fol_train_values_d = {}
fol_values_d = {}
print >> sys.stderr, 'load second pass blob outlines; calculate object properties',
for i in range(nreps):
offset = (i * seglen * step) + start_offset
source_f = mousezopt.generate_outfile_names(vid, seglen, offset,
nframes, peak_max,
peak_min,
target_int_step)[1]
ols_d = eval(open(source_f).read())
ols = ([
v for k, v in ols_d.items()
if numpy.abs(cutoff - k) < target_int_step / 2
] + [[]])[0]
objs_train = [
o for o in vidtools.find_objs(ols, SHAPE, n_obj, n_obj)[0]
if len(o) > 0
]
obj_train_arcs_d[offset] = objs_train
size_train_values_d[offset] = [[
vidtools.size_of_polygon(ols[i][j]) for i, j in o
] for o in objs_train]
fol_train_values_d[offset] = [
vidtools.fol_from_obj_arc(o, ols, SHAPE) for o in objs_train
]
objs = [o for o in vidtools.find_objs(ols, SHAPE)[0] if len(o) > 0]
obj_arcs_d[offset] = objs
size_values_d[offset] = [
[vidtools.size_of_polygon(ols[i][j]) for i, j in o] for o in objs
]
fol_values_d[offset] = [
def score_segment_by_cutoff(mms,maxint,minint,stepint,maxblobs = 20,size_binw=50,fol_binw=0.05):
import time
ols_by_cut = {}
scores = {}
times = []
for cutoff in numpy.arange(maxint,minint,-stepint):
print >> sys.stderr, '\nCUTOFF',cutoff
ols_this = []
t1 = time.time()
stop_seg = False
for mm in mms:
ols_this.append(vidtools.chain_outlines_from_mask(mm>cutoff, \
preshrink=1,debug=False,return_termini=False, \
order_points=False,sort_outlines=False))
if len(ols_this[-1]) > maxblobs:
print >> sys.stderr, 'segmentation result (%s) exceeds maximum number of blobs per frame (%s); break' \
% (max([len(ol) for ol in ols_this]),maxblobs)
stop_seg = True
break
t2 = time.time()
print >> sys.stderr, 'find blobs %s sec' % (t2-t1)
if stop_seg:
scores[cutoff] = 0
break
ols_by_cut[cutoff] = ols_this
if fol_binw is None or size_binw is None:
scores[cutoff] = 0
times.append(t2-t1)
continue
nblobs = len(reduce(lambda x,y:x+y,[[]]+[[vidtools.size_of_polygon(p) for p in ol if len(p) > 0] for ol in ols_this if len(ol) > 0]))
if nblobs == 0:
print >> sys.stderr, 'no blobs; skip'
scores[cutoff] = 0
continue
t3 = time.time()
objs_this,splits_this,joins_this = vidtools.find_objs(ols_by_cut[cutoff],mm.shape)
t4 = time.time()
print >> sys.stderr, 'track objects %s sec' % (t4-t3)
t5 = time.time()
size_h,size_bins,fol_h,fol_bins = vidtools.get_object_arc_param_dists(ols_by_cut[cutoff],mm.shape,n_obj,size_binw=size_binw,fol_binw=fol_binw)
t6 = time.time()
if size_h is None:
print >> sys.stderr, 'no object arcs; skip'
scores[cutoff] = 0
continue
print >> sys.stderr, 'calculate scoring distributions %s sec' % (t6-t5)
keep,drop = vidtools.greedy_objs_filter(objs_this,ols_by_cut[cutoff],size_h,size_bins,fol_h,fol_bins,mm.shape)
sscore = sum([vidtools.score_object_arc_size(o,ols_by_cut[cutoff],size_h,size_bins) for o in keep])
fscore = sum([vidtools.score_object_arc_fol(o,ols_by_cut[cutoff],fol_h,fol_bins,mm.shape) for o in keep])
t7 = time.time()
print >> sys.stderr, cutoff, nblobs, sscore, fscore, sscore+fscore
scores[cutoff] = sscore+fscore
times.append(t7-t1)
print >> sys.stderr, "total time %s minutes" % (sum(times)/60.0)
return scores,ols_by_cut
def score_segment_by_cutoff(mms,
maxint,
minint,
stepint,
maxblobs=20,
size_binw=50,
fol_binw=0.05):
import time
ols_by_cut = {}
scores = {}
times = []
for cutoff in numpy.arange(maxint, minint, -stepint):
print >> sys.stderr, '\nCUTOFF', cutoff
ols_this = []
t1 = time.time()
stop_seg = False
for mm in mms:
ols_this.append(vidtools.chain_outlines_from_mask(mm>cutoff, \
