"""
Copyright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import time, os
from scipy.ndimage import maximum_filter1d, find_objects, center_of_mass
import torch
import numpy as np
import tifffile
from tqdm import trange
from numba import njit, prange, float32, int32, vectorize
import cv2
import fastremap
import logging
dynamics_logger = logging.getLogger(__name__)
from . import utils, metrics, transforms
import torch
from torch import optim, nn
import torch.nn.functional as F
from . import resnet_torch
TORCH_ENABLED = True
torch_GPU = torch.device("cuda")
torch_CPU = torch.device("cpu")
@njit("(float64[:], int32[:], int32[:], int32, int32, int32, int32)", nogil=True)
def _extend_centers(T, y, x, ymed, xmed, Lx, niter):
"""Run diffusion from the center of the mask on the mask pixels.
Args:
T (numpy.ndarray): Array of shape (Ly * Lx) where diffusion is run.
y (numpy.ndarray): Array of y-coordinates of pixels inside the mask.
x (numpy.ndarray): Array of x-coordinates of pixels inside the mask.
ymed (int): Center of the mask in the y-coordinate.
xmed (int): Center of the mask in the x-coordinate.
Lx (int): Size of the x-dimension of the masks.
niter (int): Number of iterations to run diffusion.
Returns:
numpy.ndarray: Array of shape (Ly * Lx) representing the amount of diffused particles at each pixel.
"""
for t in range(niter):
T[ymed * Lx + xmed] += 1
T[y * Lx +
x] = 1 / 9. * (T[y * Lx + x] + T[(y - 1) * Lx + x] + T[(y + 1) * Lx + x] +
T[y * Lx + x - 1] + T[y * Lx + x + 1] +
T[(y - 1) * Lx + x - 1] + T[(y - 1) * Lx + x + 1] +
T[(y + 1) * Lx + x - 1] + T[(y + 1) * Lx + x + 1])
return T
def _extend_centers_gpu(neighbors, meds, isneighbor, shape, n_iter=200,
device=torch.device("cuda")):
"""Runs diffusion on GPU to generate flows for training images or quality control.
Args:
neighbors (torch.Tensor): 9 x pixels in masks.
meds (torch.Tensor): Mask centers.
isneighbor (torch.Tensor): Valid neighbor boolean 9 x pixels.
shape (tuple): Shape of the tensor.
n_iter (int, optional): Number of iterations. Defaults to 200.
device (torch.device, optional): Device to run the computation on. Defaults to torch.device("cuda").
Returns:
torch.Tensor: Generated flows.
"""
if device is None:
device = torch.device("cuda")
T = torch.zeros(shape, dtype=torch.double, device=device)
for i in range(n_iter):
T[tuple(meds.T)] += 1
Tneigh = T[tuple(neighbors)]
Tneigh *= isneighbor
T[tuple(neighbors[:, 0])] = Tneigh.mean(axis=0)
del meds, isneighbor, Tneigh
if T.ndim == 2:
grads = T[neighbors[0, [2, 1, 4, 3]], neighbors[1, [2, 1, 4, 3]]]
del neighbors
dy = grads[0] - grads[1]
dx = grads[2] - grads[3]
del grads
mu_torch = np.stack((dy.cpu().squeeze(0), dx.cpu().squeeze(0)), axis=-2)
else:
grads = T[tuple(neighbors[:,1:])]
del neighbors
dz = grads[0] - grads[1]
dy = grads[2] - grads[3]
dx = grads[4] - grads[5]
del grads
mu_torch = np.stack(
(dz.cpu().squeeze(0), dy.cpu().squeeze(0), dx.cpu().squeeze(0)), axis=-2)
return mu_torch
@njit(nogil=True)
def get_centers(masks, slices):
"""
Get the centers of the masks and their extents.
Args:
masks (ndarray): The labeled masks.
slices (ndarray): The slices of the masks.
Returns:
tuple containing
- centers (ndarray): The centers of the masks.
- ext (ndarray): The extents of the masks.
