def hist2d_from_blobs(blobslists_list,SHAPE): '''takes list of lists of blob polys (i.e. contents of a single mouseols.tar) returns 2-d hist of occupied positions''' mh = numpy.zeros(SHAPE,int) for ols in blobslists_list: for poly in ols: mh += vidtools.mask_from_outline(poly,SHAPE) return mh
def load_mouse_hist2d_segments(tarh,SHAPE,return_sizes=False, max_pix_mult=None,max_obj=None,max_dist_mult=None,svec=None): mm_hists = [] segments = sorted(tarh.getnames()) print >> sys.stderr, 'Load mouse locations for %s segments' % len(segments) lastmax = 0 if svec is None: svec = [] print >> sys.stderr, 'mouse sizes:' for i,olsf in enumerate(segments,1): print >> sys.stderr, '\r %s / %s' % (i,len(segments)), blobslists_list = Util.tar2obj(olsf,tarh) for ols in blobslists_list: if len(ols) == 1: svec.append(vidtools.size_of_polygon(ols[0])) mouse_size = numpy.mean(svec) mouse_radius = math.sqrt(mouse_size/math.pi) print >> sys.stderr, 'mouse locations:' for i,olsf in enumerate(segments,1): print >> sys.stderr, '\r %s / %s (lastmax %s)' % (i,len(segments),lastmax), blobslists_list = Util.tar2obj(olsf,tarh) mh = numpy.zeros(SHAPE,dtype=int) for ols in blobslists_list: if len(ols) == 0: continue if max_obj is not None and len(ols) > max_obj: print >> sys.stderr, 'max number of objects (%s) exceeded: %s' % (max_obj,len(ols)) continue this_mh = numpy.zeros(SHAPE,dtype=int) dist_to_all = [] for poly in ols: this_mh += vidtools.mask_from_outline(poly,SHAPE) if max_dist_mult is not None: for o_poly in ols: dist_to_all.append(vidtools.hypotenuse(*vidtools.apogee(poly,o_poly))) if max_pix_mult is not None: tot_pix = numpy.array(this_mh,dtype=bool).sum() if tot_pix > max_pix_mult*mouse_size: print >> sys.stderr, 'max total pixels of objects (%s) exceeded: %s' % (max_pix_mult*mouse_size,tot_pix) continue if max_dist_mult is not None and max(dist_to_all) > max_dist_mult*mouse_radius: print >> sys.stderr, 'max separation of objects (%s) exceeded: %s' % (max_dist_mult*mouse_radius,max(dist_to_all)) continue mh += this_mh mm_hists.append(mh) lastmax = mm_hists[-1].max() if return_sizes: return mm_hists,svec else: return mm_hists
fcopy = frameav.copy() print >>sys.stderr,'finalize mouse calls' for i,f in enumerate(frames): #print i #get xy coords, outlines of above-cut regions, and mean z-scores in each outline xy,ol,zsc = vidtools.find_mouse(f,fcopy,zcut=mousezcut,outline_zcut=mousezcut/2.0,grow_by=mouse_grow_by,preshrink=mouse_preshrink) if opts['ground_anchors'] is None and opts['burrowz'] is None: pass else: for p in ol: try: vidtools.subtract_outline(p,frameav) except: print >> sys.stderr, 'failure in frame %s' % i size = [len(filter(None,vidtools.shrink_mask(vidtools.mask_from_outline(p,f.shape),mouse_grow_by).flat)) for p in ol] mlen = [max([vidtools.hypotenuse(p1,p2) for p1 in p for p2 in p]) for p in ol] miceli.append( (images[i],xy) ) miceoutli.append( (images[i],ol) ) micez.append( (images[i],zsc) ) micesize.append( (images[i],size) ) micelen.append( (images[i],mlen) ) if i % tick == 0: print >> sys.stderr, 'frame %s done' % i mice = dict(miceli) miceout = dict(miceoutli) micez = dict(micez) micesize = dict(micesize) micelen = dict(micelen) try:
