Image Hide-and-Seek

W tym wyzwaniu musisz znaleźć określony piksel na zdjęciu (zrobionym prawdziwym aparatem).

Otrzymujesz krotkę (R, G, B) i obraz, i musisz zwrócić punkt (x, y) w obrazie, który odpowiada podanemu kolorowi RGB . Obraz może zawierać wiele punktów pasujących do koloru; musisz tylko znaleźć 1.

Wyzwaniem jest to, że trzeba to zrobić podczas czytania jak kilka pikseli, jak to możliwe . Twój wynik będzie całkowitą liczbą pikseli odczytanych we wszystkich przypadkach testowych.

Jeśli chcesz, możesz odczytać cały obraz w tablicy wartości RGB, o ile nie wykonujesz żadnego przetwarzania pikseli. Pozwalam na to wyłącznie w celu zwiększenia wydajności. Na przykład w Pythonie list(Image.open("image_name+".jpg").convert("RGB").getdata())jest w porządku.

Lokalizacje na stałe są niedozwolone. Twój algorytm powinien działać dobrze w przypadku więcej niż tylko przypadków testowych wymienionych poniżej. Nie wolno zapisywać danych między przypadkami testowymi. Wybrałem wartości RGB, które pojawiają się rzadko ( <10) na obrazie (w przypadku, gdy ma to znaczenie dla twojego algorytmu). Jeśli używasz losowości w algorytmie, ustaw ziarno, aby twój wynik był stały.

Obrazy można znaleźć na Github

Przypadki testowe:

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, wynik: 14.035.624

Po pierwsze, oto kod:

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

a oto gif pokazujący, jak algorytm sprawdza piksele:

Tak więc, oto co ten kod robi: Zmienna heapjest priorytetem kolejka z(x, y) współrzędnymi w obrazie, posortowana według odległości euklidesowej koloru na które koordynują do koloru docelowego. Zostaje zainicjowany z 10.200 punktów, które są równomiernie rozmieszczone na całym obrazie.

Po zainicjowaniu stosu, zsuwamy punkt z minimalną odległością od koloru docelowego. Jeśli kolor w tym punkcie ma odległość> 0, to znaczy, jeśli kolor w tym punkcie NIE jest kolorem docelowym, dodajemy od niego 8 otaczających punktów heap. Aby upewnić się, że dany punkt nie jest rozpatrywany więcej niż jeden raz, zachowujemy zmienną visited, która jest zbiorem wszystkich punktów, które zostały zbadane do tej pory.

Czasami zamiast bezpośrednio zajmować punkt przy minimalnej odległości koloru, losowo wybieramy inny punkt blisko szczytu kolejce. Nie jest to absolutnie konieczne, ale w moich testach goli około 1 000 000 pikseli z całego wyniku. Po znalezieniu docelowego koloru po prostu zwracamy długość visitedzestawu.

Podobnie jak @Karl Napf, zignorowałem przypadki testowe, w których określony kolor nie był obecny na obrazie. Możesz znaleźć program sterownika do uruchomienia przez wszystkie przypadki testowe na repozytorium GitHub, które utworzyłem dla tej odpowiedzi, .

Oto wyniki z każdego konkretnego przypadku testowego:

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, wynik: 396,250,646

• Chociaż nie ma parsowania plików PNG i nadal występują problemy z przypadkami testowymi, zdecydowałem się na programowanie.
• Te przypadki testowe, w których wartość nie jest obecna na obrazie, zostały zignorowane (porównane z tradycyjnym wyszukiwaniem liniowym, które uzyskało wynik 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()

Jest to rekurencyjne wyszukiwanie w quad-section. Czasami znajduje prawidłową wartość w kilku procentach, czasem ponad 75%. Oto wyniki dla wszystkich przypadków testowych:

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)