Mercurial > repos > perssond > coreograph
changeset 0:99308601eaa6 draft
"planemo upload for repository https://github.com/ohsu-comp-bio/UNetCoreograph commit fb90660a1805b3f68fcff80d525b5459c3f7dfd6-dirty"
| author | perssond |
|---|---|
| date | Wed, 19 May 2021 21:34:38 +0000 |
| parents | |
| children | 57f1260ca94e |
| files | UNet2DtCycifTRAINCoreograph.py UNetCoreograph.py coreograph.xml images/TMA_MAP.jpg images/TMA_MAP.tif 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 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 | 24 files changed, 2153 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/UNet2DtCycifTRAINCoreograph.py Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,586 @@ +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) + + # I = im2double(tifread('/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/SinemSaka_NucleiSegmentation_SingleImageInferenceTest3.tif')) + # UNet2D.singleImageInferenceSetup(modelPath,0) + # J = UNet2D.singleImageInference(I,'accumulate',0) + # UNet2D.singleImageInferenceCleanup() + # # imshowlist([I,J]) + # # sys.exit(0) + # # tifwrite(np.uint8(255*I),'/home/mc457/Workspace/I1.tif') + # # tifwrite(np.uint8(255*J),'/home/mc457/Workspace/I2.tif') + # K = np.zeros((2,I.shape[0],I.shape[1])) + # K[0,:,:] = I + # K[1,:,:] = J + # tifwrite(np.uint8(255*K),'/home/mc457/Workspace/Sinem_NucSeg.tif') + + # UNet2D.singleImageInferenceSetup(modelPath,0) + # imagePath = 'Y://sorger//data//RareCyte//Connor//Topacio_P2_AF//ashlar//C0078' + # + # fileList = glob.glob(imagePath + '//registration//C0078.ome.tif') + # print(fileList) + # for iFile in fileList: + # fileName = os.path.basename(iFile) + # fileNamePrefix = fileName.split(os.extsep, 1) + # I = im2double(tifffile.imread(iFile, key=0)) + # hsize = int((float(I.shape[0])*float(0.75))) + # vsize = int((float(I.shape[1])*float(0.75))) + # I = resize(I,(hsize,vsize)) + # J = UNet2D.singleImageInference(I,'accumulate',1) + # K = np.zeros((3,I.shape[0],I.shape[1])) + # K[2,:,:] = I + # K[0,:,:] = J + # J = UNet2D.singleImageInference(I, 'accumulate', 2) + # K[1, :, :] = J + # outputPath = imagePath + '//prob_maps' + # if not os.path.exists(outputPath): + # os.makedirs(outputPath) + # tifwrite(np.uint8(255*K),outputPath + '//' + fileNamePrefix[0] +'_NucSeg.tif') + # UNet2D.singleImageInferenceCleanup() + + + # ----- test 2 ----- + + # imPath = '/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/ClarenceYapp_NucleiSegmentation' + # UNet2D.setup(128,1,2,8,2,2,3,1,0.1,3,4) + # UNet2D.train(imPath,logPath,modelPath,pmPath,800,100,100,False,10,1) + # UNet2D.deploy(imPath,100,modelPath,pmPath,1) + + + # ----- test 3 ----- + + # imPath = '/home/mc457/files/CellBiology/IDAC/Marcelo/Etc/UNetTestSets/CarmanLi_CellTypeSegmentation' + # # UNet2D.setup(256,1,2,8,2,2,3,1,0.1,3,4) + # # UNet2D.train(imPath,logPath,modelPath,pmPath,1400,100,164,False,10000,1) + # UNet2D.deploy(imPath,164,modelPath,pmPath,1) + + + # ----- test 4 ----- + + # imPath = '/home/cicconet/Downloads/TrainSet1' + # UNet2D.setup(64,1,2,8,2,2,3,1,0.1,3,4) + # UNet2D.train(imPath,logPath,modelPath,pmPath,200,8,8,False,2000,1,0) + # # UNet2D.deploy(imPath,164,modelPath,pmPath,1) \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/UNetCoreograph.py Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,802 @@ +import numpy as np +from scipy import misc as sm +import shutil +import scipy.io as sio +import os +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 +from skimage.feature import peak_local_max,blob_log +from skimage.color import label2rgb +import skimage.io as skio +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): + tiff = tifffile.TiffFile(path) + shape = tiff.pages[0].shape + numChan=None + for i, page in enumerate(tiff.pages): + if page.shape != shape: + numChan = i + return numChan + break +# else: +# raise Exception("Did not find any pyramid subresolutions") + + if not numChan: + numChan = len(tiff.pages) + return numChan + +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, 1) + + 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,probMap,initialmask,preBlur,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) +# Irs = cv2.resize(I,(vsize,hsize),cv2.INTER_CUBIC) +# I=I.astype(np.float) +# r,c = I.shape +# I+=np.random.rand(r,c)*1e-6 +# c1 = uniform_filter(I, 3, mode='reflect') +# c2 = uniform_filter(I*I, 3, mode='reflect') +# nucGF = np.sqrt(c2 - c1*c1)*np.sqrt(9./8) +# nucGF[np.isnan(nucGF)]=0 + #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 + +# init=np.argwhere(eroded>0) + nucGF = gaussian(nucGF,0.7) + nucGF=nucGF/np.amax(nucGF) + + +# initialmask = nucGF>0 + nuclearMask = morphological_chan_vese(nucGF, 100, init_level_set=initialmask, smoothing=10,lambda1=1.001, lambda2=1) + +# nuclearMask = chan_vese(nucGF, mu=1.5, lambda1=6, lambda2=1, tol=0.0005, max_iter=2000, dt=15, init_level_set=initialmask, extended_output=True) +# nuclearMask = nuclearMask[0] + + + TMAmask = nuclearMask +# nMaskDist =distance_transform_edt(nuclearMask) +# fgm = peak_local_max(h_maxima(nMaskDist, 2*preBlur),indices =False) +# markers= np.logical_or(erosion(1-nuclearMask,disk(3)),fgm) +# TMAmask=watershed(-nMaskDist,label(markers),watershed_line=True) +# TMAmask = nuclearMask*(TMAmask>0) + 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("--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, 'TFModel - 3class 16 kernels 5ks 2 layers') + #modelPath = 'D:\\LSP\\Coreograph\\model-4layersMaskAug20' + scriptPath = os.path.dirname(os.path.realpath(__file__)) + modelPath = os.path.join(scriptPath, 'model') +# outputPath = 'D:\\LSP\\cycif\\testsets\\exemplar-002\\dearrayPython' ############ + maskOutputPath = os.path.join(outputPath, 'masks') +# imagePath = 'D:\\LSP\\cycif\\testsets\\exemplar-002\\registration\\exemplar-002.ome.tif'########### +# imagePath = 'Y:\\sorger\\data\\RareCyte\\Connor\\TMAs\\CAJ_TMA11_13\\original_data\\TMA11\\registration\\TMA11.ome.tif' +# imagePath = 'Y:\\sorger\\data\\RareCyte\\Connor\\TMAs\\Z124_TMA20_22\\TMA22\\registration\\TMA22.ome.tif' +# classProbsPath = 'D:\\unetcoreograph.tif' +# imagePath = 'Y:\\sorger\\data\\RareCyte\\Connor\\Z155_PTCL\\TMA_552\\registration\\TMA_552.ome.tif' +# classProbsPath = 'Y:\\sorger\\data\\RareCyte\\Connor\\Z155_PTCL\\TMA_552\\probMapCore\\TMA_552_CorePM_1.tif' +# imagePath = 'Y:\\sorger\\data\\RareCyte\\Zoltan\\Z112_TMA17_19\\190403_ashlar\\TMA17_1092.ome.tif' +# classProbsPath = 'Z:\\IDAC\\Clarence\\LSP\\CyCIF\\TMA\\probMapCore\\1new_CorePM_1.tif' +# imagePath = 'Y:\\sorger\\data\\RareCyte\\ANNIINA\\Julia\\2018\\TMA6\\julia_tma6.ome.tif' +# classProbsPath = 'Z:\\IDAC\\Clarence\\LSP\\CyCIF\\TMA\\probMapCore\\3new_CorePM_1.tif' + + +# if not os.path.exists(outputPath): +# os.makedirs(outputPath) +# else: +# shutil.rmtree(outputPath) + if not os.path.exists(maskOutputPath): + os.makedirs(maskOutputPath) + + + channel = args.channel + dsFactor = 1/(2**args.downsampleFactor) +# I = tifffile.imread(imagePath, key=channel) + 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) +# classProbs = tifffile.imread(classProbsPath,key=0) + preMask = gaussian(np.uint8(classProbs*255),1)>0.8 + + P = regionprops(label(preMask),cache=False) + area = [ele.area for ele in P] + print(str(len(P)) + ' cores detected!') + 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 + numCores = len(centroids) + estCoreDiamX = np.ones(numCores)*estCoreDiam/dsFactor + estCoreDiamY = np.ones(numCores)*estCoreDiam/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 + + + 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])] + + for iChan in range(outputChan[0],outputChan[1]+1): + with pytiff.Tiff(imagePath, "r", encoding='utf-8') as handle: + handle.set_page(iChan) + coreStack= 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',coreStack,append=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)] + 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,singleProbMap,initialmask,coreRad/20,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, (np.amax(imagesub), np.amax(imagesub), np.amax(imagesub)), 1, cv2.LINE_AA) + + skio.imsave(maskOutputPath + os.path.sep + str(iCore+1) + '_mask.tif',np.uint8(TMAmask)) + print('Segmented core ' + str(iCore+1)) + + boundaries = find_boundaries(maskTMA) + imagesub = imagesub/np.percentile(imagesub,99.9) + imagesub[boundaries==1] = 1 + skio.imsave(outputPath + os.path.sep + 'TMA_MAP.tif' ,np.uint8(imagesub*255)) + print('Segmented all cores!') + + +#restore GPU to 0 + #image load using tifffile