def draw_analysis_activity_summary(adir,fig=1,outformat=None,graph_height=0.2,show_mice=False,dawn_min=None,hill_bounds=None,skip_first=10):
'''given an analysis directory, generates a figure showing activity and grounds
if format is not None, also outputs summary figure to adir
if graph_height is not None, plots a color-coded activity graph on bottom <graph_height> portion of fig
'''
print >> sys.stderr, 'load:'
shape = eval(open(adir+'/shape.tuple').read())
if hill_bounds is None:
hill_left = 0
hill_right = shape[1]
else:
hill_left,hill_right = hill_bounds
print >> sys.stderr, '\tframes'
frames = [numpy.fromfile(f).reshape(shape) for f in sorted(glob(adir+'/*.frame'))]
actoutfs = sorted(glob(adir+'/*.newactout'))
acttermfs = sorted(glob(adir+'/*.newactterm'))
groundfs = sorted(glob(adir+'/*.ground'))
print >> sys.stderr, '\tactouts'
actouts = [eval(open(f).read()) for f in actoutfs]
print >> sys.stderr, '\tactterms'
actterms = [eval(open(f).read()) for f in acttermfs]
print >> sys.stderr, '\tgrounds'
grounds = [Util.smooth(numpy.fromfile(f,sep='\n'),10) for f in groundfs[:-1]]
hill_area = numpy.array([sum(shape[0]-g[hill_left:hill_right]) for g in grounds])
grounds = grounds[1:] #drop first ground after calculating differences
spec = iplot.subspectrum(len(actouts))
figobj = pylab.figure(fig,figsize=(9,6))
figobj.clf()
print >> sys.stderr, 'render:'
pylab.gray()
if not show_mice:
bkgd_mat = frames[-1]-(frames[0]-frames[-1])
if graph_height is not None:
h = frames[0].shape[0] * graph_height
bkgd_mat = numpy.array(list(bkgd_mat) + list(numpy.zeros((h,bkgd_mat.shape[1]))))
pylab.matshow(bkgd_mat,fignum=fig)
else:
print >> sys.stderr, 'show mice invoked; loading mice...',
mice = [eval(open(f).read()) for f in sorted(glob(adir+'/*.mice'))]
xco,yco = Util.dezip(reduce(lambda x,y: x+y,[[v for v in m.values() if v] for m in mice]))
print >> sys.stderr, 'done'
bkgd_mat = numpy.zeros(shape)
#raise NotImplementedError
if graph_height is not None:
h = frames[0].shape[0] * graph_height
bkgd_mat = numpy.array(list(bkgd_mat) + list(numpy.zeros((h,bkgd_mat.shape[1]))))
pylab.matshow(bkgd_mat,fignum=fig)
m,x,y = numpy.histogram2d(xco,yco,bins=numpy.array(shape[::-1])/5)
#the following is nasty: re-center to -0.5 bin width, log transform counts
pylab.contourf(x[1:]-(x[1]-x[0])/2,y[1:]-(y[1]-y[0])/2,numpy.log(m.transpose()+1),100)
ax = figobj.axes[0]
ax.set_xticks([])
ax.set_yticks([])
for i,poly in enumerate(actouts):
for p in poly:
if p: ax.add_patch(pylab.matplotlib.patches.Polygon(p,fc='none',ec=spec[i]))
for i, terms in enumerate(actterms):
for t in terms:
x,y = Util.dezip(t)
pylab.plot(x,y,c=spec[i])
vinf,ainf = adir.split('/analysis/')
ax.text(10,20,vinf,fontsize=8,color='w')
ax.text(10,35,ainf,fontsize=8,color='w')
if graph_height is not None:
areas = [sum([len(filter(None,vidtools.mask_from_outline(p,frames[0].shape).flat)) for p in polys]) for polys in actouts]
