def init_objects(stream,frames,currsum,denom,seglen,cutoff,frames_offset,SHAPE,size_h,size_bins,fol_h,fol_bins,transform='',outline_engine='homebrew'):
hsl = seglen/2
min_arc_score = (2*max(size_h))+max(fol_h) #score of a "perfect" object arc of length 2
ols = []
last_frames = []
ols_offset = frames_offset + hsl
c = itertools.cycle(['|','/','-','\\'])
t = time.time()
print >> sys.stderr, 'analyze %s frames:' % seglen
for i in xrange(seglen):
last_frames.append(frames[0])
frames_offset += 1
mm = vidtools.shift_frames_return_diff(stream,frames,currsum,denom,seglen,transform=transform)
if outline_engine == 'homebrew':
ol = vidtools.chain_outlines_from_mask(mm>cutoff,preshrink=1,debug=False,return_termini=False,order_points=True,sort_outlines=False) #order_points should be True
elif outline_engine == 'shapely':
ol = vidtools.chain_outlines_from_mask_shapely(mm>cutoff,preshrink=1)
else:
print >> sys.stderr, 'outline_engine must be one of %s' % (['homebrew','shapely'])
raise ValueError
ols.append(ol)
print >> sys.stderr, '\r%s %s ' % (i,c.next()),
print >> sys.stderr, 'done in', str(datetime.timedelta(seconds=int(time.time() - t)))
objs = {}
splits = defaultdict(list)
objs_sizes = {}
objs_fols = {}
print >> sys.stderr, '\nperform initial object arc tracking on %s frames' % len(ols)
#one round of object tracking; assumes next object tracking call will start a single frame from now
to_retire_objs, to_retire_objs_sizes, to_retire_objs_fols = vidtools.find_objs_progressive(ols, ols_offset, ols_offset, ols_offset+1, SHAPE, objs, splits, \
objs_sizes, objs_fols, size_h,size_bins,fol_h,fol_bins)
prelast_avg = vidtools.average_frames(last_frames[hsl:])
prelast_mm = vidtools.mousemask_from_object_arcs(frames_offset-hsl,frames_offset,min_arc_score,ols,ols_offset, \
Util.merge_dictlist([objs,to_retire_objs]), \
Util.merge_dictlist([objs_sizes,to_retire_objs_sizes]), \
Util.merge_dictlist([objs_fols,to_retire_objs_fols]), \
size_h, size_bins, fol_h, fol_bins,SHAPE)
print >> sys.stderr, 'object initialization complete in', str(datetime.timedelta(seconds=int(time.time() - t)))
return ols,ols_offset,frames_offset,objs,splits,objs_sizes,objs_fols,prelast_avg,prelast_mm,to_retire_objs,to_retire_objs_sizes,to_retire_objs_fols
def init_objects(stream,
frames,
currsum,
denom,
seglen,
cutoff,
frames_offset,
SHAPE,
size_h,
size_bins,
fol_h,
fol_bins,
transform='',
outline_engine='homebrew'):
hsl = seglen / 2
min_arc_score = (2 * max(size_h)) + max(
fol_h) #score of a "perfect" object arc of length 2
ols = []
last_frames = []
ols_offset = frames_offset + hsl
c = itertools.cycle(['|', '/', '-', '\\'])
t = time.time()
print >> sys.stderr, 'analyze %s frames:' % seglen
for i in xrange(seglen):
last_frames.append(frames[0])
frames_offset += 1
mm = vidtools.shift_frames_return_diff(stream,
frames,
currsum,
denom,
seglen,
transform=transform)
if outline_engine == 'homebrew':
ol = vidtools.chain_outlines_from_mask(
mm > cutoff,
preshrink=1,
debug=False,
return_termini=False,
order_points=True,
sort_outlines=False) #order_points should be True
elif outline_engine == 'shapely':
ol = vidtools.chain_outlines_from_mask_shapely(mm > cutoff,
preshrink=1)
else:
print >> sys.stderr, 'outline_engine must be one of %s' % (
['homebrew', 'shapely'])
raise ValueError
ols.append(ol)
print >> sys.stderr, '\r%s %s ' % (i, c.next()),
print >> sys.stderr, 'done in', str(
datetime.timedelta(seconds=int(time.time() - t)))
objs = {}
splits = defaultdict(list)
objs_sizes = {}
objs_fols = {}
print >> sys.stderr, '\nperform initial object arc tracking on %s frames' % len(
ols)
#one round of object tracking; assumes next object tracking call will start a single frame from now
to_retire_objs, to_retire_objs_sizes, to_retire_objs_fols = vidtools.find_objs_progressive(ols, ols_offset, ols_offset, ols_offset+1, SHAPE, objs, splits, \
objs_sizes, objs_fols, size_h,size_bins,fol_h,fol_bins)
