def mergeWith(a, b):
if a is None:
return b
tot = float(a.count + b.count)
r = (a.count / tot) * a.rgb_aves[0] + (b.count / tot) * b.rgb_aves[0]
g = (a.count / tot) * a.rgb_aves[1] + (b.count / tot) * b.rgb_aves[1]
b = (a.count / tot) * a.rgb_aves[2] + (b.count / tot) * b.rgb_aves[2]
return Bucket(r, g, b, tot)
def bucketize(r, g, b, buckets):
nb = Bucket(r, g, b)
matches = filter(cutoff, [(nb.col.delta_e(bucket.col), bucket)
for bucket in buckets])
numMatches = len(matches)
if numMatches == 0:
buckets.append(nb)
elif numMatches == 1:
ind = buckets.index(matches[0][1])
buckets[ind].count += 1
else:
for (diff, bucket) in matches:
buckets.remove(bucket)
b = reduce(mergeWith, [bucket for (diff, bucket) in matches], nb)
buckets.append(b)
def getInverseWeightedAverage(x, y, neighbors): totalDist = 0 buckets = [] for (n_x, n_y, (r,g,b)) in neighbors: xDif = x - n_x yDif = y - n_y dist = math.sqrt(xDif*xDif + yDif*yDif) totalDist += dist buckets.append( (Bucket(r,g,b), dist) ) for (bucket, dist) in buckets: if dist == 0: bucket.count = 1 else: bucket.count = (totalDist - dist) / dist return reduce(mergeWith, [bucket for (bucket, dist) in buckets])
def createMosaic(db, photo_filename): im = Image.open(photo_filename) pix = im.load() size = im.size print size labels = [[None for x in range(size[1])] for y in range(size[0])] d = collections.defaultdict(int) for x in range(size[0]): if x % 25 == 0: print x / float(size[0]) for y in range(size[1]): (r,g,b) = im.getpixel((x,y)) pix = [ Bucket(r,g,b) ] bestSample = selectBestImage(db, pix) d[bestSample] += 1 labels[x][y] = bestSample print "Building photo" thumbs = createThumbnails(d) result = generateImage(labels, thumbs, size) return result