progs = [True and sum([vidtools.hypotenuse(*t) for t in terms]) or 0 for terms in actterms]
hill_increase = []
for v in hill_area[1:] - hill_area[:-1]:
if 0 < v:
hill_increase.append(v)
else:
hill_increase.append(0)
hill_increase = numpy.array(hill_increase)
z3 = (3*hill_increase.std()) + hill_increase.mean()
#willing to accept that a mouse kicks out at no more than 2x max observed burrow-build
max_increase = max(2*max(areas),z3)
print >> sys.stderr, 'for max ground change, z3 = %s; 2*max_areas = %s. Choose %s' % (z3,2*max(areas),max_increase)
for i,v in enumerate(hill_increase):
if v > max_increase:
hill_increase[i] = 0
h = frames[0].shape[0] * graph_height
base = frames[0].shape[0] + h/2 - 10
scale = frames[0].shape[1]/float(len(areas))
x = numpy.arange(0,frames[0].shape[1],scale)[:len(areas)]
if dawn_min:
pylab.bar(x[dawn_min],1*h/2,color='w',edgecolor='w',bottom=base)
pylab.bar(x[dawn_min],-1*h/2,color='w',edgecolor='w',bottom=base)
hours = []
for m in range(len(spec)):
if m%60==0:
hours.append(1)
else:
hours.append(0)
print len(hours)
pylab.bar(x,numpy.array(hours)*h/2,color='w',edgecolor='w',alpha=0.4,linewidth=0,bottom=base)
pylab.bar(x,-1*numpy.array(hours)*h/2,color='w',edgecolor='w',alpha=0.4,linewidth=0,bottom=base)
pylab.bar(x[skip_first:],Util.normalize(numpy.array(hill_increase[skip_first:]))*h/2,color=spec,edgecolor=spec,bottom=base)
pylab.plot(x[skip_first:],base+Util.normalize(numpy.cumsum(numpy.array(hill_increase[skip_first:])))*h/2,'--w')
for i,v in zip(x[skip_first:],Util.normalize(numpy.cumsum(numpy.array(hill_increase[skip_first:])))):
if v > 0.5:
break
pylab.plot((i,i),(base,base+(h/2)),'--w',lw=2)
pylab.bar(x[skip_first:],-1*Util.normalize(numpy.array(areas[skip_first:]))*h/2,color=spec,edgecolor=spec,bottom=base)
pylab.plot(x[skip_first:],base-Util.normalize(numpy.cumsum(numpy.array(areas[skip_first:])))*h/2,'--w')
for i,v in zip(x[skip_first:],Util.normalize(numpy.cumsum(numpy.array(areas[skip_first:])))):
if v > 0.5:
break
pylab.plot((i,i),(base,base-(h/2)),'--w',lw=2)
pylab.text(5,base+h/2,'%0.1f' % max(hill_increase[skip_first:]),color='w')
pylab.text(5,base-h/2,'%0.1f' % max(areas[skip_first:]),color='w')
#'''
spec.reverse() #flip spectrum to draw grounds back-to-front
for i, g in enumerate(grounds[::-1]):
pylab.plot(numpy.arange(hill_left,hill_right),g[hill_left:hill_right],c=spec[i])
spec.reverse() #flip back
#'''
pylab.plot()
pylab.ylim(1.2*shape[0],0)
pylab.xlim(0,shape[1])
if outformat is not None:
print >> sys.stderr, 'save as '+adir+'/activity_summary.'+outformat
figobj.savefig(adir+'/activity_summary.'+outformat)
def draw_rainbowtron_opencv(analysis_dir,fig=1,outformat='pdf',vid_fps=30,graph_height=0.2, \
bkgd_data='fullmice',spectrum_len=None,dawn_min=None,activity_percentile_plot=0.5, \
hill_bounds=None,skip_first=0,hsl_per_min=2,set_hill_max=None,set_dig_max=None, \
post_summarize_analysis=True,outfile_prefix=None, \
ground_step_coeff=1,max_pix_mult=None,max_obj=None,max_dist_mult=None,hill_win_coeff=None,write_yval=False, \
