| Previous changeset 1:57f1260ca94e (2022-03-11) Next changeset 3:ee92746d141a (2022-09-20) |
|
Commit message:
planemo upload for repository https://github.com/ohsu-comp-bio/UNetCoreograph commit cb09eb9d2fa0feae993ae994b6beae05972c644b |
|
modified:
coreograph.xml macros.xml |
|
added:
test-data/coreograph_test.tiff |
|
removed:
Dockerfile LICENSE README.md UNet2DtCycifTRAINCoreograph.py UNetCoreograph.py 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 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 |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb Dockerfile --- a/Dockerfile Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -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 |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb LICENSE --- a/LICENSE Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -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. |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb README.md --- 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 - |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb UNet2DtCycifTRAINCoreograph.py --- a/UNet2DtCycifTRAINCoreograph.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,529 +0,0 @@\n-import numpy as np\r\n-from scipy import misc\r\n-import tensorflow as tf\r\n-import shutil\r\n-import scipy.io as sio\r\n-import os,fnmatch,PIL,glob\r\n-\r\n-import sys\r\n-sys.path.insert(0, \'C:\\\\Users\\\\Public\\\\Documents\\\\ImageScience\')\r\n-from toolbox.imtools import *\r\n-from toolbox.ftools import *\r\n-from toolbox.PartitionOfImage import PI2D\r\n-\r\n-\r\n-def concat3(lst):\r\n-\t\treturn tf.concat(lst,3)\r\n-\r\n-class UNet2D:\r\n-\thp = None # hyper-parameters\r\n-\tnn = None # network\r\n-\ttfTraining = None # if training or not (to handle batch norm)\r\n-\ttfData = None # data placeholder\r\n-\tSession = None\r\n-\tDatasetMean = 0\r\n-\tDatasetStDev = 0\r\n-\r\n-\tdef setupWithHP(hp):\r\n-\t\tUNet2D.setup(hp[\'imSize\'],\r\n-\t\t\t\t\t hp[\'nChannels\'],\r\n-\t\t\t\t\t hp[\'nClasses\'],\r\n-\t\t\t\t\t hp[\'nOut0\'],\r\n-\t\t\t\t\t hp[\'featMapsFact\'],\r\n-\t\t\t\t\t hp[\'downSampFact\'],\r\n-\t\t\t\t\t hp[\'ks\'],\r\n-\t\t\t\t\t hp[\'nExtraConvs\'],\r\n-\t\t\t\t\t hp[\'stdDev0\'],\r\n-\t\t\t\t\t hp[\'nLayers\'],\r\n-\t\t\t\t\t hp[\'batchSize\'])\r\n-\r\n-\tdef setup(imSize,nChannels,nClasses,nOut0,featMapsFact,downSampFact,kernelSize,nExtraConvs,stdDev0,nDownSampLayers,batchSize):\r\n-\t\tUNet2D.hp = {\'imSize\':imSize,\r\n-\t\t\t\t\t \'nClasses\':nClasses,\r\n-\t\t\t\t\t \'nChannels\':nChannels,\r\n-\t\t\t\t\t \'nExtraConvs\':nExtraConvs,\r\n-\t\t\t\t\t \'nLayers\':nDownSampLayers,\r\n-\t\t\t\t\t \'featMapsFact\':featMapsFact,\r\n-\t\t\t\t\t \'downSampFact\':downSampFact,\r\n-\t\t\t\t\t \'ks\':kernelSize,\r\n-\t\t\t\t\t \'nOut0\':nOut0,\r\n-\t\t\t\t\t \'stdDev0\':stdDev0,\r\n-\t\t\t\t\t \'batchSize\':batchSize}\r\n-\r\n-\t\tnOutX = [UNet2D.hp[\'nChannels\'],UNet2D.hp[\'nOut0\']]\r\n-\t\tdsfX = []\r\n-\t\tfor i in range(UNet2D.hp[\'nLayers\']):\r\n-\t\t\tnOutX.append(nOutX[-1]*UNet2D.hp[\'featMapsFact\'])\r\n-\t\t\tdsfX.append(UNet2D.hp[\'downSampFact\'])\r\n-\r\n-\r\n-\t\t# --------------------------------------------------\r\n-\t\t# downsampling layer\r\n-\t\t# --------------------------------------------------\r\n-\r\n-\t\twith tf.name_scope(\'placeholders\'):\r\n-\t\t\tUNet2D.tfTraining = tf.placeholder(tf.bool, name=\'training\')\r\n-\t\t\tUNet2D.tfData = tf.placeholder("float", shape=[None,UNet2D.hp[\'imSize\'],UNet2D.hp[\'imSize\'],UNet2D.hp[\'nChannels\']],name=\'data\')\r\n-\r\n-\t\tdef down_samp_layer(data,index):\r\n-\t\t\twith tf.name_scope(\'ld%d\' % index):\r\n-\t\t\t\tldXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp[\'ks\'], UNet2D.hp[\'ks\'], nOutX[index], nOutX[index+1]], stddev=stdDev0),name=\'kernel1\')\r\n-\t\t\t\tldXWeightsExtra = []\r\n-\t\t\t\tfor i in range(nExtraConvs):\r\n-\t\t\t\t\tldXWeightsExtra.append(tf.Variable(tf.truncated_normal([UNet2D.hp[\'ks\'], UNet2D.hp[\'ks\'], nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name=\'kernelExtra%d\' % i))\r\n-\t\t\t\t\r\n-\t\t\t\tc00 = tf.nn.conv2d(data, ldXWeights1, strides=[1, 1, 1, 1], padding=\'SAME\')\r\n-\t\t\t\tfor i in range(nExtraConvs):\r\n-\t\t\t\t\tc00 = tf.nn.conv2d(tf.nn.relu(c00), ldXWeightsExtra[i], strides=[1, 1, 1, 1], padding=\'SAME\')\r\n-\r\n-\t\t\t\tldXWeightsShortcut = tf.Variable(tf.truncated_normal([1, 1, nOutX[index], nOutX[index+1]], stddev=stdDev0),name=\'shortcutWeights\')\r\n-\t\t\t\tshortcut = tf.nn.conv2d(data, ldXWeightsShortcut, strides=[1, 1, 1, 1], padding=\'SAME\')\r\n-\r\n-\t\t\t\tbn = tf.layers.batch_normalization(tf.nn.relu(c00+shortcut), training=UNet2D.tfTraining)\r\n-\r\n-\t\t\t\treturn