Paintera is a general visualization tool for 3D volumetric data and proof-reading in segmentation/reconstruction with a primary focus on neuron reconstruction from electron micrographs in connectomics. It features/supports:
- Views of orthogonal 2D cross-sections of the data at arbitrary angles and zoom levels
- Mipmaps for efficient display of arbitrarily large data at arbitrary scale levels
- Label data
- Painting
- Manual agglomeration
- 3D visualization as polygon meshes
- Meshes for each mipmap level
- Mesh generation on-the-fly via marching cubes to incorporate painted labels and agglomerations in 3D visualization. Marching Cubes is parallelized over small blocks. Only relevant blocks are considered (huge speed-up for sparse label data).
- Adaptive mesh details, i.e. only show high-resolution meshes for blocks that are closer to camera.
Paintera is available for installation through conda and the Python Package Index. Alternatively, you can use jgo to run Paintera from an existing Maven artifact on the SciJava Maven repository or your local maven repository, typically located at $HOME/.m2 on your computer. For all of these options, Java 8 with JavaFX and Apache Maven are requirement. Efforts to create a standalone app have not been successful so far (#253).
Java 8 (through OpenJDK), JavaFX, and Apache Maven are available for installation on many Linux distributions.
On Windows and macOS the use of Oracle Java 8 (comes with JavaFX) is recommended and Apache Maven needs to be downloaded and installed manually. Make sure that both Java and Maven are available on the PATH after installation. Note that our experience with Windows and macOS installations is very limited and there may be better ways to install Java 8 with JavaFX and Maven on these operating systems. If you are aware of any, please create a pull request to add these to this README.
Alternatively, OpenJDK and Maven are available through the default (pkgs/main) and conda-forge channels on conda, respectively.
conda install -c pkgs/main openjdk
conda install -c conda-forge mavenNote that OpenJDK on the conda-forge channels does not ship with JavaFX and cannot be used.
pacman -S jdk8-openjdk
pacman -S java8-openjfx
pacman -S mavensudo apt install default-jre default-jdk
sudo apt install maven
sudo apt install openjfxOn Ubuntu 18.04, you will have to install OpenJFX 8 explicitly and then immediately pin this version:
sudo apt install openjfx=8u161-b12-1ubuntu2 libopenjfx-java=8u161-b12-1ubuntu2 libopenjfx-jni=8u161-b12-1ubuntu2
sudo apt-mark hold openjfx libopenjfx-java libopenjfx-jniIf you are on 18.10 or newer, add the bionic repositories following instruction on https://bugs.launchpad.net/ubuntu/+source/openjfx/+bug/1799946:
sudo apt-add-repository 'deb http://de.archive.ubuntu.com/ubuntu/ bionic universe'Installation through conda requires an installation of the conda package manager.
Paintera is available for installation from the conda-forge channel:
conda install -c conda-forge painteraIt is recommended to install Paintera into a separate environment (outside the root environment), e.g.
conda create -n paintera -c conda-forge paintera
conda activate painteraPaintera can then be executed with the paintera command:
paintera [[JGO ARG...] [JVM ARG...] --] [ARG...]
By default, org.slf4j:slf4j-simple is used as slf4j binding for logging. This can be replaced by setting the PAINTERA_SLF4J_BINDING environment variable:
PAINTERA_SLF4J_BINDING=groupId:artifactId[:version]For example, to replace org.slf4j:slf4j-simple with ch.qos.logback:logback-classic, run (on Linux/OSX):
PAINTERA_SLF4J_BINDING=ch.qos.logback:logback-classic paintera
Paintera is available on the Python Package Index and can be installed through pip (Python >= 3.6 required):
pip install painteraGenerally, it is adivsable to install packages into user space with pip, i.e.
pip install --user painteraOn Linux, the packages will be installed into $HOME/.local and Paintera will be located at
$HOME/.local/bin/painteraYou can add $HOME/.local/bin to the PATH to make the paintera accessible from different locations.
Paintera can then be executed with the paintera command:
paintera [[JGO ARG...] [JVM ARG...] --] [ARG...]