"""
centers = np.zeros((len(slices), 2), "int32")
ext = np.zeros((len(slices),), "int32")
for p in prange(len(slices)):
si = slices[p]
i = si[0]
sr, sc = si[1:3], si[3:5]
# find center in slice around mask
yi, xi = np.nonzero(masks[sr[0]:sr[-1], sc[0]:sc[-1]] == (i + 1))
ymed = yi.mean()
xmed = xi.mean()
# center is closest point to (ymed, xmed) within mask
imin = ((xi - xmed)**2 + (yi - ymed)**2).argmin()
ymed = yi[imin] + sr[0]
xmed = xi[imin] + sc[0]
centers[p] = np.array([ymed, xmed])
ext[p] = (sr[-1] - sr[0]) + (sc[-1] - sc[0]) + 2
return centers, ext
[docs]def masks_to_flows_gpu(masks, device=None, niter=None):
"""Convert masks to flows using diffusion from center pixel.
Center of masks where diffusion starts is defined using COM.
Args:
masks (int, 2D or 3D array): Labelled masks. 0=NO masks; 1,2,...=mask labels.
Returns:
tuple containing
- mu (float, 3D or 4D array): Flows in Y = mu[-2], flows in X = mu[-1].
If masks are 3D, flows in Z = mu[0].
- meds_p (float, 2D or 3D array): cell centers
"""
if device is None:
device = torch.device("cuda")
Ly0, Lx0 = masks.shape
Ly, Lx = Ly0 + 2, Lx0 + 2
masks_padded = torch.from_numpy(masks.astype("int64")).to(device)
masks_padded = F.pad(masks_padded, (1, 1, 1, 1))
### get mask pixel neighbors
y, x = torch.nonzero(masks_padded, as_tuple=True)
neighborsY = torch.stack((y, y - 1, y + 1, y, y, y - 1, y - 1, y + 1, y + 1), dim=0)
neighborsX = torch.stack((x, x, x, x - 1, x + 1, x - 1, x + 1, x - 1, x + 1), dim=0)
neighbors = torch.stack((neighborsY, neighborsX), dim=0)
neighbor_masks = masks_padded[tuple(neighbors)]
isneighbor = neighbor_masks == neighbor_masks[0]
### get center-of-mass within cell
slices = find_objects(masks)
# turn slices into array
slices = np.array([
np.array([i, si[0].start, si[0].stop, si[1].start, si[1].stop])
for i, si in enumerate(slices)
if si is not None
])
centers, ext = get_centers(masks, slices)
meds_p = torch.from_numpy(centers).to(device).long()
meds_p += 1 # for padding
### run diffusion
n_iter = 2 * ext.max() if niter is None else niter
shape = masks_padded.shape
mu = _extend_centers_gpu(neighbors, meds_p, isneighbor, shape, n_iter=n_iter,
device=device)
# new normalization
mu /= (1e-60 + (mu**2).sum(axis=0)**0.5)
#mu /= (1e-20 + (mu**2).sum(axis=0)**0.5)
# put into original image
mu0 = np.zeros((2, Ly0, Lx0))
mu0[:, y.cpu().numpy() - 1, x.cpu().numpy() - 1] = mu
return mu0, meds_p.cpu().numpy() - 1
[docs]def masks_to_flows_gpu_3d(masks, device=None):
"""Convert masks to flows using diffusion from center pixel.
Args:
masks (int, 2D or 3D array): Labelled masks. 0=NO masks; 1,2,...=mask labels.
Returns:
tuple containing
- mu (float, 3D or 4D array): Flows in Y = mu[-2], flows in X = mu[-1]. If masks are 3D, flows in Z = mu[0].