tf.nn.max_pool(bn, ksize=[1, dsfX[index], dsfX[index], 1], strides=[1, dsfX[index], dsfX[index], 1], padding=\'SAME\',name=\'maxpool\')\r\n-\r\n-\t\t# --------------------------------------------------\r\n-\t\t# bottom layer\r\n-\t\t# --------------------------------------------------\r\n-\r\n-\t\twith tf.name_scope(\'lb\'):\r\n-\t\t\tlbWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp[\'ks\'], UNet2D.hp[\'ks\'], nOutX[UNet2D.hp[\'nLayers\']], nOutX[UNet2D.hp[\'nLayers\']+1]], stddev=stdDev0),name=\'kernel1\')\r\n-\t\t\tdef lb(hidden):\r\n-\t\t\t\treturn tf.nn.relu(tf.nn.conv2d(hidden, lbWeights1, strides=[1, 1, 1, 1], padding=\'SAME\'),name=\'conv\')\r\n-\r\n-\t\t# --------------------------------------------------\r\n-\t\t# downsampling\r\n-\t\t# --------------------------------------------------\r\n-\r\n-\t\twith tf.name_scope(\'downsampling\'): \r\n-\t\t\tdsX = []\r\n-\t\t\tdsX.append(UNet2D.tfData)\r\n-\r\n-\t\t\tfor i in range(UNet2D.hp[\'nLayers\']):\r\n-\t\t\t\tdsX.append(down_samp_layer(dsX[i],i))\r\n-\r\n-\t\t\tb = lb(dsX[U'..b'(tifread(path))\r\n-\t\t\t#im = im[0, 0, 0, :, :]\r\n-\t\t\tData[iSample,:,:,0] = (im-datasetMean)/datasetStDev\r\n-\r\n-\t\t# --------------------------------------------------\r\n-\t\t# session\r\n-\t\t# --------------------------------------------------\r\n-\r\n-\t\tsaver = tf.train.Saver()\r\n-\t\tsess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU\r\n-\r\n-\t\tsaver.restore(sess, variablesPath)\r\n-\t\tprint("Model restored.")\r\n-\r\n-\t\t# --------------------------------------------------\r\n-\t\t# deploy\r\n-\t\t# --------------------------------------------------\r\n-\r\n-\t\tbatchData = np.zeros((batchSize,imSize,imSize,nChannels))\r\n-\r\n-\t\tif not os.path.exists(outPMPath):\r\n-\t\t\tos.makedirs(outPMPath)\r\n-\r\n-\t\tfor i in range(nImages):\r\n-\t\t\tprint(i,nImages)\r\n-\r\n-\t\t\tj = np.mod(i,batchSize)\r\n-\r\n-\t\t\tbatchData[j,:,:,:] = Data[i,:,:,:]\r\n-\t\t \r\n-\t\t\tif j == batchSize-1 or i == nImages-1:\r\n-\r\n-\t\t\t\toutput = sess.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0})\r\n-\r\n-\t\t\t\tfor k in range(j+1):\r\n-\t\t\t\t\tpm = output[k,:,:,pmIndex]\r\n-\t\t\t\t\tim = np.sqrt(normalize(batchData[k,:,:,0]))\r\n-\t\t\t\t\t# imwrite(np.uint8(255*np.concatenate((im,pm),axis=1)),\'%s/I%05d.png\' % (outPMPath,i-j+k+1))\r\n-\t\t\t\t\timwrite(np.uint8(255*im),\'%s/I%05d_Im.png\' % (outPMPath,i-j+k+1))\r\n-\t\t\t\t\timwrite(np.uint8(255*pm),\'%s/I%05d_PM.png\' % (outPMPath,i-j+k+1))\r\n-\r\n-\r\n-\t\t# --------------------------------------------------\r\n-\t\t# clean-up\r\n-\t\t# --------------------------------------------------\r\n-\r\n-\t\tsess.close()\r\n-\r\n-\tdef singleImageInferenceSetup(modelPath,gpuIndex):\r\n-\t\tos.environ[\'CUDA_VISIBLE_DEVICES\']= \'%d\' % gpuIndex\r\n-\r\n-\t\tvariablesPath = pathjoin(modelPath,\'model.ckpt\')\r\n-\r\n-\t\thp = loadData(pathjoin(modelPath,\'hp.data\'))\r\n-\t\tUNet2D.setupWithHP(hp)\r\n-\r\n-\t\tUNet2D.DatasetMean = loadData(pathjoin(modelPath,\'datasetMean.data\'))\r\n-\t\tUNet2D.DatasetStDev = loadData(pathjoin(modelPath,\'datasetStDev.data\'))\r\n-\t\tprint(UNet2D.DatasetMean)\r\n-\t\tprint(UNet2D.DatasetStDev)\r\n-\r\n-\t\t# --------------------------------------------------\r\n-\t\t# session\r\n-\t\t# --------------------------------------------------\r\n-\r\n-\t\tsaver = tf.train.Saver()\r\n-\t\tUNet2D.Session = tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) # config parameter needed to save variables when using GPU\r\n-\r\n-\t\tsaver.restore(UNet2D.Session, variablesPath)\r\n-\t\tprint("Model restored.")\r\n-\r\n-\tdef singleImageInferenceCleanup():\r\n-\t\tUNet2D.Session.close()\r\n-\r\n-\tdef singleImageInference(image,mode,pmIndex):\r\n-\t\tprint(\'Inference...\')\r\n-\r\n-\t\tbatchSize = UNet2D.hp[\'batchSize\']\r\n-\t\timSize = UNet2D.hp[\'imSize\']\r\n-\t\tnChannels = UNet2D.hp[\'nChannels\']\r\n-\r\n-\t\tPI2D.setup(image,imSize,int(imSize/8),mode)\r\n-\t\tPI2D.createOutput(nChannels)\r\n-\r\n-\t\tbatchData = np.zeros((batchSize,imSize,imSize,nChannels))\r\n-\t\tfor i in range(PI2D.NumPatches):\r\n-\t\t\tj = np.mod(i,batchSize)\r\n-\t\t\tbatchData[j,:,:,0] = (PI2D.getPatch(i)-UNet2D.DatasetMean)/UNet2D.DatasetStDev\r\n-\t\t\tif j == batchSize-1 or i == PI2D.NumPatches-1:\r\n-\t\t\t\toutput = UNet2D.Session.run(UNet2D.nn,feed_dict={UNet2D.tfData: batchData, UNet2D.tfTraining: 0})\r\n-\t\t\t\tfor k in range(j+1):\r\n-\t\t\t\t\tpm = output[k,:,:,pmIndex]\r\n-\t\t\t\t\tPI2D.patchOutput(i-j+k,pm)\r\n-\t\t\t\t\t# PI2D.patchOutput(i-j+k,normalize(imgradmag(PI2D.getPatch(i-j+k),1)))\r\n-\r\n-\t\treturn PI2D.getValidOutput()\r\n-\r\n-\r\n-if __name__ == \'__main__\':\r\n-\tlogPath = \'D:\\\\LSP\\\\UNet\\\\Coreograph\\\\TFLogs\'\r\n-\tmodelPath = \'D:\\\\LSP\\\\Coreograph\\\\model-4layersMaskAug20New\'\r\n-\tpmPath = \'D:\\\\LSP\\\\UNet\\\\Coreograph\\\\TFProbMaps\'\r\n-\r\n-\r\n-\t# ----- test 1 -----\r\n-\r\n-\t# imPath = \'D:\\\\LSP\\\\UNet\\\\tonsil20x1bin1chan\\\\tonsilAnnotations\'\r\n-\timPath = \'Z:/IDAC/Clarence/LSP/CyCIF/TMA/training data custom unaveraged\'\r\n-\t# UNet2D.setup(128,1,2,8,2,2,3,1,0.1,2,8)\r\n-\t# UNet2D.train(imPath,logPath,modelPath,pmPath,500,100,40,True,20000,1,0)\r\n-\tUNet2D.setup(128, 1, 2, 20, 2, 2, 3, 2, 0.03, 4, 32)\r\n-\tUNet2D.train(imPath, logPath, modelPath, pmPath, 2053, 513 , 641, True, 10, 1, 1)\r\n-\tUNet2D.deploy(imPath,100,modelPath,pmPath,1,1)\r\n-\r\n-\r\n-\r\n-\r\n' |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb UNetCoreograph.py --- a/UNetCoreograph.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b'@@ -1,800 +0,0 @@\n-import numpy as np\r\n-from scipy import misc as sm\r\n-import shutil\r\n-import scipy.io as sio\r\n-import os\r\n-os.environ[\'TF_CPP_MIN_LOG_LEVEL\'] = \'2\'\r\n-import logging\r\n-logging.getLogger(\'tensorflow\').setLevel(logging.FATAL)\r\n-import skimage.exposure as sk\r\n-import cv2\r\n-import argparse\r\n-import pytiff\r\n-import tifffile\r\n-import tensorflow as tf\r\n-from skimage.morphology import *\r\n-from skimage.exposure import rescale_intensity\r\n-from skimage.segmentation import chan_vese, find_boundaries, morphological_chan_vese\r\n-from skimage.measure import regionprops,label, find_contours\r\n-from skimage.transform import resize\r\n-from skimage.filters import gaussian, threshold_otsu\r\n-from skimage.feature import peak_local_max,blob_log\r\n-from skimage.color import gray2rgb as gray2rgb\r\n-import skimage.io as skio\r\n-from scipy.ndimage.morphology import binary_fill_holes\r\n-from skimage import img_as_bool\r\n-from skimage.draw import circle_perimeter\r\n-from scipy.ndimage.filters import uniform_filter\r\n-from scipy.ndimage import gaussian_laplace\r\n-from os.path import *\r\n-from os import listdir, makedirs, remove\r\n-\r\n-\r\n-\r\n-import sys\r\n-from typing import Any\r\n-\r\n-#sys.path.insert(0, \'C:\\\\Users\\\\Public\\\\Documents\\\\ImageScience\')\r\n-from toolbox.imtools import *\r\n-from toolbox.ftools import *\r\n-from toolbox.PartitionOfImage import PI2D\r\n-\r\n-\r\n-def concat3(lst):\r\n-\t\treturn tf.concat(lst,3)\r\n-\r\n-class UNet2D:\r\n-\thp = None # hyper-parameters\r\n-\tnn = None # network\r\n-\ttfTraining = None # if training or not (to handle batch norm)\r\n-\ttfData = None # data placeholder\r\n-\tSession = None\r\n-\tDatasetMean = 0\r\n-\tDatasetStDev = 0\r\n-\r\n-\tdef setupWithHP(hp):\r\n-\t\tUNet2D.setup(hp[\'imSize\'],\r\n-\t\t\t\t\t hp[\'nChannels\'],\r\n-\t\t\t\t\t hp[\'nClasses\'],\r\n-\t\t\t\t\t hp[\'nOut0\'],\r\n-\t\t\t\t\t hp[\'featMapsFact\'],\r\n-\t\t\t\t\t hp[\'downSampFact\'],\r\n-\t\t\t\t\t hp[\'ks\'],\r\n-\t\t\t\t\t hp[\'nExtraConvs\'],\r\n-\t\t\t\t\t hp[\'stdDev0\'],\r\n-\t\t\t\t\t hp[\'nLayers\'],\r\n-\t\t\t\t\t hp[\'batchSize\'])\r\n-\r\n-\tdef setup(imSize,nChannels,nClasses,nOut0,featMapsFact,downSampFact,kernelSize,nExtraConvs,stdDev0,nDownSampLayers,batchSize):\r\n-\t\tUNet2D.hp = {\'imSize\':imSize,\r\n-\t\t\t\t\t \'nClasses\':nClasses,\r\n-\t\t\t\t\t \'nChannels\':nChannels,\r\n-\t\t\t\t\t \'nExtraConvs\':nExtraConvs,\r\n-\t\t\t\t\t \'nLayers\':nDownSampLayers,\r\n-\t\t\t\t\t \'featMapsFact\':featMapsFact,\r\n-\t\t\t\t\t \'downSampFact\':downSampFact,\r\n-\t\t\t\t\t \'ks\':kernelSize,\r\n-\t\t\t\t\t \'nOut0\':nOut0,\r\n-\t\t\t\t\t \'stdDev0\':stdDev0,\r\n-\t\t\t\t\t \'batchSize\':batchSize}\r\n-\r\n-\t\tnOutX = [UNet2D.hp[\'nChannels\'],UNet2D.hp[\'nOut0\']]\r\n-\t\tdsfX = []\r\n-\t\tfor i in range(UNet2D.hp[\'nLayers\']):\r\n-\t\t\tnOutX.append(nOutX[-1]*UNet2D.hp[\'featMapsFact\'])\r\n-\t\t\tdsfX.append(UNet2D.hp[\'downSampFact\'])\r\n-\r\n-\r\n-\t\t# --------------------------------------------------\r\n-\t\t# downsampling layer\r\n-\t\t# --------------------------------------------------\r\n-\r\n-\t\twith tf.name_scope(\'placeholders\'):\r\n-\t\t\tUNet2D.tfTraining = tf.placeholder(tf.bool, name=\'training\')\r\n-\t\t\tUNet2D.tfData = tf.placeholder("float", shape=[None,UNet2D.hp[\'imSize\'],UNet2D.hp[\'imSize\'],UNet2D.hp[\'nChannels\']],name=\'data\')\r\n-\r\n-\t\tdef down_samp_layer(data,index):\r\n-\t\t\twith tf.name_scope(\'ld%d\' % index):\r\n-\t\t\t\tldXWeights1 = tf.Variable(tf.truncated_normal([UNet2D.hp[\'ks\'], UNet2D.hp[\'ks\'], nOutX[index], nOutX[index+1]], stddev=stdDev0),name=\'kernel1\')\r\n-\t\t\t\tldXWeightsExtra = []\r\n-\t\t\t\tfor i in