feat: stacked CCL for huge arrays

.gitignore

@@ -144,4 +144,5 @@ callgrind*
 subvol.bin
 
 
-cc3d.cpp
+cc3d.cpp
+cc3d/fastcc3d.cpp

MANIFEST.in

@@ -1,5 +1,5 @@
-include cc3d.hpp
-include cc3d_graphs.hpp
-include cc3d_continuous.hpp
+include cc3d/cc3d.hpp
+include cc3d/cc3d_graphs.hpp
+include cc3d/cc3d_continuous.hpp
 include LICENSE
 include COPYING.LESSER

README.md

@@ -99,6 +99,22 @@ labels_out = cc3d.connected_components(labels_in, delta=10)
 labels_in = np.memmap("labels.bin", order="F", dtype=np.uint32, shape=(5000, 5000, 2000))
 labels_out = cc3d.connected_components(labels_in, out_file="out.bin")
 
+# Here's another strategy that you can use for huge files that won't even
+# take up any disk space. Provide any iterator to this function that produces
+# thick z sections of the input array that are in sequential order.
+# The output is a highly compressed CrackleArray that is still random access.
+# See: https://github.com/seung-lab/crackle
+# You need to pip install connected-components-3d[stack] to get the extra modules.
+def sections(labels_in):
+  """
+  A generator that produces thick Z slices
+  of an image
+  """
+  for z in range(0, labels_in.shape[2], 100):
+    yield labels_in[:,:,z:z+100]
+
+compressed_labels_out = cc3d.connected_components_stack(sections(labels))
+
 # You can extract the number of labels (which is also the maximum 
 # label value) like so:
 labels_out, N = cc3d.connected_components(labels_in, return_N=True) # free

automated_test.py

@@ -1,6 +1,7 @@
 import pytest
 import sys
 
+import fastremap
 import cc3d
 import numpy as np
 
@@ -1490,15 +1491,43 @@ def test_pytorch_integration_ccl_doesnt_crash():
     assert isinstance(out, torch.Tensor)
     assert torch.all(out == labels)
 
+@pytest.mark.parametrize("connectivity", [6, 26])
+def test_connected_components_stack(connectivity):
+  stack = [
+    np.ones([100,100,100], dtype=np.uint32)
+    for i in range(4)
+  ]
 
+  stack += [ np.zeros([100,100,1], dtype=np.uint32) ]
 
+  stack += [
+    np.ones([100,100,100], dtype=np.uint32)
+    for i in range(2)
+  ]
 
+  arr = cc3d.connected_components_stack(stack, connectivity=connectivity)
 
+  ans = np.ones([100,100,601], dtype=np.uint32)
+  ans[:,:,400] = 0
+  ans[:,:,401:] = 2
 
+  assert np.all(np.unique(arr[:]) == [ 0, 1, 2 ])
+  assert np.all(arr[:] == ans)
 
+  image = np.random.randint(0,100, size=[100,100,11], dtype=np.uint8)
 
+  ans = cc3d.connected_components(image, connectivity=connectivity)
+  ans, _ = fastremap.renumber(ans[:])
 
+  stack = [
+    image[:,:,:2],
+    image[:,:,2:6],
+    image[:,:,6:9],
+    image[:,:,9:11],
+  ]
 
+  res = cc3d.connected_components_stack(stack, connectivity=connectivity)
+  res, _ = fastremap.renumber(res[:])
 
-
+  assert np.all(res[:] == ans[:,:,:11])
 

