Mesh does not render in neuroglancer #181
Description
Hi, I am creating a precomputed segmentation with cloudvolume and igneous, and generating the mesh on the fly. The mesh directory is not empty, and neuroglancer can detect the "mesh" entry in the info file, but it does not render. (Unfortunately, the gs bucket is private, I cannot share the link directly. Instead, I provide the MWE below with a spherical phantom.)
Phantom creation script
import numpy as np
import tifffile as tiff
size_xy = 1000
size_z = 1200
radius = 300
x0, y0, z0 = (500, 500, 500)
x, y, z = np.mgrid[0:size_xy:1, 0:size_xy:1, 0:size_z:1]
r = np.sqrt((x - x0)**2 + (y - y0)**2 + (z - z0)**2)
img = np.ones((size_xy,size_xy,size_z),dtype=np.uint8)
img[r > radius] = 0
tiff.imwrite("input/sphere_phantom.tif",img)`cloudvolume` & `igneous` script
import tifffile as tiff
import os
from tqdm import tqdm
from cloudvolume import CloudVolume
from cloudvolume.lib import mkdir
from pathlib import Path
import numpy as np
image_filename = Path("input/sphere_phantom.tif")
is_zyx_instead_of_xyz = False
first_image = tiff.imread(image_filename)
img_shape = first_image.shape
dtype = first_image.dtype
print(f"Dataset shape: {img_shape}")
print(f"Dataset dtype: {dtype}")
output_dir = "output/phantom.precomputed/"
output_dir = Path(output_dir)
mkdir(output_dir)
output_dir = output_dir.absolute().as_uri() + "/"
print(output_dir)
native_resolution_nm = 100
binned_resolution_nm = native_resolution_nm*16
# Create a CloudVolume object for the Neuroglancer precomputed format
info = CloudVolume.create_new_info(
num_channels = 1,
layer_type = 'segmentation', # 'image' or 'segmentation'
data_type = 'uint8', # can pick any popular uint
encoding = 'compresso', # see: https://github.com/seung-lab/cloud-volume/wiki/Compression-Choices
resolution = [ binned_resolution_nm ] * 3, # X,Y,Z values in nanometers
voxel_offset = [ 0, 0, 0 ], # values X,Y,Z values in voxels
chunk_size = [ 256 ] * 3, # rechunk of image X,Y,Z in voxels
volume_size = img_shape[::-1] if is_zyx_instead_of_xyz else img_shape, # X,Y,Z size in voxels
max_mip = 4,
factor = (2,2,2),
)
vol = CloudVolume(
output_dir,
info=info,
progress=False,
parallel=False,
cache_locking=True,
compress=False,
delete_black_uploads = True,
)
vol.commit_info()
print("CloudVolume info:")
print(vol.info)
data_to_write = tiff.imread(image_filename).astype(np.uint8)
if is_zyx_instead_of_xyz:
data_to_write = np.transpose(data_to_write, (2, 1, 0))[..., np.newaxis]
else:
data_to_write = data_to_write[..., np.newaxis]
vol[:,:,:, 1] = data_to_write
# ------------------------
from taskqueue import LocalTaskQueue
import igneous.task_creation as tc
tq = LocalTaskQueue(parallel=True)
tasks = tc.create_downsampling_tasks(output_dir, mip=0, num_mips = 3, factor= (2,2,2), fill_missing=True, delete_black_uploads=True,sparse=True,memory_target=int(500e9))
tq.insert(tasks)
tq.execute()
# tasks = tc.create_mesh_manifest_tasks(output_dir)
# tq.insert(tasks)
# tq.execute()
print("Done!")
# ----------------------
tq = LocalTaskQueue(parallel=True)
tasks = tc.create_meshing_tasks(output_dir, mip=0, shape=(256, 256, 256), fill_missing=True, sharded=False)
tq.insert(tasks)
tq.execute()
tasks = tc.create_unsharded_multires_mesh_tasks(output_dir)
tq.insert(tasks)
tq.execute()
tasks = tc.create_mesh_manifest_tasks(output_dir)
tq.insert(tasks)
tq.execute()
print("Done!")Info file
{
"data_type": "uint8",
"mesh": "mesh_mip_0_err_40",
"num_channels": 1,
"scales": [
{
"chunk_sizes": [
[
256,
256,
256
]
],
"encoding": "compresso",
"key": "1600_1600_1600",
"resolution": [
1600,
1600,
1600
],
"size": [
1000,
1000,
1200
],
"voxel_offset": [
0,
0,
0
]
},
{
"chunk_sizes": [
[
256,
256,
256
]
],
"encoding": "compresso",
"key": "3200_3200_3200",
"resolution": [
3200,
3200,
3200
],
"size": [
500,
500,
600
],
"voxel_offset": [
0,
0,
0
]
},
{
"chunk_sizes": [
[
256,
256,
256
]
],
"encoding": "compresso",
"key": "6400_6400_6400",
"resolution": [
6400,
6400,
6400
],
"size": [
250,
250,
300
],
"voxel_offset": [
0,
0,
0
]
},
{
"chunk_sizes": [
[
256,
256,
256
]
],
"encoding": "compresso",
"key": "12800_12800_12800",
"resolution": [
12800,
12800,
12800
],
"size": [
125,
125,
150
],
"voxel_offset": [
0,
0,
0
]
},
{
"chunk_sizes": [
[
256,
256,
256
]
],
"encoding": "compresso",
"key": "25600_25600_25600",
"resolution": [
25600,
25600,
25600
],
"size": [
63,
63,
75
],
"voxel_offset": [
0,
0,
0
]
}
],
"type": "segmentation"
}