def do_fuse(substack,C_firstview,C_secondview,max_distance,verbose=False):
# ============ max-cardinality bipartite matching
if len(C_firstview)==0 or len(C_secondview)==0:
if verbose:
print('total=%d merged=%d only_firstview=%d only_secondview=%d'%(len(C_firstview+C_secondview),len([]),len(C_firstview),len(C_secondview)))
return [],C_firstview,C_secondview,C_firstview+C_secondview
true_positives_firstview = set() # subset of C_true that are true positives
true_positives_secondview = set() # subset of C_pred that are true positives
G,mate,node2center = match_markers(C_firstview,C_secondview,max_distance*2)
C_merged = []
for k1,k2 in mate.iteritems():
if k1[0] == 'p': # mate is symmetric
continue
c1 = node2center[k1]
c2 = node2center[k2]
d = distance((c1.x,c1.y,c1.z),(c2.x,c2.y,c2.z))
if d < (max_distance): # a constant criterion is needed!
true_positives_firstview.add(c1)
true_positives_secondview.add(c2)
c_merged = c1
c_merged.x = (c_merged.x + c2.x)/2
c_merged.y = (c_merged.y + c2.y)/2
c_merged.z = (c_merged.z + c2.z)/2
if c_merged.x<0 or c_merged.y<0 or c_merged.z<0 or c_merged.x>substack.info['Width'] or c_merged.y>substack.info['Height'] or c_merged.z>substack.info['Depth']:
continue
C_merged.append(c_merged)
C_onlyfirstview=[]
for i,c in enumerate(C_firstview):
if c not in true_positives_firstview:
cx,cy,cz = c.x,c.y,c.z
if cx<0 or cy<0 or cz<0 or cx>substack.info['Width'] or cy>substack.info['Height'] or cz>substack.info['Depth']:
continue
else:
C_onlyfirstview.append(c)
C_onlysecondview=[]
for i,c in enumerate(C_secondview):
if c not in true_positives_secondview:
cx,cy,cz = c.x,c.y,c.z
if cx<0 or cy<0 or cz<0 or cx>substack.info['Width'] or cy>substack.info['Height'] or cz>substack.info['Depth']:
continue
else:
C_onlysecondview.append(c)
C_total=C_merged+C_onlyfirstview+C_onlysecondview
if verbose:
print('total=%d merged=%d only_firstview=%d only_secondview=%d'%(len(C_total),len(C_merged),len(C_onlyfirstview),len(C_onlysecondview)))
return C_merged,C_onlyfirstview,C_onlysecondview,C_total
def eval_perf(substack,C_true,C_pred,verbose=True,errors_marker_file=None,rp_file=None, max_cell_diameter=None):
# max-cardinality bipartite matching
C_rejected = [c for c in C_pred if c.rejected]
C_pred = [c for c in C_pred if not c.rejected]
true_positives_true = set() # subset of C_true that are true positives
true_positives_pred = set() # subset of C_pred that are true positives
TP = 0
TP_inside = []
G,mate,node2center = match_markers(C_true,C_pred, 2*max_cell_diameter)
kw = 0
tee.log(mate)
#todo modificato
#tee.log(type(mate))
#for k1,k2 in mate.iteritems():
for k1, k2 in mate:
if k1[0] == 'p': # mate is symmetric
continue
c1 = node2center[k1]
c2 = node2center[k2]
d = distance((c1.x,c1.y,c1.z),(c2.x,c2.y,c2.z))
if d < max_cell_diameter/2:
true_positives_pred.add(c2)
true_positives_true.add(c1)
TP += 1
if inside(c1,substack):
if verbose:
print(' TP:', k2, c2.name,c2.x,c2.y,c2.z,c2, k1, c1.name,c1.x,c1.y,c1.z,d)
TP_inside.append(c2)
kw += 1
else:
if verbose:
print('OTP:', k2, c2.name,c2.x,c2.y,c2.z,c2, k1, c1.name,c1.x,c1.y,c1.z,'d:',d)
else:
if verbose:
print('---> too far', c2.name,c2.x,c2.y,c2.z,c2,c1.name,c1.x,c1.y,c1.z,d)
FP_inside,FN_inside = [],[]
if errors_marker_file is not None:
ostream = open(errors_marker_file,'w')
print('##x,y,z,radius,shape,name,comment, color_r,color_g,color_b',file=ostream)
for i,c in enumerate(C_true):
if c not in true_positives_true:
if inside(c,substack):
r,g,b = 255,0,255
name = 'FN_%03d (%s)' % (i+1,c.name)
