def _model3d():
from utils import path_model3d
from stardist.models import StarDist3D
model_path = path_model3d()
return StarDist3D(None,
name=model_path.name,
basedir=str(model_path.parent))
def test_load_and_export_TF():
model_path = path_model3d()
model = StarDist3D(None,
name=model_path.name,
basedir=str(model_path.parent))
model.export_TF(single_output=True, upsample_grid=False)
model.export_TF(single_output=True, upsample_grid=True)
def test_load_and_export_TF():
model_path = path_model3d()
model = StarDist3D(None,
name=model_path.name,
basedir=str(model_path.parent))
assert any(g > 1 for g in model.config.grid)
# model.export_TF(single_output=False, upsample_grid=False)
# model.export_TF(single_output=False, upsample_grid=True)
model.export_TF(single_output=True, upsample_grid=False)
model.export_TF(single_output=True, upsample_grid=True)
def test_load_and_predict_with_overlap():
model_path = path_model3d()
model = StarDist3D(None, name=model_path.name, basedir=str(model_path.parent))
img, mask = real_image3d()
x = normalize(img,1,99.8)
prob, dist = model.predict(x, n_tiles=(1,2,2))
assert prob.shape == dist.shape[:3]
assert model.config.n_rays == dist.shape[-1]
labels, _ = model.predict_instances(x, nms_thresh = .5,
overlap_label = -3)
assert np.min(labels) == -3
return model, labels
def test_load_and_predict():
model_path = path_model3d()
model = StarDist3D(None, name=model_path.name, basedir=str(model_path.parent))
img, mask = real_image3d()
x = normalize(img,1,99.8)
prob, dist = model.predict(x, n_tiles=(1,2,2))
assert prob.shape == dist.shape[:3]
assert model.config.n_rays == dist.shape[-1]
labels, _ = model.predict_instances(x)
assert labels.shape == img.shape[:3]
stats = matching(mask, labels, thresh=0.5)
assert (stats.fp, stats.tp, stats.fn) == (0, 30, 21)
return model, labels
def test_predict_dense_sparse():
model_path = path_model3d()
model = StarDist3D(None,
name=model_path.name,
basedir=str(model_path.parent))
img, mask = real_image3d()
x = normalize(img, 1, 99.8)
labels1, res1 = model.predict_instances(x, n_tiles=(1, 2, 2), sparse=False)
labels2, res2 = model.predict_instances(x, n_tiles=(1, 2, 2), sparse=True)
assert np.allclose(labels1, labels2)
assert all(
np.allclose(res1[k], res2[k])
for k in set(res1.keys()).union(set(res2.keys())))
return labels2, labels2
def test_optimize_thresholds():
model_path = path_model3d()
model = StarDist3D(None, name=model_path.name, basedir=str(model_path.parent))
img, mask = real_image3d()
x = normalize(img,1,99.8)
def _opt(model):
return model.optimize_thresholds([x],[mask],
nms_threshs = [.3,.5],
iou_threshs = [.3,.5],
optimize_kwargs = dict(tol=1e-1),
save_to_json = False)
t1 = _opt(model)
# enforce implicit tiling
model.config.train_batch_size = 1
model.config.train_patch_size = tuple(s-1 for s in x.shape)
t2 = _opt(model)
assert all(np.allclose(t1[k],t2[k]) for k in t1.keys())
return model