- mu_c (float, 2D or 3D array): zeros
"""
if device is None:
device = torch.device("cuda")
Lz0, Ly0, Lx0 = masks.shape
Lz, Ly, Lx = Lz0 + 2, Ly0 + 2, Lx0 + 2
masks_padded = torch.from_numpy(masks.astype("int64")).to(device)
masks_padded = F.pad(masks_padded, (1, 1, 1, 1, 1, 1))
# get mask pixel neighbors
z, y, x = torch.nonzero(masks_padded).T
neighborsZ = torch.stack((z, z + 1, z - 1, z, z, z, z))
neighborsY = torch.stack((y, y, y, y + 1, y - 1, y, y), axis=0)
neighborsX = torch.stack((x, x, x, x, x, x + 1, x - 1), axis=0)
neighbors = torch.stack((neighborsZ, neighborsY, neighborsX), axis=0)
# get mask centers
slices = find_objects(masks)
centers = np.zeros((masks.max(), 3), "int")
for i, si in enumerate(slices):
if si is not None:
sz, sy, sx = si
#lz, ly, lx = sr.stop - sr.start + 1, sc.stop - sc.start + 1
zi, yi, xi = np.nonzero(masks[sz, sy, sx] == (i + 1))
zi = zi.astype(np.int32) + 1 # add padding
yi = yi.astype(np.int32) + 1 # add padding
xi = xi.astype(np.int32) + 1 # add padding
zmed = np.mean(zi)
ymed = np.mean(yi)
xmed = np.mean(xi)
imin = np.argmin((zi - zmed)**2 + (xi - xmed)**2 + (yi - ymed)**2)
zmed = zi[imin]
ymed = yi[imin]
xmed = xi[imin]
centers[i, 0] = zmed + sz.start
centers[i, 1] = ymed + sy.start
centers[i, 2] = xmed + sx.start
# get neighbor validator (not all neighbors are in same mask)
neighbor_masks = masks_padded[tuple(neighbors)]
isneighbor = neighbor_masks == neighbor_masks[0]
ext = np.array(
[[sz.stop - sz.start + 1, sy.stop - sy.start + 1, sx.stop - sx.start + 1]
for sz, sy, sx in slices])
n_iter = 6 * (ext.sum(axis=1)).max()
# run diffusion
shape = masks_padded.shape
mu = _extend_centers_gpu(neighbors, centers, isneighbor, shape, n_iter=n_iter,
device=device)
# normalize
mu /= (1e-60 + (mu**2).sum(axis=0)**0.5)
# put into original image
mu0 = np.zeros((3, Lz0, Ly0, Lx0))
mu0[:, z.cpu().numpy() - 1, y.cpu().numpy() - 1, x.cpu().numpy() - 1] = mu
mu_c = np.zeros_like(mu0)
return mu0, mu_c
[docs]def masks_to_flows_cpu(masks, device=None, niter=None):
"""Convert masks to flows using diffusion from center pixel.
Center of masks where diffusion starts is defined to be the closest pixel to the mean of all pixels that is inside the mask.
Result of diffusion is converted into flows by computing the gradients of the diffusion density map.
Args:
masks (int, 2D or 3D array): Labelled masks 0=NO masks; 1,2,...=mask labels
Returns:
tuple containing
- mu (float, 3D or 4D array): Flows in Y = mu[-2], flows in X = mu[-1].
If masks are 3D, flows in Z = mu[0].
- meds (float, 2D or 3D array): cell centers
"""
Ly, Lx = masks.shape
mu = np.zeros((2, Ly, Lx), np.float64)
slices = find_objects(masks)
meds = []
for i in prange(len(slices)):
si = slices[i]
if si is not None:
sr, sc = si
ly, lx = sr.stop - sr.start + 2, sc.stop - sc.start + 2
### get center-of-mass within cell
y, x = np.nonzero(masks[sr, sc] == (i + 1))
y = y.astype(np.int32) + 1
x = x.astype(np.int32) + 1
ymed = y.mean()
xmed = x.mean()
imin = ((x - xmed)**2 + (y - ymed)**2).argmin()
xmed = x[imin]
ymed = y[imin]
n_iter = 2 * np.int32(ly + lx) if niter is None else niter
T = np.zeros((ly) * (lx), np.float64)
T = _extend_centers(T, y, x, ymed, xmed, np.int32(lx), np.int32(n_iter))
dy = T[(y + 1) * lx + x] - T[(y - 1) * lx + x]
dx = T[y * lx + x + 1] - T[y * lx + x - 1]
mu[:, sr.start + y - 1, sc.start + x - 1] = np.stack((dy, dx))
meds.append([ymed - 1, xmed - 1])
# new normalization
mu /= (1e-60 + (mu**2).sum(axis=0)**0.5)
return mu, meds
[docs]def masks_to_flows(masks, device=None, niter=None):
"""Convert masks to flows using diffusion from center pixel.
Center of masks where diffusion starts is defined to be the closest pixel to the mean of all pixels that is inside the mask.
Result of diffusion is converted into flows by computing the gradients of the diffusion density map.
Args:
masks (int, 2D or 3D array): Labelled masks 0=NO masks; 1,2,...=mask labels
Returns:
mu (float, 3D or 4D array): Flows in Y = mu[-2], flows in X = mu[-1].
If masks are 3D, flows in Z = mu[0].
