画像のかくれんぼ

この課題では、写真（実際のカメラで撮影）内の特定のピクセルを見つける必要があります。

（R、G、B）タプルと画像が渡され、指定されたRGBカラーに一致する画像内のポイント（x、y）を返す必要があります。画像には、色と一致する複数のポイントがある場合があります。あなただけを見つける必要があります1。

課題は、あなたがそれを行う必要があるということですできるだけ少数の画素として読みながら。スコアは、すべてのテストケースで読み取られたピクセルの総数になります。

必要に応じて、ピクセルで処理を行わない限り、画像全体をRGB値の配列に読み込むことができます。これは純粋に効率化の目的で許可しています。たとえば、Pythonではlist(Image.open("image_name+".jpg").convert("RGB").getdata())大丈夫です。

場所のハードコーディングは許可されていません。アルゴリズムは、以下にリストされているテストケース以外にも機能するはずです。テストケース間でデータを保存することはできません。<10画像にまれにしか表示されないRGB値（）を選択しました（アルゴリズムに違いがある場合）。アルゴリズムでランダム性を使用している場合、スコアが一定になるようにシードを設定してください。

画像はGithubにあります

テストケース：

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、スコア：14,035,624

まず最初に、ここにコードがあります：

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

そして、アルゴリズムがピクセルをどのように検査するかを示すgifです：

そのため、このコードが行うことは次のとおりです。変数heapは、画像内の座標の優先キューで(x, y)あり、その座標の色とターゲットカラーのユークリッド距離でソートされます。画像全体に均等に分布する10,200ポイントで初期化されます。

ヒープが初期化されたら、ターゲットカラーまでの最小距離でポイントをポップします。そのポイントの色の距離が0より大きい場合、つまりそのポイントの色がターゲットカラーではない場合、周囲の8つのポイントをに追加しheapます。特定のポイントが複数回考慮されないようにするために、変数を維持しますvisited。これは、これまでに調べられたすべてのポイントのセットです。

場合によっては、最小色距離のポイントを直接取得する代わりに、キューの上部付近から他のポイントをランダムに選択します。これは厳密に必要というわけではありませんが、私のテストでは、合計スコアから約1,000,000ピクセルを削ぎ落とします。ターゲットカラーが見つかったら、visitedセットの長さを返すだけです。

@Karl Napfのように、指定した色が画像に存在しないテストケースを無視しました。この回答用に作成したGitHubリポジトリで、すべてのテストケースを実行するドライバープログラムを見つけることができます。

特定の各テストケースの結果は次のとおりです。

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、スコア：396,250,646

• 解析するPNGはなく、テストケースにはまだ問題がありますが、とにかくプログラムすることにしました。
• 画像に値が存在しないテストケースは無視されました（スコアが544,017,431だった従来の線形検索と比較してチェックされました）

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()

これは、再帰的なクワッドセクション検索です。正しい値が数パーセントで見つかることもあれば、75％以上になることもあります。すべてのテストケースの結果は次のとおりです。

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)