> + </output_collection> + <output_collection name="masks" type="list"> + <element name="1" ftype="tiff"> + <assert_contents> + <has_size value="345" delta="100" /> + </assert_contents> + </element> + <element name="2" ftype="tiff"> + <assert_contents> + <has_size value="345" delta="100" /> + </assert_contents> + </element> + </output_collection> + <output name="TMA_MAP" ftype="tiff"> + <assert_contents> + <has_size value="530" delta="100" /> + </assert_contents> + </output> + </test> + </tests> <help><![CDATA[ +------------------- +UNet Coreograph +------------------- +**Coreograph** uses UNet, a deep learning model, to identify complete/incomplete tissue cores on a tissue microarray. It has been trained on 9 TMA slides of different sizes and tissue types + +Training sets were acquired at 0.2micron/pixel resolution and downsampled 1/32 times to speed up performance. Once the center of each core has been identifed, active contours is used to generate a tissue mask of each core that can aid downstream single cell segmentation. A GPU is not required but will reduce computation time. + +**Inputs** +A tif or ome.tiff image multiple tissues, such as a tissue microarray. + +**Outputs** +Coreograph exports these files: +1. individual cores as tiff stacks with user-selectable channel ranges +2. binary tissue masks (saved in the 'mask' subfolder) +3. a TMA map showing the labels and outlines of each core for quality control purposes ]]></help> <expand macro="citations" /> </tool>
--- a/macros.xml Fri Mar 11 23:40:51 2022 +0000 +++ b/macros.xml Thu Sep 01 22:43:42 2022 +0000 @@ -2,7 +2,7 @@ <macros> <xml name="requirements"> <requirements> - <container type="docker">labsyspharm/unetcoreograph:@VERSION@</container> + <!-- <requirement type="package" version="3.6">python</requirement> <requirement type="package" version="1.15.1">tensorflow-estimator</requirement> <requirement type="package" version="1.15">tensorflow</requirement> @@ -13,24 +13,27 @@ <requirement type="package" version="1.1.0">scipy</requirement> <requirement type="package">opencv</requirement> <requirement type="package" version="0.8.1">pytiff</requirement> + --> + <container type="docker">labsyspharm/unetcoreograph:@TOOL_VERSION@</container> </requirements> </xml> <xml name="version_cmd"> - <version_command>echo @VERSION@</version_command> + <version_command>echo @TOOL_VERSION@</version_command> </xml> <xml name="citations"> <citations> </citations> </xml> - <token name="@VERSION@">2.2.8</token> + <token name="@TOOL_VERSION@">2.2.8</token> + <token name="@VERSION_SUFFIX@">0</token> <token name="@CMD_BEGIN@"><![CDATA[ - UNET_PATH=""; + UNET_PATH="" && if [ -f "/app/UNetCoreograph.py" ]; then export UNET_PATH="/app/UNetCoreograph.py"; else export UNET_PATH="${__tool_directory__}/UNetCoreograph.py"; - fi; + fi && ]]></token> </macros>
--- a/model/checkpoint Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,2 +0,0 @@ -model_checkpoint_path: "D:\\LSP\\Coreograph\\model-4layersMaskAug20New\\model.ckpt" -all_model_checkpoint_paths: "D:\\LSP\\Coreograph\\model-4layersMaskAug20New\\model.ckpt"
--- a/model/datasetMean.data Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,3 +0,0 @@ -€G?· -=p£× -. \ No newline at end of file
--- a/model/datasetStDev.data Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,3 +0,0 @@ -€G?· -=p£× -. \ No newline at end of file