"""
if masks.max() == 0:
dynamics_logger.warning("empty masks!")
return np.zeros((2, *masks.shape), "float32")
if device is not None:
if device.type == "cuda" or device.type == "mps":
masks_to_flows_device = masks_to_flows_gpu
else:
masks_to_flows_device = masks_to_flows_cpu
else:
masks_to_flows_device = masks_to_flows_cpu
if masks.ndim == 3:
Lz, Ly, Lx = masks.shape
mu = np.zeros((3, Lz, Ly, Lx), np.float32)
for z in range(Lz):
mu0 = masks_to_flows_device(masks[z], device=device, niter=niter)[0]
mu[[1, 2], z] += mu0
for y in range(Ly):
mu0 = masks_to_flows_device(masks[:, y], device=device, niter=niter)[0]
mu[[0, 2], :, y] += mu0
for x in range(Lx):
mu0 = masks_to_flows_device(masks[:, :, x], device=device, niter=niter)[0]
mu[[0, 1], :, :, x] += mu0
return mu
elif masks.ndim == 2:
mu, mu_c = masks_to_flows_device(masks, device=device, niter=niter)
return mu
else:
raise ValueError("masks_to_flows only takes 2D or 3D arrays")
[docs]def labels_to_flows(labels, files=None, device=None,
redo_flows=False, niter=None, return_flows=True):
"""Converts labels (list of masks or flows) to flows for training model.
Args:
labels (list of ND-arrays): The labels to convert. labels[k] can be 2D or 3D. If [3 x Ly x Lx],
it is assumed that flows were precomputed. Otherwise, labels[k][0] or labels[k] (if 2D)
is used to create flows and cell probabilities.
files (list of str, optional): The files to save the flows to. If provided, flows are saved to
files to be reused. Defaults to None.
device (str, optional): The device to use for computation. Defaults to None.
redo_flows (bool, optional): Whether to recompute the flows. Defaults to False.
niter (int, optional): The number of iterations for computing flows. Defaults to None.
Returns:
list of [4 x Ly x Lx] arrays: The flows for training the model. flows[k][0] is labels[k],
flows[k][1] is cell distance transform, flows[k][2] is Y flow, flows[k][3] is X flow,
and flows[k][4] is heat distribution.
"""
nimg = len(labels)
if labels[0].ndim < 3:
labels = [labels[n][np.newaxis, :, :] for n in range(nimg)]
flows = []
# flows need to be recomputed
if labels[0].shape[0] == 1 or labels[0].ndim < 3 or redo_flows:
dynamics_logger.info("computing flows for labels")
# compute flows; labels are fixed here to be unique, so they need to be passed back
# make sure labels are unique!
labels = [fastremap.renumber(label, in_place=True)[0] for label in labels]
iterator = trange if nimg > 1 else range
for n in iterator(nimg):
labels[n][0] = fastremap.renumber(labels[n][0], in_place=True)[0]
vecn = masks_to_flows(labels[n][0].astype(int), device=device, niter=niter)
# concatenate labels, distance transform, vector flows, heat (boundary and mask are computed in augmentations)
flow = np.concatenate((labels[n], labels[n] > 0.5, vecn),
axis=0).astype(np.float32)
if files is not None:
file_name = os.path.splitext(files[n])[0]
tifffile.imwrite(file_name + "_flows.tif", flow)
if return_flows:
flows.append(flow)
else:
dynamics_logger.info("flows precomputed")
if return_flows:
flows = [labels[n].astype(np.float32) for n in range(nimg)]
return flows
@njit([
"(int16[:,:,:], float32[:], float32[:], float32[:,:])",
"(float32[:,:,:], float32[:], float32[:], float32[:,:])"
], cache=True)
def map_coordinates(I, yc, xc, Y):
"""
Bilinear interpolation of image "I" in-place with y-coordinates yc and x-coordinates xc to Y.
Args:
I (numpy.ndarray): Input image of shape (C, Ly, Lx).
yc (numpy.ndarray): New y-coordinates.
xc (numpy.ndarray): New x-coordinates.
Y (numpy.ndarray): Output array of shape (C, ni).
