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from __future__ import print_function, unicode_literals, absolute_import, division
import numpy as np
from time import time
from .utils import _normalize_grid
def _ind_prob_thresh(prob, prob_thresh, b=2):
if b is not None and np.isscalar(b):
b = ((b,b),)*prob.ndim
ind_thresh = prob > prob_thresh
if b is not None:
_ind_thresh = np.zeros_like(ind_thresh)
ss = tuple(slice(_bs[0] if _bs[0]>0 else None,
-_bs[1] if _bs[1]>0 else None) for _bs in b)
_ind_thresh[ss] = True
ind_thresh &= _ind_thresh
return ind_thresh
def _non_maximum_suppression_old(coord, prob, grid=(1,1), b=2, nms_thresh=0.5, prob_thresh=0.5, verbose=False, max_bbox_search=True):
"""2D coordinates of the polys that survive from a given prediction (prob, coord)
prob.shape = (Ny,Nx)
coord.shape = (Ny,Nx,2,n_rays)
b: don't use pixel closer than b pixels to the image boundary
returns retained points
"""
from .lib.stardist2d import c_non_max_suppression_inds_old
# TODO: using b>0 with grid>1 can suppress small/cropped objects at the image boundary
assert prob.ndim == 2
assert coord.ndim == 4
grid = _normalize_grid(grid,2)
# mask = prob > prob_thresh
# if b is not None and b > 0:
# _mask = np.zeros_like(mask)
# _mask[b:-b,b:-b] = True
# mask &= _mask
mask = _ind_prob_thresh(prob, prob_thresh, b)
polygons = coord[mask]
scores = prob[mask]
# sort scores descendingly
ind = np.argsort(scores)[::-1]
survivors = np.zeros(len(ind), bool)
polygons = polygons[ind]
scores = scores[ind]
if max_bbox_search:
# map pixel indices to ids of sorted polygons (-1 => polygon at that pixel not a candidate)
mapping = -np.ones(mask.shape,np.int32)
mapping.flat[ np.flatnonzero(mask)[ind] ] = range(len(ind))
else:
mapping = np.empty((0,0),np.int32)
if verbose:
t = time()
survivors[ind] = c_non_max_suppression_inds_old(np.ascontiguousarray(polygons.astype(np.int32)),
mapping, np.float32(nms_thresh), np.int32(max_bbox_search),
np.int32(grid[0]), np.int32(grid[1]),np.int32(verbose))
if verbose:
print("keeping %s/%s polygons" % (np.count_nonzero(survivors), len(polygons)))
print("NMS took %.4f s" % (time() - t))
points = np.stack([ii[survivors] for ii in np.nonzero(mask)],axis=-1)
return points
def non_maximum_suppression(dist, prob, grid=(1,1), b=2, nms_thresh=0.5, prob_thresh=0.5,
use_bbox=True, use_kdtree=True, verbose=False,cut=False):
"""Non-Maximum-Supression of 2D polygons
Retains only polygons whose overlap is smaller than nms_thresh
dist.shape = (Ny,Nx, n_rays)
prob.shape = (Ny,Nx)
returns the retained points, probabilities, and distances:
points, prob, dist = non_maximum_suppression(dist, prob, ....
"""
# TODO: using b>0 with grid>1 can suppress small/cropped objects at the image boundary
assert prob.ndim == 2 and dist.ndim == 3 and prob.shape == dist.shape[:2]
dist = np.asarray(dist)
prob = np.asarray(prob)
n_rays = dist.shape[-1]
grid = _normalize_grid(grid,2)
# mask = prob > prob_thresh
# if b is not None and b > 0:
# _mask = np.zeros_like(mask)
# _mask[b:-b,b:-b] = True
# mask &= _mask
mask = _ind_prob_thresh(prob, prob_thresh, b)
points = np.stack(np.where(mask), axis=1)
dist = dist[mask]
scores = prob[mask]
# sort scores descendingly
ind = np.argsort(scores)[::-1]
if cut is True and ind.shape[0] > 20000:
#if cut is True and :
ind = ind[:round(ind.shape[0]*0.5)]
dist = dist[ind]
scores = scores[ind]
points = points[ind]
points = (points * np.array(grid).reshape((1,2)))
if verbose:
t = time()
inds = non_maximum_suppression_inds(dist, points.astype(np.int32, copy=False), scores=scores,
use_bbox=use_bbox, use_kdtree=use_kdtree,
thresh=nms_thresh, verbose=verbose)
if verbose:
print("keeping %s/%s polygons" % (np.count_nonzero(inds), len(inds)))
print("NMS took %.4f s" % (time() - t))
return points[inds], scores[inds], dist[inds]
def non_maximum_suppression_sparse(dist, prob, points, b=2, nms_thresh=0.5,
use_bbox=True, use_kdtree = True, verbose=False):
"""Non-Maximum-Supression of 2D polygons from a list of dists, probs (scores), and points
Retains only polyhedra whose overlap is smaller than nms_thresh
dist.shape = (n_polys, n_rays)
prob.shape = (n_polys,)
points.shape = (n_polys,2)
returns the retained instances
(pointsi, probi, disti, indsi)
with
pointsi = points[indsi] ...
