From a912f822822636e8aae575262b3cb4d53685a996 Mon Sep 17 00:00:00 2001 From: k-dominik Date: Mon, 25 Sep 2023 11:42:04 +0200 Subject: [PATCH 1/6] doc: save for training func no longer exists removed documentation entry as well as related images. --- README.md | 29 +++++++++++++------------- doc/screenshots/IJ-Train-Checkbox.png | Bin 878977 -> 0 bytes doc/screenshots/IJ-Train-Console.png | Bin 158973 -> 0 bytes 3 files changed, 15 insertions(+), 14 deletions(-) delete mode 100644 doc/screenshots/IJ-Train-Checkbox.png delete mode 100644 doc/screenshots/IJ-Train-Console.png diff --git a/README.md b/README.md index 9d97fec9..73705449 100644 --- a/README.md +++ b/README.md @@ -91,20 +91,21 @@ The plugin uses (lossless) gzip compression. ![ImageJ Menu](./doc/screenshots/IJ-Export.png) -### How to train an ilastik project to be used with those wrappers - -For the workflow wrappers below, it is important that ilastik projects are trained from data that was -preprocessed and exported the same way as all further datasets will be. There are two ways how this can be achieved with this plugin: - -1. You can manually use the export option as described [above](#export) to export your images from ImageJ to an ilastik-compatible - HDF5 dataset with 5 dimensions. Then when you create an ilastik project, read the raw data / probabilities / segmentation from the exported HDF5 files. -2. Each workflow wrapper has a `Save temporary file for training only, no prediction` option. If you select this option and run the plugin, - the input files -- that would otherwise be passed to ilastik -- are exported to temporary files. - ![Save temporary file checkbox](./doc/screenshots/IJ-Train-Checkbox.png) - The locations and names of those files are written to ImageJ's console, which you should open using `Window->Console`. - ![Temporary file location in console](./doc/screenshots/IJ-Train-Console.png) - Use this file as input in your ilastik project. Then processing any further files from ImageJ through the ilastik workflow wrappers - should give the desired results. +### How to train an ilastik project for use in the plugins + +In general you can use any project of the supported workflows that you trained. +However, there are some things to keep in mind: + * The spatial dimensions (not the size along those) need to match. + Meaning if you trained your project with 3D (`x`,`y`,`z`) data, you can only process 3D images with it. + * The largest 3D filter you selected in ilastik, must fit inside the image. + It's roughly the largest 3D sigma you selected times 3.5 (so with default largest sigma `10.0` it would be a minimum of `35` pixels in `z`-dimension). + * The number of channels needs to match (also they should be in the same order), if you train e.g. on dapi + fitc, you should only process files with dapi + fitc channels. + + +Ideally, you train your ilastik already with `.h5` files manually generated by the [above export functionality](#export). +The export creates files with maximum compatibility with ilastik - and you will also get the best performance during training. +With this you make sure that the whole processing chain will work. + ### ilastik configuration of the workflow wrappers Found at `Plugins -> ilastik -> Configure ilastik executable location`. diff --git a/doc/screenshots/IJ-Train-Checkbox.png b/doc/screenshots/IJ-Train-Checkbox.png deleted file mode 100644 index 5d6888f840dac06a7977deaa8c07972b7a7eb2cb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 878977 zcmZ^}1yCH(w>OBph6Hy41HqlaU4larB)Gc`GQeQL-GW;J32uYCyZhko?k=1Ed*6QF z+uGf(>Z;r4oZrz~ed?a>a5WVtI5@-^G?ago6mum_I5;dq zYbhx;1t}?NH5W$L#7c32qLqemM zlvIPJn19e~)~+nwFAggyM~;hCi~>5Ar6C$HSdjx8J;eC?=>e1 zfh^#7vOHyEkQ)fae>X@c;Ng!bD~_*E2?oH?{R%*Vi)ClUh=`4a{}}kPwsYgwvHkKv z&a>2y;;kZ=74P!{oVIY64gym<-bNtYAJKMs4Fqv(39m>ddKs7H$jWjge579qE}3~f 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zJF!v(!dOz6FmkqaS$ToC(N}I`08^NozWw7rclCWpf8^{pMwSTL%6&~#7C(c`Bh(_S?mCwcxXHh(*;;4^@?) zyPu4)`;y(7rwL~A^e-b~HMnK1AFM{RBHU}B&OV%u-U>csEq+iD6(3{z zB-nR^J7}hwYmGG#9(~$x?ltD!o&SIfuh+a$z0Aq|x%TgK$@3Km_mw^WD;)gK=iUfB z$@#|8ZGM=nS?KbaPsTs1sSYKx7ACYoh*Z<}L!n?nYt*>Xcs zvOryha9_T>GJD^PPev5omd+o0y!urm>2)DZxa-wCm+DV5`&T;eJ!d{z3snytPI@Vl z;fsgzCGja(x1Ny@SDp|*#C}$L{2J2x*76_x;_1I7-c=uVHAdZTybzD&?BsAH^u*-7 zzlsQG!A5A)Z&><%s-aZJ#Lewp!yCsW{q|20?