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[cx,cy,cz,0,1,name,comment,r,g,b])), file=ostream)
FN_inside.append(c)
if verbose:
print('FN: ', c.name,c.x,c.y,c.z,c)
for i,c in enumerate(C_pred):
c.is_false_positive = False
if c not in true_positives_pred:
if inside(c,substack):
r,g,b = 255,0,0
name = 'FP_%03d (%s)' % (i+1,c.name)
c.is_false_positive = True
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[1+cx,1+cy,1+cz,0,1,name,comment,r,g,b])), file=ostream)
FP_inside.append(c)
if verbose:
print('FP: ', c.name,c.x,c.y,c.z,c)
# Also print predicted TP in error marker file (helps debugging)
for i,c in enumerate(C_pred):
if c in true_positives_pred:
if inside(c,substack):
r,g,b = 0,255,0
name = 'TP_%03d (%s)' % (i+1,c.name)
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[1+cx,1+cy,1+cz,0,1,name,comment,r,g,b])), file=ostream)
# Also print true TP in error marker file (helps debugging)
for i,c in enumerate(C_true):
if c in true_positives_true:
if inside(c,substack):
r,g,b = 0,255,255
name = 'TP_%03d (%s)' % (i+1,c.name)
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[1+cx,1+cy,1+cz,0,1,name,comment,r,g,b])), file=ostream)
# Finally, print rejected predictions error marker file (to show the benefit of the filter)
for i,c in enumerate(C_rejected):
if inside(c,substack):
r,g,b = 255,128,0
name = 'REJ_%03d (%s)' % (i+1,c.name)
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[1+cx,1+cy,1+cz,0,1,name,comment,r,g,b])), file=ostream)
if errors_marker_file is not None:
ostream.close()
if len(C_pred):
if hasattr(C_pred[0],'distance') and rp_file is not None:
with open(rp_file, 'w') as ostream:
for i,c in enumerate(C_pred):
if inside(c,substack):
if c in true_positives_pred:
print(-c.distance, '1', file=ostream)
else:
print(-c.distance, '0', file=ostream)
# Add also the false negatives with infinite distance so they will always be rejected
for i,c in enumerate(C_true):
if c not in true_positives_true:
if inside(c,substack):
print(-1000, '1', file=ostream)
if len(TP_inside) > 0:
precision = float(len(TP_inside))/float(len(TP_inside)+len(FP_inside))
recall = float(len(TP_inside))/float(len(TP_inside)+len(FN_inside))
else:
precision = int(len(FP_inside) == 0)
recall = int(len(FN_inside) == 0)
if len(TP_inside)==0 and len(FP_inside)>0 and len(FN_inside)>0:
F1 = 0.00
else:
F1 = 2*precision*recall/(precision+recall)
C_pred_inside = [c for c in C_pred if inside(c,substack)]
C_true_inside = [c for c in C_true if inside(c,substack)]
print('|pred|=%d |true|=%d P: %.2f / R: %.2f / F1: %.2f ==== TP: %d / FP: %d / FN: %d' % (len(C_pred_inside),len(C_true_inside),precision*100,recall*100,F1*100,len(TP_inside),len(FP_inside),len(FN_inside)))
return precision,recall,F1,TP_inside,FP_inside,FN_inside
def eval_perf(substack,C_true,C_pred,verbose=True,errors_marker_file=None,rp_file=None, max_cell_diameter=None):
# max-cardinality bipartite matching
C_rejected = [c for c in C_pred if c.rejected]
C_pred = [c for c in C_pred if not c.rejected]
true_positives_true = set() # subset of C_true that are true positives
true_positives_pred = set() # subset of C_pred that are true positives
TP = 0
TP_inside = []
G,mate,node2center = match_markers(C_true,C_pred, 2*max_cell_diameter)
kw = 0
for k1,k2 in mate.iteritems():
if k1[0] == 'p': # mate is symmetric
continue
c1 = node2center[k1]
c2 = node2center[k2]
d = distance((c1.x,c1.y,c1.z),(c2.x,c2.y,c2.z))
if d < max_cell_diameter/2:
true_positives_pred.add(c2)
true_positives_true.add(c1)
TP += 1
