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  • Precomputed neuroglancer format

The precomputed format specification is a data source format for loading data into neuroglancer. This guide demonstrates how to generate precomputed compressed segmentations & meshes from a BOSS segmentation volume.

Steps

  1. Generate the precomputed compressed segmentation files using neuroglancer-scripts
  2. Generate meshes using neuroglancer python and custom script
  3. Host them somewhere and point neuroglancer at the directory

Prerequisites

Install the following software:

  1. python3
  2. Create Python virtual environment
    • python3 -m venv env/
    • . env/bin/activate
  3. neuroglancer-scripts
    • install dev version (pypi version has bugs)
      • git clone https://github.com/HumanBrainProject/neuroglancer-scripts.git
      • cd neuroglancer-scripts
      • pip install -e .[dev]
  4. neuroglancer (for meshing)
    1. Optional:
      1. node (package for Ubuntu)
      2. install Java (needed for webpack)
      3. run the install scripts from neuroglancer root directory (npm i)
      4. start the web server npm run dev-server (this compiles webpack)
    2. navigate to python directory and run (installs neuroglancer package)
      1. pip install numpy tornado==4.5.3
      2. python setup.py develop
      3. python setup.py bundle_client

Create compressed segmentations

  1. Create basic info file:

    {
        "encoding": "compressed_segmentation",
        "scales": [
            {
                "voxel_offset": [0,0,0],
                "resolution": [798,798,2000],
                "size": [1406,621,138]
            }
        ],
        "compressed_segmentation_block_size": [8,8,8],
        "data_type": "uint64",
        "mesh": "mesh",
        "type": "segmentation",
        "num_channels": 1
    }
    
  2. Create additional scales (chunk size 128)

    mkdir output; generate-scales-info info output --target-chunk-size 128

  3. Create res 0 precomputed data:

    slices-to-precomputed data/ output/ --flat

  4. Create the downsampled data:

    compute-scales --flat --downscaling-method majority output/

Create the meshes using neuroglancer

  1. create mesh directory mkdir mesh
  2. change directory to neuroglancer/python
  3. run the following script: python mesh.py DATA/ XXX YYY ZZZ

mesh.py

from pathlib import Path
import argparse

import numpy as np
import tifffile as tiff

import neuroglancer

parser = argparse.ArgumentParser(description='create meshes')
parser.add_argument('img_path_str', type=str, help='path to images')
parser.add_argument('voxel_size', nargs=3, type=int,
                    help='voxel size (x, y, z)')

args = parser.parse_args()

img_path_str = args.img_path_str
voxel_size = args.voxel_size

json_descriptor = '{{"fragments": ["mesh.{}.{}"]}}'

img_path = Path(img_path_str)

mesh_list = []
for f in sorted(img_path.glob('*.tif')):
    A = tiff.imread(str(f))
    mesh_list.append(A)
mesh = np.dstack(mesh_list)
mesh = np.transpose(mesh, (2, 0, 1))

ids = [int(i) for i in np.unique(mesh[:])]

vol = neuroglancer.LocalVolume(
    data=mesh,
    voxel_size=voxel_size,
)

img_path.parent.joinpath(
    'output', 'mesh').mkdir(exist_ok=True, parents=True)

for ID in ids[1:]:
    print(ID)
    mesh_data = vol.get_object_mesh(ID)
    with open(
            str(img_path.parent / 'output' / 'mesh' / '.'.join(
                ('mesh', str(ID), str(ID)))), 'wb') as meshfile:
        meshfile.write(mesh_data)
    with open(
            str(img_path.parent / 'output' / 'mesh' / ''.join(
                (str(ID), ':0'))), 'w') as ff:
        ff.write(json_descriptor.format(ID, ID))

print('ids to insert into URL:')
ids_string = '[\'' + '\'_\''.join([str(i) for i in ids[1:]]) + '\']'
print(ids_string)

print('done')

Host the directory

nginx

  1. Copy the output directory to a server to host
  2. Enable CORS: nginx config
  3. Enable sending compressed files in place of uncompressed files. nginx config

S3

1. Strip the .gz extension from each file (script below)

# from within the output directory:
# python rename_files.py .
from pathlib import Path
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('path', type=str)
args = parser.parse_args()

p = Path(args.path)
all = set(p.glob('**/*'))
mesh = set(p.glob('mesh*'))
ids_files = all-mesh

for f in ids_files:
    f.rename(f.with_suffix(''))

2. Add CORS support to the bucket (in bucket properties) -- should look something like:

<?xml version="1.0" encoding="UTF-8"?>
<CORSConfiguration xmlns="http://s3.amazonaws.com/doc/2006-03-01/">
<CORSRule>
    <AllowedOrigin>*</AllowedOrigin>
    <AllowedMethod>GET</AllowedMethod>
    <MaxAgeSeconds>3000</MaxAgeSeconds>
    <AllowedHeader>Authorization</AllowedHeader>
</CORSRule>
</CORSConfiguration>

3. Give the bucket read permissions using bucket policy

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "PublicReadGetObject",
            "Effect": "Allow",
            "Principal": "*",
            "Action": "s3:GetObject",
            "Resource": "arn:aws:s3:::BUCKETNAME/*"
        }
    ]
}

4. Upload the directory to to the bucket

`aws s3 sync . s3://BUCKET_NAME/PROJECT_NAME --profile AWS_PROFILE`

5. Add metadata to all objects that are gzipped (e.g. the segments but not the meshes) to have Content-Encoding be gzip (can do this in bulk on AWS Console)

View the output

The final URL to construct should be a neuroglancer URL with precomputed as the source. Use the ids printed at the end of the mesh creation for the segments field.

Here's an examples of the json for one of our publicly available datasets:

{
  "layers": {
    "em_clahe": {
      "source": "boss://https://api.boss.neurodata.io/collman/M247514_Rorb_1_Site3Align2_EM/em_clahe?",
      "type": "image",
      "opacity": 1,
      "blend": "additive"
    },
    "m247514_Site3Annotation_MN_global": {
      "source": "precomputed://https://s3.amazonaws.com/synaptomes-precomputed/M247514_Rorb_1_Site3Align2_EM",
      "type": "segmentation",
      "segments": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96", "97", "98", "99", "100", "101", "102", "103", "104", "105", "106", "107", "108", "109", "110", "111", "112", "113", "114", "115", "116", "117", "118", "119", "120", "121", "122", "123", "124", "125", "126", "127", "128", "129", "130", "131", "132", "133", "134", "135", "136", "137", "138", "139", "140", "141", "142", "143", "144", "145", "146", "147", "148", "149", "150", "151", "152", "153", "154", "155", 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    }
  },
  "navigation": {
    "pose": {
      "position": {
        "voxelSize": [
          3,
          3,
          50
        ],
        "voxelCoordinates": [
          7010,
          7361.5,
          24.5
        ]
      }
    },
    "zoomFactor": 3
  },
  "layout": "xy"
}

Questions/comments?

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