--- a/toolbox/PartitionOfImage.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,305 +0,0 @@ -import numpy as np -from toolbox.imtools import * -# from toolbox.ftools import * -# import sys - -class PI2D: - Image = None - PaddedImage = None - PatchSize = 128 - Margin = 14 - SubPatchSize = 100 - PC = None # patch coordinates - NumPatches = 0 - Output = None - Count = None - NR = None - NC = None - NRPI = None - NCPI = None - Mode = None - W = None - - def setup(image,patchSize,margin,mode): - PI2D.Image = image - PI2D.PatchSize = patchSize - PI2D.Margin = margin - subPatchSize = patchSize-2*margin - PI2D.SubPatchSize = subPatchSize - - W = np.ones((patchSize,patchSize)) - W[[0,-1],:] = 0 - W[:,[0,-1]] = 0 - for i in range(1,2*margin): - v = i/(2*margin) - W[i,i:-i] = v - W[-i-1,i:-i] = v - W[i:-i,i] = v - W[i:-i,-i-1] = v - PI2D.W = W - - if len(image.shape) == 2: - nr,nc = image.shape - elif len(image.shape) == 3: # multi-channel image - nz,nr,nc = image.shape - - PI2D.NR = nr - PI2D.NC = nc - - npr = int(np.ceil(nr/subPatchSize)) # number of patch rows - npc = int(np.ceil(nc/subPatchSize)) # number of patch cols - - nrpi = npr*subPatchSize+2*margin # number of rows in padded image - ncpi = npc*subPatchSize+2*margin # number of cols in padded image - - PI2D.NRPI = nrpi - PI2D.NCPI = ncpi - - if len(image.shape) == 2: - PI2D.PaddedImage = np.zeros((nrpi,ncpi)) - PI2D.PaddedImage[margin:margin+nr,margin:margin+nc] = image - elif len(image.shape) == 3: - PI2D.PaddedImage = np.zeros((nz,nrpi,ncpi)) - PI2D.PaddedImage[:,margin:margin+nr,margin:margin+nc] = image - - PI2D.PC = [] # patch coordinates [r0,r1,c0,c1] - for i in range(npr): - r0 = i*subPatchSize - r1 = r0+patchSize - for j in range(npc): - c0 = j*subPatchSize - c1 = c0+patchSize - PI2D.PC.append([r0,r1,c0,c1]) - - PI2D.NumPatches = len(PI2D.PC) - PI2D.Mode = mode # 'replace' or 'accumulate' - - def getPatch(i): - r0,r1,c0,c1 = PI2D.PC[i] - if len(PI2D.PaddedImage.shape) == 2: - return PI2D.PaddedImage[r0:r1,c0:c1] - if len(PI2D.PaddedImage.shape) == 3: - return PI2D.PaddedImage[:,r0:r1,c0:c1] - - def createOutput(nChannels): - if nChannels == 1: - PI2D.Output = np.zeros((PI2D.NRPI,PI2D.NCPI),np.float16) - else: - PI2D.Output = np.zeros((nChannels,PI2D.NRPI,PI2D.NCPI),np.float16) - if PI2D.Mode == 'accumulate': - PI2D.Count = np.zeros((PI2D.NRPI,PI2D.NCPI),np.float16) - - def patchOutput(i,P): - r0,r1,c0,c1 = PI2D.PC[i] - if PI2D.Mode == 'accumulate': - PI2D.Count[r0:r1,c0:c1] += PI2D.W - if len(P.shape) == 2: - if PI2D.Mode == 'accumulate': - PI2D.Output[r0:r1,c0:c1] += np.multiply(P,PI2D.W) - elif PI2D.Mode == 'replace': - PI2D.Output[r0:r1,c0:c1] = P - elif len(P.shape) == 3: - if PI2D.Mode == 'accumulate': - for i in range(P.shape[0]): - PI2D.Output[i,r0:r1,c0:c1] += np.multiply(P[i,:,:],PI2D.W) - elif PI2D.Mode == 'replace': - PI2D.Output[:,r0:r1,c0:c1] = P - - def getValidOutput(): - margin = PI2D.Margin - nr, nc = PI2D.NR, PI2D.NC - if PI2D.Mode == 'accumulate': - C = PI2D.Count[margin:margin+nr,margin:margin+nc] - if len(PI2D.Output.shape) == 2: - if PI2D.Mode == 'accumulate': - return np.divide(PI2D.Output[margin:margin+nr,margin:margin+nc],C) - if PI2D.Mode == 'replace': - return PI2D.Output[margin:margin+nr,margin:margin+nc] - if len(PI2D.Output.shape) == 3: - if PI2D.Mode == 'accumulate': - for i in range(PI2D.Output.shape[0]): - PI2D.Output[i,margin:margin+nr,margin:margin+nc] = np.divide(PI2D.Output[i,margin:margin+nr,margin:margin+nc],C) - return PI2D.Output[:,margin:margin+nr,margin:margin+nc] - - - def demo(): - I = np.random.rand(128,128) - # PI2D.setup(I,128,14) - PI2D.setup(I,64,4,'replace') - - nChannels = 2 - PI2D.createOutput(nChannels) - - for i in range(PI2D.NumPatches): - P = PI2D.getPatch(i) - Q = np.zeros((nChannels,P.shape[0],P.shape[1])) - for j in range(nChannels): - Q[j,:,:] = P - PI2D.patchOutput(i,Q) - - J = PI2D.getValidOutput() - J = J[0,:,:] - - D = np.abs(I-J) - print(np.max(D)) - - K = cat(1,cat(1,I,J),D) - imshow(K) - - -class PI3D: - Image = None - PaddedImage = None - PatchSize = 128 - Margin = 14 - SubPatchSize = 100 - PC = None # patch coordinates - NumPatches = 0 - Output = None - Count = None - NR = None # rows - NC = None # cols - NZ = None # planes - NRPI = None - NCPI = None - NZPI = None - Mode = None - W = None - - def setup(image,patchSize,margin,mode): - PI3D.Image = image - PI3D.PatchSize = patchSize - PI3D.Margin = margin - subPatchSize = patchSize-2*margin - PI3D.SubPatchSize = subPatchSize - - W = np.ones((patchSize,patchSize,patchSize)) - W[[0,-1],:,:] = 0 - W[:,[0,-1],:] = 0 - W[:,:,[0,-1]] = 0 - for i in range(1,2*margin): - v = i/(2*margin) - W[[i,-i-1],i:-i,i:-i] = v - W[i:-i,[i,-i-1],i:-i] = v - W[i:-i,i:-i,[i,-i-1]] = v - - PI3D.W = W - - if len(image.shape) == 3: - nz,nr,nc = image.shape - elif len(image.shape) == 4: # multi-channel image - nz,nw,nr,nc = image.shape - - PI3D.NR = nr - PI3D.NC = nc - PI3D.NZ = nz - - npr = int(np.ceil(nr/subPatchSize)) # number of patch rows - npc = int(np.ceil(nc/subPatchSize)) # number of patch cols - npz = int(np.ceil(nz/subPatchSize)) # number of patch planes - - nrpi = npr*subPatchSize+2*margin # number of rows in padded image - ncpi = npc*subPatchSize+2*margin # number of cols in padded image - nzpi = npz*subPatchSize+2*margin # number of plns in padded image - - PI3D.NRPI = nrpi - PI3D.NCPI = ncpi - PI3D.NZPI = nzpi - - if len(image.shape) == 3: - PI3D.PaddedImage = np.zeros((nzpi,nrpi,ncpi)) - PI3D.PaddedImage[margin:margin+nz,margin:margin+nr,margin:margin+nc] = image - elif len(image.shape) == 4: - PI3D.PaddedImage = np.zeros((nzpi,nw,nrpi,ncpi)) - PI3D.PaddedImage[margin:margin+nz,:,margin:margin+nr,margin:margin+nc] = image - - PI3D.PC = [] # patch coordinates [z0,z1,r0,r1,c0,c1] - for iZ in range(npz): - z0 = iZ*subPatchSize - z1 = z0+patchSize - for i in range(npr): - r0 = i*subPatchSize - r1 = r0+patchSize - for j in range(npc): - c0 = j*subPatchSize - c1 = c0+patchSize - PI3D.PC.append([z0,z1,r0,r1,c0,c1]) - - PI3D.NumPatches = len(PI3D.PC) - PI3D.Mode = mode # 'replace' or 'accumulate' - - def getPatch(i): - z0,z1,r0,r1,c0,c1 = PI3D.PC[i] - if len(PI3D.PaddedImage.shape) == 3: - return PI3D.PaddedImage[z0:z1,r0:r1,c0:c1] - if len(PI3D.PaddedImage.shape) == 4: - return PI3D.PaddedImage[z0:z1,:,r0:r1,c0:c1] - - def createOutput(nChannels): - if nChannels == 1: - PI3D.Output = np.zeros((PI3D.NZPI,PI3D.NRPI,PI3D.NCPI)) - else: - PI3D.Output = np.zeros((PI3D.NZPI,nChannels,PI3D.NRPI,PI3D.NCPI)) - if PI3D.Mode == 'accumulate': - PI3D.Count = np.zeros((PI3D.NZPI,PI3D.NRPI,PI3D.NCPI)) - - def patchOutput(i,P): - z0,z1,r0,r1,c0,c1 = PI3D.PC[i] - if