range(nExtraConvs):\r\n-\t\t\t\t\tldXWeightsExtra.append(tf.Variable(tf.truncated_normal([UNet2D.hp[\'ks\'], UNet2D.hp[\'ks\'], nOutX[index+1], nOutX[index+1]], stddev=stdDev0),name=\'kernelExtra%d\' % i))\r\n-\t\t\t\t\r\n-\t\t\t\tc00 = tf.nn.conv2d(data, ldXWeights1, strides=[1, 1, 1, 1], padding=\'SAME\')\r\n-\t\t\t\tfor i in range(nExtraConvs):\r\n-\t\t\t\t\tc00 = tf.nn.conv2d(tf.nn.relu(c00), ldXWeightsExtra[i], strides=[1, 1, 1, 1], padding=\'SAME\')\r\n-\r\n-\t\t\t\tldXWeightsShortcut = tf.Variable(tf.truncated_normal([1, 1, nOutX[index], nOutX[index+1]], stddev=stdDev0),name=\'shortcutWeights\')\r\n-\t\t\t\tshortcut = tf.nn.conv2d(data, ldXWeightsShortcut, strides=[1, 1, 1, 1], padding=\'SAME\')\r\n-\r\n-\t\t\t\tbn = tf.layers.batch_normalization(tf.nn.relu(c00+short'..b's)\r\n-\t\tprint(str(numCores) + \' tissues detected!\')\r\n-\t\testCoreDiamX = np.array([(ele.bbox[3]-ele.bbox[1])*1.1 for ele in P]) / dsFactor\r\n-\t\testCoreDiamY = np.array([(ele.bbox[2]-ele.bbox[0])*1.1 for ele in P]) / dsFactor\r\n-\r\n-\tif numCores ==0 & args.cluster:\r\n-\t\tprint(\'No cores detected. Try adjusting the downsample factor\')\r\n-\t\tsys.exit(255)\r\n-\r\n-\tsingleMaskTMA = np.zeros(imagesub.shape)\r\n-\tmaskTMA = np.zeros(imagesub.shape)\r\n-\tbbox = [None] * numCores\r\n-\timagesub = imagesub/np.percentile(imagesub,99.9)\r\n-\timagesub = (imagesub * 255).round().astype(np.uint8)\r\n-\timagesub = gray2rgb(imagesub)\r\n-\tx=np.zeros(numCores)\r\n-\txLim=np.zeros(numCores)\r\n-\ty=np.zeros(numCores)\r\n-\tyLim=np.zeros(numCores)\r\n-\t\r\n-# segmenting each core \t\r\n-\t#######################\r\n-\tfor iCore in range(numCores):\r\n-\t\tx[iCore] = centroids[iCore,1] - estCoreDiamX[iCore]/2\r\n-\t\txLim[iCore] = x[iCore]+estCoreDiamX[iCore]\r\n-\t\tif xLim[iCore] > I.shape[1]:\r\n-\t\t\txLim[iCore] = I.shape[1]\r\n-\t\tif x[iCore]<1:\r\n-\t\t\tx[iCore]=1\r\n-\r\n-\t\ty[iCore] = centroids[iCore,0] - estCoreDiamY[iCore]/2\r\n-\t\tyLim[iCore] = y[iCore] + estCoreDiamY[iCore]\r\n-\t\tif yLim[iCore] > I.shape[0]:\r\n-\t\t\tyLim[iCore] = I.shape[0]\r\n-\t\tif y[iCore]<1:\r\n-\t\t\ty[iCore]=1\r\n-\r\n-\t\tbbox[iCore] = [round(x[iCore]), round(y[iCore]), round(xLim[iCore]), round(yLim[iCore])]\r\n-\t\tcoreStack = 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\')\r\n-\r\n-\t\tfor iChan in range(outputChan[0],outputChan[1]+1):\r\n-\t\t\twith pytiff.Tiff(imagePath, "r", encoding=\'utf-8\') as handle:\r\n-\t\t\t\thandle.set_page(iChan)\r\n-\t\t\t\tcoreStack[iChan,:,:] =handle[np.uint32(bbox[iCore][1]):np.uint32(bbox[iCore][3]-1), np.uint32(bbox[iCore][0]):np.uint32(bbox[iCore][2]-1)]\r\n-\r\n-\t\tskio.imsave(outputPath + os.path.sep + str(iCore+1) + \'.tif\',np.uint16(coreStack),imagej=True,bigtiff=True)\r\n-\t\twith pytiff.Tiff(imagePath, "r", encoding=\'utf-8\') as handle:\r\n-\t\t\thandle.set_page(args.channel)\r\n-\t\t\tcoreSlice= handle[np.uint32(bbox[iCore][1]):np.uint32(bbox[iCore][3]-1), np.uint32(bbox[iCore][0]):np.uint32(bbox[iCore][2]-1)]\r\n-\r\n-\t\tcore = (coreLabel ==(iCore+1))\r\n-\t\tinitialmask = core[np.uint32(y[iCore] * dsFactor):np.uint32(yLim[iCore] * dsFactor),\r\n-\t\t\t\t\t np.uint32(x[iCore] * dsFactor):np.uint32(xLim[iCore] * dsFactor)]\r\n-\t\tif not args.tissue:\r\n-\t\t\tinitialmask = resize(initialmask,size(coreSlice),cv2.INTER_NEAREST)\r\n-\r\n-\t\t\tsingleProbMap = classProbs[np.uint32(y[iCore]*dsFactor):np.uint32(yLim[iCore]*dsFactor),np.uint32(x[iCore]*dsFactor):np.uint32(xLim[iCore]*dsFactor)]\r\n-\t\t\tsingleProbMap = resize(np.uint8(255*singleProbMap),size(coreSlice),cv2.INTER_NEAREST)\r\n-\t\t\tTMAmask = coreSegmenterOutput(coreSlice,initialmask,False)\r\n-\t\telse:\r\n-\t\t\tIrs = resize(coreSlice,(int((float(coreSlice.shape[0]) * 0.25)),int((float(coreSlice.shape[1]) * 0.25))))\r\n-\t\t\tTMAmask = coreSegmenterOutput(Irs, np.uint8(initialmask), False)\r\n-\r\n-\t\tif np.sum(TMAmask)==0:\r\n-\t\t\tTMAmask = np.ones(TMAmask.shape)\r\n-\t\tvsize = int(float(coreSlice.shape[0]))\r\n-\t\thsize = int(float(coreSlice.shape[1]))\r\n-\t\tmasksub = resize(resize(TMAmask,(vsize,hsize),cv2.INTER_NEAREST),(int((float(coreSlice.shape[0])*dsFactor)),int((float(coreSlice.shape[1])*dsFactor))),cv2.INTER_NEAREST)\r\n-\t\tsingleMaskTMA[int(y[iCore]*dsFactor):int(y[iCore]*dsFactor)+masksub.shape[0],int(x[iCore]*dsFactor):int(x[iCore]*dsFactor)+masksub.shape[1]]=masksub\r\n-\t\tmaskTMA = maskTMA + resize(singleMaskTMA,maskTMA.shape,cv2.INTER_NEAREST)\r\n-\r\n-\t\tcv2.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)\r\n-\t\t\r\n-\t\tskio.imsave(maskOutputPath + os.path.sep + str(iCore+1) + \'_mask.tif\',np.uint8(TMAmask))\r\n-\t\tprint(\'Segmented core/tissue \' + str(iCore+1))\r\n-\t\t\r\n-\tboundaries = find_boundaries(maskTMA)\r\n-\timagesub[boundaries==1] = 255\r\n-\tskio.imsave(outputPath + os.path.sep + \'TMA_MAP.tif\' ,imagesub)\r\n-\tprint(\'Segmented all cores/tissues!\')\r\n-\r\n-#restore GPU to 0\r\n-\t#image load using tifffile\r\n' |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb coreograph.xml --- 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> |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/TMA_MAP.jpg |
