Immagine a nascondino

In questa sfida, è necessario trovare un pixel specifico all'interno di una fotografia (scattata con una vera fotocamera).

Hai passato una tupla (R, G, B) e un'immagine e devi restituire un punto (x, y) all'interno dell'immagine che corrisponde al colore RGB indicato . L'immagine può avere più punti che corrispondono al colore; devi solo trovare 1.

La sfida è che devi farlo leggendo il minor numero di pixel possibile . Il tuo punteggio sarà il numero totale di pixel letti in tutti i casi di test.

Se lo desideri, puoi leggere l'intera immagine in una matrice di valori RGB, purché non esegua alcuna elaborazione sui pixel. Lo autorizzo esclusivamente a fini di efficienza. Ad esempio, in Python, list(Image.open("image_name+".jpg").convert("RGB").getdata())va bene.

Posizioni hardcoding non consentite. Il tuo algoritmo dovrebbe funzionare bene solo per i casi di test elencati di seguito. Non è consentito salvare i dati tra i casi di test. Ho scelto valori RGB che appaiono di rado ( <10) nell'immagine (nel caso in cui ciò faccia la differenza per il tuo algoritmo). Se stai usando la casualità nel tuo algoritmo, ti preghiamo di impostare un seme, in modo che il tuo punteggio sia costante.

Le immagini possono essere trovate su Github

Casi test:

image_name: 
(r, g, b) [all possible answers]