"""
# TODO: using b>0 with grid>1 can suppress small/cropped objects at the image boundary
dist = np.asarray(dist)
prob = np.asarray(prob)
points = np.asarray(points)
n_rays = dist.shape[-1]
assert dist.ndim == 2 and prob.ndim == 1 and points.ndim == 2 and \
points.shape[-1]==2 and len(prob) == len(dist) == len(points)
verbose and print("predicting instances with nms_thresh = {nms_thresh}".format(nms_thresh=nms_thresh), flush=True)
inds_original = np.arange(len(prob))
_sorted = np.argsort(prob)[::-1]
probi = prob[_sorted]
disti = dist[_sorted]
pointsi = points[_sorted]
inds_original = inds_original[_sorted]
if verbose:
print("non-maximum suppression...")
t = time()
inds = non_maximum_suppression_inds(disti, pointsi, scores=probi, thresh=nms_thresh, use_kdtree = use_kdtree, verbose=verbose)
if verbose:
print("keeping %s/%s polyhedra" % (np.count_nonzero(inds), len(inds)))
print("NMS took %.4f s" % (time() - t))
return pointsi[inds], probi[inds], disti[inds], inds_original[inds]
def non_maximum_suppression_inds(dist, points, scores, thresh=0.5, use_bbox=True, use_kdtree = True, verbose=1):
"""
Applies non maximum supression to ray-convex polygons given by dists and points
sorted by scores and IoU threshold
P1 will suppress P2, if IoU(P1,P2) > thresh
with IoU(P1,P2) = Ainter(P1,P2) / min(A(P1),A(P2))
i.e. the smaller thresh, the more polygons will be supressed
dist.shape = (n_poly, n_rays)
point.shape = (n_poly, 2)
score.shape = (n_poly,)
returns indices of selected polygons
"""
from stardist.lib.stardist2d import c_non_max_suppression_inds
assert dist.ndim == 2
assert points.ndim == 2
n_poly = dist.shape[0]
if scores is None:
scores = np.ones(n_poly)
assert len(scores) == n_poly
assert points.shape[0] == n_poly
def _prep(x, dtype):
return np.ascontiguousarray(x.astype(dtype, copy=False))
inds = c_non_max_suppression_inds(_prep(dist, np.float32),
_prep(points, np.float32),
int(use_kdtree),
int(use_bbox),
int(verbose),
np.float32(thresh))
return inds
#########
def non_maximum_suppression_3d(dist, prob, rays, grid=(1,1,1), b=2, nms_thresh=0.5, prob_thresh=0.5, use_bbox=True, use_kdtree=True, verbose=False):
"""Non-Maximum-Supression of 3D polyhedra
Retains only polyhedra whose overlap is smaller than nms_thresh
dist.shape = (Nz,Ny,Nx, n_rays)
prob.shape = (Nz,Ny,Nx)
returns the retained points, probabilities, and distances:
points, prob, dist = non_maximum_suppression_3d(dist, prob, ....