hill_max_cumul_digscale=None,dig_max_cumul_digscale=None):
'''given a summarize_segment_opencv.py analysis directory (i.e. tarballs of analysis results by half-segment-length)
generates a figure showing burrowing activity and sand-hill accumulation
if outformat is not None, save figure to analysis_dir
if vidformat is not None, record rainbowtron over time to analysis_dir
bkgd_data: data to display as background for image. one of ["fullmice","flatmice","hillchange"]
if post_summarize_analysis is True, look for ground and activity from vidtools.find_ground_over_frames_matrix; vidtools.find_burrow_over_frames_matrix
HACK: if bkgd_data is NOT "fullmice", the mouse size vector will not be loaded, and a mouse_radius of 10 will be used
'''
if outfile_prefix is None:
outfile_prefix = analysis_dir.rstrip('/').split('/')[-4].split('-')[0] + '-' + ('_'.join(analysis_dir.rstrip('/').split('/')[-3].split('_')[-2:]))
outfile_prefix = '%s-hillmax%s-digmax%s-gsc%s-mpm%s-mo%s-mdm%s-hwc%s-hmcd%s-dmcd%s-bkgd_%s' % \
(outfile_prefix, set_hill_max,set_dig_max, \
ground_step_coeff,max_pix_mult,max_obj,max_dist_mult,hill_win_coeff,hill_max_cumul_digscale,dig_max_cumul_digscale, \
bkgd_data)
#will get overwritten later if possible
mouse_radius = 10
print >> sys.stderr, 'load:'
SHAPE = eval(open(analysis_dir+'/SHAPE').read())
config = eval(open(os.path.split(os.path.split(analysis_dir.rstrip('/'))[0])[0]+'-config.dict').read())
if hill_bounds is None:
hill_left = 0
hill_right = SHAPE[1]
elif hill_bounds == 'config':
hill_left,hill_right = config['hill_bounds']
else:
hill_left,hill_right = hill_bounds
print >> sys.stderr, '\ttar handles'
tars_d = dict([(os.path.basename(f),tarfile.open(f)) for f in glob(os.path.join(analysis_dir,'*.tar'))])
print >> sys.stderr, '\tframes'
frames = Util.tar2ar_all(tars_d['segavgs.tar'],SHAPE)
if bkgd_data == 'hillchange':
bkgd_mat = frames[-1]-(frames[0]-frames[-1])
else:
if bkgd_data == 'flatmice':
print >> sys.stderr, '\tmouse masks'
mouse_hists = Util.tar2ar_all(tars_d['mousemasks.tar'],SHAPE,bool)
elif bkgd_data == 'fullmice':
print >> sys.stderr, '\tmouse locations'
hist2d_tarf = os.path.join(analysis_dir,'mousehists-mpm%s-mo%s-mdm%s.tar' % (max_pix_mult,max_obj,max_dist_mult) )
outlines_tarf = os.path.join(analysis_dir,'miceols.tar')
# this will save tons of time...eventually...
mouse_hists,svec = load_cached_mouse_hist2d_segments(hist2d_tarf,outlines_tarf,SHAPE,check_lengths=True,return_sizes=True, \
max_pix_mult=max_pix_mult,max_obj=max_obj,max_dist_mult=max_dist_mult)
#mouse_hists = load_mouse_hist2d_segments(tars_d['miceols.tar'],SHAPE)
mouse_radius = math.sqrt(numpy.mean(svec)/math.pi)
else:
errstr = 'bkgd_data must be one of ["hillchange","flatmice","fullmice"]; got %s' % bkgd_data
raise ValueError, errstr
bkgd_mat = numpy.log1p(reduce(lambda x,y:x+numpy.array(y,dtype=int), mouse_hists))
print >> sys.stderr, '\tgrounds'
if post_summarize_analysis:
grounds_postsummary_tarbase = 'grounds_postsummary-gsc%s.tar' % (ground_step_coeff)
if grounds_postsummary_tarbase in tars_d:
grounds = Util.tar2obj_all(tars_d[grounds_postsummary_tarbase],lambda t:numpy.array(eval(t),dtype=float))
print >> sys.stderr, 'loaded %s grounds' % len(grounds)
else:
grounds_postsummary_tarf = os.path.join(analysis_dir,grounds_postsummary_tarbase)
grounds = vidtools.find_ground_over_frames_matrix(frames,config['ground_anchors'],config['end_ground_anchors'],max_step_limit=mouse_radius*ground_step_coeff)
#write grounds so future runs can load cached
tarh = tarfile.open(grounds_postsummary_tarf,'w')
for g,n in zip(grounds,sorted(tars_d['grounds.tar'].getnames())):
Util.obj2tar(g,n,tarh)
tarh.close()
grounds = map(numpy.array,grounds)
else:
grounds = [Util.smooth(g,10) for g in Util.tar2obj_all(tars_d['grounds.tar'],lambda t:numpy.array(eval(t),dtype=float))]
print >> sys.stderr, '\tactouts'
if post_summarize_analysis:
newact_ols_postsummary_tarbase = 'newact_ols_postsummary-gsc%s-mpm%s-mo%s-mdm%s.tar' % \
(ground_step_coeff,max_pix_mult,max_obj,max_dist_mult)
pd_ols_postsummary_tarbase = 'pd_ols_postsummary-gsc%s-mpm%s-mo%s-mdm%s.tar' % \
(ground_step_coeff,max_pix_mult,max_obj,max_dist_mult)
newact_ols_postsummary_tarf = os.path.join(analysis_dir,newact_ols_postsummary_tarbase)
pd_ols_postsummary_tarf = os.path.join(analysis_dir,pd_ols_postsummary_tarbase)
if newact_ols_postsummary_tarbase in tars_d and pd_ols_postsummary_tarbase in tars_d:
actouts = Util.tar2obj_all(tars_d[newact_ols_postsummary_tarbase])
pdols = Util.tar2obj_all(tars_d[pd_ols_postsummary_tarbase])
print >> sys.stderr, 'loaded %s activity outlines' % len(actouts)
print >> sys.stderr, 'loaded %s predug outlines' % len(pdols)
else:
if bkgd_data == 'hillchange':
mousemasks = Util.tar2ar_all(tars_d['mousemasks.tar'],SHAPE,bool)
else:
mousemasks = [numpy.array(m,dtype=bool) for m in mouse_hists]
pdmask,pdols,prevactols,actouts = vidtools.find_burrow_over_frames_matrix(frames,mousemasks,grounds,mouse_radius,predug_poly=config.get('predug',None))
na_tarh = tarfile.open(newact_ols_postsummary_tarf,'w')
pd_tarh = tarfile.open(pd_ols_postsummary_tarf,'w')
for na,pd,n in zip(actouts,pdols,sorted(tars_d['newact_ols.tar'].getnames())):
Util.obj2tar(na,n,na_tarh)
Util.obj2tar(pd,n,pd_tarh)
na_tarh.close()
pd_tarh.close()
else:
actouts = Util.tar2obj_all(tars_d['newact_ols.tar'])
if hill_win_coeff is None:
hill_area = numpy.array([sum(SHAPE[0]-g[hill_left:hill_right]) for g in grounds])
hill_increase = []
for v in hill_area[1:] - hill_area[:-1]:
if 0 < v:
hill_increase.append(v)
else:
hill_increase.append(0)
else:
#ADD HILL AREA WINDOW CALC
win = mouse_radius * hill_win_coeff
hill_increase = []
for i,g in enumerate(grounds[1:]):
diff_vec = grounds[i] - g
hi,win_start = max([(sum(diff_vec[x:x+win]),x) for x in range(len(diff_vec))])
hill_increase.append(hi)
hill_increase = numpy.array(hill_increase)
grounds = grounds[1:] #drop first ground after calculating differences
# takes second (1-th) actout since first is always blank
areas = [sum([len(filter(None,vidtools.mask_from_outline(p,frames[0].shape).flat)) for p in polys]) for polys in actouts[1:]]
