我遇到的问题是我有两个列表可以创建一个非常慢的大型循环...慢到3.5到4秒。我希望改善这一点。我使用的两个列表都包含Lab Colors。第一个列表是调色板，称之为palette_colors。
第二个列表有我用来比较的各个实验室颜色，称之为query_colors。
我遍历第二个query_colors，将列表中的每种颜色与palette_colors列表中的每种颜色进行比较。从那里我们得到一个距离，用于检查颜色是否落在某个阈值内。
我遇到的问题是，由于palette_colors是一个大型列表（大约300个项目），而query_colors大约有100个，因此它会迭代大约30,000次。
所以问题是，如何才能提高运行速度？
以下是我的一些想法：
并行处理：我尝试使用并行处理，但要么是正确的上下文才能使用它，或者我只是不知道我在做什么......我倾向于不知道我在做什么。
处理后的十六进制值之间的缓存我首先想到的是缓存颜色组合之间的距离，然而，这并没有多大帮助，因为颜色非常具体：FFFFFF！= FFFFFE，即使它们明显相同。
初始Hex查找缓存：另一个想法是比较十六进制值...如果十六进制值匹配，则返回该匹配。但是，Idea 1存在同样的问题。
Numpy Arrays + Distance Function：也许如果有办法将两个列表转换为仅包含Lab值的numpy数组，那么使用CIELAB2000距离函数比较每个？
这是我的全功能脚本（确保安装colormath）：
from time import time
from colormath.color_diff import delta_e_cie2000
from colormath.color_objects import LabColor
from operator import itemgetter
# Helper function for timing
milli_time = lambda: int(round(time() * 1000))
# when merging similar colors, check to see how much of that color there is before merging
def map_colors(query_colors, max_dist=100):
# Contains colors from the palette that are closest to each color
close_colors = []
# loop through colors that we want to map
for color_to_compare in query_colors:
# compare lab distance with palette colors
closest = [check_distance(palette_color, color_to_compare, max_dist) for palette_color in palette_colors]
# Remove "none" values
closest = [c for c in closest if c is not None]
# sort by distance (ascending)
closest = sorted(closest, key=itemgetter('distance'))[:1][0]['hex']
# Remove hash
closest = closest.replace('#','').lower()
# Add to main list of closest colors
close_colors.append(closest)
return close_colors
# Checks the distance betwen lab colors
def check_distance(color_1, color_2, max_dist):
distance = delta_e_cie2000(color_1['lab'], color_2['lab'])
if distance < max_dist:
return {
'hex': color_1['hex'],
'lab': color_1['lab'],
'distance': distance
}
# list of palette colors
# Stack overflow doesn't allow this many characters,
# so you'll have to copy and past the color palette from this url:
# https://codepen.io/anon/pen/bvrwzE?editors=1010
palette_colors = [] # ^^^^
# list of colors to compare
query_colors = [{'lab': LabColor(lab_l=89.82760556495964,lab_a=-3.4924545681218055,lab_b=13.558600954011734)}, {'lab': LabColor(lab_l=2.014962108133794,lab_a=0.22811941599047703,lab_b=1.790011046195017)}, {'lab': LabColor(lab_l=40.39474520781096,lab_a=2.901069537563777,lab_b=11.280131535056025)}, {'lab': LabColor(lab_l=67.39662457756837,lab_a=-2.5976442408520706,lab_b=26.652254040495404)}, {'lab': LabColor(lab_l=32.389426017556374,lab_a=1.0164239936505115,lab_b=12.27627339551004)}, {'lab': LabColor(lab_l=55.13922546782179,lab_a=-1.435016766528352,lab_b=35.18742442417581)}, {'lab': LabColor(lab_l=73.96645091673257,lab_a=1.0198226618362005,lab_b=18.548230422095546)}, {'lab': LabColor(lab_l=44.90651839131053,lab_a=-1.4672716457064805,lab_b=18.154138443480683)}, {'lab': LabColor(lab_l=60.80