prelast_avg = vidtools.average_frames(last_frames[hsl:])
prelast_mm = vidtools.mousemask_from_object_arcs(frames_offset-hsl,frames_offset,min_arc_score,ols,ols_offset, \
Util.merge_dictlist([objs,to_retire_objs]), \
Util.merge_dictlist([objs_sizes,to_retire_objs_sizes]), \
Util.merge_dictlist([objs_fols,to_retire_objs_fols]), \
size_h, size_bins, fol_h, fol_bins,SHAPE)
print >> sys.stderr, 'object initialization complete in', str(
datetime.timedelta(seconds=int(time.time() - t)))
return ols, ols_offset, frames_offset, objs, splits, objs_sizes, objs_fols, prelast_avg, prelast_mm, to_retire_objs, to_retire_objs_sizes, to_retire_objs_fols
last_newactols_file = '%07d-%07d-newact_ols.list' % (frames_offset,frames_offset+hsl)
last_ground_file = '%07d-%07d-ground.list' % (frames_offset,frames_offset+hsl)
last_digdiff_file = '%07d-%07d-digdiff.mat' % (frames_offset,frames_offset+hsl)
t = time.time()
#eventually, choice to reload previous analysis goes here
last_frames = []
while len(last_frames) < hsl:
ols_offset, frames_offset = advance_analysis(ols,ols_offset,objs,splits,objs_sizes,objs_fols, \
to_retire_objs,to_retire_objs_sizes,to_retire_objs_fols, \
last_frames,stream,frames,currsum,denom,opts.seglen,cutoff,frames_offset, \
SHAPE,size_h,size_bins,fol_h,fol_bins,transform=opts.transform,outline_engine=opts.outline_engine)
last_avg = vidtools.average_frames(last_frames)
last_mm = vidtools.mousemask_from_object_arcs(frames_offset-len(last_frames),frames_offset,min_arc_score,ols,ols_offset, \
Util.merge_dictlist([objs,to_retire_objs]), \
Util.merge_dictlist([objs_sizes,to_retire_objs_sizes]), \
Util.merge_dictlist([objs_fols,to_retire_objs_fols]), \
size_h, size_bins, fol_h, fol_bins,SHAPE)
this_avg = vidtools.average_frames(frames[:hsl])
this_mm = vidtools.mousemask_from_object_arcs(frames_offset,frames_offset+hsl,min_arc_score,ols,ols_offset, \
Util.merge_dictlist([objs,to_retire_objs]), \
Util.merge_dictlist([objs_sizes,to_retire_objs_sizes]), \
Util.merge_dictlist([objs_fols,to_retire_objs_fols]), \
size_h, size_bins, fol_h, fol_bins,SHAPE)
prelast_masked = prelast_avg.copy()
prelast_masked[prelast_mm] = numpy.mean(prelast_avg[:50,:50])
last_masked = last_avg.copy()
last_masked[last_mm] = numpy.mean(last_avg[:50,:50])
this_masked = this_avg.copy()
this_masked[this_mm] = numpy.mean(this_avg[:50,:50])
frames_offset + hsl)
last_digdiff_file = '%07d-%07d-digdiff.mat' % (frames_offset,
frames_offset + hsl)
t = time.time()
#eventually, choice to reload previous analysis goes here
last_frames = []
while len(last_frames) < hsl:
ols_offset, frames_offset = advance_analysis(ols,ols_offset,objs,splits,objs_sizes,objs_fols, \
to_retire_objs,to_retire_objs_sizes,to_retire_objs_fols, \
last_frames,stream,frames,currsum,denom,opts.seglen,cutoff,frames_offset, \
SHAPE,size_h,size_bins,fol_h,fol_bins,transform=opts.transform,outline_engine=opts.outline_engine)
last_avg = vidtools.average_frames(last_frames)
last_mm = vidtools.mousemask_from_object_arcs(frames_offset-len(last_frames),frames_offset,min_arc_score,ols,ols_offset, \
Util.merge_dictlist([objs,to_retire_objs]), \
Util.merge_dictlist([objs_sizes,to_retire_objs_sizes]), \
Util.merge_dictlist([objs_fols,to_retire_objs_fols]), \
size_h, size_bins, fol_h, fol_bins,SHAPE)
this_avg = vidtools.average_frames(frames[:hsl])
this_mm = vidtools.mousemask_from_object_arcs(frames_offset,frames_offset+hsl,min_arc_score,ols,ols_offset, \
Util.merge_dictlist([objs,to_retire_objs]), \
Util.merge_dictlist([objs_sizes,to_retire_objs_sizes]), \
Util.merge_dictlist([objs_fols,to_retire_objs_fols]), \
size_h, size_bins, fol_h, fol_bins,SHAPE)
prelast_masked = prelast_avg.copy()
prelast_masked[prelast_mm] = numpy.mean(prelast_avg[:50, :50])
last_masked = last_avg.copy()
last_masked[last_mm] = numpy.mean(last_avg[:50, :50])
this_masked = this_avg.copy()
this_masked[this_mm] = numpy.mean(this_avg[:50, :50])