barn:
(143,91,33) [(887,1096),(2226,1397),(2007,1402),(2161,1508),(1187,1702)]
(53,35,59) [(1999,1260)]
(20,24,27) [(1328,1087),(154,1271)]
(167,148,176) [(1748,1204)]
(137,50,7) [(596,1498)]
(116,95,94) [(1340,1123)]
(72,49,59) [(1344,857),(1345,858),(1380,926),(1405,974),(1480,1117)]
(211,163,175) [(1963,745)]
(30,20,0) [(1609,1462),(1133,1477),(1908,1632)]
(88,36,23) [(543,1494),(431,1575)]
daisy:
(21,57,91) [(1440,1935),(2832,2090),(2232,2130),(1877,2131),(1890,2132)]
(201,175,140) [(1537,1749),(2319,1757)]
(169,160,0) [(2124,759)]
(113,123,114) [(1012,994),(2134,1060),(1803,1183),(1119,1335)]
(225,226,231) [(3207,829),(3256,889),(3257,889),(1434,981),(2599,1118),(2656,1348),(2656,1351)]
(17,62,117) [(2514,3874),(2336,3885)]
(226,225,204) [(3209,812)]
(119,124,146) [(2151,974),(2194,1021),(2194,1022),(2202,1034),(2812,1500)]
(2,63,120) [(2165,3881),(2326,3882),(2330,3882),(2228,3887)]
(200,167,113) [(1453,1759)]
dandelion:
(55,2,46) [(667,825),(668,825)]
(95,37,33) [(1637,1721),(1625,1724),(1405,1753),(2026,2276),(2016,2298)]
(27,41,50) [(1267,126),(424,519),(2703,1323),(1804,3466)]
(58,92,129) [(2213,3274)]
(136,159,105) [(1300,2363),(2123,2645),(1429,3428),(1430,3432),(1417,3467),(1393,3490),(1958,3493)]
(152,174,63) [(2256,2556)]
(78,49,19) [(2128,2836)]
(217,178,205) [(2736,3531)]
(69,95,130) [(870,305),(493,460),(2777,1085),(2791,1292),(2634,3100)]
(150,171,174) [(2816,1201),(2724,2669),(1180,2706),(1470,3215),(1471,3215)]
gerbera:
(218,186,171) [(4282,1342)]
(180,153,40) [(4596,1634),(4369,1682),(4390,1708),(4367,1750)]
(201,179,119) [(4282,1876),(4479,1928)]
(116,112,149) [(5884,252),(4168,371),(4169,372),(4164,384),(5742,576)]
(222,176,65) [(4232,1548)]
(108,129,156) [(5341,3574),(5339,3595),(5302,3734)]
(125,99,48) [(4548,1825),(4136,1932),(5054,2013),(5058,2023),(5058,2035),(5055,2050),(5031,2073)]
(170,149,32) [(4461,1630),(4520,1640)]
(156,185,203) [(3809,108)]
(103,67,17) [(4844,1790)]
hot-air:
(48,21,36) [(1992,1029),(2005,1030),(2015,1034),(2018,1036)]
(104,65,36) [(3173,1890),(3163,1893)]
(169,89,62) [(4181,931),(4210,938),(4330,1046),(4171,1056),(3117,1814)]
(68,59,60) [(1872,220),(1874,220),(1878,220),(1696,225),(3785,429)]
(198,96,74) [(4352,1057)]
(136,43,53) [(1700,931)]
(82,42,32) [(4556,961),(4559,973),(4563,989),(4563,990),(4441,1004),(4387,1126),(4378,1128)]
(192,132,72) [(1399,900),(3105,1822),(3104,1824),(3105,1824),(3107,1826),(3107,1827),(3104,1839),(3119,1852)]
(146,21,63) [(1716,993)]
(125,64,36) [(4332,937)]
in-input:
(204,90,1) [(1526,1997),(1385,2145),(4780,2807),(4788,3414)]
(227,163,53) [(1467,1739),(2414,1925),(2441,2198),(134,2446)]
(196,179,135) [(3770,2740),(1110,3012),(3909,3216),(1409,3263),(571,3405)]
(208,59,27) [(1134,1980),(4518,2108),(4515,2142)]
(149,70,1) [(4499,1790),(2416,2042),(1338,2150),(3731,2408),(3722,2409),(4400,3618)]
(168,3,7) [(987,402),(951,432),(1790,1213),(1790,1214),(1848,1217),(4218,1840),(4344,1870),(1511,1898)]
(218,118,4) [(3857,1701),(1442,1980),(1411,2156),(25,2606)]
(127,153,4) [(3710,2813)]
(224,230,246) [(2086,160),(2761,222),(4482,1442)]
(213,127,66) [(4601,1860),(4515,2527),(4757,2863)]
klatschmohn:
(170,133,19) [(1202,2274),(1202,2275),(957,2493),(1034,2633),(3740,3389),(3740,3391),(3683,3439)]
(162,92,4) [(489,2854)]
(159,175,104) [(3095,2475),(3098,2481)]
(199,139,43) [(1956,3055)]
(171,169,170) [(3669,1487),(3674,1490),(3701,1507)]
(184,115,58) [(1958,2404)]
(228,169,5) [(1316,2336),(1317,2336)]
(179,165,43) [(3879,2380),(1842,2497),(1842,2498)]
(67,21,6) [(1959,2197),(2157,2317),(2158,2317),(2158,2318),(2116,2373)]
(213,100,106) [(1303,1816)]
tajinaste-rojo:
(243,56,99) [(1811,2876),(1668,4141),(2089,4518),(1981,4732),(1659,4778),(2221,5373),(1779,5598),(2210,5673),(2373,5860)]
(147,157,210) [(1835,1028),(1431,3358)]
(114,37,19) [(1792,3572),(1818,3592)]
(108,117,116) [(2772,4722),(1269,5672),(2512,5811),(2509,5830),(2186,5842),(2186,5846),(2190,5851),(2211,5884)]
(214,197,93) [(1653,4386)]
(163,102,101) [(2226,2832),(2213,3683),(1894,4091),(1875,4117)]
(192,192,164) [(2175,2962),(2206,3667),(2315,3858),(1561,3977),(3039,5037),(3201,5641)]
(92,118,45) [(1881,1704),(1983,1877),(2254,2126),(3753,5862),(3766,5883)]
(145,180,173) [(1826,1585)]
(181,124,105) [(1969,3892)]
turret-arch:
(116,70,36) [(384,648),(516,669)]
(121,115,119) [(2419,958)]
(183,222,237) [(172,601),(183,601),(110,611),(111,617)]
(237,136,82) [(2020,282),(676,383),(748,406),(854,482),(638,497),(647,661),(1069,838),(1809,895),(1823,911)]
(193,199,215) [(1567,919),(1793,1047)]
(33,30,25) [(1307,861),(309,885),(1995,895),(504,1232),(2417,1494)]
(17,23,39) [(1745,1033),(788,1090),(967,1250)]
(192,139,95) [(1445,1337)]
(176,125,98) [(1197,1030)]
(178,83,0) [(2378,1136)]
water-lilies:
(86,140,80) [(2322,2855),(4542,3005),(4540,3006),(4577,3019)]
(218,124,174) [(1910,2457)]
(191,77,50) [(2076,1588)]
(197,211,186) [(4402,1894)]
(236,199,181) [(2154,1836)]
(253,242,162) [(1653,1430)]
(114,111,92) [(1936,2499)]
(111,93,27) [(2301,2423),(2127,2592),(2137,2717),(2147,2717)]
(139,92,102) [(1284,2243),(1297,2258)]
(199,157,117) [(3096,993)]

