def create_random_suppressed(nms_thresh=.4, shape=(64, 65, 67), noise=.1, n_rays=32):
prob, dist, rays = create_random_data(shape, noise, n_rays)
points, probi, disti = non_maximum_suppression_3d(dist, prob, rays,
prob_thresh=0.9,
nms_thresh=nms_thresh,
verbose=True)
return points, probi, disti, rays
def test_nms_kdtree(nms_thresh=0.1, shape=(33, 44, 55), noise=.1, n_rays=32):
prob, dist, rays = create_random_data(shape, noise, n_rays)
points1, probi1, disti1 = non_maximum_suppression_3d(dist,
prob,
rays,
prob_thresh=0.9,
nms_thresh=nms_thresh,
use_kdtree=False,
verbose=True)
points2, probi2, disti2 = non_maximum_suppression_3d(dist,
prob,
rays,
prob_thresh=0.9,
nms_thresh=nms_thresh,
use_kdtree=True,
verbose=True)
assert np.allclose(points1, points2)
assert np.allclose(probi1, probi2)
assert np.allclose(disti1, disti2)
return (points1, probi1, disti1), (points2, probi2, disti2)
def test_nms():
dist = 10 * np.ones((33, 44, 55, 32))
prob = np.random.uniform(0, 1, dist.shape[:3])
rays = Rays_GoldenSpiral(dist.shape[-1])
points, probi, disti = non_maximum_suppression_3d(dist,
prob,
rays,
prob_thresh=0.8,
nms_thresh=0,
verbose=True)
return points, rays, disti