Package: zwatershed
# convolutional network metric scripts- Code for fast watersheds. Code is based around code from https://bitbucket.org/poozh/watershed described in http://arxiv.org/abs/1505.00249.  For use in https://github.com/naibaf7/PyGreentea.# building### conda- `conda install -c conda-forge zwatershed`### pip [<img src="https://img.shields.io/pypi/v/zwatershed.svg?maxAge=2592000">](https://pypi.python.org/pypi/zwatershed/)- `pip install zwatershed`### from source- clone the repository- run ./make.sh### 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_len2. Zwatershed the subvolumes1. `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 subvolume4. Threshold individual subvolumes by merging- `seg_merged = merge_by_thresh(seg,seg_sizes,rg,thresh)`- load in these areguments from outname