#progs = [True and sum([vidtools.hypotenuse(*t) for t in terms]) or 0 for terms in actterms]
#write hill increase and dig areas
areas_out = os.path.join(analysis_dir,outfile_prefix+'-dig_areas.list')
hills_out = os.path.join(analysis_dir,outfile_prefix+'-hill_increases.list')
open(areas_out,'w').write(list(areas).__repr__())
open(hills_out,'w').write(list(hill_increase).__repr__())
#PLOT
if hill_max_cumul_digscale:
hill_scale_factor = sum(hill_increase)/float(hill_max_cumul_digscale)
else:
hill_scale_factor = 1
if dig_max_cumul_digscale:
dig_scale_factor = sum(areas)/float(dig_max_cumul_digscale)
else:
dig_scale_factor = 1
if spectrum_len is None:
spectrum_len = len(actouts)
spec = iplot.subspectrum(spectrum_len,reversed=True,noUV=True)
figobj = pylab.figure(fig,figsize=(9,6))
figobj.clf()
print >> sys.stderr, 'render:'
pylab.gray()
if graph_height is not None:
h = frames[0].shape[0] * graph_height
bkgd_mat = numpy.array(list(bkgd_mat) + list(numpy.zeros((h,bkgd_mat.shape[1]))))
pylab.matshow(bkgd_mat,fignum=fig)
ax = figobj.axes[0]
ax.set_xticks([])
ax.set_yticks([])
for i,poly in enumerate(actouts):
for p in poly:
if p: ax.add_patch(pylab.matplotlib.patches.Polygon(p,fc='none',ec=spec[i]))
if post_summarize_analysis and len(config.get('predug',[])) > 0:
for i,poly in enumerate(pdols):
for p in poly:
if p: ax.add_patch(matplotlib.patches.Polygon(p,ec='gray',fc='none',hatch='/'))
vinf,ainf = analysis_dir.split('/analysis/')
ax.text(10,20,vinf,fontsize=8,color='w')
ax.text(10,35,ainf,fontsize=8,color='w')
if graph_height is not None:
z3 = (3*hill_increase.std()) + hill_increase.mean()
#willing to accept that a mouse kicks out at no more than 2x max observed burrow-build?
max_increase = max(2*max(areas),z3)
print >> sys.stderr, 'for max ground change, z3 = %s; 2*max_areas = %s. Choose %s' % (z3,2*max(areas),max_increase)
for i,v in enumerate(hill_increase):
if v > max_increase:
hill_increase[i] = 0
h = frames[0].shape[0] * graph_height
base = frames[0].shape[0] + h/2 - 10
scale = frames[0].shape[1]/float(len(areas))
x = numpy.arange(0,frames[0].shape[1],scale)[:len(areas)]
print len(x),len(hill_increase)
if dawn_min:
pylab.bar(x[dawn_min],1*h/2,color='w',edgecolor='w',bottom=base)
pylab.bar(x[dawn_min],-1*h/2,color='w',edgecolor='w',bottom=base)
hours = []
for m in range(min(len(spec),len(areas))):
if m%(60*hsl_per_min)==0:
hours.append(1)
else:
hours.append(0)
print len(hours)
pylab.bar(x,numpy.array(hours)*h/2,color='w',edgecolor='w',alpha=0.4,linewidth=0,bottom=base)
pylab.bar(x,-1*numpy.array(hours)*h/2,color='w',edgecolor='w',alpha=0.4,linewidth=0,bottom=base)
pylab.bar(x[skip_first:],-1*Util.normalize(numpy.array(hill_increase[skip_first:]),set_max=set_hill_max)*h/2,color=spec,edgecolor=spec,bottom=base)
pylab.plot(x[skip_first:],base-Util.normalize(numpy.cumsum(numpy.array(hill_increase[skip_first:])))*h/2*hill_scale_factor,'--w')
for i,v in zip(x[skip_first:],Util.normalize(numpy.cumsum(numpy.array(hill_increase[skip_first:])))):
if activity_percentile_plot and v > activity_percentile_plot:
break