PI3D.Mode == 'accumulate': - PI3D.Count[z0:z1,r0:r1,c0:c1] += PI3D.W - if len(P.shape) == 3: - if PI3D.Mode == 'accumulate': - PI3D.Output[z0:z1,r0:r1,c0:c1] += np.multiply(P,PI3D.W) - elif PI3D.Mode == 'replace': - PI3D.Output[z0:z1,r0:r1,c0:c1] = P - elif len(P.shape) == 4: - if PI3D.Mode == 'accumulate': - for i in range(P.shape[1]): - PI3D.Output[z0:z1,i,r0:r1,c0:c1] += np.multiply(P[:,i,:,:],PI3D.W) - elif PI3D.Mode == 'replace': - PI3D.Output[z0:z1,:,r0:r1,c0:c1] = P - - def getValidOutput(): - margin = PI3D.Margin - nz, nr, nc = PI3D.NZ, PI3D.NR, PI3D.NC - if PI3D.Mode == 'accumulate': - C = PI3D.Count[margin:margin+nz,margin:margin+nr,margin:margin+nc] - if len(PI3D.Output.shape) == 3: - if PI3D.Mode == 'accumulate': - return np.divide(PI3D.Output[margin:margin+nz,margin:margin+nr,margin:margin+nc],C) - if PI3D.Mode == 'replace': - return PI3D.Output[margin:margin+nz,margin:margin+nr,margin:margin+nc] - if len(PI3D.Output.shape) == 4: - if PI3D.Mode == 'accumulate': - for i in range(PI3D.Output.shape[1]): - PI3D.Output[margin:margin+nz,i,margin:margin+nr,margin:margin+nc] = np.divide(PI3D.Output[margin:margin+nz,i,margin:margin+nr,margin:margin+nc],C) - return PI3D.Output[margin:margin+nz,:,margin:margin+nr,margin:margin+nc] - - - def demo(): - I = np.random.rand(128,128,128) - PI3D.setup(I,64,4,'accumulate') - - nChannels = 2 - PI3D.createOutput(nChannels) - - for i in range(PI3D.NumPatches): - P = PI3D.getPatch(i) - Q = np.zeros((P.shape[0],nChannels,P.shape[1],P.shape[2])) - for j in range(nChannels): - Q[:,j,:,:] = P - PI3D.patchOutput(i,Q) - - J = PI3D.getValidOutput() - J = J[:,0,:,:] - - D = np.abs(I-J) - print(np.max(D)) - - pI = I[64,:,:] - pJ = J[64,:,:] - pD = D[64,:,:] - - K = cat(1,cat(1,pI,pJ),pD) - imshow(K) -
--- a/toolbox/ftools.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,55 +0,0 @@ -from os.path import * -from os import listdir, makedirs, remove -import pickle -import shutil - -def fileparts(path): # path = file path - [p,f] = split(path) - [n,e] = splitext(f) - return [p,n,e] - -def listfiles(path,token): # path = folder path - l = [] - for f in listdir(path): - fullPath = join(path,f) - if isfile(fullPath) and token in f: - l.append(fullPath) - l.sort() - return l - -def listsubdirs(path): # path = folder path - l = [] - for f in listdir(path): - fullPath = join(path,f) - if isdir(fullPath): - l.append(fullPath) - l.sort() - return l - -def pathjoin(p,ne): # '/path/to/folder', 'name.extension' (or a subfolder) - return join(p,ne) - -def saveData(data,path): - print('saving data') - dataFile = open(path, 'wb') - pickle.dump(data, dataFile) - -def loadData(path): - print('loading data') - dataFile = open(path, 'rb') - return pickle.load(dataFile) - -def createFolderIfNonExistent(path): - if not exists(path): # from os.path - makedirs(path) - -def moveFile(fullPathSource,folderPathDestination): - [p,n,e] = fileparts(fullPathSource) - shutil.move(fullPathSource,pathjoin(folderPathDestination,n+e)) - -def copyFile(fullPathSource,folderPathDestination): - [p,n,e] = fileparts(fullPathSource) - shutil.copy(fullPathSource,pathjoin(folderPathDestination,n+e)) - -def removeFile(path): - remove(path) \ No newline at end of file