cc3d/__init__.py

@@ -0,0 +1,280 @@
+from typing import (
+  Dict, Union, Tuple, Iterator, 
+  Sequence, Optional, Any, BinaryIO
+)
+
+import fastcc3d
+from fastcc3d import (
+  connected_components,
+  statistics,
+  each,
+  contacts,
+  region_graph,
+  voxel_connectivity_graph,
+  color_connectivity_graph,
+  estimate_provisional_labels,
+)
+
+import numpy as np
+
+def dust(
+  img:np.ndarray, 
+  threshold:Union[int,float], 
+  connectivity:int = 26,
+  in_place:bool = False,
+) -> np.ndarray:
+  """
+  Remove from the input image connected components
+  smaller than threshold ("dust"). The name of the function
+  can be read as a verb "to dust" the image.
+
+  img: 2D or 3D image
+  threshold: discard components smaller than this in voxels
+  connectivity: cc3d connectivity to use
+  in_place: whether to modify the input image or perform
+    dust 
+
+  Returns: dusted image
+  """
+  orig_dtype = img.dtype
+  img = _view_as_unsigned(img)
+
+  if not in_place:
+    img = np.copy(img)
+
+  cc_labels, N = connected_components(
+    img, connectivity=connectivity, return_N=True
+  )
+  stats = statistics(cc_labels)
+  mask_sizes = stats["voxel_counts"]
+  del stats
+
+  to_mask = [ 
+    i for i in range(1, N+1) if mask_sizes[i] < threshold 
+  ]
+
+  if len(to_mask) == 0:
+    return img
+
+  mask = np.isin(cc_labels, to_mask)
+  del cc_labels
+  np.logical_not(mask, out=mask)
+  np.multiply(img, mask, out=img)
+  return img.view(orig_dtype)
+
+def largest_k(
+  img:np.ndarray,
+  k:int,
+  connectivity:int = 26,
+  delta:Union[int,float] = 0,
+  return_N:bool = False,
+) -> np.ndarray:
+  """
+  Returns the k largest connected components
+  in the image.
+  """
+  assert k >= 0
+
+  order = "C" if img.flags.c_contiguous else "F"
+
+  if k == 0:
+    return np.zeros(img.shape, dtype=np.uint16, order=order)
+
+  cc_labels, N = connected_components(
+    img, connectivity=connectivity, 
+    return_N=True, delta=delta,
+  )
+  if N <= k:
+    if return_N:
+      return cc_labels, N
+    return cc_labels
+
+  cts = statistics(cc_labels)["voxel_counts"]  
+  preserve = [ (i,ct) for i,ct in enumerate(cts) if i > 0 ]
+  preserve.sort(key=lambda x: x[1])
+  preserve = [ x[0] for x in preserve[-k:] ]
+
+  shape, dtype = cc_labels.shape, cc_labels.dtype
+  rns = fastcc3d.runs(cc_labels)
+
+  order = "C" if cc_labels.flags.c_contiguous else "F"
+  del cc_labels
+
+  cc_out = np.zeros(shape, dtype=dtype, order=order)
+  for i, label in enumerate(preserve):
+    fastcc3d.draw(i+1, rns[label], cc_out)
+
+  if return_N:
+    return cc_out, len(preserve)
+  return cc_out
+
+def _view_as_unsigned(img:np.ndarray) -> np.ndarray:
+  if np.issubdtype(img.dtype, np.unsignedinteger) or img.dtype == bool:
+    return img
+  elif img.dtype == np.int8:
+    return img.view(np.uint8)
+  elif img.dtype == np.int16:
+    return img.view(np.uint16)
+  elif img.dtype == np.int32:
+    return img.view(np.uint32)
+  elif img.dtype == np.int64:
+    return img.view(np.uint64)
+
+  return img
+
+
+class DisjointSet:
+  def __init__(self):
+    self.data = {} 
+  def makeset(self, x):
+    self.data[x] = x
+    return x
+  def find(self, x):
+    if not x in self.data:
+      return None
+    i = self.data[x]
+    while i != self.data[i]:
+      self.data[i] = self.data[self.data[i]]
+      i = self.data[i]
+    return i
+  def union(self, x, y):
+    i = self.find(x)
+    j = self.find(y)
+    if i is None:
+      i = self.makeset(x)
+    if j is None:
+      j = self.makeset(y)
+
+    if i < j:
+      self.data[j] = i
+    else:
+      self.data[i] = j
+
+def connected_components_stack(
+  stacked_images:Sequence[np.ndarray], 
+  connectivity:int = 26,
+  return_N:bool = False,
+  out_dtype:Optional[Any] = None,
+):
