Python 提高将实验室颜色列表映射到第二个实验室颜色列表的速度

我遇到的问题是，我有两个列表，它们创建了一个相当大的循环，速度非常慢。。。慢3.5到4秒。我希望能改善这一点。我使用的两个列表都包含实验室颜色。第一个列表是调色板，称之为

palete\u colors

第二个列表有我用来比较的各个实验室颜色，称之为

query\u colors

我在第二个

query\u colors

中循环，将列表中的每种颜色与

palete\u colors

列表中的每种颜色进行比较。由此我们得到一个距离，用于检查颜色是否在某个阈值内

我遇到的问题是，由于

palete\u colors

是一个很大的列表（大约300项），而

query\u colors

大约有100项，因此它会重复大约30000次

所以问题是，如何改进这一点以更快地运行？

---

以下是我的一些想法：

并行处理：我试着使用并行处理，但不是因为它不适合在上下文中使用，就是因为我不知道自己在做什么。。。我倾向于不知道自己在做什么

在处理的十六进制值之间缓存：我的第一个想法是缓存颜色组合之间的距离，但是，这没有多大帮助，因为颜色非常具体：FFFFFF！=FFFFF E，即使它们明显相同

初始十六进制查找缓存：另一个想法是比较十六进制值……如果十六进制值匹配，则只需返回该匹配。然而，想法1也存在同样的问题

Numpy数组+距离函数：如果有办法将两个列表都转换为仅包含Lab值的Numpy数组，然后使用CIELAB2000距离函数对每个列表进行比较

以下是我的完整脚本（请确保安装colormath）：

从时间导入时间
从colormath.color_diff导入delta_e_cie2000
从colormath.color\u对象导入LabColor
从运算符导入itemgetter
#定时辅助函数
毫秒时间=λ：整数（四舍五入（时间（）*1000））
#当合并相似的颜色时，在合并之前检查该颜色有多少
def映射颜色（查询颜色，最大距离=100）：
#包含调色板中最接近每种颜色的颜色
关闭颜色=[]
#循环我们要映射的颜色
对于查询颜色中的颜色\u到\u比较：
#将实验室距离与调色板颜色进行比较
最近=[检查调色板颜色中调色板颜色的距离（调色板颜色、颜色之间的比较、最大距离）]
#删除“无”值
最近的=[c表示最近的c，如果c不是无]
#按距离排序（升序）
最近的=排序的（最近的，key=itemgetter（'distance'））[：1][0]['hex']
#删除散列
最近的=最近的。替换（'#'，''）。下（）
#添加到最接近颜色的主列表
关闭颜色。附加（最近）
返回接近的颜色
#检查实验室颜色之间的距离
def检查距离（颜色1、颜色2、最大距离）：
距离=delta_e_cie2000（颜色1['lab']，颜色2['lab']）
如果距离<最大距离：
返回{
“十六进制”：颜色_1[“十六进制”]，
“实验室”：颜色_1[“实验室”]，
“距离”：距离
}
#调色板颜色列表
#堆栈溢出不允许这么多字符，
#因此，您必须复制并通过此url的调色板：
# https://codepen.io/anon/pen/bvrwzE?editors=1010
调色板颜色=[]#^^^^
#要比较的颜色列表
查询-{'lab'：LabColor（lab'l=89.82760556495964，lab'a=-3.492454568128055，lab'b=13.558600954011734）}，{'lab'：LabColor（lab'l=2.01496108133794，lab'a=0.228119415990447703，lab'b=1.790011046195017）}，{'LabColor（lab'l=40.39474520781096，lab'a=2.9037373777，lab'b'}，{'lab'(lab)LabColor(lab)(lab)LabColor(lab)(lab)LabColor(lab)lab(lab)lab(lab)lab(lab)LabColor(lab)(lab)LabColor(lab)(lab)(lab)lab(lab)lab(lab)lab(lab)lab（lab_l=4.179197112322317，lab_a=3.6420050506552555，lab_b=3.04072669339523646）{'lab'：LabColor（lab_l=30.180289034511695，lab_a=1.7045267250474505，lab_b=28.01083333844222）}，{'LabColor（lab_l=44.3100506010243;，lab a=-4.362010483995816，lab_b=18.43963528）{'(lab_l=0.8423115373777676,lab_a=0.13906540788867494,lab_b=-0.37869203709088){'lab'lab'：LabColor{lab l=52.12865600856179,lab_a=-0.579700071502412,lab_b=31.8790459272144}}{'lab(lab)LabColor(lab)(lab)LabColor(lab)LabColor(lab)lab(lab)lab(lab)lab(lab)lab(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 lab（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=1.93937585886，lab_b=13.198731）
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.')

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

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