| b |
| Binary file images/TMA_MAP.jpg has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/TMA_MAP.tif |
| b |
| Binary file images/TMA_MAP.tif has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/coreographbanner.png |
| b |
| Binary file images/coreographbanner.png has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/coreographbannerv2.png |
| b |
| Binary file images/coreographbannerv2.png has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/coreographbannerv3.png |
| b |
| Binary file images/coreographbannerv3.png has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/coreographbannerv4.png |
| b |
| Binary file images/coreographbannerv4.png has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/coreographbannerv5.png |
| b |
| Binary file images/coreographbannerv5.png has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/probmap.jpg |
| b |
| Binary file images/probmap.jpg has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/probmap.tif |
| b |
| Binary file images/probmap.tif has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/raw.jpg |
| b |
| Binary file images/raw.jpg has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb images/raw.tif |
| b |
| Binary file images/raw.tif has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb macros.xml --- 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> |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb model/checkpoint --- a/model/checkpoint Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -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" |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb model/datasetMean.data --- a/model/datasetMean.data Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,3 +0,0 @@ -�G?� -=p�� -. \ No newline at end of file |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb model/datasetStDev.data --- a/model/datasetStDev.data Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| b |
| @@ -1,3 +0,0 @@ -�G?� -=p�� -. \ No newline at end of file |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb model/hp.data |
| b |
| Binary file model/hp.data has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb model/model.ckpt.data-00000-of-00001 |
| b |
| Binary file model/model.ckpt.data-00000-of-00001 has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb model/model.ckpt.index |
| b |
| Binary file model/model.ckpt.index has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb model/model.ckpt.meta |
| b |
| Binary file model/model.ckpt.meta has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb test-data/coreograph_test.tiff |
| b |
| Binary file test-data/coreograph_test.tiff has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb toolbox/PartitionOfImage.py --- a/toolbox/PartitionOfImage.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b"@@ -1,305 +0,0 @@\n-import numpy as np\r\n-from toolbox.imtools import *\r\n-# from toolbox.ftools import *\r\n-# import sys\r\n-\r\n-class PI2D:\r\n- Image = None\r\n- PaddedImage = None\r\n- PatchSize = 128\r\n- Margin = 14\r\n- SubPatchSize = 100\r\n- PC = None # patch coordinates\r\n- NumPatches = 0\r\n- Output = None\r\n- Count = None\r\n- NR = None\r\n- NC = None\r\n- NRPI = None\r\n- NCPI = None\r\n- Mode = None\r\n- W = None\r\n-\r\n- def setup(image,patchSize,margin,mode):\r\n- PI2D.Image = image\r\n- PI2D.PatchSize = patchSize\r\n- PI2D.Margin = margin\r\n- subPatchSize = patchSize-2*margin\r\n- PI2D.SubPatchSize = subPatchSize\r\n-\r\n- W = np.ones((patchSize,patchSize))\r\n- W[[0,-1],:] = 0\r\n- W[:,[0,-1]] = 0\r\n- for i in range(1,2*margin):\r\n- v = i/(2*margin)\r\n- W[i,i:-i] = v\r\n- W[-i-1,i:-i] = v\r\n- W[i:-i,i] = v\r\n- W[i:-i,-i-1] = v\r\n- PI2D.W = W\r\n-\r\n- if len(image.shape) == 2:\r\n- nr,nc = image.shape\r\n- elif len(image.shape) == 3: # multi-channel image\r\n- nz,nr,nc = image.shape\r\n-\r\n- PI2D.NR = nr\r\n- PI2D.NC = nc\r\n-\r\n- npr = int(np.ceil(nr/subPatchSize)) # number of patch rows\r\n- npc = int(np.ceil(nc/subPatchSize)) # number of patch cols\r\n-\r\n- nrpi = npr*subPatchSize+2*margin # number of rows in padded image \r\n- ncpi = npc*subPatchSize+2*margin # number of cols in padded image \r\n-\r\n- PI2D.NRPI = nrpi\r\n- PI2D.NCPI = ncpi\r\n-\r\n- if len(image.shape) == 2:\r\n- PI2D.PaddedImage = np.zeros((nrpi,ncpi))\r\n- PI2D.PaddedImage[margin:margin+nr,margin:margin+nc] = image\r\n- elif len(image.shape) == 3:\r\n- PI2D.PaddedImage = np.zeros((nz,nrpi,ncpi))\r\n- PI2D.PaddedImage[:,margin:margin+nr,margin:margin+nc] = image\r\n-\r\n- PI2D.PC = [] # patch coordinates [r0,r1,c0,c1]\r\n- for i in range(npr):\r\n- r0 = i*subPatchSize\r\n- r1 = r0+patchSize\r\n- for j in range(npc):\r\n- c0 = j*subPatchSize\r\n- c1 = c0+patchSize\r\n- PI2D.PC.append([r0,r1,c0,c1])\r\n-\r\n- PI2D.NumPatches = len(PI2D.PC)\r\n- PI2D.Mode = mode # 'replace' or 'accumulate'\r\n-\r\n- def getPatch(i):\r\n- r0,r1,c0,c1 = PI2D.PC[i]\r\n- if len(PI2D.PaddedImage.shape) == 2:\r\n- return PI2D.PaddedImage[r0:r1,c0:c1]\r\n- if len(PI2D.PaddedImage.shape) == 3:\r\n- return PI2D.PaddedImage[:,r0:r1,c0:c1]\r\n-\r\n- def createOutput(nChannels):\r\n- if nChannels == 1:\r\n- PI2D.Output = np.zeros((PI2D.NRPI,PI2D.NCPI),np.float16)\r\n- else:\r\n- PI2D.Output = np.zeros((nChannels,PI2D.NRPI,PI2D.NCPI),np.float16)\r\n- if PI2D.Mode == 'accumulate':\r\n- PI2D.Count = np.zeros((PI2D.NRPI,PI2D.NCPI),np.float16)\r\n-\r\n- def patchOutput(i,P):\r\n- r0,r1,c0,c1 = PI2D.PC[i]\r\n- if PI2D.Mode == 'accumulate':\r\n- PI2D.Count[r0:r1,c0:c1] += PI2D.W\r\n- if len(P.shape) == 2:\r\n- if PI2D.Mode == 'accumulate':\r\n- PI2D.Output[r0:r1,c0:c1] += np.multiply(P,PI2D.W)\r\n- elif PI2D.Mode == 'replace':\r\n- PI2D.Output[r0:r1,c0:c1] = P\r\n- elif len(P.shape) == 3:\r\n- if PI2D.Mode == 'accumulate':\r\n- for i in range(P.shape[0]):\r\n- PI2D.Output[i,r0:r1,c0:c1] += np.multiply(P[i,:,:],PI2D.W)\r\n- elif PI2D.Mode == 'replace':\r\n- PI2D.Output[:,r0:r1,c0:c1] = P\r\n-\r\n- def getValidOutput():\r\n- margin = PI2D.Margin\r\n- nr, nc = PI2D.NR, PI2D.NC\r\n- if PI2D.Mode == 'accumulate':\r\n- C = PI2D.Count[margin:margin+nr,margin:margin+nc]\r\n- if len(PI2D.Output.shape) == 2:\r\n- if PI2D.Mode == 'accumulate':\r\n- return np.divide(PI2D.Output[margin:margin+nr,margin:margin+nc],C)\r\n- if PI2D.Mode ="..b" PI3D.NRPI = nrpi\r\n- PI3D.NCPI = ncpi\r\n- PI3D.NZPI = nzpi\r\n-\r\n- if len(image.shape) == 3:\r\n- PI3D.PaddedImage = np.zeros((nzpi,nrpi,ncpi))\r\n- PI3D.PaddedImage[margin:margin+nz,margin:margin+nr,margin:margin+nc] = image\r\n- elif len(image.shape) == 4:\r\n- PI3D.PaddedImage = np.zeros((nzpi,nw,nrpi,ncpi))\r\n- PI3D.PaddedImage[margin:margin+nz,:,margin:margin+nr,margin:margin+nc] = image\r\n-\r\n- PI3D.PC = [] # patch coordinates [z0,z1,r0,r1,c0,c1]\r\n- for iZ in range(npz):\r\n- z0 = iZ*subPatchSize\r\n- z1 = z0+patchSize\r\n- for i in range(npr):\r\n- r0 = i*subPatchSize\r\n- r1 = r0+patchSize\r\n- for j in range(npc):\r\n- c0 = j*subPatchSize\r\n- c1 = c0+patchSize\r\n- PI3D.PC.append([z0,z1,r0,r1,c0,c1])\r\n-\r\n- PI3D.NumPatches = len(PI3D.PC)\r\n- PI3D.Mode = mode # 'replace' or 'accumulate'\r\n-\r\n- def getPatch(i):\r\n- z0,z1,r0,r1,c0,c1 = PI3D.PC[i]\r\n- if len(PI3D.PaddedImage.shape) == 3:\r\n- return PI3D.PaddedImage[z0:z1,r0:r1,c0:c1]\r\n- if len(PI3D.PaddedImage.shape) == 4:\r\n- return PI3D.PaddedImage[z0:z1,:,r0:r1,c0:c1]\r\n-\r\n- def createOutput(nChannels):\r\n- if nChannels == 1:\r\n- PI3D.Output = np.zeros((PI3D.NZPI,PI3D.NRPI,PI3D.NCPI))\r\n- else:\r\n- PI3D.Output = np.zeros((PI3D.NZPI,nChannels,PI3D.NRPI,PI3D.NCPI))\r\n- if PI3D.Mode == 'accumulate':\r\n- PI3D.Count = np.zeros((PI3D.NZPI,PI3D.NRPI,PI3D.NCPI))\r\n-\r\n- def patchOutput(i,P):\r\n- z0,z1,r0,r1,c0,c1 = PI3D.PC[i]\r\n- if PI3D.Mode == 'accumulate':\r\n- PI3D.Count[z0:z1,r0:r1,c0:c1] += PI3D.W\r\n- if len(P.shape) == 3:\r\n- if PI3D.Mode == 'accumulate':\r\n- PI3D.Output[z0:z1,r0:r1,c0:c1] += np.multiply(P,PI3D.W)\r\n- elif PI3D.Mode == 'replace':\r\n- PI3D.Output[z0:z1,r0:r1,c0:c1] = P\r\n- elif len(P.shape) == 4:\r\n- if PI3D.Mode == 'accumulate':\r\n- for i in range(P.shape[1]):\r\n- PI3D.Output[z0:z1,i,r0:r1,c0:c1] += np.multiply(P[:,i,:,:],PI3D.W)\r\n- elif PI3D.Mode == 'replace':\r\n- PI3D.Output[z0:z1,:,r0:r1,c0:c1] = P\r\n-\r\n- def getValidOutput():\r\n- margin = PI3D.Margin\r\n- nz, nr, nc = PI3D.NZ, PI3D.NR, PI3D.NC\r\n- if PI3D.Mode == 'accumulate':\r\n- C = PI3D.Count[margin:margin+nz,margin:margin+nr,margin:margin+nc]\r\n- if len(PI3D.Output.shape) == 3:\r\n- if PI3D.Mode == 'accumulate':\r\n- return np.divide(PI3D.Output[margin:margin+nz,margin:margin+nr,margin:margin+nc],C)\r\n- if PI3D.Mode == 'replace':\r\n- return PI3D.Output[margin:margin+nz,margin:margin+nr,margin:margin+nc]\r\n- if len(PI3D.Output.shape) == 4:\r\n- if PI3D.Mode == 'accumulate':\r\n- for i in range(PI3D.Output.shape[1]):\r\n- 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)\r\n- return