copick-easymode

Easymode pretrained segmentation integration for copick CLI.

This plugin provides CLI commands to run easymode pretrained segmentation models on tomograms stored in copick projects. For more information about easymode, visit the documentation. If you use this plugin, please cite the easymode authors.

Installation

git clone https://github.com/copick/copick-easymode.git
cd copick-easymode
pip install -e .

Usage

After installation, the copick inference easymode command becomes available:

# Basic usage - segment ribosomes in all runs
copick inference easymode -c config.json -m ribosome -t wbp@10.0

# Segment multiple features
copick inference easymode -c config.json -m ribosome,membrane,microtubule -t wbp@10.0

# Segment specific runs
copick inference easymode -c config.json -m membrane -t wbp@10.0 --run run001,run002

# Use specific GPUs
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --gpus 0,1

# High quality with test-time augmentation
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --tta 16

# Don't add object definitions to config
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --no-add-objects

# Overwrite existing segmentations
copick inference easymode -c config.json -m ribosome -t wbp@10.0 --overwrite

Available Models

The following pretrained segmentation models are available:

Model	Description
ribosome	Ribosome particles
membrane	Cellular membranes
microtubule	Microtubules
actin	Actin filaments
cytoplasm	Cytoplasm region
mitochondrion	Mitochondria
nucleus	Nuclear region
nuclear_envelope	Nuclear envelope
npc	Nuclear pore complex
cytoplasmic_granule	Cytoplasmic granules
mitochondrial_granule	Mitochondrial granules
prohibitin	Prohibitin complexes
tric	TRiC/CCT chaperonin
vault	Vault particles
void	Void/empty regions

Command Options

Option	Description
-c, --config	Path to copick configuration file (or set COPICK_CONFIG env var)
-m, --model	Comma-separated list of models to run (required)
-t, --tomogram	Tomogram URI as type@voxel_size e.g., wbp@10.0 (required)
-r, --run	Run name(s) to process, comma-separated. Empty = all runs
--gpus	Comma-separated GPU IDs. Default: all available
--tta	Test-time augmentation level 1-16. Higher = better but slower. Default: 4
--batch-size	Batch size for inference. Default: 1
--add-objects/--no-add-objects	Add object definitions to config if missing. Default: enabled
--overwrite/--no-overwrite	Overwrite existing segmentations. Default: disabled
--user-id	User ID for created segmentations. Default: copick
--session-id	Session ID for created segmentations. Default: 1
--debug/--no-debug	Enable debug logging

Object Definitions

When --add-objects is enabled (default), the plugin automatically adds object definitions to your copick config for any segmented features that don't already exist. These are added with minimal defaults:

• is_particle: False (segmentation target)
• label: Auto-assigned (next available integer)
• color: Auto-assigned

You can edit the config file afterward to add additional metadata like emdb_id, pdb_id, radius, etc.

Output

Segmentations are stored in the copick project at:

{overlay_root}/ExperimentRuns/{run_name}/VoxelSpacing{voxel_size:.3f}/Segmentations/

Each segmentation is stored as a zarr array with OME-Zarr metadata.

Requirements

• Python >= 3.10
• copick >= 0.8.0
• easymode
• TensorFlow >= 2.10.0

License

GPLv3 License