Returns:
None
"""
C, Ly, Lx = I.shape
yc_floor = yc.astype(np.int32)
xc_floor = xc.astype(np.int32)
yc = yc - yc_floor
xc = xc - xc_floor
for i in range(yc_floor.shape[0]):
yf = min(Ly - 1, max(0, yc_floor[i]))
xf = min(Lx - 1, max(0, xc_floor[i]))
yf1 = min(Ly - 1, yf + 1)
xf1 = min(Lx - 1, xf + 1)
y = yc[i]
x = xc[i]
for c in range(C):
Y[c, i] = (np.float32(I[c, yf, xf]) * (1 - y) * (1 - x) +
np.float32(I[c, yf, xf1]) * (1 - y) * x +
np.float32(I[c, yf1, xf]) * y * (1 - x) +
np.float32(I[c, yf1, xf1]) * y * x)
[docs]def steps2D_interp(p, dP, niter, device=None):
""" Run dynamics of pixels to recover masks in 2D, with interpolation between pixel values.
Euler integration of dynamics dP for niter steps.
Args:
p (numpy.ndarray): Array of shape (n_points, 2) representing the initial pixel locations.
dP (numpy.ndarray): Array of shape (2, Ly, Lx) representing the flow field.
niter (int): Number of iterations to perform.
device (torch.device, optional): Device to use for computation. Defaults to None.
Returns:
numpy.ndarray: Array of shape (n_points, 2) representing the final pixel locations.
Raises:
None
"""
shape = dP.shape[1:]
if device is not None and device.type == "cuda":
shape = np.array(shape)[[
1, 0
]].astype("float") - 1 # Y and X dimensions (dP is 2.Ly.Lx), flipped X-1, Y-1
pt = torch.from_numpy(p[[1, 0]].T).float().to(device).unsqueeze(0).unsqueeze(
0) # p is n_points by 2, so pt is [1 1 2 n_points]
im = torch.from_numpy(dP[[1, 0]]).float().to(device).unsqueeze(
0) #covert flow numpy array to tensor on GPU, add dimension
# normalize pt between 0 and 1, normalize the flow
for k in range(2):
im[:, k, :, :] *= 2. / shape[k]
pt[:, :, :, k] /= shape[k]
# normalize to between -1 and 1
pt = pt * 2 - 1
#here is where the stepping happens
for t in range(niter):
# align_corners default is False, just added to suppress warning
dPt = torch.nn.functional.grid_sample(im, pt, align_corners=False)
for k in range(2): #clamp the final pixel locations
pt[:, :, :, k] = torch.clamp(pt[:, :, :, k] + dPt[:, k, :, :], -1., 1.)
#undo the normalization from before, reverse order of operations
pt = (pt + 1) * 0.5
for k in range(2):
pt[:, :, :, k] *= shape[k]
p = pt[:, :, :, [1, 0]].cpu().numpy().squeeze().T
return p
else:
dPt = np.zeros(p.shape, np.float32)
for t in range(niter):
map_coordinates(dP.astype(np.float32), p[0], p[1], dPt)
for k in range(len(p)):
p[k] = np.minimum(shape[k] - 1, np.maximum(0, p[k] + dPt[k]))
return p
@njit("(float32[:,:,:,:],float32[:,:,:,:], int32[:,:], int32)", nogil=True)
def steps3D(p, dP, inds, niter):
""" Run dynamics of pixels to recover masks in 3D.
Euler integration of dynamics dP for niter steps.
Args:
p (np.ndarray): Pixel locations [axis x Lz x Ly x Lx] (start at initial meshgrid).
dP (np.ndarray): Flows [axis x Lz x Ly x Lx].
inds (np.ndarray): Non-zero pixels to run dynamics on [npixels x 3].
niter (int): Number of iterations of dynamics to run.
Returns:
np.ndarray: Final locations of each pixel after dynamics.
"""
shape = p.shape[1:]
for t in range(niter):
#pi = p.astype(np.int32)
for j in range(inds.shape[0]):
z = inds[j, 0]
y = inds[j, 1]
x = inds[j, 2]
p0, p1, p2 = int(p[0, z, y, x]), int(p[1, z, y, x]), int(p[2, z, y, x])
p[0, z, y, x] = min(shape[0] - 1, max(0, p[0, z, y, x] + dP[0, p0, p1, p2]))
p[1, z, y, x] = min(shape[1] - 1, max(0, p[1, z, y, x] + dP[1, p0, p1, p2]))
p[2, z, y, x] = min(shape[2] - 1, max(0, p[2, z, y, x] + dP[2, p0, p1, p2]))
return p
@njit("(float32[:,:,:], float32[:,:,:], int32[:,:], int32)", nogil=True)
def steps2D(p, dP, inds, niter):
"""Run dynamics of pixels to recover masks in 2D.
