from aubio.task.task import task from aubio.task.utils import * from aubio.aubioclass import * class taskonset(task): def __init__(self,input,output=None,params=None): """ open the input file and initialize arguments parameters should be set *before* calling this method. """ task.__init__(self,input,params=params) self.opick = onsetpick(self.params.bufsize, self.params.hopsize, self.channels, self.myvec, self.params.threshold, mode=get_onset_mode(self.params.onsetmode), dcthreshold=self.params.dcthreshold, derivate=self.params.derivate) self.olist = [] self.ofunc = [] self.maxofunc = 0 self.last = 0 if self.params.localmin: self.ovalist = [0., 0., 0., 0., 0.] def __call__(self): task.__call__(self) isonset,val = self.opick.do(self.myvec) if (aubio_silence_detection(self.myvec(),self.params.silence)): isonset=0 if self.params.storefunc: self.ofunc.append(val) if self.params.localmin: if val > 0: self.ovalist.append(val) else: self.ovalist.append(0) self.ovalist.pop(0) if (isonset == 1): if self.params.localmin: # find local minima before peak i=len(self.ovalist)-1 while self.ovalist[i-1] < self.ovalist[i] and i > 0: i -= 1 now = (self.frameread+1-i) else: now = self.frameread # take back delay if self.params.delay != 0.: now -= self.params.delay if now < 0 : now = 0 if self.params.mintol: # prune doubled if (now - self.last) > self.params.mintol: self.last = now return now, val else: return now, val def fprint(self,foo): print self.params.step*foo[0] def eval(self,inputdata,ftru,mode='roc',vmode=''): from aubio.txtfile import read_datafile from aubio.onsetcompare import onset_roc, onset_diffs, onset_rocloc ltru = read_datafile(ftru,depth=0) lres = [] for i in range(len(inputdata)): lres.append(inputdata[i][0]*self.params.step) if vmode=='verbose': print "Running with mode %s" % self.params.onsetmode, print " and threshold %f" % self.params.threshold, print " on file", self.input #print ltru; print lres if mode == 'local': l = onset_diffs(ltru,lres,self.params.tol) mean = 0 for i in l: mean += i if len(l): mean = "%.3f" % (mean/len(l)) else: mean = "?0" return l, mean elif mode == 'roc': self.orig, self.missed, self.merged, \ self.expc, self.bad, self.doubled = \ onset_roc(ltru,lres,self.params.tol) elif mode == 'rocloc': self.v = {} self.v['orig'], self.v['missed'], self.v['Tm'], \ self.v['expc'], self.v['bad'], self.v['Td'], \ self.v['l'], self.v['labs'] = \ onset_rocloc(ltru,lres,self.params.tol) def plot(self,onsets,ofunc,wplot,oplots,nplot=False): import Gnuplot, Gnuplot.funcutils import aubio.txtfile import os.path import numarray from aubio.onsetcompare import onset_roc x1,y1,y1p = [],[],[] oplot = [] if self.params.onsetmode in ('mkl','kl'): ofunc[0:10] = [0] * 10 self.lenofunc = len(ofunc) self.maxofunc = max(ofunc) # onset detection function downtime = numarray.arange(len(ofunc))*self.params.step oplot.append(Gnuplot.Data(downtime,ofunc,with='lines',title=self.params.onsetmode)) # detected onsets if not nplot: for i in onsets: x1.append(i[0]*self.params.step) y1.append(self.maxofunc) y1p.append(-self.maxofunc) #x1 = numarray.array(onsets)*self.params.step #y1 = self.maxofunc*numarray.ones(len(onsets)) if x1: oplot.append(Gnuplot.Data(x1,y1,with='impulses')) wplot.append(Gnuplot.Data(x1,y1p,with='impulses')) oplots.append((oplot,self.params.onsetmode,self.maxofunc)) # check if ground truth datafile exists datafile = self.input.replace('.wav','.txt') if datafile == self.input: datafile = "" if not os.path.isfile(datafile): self.title = "" #"(no ground truth)" else: t_onsets = aubio.txtfile.read_datafile(datafile) x2 = numarray.array(t_onsets).resize(len(t_onsets)) y2 = self.maxofunc*numarray.ones(len(t_onsets)) wplot.append(Gnuplot.Data(x2,y2,with='impulses')) tol = 0.050 orig, missed, merged, expc, bad, doubled = \ onset_roc(x2,x1,tol) self.title = "GD %2.3f%% FP %2.3f%%" % \ ((100*float(orig-missed-merged)/(orig)), (100*float(bad+doubled)/(orig))) def plotplot(self,wplot,oplots,outplot=None,extension=None,xsize=1.,ysize=1.,spectro=False): from aubio.gnuplot import gnuplot_create, audio_to_array, make_audio_plot, audio_to_spec import re # prepare the plot g = gnuplot_create(outplot=outplot, extension=extension) g('set title \'%s\'' % (re.sub('.*/','',self.input))) if spectro: g('set size %f,%f' % (xsize,1.3*ysize) ) else: g('set size %f,%f' % (xsize,ysize) ) g('set multiplot') # hack to align left axis g('set lmargin 3') g('set rmargin 6') if spectro: import Gnuplot minf = 50 maxf = 500 data,time,freq = audio_to_spec(self.input,minf=minf,maxf=maxf) g('set size %f,%f' % (1.24*xsize , 0.34*ysize) ) g('set origin %f,%f' % (-0.12,0.65*ysize)) g('set xrange [0.:%f]' % time[-1]) g('set yrange [%f:%f]' % (minf,maxf)) g('set pm3d map') g('unset colorbox') g('set lmargin 0') g('set rmargin 0') g('set tmargin 0') g('set palette rgbformulae -25,-24,-32') g.xlabel('time (s)',offset=(0,1.)) g.ylabel('freq (Hz)') g('set origin 0,%f' % (1.0*ysize) ) g('set format x "%1.1f"') #if log: # g('set yrange [%f:%f]' % (max(10,minf),maxf)) # g('set log y') g.splot(Gnuplot.GridData(data,time,freq, binary=1, title='')) else: # plot waveform and onsets time,data = audio_to_array(self.input) wplot = [make_audio_plot(time,data)] + wplot g('set origin 0,%f' % (0.7*ysize) ) g('set size %f,%f' % (xsize,0.3*ysize)) g('set format y "%1f"') g('set xrange [0:%f]' % max(time)) g('set yrange [-1:1]') g('set noytics') g('set y2tics -1,1') g.xlabel('time (s)',offset=(0,0.7)) g.ylabel('amplitude') g.plot(*wplot) # default settings for next plots g('unset title') g('set format x ""') g('set format y "%3e"') g('set tmargin 0') g.xlabel('') N = len(oplots) y = 0.7*ysize # the vertical proportion of the plot taken by onset functions delta = 0.035 # the constant part of y taken by last plot label and data for i in range(N): # plot onset detection functions g('set size %f,%f' % ( xsize, (y-delta)/N)) g('set origin 0,%f' % ((N-i-1)*(y-delta)/N + delta )) g('set nokey') g('set xrange [0:%f]' % (self.lenofunc*self.params.step)) g('set yrange [0:%f]' % (1.1*oplots[i][2])) g('set y2tics ("0" 0, "%d" %d)' % (round(oplots[i][2]),round(oplots[i][2]))) g.ylabel(oplots[i][1]) if i == N-1: g('set size %f,%f' % ( xsize, (y-delta)/N + delta ) ) g('set origin 0,0') g.xlabel('time (s)', offset=(0,0.7)) g('set format x') g.plot(*oplots[i][0]) g('unset multiplot')