Python, punteggio: 14.035.624

Per prima cosa, ecco il codice:

from heapq import heappush, heappop
from PIL import Image
import random

random.seed(1)


def dist(x, y):
    return sum([(x[i] - y[i]) ** 2 for i in range(3)])


def gradient_descent(image_name, c):
    im = Image.open(image_name + '.jpg').convert('RGB')
    L = im.load()
    sx, sy = im.size
    heap = []
    visited = set()
    count = 0
    points = []
    for i in range(0, sx, sx / 98):
        for j in range(0, sy, sy / 98):
            points.append((i, j))
    for x in points:
        heappush(heap, [dist(c, L[x[0], x[1]]), [x[0], x[1]]])
        visited.add((x[0], x[1]))

    while heap:
        if count % 10 == 0:
            x = random.random()
            if x < 0.5:
                n = heap.pop(random.randint(10, 100))
            else:
                n = heappop(heap)
        else:
            n = heappop(heap)
        x, y = n[1]
        c_color = L[x, y]
        count += 1

        if c_color == c:
            p = float(len(visited)) / (sx * sy) * 100
            print('count: {}, percent: {}, position: {}'.format(len(visited), p, (x, y)))
            return len(visited)

        newpoints = []
        newpoints.append((x + 1, y))
        newpoints.append((x - 1, y))
        newpoints.append((x, y + 1))
        newpoints.append((x, y - 1))
        newpoints.append((x + 1, y + 1))
        newpoints.append((x + 1, y - 1))
        newpoints.append((x - 1, y + 1))
        newpoints.append((x - 1, y - 1))

        for p in newpoints:
            if p not in visited:
                try:
                    d = dist(c, L[p[0], p[1]])
                    heappush(heap, [d, [p[0], p[1]]])
                    visited.add(p)
                except IndexError:
                    pass

ed ecco una gif che mostra come l'algoritmo esamina i pixel:

Quindi, ecco cosa sta facendo questo codice: la variabile heapè una coda prioritaria di (x, y)coordinate nell'immagine, ordinate in base alla distanza euclidea del colore a quella coordinata rispetto al colore target. Viene inizializzato con 10.200 punti che sono distribuiti uniformemente sull'intera immagine.

Con l'heap inizializzato, pop-up il punto con la distanza minima dal colore target. Se il colore in quel punto ha una distanza> 0, vale a dire, se il colore in quel punto NON è il colore target, aggiungeremo gli 8 punti circostanti da esso a heap. Per garantire che un dato punto non venga considerato più di una volta, manteniamo la variabile visited, che è un insieme di tutti i punti che sono stati esaminati finora.

Di tanto in tanto, invece di prendere direttamente il punto con la distanza minima di colore, sceglieremo a caso qualche altro punto da vicino la parte superiore della coda. Questo non è strettamente necessario, ma nei miei test, si riduce di circa 1.000.000 di pixel dal punteggio totale. Una volta trovato il colore target, restituiamo semplicemente la lunghezza visiteddell'insieme.

Come @Karl Napf, ho ignorato i casi di test in cui il colore specificato non era presente nell'immagine. Puoi trovare un programma driver per eseguire tutti i casi di test nel repository GitHub che ho creato per questa risposta.