Euler integration of dynamics dP for niter steps.
Args:
p (np.ndarray): Pixel locations [axis x Ly x Lx] (start at initial meshgrid).
dP (np.ndarray): Flows [axis x Ly x Lx].
inds (np.ndarray): Non-zero pixels to run dynamics on [npixels x 2].
niter (int): Number of iterations of dynamics to run.
Returns:
np.ndarray: Final locations of each pixel after dynamics.
"""
shape = p.shape[1:]
for t in range(niter):
for j in range(inds.shape[0]):
# starting coordinates
y = inds[j, 0]
x = inds[j, 1]
p0, p1 = int(p[0, y, x]), int(p[1, y, x])
step = dP[:, p0, p1]
for k in range(p.shape[0]):
p[k, y, x] = min(shape[k] - 1, max(0, p[k, y, x] + step[k]))
return p
[docs]def follow_flows(dP, mask=None, niter=200, interp=True, device=None):
""" Run dynamics to recover masks in 2D or 3D.
Pixels are represented as a meshgrid. Only pixels with non-zero cell-probability
are used (as defined by inds).
Args:
dP (np.ndarray): Flows [axis x Ly x Lx] or [axis x Lz x Ly x Lx].
mask (np.ndarray, optional): Pixel mask to seed masks. Useful when flows have low magnitudes.
niter (int, optional): Number of iterations of dynamics to run. Default is 200.
interp (bool, optional): Interpolate during 2D dynamics (not available in 3D). Default is True.
use_gpu (bool, optional): Use GPU to run interpolated dynamics (faster than CPU). Default is False.
Returns:
tuple containing:
- p (np.ndarray): Final locations of each pixel after dynamics; [axis x Ly x Lx] or [axis x Lz x Ly x Lx].
- inds (np.ndarray): Indices of pixels used for dynamics; [axis x Ly x Lx] or [axis x Lz x Ly x Lx].
"""
shape = np.array(dP.shape[1:]).astype(np.int32)
niter = np.uint32(niter)
if len(shape) > 2:
p = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), np.arange(shape[2]),
indexing="ij")
p = np.array(p).astype(np.float32)
# run dynamics on subset of pixels
inds = np.array(np.nonzero(np.abs(dP).max(axis=0) > 1e-3)).astype(np.int32).T
p = steps3D(p, dP, inds, niter)
else:
p = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing="ij")
p = np.array(p).astype(np.float32)
inds = np.array(np.nonzero(np.abs(dP).max(axis=0) > 1e-3)).astype(np.int32).T
if inds.ndim < 2 or inds.shape[0] < 5:
dynamics_logger.warning("WARNING: no mask pixels found")
return p, None
if not interp:
p = steps2D(p, dP.astype(np.float32), inds, niter)
else:
p_interp = steps2D_interp(p[:, inds[:, 0], inds[:, 1]], dP, niter,
device=device)
p[:, inds[:, 0], inds[:, 1]] = p_interp
return p, inds
[docs]def remove_bad_flow_masks(masks, flows, threshold=0.4, device=None):
"""Remove masks which have inconsistent flows.
Uses metrics.flow_error to compute flows from predicted masks
and compare flows to predicted flows from the network. Discards
masks with flow errors greater than the threshold.
Args:
masks (int, 2D or 3D array): Labelled masks, 0=NO masks; 1,2,...=mask labels,
size [Ly x Lx] or [Lz x Ly x Lx].
flows (float, 3D or 4D array): Flows [axis x Ly x Lx] or [axis x Lz x Ly x Lx].
threshold (float, optional): Masks with flow error greater than threshold are discarded.
Default is 0.4.
Returns:
masks (int, 2D or 3D array): Masks with inconsistent flow masks removed,
0=NO masks; 1,2,...=mask labels, size [Ly x Lx] or [Lz x Ly x Lx].