preshrink=1,debug=False,return_termini=False, \
order_points=False,sort_outlines=False))
if len(ols_this[-1]) > maxblobs:
print >> sys.stderr, 'segmentation result (%s) exceeds maximum number of blobs per frame (%s); break' \
% (max([len(ol) for ol in ols_this]),maxblobs)
stop_seg = True
break
t2 = time.time()
print >> sys.stderr, 'find blobs %s sec' % (t2 - t1)
if stop_seg:
scores[cutoff] = 0
break
ols_by_cut[cutoff] = ols_this
if fol_binw is None or size_binw is None:
scores[cutoff] = 0
times.append(t2 - t1)
continue
nblobs = len(
reduce(lambda x, y: x + y, [[]] +
[[vidtools.size_of_polygon(p) for p in ol if len(p) > 0]
for ol in ols_this if len(ol) > 0]))
if nblobs == 0:
print >> sys.stderr, 'no blobs; skip'
scores[cutoff] = 0
continue
t3 = time.time()
objs_this, splits_this, joins_this = vidtools.find_objs(
ols_by_cut[cutoff], mm.shape)
t4 = time.time()
print >> sys.stderr, 'track objects %s sec' % (t4 - t3)
t5 = time.time()
size_h, size_bins, fol_h, fol_bins = vidtools.get_object_arc_param_dists(
ols_by_cut[cutoff],
mm.shape,
n_obj,
size_binw=size_binw,
fol_binw=fol_binw)
t6 = time.time()
if size_h is None:
print >> sys.stderr, 'no object arcs; skip'
scores[cutoff] = 0
continue
print >> sys.stderr, 'calculate scoring distributions %s sec' % (t6 -
t5)
keep, drop = vidtools.greedy_objs_filter(objs_this, ols_by_cut[cutoff],
size_h, size_bins, fol_h,
fol_bins, mm.shape)
sscore = sum([
vidtools.score_object_arc_size(o, ols_by_cut[cutoff], size_h,
size_bins) for o in keep
])
fscore = sum([
vidtools.score_object_arc_fol(o, ols_by_cut[cutoff], fol_h,
fol_bins, mm.shape) for o in keep
])
t7 = time.time()
print >> sys.stderr, cutoff, nblobs, sscore, fscore, sscore + fscore
scores[cutoff] = sscore + fscore
times.append(t7 - t1)
print >> sys.stderr, "total time %s minutes" % (sum(times) / 60.0)
return scores, ols_by_cut
stream = cv.CaptureFromFile(vid)
SHAPE = vidtools.array_from_stream(stream).shape
obj_train_arcs_d = {}
obj_arcs_d = {}
size_train_values_d = {}
size_values_d = {}
fol_train_values_d = {}
fol_values_d = {}
print >> sys.stderr, 'load second pass blob outlines; calculate object properties',
for i in range(nreps):
offset = (i*seglen*step)+start_offset
source_f = mousezopt.generate_outfile_names(vid,seglen,offset,nframes,peak_max,peak_min,target_int_step)[1]
ols_d = eval(open(source_f).read())
ols = ([v for k,v in ols_d.items() if numpy.abs(cutoff-k) < target_int_step/2]+[[]])[0]
objs_train = [o for o in vidtools.find_objs(ols,SHAPE,n_obj,n_obj)[0] if len(o) > 0]
obj_train_arcs_d[offset] = objs_train
size_train_values_d[offset] = [[vidtools.size_of_polygon(ols[i][j]) for i,j in o] for o in objs_train]
fol_train_values_d[offset] = [vidtools.fol_from_obj_arc(o,ols,SHAPE) for o in objs_train]
objs = [o for o in vidtools.find_objs(ols,SHAPE)[0] if len(o) > 0]
obj_arcs_d[offset] = objs
size_values_d[offset] = [[vidtools.size_of_polygon(ols[i][j]) for i,j in o] for o in objs]
fol_values_d[offset] = [vidtools.fol_from_obj_arc(o,ols,SHAPE) for o in objs]
print >> sys.stderr, '.',
print >> sys.stderr, 'done'
if len(reduce(lambda x,y:x+y,size_train_values_d.values())) > 0 and len(reduce(lambda x,y:x+y,fol_train_values_d.values())) > 0:
print >> sys.stderr, 'calculate scoring distributions ...' ,
size_h,size_bins,fol_h,fol_bins = vidtools.get_object_arc_param_dists_from_values_dict(size_train_values_d,fol_train_values_d,size_binw=20,fol_binw=0.01)
print >> sys.stderr, 'done'
print >> sys.stderr, 'score objects ...' ,