Ecco i risultati di ciascun caso di test specifico:

barn
color: (143, 91, 33), count: 20388 / 0.452483465755%, position: (612, 1131)
color: (53, 35, 59), count: 99606 / 2.21061742643%, position: (1999, 1260)
color: (72, 49, 59), count: 705215 / 15.6512716943%, position: (1405, 974)

daisy
color: (21, 57, 91), count: 37393 / 0.154770711039%, position: (1877, 2131)
color: (169, 160, 0), count: 10659 / 0.0441179100089%, position: (2124, 759)
color: (113, 123, 114), count: 674859 / 2.79326096545%, position: (1119, 1335)
color: (225, 226, 231), count: 15905 / 0.0658312560927%, position: (3256, 889)
color: (17, 62, 117), count: 15043 / 0.0622634131029%, position: (2514, 3874)
color: (226, 225, 204), count: 138610 / 0.573710808362%, position: (1978, 1179)
color: (119, 124, 146), count: 390486 / 1.61623287435%, position: (2357, 917)
color: (2, 63, 120), count: 10063 / 0.0416510487306%, position: (2324, 3882)
color: (200, 167, 113), count: 16393 / 0.06785110224%, position: (1453, 1759)

dandelion
color: (95, 37, 33), count: 10081 / 0.0686342592593%, position: (1625, 1724)
color: (27, 41, 50), count: 2014910 / 13.7180691721%, position: (1267, 126)
color: (58, 92, 129), count: 48181 / 0.328029684096%, position: (1905, 756)
color: (136, 159, 105), count: 10521 / 0.0716299019608%, position: (1416, 3467)
color: (152, 174, 63), count: 10027 / 0.0682666122004%, position: (2256, 2558)
color: (69, 95, 130), count: 201919 / 1.37472086057%, position: (2708, 2943)
color: (150, 171, 174), count: 29714 / 0.202301198257%, position: (1180, 2706)

gerbera
color: (180, 153, 40), count: 21904 / 0.0906612910062%, position: (4459, 1644)
color: (116, 112, 149), count: 14896 / 0.0616549758413%, position: (5884, 252)
color: (222, 176, 65), count: 76205 / 0.315414704215%, position: (4313, 2097)
color: (108, 129, 156), count: 12273 / 0.0507983027994%, position: (5302, 3734)
color: (125, 99, 48), count: 26968 / 0.111621333814%, position: (5054, 2013)
color: (170, 149, 32), count: 89591 / 0.370819746281%, position: (4478, 1647)
color: (156, 185, 203), count: 177373 / 0.734151989118%, position: (4096, 368)
color: (103, 67, 17), count: 11035 / 0.0456741849093%, position: (4844, 1790)

hot-air
color: (48, 21, 36), count: 49711 / 0.24902994992%, position: (1990, 1095)
color: (104, 65, 36), count: 9927 / 0.0497298447599%, position: (3191, 1846)
color: (68, 59, 60), count: 195418 / 0.978957066918%, position: (3948, 470)
color: (82, 42, 32), count: 12216 / 0.0611967143737%, position: (4559, 984)
color: (192, 132, 72), count: 116511 / 0.583668171938%, position: (3103, 1844)

in-input
color: (204, 90, 1), count: 44058 / 0.248299807393%, position: (4695, 2559)
color: (227, 163, 53), count: 12654 / 0.0713147615132%, position: (221, 2384)
color: (196, 179, 135), count: 181534 / 1.02307996812%, position: (1030, 3486)
color: (208, 59, 27), count: 9956 / 0.0561095120614%, position: (4518, 2108)
color: (149, 70, 1), count: 13698 / 0.0771984829467%, position: (3731, 2408)
color: (168, 3, 7), count: 19381 / 0.10922644167%, position: (942, 398)
color: (218, 118, 4), count: 36648 / 0.206538911011%, position: (25, 2606)
color: (224, 230, 246), count: 1076427 / 6.06647185011%, position: (4482, 1442)
color: (213, 127, 66), count: 62673 / 0.353209265712%, position: (4701, 2579)

klatschmohn
color: (170, 133, 19), count: 11535 / 0.0724321530189%, position: (1034, 2633)
color: (162, 92, 4), count: 103795 / 0.651763790429%, position: (489, 2854)
color: (159, 175, 104), count: 10239 / 0.0642941321856%, position: (3098, 2481)
color: (171, 169, 170), count: 10119 / 0.063540611738%, position: (3674, 1490)
color: (184, 115, 58), count: 22425 / 0.140814133632%, position: (1958, 2404)
color: (228, 169, 5), count: 10449 / 0.0656127929688%, position: (1316, 2336)
color: (179, 165, 43), count: 10285 / 0.0645829816905%, position: (1842, 2498)
color: (67, 21, 6), count: 10206 / 0.0640869140625%, position: (2116, 2373)
color: (213, 100, 106), count: 11712 / 0.073543595679%, position: (1303, 1816)