PI3D.Output[margin:margin+nz,:,margin:margin+nr,margin:margin+nc]\r\n-\r\n-\r\n- def demo():\r\n- I = np.random.rand(128,128,128)\r\n- PI3D.setup(I,64,4,'accumulate')\r\n-\r\n- nChannels = 2\r\n- PI3D.createOutput(nChannels)\r\n-\r\n- for i in range(PI3D.NumPatches):\r\n- P = PI3D.getPatch(i)\r\n- Q = np.zeros((P.shape[0],nChannels,P.shape[1],P.shape[2]))\r\n- for j in range(nChannels):\r\n- Q[:,j,:,:] = P\r\n- PI3D.patchOutput(i,Q)\r\n-\r\n- J = PI3D.getValidOutput()\r\n- J = J[:,0,:,:]\r\n-\r\n- D = np.abs(I-J)\r\n- print(np.max(D))\r\n-\r\n- pI = I[64,:,:]\r\n- pJ = J[64,:,:]\r\n- pD = D[64,:,:]\r\n-\r\n- K = cat(1,cat(1,pI,pJ),pD)\r\n- imshow(K)\r\n-\r\n" |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb toolbox/__pycache__/PartitionOfImage.cpython-36.pyc |
| b |
| Binary file toolbox/__pycache__/PartitionOfImage.cpython-36.pyc has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb toolbox/__pycache__/__init__.cpython-36.pyc |
| b |
| Binary file toolbox/__pycache__/__init__.cpython-36.pyc has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb toolbox/__pycache__/ftools.cpython-36.pyc |
| b |
| Binary file toolbox/__pycache__/ftools.cpython-36.pyc has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb toolbox/__pycache__/imtools.cpython-36.pyc |
| b |
| Binary file toolbox/__pycache__/imtools.cpython-36.pyc has changed |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb toolbox/ftools.py --- 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 |
| b |
| diff -r 57f1260ca94e -r 224e0cf4aaeb toolbox/imtools.py --- a/toolbox/imtools.py Fri Mar 11 23:40:51 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
| [ |
| b"@@ -1,312 +0,0 @@\n-import matplotlib.pyplot as plt\r\n-import tifffile\r\n-import os\r\n-import numpy as np\r\n-from skimage import io as skio\r\n-from scipy.ndimage import *\r\n-from skimage.morphology import *\r\n-from skimage.transform import resize\r\n-\r\n-def tifread(path):\r\n- return tifffile.imread(path)\r\n-\r\n-def tifwrite(I,path):\r\n- tifffile.imsave(path, I)\r\n-\r\n-def imshow(I,**kwargs):\r\n- if not kwargs:\r\n- plt.imshow(I,cmap='gray')\r\n- else:\r\n- plt.imshow(I,**kwargs)\r\n- \r\n- plt.axis('off')\r\n- plt.show()\r\n-\r\n-def imshowlist(L,**kwargs):\r\n- n = len(L)\r\n- for i in range(n):\r\n- plt.subplot(1, n, i+1)\r\n- if not kwargs:\r\n- plt.imshow(L[i],cmap='gray')\r\n- else:\r\n- plt.imshow(L[i],**kwargs)\r\n- plt.axis('off')\r\n- plt.show()\r\n-\r\n-def imread(path):\r\n- return skio.imread(path)\r\n-\r\n-def imwrite(I,path):\r\n- skio.imsave(path,I)\r\n-\r\n-def im2double(I):\r\n- if I.dtype == 'uint16':\r\n- return I.astype('float64')/65535\r\n- elif I.dtype == 'uint8':\r\n- return I.astype('float64')/255\r\n- elif I.dtype == 'float32':\r\n- return I.astype('float64')\r\n- elif I.dtype == 'float64':\r\n- return I\r\n- else:\r\n- print('returned original image type: ', I.dtype)\r\n- return I\r\n-\r\n-def size(I):\r\n- return list(I.shape)\r\n-\r\n-def imresizeDouble(I,sizeOut): # input and output are double\r\n- return resize(I,(sizeOut[0],sizeOut[1]),mode='reflect')\r\n-\r\n-def imresize3Double(I,sizeOut): # input and output are double\r\n- return resize(I,(sizeOut[0],sizeOut[1],sizeOut[2]),mode='reflect')\r\n-\r\n-def imresizeUInt8(I,sizeOut): # input and output are UInt8\r\n- return np.uint8(resize(I.astype(float),(sizeOut[0],sizeOut[1]),mode='reflect',order=0))\r\n-\r\n-def imresize3UInt8(I,sizeOut): # input and output are UInt8\r\n- return np.uint8(resize(I.astype(float),(sizeOut[0],sizeOut[1],sizeOut[2]),mode='reflect',order=0))\r\n-\r\n-def normalize(I):\r\n- m = np.min(I)\r\n- M = np.max(I)\r\n- if M > m:\r\n- return (I-m)/(M-m)\r\n- else:\r\n- return I\r\n-\r\n-def snormalize(I):\r\n- m = np.mean(I)\r\n- s = np.std(I)\r\n- if s > 0:\r\n- return (I-m)/s\r\n- else:\r\n- return I\r\n-\r\n-def cat(a,I,J):\r\n- return np.concatenate((I,J),axis=a)\r\n-\r\n-def imerode(I,r):\r\n- return binary_erosion(I, disk(r))\r\n-\r\n-def imdilate(I,r):\r\n- return binary_dilation(I, disk(r))\r\n-\r\n-def imerode3(I,r):\r\n- return morphology.binary_erosion(I, ball(r))\r\n-\r\n-def imdilate3(I,r):\r\n- return morphology.binary_dilation(I, ball(r))\r\n-\r\n-def sphericalStructuralElement(imShape,fRadius):\r\n- if len(imShape) == 2:\r\n- return disk(fRadius,dtype=float)\r\n- if len(imShape) == 3:\r\n- return ball(fRadius,dtype=float)\r\n-\r\n-def medfilt(I,filterRadius):\r\n- return median_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius))\r\n-\r\n-def maxfilt(I,filterRadius):\r\n- return maximum_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius))\r\n-\r\n-def minfilt(I,filterRadius):\r\n- return