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def test_nms(images, n_rays):
for img in images:
prob = edt_prob(img)
dist = _cpp_star_dist(img, n_rays)
coord = dist_to_coord(dist)
nms_a = non_maximum_suppression(coord, prob, prob_thresh=0.4, verbose=False, max_bbox_search=False)
nms_b = non_maximum_suppression(coord, prob, prob_thresh=0.4, verbose=False, max_bbox_search=True)
check_similar(nms_a,nms_b)
from utils import prob_dist_image2d
np.random.seed(42)
from scipy.ndimage import zoom
from stardist.geometry.geom2d import dist_to_coord, polygons_to_label
from stardist.nms import non_maximum_suppression
from skimage.morphology import watershed
if __name__ == '__main__':
img, prob, dist = prob_dist_image2d()
grid = (2,2)
coord = dist_to_coord(dist, grid = grid)
points = non_maximum_suppression(coord, prob, grid = grid, prob_thresh=.4, nms_thresh=.3)
labels0 = polygons_to_label(coord, prob, points, shape=img.shape)
aff, aff_neg = dist_to_affinity2D(dist,
weights = prob>0.03,
grid = grid,
normed=True, verbose = True);
factor = tuple(s1/s2 for s1, s2 in zip(img.shape, prob.shape))
potential = (np.mean(aff,-1))*prob
potential = zoom(potential, factor, order=1)
markers = np.zeros(img.shape, np.int32)
def test_nms(images, n_rays):
for img in images:
prob = edt_prob(img)
dist = _cpp_star_dist(img, n_rays)
coord = dist_to_coord(dist)
nms_a = non_maximum_suppression(coord, prob, prob_thresh=0.4, verbose=False, max_bbox_search=False)
nms_b = non_maximum_suppression(coord, prob, prob_thresh=0.4, verbose=False, max_bbox_search=True)
check_similar(nms_a,nms_b)
def _instances_from_prediction(self, img_shape, prob, dist, prob_thresh=None, nms_thresh=None, overlap_label = None, **nms_kwargs):
if prob_thresh is None: prob_thresh = self.thresholds.prob
if nms_thresh is None: nms_thresh = self.thresholds.nms
if overlap_label is not None: raise NotImplementedError("overlap_label not supported for 2D yet!")
coord = dist_to_coord(dist, grid=self.config.grid)
points = non_maximum_suppression(coord, prob, grid=self.config.grid,
prob_thresh=prob_thresh, nms_thresh=nms_thresh, **nms_kwargs)
labels = polygons_to_label(coord, prob, points, shape=img_shape)
return labels, dict(coord=coord[points[:,0],points[:,1]], points=points, prob=prob[points[:,0],points[:,1]])