tajinaste-rojo
color: (243, 56, 99), count: 126561 / 0.5273375%, position: (2241, 5424)
color: (114, 37, 19), count: 11285 / 0.0470208333333%, position: (1818, 3583)
color: (108, 117, 116), count: 33855 / 0.1410625%, position: (1269, 5672)
color: (163, 102, 101), count: 1058090 / 4.40870833333%, position: (1546, 4867)
color: (192, 192, 164), count: 10118 / 0.0421583333333%, position: (1919, 3171)
color: (92, 118, 45), count: 13431 / 0.0559625%, position: (3766, 5883)
color: (145, 180, 173), count: 1207713 / 5.0321375%, position: (1863, 955)

turret-arch
color: (116, 70, 36), count: 145610 / 3.23161258822%, position: (96, 671)
color: (183, 222, 237), count: 11704 / 0.259754094722%, position: (140, 604)
color: (237, 136, 82), count: 60477 / 1.34220338231%, position: (1063, 993)
color: (193, 199, 215), count: 359671 / 7.98240046163%, position: (2259, 953)
color: (33, 30, 25), count: 148195 / 3.28898308846%, position: (1307, 861)
color: (17, 23, 39), count: 10601 / 0.235274535044%, position: (2080, 1097)
color: (192, 139, 95), count: 219732 / 4.87664787607%, position: (1127, 970)
color: (176, 125, 98), count: 2471787 / 54.8578942696%, position: (147, 734)

water-lilies
color: (86, 140, 80), count: 10371 / 0.0717376936238%, position: (4542, 3005)
color: (218, 124, 174), count: 25655 / 0.177459312498%, position: (1910, 2457)
color: (197, 211, 186), count: 1144341 / 7.91557073177%, position: (4402, 1894)
color: (253, 242, 162), count: 14174 / 0.0980435897622%, position: (1672, 1379)
color: (111, 93, 27), count: 10405 / 0.0719728764975%, position: (2147, 2717)
color: (199, 157, 117), count: 10053 / 0.0695380420403%, position: (3096, 993)

Total: 14035624

Python, punteggio: 396.250.646

• Mentre non ci sono PNG da analizzare e ci sono ancora problemi con i test, ho deciso di programmare comunque.
• Quelle prove in cui il valore non è presente nell'immagine sono state ignorate (confrontate con una ricerca lineare tradizionale, che aveva un punteggio di 544.017.431 )

from PIL import Image

def dist(x,y):
 d = 0
 for i in range(3):
  d += (x[i]-y[i])**2
 return d

def mid(x,y):
 return (x+y)/2

class Finder:
 def __init__(self, image_name, c):
  self.image_name = image_name,
  self.c = c
  self.found = False
  self.position = None
  self.im = Image.open(image_name+".jpg").convert("RGB")
  self.L = self.im.load()
  self.visited = set()

 def quadsearch(self,x0,x1,y0,y1):
  if x0==x1 and y0==y1: return
  xm=mid(x0,x1)
  ym=mid(y0,y1)
  R = [
   (x0,xm,y0,ym),
   (xm,x1,y0,ym),
   (x0,xm,ym,y1),
   (xm,x1,ym,y1),
   ]
  P = [(mid(t[0],t[1]), mid(t[2],t[3])) for t in R]
  DR = []
  for i in range(len(P)):
   p = P[i]
   if p in self.visited: continue
   self.visited.add(p)
   u = self.L[p[0], p[1]]
   d = dist(u, self.c)
   if d == 0:
    self.found = True
    self.position = (p[0], p[1])
    return
   DR.append((d,R[i]))
  S = sorted(range(len(DR)), key=lambda k: DR[k][0])
  for i in S:
   if self.found == True: return
   r = DR[i][1]
   self.quadsearch(r[0], r[1], r[2], r[3])

 def start(self):
  sx,sy = self.im.size
  self.quadsearch(0,sx,0,sy)

 def result(self):
  if self.found:
   count = len(self.visited)
   sx,sy = self.im.size
   ratio = float(count)/(sx*sy)
   print len(self.visited), ratio, self.position, self.L[self.position[0], self.position[1]], "=", self.c
  else:
   print self.c, "not found"

if __name__ == "__main__":
 image_name="turret-arch"
 c=(116,70,36)
 F = Finder(image_name, c)
 F.start()
 F.result()

