Exp_ID + '.h5') # The path of the file of the input video.
# The entire process of SUNS online
Masks, Masks_2, time_total, time_frame, list_time_per = suns_online(
filename_video, filename_CNN, Params_pre, Params_post, \
dims, frames_init, merge_every, batch_size_init, \
useSF=useSF, useTF=useTF, useSNR=useSNR, med_subtract=med_subtract, \
update_baseline=update_baseline, useWT=useWT, \
show_intermediate=show_intermediate, prealloc=prealloc, display=display, p=p)
# %% Evaluation of the segmentation accuracy compared to manual ground truth
filename_GT = dir_GTMasks + Exp_ID + '_sparse.mat'
data_GT = loadmat(filename_GT)
GTMasks_2 = data_GT['GTMasks_2'].transpose()
(Recall, Precision, F1) = GetPerformance_Jaccard_2(GTMasks_2,
Masks_2,
ThreshJ=0.5)
print({'Recall': Recall, 'Precision': Precision, 'F1': F1})
savemat(os.path.join(dir_output, 'Output_Masks_CV{}_{}.mat'.format(CV, Exp_ID)), \
{'Masks':Masks, 'list_time_per':list_time_per}, do_compression=True)
# %% Save recall, precision, F1, total processing time, and average processing time per frame
list_Recall[CV, eid] = Recall
list_Precision[CV, eid] = Precision
list_F1[CV, eid] = F1
list_time[CV, eid] = time_total
list_time_frame[CV, eid] = time_frame
Info_dict = {
'list_Recall': list_Recall,
'list_Precision': list_Precision,
def refine_seperate_multi(GTMasks_2,
masks_final_2,
times_final,
list_cons,
thresh_mask=0.5,
ThreshJ=0.5,
display=False):
'''Refine segmented neurons by requiring them to be active for "cons" consecutive frames.
The outputs are the recall, precision, and F1 calculated using all values in "list_cons".
Used to search the optimal "cons".
Inputs:
GTMasks_2 (sparse.csr_matrix): Ground truth masks.
masks_final_2 (sparse.csr_matrix of float32): the segmented neuron masks.
times_final (list of 1D numpy.array): indices of frames when the neuron is active.
list_cons (list of int): A list of minimum number of consecutive frames that a neuron should be active for.
thresh_mask (float between 0 and 1, default to 0.5): Threashold to binarize the real-number mask.
values higher than "thresh_mask" times the maximum value are set to be True.
ThreshJ (float, default to 0.5): Threshold Jaccard distance for two neurons to match.
display (bool, default to False): Indicator of whether to show the optimal "cons"
Outputs:
Recall_k (1D numpy.array of float): Percentage of matched neurons over all GT neurons.
Precision_k (1D numpy.array of float): Percentage of matched neurons over all segmented neurons.
F1_k (1D numpy.array of float): Harmonic mean of Recall and Precision.
'''
L_cons = len(list_cons) # number of "cons" to optimize over
num_masks = len(times_final) # Number of segmented neurons
Precision_k = np.zeros(L_cons)
Recall_k = np.zeros(L_cons)
F1_k = np.zeros(L_cons)
for (k1, cons) in enumerate(list_cons):
if cons > 1:
have_cons = np.zeros(num_masks, dtype='bool')
for kk in range(num_masks):
times_diff1 = times_final[kk][cons -
1:] - times_final[kk][:1 - cons]
# indicators of whether the neuron was active for "cons" frames
have_cons[kk] = np.any(times_diff1 == cons - 1)
if np.any(have_cons):
masks_select_2 = masks_final_2[have_cons]
Masks_2 = sparse.vstack(
[x >= x.max() * thresh_mask for x in masks_select_2])
# Evalueate the accuracy of the result using recall, precision, and F1
(Recall_k[k1], Precision_k[k1],
F1_k[k1]) = GetPerformance_Jaccard_2(GTMasks_2, Masks_2,
ThreshJ)
else:
(Recall_k[k1], Precision_k[k1], F1_k[k1]) = (0, 0, 0)
else:
masks_select_2 = masks_final_2
Masks_2 = sparse.vstack(
[x >= x.max() * thresh_mask for x in masks_select_2])
(Recall_k[k1], Precision_k[k1],
F1_k[k1]) = GetPerformance_Jaccard_2(GTMasks_2, Masks_2, ThreshJ)
if display:
ind = F1_k.argmax()
print('Recall={:0.6f}, Precision={:0.6f}, F1={:0.6f}, cons={}.'.format(
Recall_k[ind], Precision_k[ind], F1_k[ind], list_cons[ind]))
return Recall_k, Precision_k, F1_k