Changeset 75139a9 for python

Timestamp:

Dec 16, 2005, 2:26:25 AM (19 years ago)

Author:

Paul Brossier <piem@altern.org>

Branches:

feature/autosink, feature/cnn, feature/cnn_org, feature/constantq, feature/crepe, feature/crepe_org, feature/pitchshift, feature/pydocstrings, feature/timestretch, fix/ffmpeg5, master, pitchshift, sampler, timestretch, yinfft+

Children:

336cf77

Parents:

257debc

Message:

complete rewrite of bench onset, no more command line call
complete rewrite of bench onset, no more command line call

File:

• python/bench-onset (modified) (1 diff)

python/bench-onset

@@ -4 +4 @@
 from aubio.bench.node import *
 
-datapath = "%s%s" % (DATADIR,'/onset/DB')
+class onset_parameters:
+        def __init__(self):
+                """ set default parameters """
+                self.silence = -70
+                self.derivate = False
+                self.localmin = False
+                self.bufsize = 512
+                self.hopsize = 256
+                self.samplerate = 44100
+                self.tol = 0.05
+                self.step = float(self.hopsize)/float(self.samplerate)
+                self.threshold = 0.1
+                self.mode = 'dual'
+
+class taskonset(task):
+        
+        def pretty_print(self,values):
+                for i in range(len(values)):
+                        print self.formats[i] % values[i],
+                print
+
+        def compute_results(self):
+                self.P = 100*float(self.expc-self.missed-self.merged)/(self.expc-self.missed-self.merged + self.bad+self.doubled)
+                self.R = 100*float(self.expc-self.missed-self.merged)/(self.expc-self.missed-self.merged + self.missed+self.merged)
+                if self.R < 0: self.R = 0
+                self.F = 2* self.P*self.R / (self.P+self.R)
+
+                self.values = [self.params.mode, 
+                "%2.3f" % self.params.threshold,
+                self.orig,
+                self.expc,
+                self.missed,
+                self.merged,
+                self.bad,
+                self.doubled,
+                (self.orig-self.missed-self.merged),
+                "%2.3f" % (100*float(self.orig-self.missed-self.merged)/(self.orig)),
+                "%2.3f" % (100*float(self.bad+self.doubled)/(self.orig)), 
+                "%2.3f" % (100*float(self.orig-self.missed)/(self.orig)), 
+                "%2.3f" % (100*float(self.bad)/(self.orig)),
+                "%2.3f" % self.P,
+                "%2.3f" % self.R,
+                "%2.3f" % self.F  ]
+
+        def compute_onset(self,input,output):
+                from aubio.tasks import getonsets, get_onset_mode
+                from aubio.onsetcompare import onset_roc, onset_diffs
+                from aubio.txtfile import read_datafile
+                amode = 'roc'
+                vmode = 'verbose'
+                vmode = ''
+                lres, ofunc = getonsets(input,
+                        self.params.threshold,
+                        self.params.silence,
+                        mode=get_onset_mode(self.params.mode),
+                        localmin=self.params.localmin,
+                        derivate=self.params.derivate,
+                        bufsize=self.params.bufsize,
+                        hopsize=self.params.hopsize,
+                        storefunc=False)
+
+                for i in range(len(lres)): lres[i] = lres[i]*self.params.step
+                ltru = read_datafile(input.replace('.wav','.txt'),depth=0)
+                if vmode=='verbose':
+                        print "Running with mode %s" % self.params.mode, 
+                        print " and threshold %f" % self.params.threshold, 
+                        print " on file", input
+                #print ltru; print lres
+                if amode == 'localisation':
+                        l = onset_diffs(ltru,lres,self.params.tol)
+                        mean = 0
+                        for i in l: mean += i
+                        if len(l): print "%.3f" % (mean/len(l))
+                        else: print "?0"
+                elif amode == 'roc':
+                        orig, missed, merged, expc, bad, doubled = onset_roc(ltru,lres,self.params.tol)
+                        self.orig    += orig
+                        self.missed  += missed
+                        self.merged  += merged
+                        self.expc    += expc
+                        self.bad     += bad
+                        self.doubled += doubled
+                self.compute_results()
+                        
+        def compute_data(self):
+                self.orig, self.missed, self.merged, self.expc, \
+                        self.bad, self.doubled = 0, 0, 0, 0, 0, 0
+                act_on_data(self.compute_onset,self.datadir,self.resdir, \
+                        suffix='',filter='f -name \'*.wav\'')
+
+        def run_bench(self,modes=['dual'],thresholds=[0.5]):
+                self.modes = modes
+                self.thresholds = thresholds
+
+                self.pretty_print(self.titles)
+                for mode in self.modes:
+                        self.params.mode = mode
+                        for threshold in self.thresholds:
+                                self.params.threshold = threshold
+                                self.compute_data()
+                                self.compute_results()
+                                self.pretty_print(self.values)
+
+        def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]):
+                """ simple dichotomia like algorithm to optimise threshold """
+                self.modes = modes
+                self.pretty_print(self.titles)
+                for mode in self.modes:
+                        steps = 10 
+                        lesst = thresholds[0] 
+                        topt = thresholds[1]
+                        self.params.mode = mode
+
+                        self.params.threshold = topt 
+                        self.compute_data()
+                        self.pretty_print(self.values)
+                        topF = self.F 
+
+                        self.params.threshold = lesst 
+                        self.compute_data()
+                        self.pretty_print(self.values)
+                        lessF = self.F 
+
+                        for i in range(steps):
+                                self.params.threshold = ( lesst + topt ) * .5 
+                                self.compute_data()
+                                self.pretty_print(self.values)
+                                if self.F == 100.0 or self.F == topF: 
+                                        print "assuming we converged, stopping" 
+                                        break
+                                #elif abs(self.F - topF) < 0.01 :
+                                #       print "done converging"
+                                #       break
+                                if topF < self.F:
+                                        #lessF = topF
+                                        #lesst = topt 
+                                        topF = self.F
+                                        topt = self.params.threshold
+                                elif lessF < self.F:
+                                        lessF = self.F
+                                        lesst = self.params.threshold
+                                if topt == lesst:
+                                        lesst /= 2.
+
+
+#modes = [ 'complex' ]
+modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']
+#thresholds = [1.5]
+thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
+
+#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
+datapath = "%s%s" % (DATADIR,'/onset/DB/PercussivePhrases/RobertRich')
 respath = '/var/tmp/DB-testings'
 
