The Space reads open-data meshes and CT by URL and keeps none of it; here are the same sources for your own tools, and the page's API and MCP server, which checks any segment with the free judge.
https://vesuvius-challenge-open-data.s3.us-east-1.amazonaws.com/PHerc0500P2/segments/20250611171318-z_dbg_gen_00440_inp_hr/mesh/20250611171318-on-20250526151718-2.215um.tifxyz/# pip install numpy tifffile
import io, json, urllib.request
import numpy as np, tifffile
T = "https://vesuvius-challenge-open-data.s3.us-east-1.amazonaws.com/PHerc0500P2/segments/20250611171318-z_dbg_gen_00440_inp_hr/mesh/20250611171318-on-20250526151718-2.215um.tifxyz"
meta = json.load(urllib.request.urlopen(T + "/meta.json")) # scale, bbox, format
get = lambda c: tifffile.imread(io.BytesIO(urllib.request.urlopen(f"{T}/{c}.tif").read()))
xyz = np.stack([get(c) for c in "xyz"], -1) # xyz[i, j] = (x, y, z) in scan voxels; -1 = no surfacehttps://vesuvius-challenge-open-data.s3.us-east-1.amazonaws.com/PHerc0500P2/volumes/20250526151718-2.215um-0.4m-111keV-masked.zarr# pip install "zarr>=3" fsspec aiohttp
import zarr
root = zarr.open_group("https://vesuvius-challenge-open-data.s3.us-east-1.amazonaws.com/PHerc0500P2/volumes/20250526151718-2.215um-0.4m-111keV-masked.zarr", mode="r") # OME-Zarr, zarr v2
a = root["2"] # level 0 is full resolution; level L is 2**L times coarser
print(a.shape, a.dtype, a.chunks)
block = a[1500:1564, 2000:2064, 3000:3064] # (z, y, x); reads only the chunks it touches# pip install "vesuvius[volume-only]"
from vesuvius import Volume
v = Volume(type="zarr", path="https://vesuvius-challenge-open-data.s3.us-east-1.amazonaws.com/PHerc0500P2/volumes/20250526151718-2.215um-0.4m-111keV-masked.zarr/2") # one pyramid level
block = v[1500:1564, 2000:2064, 3000:3064]In VC3D (villa's volume-cartographer), open or create a project, then
File > Attach Remote Zarr... and paste:
https://vesuvius-challenge-open-data.s3.us-east-1.amazonaws.com/PHerc0500P2/volumes/20250526151718-2.215um-0.4m-111keV-masked.zarr
It accepts http, https and s3 OME-Zarr roots.https://neuroglancer-demo.appspot.com/#!%7B%22layers%22%3A%5B%7B%22type%22%3A%22image%22%2C%22source%22%3A%22zarr%3A%2F%2Fhttps%3A%2F%2Fvesuvius-challenge-open-data.s3.us-east-1.amazonaws.com%2FPHerc0500P2%2Fvolumes%2F20250526151718-2.215um-0.4m-111keV-masked.zarr%2F%22%2C%22name%22%3A%22The%20CT%20that%20mesh%20is%20on%2C%20PHerc0500P2%202.215%20um%22%7D%5D%2C%22layout%22%3A%224panel%22%7Dcurl -s https://vesuvius-challenge-open-data.s3.us-east-1.amazonaws.com/PHerc0500P2/volumes/20250526151718-2.215um-0.4m-111keV-masked.zarr/.zattrs # OME multiscales metadata
curl -s https://vesuvius-challenge-open-data.s3.us-east-1.amazonaws.com/PHerc0500P2/volumes/20250526151718-2.215um-0.4m-111keV-masked.zarr/2/.zarray # one level: shape, chunks, dtype, codechttps://huggingface.co/spaces/SharpMeow/fiber-checker# pip install gradio_client
from gradio_client import Client
client = Client("SharpMeow/fiber-checker")
result = client.predict(api_name="/list_scrolls")# pip install gradio_client
from gradio_client import Client
client = Client("SharpMeow/fiber-checker")
result = client.predict(api_name="/example_segments")# pip install gradio_client
from gradio_client import Client
client = Client("SharpMeow/fiber-checker")
result = client.predict(sample="PHerc0343P", api_name="/list_segments")URL=https://sharpmeow-fiber-checker.hf.space
EVENT=$(curl -s -X POST $URL/gradio_api/call/list_scrolls -H 'Content-Type: application/json' \
-d '{"data": []}' | python3 -c 'import json,sys; print(json.load(sys.stdin)["event_id"])')
curl -sN $URL/gradio_api/call/list_scrolls/$EVENT{
"mcpServers": {
"fiber-checker": {
"url": "https://sharpmeow-fiber-checker.hf.space/gradio_api/mcp/sse"
}
}
}
Tool list: https://sharpmeow-fiber-checker.hf.space/gradio_api/mcp/schema