import time, os
from scipy.ndimage import maximum_filter1d, find_objects
import torch
import numpy as np
import tifffile
from tqdm import trange
from numba import njit, 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
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 center of mask (ymed, xmed) on mask pixels (y, x)
Parameters
--------------
T: float64, array
_ x Lx array that diffusion is run in
y: int32, array
pixels in y inside mask
x: int32, array
pixels in x inside mask
ymed: int32
center of mask in y
xmed: int32
center of mask in x
Lx: int32
size of x-dimension of masks
niter: int32
number of iterations to run diffusion
Returns
---------------
T: float64, array
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, centers, isneighbor, Ly, Lx, n_iter=200, device=torch.device('cuda')):
""" runs diffusion on GPU to generate flows for training images or quality control
neighbors is 9 x pixels in masks,
centers are mask centers,
isneighbor is valid neighbor boolean 9 x pixels
"""
if device is not None:
device = device
nimg = neighbors.shape[0] // 9
pt = torch.from_numpy(neighbors).to(device)
T = torch.zeros((nimg,Ly,Lx), dtype=torch.double, device=device)
meds = torch.from_numpy(centers.astype(int)).to(device).long()
isneigh = torch.from_numpy(isneighbor).to(device)
for i in range(n_iter):
T[:, meds[:,0], meds[:,1]] +=1
Tneigh = T[:, pt[:,:,0], pt[:,:,1]]
Tneigh *= isneigh
T[:, pt[0,:,0], pt[0,:,1]] = Tneigh.mean(axis=1)
del meds, isneigh, Tneigh
T = torch.log(1.+ T)
# gradient positions
grads = T[:, pt[[2,1,4,3],:,0], pt[[2,1,4,3],:,1]]
del pt
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)
return mu_torch
[docs]def masks_to_flows_gpu(masks, device=None):
""" convert masks to flows using diffusion from center pixel
Center of masks where diffusion starts is defined using COM
Parameters
-------------
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].
mu_c: float, 2D or 3D array
for each pixel, the distance to the center of the mask
in which it resides
"""
if device is None:
device = torch.device('cuda')
Ly0,Lx0 = masks.shape
Ly, Lx = Ly0+2, Lx0+2
masks_padded = np.zeros((Ly, Lx), np.int64)
masks_padded[1:-1, 1:-1] = masks
# get mask pixel neighbors
y, x = np.nonzero(masks_padded)
neighborsY = np.stack((y, y-1, y+1,
y, y, y-1,
y-1, y+1, y+1), axis=0)
neighborsX = np.stack((x, x, x,
x-1, x+1, x-1,
x+1, x-1, x+1), axis=0)
neighbors = np.stack((neighborsY, neighborsX), axis=-1)
# get mask centers
slices = find_objects(masks)
centers = np.zeros((masks.max(), 2), 'int')
for i,si in enumerate(slices):
if si is not None:
sr,sc = si
ly, lx = sr.stop - sr.start + 1, sc.stop - sc.start + 1
yi,xi = np.nonzero(masks[sr, sc] == (i+1))
yi = yi.astype(np.int32) + 1 # add padding
xi = xi.astype(np.int32) + 1 # add padding
ymed = np.median(yi)
xmed = np.median(xi)
imin = np.argmin((xi-xmed)**2 + (yi-ymed)**2)
xmed = xi[imin]
ymed = yi[imin]
centers[i,0] = ymed + sr.start
centers[i,1] = xmed + sc.start
# get neighbor validator (not all neighbors are in same mask)
neighbor_masks = masks_padded[neighbors[:,:,0], neighbors[:,:,1]]
isneighbor = neighbor_masks == neighbor_masks[0]
ext = np.array([[sr.stop - sr.start + 1, sc.stop - sc.start + 1] for sr, sc in slices])
n_iter = 2 * (ext.sum(axis=1)).max()
# run diffusion
mu = _extend_centers_gpu(neighbors, centers, isneighbor, Ly, Lx,
n_iter=n_iter, device=device)
# normalize
mu /= (1e-20 + (mu**2).sum(axis=0)**0.5)
# put into original image
mu0 = np.zeros((2, Ly0, Lx0))
mu0[:, y-1, x-1] = mu
mu_c = np.zeros_like(mu0)
return mu0, mu_c
[docs]def masks_to_flows_cpu(masks, device=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 median of all pixels that is inside the
mask. Result of diffusion is converted into flows by computing
the gradients of the diffusion density map.