488926260843,lab_a=-8.077128235007613,lab_b=16.719069040228884)}, {'lab': LabColor(lab_l=4.179197112322317,lab_a=3.642005050652555,lab_b=3.0407269339523646)}, {'lab': LabColor(lab_l=30.180289034511695,lab_a=1.7045267250474505,lab_b=28.01083333844222)}, {'lab': LabColor(lab_l=44.31005006010243,lab_a=-4.362010483995816,lab_b=18.432029645523528)}, {'lab': LabColor(lab_l=0.8423115373777676,lab_a=0.13906540788867494,lab_b=-0.3786920370309088)}, {'lab': LabColor(lab_l=52.12865600856179,lab_a=-0.5797000071502412,lab_b=31.8790459272144)}, {'lab': LabColor(lab_l=67.92970225276791,lab_a=-4.149165904914209,lab_b=33.253179101415256)}, {'lab': LabColor(lab_l=60.97889320274747,lab_a=3.338501380000247,lab_b=20.062676387837676)}, {'lab': LabColor(lab_l=2.593838857738689,lab_a=2.824229469131745,lab_b=2.704743489514988)}, {'lab': LabColor(lab_l=7.392989008245966,lab_a=9.59267973632079,lab_b=6.729836507330539)}, {'lab': LabColor(lab_l=98.10223593819727,lab_a=-1.3873907335449909,lab_b=4.897317053977535)}, {'lab': LabColor(lab_l=82.313865698896,lab_a=2.588499921779952,lab_b=2.5971717623187507)}, {'lab': LabColor(lab_l=28.371415683395696,lab_a=5.560367090545137,lab_b=0.6970013651421025)}, {'lab': LabColor(lab_l=41.300756170362206,lab_a=-1.8010193876651093,lab_b=5.122094973647007)}, {'lab': LabColor(lab_l=5.26507956373176,lab_a=4.548521840585698,lab_b=-0.8421897365563757)}, {'lab': LabColor(lab_l=60.53644890578005,lab_a=1.9353937585603886,lab_b=13.731983810148996)}, {'lab': LabColor(lab_l=18.50664175674912,lab_a=4.127558915370255,lab_b=1.5318785538835367)}, {'lab': LabColor(lab_l=46.121107041110534,lab_a=-4.738660301778608,lab_b=11.46208844171116)}, {'lab': LabColor(lab_l=35.096818879142134,lab_a=3.865379674380942,lab_b=8.636348905128832)}, {'lab': LabColor(lab_l=23.053962804968776,lab_a=1.7671822304096418,lab_b=2.044120086931378)}, {'lab': LabColor(lab_l=34.77343072376579,lab_a=-3.57662664587155,lab_b=9.259575358162131)}, {'lab': LabColor(lab_l=35.35931031618316,lab_a=5.074166825160403,lab_b=7.782881046177659)}, {'lab': LabColor(lab_l=21.404442965730887,lab_a=3.157463425084356,lab_b=18.391549176595827)}, {'lab': LabColor(lab_l=86.26486893959512,lab_a=4.032848274744483,lab_b=-8.58323099615992)}, {'lab': LabColor(lab_l=45.991759128676385,lab_a=0.491023915355826,lab_b=10.794889190806279)}, {'lab': LabColor(lab_l=8.10395281254021,lab_a=2.434569728945693,lab_b=12.18393849532981)}, {'lab': LabColor(lab_l=37.06003096203893,lab_a=1.8239118316595027,lab_b=25.900755157740306)}, {'lab': LabColor(lab_l=34.339870663873945,lab_a=4.98653095415319,lab_b=1.8327067580758416)}, {'lab': LabColor(lab_l=46.981747324933046,lab_a=5.292489697923786,lab_b=6.937195284587405)}, {'lab': LabColor(lab_l=35.813822728158144,lab_a=29.12172183663478,lab_b=31.259045232888216)}, {'lab': LabColor(lab_l=83.84664420563516,lab_a=4.076393227849973,lab_b=7.589758095027621)}, {'lab': LabColor(lab_l=4.862540354567976,lab_a=3.691877768850965