Locate RGB values in a picture! Up to 10x faster than NumPy, 100x faster than PIL.

How to install

pip install locate-pixelcolor-cpp

Please install this C++ compiler:

MSVC ..... C++ x64/x86 build tools from: https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&channel=Release&version=VS2022&source=VSLandingPage&passive=false&cid=2030

Localize the following files (Version number might vary) and copy their path: vcvarsall_bat = r"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Auxiliary\Build\vcvarsall.bat"

cl_exe = r"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.34.31933\bin\Hostx86\x64\cl.exe"

link_exe = r"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.34.31933\bin\Hostx86\x64\link.exe"

Compile the code

python from locate_pixelcolor_cpp import compile_localize_picture_color_with_cpp compile_localize_picture_color_with_cpp( vcvarsall_bat=r"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Auxiliary\Build\vcvarsall.bat", cl_exe=r"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.34.31933\bin\Hostx86\x64\cl.exe", link_exe=r"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.34.31933\bin\Hostx86\x64\link.exe", )

Benchmark

```python

Let's use a 4525 x 6623 x 3 picture https://www.pexels.com/pt-br/foto/foto-da-raposa-sentada-no-chao-2295744/

from locatepixelcolorcpp import searchcolors # The function can only be imported when the compilation was successful ( compilelocalizepicturecolorwithcpp ) import cv2 path=r"C:\Users\Gamer\Documents\Downloads\pexels-alex-andrews-2295744.jpg" im = cv2.imread(path)

colors=[(66, 71, 69),(62, 67, 65),(144, 155, 153),(52, 57, 55),(127, 138, 136),(53, 58, 56),(51, 56, 54),(32, 27, 18),(24, 17, 8),]

%timeit search_colors(im, colors=colors)

127 ms ± 3.61 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

from locatepixelcolor import searchcolors as search_colors2

first version with numexpr

https://github.com/hansalemaos/locate_pixelcolor

%timeit search_colors2(im,colors)

400 ms ± 18.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

import numpy as np b,g,r = im[...,0],im[...,1],im[...,2]

%timeit np.where(((b==66)&(g==71)&(r==69))|((b==62)&(g==67)&(r==65))|((b==144)&(g==155)&(r==153))|((b==52)&(g==57)&(r==55))|((b==127)&(g==138)&(r==136))|((b==53)&(g==58)&(r==56))|((b==51)&(g==56)&(r==54))|((b==32)&(g==27)&(r==18))|((b==24)&(g==17)&(r==8)))

1 s ± 16.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

from PIL import Image img = Image.open(path) img = img.convert("RGB") datas = img.getdata()

def pi(): newData = [] for item in datas: if (item[0] == 66 and item[1] == 71 and item[2] == 69) or (item[0] == 62 and item[1] == 67 and item[2] == 65) or (item[0] == 144 and item[1] == 155 and item[2] == 153) or (item[0] == 52 and item[1] == 57 and item[2] == 55) or (item[0] == 127 and item[1] == 138 and item[2] == 136) or (item[0] == 53 and item[1] == 58 and item[2] == 56) or (item[0] == 51 and item[1] == 56 and item[2] == 54) or (item[0] == 32 and item[1] == 27 and item[2] == 18) or (item[0] == 24 and item[1] == 17 and item[2] == 8): newData.append(item) return newData %timeit pi()

10.6 s ± 51.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

One color

from locatepixelcolorcpp import search_colors import cv2 path=r"C:\Users\Gamer\Documents\Downloads\pexels-alex-andrews-2295744.jpg" im = cv2.imread(path)

%timeit search_colors(im, colors=[(255,255,255)])

75.3 ms ± 247 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

first version with numexpr

https://github.com/hansalemaos/locate_pixelcolor

from locatepixelcolor import searchcolors import cv2 path=r"C:\Users\Gamer\Documents\Downloads\pexels-alex-andrews-2295744.jpg" im = cv2.imread(path)

%timeit search_colors(im, colors=[(255,255,255)])

98 ms ± 422 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

b,g,r = im[...,0],im[...,1],im[...,2]

%timeit np.where(((b==255)&(g==255)&(r==255)))

150 ms ± 209 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

from PIL import Image img = Image.open(path) img = img.convert("RGB") datas = img.getdata() def getcoordswithpil(col): newData = [] for item in datas: if item[0] == col[0] and item[1] == col[1] and item[2] == col[2]: newData.append(item) return newData %timeit getcoordswithpil(col=(255,255,255)) 3.41 s ± 14.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

```