pylab.plot((i,i),(base,base-(h/2)),'--w',lw=2)
pylab.bar(x[skip_first:],Util.normalize(numpy.array(areas[skip_first:]),set_max=set_dig_max)*h/2,color=spec,edgecolor=spec,bottom=base)
pylab.plot(x[skip_first:],base+Util.normalize(numpy.cumsum(numpy.array(areas[skip_first:])))*h/2*dig_scale_factor,'--w')
for i,v in zip(x[skip_first:],Util.normalize(numpy.cumsum(numpy.array(areas[skip_first:])))):
if activity_percentile_plot and v > activity_percentile_plot:
break
pylab.plot((i,i),(base,base+(h/2)),'--w',lw=2)
if write_yval:
pylab.text(5,base-h/2,'hill %0.1f' % max(hill_increase[skip_first:]),color='w')
pylab.text(5,base+h/2,'dig %0.1f' % max(areas[skip_first:]),color='w')
#'''
for i, g in enumerate(grounds):
pylab.plot(numpy.arange(hill_left,hill_right),g[hill_left:hill_right],c=spec[i])
#'''
pylab.plot()
pylab.ylim((1+graph_height)*SHAPE[0],0)
pylab.xlim(0,SHAPE[1])
if outformat is not None:
out_img = os.path.join(analysis_dir,outfile_prefix+'-activity_summary.'+outformat)
print >> sys.stderr, 'save as %s' % out_img
figobj.savefig(out_img)
#VIDEO!
spec = iplot.subspectrum(spectrum_len,noUV=True)
if vid_fps:
import cv
vidout = os.path.join(analysis_dir,outfile_prefix+'-activity_summary.avi')
if os.path.exists(vidout): os.unlink(vidout)
pixdim = tuple(reversed(SHAPE))
pixdim = tuple(numpy.array(pixdim)*2)
vidwriter = cv.CreateVideoWriter(vidout , cv.FOURCC('x','v','i','d'), vid_fps, pixdim,1)
cols_polys = []
cols_hatch_polys = []
cols_lines = []
hourbar_kwargs = {'alpha':0.4,'linewidth':0}
bars = []
bars.append(('w',(x,numpy.array(hours)*h/2,base,hourbar_kwargs)))
bars.append(('w',(x,-1*numpy.array(hours)*h/2,base,hourbar_kwargs)))
for vfi,m in enumerate(mouse_hists[1:]):
if graph_height is not None:
h = frames[0].shape[0] * graph_height
m = numpy.array(list(m) + list(numpy.zeros((h,m.shape[1]))))
if len(actouts[vfi]):
cols_polys.extend(zip([spec[vfi]]*len(actouts[vfi]),actouts[vfi]))
if post_summarize_analysis and len(config.get('predug',[])) > 0 and len(pdols[vfi]):
cols_hatch_polys.extend(zip(['gray']*len(pdols[vfi]),['/']*len(pdols[vfi]),pdols[vfi]))
cols_lines.append((spec[vfi],(numpy.arange(hill_left,hill_right),grounds[vfi][hill_left:hill_right])))
this_bars = []
#pylab.bar(x[skip_first:],-1*Util.normalize(numpy.array(hill_increase[skip_first:]))*h/2,color=spec,edgecolor=spec,bottom=base)
this_bars.append((spec[:vfi],(x[:vfi],(-1*Util.normalize(numpy.array(hill_increase))*h/2)[:vfi],base,{})))
#pylab.plot(x[skip_first:],base-Util.normalize(numpy.cumsum(numpy.array(hill_increase[skip_first:])))*h/2,'--w')
hi_line = [('w',(x[:vfi],(base-Util.normalize(numpy.cumsum(numpy.array(hill_increase)))*h/2)[:vfi]))]
#pylab.bar(x[skip_first:],Util.normalize(numpy.array(areas[skip_first:]))*h/2,color=spec,edgecolor=spec,bottom=base)
this_bars.append((spec[:vfi],(x[:vfi],(Util.normalize(numpy.array(areas))*h/2)[:vfi],base,{})))
#pylab.plot(x[skip_first:],base+Util.normalize(numpy.cumsum(numpy.array(areas[skip_first:])))*h/2,'--w')
ba_line = [('w',(x[:vfi],(base+Util.normalize(numpy.cumsum(numpy.array(areas)))*h/2)[:vfi]))]