È una ricerca ricorsiva in quattro sezioni. A volte trova il valore corretto in pochi percento, a volte oltre il 75%. Ecco i risultati per tutti i test:

pixels_visited, percentage, (position) (RGB at position) = (RGB searched)

tajinaste-rojo
1500765 0.062531875 (2329, 5146) (243, 56, 99) = (243, 56, 99)
(147, 157, 210) not found
335106 0.01396275 (2116, 5791) (114, 37, 19) = (114, 37, 19)
1770396 0.0737665 (1269, 5672) (108, 117, 116) = (108, 117, 116)
(214, 197, 93) not found
8086276 0.336928166667 (1546, 4867) (163, 102, 101) = (163, 102, 101)
12859 0.000535791666667 (1476, 4803) (192, 192, 164) = (192, 192, 164)
7505961 0.312748375 (3766, 5883) (92, 118, 45) = (92, 118, 45)
15057489 0.627395375 (1871, 1139) (145, 180, 173) = (145, 180, 173)
(181, 124, 105) not found
in-input
35754 0.00201500551852 (4695, 2559) (204, 90, 1) = (204, 90, 1)
5029615 0.283456451895 (10, 2680) (227, 163, 53) = (227, 163, 53)
6986547 0.393744217722 (1383, 3446) (196, 179, 135) = (196, 179, 135)
1608341 0.090642053775 (4518, 2108) (208, 59, 27) = (208, 59, 27)
581774 0.0327873194757 (3750, 2798) (149, 70, 1) = (149, 70, 1)
1302581 0.0734101891628 (4374, 1941) (168, 3, 7) = (168, 3, 7)
6134761 0.345739701008 (25, 2606) (218, 118, 4) = (218, 118, 4)
(127, 153, 4) not found
9760033 0.550050913352 (4482, 1442) (224, 230, 246) = (224, 230, 246)
212816 0.0119937745268 (4701, 2579) (213, 127, 66) = (213, 127, 66)
water-lilies
5649260 0.390767412093 (4577, 3019) (86, 140, 80) = (86, 140, 80)
12600699 0.871608412215 (1910, 2457) (218, 124, 174) = (218, 124, 174)
(191, 77, 50) not found
3390653 0.234536328318 (4402, 1894) (197, 211, 186) = (197, 211, 186)
(236, 199, 181) not found
7060220 0.488365537823 (1672, 1379) (253, 242, 162) = (253, 242, 162)
(114, 111, 92) not found
6852380 0.473988947097 (2147, 2717) (111, 93, 27) = (111, 93, 27)
(139, 92, 102) not found
14105709 0.975712111261 (3096, 993) (199, 157, 117) = (199, 157, 117)
dandelion
(55, 2, 46) not found
9094264 0.619162854031 (1637, 1721) (95, 37, 33) = (95, 37, 33)
2358912 0.16060130719 (1526, 3129) (27, 41, 50) = (27, 41, 50)
11729837 0.798600013617 (1905, 756) (58, 92, 129) = (58, 92, 129)
6697060 0.455954520697 (2246, 2685) (136, 159, 105) = (136, 159, 105)
6429635 0.437747480937 (2148, 2722) (152, 174, 63) = (152, 174, 63)
(78, 49, 19) not found
(217, 178, 205) not found
80727 0.00549611928105 (2481, 3133) (69, 95, 130) = (69, 95, 130)
239962 0.0163372821351 (2660, 917) (150, 171, 174) = (150, 171, 174)
turret-arch
210562 0.0467313240712 (725, 655) (116, 70, 36) = (116, 70, 36)
(121, 115, 119) not found
2548703 0.565649385237 (140, 604) (183, 222, 237) = (183, 222, 237)
150733 0.033453104887 (2165, 601) (237, 136, 82) = (237, 136, 82)
3458188 0.767497003862 (2259, 953) (193, 199, 215) = (193, 199, 215)
2430256 0.539361711572 (265, 1222) (33, 30, 25) = (33, 30, 25)
638995 0.141816103689 (1778, 1062) (17, 23, 39) = (17, 23, 39)
