def similarity(self, pict): # distance of sizes #dim=sum(map(lambda (x,y):(x-y)**2, zip(self.size, pict.size))) #dim/=self.size[0]**2+self.size[1]**2 msr=[] #dimensions=zip(self.size, pict.size) #widths=sorted(dimensions[0]) #heights=sorted(dimensions[1]) #msr.append(sqr(1.*widths[0]/widths[1]*heights[0]/heights[1])) #FIXME: hier irgendwo bleibts haengen, wenn ca. bilder # beim instanziieren ihre local copies nicht laden konnten dimcor = stats.pearson(self.size, pict.size) if dimcor: msr.append(dimcor) #hst=sum(map(lambda (x,y):(x-y)**2, zip(self.histogram, pict.histogram))) hstcor=measure.image_histograms(self, pict) if hstcor: msr.extend(hstcor) mood=measure.image_histmediandist(self, pict) if mood: msr.append(1.-mood) #colorful=measure.image_histrelcor(self, pict) #if colorful: #msr.extend(colorful) #dist = measure.image_hist_dist(self, pict) #if dist: #msr.append(1.-dist/255.) res = 1. #while len(msr)>0: #res *= msr.pop() return sum(msr)/len(msr)
def compare(self, pict):
sim=self.similarity(pict)
if sim>.85:
connect(self,pict,sim)
print 'Similarity: {:2.3f}'.format(sim)
print 'Color mood dist: {:2.3f}'.format(
measure.image_histmediandist(self, pict))
for p in [self, pict]:
print '\tInfo: \t{}'.format(p.info)
print '\tNamespace:\t{}'.format(p.path)
print '\tFilename: \t{}'.format(p.name)
print '\tHistogram:\t{}'.format(p.hist)
print '\tSources:\n\t\t',
for source in p.sources:
print source.name,
print
def similarity(self, pict): # distance of sizes #dim=sum(map(lambda (x,y):(x-y)**2, zip(self.size, pict.size))) #dim/=self.size[0]**2+self.size[1]**2 msr=[] dimensions=zip(self.size, pict.size) widths=sorted(dimensions[0]) heights=sorted(dimensions[1]) msr.append(sqr(1.*widths[0]/widths[1]*heights[0]/heights[1])) #hst=sum(map(lambda (x,y):(x-y)**2, zip(self.histogram, pict.histogram))) hstcor=measure.image_histograms(self, pict) msr.extend(hstcor) mood=measure.image_histmediandist(self, pict) msr.append(1-mood) #colorful=measure.image_histrelcor(self, pict) #msr.extend(colorful) return sum(msr)/len(msr)