minimum_filter(I,footprint=sphericalStructuralElement(I.shape,filterRadius))\r\n-\r\n-def ptlfilt(I,percentile,filterRadius):\r\n- return percentile_filter(I,percentile,footprint=sphericalStructuralElement(I.shape,filterRadius))\r\n-\r\n-def imgaussfilt(I,sigma,**kwargs):\r\n- return gaussian_filter(I,sigma,**kwargs)\r\n-\r\n-def imlogfilt(I,sigma,**kwargs):\r\n- return -gaussian_laplace(I,sigma,**kwargs)\r\n-\r\n-def imgradmag(I,sigma):\r\n- if len(I.shape) == 2:\r\n- dx = imgaussfilt(I,sigma,order=[0,1])\r\n- dy = imgaussfilt(I,sigma,order=[1,0])\r\n- return np.sqrt(dx**2+dy**2)\r\n- if len(I.shape) == 3:\r\n- dx = imgaussfilt(I,sigma,order=[0,0,1])\r\n- dy = imgaussfilt(I,sigma,order=[0,1,0])\r\n- dz = imgaussfilt(I,sigma,order=[1,0,0])\r\n- return np.sqrt(dx**2+dy**2+dz**2)\r\n-\r\n-def localstats(I,radius,justfeatnames=False):\r\n- ptls = [10,30,50,70,90]\r\n- featNames = []\r\n- for i in range(len(ptls)):\r\n- "..b":,:,nDerivativesPerSigma*i ] = imgaussfilt(I,sigma)\r\n- D[:,:,:,nDerivativesPerSigma*i+1 ] = dx\r\n- D[:,:,:,nDerivativesPerSigma*i+2 ] = dy\r\n- D[:,:,:,nDerivativesPerSigma*i+3 ] = dz\r\n- D[:,:,:,nDerivativesPerSigma*i+4 ] = dxx\r\n- D[:,:,:,nDerivativesPerSigma*i+5 ] = imgaussfilt(I,sigma,order=[0,1,1])\r\n- D[:,:,:,nDerivativesPerSigma*i+6 ] = imgaussfilt(I,sigma,order=[1,0,1])\r\n- D[:,:,:,nDerivativesPerSigma*i+7 ] = dyy\r\n- D[:,:,:,nDerivativesPerSigma*i+8 ] = imgaussfilt(I,sigma,order=[1,1,0])\r\n- D[:,:,:,nDerivativesPerSigma*i+9 ] = dzz\r\n- D[:,:,:,nDerivativesPerSigma*i+10] = np.sqrt(dx**2+dy**2+dz**2)\r\n- D[:,:,:,nDerivativesPerSigma*i+11] = np.sqrt(dxx**2+dyy**2+dzz**2)\r\n-\r\n- # D[:,:,:,nDerivativesPerSigma*i ] = imgaussfilt(I,sigma)\r\n- # D[:,:,:,nDerivativesPerSigma*i+1 ] = np.sqrt(dx**2+dy**2+dz**2)\r\n- # D[:,:,:,nDerivativesPerSigma*i+2 ] = np.sqrt(dxx**2+dyy**2+dzz**2)\r\n- return D\r\n- # derivatives are indexed by the last dimension, which is good for ML features but not for visualization,\r\n- # in which case the expected dimensions are [plane,y(row),x(col)]; to obtain that ordering, do\r\n- # D = np.moveaxis(D,[2,0,1],[0,1,2])\r\n-\r\n-def imfeatures(I=[],sigmaDeriv=1,sigmaLoG=1,locStatsRad=0,justfeatnames=False):\r\n- if type(sigmaDeriv) is not list:\r\n- sigmaDeriv = [sigmaDeriv]\r\n- if type(sigmaLoG) is not list:\r\n- sigmaLoG = [sigmaLoG]\r\n- derivFeatNames = imderivatives([],sigmaDeriv,justfeatnames=True)\r\n- nLoGFeats = len(sigmaLoG)\r\n- locStatsFeatNames = []\r\n- if locStatsRad > 1:\r\n- locStatsFeatNames = localstats([],locStatsRad,justfeatnames=True)\r\n- nLocStatsFeats = len(locStatsFeatNames)\r\n- if justfeatnames == True:\r\n- featNames = derivFeatNames\r\n- for i in range(nLoGFeats):\r\n- featNames.append('logSigma%d' % sigmaLoG[i])\r\n- for i in range(nLocStatsFeats):\r\n- featNames.append(locStatsFeatNames[i])\r\n- return featNames\r\n- nDerivFeats = len(derivFeatNames)\r\n- nFeatures = nDerivFeats+nLoGFeats+nLocStatsFeats\r\n- sI = size(I)\r\n- F = np.zeros((sI[0],sI[1],nFeatures))\r\n- F[:,:,:nDerivFeats] = imderivatives(I,sigmaDeriv)\r\n- for i in range(nLoGFeats):\r\n- F[:,:,nDerivFeats+i] = imlogfilt(I,sigmaLoG[i])\r\n- if locStatsRad > 1:\r\n- F[:,:,nDerivFeats+nLoGFeats:] = localstats(I,locStatsRad)\r\n- return F\r\n-\r\n-def imfeatures3(I=[],sigmaDeriv=2,sigmaLoG=2,locStatsRad=0,justfeatnames=False):\r\n- if type(sigmaDeriv) is not list:\r\n- sigmaDeriv = [sigmaDeriv]\r\n- if type(sigmaLoG) is not list:\r\n- sigmaLoG = [sigmaLoG]\r\n- derivFeatNames = imderivatives3([],sigmaDeriv,justfeatnames=True)\r\n- nLoGFeats = len(sigmaLoG)\r\n- locStatsFeatNames = []\r\n- if locStatsRad > 1:\r\n- locStatsFeatNames = localstats3([],locStatsRad,justfeatnames=True)\r\n- nLocStatsFeats = len(locStatsFeatNames)\r\n- if justfeatnames == True:\r\n- featNames = derivFeatNames\r\n- for i in range(nLoGFeats):\r\n- featNames.append('logSigma%d' % sigmaLoG[i])\r\n- for i in range(nLocStatsFeats):\r\n- featNames.append(locStatsFeatNames[i])\r\n- return featNames\r\n- nDerivFeats = len(derivFeatNames)\r\n- nFeatures = nDerivFeats+nLoGFeats+nLocStatsFeats\r\n- sI = size(I)\r\n- F = np.zeros((sI[0],sI[1],sI[2],nFeatures))\r\n- F[:,:,:,:nDerivFeats] = imderivatives3(I,sigmaDeriv)\r\n- for i in range(nLoGFeats):\r\n- F[:,:,:,nDerivFeats+i] = imlogfilt(I,sigmaLoG[i])\r\n- if locStatsRad > 1:\r\n- F[:,:,:,nDerivFeats+nLoGFeats:] = localstats3(I,locStatsRad)\r\n- return F\r\n-\r\n-def stack2list(S):\r\n- L = []\r\n- for i in range(size(S)[2]):\r\n- L.append(S[:,:,i])\r\n- return L\r\n-\r\n-def thrsegment(I,wsBlr,wsThr): # basic threshold segmentation\r\n- G = imgaussfilt(I,sigma=(1-wsBlr)+wsBlr*5) # min 1, max 5\r\n- M = G > wsThr\r\n- return M\n\\ No newline at end of file\n" |