,lab_b=4.065132741305494)}, {'lab': LabColor(lab_l=29.520608025204446,lab_a=15.21028328876109,lab_b=-1.9817725741452907)}, {'lab': LabColor(lab_l=2.9184863831701477,lab_a=3.1009055082606847,lab_b=2.374657313916806)}, {'lab': LabColor(lab_l=25.119337116801645,lab_a=6.36800573668811,lab_b=5.191791275068236)}, {'lab': LabColor(lab_l=32.49319565030376,lab_a=4.09934993369665,lab_b=4.837690385449466)}, {'lab': LabColor(lab_l=6.09612588470991,lab_a=9.66024466422727,lab_b=2.297265839425217)}, {'lab': LabColor(lab_l=32.607204509025415,lab_a=37.17700423170081,lab_b=11.087136268936316)}, {'lab': LabColor(lab_l=45.72621067797596,lab_a=4.995679962723376,lab_b=8.10305144884066)}, {'lab': LabColor(lab_l=15.182103174406642,lab_a=17.3648698250356,lab_b=16.351707883547945)}, {'lab': LabColor(lab_l=30.735504056893177,lab_a=20.749263489097476,lab_b=11.103091166084845)}, {'lab': LabColor(lab_l=47.58987428222485,lab_a=21.4969535181187,lab_b=24.91820246623675)}, {'lab': LabColor(lab_l=3.2817937526961423,lab_a=7.0384930526659755,lab_b=5.0447129238750605)}, {'lab': LabColor(lab_l=39.176664955904386,lab_a=7.001035374555848,lab_b=7.1369181820884915)}, {'lab': LabColor(lab_l=32.47219675839261,lab_a=-2.4501733403216597,lab_b=10.408787644368223)}, {'lab': LabColor(lab_l=8.87372837821,lab_a=-2.5643873356231834,lab_b=5.64931313305761)}, {'lab': LabColor(lab_l=1.742927713725976,lab_a=0.539611795069117,lab_b=-0.6652519493932862)}, {'lab': LabColor(lab_l=33.873919675420986,lab_a=5.764566965886092,lab_b=-17.964944971494113)}, {'lab': LabColor(lab_l=40.693479627397174,lab_a=6.595272818345682,lab_b=5.018268124660407)}, {'lab': LabColor(lab_l=88.60103885061399,lab_a=2.6126810949935186,lab_b=-2.945792185321894)}, {'lab': LabColor(lab_l=55.70462312256947,lab_a=6.028112199048142,lab_b=-13.056527815975972)}, {'lab': LabColor(lab_l=9.115995988538636,lab_a=31.807462808077545,lab_b=-35.11774548995232)}, {'lab': LabColor(lab_l=38.051505820085076,lab_a=34.8155573981796,lab_b=-18.475401488472354)}, {'lab': LabColor(lab_l=71.92703712306943,lab_a=-3.471403558562458,lab_b=-10.445020993962896)}, {'lab': LabColor(lab_l=26.243044230459148,lab_a=46.369628814522414,lab_b=34.6338595372704)}, {'lab': LabColor(lab_l=66.76005751735073,lab_a=20.035224514354134,lab_b=25.87283658575612)}, {'lab': LabColor(lab_l=63.60391924768574,lab_a=-2.891469413896064,lab_b=9.573769130513398)}, {'lab': LabColor(lab_l=41.24069266482021,lab_a=16.278878911463096,lab_b=9.759226052984914)}, {'lab': LabColor(lab_l=27.25079531257893,lab_a=24.94884066949429,lab_b=-48.598531002024316)}, {'lab': LabColor(lab_l=4.265814465219158,lab_a=10.473548710425703,lab_b=4.1174226612907985)}, {'lab': LabColor(lab_l=87.15090987843114,lab_a=7.229747311809753,lab_b=-14.635793427155486)}, {'lab': LabColor(lab_l=54.54311545632727,lab_a=8.647572834710072,lab_b=-18.893603550071546)}, {'lab': LabColor(lab_l=11.276968541214082,lab_a=18.169882892627108,lab_b=-30.249378295412065)}, {'