By default, org.slf4j:slf4j-simple is used as slf4j binding for logging. This can be replaced by setting the PAINTERA_SLF4J_BINDING environment variable:
PAINTERA_SLF4J_BINDING=groupId:artifactId[:version]For example, to replace org.slf4j:slf4j-simple with ch.qos.logback:logback-classic, run (on Linux/OSX):
PAINTERA_SLF4J_BINDING=ch.qos.logback:logback-classic paintera
You can use jgo to run Paintera from an existing Maven artifact on the SciJava Maven repository or your local maven repository, typically located at $HOME/.m2 on your computer. Jgo is available through PyPI and conda. Please follow the instructions on the jgo GitHub repository for installation.
jgo \
[JGO ARG...] [JVM ARG...] \
-r scijava.public=https://maven.scijava.org/content/groups/public \
org.janelia.saalfeldlab:paintera[:$VERSION][+org.slf4j:slf4j-simple:1.7.25]Brackets indicate optional parts of the command that can be used to specify a version other than the latest release ([:$VERSION], e.g. 0.17.1-SNAPSHOT) or add logging (+org.slf4j:slf4j-simple:1.7.25). slf4j-simple can be replaced with any slf4j binding in the appropriate version (currently 1.7.25). The -r scijava.public=https://maven.scijava.org/content/groups/public option adds the SciJava Maven repository and can be omitted if $HOME/.jgorc is set up appropriately.
Current development on master or feature branches can be installed through maven and subsequently executed through jgo:
git clone https://github.com/saalfeldlab/paintera
cd paintera
# optionally checkout a feature branch (skip if running on master)
git checkout <branch-name>
mvn clean install
# maven will log the version it installs. Replace <VERSION> below with that version
jgo \
[JGO ARG...] [JVM ARG...] \
org.janelia.saalfeldlab:paintera:<VERSION>+org.slf4j:slf4j-simple:1.7.25The -r flag can be ommitted here because Paintera is available in the local maven repository.
We recommend setting these JVM options:
| Option | Description |
|---|---|
| -Xmx16G | Maximum Java heap space (replace 16G with desired amount) |
| -XX:+UseConcMarkSweepGC | Concurrent garbage collector, generally better for UI applications |
The following assumes that Paintera was installed through conda or pip and the paintera command is available on the command line. Replace paintera with an appropriate substitute if executed in a different way, e.g. through jgo.
$ paintera --help
Usage: Paintera [--add-n5-container=<container>...
[--add-n5-container=<container>...]... (-d=DATASET...
[-d=DATASET...]... [-r=RESOLUTION] [-r=RESOLUTION]...
[-o=OFFSET] [-o=OFFSET]... [-R] [--min=MIN] [--max=MAX]
[--channel-dimension=CHANNEL_DIMENSION]
[--channels=CHANNELS...] [--channels=CHANNELS...]...
[--name=NAME] [--name=NAME]...
[--id-service-fallback=ID_SERVICE_FALLBACK]
[--label-block-lookup-fallback=LABEL_BLOCK_LOOKUP_FALLBACK]
[--entire-container] [--exclude=EXCLUDE...]
[--exclude=EXCLUDE...]... [--include=INCLUDE...]
[--include=INCLUDE...]... [--only-explicitly-included])]...
[-h] [--default-to-temp-directory] [--print-error-codes]
[--version] [--height=HEIGHT]
[--highest-screen-scale=HIGHEST_SCREEN_SCALE]
[--num-screen-scales=NUM_SCREEN_SCALES]
[--screen-scale-factor=SCREEN_SCALE_FACTOR] [--width=WIDTH]
[--screen-scales=SCREEN_SCALES[,SCREEN_SCALES...]...]...
[PROJECT]
[PROJECT] Optional project N5 root (N5 or FileSystem).
--add-n5-container=<container>...
Container of dataset(s) to be added. If none is
provided, default to Paintera project (if any).
Currently N5 file system and HDF5 containers are
supported.
--channel-dimension=CHANNEL_DIMENSION
Defines the dimension of a 4D dataset to be
interpreted as channel axis. 0 <=
CHANNEL_DIMENSION <= 3
Default: 3
--channels=CHANNELS... Use only this subset of channels for channel (4D)
data. Multiple subsets can be specified. If no
channels are specified, use all channels.
-d, --dataset=DATASET... Dataset(s) within CONTAINER to be added. TODO: If
no datasets are specified, all datasets will be
added (or use a separate option for this).
--default-to-temp-directory
Default to temporary directory instead of showing
dialog when PROJECT is not specified.
--entire-container If set to true, discover all datasets (Paintera
format, multi-scale group, and N5 dataset)
inside CONTAINER and add to Paintera. The -d,
--dataset and --name options will be ignored if
ENTIRE_CONTAINER is set. Datasets can be
excluded through the --exclude option. The
--include option overrides any exclusions.
