Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

Fast watersheds

Project Description

# convolutional network metric scripts
- Code for fast watersheds. Code is based around code from described in For use in

# building

### conda
- `conda install -c conda-forge zwatershed`

### pip [<img src="">](
- `pip install zwatershed`

### from source
- clone the repository
- run ./

### requirements
- numpy, h5py, cython
- if using parallel watershed, also requires multiprocessing or pyspark
- in order to build the cython, requires a c++ compiler and boost

# function api
- `(segs, rand) = zwatershed_and_metrics(segTrue, aff_graph, eval_thresh_list, seg_save_thresh_list)`
- *returns segmentations and metrics*
- `segs`: list of segmentations
- `len(segs) == len(seg_save_thresh_list)`
- `rand`: dict
- `rand['V_Rand']`: V_Rand score (scalar)
- `rand['V_Rand_split']`: list of score values
- `len(rand['V_Rand_split']) == len(eval_thresh_list)`
- `rand['V_Rand_merge']`: list of score values,
- `len(rand['V_Rand_merge']) == len(eval_thresh_list)`
- `segs = zwatershed(aff_graph, seg_save_thresh_list)`
- *returns segmentations*
- `segs`: list of segmentations
- `len(segs) == len(seg_save_thresh_list)`

##### These methods have versions which save the segmentations to hdf5 files instead of returning them
- `rand = zwatershed_and_metrics_h5(segTrue, aff_graph, eval_thresh_list, seg_save_thresh_list, seg_save_path)`
- `zwatershed_h5(aff_graph, eval_thresh_list, seg_save_path)`

##### All 4 methods have versions which take an edgelist representation of the affinity graph
- `(segs, rand) = zwatershed_and_metrics_arb(segTrue, node1, node2, edgeWeight, eval_thresh_list, seg_save_thresh_list)`
- `segs = zwatershed_arb(seg_shape, node1, node2, edgeWeight, seg_save_thresh_list)`
- `rand = zwatershed_and_metrics_h5_arb(segTrue, node1, node2, edgeWeight, eval_thresh_list, seg_save_thresh_list, seg_save_path)`
- `zwatershed_h5_arb(seg_shape, node1, node2, edgeWeight, eval_thresh_list, seg_save_path)`

# parallel watershed - 4 steps
- *a full example is given in par_ex.ipynb*

1. Partition the subvolumes
- `partition_data = partition_subvols(pred_file,out_folder,max_len)`
- evenly divides the data in *pred_file* with the constraint that no dimension of any subvolume is longer than max_len
2. Zwatershed the subvolumes
1. `eval_with_spark(partition_data[0])`
- *with spark*
2. `eval_with_par_map(partition_data[0],NUM_WORKERS)`
- *with python multiprocessing map*
- after evaluating, subvolumes will be saved into the out\_folder directory named based on their smallest indices in each dimension (ex. path/to/out\_folder/0\_0\_0\_vol)
3. Stitch the subvolumes together
- `stitch_and_save(partition_data,outname)`
- stitch together the subvolumes in partition_data
- save to the hdf5 file outname
- outname['starts'] = list of min_indices of each subvolume
- outname['ends'] = list of max_indices of each subvolume
- outname['seg'] = full stitched segmentation
- outname['seg_sizes'] = array of size of each segmentation
- outname['rg_i'] = region graph for ith subvolume
4. Threshold individual subvolumes by merging
- `seg_merged = merge_by_thresh(seg,seg_sizes,rg,thresh)`
- load in these areguments from outname

Release History

This version
History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


History Node


Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
(29.6 kB) Copy SHA256 Hash SHA256
Source None Aug 5, 2016

Supported By

Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Google Google Cloud Servers