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_bbox_search(img):
prob = edt_prob(img)
dist = star_dist(img, n_rays=32, mode="cpp")
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 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 get_objectprobs(self, labels):
objectprobs = np.zeros((1, labels.shape[1], labels.shape[2]),
dtype="float32")
for i in range(labels.shape[0]):
objectprobs += edt_prob(labels[i].astype("int32"))
objectprobs[:, labels.sum(axis=0) > 1.5] = -1
return objectprobs
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_bbox_search_old(img):
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)
dist = star_dist(img, n_rays=32, mode="cpp")
coord = _dist_to_coord_old(dist)
points_a = _non_maximum_suppression_old(coord, prob, prob_thresh=0.4, verbose=False, max_bbox_search=False)
points_b = _non_maximum_suppression_old(coord, prob, prob_thresh=0.4, verbose=False, max_bbox_search=True)
img2_a = _polygons_to_label_old(coord, prob, points_a, shape=img.shape)
img2_b = _polygons_to_label_old(coord, prob, points_b, shape=img.shape)
check_similar(points_a, points_b)
check_similar(img2_a, img2_b)
def __getitem__(self, idx):
assert self.raw_files[idx] == self.target_files[idx]
img_name = os.path.join(self.root_dir, 'images', self.raw_files[idx])
image = io.imread(img_name)
image = normalize(image, 1, 99.8, axis=(0, 1))
image = np.expand_dims(image, 0)
target_name = os.path.join(self.root_dir, 'masks',
self.target_files[idx])
target = io.imread(target_name)
distances = star_dist(target, self.n_rays, opencl=False)
if self.max_dist:
distances[distances > self.max_dist] = self.max_dist
distances = np.transpose(distances, (2, 0, 1))
obj_probabilities = edt_prob(target)
obj_probabilities = np.expand_dims(obj_probabilities, 0)
if self.transform:
image = self.transform(image)
if self.target_transform:
distances = self.target_transform(distances)
obj_probabilities = self.target_transform(obj_probabilities)
return image, obj_probabilities, distances