%!MYI)HilAL6Dy8vBOyTkDKk@${M3 z|LscuWmn7PAD(U`dHsaL^H-w{41%-eC+pvfbk)4)kIl^XO39&P@vrMDH1e-iK|``t z8KD_JOnj2CEqKDIKiCdl`lsB_5zLLVk#6^it|tiFpfj$43c`Ek-T=ec=X=_;VR~JJ zjyS*R@#sBVLtr>WA-pyGQlM&W`9`fv9%zVk?cDf_{-qx;iC^_J Date: Mon, 25 Sep 2023 13:43:26 +0200 Subject: [PATCH 2/6] doc: Update pixel classification * updated screenshots for 2.0.0 * simplified example macro (with more complex macro in `examples/`). * fix: pc output is _not_ a label image! --- README.md | 51 +++++++++---------------- doc/screenshots/IJ-PC-dialog.png | Bin 72026 -> 152552 bytes doc/screenshots/IJ-PC-input.png | Bin 1090319 -> 1333092 bytes doc/screenshots/IJ-PC-predictions.png | Bin 822083 -> 672619 bytes doc/screenshots/IJ-PC-segmentation.png | Bin 94240 -> 230943 bytes examples/pixel_classification.ijm | 38 +++++++++++++++--- 6 files changed, 50 insertions(+), 39 deletions(-) diff --git a/README.md b/README.md index 73705449..c6d592ff 100644 --- a/README.md +++ b/README.md @@ -118,7 +118,7 @@ Found at `Plugins -> ilastik -> Configure ilastik executable location`. ### Pixel Classification and Autocontext -Pixel Classification and Autocontext workflow have similar input settings. +[Pixel Classification](https://www.ilastik.org/documentation/pixelclassification/pixelclassification) and [Autocontext](https://www.ilastik.org/documentation/autocontext/autocontext) workflow have similar input settings. The Pixel Classification Workflow can be found at `Plugins -> ilastik -> Run Pixel Classification Prediction`, the Autocontext Workflow at `Plugins -> ilastik -> Run Autocontext Prediction`. @@ -135,43 +135,28 @@ The Pixel Classification Workflow can be found at `Plugins -> ilastik -> Run Pix * if _Probabilities_ was selected: a multi-channel float image that you can _e.g._ threshold to obtain a segmentation ![Pixel Classification Output: Probabilities](./doc/screenshots/IJ-PC-predictions.png) -* or a _Segmentation_:a single-channel image where each pixel gets a value corresponding to an _object ID_ inside a _connected component_. -It is recommended to apply a LUT (e.g. `Image -> Lookup Tables -> glasbey`) to the result in order to see the _segmentation_ output. +* or a _Segmentation_:a single-channel image where each pixel gets a value corresponding to the label class. + e.g. all pixels belonging to your first label, will have the value `1`, all that belong to the 2nd class, will have the value `2` and so on. + ilastik will assign classes based on the _highest_ probability label for each pixel. + It is recommended to apply a LUT (e.g. `Image -> Lookup Tables -> glasbey`) to the result in order to see the _segmentation_ output. ![Pixel Classification Output: Segmentation](./doc/screenshots/IJ-PC-segmentation.png). -#### Batch processing -The macro below demonstrates how to apply pixel classification to all HDF5 files in a given input directory and save -resulting probability maps in separate HDF5 files inside the input directory. -``` -// set global variables -pixelClassificationProject = ""; -outputType = "Probabilities"; // or "Segmentation" -inputDataset = "data"; -outputDataset = "exported_data"; -axisOrder = "tzyxc"; -compressionLevel = 0; - -// process all H5 files in a given directory -dataDir = ""; -fileList = getFileList(dataDir); -for (i = 0; i < fileList.length; i++) { - // import image from the H5 - fileName = dataDir + fileList[i]; - importArgs = "select=" + fileName + " datasetname=" + inputDataset + " axisorder=" + axisOrder; - run("Import HDF5", importArgs); +#### Example macro usage - // run pixel classification - inputImage = fileName + "/" + inputDataset; - pixelClassificationArgs = "projectfilename=" + pixelClassificationProject + " saveonly=false inputimage=" + inputImage + " pixelclassificationtype=" + outputType; - run("Run Pixel Classification Prediction", pixelClassificationArgs); +A simple example macro that opens a `.tif` file and processes it with a pre-trained ilastik pixel classification project would look like the following: - // export probability maps to H5 - outputFile = dataDir + "output" + i + ".h5"; - exportArgs = "select=" + outputFile + " datasetname=" + outputDataset + " compressionlevel=" + compressionLevel; - run("Export HDF5", exportArgs); -} +```javascript +project = "/absolute/path/to/some/directory/pixel_class_2d_cells_apoptotic.ilp"; +input = "/absolute/path/to/some/directory/2d_cells_apoptotic.tif"; + +type = "Probabilities"; + +open(input); +run("Run Pixel Classification Prediction", "projectfilename=[" + project + "] input=[" + input + "] pixelclassificationtype=[" + type + "]"); ``` -replace `` with the input dataset where the HDF5 files reside (don't forget the trailing slash `/`) and `` with the path to your ilastik Pixel Classification project file. + +A more complex example can be found in [`./examples/pixel_classification.ijm`](./examples/pixel_classification.ijm). + ### Object Classification Found at `Plugins -> ilastik -> Run Object Classification Prediction`. diff --git a/doc/screenshots/IJ-PC-dialog.png b/doc/screenshots/IJ-PC-dialog.png index bd52a26ea5dc82e1222af8053ed77cc21b9765a2..ca6e96309e08086d052ac09a2e32aa9d96a1dcec 100644 GIT binary patch literal 152552 zcmeFYi9eLz`#-J}E!I@XGPXj+gtD8l?@?5UOj(jFlQlGqlI(^MvP_|*EZHT^D9hNg zWZ$w3Mi`8-j4{ja*8BDTeBQs$@ALZuzK_RuJUs4u&N^18M*>lU+; 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