Parameters
-------------
masks: int, 2D array
labelled masks 0=NO masks; 1,2,...=mask labels
Returns
-------------
mu: float, 3D array
flows in Y = mu[-2], flows in X = mu[-1].
if masks are 3D, flows in Z = mu[0].
mu_c: float, 2D array
for each pixel, the distance to the center of the mask
in which it resides
"""
Ly, Lx = masks.shape
mu = np.zeros((2, Ly, Lx), np.float64)
mu_c = np.zeros((Ly, Lx), np.float64)
nmask = masks.max()
slices = find_objects(masks)
dia = utils.diameters(masks)[0]
s2 = (.15 * dia)**2
for i,si in enumerate(slices):
if si is not None:
sr,sc = si
ly, lx = sr.stop - sr.start + 1, sc.stop - sc.start + 1
y,x = np.nonzero(masks[sr, sc] == (i+1))
y = y.astype(np.int32) + 1
x = x.astype(np.int32) + 1
ymed = np.median(y)
xmed = np.median(x)
imin = np.argmin((x-xmed)**2 + (y-ymed)**2)
xmed = x[imin]
ymed = y[imin]
d2 = (x-xmed)**2 + (y-ymed)**2
mu_c[sr.start+y-1, sc.start+x-1] = np.exp(-d2/s2)
niter = 2*np.int32(np.ptp(x) + np.ptp(y))
T = np.zeros((ly+2)*(lx+2), np.float64)
T = _extend_centers(T, y, x, ymed, xmed, np.int32(lx), np.int32(niter))
T[(y+1)*lx + x+1] = np.log(1.+T[(y+1)*lx + x+1])
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))
mu /= (1e-20 + (mu**2).sum(axis=0)**0.5)
return mu, mu_c
[docs]def masks_to_flows(masks, use_gpu=False, device=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 median of all pixels that is inside the
mask. Result of diffusion is converted into flows by computing
the gradients of the diffusion density map.
Parameters
-------------
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].
mu_c: float, 2D or 3D array
for each pixel, the distance to the center of the mask
in which it resides
"""
if masks.max() == 0:
dynamics_logger.warning('empty masks!')
return np.zeros((2, *masks.shape), 'float32')
if use_gpu:
if use_gpu and device is None:
device = torch_GPU
elif device is None:
device = torch_CPU
masks_to_flows_device = masks_to_flows_gpu
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)[0]
mu[[1,2], z] += mu0
for y in range(Ly):
mu0 = masks_to_flows_device(masks[:,y], device=device)[0]
mu[[0,2], :, y] += mu0
for x in range(Lx):
mu0 = masks_to_flows_device(masks[:,:,x], device=device)[0]
mu[[0,1], :, :, x] += mu0
return mu
elif masks.ndim==2:
mu, mu_c = masks_to_flows_device(masks, device=device)
return mu
else:
raise ValueError('masks_to_flows only takes 2D or 3D arrays')
[docs]def labels_to_flows(labels, files=None, use_gpu=False, device=None, redo_flows=False):
""" convert labels (list of masks or flows) to flows for training model
if files is not None, flows are saved to files to be reused
Parameters
--------------
labels: list of ND-arrays
labels[k] can be 2D or 3D, if [3 x Ly x Lx] then it is assumed that flows were precomputed.
Otherwise labels[k][0] or labels[k] (if 2D) is used to create flows and cell probabilities.
