def render_label_pred_example():
model_path = path_model2d()
model = StarDist2D(None,
name=model_path.name,
basedir=str(model_path.parent))
img, y_gt = real_image2d()
x = normalize(img, 1, 99.8)
y, _ = model.predict_instances(x)
im = render_label_pred(y_gt, y, img=x)
import matplotlib.pyplot as plt
plt.figure(1, figsize=(12, 4))
plt.subplot(1, 4, 1)
plt.imshow(x)
plt.title("img")
plt.subplot(1, 4, 2)
plt.imshow(render_label(y_gt, img=x))
plt.title("gt")
plt.subplot(1, 4, 3)
plt.imshow(render_label(y, img=x))
plt.title("pred")
plt.subplot(1, 4, 4)
plt.imshow(im)
plt.title("tp (green) fp (red) fn(blue)")
plt.tight_layout()
plt.show()
return im
def _model2d():
from utils import path_model2d
from stardist.models import StarDist2D
model_path = path_model2d()
return StarDist2D(None,
name=model_path.name,
basedir=str(model_path.parent))
def test_load_and_predict_big():
model_path = path_model2d()
model = StarDist2D(None,
name=model_path.name,
basedir=str(model_path.parent))
img, _ = real_image2d()
x = normalize(img, 1, 99.8)
x = np.tile(x, (8, 8))
labels, polygons = model.predict_instances(x)
return labels
def test_load_and_export_TF():
model_path = path_model2d()
model = StarDist2D(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():
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 render_label_example():
model_path = path_model2d()
model = StarDist2D(None,
name=model_path.name,
basedir=str(model_path.parent))
img, y_gt = real_image2d()
x = normalize(img, 1, 99.8)
y, _ = model.predict_instances(x)
# im = render_label(y,img = x, alpha = 0.3, alpha_boundary=1, cmap = (.3,.4,0))
im = render_label(y, img=x, alpha=0.3, alpha_boundary=1)
import matplotlib.pyplot as plt
plt.figure(1)
plt.imshow(im)
plt.show()
return im
def test_optimize_thresholds():
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)
res = model.optimize_thresholds([x], [mask],
nms_threshs=[.3, .5],
iou_threshs=[.3, .5],
optimize_kwargs=dict(tol=1e-1),
save_to_json=False)
np.testing.assert_almost_equal(res["prob"], 0.454617141955, decimal=3)
np.testing.assert_almost_equal(res["nms"], 0.3, decimal=3)