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/coreograph.xml Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,56 @@ +<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> + <macros> + <import>macros.xml</import> + </macros> + + <expand macro="requirements"/> + @VERSION_CMD@ + + <command detect_errors="exit_code"><![CDATA[ + #set $type_corrected = str($source_image)[:-3]+'ome.tif' + ln -s $source_image `basename $type_corrected`; + + @CMD_BEGIN@ + --imagePath `basename $type_corrected` + --downsampleFactor $downsamplefactor + --channel $channel + --buffer $buffer + --sensitivity $sensitivity + + ##if $usegrid + ##--useGrid + ##end if + + #if $cluster + --cluster + #end if + + --outputPath .; + + ]]></command> + + + <inputs> + <param name="source_image" type="data" format="tiff" label="Registered TIFF"/> + <param name="downsamplefactor" type="integer" value="5" label="Down Sample Factor"/> + <param name="channel" type="integer" value="0" label="Channel"/> + <param name="buffer" type="float" value="2.0" label="Buffer"/> + <param name="sensitivity" type="float" value="0.3" label="Sensitivity"/> + <!--<param name="usegrid" type="boolean" label="Use Grid"/>--> + <param name="cluster" type="boolean" checked="false" label="Cluster"/> + </inputs> + + <outputs> + <collection name="tma_sections" type="list" label="${tool.name} on ${on_string}: Images"> + <discover_datasets pattern="(?P<designation>[0-9]+)\.tif" format="tiff" visible="false"/> + </collection> + <collection name="masks" type="list" label="${tool.name} on ${on_string}: Masks"> + <discover_datasets pattern="(?P<designation>[0-9]+)_mask\.tif" directory="masks" format="tiff" visible="false"/> + </collection> + <data name="TMA_MAP" format="tiff" label="${tool.name} on ${on_string}: TMA Map" from_work_dir="TMA_MAP.tif"/> + </outputs> + <help><![CDATA[ + ]]></help> + <expand macro="citations" /> +</tool>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/macros.xml Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,29 @@ +<?xml version="1.0"?> +<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> + <requirement type="package">cython</requirement> + <requirement type="package" version="0.14.2">scikit-image</requirement> + <requirement type="package">matplotlib</requirement> + <requirement type="package" version="2020.2.16">tifffile</requirement> + <requirement type="package" version="1.1.0">scipy</requirement> + <requirement type="package">opencv</requirement> + <requirement type="package" version="0.8.1">pytiff</requirement> + </requirements> + </xml> + + <xml name="version_cmd"> + <version_command>echo @VERSION@</version_command> + </xml> + <xml name="citations"> + <citations> + </citations> + </xml> + + <token name="@VERSION@">2.2.0</token> + <token name="@CMD_BEGIN@">python ${__tool_directory__}/UNetCoreograph.py</token> +</macros>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/model/checkpoint Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,2 @@ +model_checkpoint_path: "D:\\LSP\\Coreograph\\model-4layersMaskAug20New\\model.ckpt" +all_model_checkpoint_paths: "D:\\LSP\\Coreograph\\model-4layersMaskAug20New\\model.ckpt"
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/model/datasetMean.data Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,3 @@ +€G?· +=p£× +. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/model/datasetStDev.data Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,3 @@ +€G?· +=p£× +. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolbox/PartitionOfImage.py Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,305 @@ +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) +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolbox/ftools.py Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,55 @@ +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
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolbox/imtools.py Wed May 19 21:34:38 2021 +0000 @@ -0,0 +1,312 @@ +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