+  """
+  This is for performing connected component labeling
+  on an array larger than RAM.
+
+  stacked_images is a sequence of 3D images that are of equal
+  width and height (x,y) and arbitrary depth (z). For example,
+  you might define a generator that produces a tenth of your
+  data at a time. The data must be sequenced in z order from
+  z = 0 to z = depth - 1.
+
+  Each 3D image will have CCL run on it and then compressed
+  into crackle format (https://github.com/seung-lab/crackle)
+  which is highly compressed but still usable and randomly
+  accessible by z-slice. 
+
+  The bottom previous slice and top current
+  slice will be analyzed to produce a merged image.
+
+  The final output will be a CrackleArray. You
+  can access parts of the image using standard array
+  operations, write the array data to disk using arr.binary
+  or fully decompressing the array using arr.decompress()
+  to obtain a numpy array (but presumably this will blow
+  out your RAM since the image is so big).
+  """
+  try:
+    import crackle
+    import fastremap
+  except ImportError:
+    print("You need to pip install connected-components-3d[stack]")
+    raise
+
+  full_binary = None
+  bottom_cc_img = None
+  bottom_cc_labels = None
+
+  if connectivity not in (6,26):
+    raise ValueError(f"Connectivity must be 6 or 26. Got: {connectivity}")
+
+  offset = 0
+
+  for image in stacked_images:
+    cc_labels, N = connected_components(
+      image, connectivity=connectivity,
+      return_N=True, out_dtype=np.uint64,
+    )
+    cc_labels[cc_labels != 0] += offset
+    offset += N
+    binary = crackle.compress(cc_labels)
+
+    if full_binary is None:
+      full_binary = binary
+      bottom_cc_img = image[:,:,-1]
+      bottom_cc_labels = cc_labels[:,:,-1]
+      continue
+
+    top_cc_labels = cc_labels[:,:,0]
+
+    equivalences = DisjointSet()
+
+    buniq = fastremap.unique(bottom_cc_labels)
+    tuniq = fastremap.unique(top_cc_labels)
+
+    for u in buniq:
+      equivalences.makeset(u)
+    for u in tuniq:
+      equivalences.makeset(u)
+
+    if connectivity == 6:
+      for y in range(image.shape[1]):
+        for x in range(image.shape[0]):
+          if bottom_cc_labels[x,y] == 0 or top_cc_labels[x,y] == 0:
+            continue
+          if bottom_cc_img[x,y] == image[x,y,0]:
+            equivalences.union(bottom_cc_labels[x,y], top_cc_labels[x,y])
+    else:
+      for y in range(image.shape[1]):
+        for x in range(image.shape[0]):
+          if bottom_cc_labels[x,y] == 0:
+            continue
+
+          for y0 in range(max(y - 1, 0), min(y + 1, image.shape[1] - 1) + 1):
+            for x0 in range(max(x - 1, 0), min(x + 1, image.shape[0] - 1) + 1):
+              if top_cc_labels[x0,y0] == 0:
+                continue
+
+              if bottom_cc_img[x,y] == image[x0,y0,0]:
+                equivalences.union(
+                  bottom_cc_labels[x,y], top_cc_labels[x0,y0]
+                )
+
+    relabel = {}
+    for u in buniq:
+      relabel[int(u)] = int(equivalences.find(u))
+    for u in tuniq:
+      relabel[int(u)] = int(equivalences.find(u))
+
+    full_binary = crackle.zstack([
+      full_binary,
+      binary,
+    ])
+    full_binary = crackle.remap(full_binary, relabel, preserve_missing_labels=True)
+
+    bottom_cc_img = image[:,:,-1]
+    bottom_cc_labels = cc_labels[:,:,-1]
+    bottom_cc_labels = fastremap.remap(bottom_cc_labels, relabel, preserve_missing_labels=True)
+
+  if crackle.contains(full_binary, 0):
+    start = 0
+  else:
+    start = 1
+
+  full_binary, mapping = crackle.renumber(full_binary, start=start)
+  arr = crackle.CrackleArray(full_binary).refit()
+
+  if return_N:
+    return arr, arr.num_labels()
+  else:
+    return arr
+
+
+