def create_random_suppressed(shape=(356, 299), radius=10, noise=.1, n_rays=32, nms_thresh=.4):
prob, dist = create_random_data(shape, radius, noise, n_rays)
coord = dist_to_coord(dist)
nms = non_maximum_suppression(coord, prob, prob_thresh=0.9,
nms_thresh=nms_thresh,
verbose=True)
return nms
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 _star_convex_polynoms(dapi_path,
membrane_dic,
model_path,
full_output=False):
"""Nuclei segmentation using the stardist algorithm.
Parameters
----------
dapi_path : string
path to the DAPI image
membrane_dic: dic obtained with extract_membranes.
Dictionnary containing relevant informations about the membranes.
model_path : string
path to the stardist model
Returns
-------
clockwise_centers : np.ndarray
An array of cell centers, sort in clockwise order.
"""
images = sorted(glob(dapi_path))
images = list(map(imread, images))
img = normalize(images[0], 1, 99.8)
model_sc = StarDist2D(None,
name="stardist_shape_completion",
basedir=model_path)
prob, dist = model_sc.predict(img)
coord = dist_to_coord(dist)
points = non_maximum_suppression(coord, prob, prob_thresh=0.4)
points = np.flip(points, 1)
rho, phi = _card_coords(points, membrane_dic["center_inside"])
cleaned = _quick_del_art(points, rho, membrane_dic["rIn"])
clockwise_centers = _quick_clockwise(cleaned, phi, rho,
membrane_dic["rIn"])
clockwise_centers = np.subtract(np.float32(clockwise_centers),
np.array(membrane_dic["img_shape"]) / 2.0)
if full_output:
return clockwise_centers, (prob, dist, points)
return clockwise_centers
plt.show()
def predictions(model_dist,i):
img = normalize(X[i],1,99.8,axis=axis_norm)
input = torch.tensor(img)
input = input.unsqueeze(0).unsqueeze(0)#unsqueeze 2 times
dist,prob = model_dist(input)
dist_numpy= dist.detach().cpu().numpy().squeeze()
prob_numpy= prob.detach().cpu().numpy().squeeze()
return dist_numpy,prob_numpy
model_dist = UNet(1,N_RAYS)
model_dist.load_state_dict(torch.load(MODEL_WEIGHTS_PATH))
print('Distance weights loaded')
apscore_nms = []
prob_thres = 0.4
for idx,img_target in enumerate(zip(X,Y)):
print(idx)
image,target = img_target
dists,probs=predictions(model_dist,idx)
dists = np.transpose(dists,(1,2,0))
coord = dist_to_coord(dists)
points = non_maximum_suppression(coord,probs,prob_thresh=prob_thres)
star_label = polygons_to_label(coord,probs,points)
apscore_nms.append(metric.calculateAPScore(star_label,target,IOU_tau=0.5))
#plot(target,star_label)
print('Total images',idx+1)
ap_nms = sum(apscore_nms)/(len(apscore_nms))
print('AP NMS',ap_nms)