lab': LabColor(lab_l=35.090989205367,lab_a=1.0233204899371962,lab_b=-0.3006113739771554)}, {'lab': LabColor(lab_l=2.9317972315881953,lab_a=0.8523516700251477,lab_b=0.29972821911726233)}, {'lab': LabColor(lab_l=42.71927233847029,lab_a=15.072870104265279,lab_b=-31.54622665459128)}, {'lab': LabColor(lab_l=1.622807369995023,lab_a=1.0292382494377224,lab_b=1.2173768955478448)}, {'lab': LabColor(lab_l=85.05833643040985,lab_a=1.955449992315006,lab_b=-9.91904370358645)}, {'lab': LabColor(lab_l=1.6648316964409666,lab_a=0.13905563573127222,lab_b=-0.37887416481000025)}, {'lab': LabColor(lab_l=53.47424677173646,lab_a=1.322931077791023,lab_b=-0.14670143432404803)}, {'lab': LabColor(lab_l=3.7059376097529935,lab_a=0.31588132922930057,lab_b=0.11051016676932868)}, {'lab': LabColor(lab_l=1.4885457056861533,lab_a=0.6786902325009586,lab_b=-1.043701149385401)}, {'lab': LabColor(lab_l=16.298330353761287,lab_a=0.4909724855400033,lab_b=3.125329071162186)}]
if __name__ == '__main__':
start_time = milli_time()
colors = map_colors(query_colors)
print(colors)
print('Script took', milli_time() - start_time, 'milliseconds to run.')
答案 0 :(得分：1)
您可以在原始实验室值上使用color_diff_matrix中的数组函数代替颜色对象和列表推导：
from colormath.color_diff_matrix import delta_e_cie2000
# Colors as raw Lab values
# Some test data
palette_colors = np.tile([ 2.01496211, 0.22811942, 1.79001105], [300, 1])
color_to_compare = np.array([ 89.82760556, -3.49245457, 13.55860095])
dist = delta_e_cie2000(color_to_compare, palette_colors)
closest = palette_colors[np.argmin(dist)] # also color as raw Lab components
这应该已经提供了一个很好的加速，但是我通过使用numba jits函数得到了另一个因子5：
from colormath.color_diff_matrix import delta_e_cie2000
from numba import jit
delta_e_cie2000_jit = jit(delta_e_cie2000)
dist = delta_e_cie2000_jit(color_to_compare, palette_colors)
... # the rest is the same
请注意，由于编译过程，第一次执行jitted函数的速度很慢。
答案 1 :(得分：0)
This is an example of the nearest neighbor problem. The usual approach for an exact answer with many query points is based on space partitioning. This would be easy for the CIE76 metric because it is Euclidean (in L*a*b* space): a k-d tree can be used directly.
It’s still possible to apply the space-partitioning approach with the newer, more complicated metrics. However, for an exact result you have to derive a bound on the distance between a point and whatever partitions you choose: that is, the minimum distance between a point and any point that would be on the other side. A coordinate-aligned plane (as used for the Euclidean case) makes for the simplest partitioning, but might make a poor bound.
If an approximate answer suffices, you can approximate the 2000 metric with the 76 one for the purposes of partitioning. You can also switch to a binning approach, where you round the coordinates onto a coarse grid and then search it in a structured fashion to find the closest match. Each of these approaches often provides but cannot guarantee an exact result.