cv_im = vidtools.mat_polys2cv(m,cols_polys,hatchpolys=cols_hatch_polys,lines=cols_lines+hi_line+ba_line,bars=bars+this_bars,scale=2)
nll = cv.WriteFrame(vidwriter,cv_im)
print >> sys.stderr, '\r\twrite video frame %s' % vfi,
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])
if opts.antfarm_config: #antfarm-specific analysis steps
digdiffs.append(prelast_masked-this_masked)
if len(digdiffs) > 3:
nll = digdiffs.pop(0)
if newactols and newactols[-1]:
prevactmask += reduce(lambda x,y:x+y, [vidtools.shrink_mask(vidtools.mask_from_outline(ol,this_masked.shape),1) for ol in newactols[-1]])
if len(digdiffs) > 1:
m = digdiffs[-2] > 0.2 #ARBITRARY
m[last_mm] = True #add mousemask to burrow area
m[vidtools.grow_mask(vidtools.shrink_mask(prevactmask,1),1)] = False #MASK PREVIOUS DIGGING
m[groundmask] = False #AND EVERYTHING ABOVE FIRST GROUND
if opts.outline_engine == 'homebrew':
newactols.append(vidtools.chain_outlines_from_mask(m,preshrink=1,grow_by=1,debug=False,return_termini=False,order_points=True,sort_outlines=False))
prevol = vidtools.chain_outlines_from_mask(prevactmask,preshrink=1,grow_by=1,debug=False,return_termini=False,order_points=True,sort_outlines=False)
elif opts.outline_engine == 'shapely':
newactols.append(vidtools.chain_outlines_from_mask_shapely(m,preshrink=1,grow_by=1))
prevol = vidtools.chain_outlines_from_mask_shapely(prevactmask,preshrink=1,grow_by=1)
else:
print >> sys.stderr, 'outline_engine must be one of %s' % (['homebrew','shapely'])
raise ValueError
digol = newactols[-1]
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])
if opts.antfarm_config: #antfarm-specific analysis steps
digdiffs.append(prelast_masked - this_masked)
if len(digdiffs) > 3:
nll = digdiffs.pop(0)
if newactols and newactols[-1]:
prevactmask += reduce(lambda x, y: x + y, [
vidtools.shrink_mask(
vidtools.mask_from_outline(ol, this_masked.shape), 1)
for ol in newactols[-1]
])
if len(digdiffs) > 1:
m = digdiffs[-2] > 0.2 #ARBITRARY
m[last_mm] = True #add mousemask to burrow area
m[vidtools.grow_mask(vidtools.shrink_mask(prevactmask, 1),
1)] = False #MASK PREVIOUS DIGGING
m[groundmask] = False #AND EVERYTHING ABOVE FIRST GROUND
if opts.outline_engine == 'homebrew':
newactols.append(
vidtools.chain_outlines_from_mask(m,
preshrink=1,
grow_by=1,
debug=False,
return_termini=False,
grow_by=mouse_grow_by,
preshrink=mouse_preshrink)
if opts['ground_anchors'] is None and opts['burrowz'] is None:
pass
else:
for p in ol:
try:
vidtools.subtract_outline(p, frameav)
except:
print >> sys.stderr, 'failure in frame %s' % i
size = [
len(
filter(
None,
vidtools.shrink_mask(
vidtools.mask_from_outline(p, f.shape),
mouse_grow_by).flat)) for p in ol
]
mlen = [
max([vidtools.hypotenuse(p1, p2) for p1 in p for p2 in p])
for p in ol
]
miceli.append((images[i], xy))
miceoutli.append((images[i], ol))
micez.append((images[i], zsc))
micesize.append((images[i], size))
micelen.append((images[i], mlen))
if i % tick == 0:
print >> sys.stderr, 'frame %s done' % i
mice = dict(miceli)
miceout = dict(miceoutli)
