chunkflow 0.6.3

Composable image chunk operators to create pipeline for distributed computation.

chunkflow

Perform Convolutional net inference to segment 3D image volume with one single command!

chunkflow read-tif --file-name path/of/image.tif -o image inference --convnet-model path/of/model.py --convnet-weight-path path/of/weight.pt --input-patch-size 20 256 256 --output-patch-overlap 4 64 64 --num-output-channels 3 -f pytorch --batch-size 12 --mask-output-chunk -i image -o affs write-h5 -i affs --file-name affs.h5 agglomerate --threshold 0.7 --aff-threshold-low 0.001 --aff-threshold-high 0.9999 -i affs -o seg write-tif -i seg -f seg.tif neuroglancer -c image,affs,seg -p 33333 -v 30 6 6

you can see your 3D image and segmentation directly in Neuroglancer!

Features

• Composable operators. The chunk operators could be freely composed in commandline for flexible usage.
• Hybrid Cloud Distributed computation in both local and cloud computers. The task scheduling frontend and computationally heavy backend are decoupled using AWS Simple Queue Service. The computational heavy backend could be any computer with internet connection and Amazon Web Services (AWS) authentication.
• All operations support 3D image volumes.

Operators

After installation, You can simply type chunkflow and it will list all the operators with help message. We list the available operators here. We keep adding new operators and will keep it update here. For the detailed usage, please checkout our Documentation.

Operator Name	Function
agglomerate	Watershed and agglomeration to segment affinity map
channel-voting	Vote across channels of semantic map
cloud-watch	Realtime speedometer in AWS CloudWatch
connected-components	Threshold the boundary map to get a segmentation
copy-var	Copy a variable to a new name
create-chunk	Create a fake chunk for easy test
crop-margin	Crop the margin of a chunk
custom-operator	Import local code as a customized operator
cutout	Cutout chunk from a local/cloud storage volume
delete-chunk	Delete chunk in task to reduce RAM requirement
delete-task-in-queue	Delete the task in AWS SQS queue
downsample-upload	Downsample the chunk hierarchically and upload to volume
evaluate-segmentation	Compare segmentation chunks
fetch-task	Fetch task from AWS SQS queue one by one
generate-tasks	Generate tasks one by one
inference	Convolutional net inference
log-summary	Summary of logs
mask	Black out the chunk based on another mask chunk
mesh	Build 3D meshes from segmentation chunk
mesh-manifest	Collect mesh fragments for object
neuroglancer	Visualize chunks using neuroglancer
normalize-section-contrast	Normalize image contrast
normalize-section-shang	Normalization algorithm created by Shang
quantize	Quantize the affinity map
read-h5	Read HDF5 files
read-tif	Read TIFF files
save	Save chunk to local/cloud storage volume
save-pngs	Save chunk as a serials of png files
setup-env	Prepare storage infor files and produce tasks
view	Another chunk viewer in browser using CloudVolume
write-h5	Write chunk as HDF5 file
write-tif	Write chunk as TIFF file

Reference

We have a paper of this repo:

@article{wu2019chunkflow,
  title={Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets},
  author={Wu, Jingpeng and Silversmith, William M and Seung, H Sebastian},
  journal={arXiv preprint arXiv:1904.10489},
  year={2019}
}