-MODES = 'hfc', 'complexdomain', 'energy', 'phase', 'specdiff', 'kl', 'mkl'
-THRESHOLD = range(1,14,1)
+taskonset = taskonset(datapath,respath)
 
-#        prepareresultpath
-act_on_results(mkdir,datapath,respath,filter='d')
+taskonset.params = onset_parameters()
 
-def compute_data(input,output):
-        aubiocmd = "%s%s %s%s" % \
-                ("LD_LIBRARY_PATH=",LD_LIBRARY_PATH,AUBIOHOME,"/examples/aubioonset")
-        for m in MODES:
-                for k in THRESHOLD:
-                        cmd = "%s --input \"%s\" --onset %s --threshold %s > \"%s--%s--%s.txt\"" \
-                                % (aubiocmd,input,m,k/10.,output,m,k/10.)
-                        runcommand(cmd,debug=1)
+taskonset.titles = [ 'mode', 'thres', 'orig', 'expc', 'missd', 'mergd',
+'bad', 'doubl', 'corrt', 'GD', 'FP', 'GD-merged', 'FP-pruned',
+'prec', 'recl', 'dist' ]
+taskonset.formats = ["%12s" , "| %6s", "| %6s", "| %6s", "| %6s", "| %6s", 
+"| %6s", "| %6s", "| %6s", "| %8s", "| %8s", "| %8s", "| %8s",
+"| %6s", "| %6s", "| %6s"] 
 
-
-#        computedata
-act_on_data(compute_data,datapath,respath,suffix='',filter='f -name \'*.wav\'')
+#taskonset.run_bench(modes=modes,thresholds=thresholds)
+taskonset.auto_learn(modes=modes)
 
 #        gatherdata