--exclude=EXCLUDE... Exclude any data set that matches any of EXCLUDE
regex patterns.
-h, --help Display this help message.
--height=HEIGHT Initial height of viewer. Defaults to 600.
Overrides height stored in project.
Default: -1
--highest-screen-scale=HIGHEST_SCREEN_SCALE
Highest screen scale, restricted to the interval
(0,1], defaults to 1. If no scale option is
specified, scales default to [1.0, 0.5, 0.25,
0.125, 0.0625].
--id-service-fallback=ID_SERVICE_FALLBACK
Set a fallback id service for scenarios in which
an id service is not provided by the data
backend, e.g. when no `maxId' attribute is
specified in an N5 dataset. Valid options are
(case insensitive): from-data — infer the max id
and id service from the dataset (may take a long
time for large datasets), none — do not use an
id service (requesting new ids will not be
possible), and ask — show a dialog to choose
between those two options
Default: ask
--include=INCLUDE... Include any data set that matches any of INCLUDE
regex patterns. Takes precedence over EXCLUDE.
--label-block-lookup-fallback=LABEL_BLOCK_LOOKUP_FALLBACK
Set a fallback label block lookup for scenarios in
which a label block lookup is not provided by
the data backend. The label block lookup is used
to process only relevant data during on-the-fly
mesh generation. Valid options are: `complete' —
always process the entire dataset (slow for
large data), `none' — do not process at all (no
3D representations/meshes available), and `ask'
— show a dialog to choose between those two
options
Default: ask
--max=MAX Maximum value of contrast range for raw and
channel data.
--min=MIN Minimum value of contrast range for raw and
channel data.
--name=NAME Specify name for dataset(s). The names are
assigned to datasets in the same order as
specified. If more datasets than names are
specified, the remaining dataset names will
default to the last segment of the dataset path.
--num-screen-scales=NUM_SCREEN_SCALES
Number of screen scales, defaults to 3. If no
scale option is specified, scales default to
[1.0, 0.5, 0.25, 0.125, 0.0625].
-o, --offset=OFFSET Spatial offset for all dataset(s) specified by
DATASET. Takes meta-data over resolution
specified in meta data of DATASET
--only-explicitly-included
When this option is set, use only data sets that
were explicitly included via INCLUDE. Equivalent
to --exclude '.*'
--print-error-codes List all error codes and exit.
-r, --resolution=RESOLUTION
Spatial resolution for all dataset(s) specified by
DATASET. Takes meta-data over resolution
specified in meta data of DATASET
-R, --revert-array-attributes
Revert array attributes found in meta data of
attributes of DATASET. Does not affect any array
attributes set explicitly through the RESOLUTION
or OFFSET options.
--screen-scale-factor=SCREEN_SCALE_FACTOR
Scalar value from the open interval (0,1) that
defines how screen scales diminish in each
dimension. Defaults to 0.5. If no scale option
is specified, scales default to [1.0, 0.5, 0.25,
0.125, 0.0625].
--screen-scales=SCREEN_SCALES[,SCREEN_SCALES...]...
Explicitly set screen scales. Must be strictly
monotonically decreasing values in from the
interval (0,1]. Overrides all other screen scale
options. If no scale option is specified, scales
default to [1.0, 0.5, 0.25, 0.125, 0.0625].
--version Print version string and exit
--width=WIDTH Initial width of viewer. Defaults to 800.
Overrides width stored in project.
