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 image_histrelcor(p,q): corrs=[] rel=[image_histrelcol(p), image_histrelcol(q)] for bank in range(0,96,32): cor=stats.pearson(rel[0][bank:bank+32], rel[1][bank:bank+32]) corrs.append(cor) return corrs
def image_histograms(p, q): # asume number color tones has been reduced to 32 by Picture class # if those two pictures are not in the same colorspace, thats no prob # while the first one might visit its B, G, and R histograms, the other # one just stays in its black'n'white space. # handle maximum colorspace, however colspace=sorted([p.mode, q.mode], key=lambda m:len(m))[-1] correlations=[] for offset,band in enumerate(colspace): off1=offset*32%len(p.histogram) off2=offset*32%len(q.histogram) corr=stats.pearson(p.histogram[off1:off1+32], q.histogram[off2:off2+32]) correlations.append(corr) # now how do we put them together? #res=sum(correlations)/len(correlations) return correlations
def image_histograms(p, q): # asume number color tones has been reduced to 32 by Picture class # if those two pictures are not in the same colorspace, thats no prob # while the first one might visit its B, G, and R histograms, the other # one just stays in its black'n'white space. # handle maximum colorspace, however #colspace=sorted([p.mode, q.mode], key=lambda m:len(m))[-1] v = lookup(hist_correlations, p, q) if v: return v bands = max(p.histogram.bands, q.histogram.bands) correlations=[] for offset in range(bands): h1 = p.histogram.array(bands=bands) h2 = q.histogram.array(bands=bands) off1=offset*32%len(h1) off2=offset*32%len(h2) corr=stats.pearson(h1[off1:off1+32], h2[off2:off2+32]) correlations.append(corr) # now how do we put them together? #res=sum(correlations)/len(correlations) register(hist_correlations, p, q, correlations) return correlations