2506522 0.556287895601 (1127, 970) (192, 139, 95) = (192, 139, 95)
1344400 0.298370988504 (147, 734) (176, 125, 98) = (176, 125, 98)
(178, 83, 0) not found
hot-air
17474837 0.875411434688 (1992, 1029) (48, 21, 36) = (48, 21, 36)
1170064 0.0586149905099 (3191, 1846) (104, 65, 36) = (104, 65, 36)
(169, 89, 62) not found
11891624 0.595717352134 (3948, 470) (68, 59, 60) = (68, 59, 60)
(198, 96, 74) not found
(136, 43, 53) not found
12476811 0.625032612198 (4387, 1126) (82, 42, 32) = (82, 42, 32)
4757856 0.238347376116 (3105, 1822) (192, 132, 72) = (192, 132, 72)
(146, 21, 63) not found
(125, 64, 36) not found
daisy
5322196 0.220287235367 (2171, 2128) (21, 57, 91) = (21, 57, 91)
(201, 175, 140) not found
22414990 0.9277629343 (2124, 759) (169, 160, 0) = (169, 160, 0)
20887184 0.864526601043 (1119, 1335) (113, 123, 114) = (113, 123, 114)
595712 0.0246566923794 (2656, 1349) (225, 226, 231) = (225, 226, 231)
3397561 0.140626034757 (2514, 3874) (17, 62, 117) = (17, 62, 117)
201068 0.00832226281046 (1978, 1179) (226, 225, 204) = (226, 225, 204)
18693250 0.773719036752 (2357, 917) (119, 124, 146) = (119, 124, 146)
3091040 0.127939041706 (2165, 3881) (2, 63, 120) = (2, 63, 120)
3567932 0.147677739839 (1453, 1759) (200, 167, 113) = (200, 167, 113)
barn
314215 0.0697356740202 (784, 1065) (143, 91, 33) = (143, 91, 33)
2448863 0.543491277908 (1999, 1260) (53, 35, 59) = (53, 35, 59)
(20, 24, 27) not found
(167, 148, 176) not found
(137, 50, 7) not found
(116, 95, 94) not found
2042891 0.453391406631 (1345, 858) (72, 49, 59) = (72, 49, 59)
(211, 163, 175) not found
(30, 20, 0) not found
(88, 36, 23) not found
klatschmohn
3048249 0.191409829222 (3683, 3439) (170, 133, 19) = (170, 133, 19)
1057649 0.0664133456509 (489, 2854) (162, 92, 4) = (162, 92, 4)
2058022 0.129230138206 (3095, 2475) (159, 175, 104) = (159, 175, 104)
(199, 139, 43) not found
2060805 0.129404892156 (3674, 1490) (171, 169, 170) = (171, 169, 170)
7725501 0.485110247577 (1958, 2404) (184, 115, 58) = (184, 115, 58)
2986734 0.187547095028 (1316, 2336) (228, 169, 5) = (228, 169, 5)
497709 0.0312528257017 (3879, 2379) (179, 165, 43) = (179, 165, 43)
3996978 0.250983720944 (2157, 2318) (67, 21, 6) = (67, 21, 6)
3332106 0.209234167028 (1303, 1816) (213, 100, 106) = (213, 100, 106)
gerbera
(218, 186, 171) not found
9445576 0.390955128952 (4377, 1750) (180, 153, 40) = (180, 153, 40)
(201, 179, 119) not found
6140398 0.254152853347 (5742, 576) (116, 112, 149) = (116, 112, 149)
6500717 0.269066561215 (4233, 1541) (222, 176, 65) = (222, 176, 65)
13307056 0.550782905612 (5302, 3734) (108, 129, 156) = (108, 129, 156)
13808847 0.571552180573 (5058, 2023) (125, 99, 48) = (125, 99, 48)
9454870 0.391339810307 (4478, 1647) (170, 149, 32) = (170, 149, 32)
2733978 0.113160142012 (4096, 368) (156, 185, 203) = (156, 185, 203)
11848606 0.490417237301 (4844, 1790) (103, 67, 17) = (103, 67, 17)