"""
device0 = device
if masks.size > 10000 * 10000 and (device is not None and device.type == "cuda"):
major_version, minor_version, _ = torch.__version__.split(".")
if major_version == "1" and int(minor_version) < 10:
# for PyTorch version lower than 1.10
def mem_info():
total_mem = torch.cuda.get_device_properties(0).total_memory
used_mem = torch.cuda.memory_allocated()
return total_mem, used_mem
else:
# for PyTorch version 1.10 and above
def mem_info():
total_mem, used_mem = torch.cuda.mem_get_info()
return total_mem, used_mem
if masks.size * 20 > mem_info()[0]:
dynamics_logger.warning(
"WARNING: image is very large, not using gpu to compute flows from masks for QC step flow_threshold"
)
dynamics_logger.info("turn off QC step with flow_threshold=0 if too slow")
device0 = None
merrors, _ = metrics.flow_error(masks, flows, device0)
badi = 1 + (merrors > threshold).nonzero()[0]
masks[np.isin(masks, badi)] = 0
return masks
[docs]def get_masks(p, iscell=None, rpad=20):
"""Create masks using pixel convergence after running dynamics.
Makes a histogram of final pixel locations p, initializes masks
at peaks of histogram and extends the masks from the peaks so that
they include all pixels with more than 2 final pixels p. Discards
masks with flow errors greater than the threshold.
Parameters:
p (float32, 3D or 4D array): Final locations of each pixel after dynamics,
size [axis x Ly x Lx] or [axis x Lz x Ly x Lx].
iscell (bool, 2D or 3D array): If iscell is not None, set pixels that are
iscell False to stay in their original location.
rpad (int, optional): Histogram edge padding. Default is 20.
Returns:
M0 (int, 2D or 3D array): Masks with inconsistent flow masks removed,
0=NO masks; 1,2,...=mask labels, size [Ly x Lx] or [Lz x Ly x Lx].
"""
pflows = []
edges = []
shape0 = p.shape[1:]
dims = len(p)
if iscell is not None:
if dims == 3:
inds = np.meshgrid(np.arange(shape0[0]), np.arange(shape0[1]),
np.arange(shape0[2]), indexing="ij")
elif dims == 2:
inds = np.meshgrid(np.arange(shape0[0]), np.arange(shape0[1]),
indexing="ij")
for i in range(dims):
p[i, ~iscell] = inds[i][~iscell]
for i in range(dims):
pflows.append(p[i].flatten().astype("int32"))
edges.append(np.arange(-.5 - rpad, shape0[i] + .5 + rpad, 1))
h, _ = np.histogramdd(tuple(pflows), bins=edges)
hmax = h.copy()
for i in range(dims):
hmax = maximum_filter1d(hmax, 5, axis=i)
seeds = np.nonzero(np.logical_and(h - hmax > -1e-6, h > 10))
Nmax = h[seeds]
isort = np.argsort(Nmax)[::-1]
for s in seeds:
s[:] = s[isort]
pix = list(np.array(seeds).T)
shape = h.shape
if dims == 3:
expand = np.nonzero(np.ones((3, 3, 3)))
else:
expand = np.nonzero(np.ones((3, 3)))
for iter in range(5):
for k in range(len(pix)):
if iter == 0:
pix[k] = list(pix[k])
newpix = []
iin = []
for i, e in enumerate(expand):
epix = e[:, np.newaxis] + np.expand_dims(pix[k][i], 0) - 1
epix = epix.flatten()
iin.append(np.logical_and(epix >= 0, epix < shape[i]))
newpix.append(epix)
iin = np.all(tuple(iin), axis=0)
for p in newpix:
p = p[iin]
newpix = tuple(newpix)
igood = h[newpix] > 2
for i in range(dims):
pix[k][i] = newpix[i][igood]
if iter == 4:
pix[k] = tuple(pix[k])
M = np.zeros(h.shape, np.uint32)
for k in range(len(pix)):
M[pix[k]] = 1 + k
for i in range(dims):
pflows[i] = pflows[i] + rpad
M0 = M[tuple(pflows)]
# remove big masks
uniq, counts = fastremap.unique(M0, return_counts=True)
big = np.prod(shape0) * 0.4
bigc = uniq[counts > big]
if len(bigc) > 0 and (len(bigc) > 1 or bigc[0] != 0):
M0 = fastremap.mask(M0, bigc)
fastremap.renumber(M0, in_place=True) #convenient to guarantee non-skipped labels
M0 = np.reshape(M0, shape0)
return M0
[docs]def resize_and_compute_masks(dP, cellprob, p=None, niter=200, cellprob_threshold=0.0,
flow_threshold=0.4, interp=True, do_3D=False, min_size=15,
resize=None, device=None):
"""Compute masks using dynamics from dP and cellprob, and resizes masks if resize is not None.