--- a/toolbox/imtools.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,312 +0,0 @@ -import matplotlib.pyplot as plt -import tifffile -import os -import numpy as np -from skimage import io as skio -from scipy.ndimage import * -from skimage.morphology import * -from skimage.transform import resize - -def tifread(path): - return tifffile.imread(path) - -def tifwrite(I,path): - tifffile.imsave(path, I) - -def imshow(I,**kwargs): - if not kwargs: - plt.imshow(I,cmap='gray') - else: - plt.imshow(I,**kwargs) - - plt.axis('off') - plt.show() - -def imshowlist(L,**kwargs): - n = len(L) - for i in range(n): - plt.subplot(1, n, i+1) - if not kwargs: - plt.imshow(L[i],cmap='gray') - else: - plt.imshow(L[i],**kwargs) - plt.axis('off') - plt.show() - -def imread(path): - return skio.imread(path) - -def imwrite(I,path): - skio.imsave(path,I) - -def im2double(I): - if I.dtype == 'uint16': - return I.astype('float64')/65535 - elif I.dtype == 'uint8': - return I.astype('float64')/255 - elif I.dtype == 'float32': - return I.astype('float64') - elif I.dtype == 'float64': - return I - else: - print('returned original image type: ', I.dtype) - return I - -def size(I): - return list(I.shape) - -def imresizeDouble(I,sizeOut): # input and output are double - return resize(I,(sizeOut[0],sizeOut[1]),mode='reflect') - -def imresize3Double(I,sizeOut): # input and output are double - return resize(I,(sizeOut[0],sizeOut[1],sizeOut[2]),mode='reflect') - -def imresizeUInt8(I,sizeOut): # input and output are UInt8 - return np.uint8(resize(I.astype(float),(sizeOut[0],sizeOut[1]),mode='reflect',order=0)) - -def imresize3UInt8(I,sizeOut): # input and output are UInt8 - return np.uint8(resize(I.astype(float),(sizeOut[0],sizeOut[1],sizeOut[2]),mode='reflect',order=0)) - -def normalize(I): - m = np.min(I) - M = np.max(I) - if M > m: - return (I-m)/(M-m) - else: - return I - -def snormalize(I): - m = np.mean(I) - s = np.std(I) - if s > 0: - return (I-m)/s - else: - return I - -def cat(a,I,J): - return np.concatenate((I,J),axis=a) - -def imerode(I,r): - return binary_erosion(I, disk(r)) - -def imdilate(I,r): - return binary_dilation(I, disk(r)) - -def imerode3(I,r): - return morphology.binary_erosion(I, ball(r)) - -def imdilate3(I,r): - return morphology.binary_dilation(I, ball(r)) - -def sphericalStructuralElement(imShape,fRadius): - if len(imShape) == 2: - return disk(fRadius,dtype=float) - if len(imShape) == 3: - return ball(fRadius,dtype=float) - -def medfilt(I,filterRadius): - return median_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius)) - -def maxfilt(I,filterRadius): - return maximum_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius)) - -def minfilt(I,filterRadius): - return minimum_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius)) - -def ptlfilt(I,percentile,filterRadius): - return percentile_filter(I,percentile,footprint=sphericalStructuralElement(I.shape,filterRadius)) - -def imgaussfilt(I,sigma,**kwargs): - return gaussian_filter(I,sigma,**kwargs) - -def imlogfilt(I,sigma,**kwargs): - return -gaussian_laplace(I,sigma,**kwargs) - -def imgradmag(I,sigma): - if len(I.shape) == 2: - dx = imgaussfilt(I,sigma,order=[0,1]) - dy = imgaussfilt(I,sigma,order=[1,0]) - return np.sqrt(dx**2+dy**2) - if len(I.shape) == 3: - dx = imgaussfilt(I,sigma,order=[0,0,1]) - dy = imgaussfilt(I,sigma,order=[0,1,0]) - dz = imgaussfilt(I,sigma,order=[1,0,0]) - return np.sqrt(dx**2+dy**2+dz**2) - -def