Default: -1| Action | Description |
|---|---|
P |
Show Status bar on right side |
(Shift +) Ctrl + Tab |
Cycle current source forward (backward) |
Ctrl + O |
Show open dataset dialog |
M |
Maximize current view |
Shift + M |
Maximize split view of one slicing viewer and 3D scene |
Shift + Z |
Un-rotate but keep scale and translation |
| left click | toggle id under cursor if current source is label source (de-select all others) |
right click / Ctrl left click |
toggle id under cursor if current source is label source (append to current selection) |
Ctrl+A |
Select all ids |
Ctrl+Shift+A |
Select all ids in current view |
Shift left click |
Merge id under cursor with id that was last toggled active (if any) |
Shift right click |
Split id under cursor from id that was last toggled active (if any) |
Space left click/drag |
Paint with id that was last toggled active (if any) |
Space right click/drag |
Erase within canvas only |
Shift + Space right click/drag |
Paint background label |
Space wheel |
change brush size |
F + left click |
2D Flood-fill in current viewer plane with id that was last toggled active (if any) |
Shift + F + left click |
Flood-fill with id that was last toggled active (if any) |
N |
Select new, previously unused id |
S |
Enter shape interpolation mode |
1 / 2 |
Edit first/second section when previewing interpolated shape |
Enter |
Commit interpolated shape into canvas |
Esc |
Abort shape interpolation mode |
Ctrl + C |
Show dialog to commit canvas and/or assignments |
C |
Increment ARGB stream seed by one |
Shift + C |
Decrement ARGB stream seed by one |
Ctrl + Shift + C |
Show ARGB stream seed spinner |
Ctrl + Enter |
Merge all selected ids |
V |
Toggle visibility of current source |
Shift + V |
Toggle visibility of not-selected ids in current source (if label source) |
R |
Clear mesh caches and refresh meshes (if current source is label source) |
L |
Lock last selected segment (if label source) |
Ctrl + S |
Save current project state. Note: This does not commit/persist canvas. Use the commit canvas dialog to persist any painted labels across sessions. |
Ctrl + Shift + N |
Create new label dataset |
Ctrl + T |
Threshold raw source (only available if current source is raw source) |
B |
Add bookmark with current global and 3D viewer transforms |
Shift + B |
Open dialog to add bookmark with text note |
Ctrl+B |
Open dialog to select bookmark |
Shortcut + Alt + T |
Open scripting REPL |
The mode is activated by pressing the S key when the current source is a label source. Then, you can select the objects in the sections by left/right clicking (scrolling automatically fixes the selection in the current section).
When you're done with selecting the objects in the second section and initiate scrolling, the preview of the interpolated shape will be displayed. If something is not right, you can edit the selection in the first or second section by pressing 1 or 2, which will update the preview. When the desired result is reached, hit Enter to commit the results into the canvas and return back to normal mode.
While in the shape interpolation mode, at any point in time you can hit Esc to discard the current state and exit the mode.
Paintera supports single and multi-channel raw data and label data from N5, HDF5, and Google Cloud storage. The preferred format is the Paintera data format but regular single or multi-scale datasets can be imported as well. Any N5-like format can be converted into the preferred Paintera format with the Paintera Conversion Helper that is automatically installed with Paintera from conda or pip. For example, to convert raw and neuron_ids of the padded sample A of the CREMI challenge, simply run (assuming the data was downloaded into the current directory):
paintera-convert to-paintera \
--scale 2,2,1 2,2,1 2,2,1 2 2 \
--revert-array-attributes \
--output-container=example.n5 \
--container=sample_A_20160501.hdf \
-d volumes/raw \
--target-dataset=volumes/raw2 \
--dataset-scale 3,3,1 3,3,1 2 2 \
--dataset-resolution 4,4,40.0 \
-d volumes/labels/neuron_idsHere,
--scalespecifies the number of downsampled mipmap levels, where each comma-separated triple specifies a downsampling factor relative to the previous level. The total number of levels in the mipmap pyramid is the number of specified factors plus one (the data at original resolution)--revert-array-attributesreverts array attributes like"resolution"and"offset"that may be available in the source datasets--output-containerspecifies the path to the output n5 container--containerspecifies the path to the input container-dadds a input dataset for conversion--target-datasetsets the name of the output dataset--dataset-scalesets the scale of this dataset, overriding the global--scaleparameter--dataset-resolutionsets the resolution of the dataset
Paintera Conversion Helper builds upon Apache Spark and can be run on any Spark Cluster, which is particularly useful for large data sets.
In #61 we introduced a specification for the preferred data format.
Accept any of these:
- any regular (i.e. default mode) three-dimensional N5 dataset that is integer or float. Optional attributes are
"resolution": [x,y,z]and"offset": [x,y,z]. - any multiscale N5 group that has
"multiScale" : trueattribute and contains three-dimensional multi-scale datasetss0...sN. Optional attributes are"resolution": [x,y,z]and"offset: [x,y,z]". In addition to the requirements from (1), alls1...sNdatasets must contain"downsamplingFactors": [x,y,z]entry (s0is exempt, will default to[1.0, 1.0, 1.0]). All datasets must have same type. Optional attributes from (1) will be ignored. - (preferred) any N5 group with attribute
"painteraData : {"type" : "raw"}and a dataset/groupdatathat conforms with (2).
