File size: 12,765 Bytes
0ce1ebe |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 |
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
|