if inside(c1,substack):
if verbose:
print(' TP:', k2, c2.name,c2.x,c2.y,c2.z,c2, k1, c1.name,c1.x,c1.y,c1.z,d)
TP_inside.append(c2)
kw += 1
else:
if verbose:
print('OTP:', k2, c2.name,c2.x,c2.y,c2.z,c2, k1, c1.name,c1.x,c1.y,c1.z,'d:',d)
else:
if verbose:
print('---> too far', c2.name,c2.x,c2.y,c2.z,c2,c1.name,c1.x,c1.y,c1.z,d)
FP_inside,FN_inside = [],[]
if errors_marker_file is not None:
ostream = open(errors_marker_file,'w')
print('##x,y,z,radius,shape,name,comment, color_r,color_g,color_b',file=ostream)
for i,c in enumerate(C_true):
if c not in true_positives_true:
if inside(c,substack):
r,g,b = 255,0,255
name = 'FN_%03d (%s)' % (i+1,c.name)
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[cx,cy,cz,0,1,name,comment,r,g,b])), file=ostream)
FN_inside.append(c)
if verbose:
print('FN: ', c.name,c.x,c.y,c.z,c)
for i,c in enumerate(C_pred):
c.is_false_positive = False
if c not in true_positives_pred:
if inside(c,substack):
r,g,b = 255,0,0
name = 'FP_%03d (%s)' % (i+1,c.name)
c.is_false_positive = True
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[1+cx,1+cy,1+cz,0,1,name,comment,r,g,b])), file=ostream)
FP_inside.append(c)
if verbose:
print('FP: ', c.name,c.x,c.y,c.z,c)
# Also print predicted TP in error marker file (helps debugging)
for i,c in enumerate(C_pred):
if c in true_positives_pred:
if inside(c,substack):
r,g,b = 0,255,0
name = 'TP_%03d (%s)' % (i+1,c.name)
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[1+cx,1+cy,1+cz,0,1,name,comment,r,g,b])), file=ostream)
# Also print true TP in error marker file (helps debugging)
for i,c in enumerate(C_true):
if c in true_positives_true:
if inside(c,substack):
r,g,b = 0,255,255
name = 'TP_%03d (%s)' % (i+1,c.name)
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[1+cx,1+cy,1+cz,0,1,name,comment,r,g,b])), file=ostream)
# Finally, print rejected predictions error marker file (to show the benefit of the filter)
for i,c in enumerate(C_rejected):
if inside(c,substack):
r,g,b = 255,128,0
name = 'REJ_%03d (%s)' % (i+1,c.name)
cx,cy,cz = int(round(c.x)),int(round(c.y)),int(round(c.z))
comment = ':'.join(map(str,[cx,cy,cz,c]))
if errors_marker_file is not None:
print(','.join(map(str,[1+cx,1+cy,1+cz,0,1,name,comment,r,g,b])), file=ostream)
if errors_marker_file is not None:
ostream.close()
if len(C_pred):
if hasattr(C_pred[0],'distance') and rp_file is not None:
with open(rp_file, 'w') as ostream:
for i,c in enumerate(C_pred):
if inside(c,substack):
if c in true_positives_pred:
print(-c.distance, '1', file=ostream)
else:
print(-c.distance, '0', file=ostream)
# Add also the false negatives with infinite distance so they will always be rejected
for i,c in enumerate(C_true):
if c not in true_positives_true:
if inside(c,substack):
print(-1000, '1', file=ostream)
if len(TP_inside) > 0:
precision = float(len(TP_inside))/float(len(TP_inside)+len(FP_inside))
recall = float(len(TP_inside))/float(len(TP_inside)+len(FN_inside))
else:
precision = int(len(FP_inside) == 0)
recall = int(len(FN_inside) == 0)
if len(TP_inside)==0 and len(FP_inside)>0 and len(FN_inside)>0:
F1 = 0.00
else:
F1 = 2*precision*recall/(precision+recall)
C_pred_inside = [c for c in C_pred if inside(c,substack)]
C_true_inside = [c for c in C_true if inside(c,substack)]
print('|pred|=%d |true|=%d P: %.2f / R: %.2f / F1: %.2f ==== TP: %d / FP: %d / FN: %d' % (len(C_pred_inside),len(C_true_inside),precision*100,recall*100,F1*100,len(TP_inside),len(FP_inside),len(FN_inside)))
return precision,recall,F1,TP_inside,FP_inside,FN_inside
