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Detects colors in images 5-10 x faster than Numpy

pip install locate-pixelcolor-cythonmulti

Tested+compiled against Windows 10 / Python 3.10 / Anaconda

If you can't import it, compile it on your system (code at the end of this page)

How to use it in Python

import numpy as np
import cv2
from locate_pixelcolor_cythonmulti import search_colors
# 4525 x 6623 x 3 picture https://www.pexels.com/pt-br/foto/foto-da-raposa-sentada-no-chao-2295744/
picx = r"C:\Users\hansc\Downloads\pexels-alex-andrews-2295744.jpg"
pic = cv2.imread(picx)
colors0 = np.array([[255, 255, 255]],dtype=np.uint8)
resus0 = search_colors(pic=pic, colors=colors0)
colors1=np.array([(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),],dtype=np.uint8)
resus1 =  search_colors(pic=pic, colors=colors1)
####################################################################
%timeit resus0=search_colors(pic,colors0)
32.3 ms ± 279 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

b,g,r = pic[...,0],pic[...,1],pic[...,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)
####################################################################
%timeit resus1=search_colors(pic, colors1)
151 ms ± 3.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%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)
####################################################################

The Cython Code

# distutils: language = c++
# cython: language_level=3
# distutils: extra_compile_args = /openmp
# distutils: extra_link_args = /openmp


from cython.parallel cimport prange
cimport cython
import numpy as np
cimport numpy as np
import cython
from collections import defaultdict

@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
cpdef searchforcolor(unsigned char[:] pic, unsigned char[:] colors, int width, int totallengthpic, int totallengthcolor):
    cdef my_dict = defaultdict(list)
    cdef int i, j
    cdef unsigned char r,g,b
    for i in prange(0, totallengthcolor, 3,nogil=True):
        r = colors[i]
        g = colors[i + 1]
        b = colors[i + 2]
        for j in range(0, totallengthpic, 3):
            if (r == pic[j]) and (g == pic[j+1]) and (b == pic[j+2]):
                with gil:
                    my_dict[(r,g,b)].append(j )

    for key in my_dict.keys():
        my_dict[key] = np.dstack(np.divmod(np.array(my_dict[key]) // 3, width))[0]
    return my_dict

setup.py to compile the code

# distutils: language = c++
# cython: language_level=3

from setuptools import Extension, setup
from Cython.Build import cythonize
import numpy as np
ext_modules = [
    Extension("colorsearchcythonmulti", ["colorsearchcythonmulti.pyx"], include_dirs=[np.get_include()],define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")])
]

setup(
    name='colorsearchcythonmulti',
    ext_modules=cythonize(ext_modules),
)


# .\python.exe .\colorsearchcythonmultisetup.py build_ext --inplace

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Compiled Cython Code (parallel) - Detects colors in images 5-10 x faster than Numpy

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