william-silversmith

"""
Extracts tissue from an overview montage and calculates the centroid.

python resin_mask.py .../bladeseq-2025.02.03-11.41.01/s021-2025.02.03-11.41.01/

To install dependencies:
pip install simplejpeg tqdm pyspng-seunglab numpy scipy connected-components-3d fill_voids tinybrain edt opencv-python -U 
"""
from typing import List, Dict, Union, Optional, Tuple

william-silversmith

# pip install caveclient osteoid cloud-volume vtk microviewer numpy

from osteoid import Skeleton, Bbox
from caveclient import CAVEclient
from cloudvolume import CloudVolume
import numpy as np
# import microviewer
import time
import sys

william-silversmith

"""
Draws a 3D cube from a 3D image.

Can be posed in any orientation.

To install:
pip install numpy cloud-volume mayavi fastremap
pip install PyQt5
"""

william-silversmith

"""
Extracts tissue from an overview montage and calculates the centroid.

To install dependencies:
pip install simplejpeg pyspng-seunglab numpy scipy connected-components-3d tinybrain edt -U 
"""
from typing import List, Dict, Union, Optional, Tuple

import csv
import io

william-silversmith

from cloudvolume import CloudVolume

cv = CloudVolume(
    'graphene://https://fafbv2.dynamicannotationframework.com/segmentation/1.0/fly_v31',  # neuroglancer layer
    use_https=True # uses the public access interface
)

# Download and Save a Mesh to Disk
mesh = cv.mesh.get(720575940621065107)
with open('720575940621065107.ply', 'wb') as f:

william-silversmith

import numpy as np
import h5py
from tqdm import tqdm

from cloudvolume import view

def find_section_clamping_values(zlevel, lowerfract, upperfract):
  filtered = np.copy(zlevel)

  # remove pure black from frequency counts as

william-silversmith

def countless_stippled(data):
  """
  Vectorized implementation of downsampling a 2D 
  image by 2 on each side using the COUNTLESS algorithm
  that treats zero as "background" that doesn't count
  for the purposes of choosing the mode.
  
  data is a 2D numpy array with even dimensions.
  """
  sections = []

william-silversmith

from itertools import combinations
from functools import reduce
import numpy as np

def countlessND(data, factor):
  assert len(data.shape) == len(factor)

  sections = []

  mode_of = reduce(lambda x,y: x * y, factor)

william-silversmith

from itertools import combinations
from functools import reduce
import numpy as np

def dynamic_zero_corrected_countless3d(data):
  sections = []

  # shift zeros up one so they don't interfere with bitwise operators
  # we'll shift down at the end
  data += 1 

william-silversmith

from itertools import combinations
from functools import reduce
import numpy as np

def countless3d(data):
  """Now write countless8 in such a way that it could be used
  to process an image."""
  sections = []

  # shift zeros up one so they don't interfere with bitwise operators