Accept any of these:
- any regular (i.e. default mode) integer or varlength
LabelMultisetType("isLabelMultiset": true) three-dimensional N5 dataset. Required attributes are"maxId": <id>. Optional attributes are"resolution": [x,y,z],"offset": [x,y,z]. - any multiscale N5 group that has
"multiScale" : trueattribute and contains three-dimensional multi-scale datasetss0...sN. Required attributes are"maxId": <id>. Optional attributes are"resolution": [x,y,z],"offset": [x,y,z],"maxId": <id>. If"maxId"is not specified, it is determined at start-up and added (this can be expensive). In addition to the requirements from (1), alls1...sNdatasets must contain"downsamplingFactors": [x,y,z]entry (s0is exempt, will default to[1.0, 1.0, 1.0]). All datasets must have same type. Optional attributes from (1) will be ignored. - (preferred) any N5 group with attribute
"painteraData : {"type" : "label"}and a dataset/groupdatathat conforms with (2). Required attributes are"maxId": <id>. Optional sub-groups are:
fragment-segment-assignment-- Dataset to store fragment-segment lookup table. Can be empty or will be initialized empty if it does not exist.label-to-block-mapping-- Multiscale directory tree with one text files per id mapping ids to containing label:label-to-block-mapping/s<scale-level>/<id>. If not present, no meshes will be generated.unique-labels-- Multiscale N5 group holding unique label lists per block. If not present (or not usingN5FS), meshes will not be updated when commiting canvas.
Paintera uses mipmap pyramids for efficient visualization of large data: At each level of the pyramid, the level of detail (and hence the amount of data) is less than at the previous level. This means that less data needs to be loaded and processed if a lower level detail suffices, e.g. if zoomed out far. Mipmap pyramids are created by gradually downsampling the data starting at original resolution. Naturally, some information is lost at each level. In a naive approach of winner-takes-all downsampling, voxels at lower resolution are assigned the most frequent label id of all contributing voxels at higher resolution, e.g.
_____ _____
| 1 | 2 |
|-----+-----|
| 2 | 3 |
‾‾‾‾‾ ‾‾‾‾‾
will be summarized into
___________
| |
| 2 |
| |
‾‾‾‾‾‾‾‾‾‾‾
As a result, label representation does not degenerate gracefully. This becomes obvious in particular when generating 3D representations from such data:
Mehses generated at lower resolution exhibit discontinuities. Instead, we propose the use of a non-scalar representation of multi-scale label data: label multisets. A summary of all contained labels is stored at each individual voxel instead of a single label. Each voxel contains a list of label ids and the associated counts:
| Label | Count |
|---|---|
| 1 | 2 |
| 3 | 1 |
| 391 | 5 |
The list is sorted by label id to enable efficient containment checks through binary search for arbitrary labels at each voxel. Going back to the simple example for winner-takes-all downsampling,
_____ _____
| 1:1 | 2:1 |
|-----+-----|
| 2:1 | 3:1 |
‾‾‾‾‾ ‾‾‾‾‾
will be summarized into
___________
| 1:1 |
| 2:2 |
| 3:1 |
‾‾‾‾‾‾‾‾‾‾‾
As a result, label data generates gracefully and without discontinuities in 3D representation:
This becomes even more apparent when overlaying multiset downsampled labels (magenta) over winner-takes-all downsampled labels (cyan):
These lists can get very large at lower resolution (in the most extreme case, all label ids of a dataset are listed in a single voxel) and it can become necessary to sacrifice some accuracy for efficiency for sufficiently large datasets. The -m N1 N2 ... flag of the Paintera conversion helper restricts the list of a label multiset to the N most frequent labels if N>0. In general, it is recommended to specify N=-1 for the first few mipmap levels and then gradually decrease accuracy at lower resolution levels.
Introduced in version 0.7.0 of the paintera-conversion-helper, the
extract-to-scalar
command extracts the highest resolution scale level of a Paintera dataset as a scalar uint64. Dataset. This is useful for using Paintera painted labels (and assignments) in downstream processing, e.g. classifier training. Optionally, the fragment-segment-assignment can be considered and additional assignments can be added (versions 0.8.0 and later). See extract-to-scalar --help for more details. The paintera-conversion-helper is installed with Paintera when installed through conda or pip but may need to be updated to support this feature.