localstats(I,radius,justfeatnames=False): - ptls = [10,30,50,70,90] - featNames = [] - for i in range(len(ptls)): - featNames.append('locPtl%d' % ptls[i]) - if justfeatnames == True: - return featNames - sI = size(I) - nFeats = len(ptls) - F = np.zeros((sI[0],sI[1],nFeats)) - for i in range(nFeats): - F[:,:,i] = ptlfilt(I,ptls[i],radius) - return F - -def localstats3(I,radius,justfeatnames=False): - ptls = [10,30,50,70,90] - featNames = [] - for i in range(len(ptls)): - featNames.append('locPtl%d' % ptls[i]) - if justfeatnames == True: - return featNames - sI = size(I) - nFeats = len(ptls) - F = np.zeros((sI[0],sI[1],sI[2],nFeats)) - for i in range(nFeats): - F[:,:,:,i] = ptlfilt(I,ptls[i],radius) - return F - -def imderivatives(I,sigmas,justfeatnames=False): - if type(sigmas) is not list: - sigmas = [sigmas] - derivPerSigmaFeatNames = ['d0','dx','dy','dxx','dxy','dyy','normGrad','normHessDiag'] - if justfeatnames == True: - featNames = []; - for i in range(len(sigmas)): - for j in range(len(derivPerSigmaFeatNames)): - featNames.append('derivSigma%d%s' % (sigmas[i],derivPerSigmaFeatNames[j])) - return featNames - nDerivativesPerSigma = len(derivPerSigmaFeatNames) - nDerivatives = len(sigmas)*nDerivativesPerSigma - sI = size(I) - D = np.zeros((sI[0],sI[1],nDerivatives)) - for i in range(len(sigmas)): - sigma = sigmas[i] - dx = imgaussfilt(I,sigma,order=[0,1]) - dy = imgaussfilt(I,sigma,order=[1,0]) - dxx = imgaussfilt(I,sigma,order=[0,2]) - dyy = imgaussfilt(I,sigma,order=[2,0]) - D[:,:,nDerivativesPerSigma*i ] = imgaussfilt(I,sigma) - D[:,:,nDerivativesPerSigma*i+1] = dx - D[:,:,nDerivativesPerSigma*i+2] = dy - D[:,:,nDerivativesPerSigma*i+3] = dxx - D[:,:,nDerivativesPerSigma*i+4] = imgaussfilt(I,sigma,order=[1,1]) - D[:,:,nDerivativesPerSigma*i+5] = dyy - D[:,:,nDerivativesPerSigma*i+6] = np.sqrt(dx**2+dy**2) - D[:,:,nDerivativesPerSigma*i+7] = np.sqrt(dxx**2+dyy**2) - return D - # derivatives are indexed by the last dimension, which is good for ML features but not for visualization, - # in which case the expected dimensions are [plane,channel,y(row),x(col)]; to obtain that ordering, do - # D = np.moveaxis(D,[0,3,1,2],[0,1,2,3]) - -def imderivatives3(I,sigmas,justfeatnames=False): - if type(sigmas) is not list: - sigmas = [sigmas] - - derivPerSigmaFeatNames = ['d0','dx','dy','dz','dxx','dxy','dxz','dyy','dyz','dzz','normGrad','normHessDiag'] - - # derivPerSigmaFeatNames = ['d0','normGrad','normHessDiag'] - - if justfeatnames == True: - featNames = []; - for i in range(len(sigmas)): - for j in range(len(derivPerSigmaFeatNames)): - featNames.append('derivSigma%d%s' % (sigmas[i],derivPerSigmaFeatNames[j])) - return featNames - nDerivativesPerSigma = len(derivPerSigmaFeatNames) - nDerivatives = len(sigmas)*nDerivativesPerSigma - sI = size(I) - D = np.zeros((sI[0],sI[1],sI[2],nDerivatives)) # plane, channel, y, x - for i in range(len(sigmas)): - sigma = sigmas[i] - dx = imgaussfilt(I,sigma,order=[0,0,1]) # z, y, x - dy = imgaussfilt(I,sigma,order=[0,1,0]) - dz = imgaussfilt(I,sigma,order=[1,0,0]) - dxx = imgaussfilt(I,sigma,order=[0,0,2]) - dyy = imgaussfilt(I,sigma,order=[0,2,0]) - dzz = imgaussfilt(I,sigma,order=[2,0,0]) - - D[:,:,:,nDerivativesPerSigma*i ] = imgaussfilt(I,sigma) - D[:,:,:,nDerivativesPerSigma*i+1 ] = dx - D[:,:,:,nDerivativesPerSigma*i+2 ] = dy - D[:,:,:,nDerivativesPerSigma*i+3 ] = dz - D[:,:,:,nDerivativesPerSigma*i+4 ] = dxx - D[:,:,:,nDerivativesPerSigma*i+5 ] = imgaussfilt(I,sigma,order=[0,1,1]) - D[:,:,:,nDerivativesPerSigma*i+6 ] = imgaussfilt(I,sigma,order=[1,0,1]) - D[:,:,:,nDerivativesPerSigma*i+7 ] = dyy - D[:,:,:,nDerivativesPerSigma*i+8 ] = imgaussfilt(I,sigma,order=[1,1,0]) - D[:,:,:,nDerivativesPerSigma*i+9 ] = dzz - D[:,:,:,nDerivativesPerSigma*i+10] = np.sqrt(dx**2+dy**2+dz**2) - D[:,:,:,nDerivativesPerSigma*i+11] = np.sqrt(dxx**2+dyy**2+dzz**2) - - # D[:,:,:,nDerivativesPerSigma*i ] = imgaussfilt(I,sigma) - # D[:,:,:,nDerivativesPerSigma*i+1 ] = np.sqrt(dx**2+dy**2+dz**2) - # D[:,:,:,nDerivativesPerSigma*i+2 ] = np.sqrt(dxx**2+dyy**2+dzz**2) - return D - # derivatives are indexed by the last dimension, which is good for ML features but not for visualization, - # in which case the expected dimensions are [plane,y(row),x(col)]; to obtain that ordering, do - # D = np.moveaxis(D,[2,0,1],[0,1,2]) - -def imfeatures(I=[],sigmaDeriv=1,sigmaLoG=1,locStatsRad=0,justfeatnames=False): - if type(sigmaDeriv) is not list: - sigmaDeriv = [sigmaDeriv] - if type(sigmaLoG) is not list: - sigmaLoG = [sigmaLoG] - derivFeatNames = imderivatives([],sigmaDeriv,justfeatnames=True) - nLoGFeats = len(sigmaLoG) - locStatsFeatNames = [] - if locStatsRad > 1: - locStatsFeatNames = localstats([],locStatsRad,justfeatnames=True) - nLocStatsFeats = len(locStatsFeatNames) - if justfeatnames == True: - featNames = derivFeatNames - for i in range(nLoGFeats): - featNames.append('logSigma%d' % sigmaLoG[i]) - for i in range(nLocStatsFeats): - featNames.append(locStatsFeatNames[i]) - return featNames - nDerivFeats = len(derivFeatNames) - nFeatures = nDerivFeats+nLoGFeats+nLocStatsFeats - sI = size(I) - F = np.zeros((sI[0],sI[1],nFeatures)) - F[:,:,:nDerivFeats] = imderivatives(I,sigmaDeriv) - for i in range(nLoGFeats): - F[:,:,nDerivFeats+i] = imlogfilt(I,sigmaLoG[i]) - if locStatsRad > 1: - F[:,:,nDerivFeats+nLoGFeats:] = localstats(I,locStatsRad) - return F - -def imfeatures3(I=[],sigmaDeriv=2,sigmaLoG=2,locStatsRad=0,justfeatnames=False): - if type(sigmaDeriv) is not list: - sigmaDeriv = [sigmaDeriv] - if type(sigmaLoG) is not list: - sigmaLoG = [sigmaLoG] - derivFeatNames = imderivatives3([],sigmaDeriv,justfeatnames=True) - nLoGFeats = len(sigmaLoG) - locStatsFeatNames = [] - if locStatsRad > 1: - locStatsFeatNames = localstats3([],locStatsRad,justfeatnames=True) - nLocStatsFeats = len(locStatsFeatNames) - if justfeatnames == True: - featNames = derivFeatNames - for i in range(nLoGFeats): - featNames.append('logSigma%d' % sigmaLoG[i]) - for i in range(nLocStatsFeats): - featNames.append(locStatsFeatNames[i]) - return featNames - nDerivFeats = len(derivFeatNames) - nFeatures = nDerivFeats+nLoGFeats+nLocStatsFeats - sI = size(I) - F = np.zeros((sI[0],sI[1],sI[2],nFeatures)) - F[:,:,:,:nDerivFeats] = imderivatives3(I,sigmaDeriv) - for i in range(nLoGFeats): - F[:,:,:,nDerivFeats+i] = imlogfilt(I,sigmaLoG[i]) - if locStatsRad > 1: - F[:,:,:,nDerivFeats+nLoGFeats:] = localstats3(I,locStatsRad) - return F - -def stack2list(S): - L = [] - for i in range(size(S)[2]): - L.append(S[:,:,i]) - return L - -def thrsegment(I,wsBlr,wsThr): # basic threshold segmentation - G = imgaussfilt(I,sigma=(1-wsBlr)+wsBlr*5) # min 1, max 5 - M = G > wsThr - return M \ No newline at end of file