def test_predict2D(model2d, use_channel):
model = model2d
img = real_image2d()[0]
img = normalize(img, 1, 99.8)
img = repeat(img, 2)
axes = 'YX'
if use_channel:
img = img[...,np.newaxis]
axes += 'C'
ref_labels, ref_polys = model.predict_instances(img, axes=axes)
res_labels, res_polys = model.predict_instances_big(img, axes=axes, block_size=288, min_overlap=32, context=96)
m = matching(ref_labels, res_labels)
assert (1.0, 1.0) == (m.accuracy, m.mean_true_score)
m = matching(render_polygons(ref_polys, shape=img.shape),
render_polygons(res_polys, shape=img.shape))
assert (1.0, 1.0) == (m.accuracy, m.mean_true_score)
# sort them first lexicographic
ref_inds = np.lexsort(ref_polys["points"].T)
res_inds = np.lexsort(res_polys["points"].T)
assert np.allclose(ref_polys["coord"][ref_inds],
res_polys["coord"][res_inds],atol=1e-2)
assert np.allclose(ref_polys["points"][ref_inds],
res_polys["points"][res_inds],atol=1e-2)
assert np.allclose(ref_polys["prob"][ref_inds],
res_polys["prob"][res_inds],atol=1e-2)
return ref_polys, res_polys
def test_predict3D(model3d):
model = model3d
img = real_image3d()[0]
img = normalize(img, 1, 99.8)
img = repeat(img, 2)
ref_labels, ref_polys = model.predict_instances(img)
res_labels, res_polys = model.predict_instances_big(img,
axes='ZYX',
block_size=(55, 105,
105),
min_overlap=(13, 25,
25),
context=(17, 30, 30))
m = matching(ref_labels, res_labels)
assert (1.0, 1.0) == (m.accuracy, m.mean_true_score)
# sort them first lexicographic
ref_inds = np.lexsort(ref_polys["points"].T)
res_inds = np.lexsort(res_polys["points"].T)
assert np.allclose(ref_polys["dist"][ref_inds],
res_polys["dist"][res_inds],
atol=1e-2)
assert np.allclose(ref_polys["points"][ref_inds],
res_polys["points"][res_inds],
atol=1e-2)
assert np.allclose(ref_polys["prob"][ref_inds],
res_polys["prob"][res_inds],
atol=1e-2)
return ref_polys, res_polys
def test_acc(img, grid):
prob = edt_prob(img)[::grid[0],::grid[1]]
dist = star_dist(img, n_rays=32, mode="cpp")[::grid[0],::grid[1]]
points, probi, disti = non_maximum_suppression(dist, prob, grid = grid, prob_thresh=0.4)
img2 = polygons_to_label(disti, points, shape=img.shape)
m = matching(img, img2)
acc = m.accuracy
print("accuracy {acc:.2f}".format(acc=acc))
assert acc > 0.9
def test_acc(img):
prob = edt_prob(img)
dist = star_dist(img, n_rays=32, mode="cpp")
coord = dist_to_coord(dist)
points = non_maximum_suppression(coord, prob, prob_thresh=0.4)
img2 = polygons_to_label(coord, prob, points, shape=img.shape)
m = matching(img, img2)
acc = m.accuracy
print("accuracy {acc:.2f}".format(acc=acc))
assert acc > 0.9
def test_load_and_predict(model3d):
model = model3d
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_acc_old(img, grid):
from stardist.geometry.geom2d import _polygons_to_label_old, _dist_to_coord_old
from stardist.nms import _non_maximum_suppression_old
prob = edt_prob(img)[::grid[0],::grid[1]]
dist = star_dist(img, n_rays=32, mode="cpp")[::grid[0],::grid[1]]
coord = _dist_to_coord_old(dist, grid = grid)
points = _non_maximum_suppression_old(coord, prob, prob_thresh=0.4, grid=grid)
img2 = _polygons_to_label_old(coord, prob, points, shape=img.shape)
m = matching(img, img2)
acc = m.accuracy
print("accuracy {acc:.2f}".format(acc=acc))
assert acc > 0.9
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_load_and_predict():
model_path = path_model2d()
model = StarDist2D(None, name=model_path.name, basedir=str(model_path.parent))
img, mask = real_image2d()
x = normalize(img,1,99.8)
prob, dist = model.predict(x, n_tiles=(2,3))
assert prob.shape == dist.shape[:2]
assert model.config.n_rays == dist.shape[-1]
labels, polygons = model.predict_instances(x)
assert labels.shape == img.shape[:2]
assert labels.max() == len(polygons['coord'])
assert len(polygons['coord']) == len(polygons['points']) == len(polygons['prob'])
stats = matching(mask, labels, thresh=0.5)
assert (stats.fp, stats.tp, stats.fn) == (1, 48, 17)
def test_load_and_predict(model2d):
model = model2d
img, mask = real_image2d()
x = normalize(img, 1, 99.8)
prob, dist = model.predict(x, n_tiles=(2, 3))
assert prob.shape == dist.shape[:2]
assert model.config.n_rays == dist.shape[-1]
labels, polygons = model.predict_instances(x)
assert labels.shape == img.shape[:2]
assert labels.max() == len(polygons['coord'])
assert len(polygons['coord']) == len(
polygons['points']) == len(polygons['prob'])
stats = matching(mask, labels, thresh=0.5)
assert (stats.fp, stats.tp, stats.fn) == (1, 48, 17)
return labels
def test_pretrained_scales():
from scipy.ndimage import zoom
from stardist.matching import matching
from skimage.measure import regionprops
model = StarDist2D.from_pretrained("2D_versatile_fluo")
img, mask = real_image2d()
x = normalize(img, 1, 99.8)
def pred_scale(scale=2):
x2 = zoom(x, scale, order=1)
labels2, _ = model.predict_instances(x2)
labels = zoom(labels2, tuple(_s1/_s2 for _s1, _s2 in zip(mask.shape, labels2.shape)), order=0)
return labels
scales = np.linspace(.5,5,10)
accs = tuple(matching(mask, pred_scale(s)).accuracy for s in scales)
print("scales ", np.round(scales,2))
print("accuracy ", np.round(accs,2))
return accs