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Composable image chunk operators to create pipeline for distributed computation.

Project description

chunkflow

Documentation Status Build Status PyPI version Coverage Status License

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 --patch-size 20 256 256 --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 in Neuroglancer!

Image_Segmentation

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.

Available 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
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-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
view Another chunk viewer in browser using CloudVolume
write-h5 Write chunk as HDF5 file
write-tif Write chunk as TIFF file

Citation

If you used this tool and is writing a paper, please cite this paper:

@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}
}

Project details


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