Args:
dP (numpy.ndarray): The dynamics flow field array.
cellprob (numpy.ndarray): The cell probability array.
p (numpy.ndarray, optional): The pixels on which to run dynamics. Defaults to None
niter (int, optional): The number of iterations for mask computation. Defaults to 200.
cellprob_threshold (float, optional): The threshold for cell probability. Defaults to 0.0.
flow_threshold (float, optional): The threshold for quality control metrics. Defaults to 0.4.
interp (bool, optional): Whether to interpolate during dynamics computation. Defaults to True.
do_3D (bool, optional): Whether to perform mask computation in 3D. Defaults to False.
min_size (int, optional): The minimum size of the masks. Defaults to 15.
resize (tuple, optional): The desired size for resizing the masks. Defaults to None.
device (str, optional): The torch device to use for computation. Defaults to None.
Returns:
tuple: A tuple containing the computed masks and the final pixel locations.
"""
mask, p = compute_masks(dP, cellprob, p=p, niter=niter,
cellprob_threshold=cellprob_threshold,
flow_threshold=flow_threshold, interp=interp, do_3D=do_3D,
min_size=min_size, device=device)
if resize is not None:
mask = transforms.resize_image(mask, resize[0], resize[1],
interpolation=cv2.INTER_NEAREST)
p = np.array([
transforms.resize_image(pi, resize[0], resize[1],
interpolation=cv2.INTER_NEAREST) for pi in p
])
return mask, p
[docs]def compute_masks(dP, cellprob, p=None, niter=200, cellprob_threshold=0.0,
flow_threshold=0.4, interp=True, do_3D=False, min_size=15,
device=None):
"""Compute masks using dynamics from dP and cellprob.
Args:
dP (numpy.ndarray): The dynamics flow field array.
cellprob (numpy.ndarray): The cell probability array.
p (numpy.ndarray, optional): The pixels on which to run dynamics. Defaults to None
niter (int, optional): The number of iterations for mask computation. Defaults to 200.
cellprob_threshold (float, optional): The threshold for cell probability. Defaults to 0.0.
flow_threshold (float, optional): The threshold for quality control metrics. Defaults to 0.4.
interp (bool, optional): Whether to interpolate during dynamics computation. Defaults to True.
do_3D (bool, optional): Whether to perform mask computation in 3D. Defaults to False.
min_size (int, optional): The minimum size of the masks. Defaults to 15.
device (str, optional): The torch device to use for computation. Defaults to None.
Returns:
tuple: A tuple containing the computed masks and the final pixel locations.
"""
cp_mask = cellprob > cellprob_threshold
if np.any(cp_mask): #mask at this point is a cell cluster binary map, not labels
# follow flows
if p is None:
p, inds = follow_flows(dP * cp_mask / 5., niter=niter, interp=interp,
device=device)
if inds is None:
dynamics_logger.info("No cell pixels found.")
shape = cellprob.shape
mask = np.zeros(shape, np.uint16)
p = np.zeros((len(shape), *shape), np.uint16)
return mask, p
#calculate masks
mask = get_masks(p, iscell=cp_mask)
# flow thresholding factored out of get_masks
if not do_3D:
if mask.max() > 0 and flow_threshold is not None and flow_threshold > 0:
# make sure labels are unique at output of get_masks
mask = remove_bad_flow_masks(mask, dP, threshold=flow_threshold,
device=device)
if mask.max() > 2**16 - 1:
recast = True
mask = mask.astype(np.float32)
else:
recast = False
mask = mask.astype(np.uint16)
if recast:
mask = mask.astype(np.uint32)
if mask.max() < 2**16:
mask = mask.astype(np.uint16)
else: # nothing to compute, just make it compatible
dynamics_logger.info("No cell pixels found.")
shape = cellprob.shape
mask = np.zeros(cellprob.shape, np.uint16)
p = np.zeros((len(shape), *shape), np.uint16)
return mask, p
mask = utils.fill_holes_and_remove_small_masks(mask, min_size=min_size)
if mask.dtype == np.uint32:
dynamics_logger.warning(
"more than 65535 masks in image, masks returned as np.uint32")
return mask, p