def calculate_vi_ri_ari(result, gt):
# false merges(缺失), false splits（划痕）
merger_error, split_error = ev.split_vi(result, gt)
vi = merger_error + split_error
ri = ev.rand_index(result, gt)
adjust_ri = ev.adj_rand_index(result, gt)
return {'vi':vi,'ri':ri,'adjust_ri':adjust_ri,
'merger_error':merger_error,
'split_error':split_error}
def get_ari(pred: np.ndarray, mask: np.ndarray, bg_value: int = 0) -> float:
"""
Adjusted rand index
Implemented by gala (https://github.com/janelia-flyem/gala.)
"""
label_pred, num_pred = label(pred,
connectivity=1,
background=bg_value,
return_num=True)
label_mask, num_mask = label(mask,
connectivity=1,
background=bg_value,
return_num=True)
value = ev.adj_rand_index(label_pred, label_mask)
return value
ws_train = ws_train.astype('int64')
print "unique labels in ws:", np.unique(ws_train).size
ws_train = optimized.despeckle_watershed(ws_train)
print "unique labels after despeckling:", np.unique(ws_train).size
ws_train, _, _ = evaluate.relabel_from_one(ws_train)
if ws_train.min() < 1:
ws_train += (1 - ws_train.min())
ws_train = ws_train.astype('int64')
print "Training watershed complete"
print "Watershed train (VI, ARI)"
vi_ws_train = ev.split_vi(ws_train, gt_train),
ari_ws_train = ev.adj_rand_index(ws_train, gt_train)
print vi_ws_train
print ari_ws_train
scipy.io.savemat('trainWS.mat', mdict={'ws_train': ws_train})
scipy.io.savemat('trainMembrane.mat', mdict={'membraneTrain': membraneTrain})
# create a feature manager
fc = features.base.Composite(children=[
features.moments.Manager(),
features.histogram.Manager(25, 0, 1, [0.1, 0.5, 0.9]),
features.graph.Manager(),
features.contact.Manager([0.1, 0.5, 0.9])
])
#feature_manager=features.base.Composite(children=[features.moments.Manager])
# print "Running watershed"
# Now we want to separate the two objects in image
# Generate the markers as local maxima of the distance to the background
distance = ndi.distance_transform_edt(foreground)
distance_float = rescale_intensity(distance,
in_range='image',
out_range='float')
# Changes local mas peak paramater
for peakPrint in peakArr:
local_maxi = peak_local_max(distance,
indices=False,
footprint=np.ones(
(peakPrint, peakPrint)),
labels=foreground)
markers = ndi.label(local_maxi)[0] #[0]
labels = watershed(foreground, markers, mask=foreground)
######## Get the watershed evaluation
split_eval = ev.split_vi(labels, membrane_ground[index])
adjusted_rand = ev.adj_rand_index(labels,
membrane_ground[index])
fm = ev.fm_index(labels, membrane_ground[index])
outFile.write(
str(index) + "_" + str(foot) + "_" + str(filt) + "_" +
str(peakPrint) + " " + str(split_eval) + " " +
str(adjusted_rand) + " " + str(fm) + "\n")
print ws_test.dtype
ws_test = ws_test.astype('int64')
print "unique labels in ws:",np.unique(ws_test).size
ws_test = optimized.despeckle_watershed(ws_test)
print "unique labels after despeckling:",np.unique(ws_test).size
ws_test, _, _ = evaluate.relabel_from_one(ws_test)
if ws_test.min() < 1:
ws_test += (1-ws_test.min())
print "Testing watershed complete"
print "Watershed test (VI, ARI)"
vi_ws_test = ev.split_vi(ws_test, gt_test),
ari_ws_test = ev.adj_rand_index(ws_test, gt_test)
print vi_ws_test
print ari_ws_test
scipy.io.savemat('testWS.mat', mdict={'ws_test':ws_test})
print 'Time Elapsed So Far: ' + str(time.time()-start)
print 'applying classifier...'
g_test = agglo.Rag(ws_test, membraneTest, learned_policy, feature_manager=fc)
print 'choosing best operating point...'
g_test.agglomerate(0.5) # best expected segmentation
segtestGala = g_test.get_segmentation()
print "Completed Gala Run"
# gala allows implementation of other agglomerative algorithms, including
threshold = threshold_otsu(img_med) binary_img = img_med > threshold # make image into binary_img and invert it image = binary_img foreground = 1 - image ######## # Watershed segmentation ######## # print "Running watershed" # Now we want to separate the two objects in image # Generate the markers as local maxima of the distance to the background distance = ndi.distance_transform_edt(foreground) distance_float = rescale_intensity(distance, in_range='image', out_range='float') # Changes local mas peak paramater for peakPrint in peakArr: local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((peakPrint, peakPrint)), labels=foreground) markers = ndi.label(local_maxi)[0] #[0] labels = watershed(foreground, markers, mask=foreground) ######## Get the watershed evaluation split_eval = ev.split_vi(labels, membrane_ground[index]) adjusted_rand = ev.adj_rand_index(labels, membrane_ground[index]) fm = ev.fm_index(labels, membrane_ground[index]) outFile.write(str(index) + "_" + str(foot) + "_" + str(filt) + "_" + str(peakPrint) + " " + str(split_eval) + " " + str(adjusted_rand) + " " + str(fm) + "\n")