Returns
--------------
flows: list of [4 x Ly x Lx] arrays
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)]
if labels[0].shape[0] == 1 or labels[0].ndim < 3 or redo_flows: # flows need to be recomputed
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]
veci = [masks_to_flows(labels[n][0],use_gpu=use_gpu, device=device) for n in trange(nimg)]
# concatenate labels, distance transform, vector flows, heat (boundary and mask are computed in augmentations)
flows = [np.concatenate((labels[n], labels[n]>0.5, veci[n]), axis=0).astype(np.float32)
for n in range(nimg)]
if files is not None:
for flow, file in zip(flows, files):
file_name = os.path.splitext(file)[0]
tifffile.imwrite(file_name+'_flows.tif', flow)
else:
dynamics_logger.info('flows precomputed')
flows = [labels[n].astype(np.float32) for n in range(nimg)]
return flows
[docs]@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 ycoordinates yc and xcoordinates xc to Y
Parameters
-------------
I : C x Ly x Lx
yc : ni
new y coordinates
xc : ni
new x coordinates
Y : C x ni
I sampled at (yc,xc)
"""
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 )
def steps2D_interp(p, dP, niter, use_gpu=False, device=None):
shape = dP.shape[1:]
if use_gpu:
if device is None:
device = torch_GPU
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
[docs]@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
Parameters
----------------
p: float32, 4D array
pixel locations [axis x Lz x Ly x Lx] (start at initial meshgrid)
dP: float32, 4D array
flows [axis x Lz x Ly x Lx]
inds: int32, 2D array
non-zero pixels to run dynamics on [npixels x 3]
niter: int32
number of iterations of dynamics to run
Returns
---------------
p: float32, 4D array
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
[docs]@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
Parameters
----------------
p: float32, 3D array
pixel locations [axis x Ly x Lx] (start at initial meshgrid)
dP: float32, 3D array
flows [axis x Ly x Lx]
inds: int32, 2D array
non-zero pixels to run dynamics on [npixels x 2]
niter: int32
number of iterations of dynamics to run
Returns
---------------
p: float32, 3D array
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, use_gpu=True, device=None):
""" define pixels and run dynamics to recover masks in 2D
Pixels are meshgrid. Only pixels with non-zero cell-probability
are used (as defined by inds)
Parameters
----------------
dP: float32, 3D or 4D array
flows [axis x Ly x Lx] or [axis x Lz x Ly x Lx]
mask: (optional, default None)
pixel mask to seed masks. Useful when flows have low magnitudes.
niter: int (optional, default 200)
number of iterations of dynamics to run
interp: bool (optional, default True)
interpolate during 2D dynamics (not available in 3D)
(in previous versions + paper it was False)
use_gpu: bool (optional, default False)
use GPU to run interpolated dynamics (faster than CPU)
Returns
---------------
p: float32, 3D or 4D array
final locations of each pixel after dynamics; [axis x Ly x Lx] or [axis x Lz x Ly x Lx]
inds: int32, 3D or 4D array
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[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[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, use_gpu=use_gpu, device=device)
p[:,inds[:,0],inds[:,1]] = p_interp
return p, inds
[docs]def remove_bad_flow_masks(masks, flows, threshold=0.4, use_gpu=False, 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 network. Discards
masks with flow errors greater than the threshold.
Parameters
----------------
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, default 0.4)
masks with flow error greater than threshold are discarded.
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]
"""
if masks.size > 10000*10000:
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')
use_gpu = False
merrors, _ = metrics.flow_error(masks, flows, use_gpu, device)
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, default 20)
histogram edge padding
threshold: float (optional, default 0.4)
masks with flow error greater than threshold are discarded
(if flows is not None)
flows: float, 3D or 4D array (optional, default None)
flows [axis x Ly x Lx] or [axis x Lz x Ly x Lx]. If flows
is not None, then masks with inconsistent flows are removed using
`remove_bad_flow_masks`.
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 e in expand:
e = np.expand_dims(e,1)
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 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,
use_gpu=False,device=None):
""" compute masks using dynamics from dP, cellprob, and boundary """
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,
use_gpu=use_gpu, device=device)
if inds is None:
dynamics_logger.info('No cell pixels found.')
shape = resize if resize is not None else 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:
shape0 = p.shape[1:]
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, use_gpu=use_gpu, device=device)
if resize is not None:
#if verbose:
# dynamics_logger.info(f'resizing output with resize = {resize}')
if mask.max() > 2**16-1:
recast = True
mask = mask.astype(np.float32)
else:
recast = False
mask = mask.astype(np.uint16)
mask = transforms.resize_image(mask, resize[0], resize[1], interpolation=cv2.INTER_NEAREST)
if recast:
mask = mask.astype(np.uint32)
Ly,Lx = mask.shape
elif 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 = resize if resize is not None else cellprob.shape
mask = np.zeros(shape, np.uint16)
p = np.zeros((len(shape), *shape), np.uint16)
return mask, p
# moving the cleanup to the end helps avoid some bugs arising from scaling...
# maybe better would be to rescale the min_size and hole_size parameters to do the
# cleanup at the prediction scale, or switch depending on which one is bigger...
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