"""
# TODO: using b>0 with grid>1 can suppress small/cropped objects at the image boundary
dist = np.asarray(dist)
prob = np.asarray(prob)
assert prob.ndim == 3 and dist.ndim == 4 and dist.shape[-1] == len(rays) and prob.shape == dist.shape[:3]
grid = _normalize_grid(grid,3)
verbose and print("predicting instances with prob_thresh = {prob_thresh} and nms_thresh = {nms_thresh}".format(prob_thresh=prob_thresh, nms_thresh=nms_thresh), flush=True)
# ind_thresh = prob > prob_thresh
# if b is not None and b > 0:
# _ind_thresh = np.zeros_like(ind_thresh)
# _ind_thresh[b:-b,b:-b,b:-b] = True
# ind_thresh &= _ind_thresh
ind_thresh = _ind_prob_thresh(prob, prob_thresh, b)
points = np.stack(np.where(ind_thresh), axis=1)
verbose and print("found %s candidates"%len(points))
probi = prob[ind_thresh]
disti = dist[ind_thresh]
_sorted = np.argsort(probi)[::-1]
probi = probi[_sorted]
disti = disti[_sorted]
points = points[_sorted]
verbose and print("non-maximum suppression...")
points = (points * np.array(grid).reshape((1,3)))
inds = non_maximum_suppression_3d_inds(disti, points, rays=rays, scores=probi, thresh=nms_thresh,
use_bbox=use_bbox, use_kdtree = use_kdtree,
verbose=verbose)
verbose and print("keeping %s/%s polyhedra" % (np.count_nonzero(inds), len(inds)))
return points[inds], probi[inds], disti[inds]
def non_maximum_suppression_3d_sparse(dist, prob, points, rays, b=2, nms_thresh=0.5, use_kdtree = True, verbose=False):
"""Non-Maximum-Supression of 3D polyhedra from a list of dists, probs and points
Retains only polyhedra whose overlap is smaller than nms_thresh
dist.shape = (n_polys, n_rays)
prob.shape = (n_polys,)
points.shape = (n_polys,3)
returns the retained instances
(pointsi, probi, disti, indsi)
with
pointsi = points[indsi] ...
"""
# TODO: using b>0 with grid>1 can suppress small/cropped objects at the image boundary
dist = np.asarray(dist)
prob = np.asarray(prob)
points = np.asarray(points)
assert dist.ndim == 2 and prob.ndim == 1 and points.ndim == 2 and \
dist.shape[-1] == len(rays) and points.shape[-1]==3 and len(prob) == len(dist) == len(points)
verbose and print("predicting instances with nms_thresh = {nms_thresh}".format(nms_thresh=nms_thresh), flush=True)
inds_original = np.arange(len(prob))
_sorted = np.argsort(prob)[::-1]
probi = prob[_sorted]
disti = dist[_sorted]
pointsi = points[_sorted]
inds_original = inds_original[_sorted]
verbose and print("non-maximum suppression...")
inds = non_maximum_suppression_3d_inds(disti, pointsi, rays=rays, scores=probi, thresh=nms_thresh, use_kdtree = use_kdtree, verbose=verbose)
verbose and print("keeping %s/%s polyhedra" % (np.count_nonzero(inds), len(inds)))
return pointsi[inds], probi[inds], disti[inds], inds_original[inds]
def non_maximum_suppression_3d_inds(dist, points, rays, scores, thresh=0.5, use_bbox=True, use_kdtree = True, verbose=1):
"""
Applies non maximum supression to ray-convex polyhedra given by dists and rays
sorted by scores and IoU threshold
P1 will suppress P2, if IoU(P1,P2) > thresh
with IoU(P1,P2) = Ainter(P1,P2) / min(A(P1),A(P2))
i.e. the smaller thresh, the more polygons will be supressed
dist.shape = (n_poly, n_rays)
point.shape = (n_poly, 3)
score.shape = (n_poly,)
returns indices of selected polygons
"""
from .lib.stardist3d import c_non_max_suppression_inds
assert dist.ndim == 2
assert points.ndim == 2
assert dist.shape[1] == len(rays)
n_poly = dist.shape[0]
if scores is None:
scores = np.ones(n_poly)
assert len(scores) == n_poly
assert points.shape[0] == n_poly
# sort scores descendingly
ind = np.argsort(scores)[::-1]
survivors = np.ones(n_poly, bool)
dist = dist[ind]
points = points[ind]
scores = scores[ind]
def _prep(x, dtype):
return np.ascontiguousarray(x.astype(dtype, copy=False))
if verbose:
t = time()
survivors[ind] = c_non_max_suppression_inds(_prep(dist, np.float32),
_prep(points, np.float32),
_prep(rays.vertices, np.float32),
_prep(rays.faces, np.int32),
_prep(scores, np.float32),
int(use_bbox),
int(use_kdtree),
int(verbose),
np.float32(thresh))
if verbose:
print("NMS took %.4f s" % (time() - t))
return survivors
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