本文首发于个人博客https://kezunlin.me/post/61d55ab4/，欢迎阅读!

opencv mat for loop

Series

• Part 1: compile opencv on ubuntu 16.04
• Part 2: compile opencv with CUDA support on windows 10
• Part 3: opencv mat for loop
• Part 4: speed up opencv image processing with openmp

Guide

Mat

• for gray image, use type

• for RGB color image，use type

gray format storage

color format storage: BGR

we can use method

isContinuous()

to judge whether the memory buffer is continuous or not.

color space reduction

uchar color_space_reduction(uchar pixel)
{
/*
0-9 ===>0
10-19===>10
20-29===>20
...
240-249===>24
250-255===>25

map from 256*256*256===>26*26*26
*/

int divideWith = 10;
uchar new_pixel = (pixel / divideWith)*divideWith;
return new_pixel;
}

color table

void get_color_table()
{
// cache color value in table[256]
int divideWith = 10;
uchar table[256];
for (int i = 0; i < 256;   i)
table[i] = divideWith* (i / divideWith);
}

C

ptr []

// C ptr []: faster but not safe
Mat& ScanImageAndReduce_Cptr(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
int channels = I.channels();
int nRows = I.rows;
int nCols = I.cols* channels;
if (I.isContinuous())
{
nCols *= nRows;
nRows = 1;
}
int i, j;
uchar* p;
for (i = 0; i < nRows;   i)
{
p = I.ptr<uchar>(i);
for (j = 0; j < nCols;   j)
{
p[j] = table[p[j]];
}
}
return I;
}

ptr

// C ptr: faster but not safe
Mat& ScanImageAndReduce_Cptr2(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
int channels = I.channels();
int nRows = I.rows;
int nCols = I.cols* channels;
if (I.isContinuous())
{
nCols *= nRows;
nRows = 1;
}
uchar* start = I.ptr<uchar>(0); // same as I.ptr<uchar>(0,0)
uchar* end = start   nRows * nCols;
for (uchar* p=start; p < end;   p)
{
*p = table[*p];
}
return I;
}

at(i,j)

// at<uchar>(i,j): random access, slow
Mat& ScanImageAndReduce_atRandomAccess(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
const int channels = I.channels();
switch (channels)
{
case 1:
{
for (int i = 0; i < I.rows;   i)
for (int j = 0; j < I.cols;   j)
I.at<uchar>(i, j) = table[I.at<uchar>(i, j)];
break;
}
case 3:
{
Mat_<Vec3b> _I = I;

for (int i = 0; i < I.rows;   i)
for (int j = 0; j < I.cols;   j)
{
_I(i, j)[0] = table[_I(i, j)[0]];
_I(i, j)[1] = table[_I(i, j)[1]];
_I(i, j)[2] = table[_I(i, j)[2]];
}
I = _I;
break;
}
}
return I;
}

Iterator

// MatIterator_<uchar>: safe but slow
Mat& ScanImageAndReduce_Iterator(Mat& I, const uchar* const table)
{
// accept only char type matrices
CV_Assert(I.depth() != sizeof(uchar));
const int channels = I.channels();
switch (channels)
{
case 1:
{
MatIterator_<uchar> it, end;
for (it = I.begin<uchar>(), end = I.end<uchar>(); it != end;   it)
*it = table[*it];
break;
}
case 3:
{
MatIterator_<Vec3b> it, end;
for (it = I.begin<Vec3b>(), end = I.end<Vec3b>(); it != end;   it)
{
(*it)[0] = table[(*it)[0]];
(*it)[1] = table[(*it)[1]];
(*it)[2] = table[(*it)[2]];
}
}
}
return I;
}

opencv LUT

// LUT
Mat& ScanImageAndReduce_LUT(Mat& I, const uchar* const table)
{
Mat lookUpTable(1, 256, CV_8U);
uchar* p = lookUpTable.data;
for (int i = 0; i < 256;   i)
p[i] = table[i];

cv::LUT(I, lookUpTable, I);
return I;
}

forEach

forEach

method of the

Mat

class that utilizes all the cores on your machine to apply any function at every pixel.

// Parallel execution with function object.
struct ForEachOperator
{
uchar m_table[256];
ForEachOperator(const uchar* const table)
{
for (size_t i = 0; i < 256; i  )
{
m_table[i] = table[i];
}
}

void operator ()(uchar& p, const int * position) const
{
// Perform a simple operation
p = m_table
;
}
};

// forEach use multiple processors, very fast
Mat& ScanImageAndReduce_forEach(Mat& I, const uchar* const table)
{
I.forEach<uchar>(ForEachOperator(table));
return I;
}

[p]

forEach with lambda

// forEach lambda use multiple processors, very fast (lambda slower than ForEachOperator)
Mat& ScanImageAndReduce_forEach_with_lambda(Mat& I, const uchar* const table)
{
I.forEach<uchar>
(
[=](uchar &p, const int * position) -> void
{
p = table
;
}
);
return I;
}

[p]

time cost

no foreach

[1 Cptr] times=5000, total_cost=988 ms, avg_cost=0.1976 ms
[1 Cptr2  ] times=5000, total_cost=1704 ms, avg_cost=0.3408 ms
[2 atRandom] times=5000, total_cost=9611 ms, avg_cost=1.9222 ms
[3 Iterator] times=5000, total_cost=20195 ms, avg_cost=4.039 ms
[4 LUT    ] times=5000, total_cost=899 ms, avg_cost=0.1798 ms

[1 Cptr] times=10000, total_cost=2425 ms, avg_cost=0.2425 ms
[1 Cptr2  ] times=10000, total_cost=3391 ms, avg_cost=0.3391 ms
[2 atRandom] times=10000, total_cost=20024 ms, avg_cost=2.0024 ms
[3 Iterator] times=10000, total_cost=39980 ms, avg_cost=3.998 ms
[4 LUT    ] times=10000, total_cost=103 ms, avg_cost=0.0103 ms

foreach

[5 forEach     ] times=200000, total_cost=199 ms, avg_cost=0.000995 ms
[5 forEach lambda] times=200000, total_cost=521 ms, avg_cost=0.002605 ms

[5 forEach     ] times=20000, total_cost=17 ms, avg_cost=0.00085 ms
[5 forEach lambda] times=20000, total_cost=23 ms, avg_cost=0.00115 ms

results

Loop Type	Time Cost (us)
ptr []	242
ptr	339
at	2002
iterator	3998
LUT	10
forEach	0.85
forEach lambda	1.15

forEach

is 10x times faster than

LUT

, 240~340x times faster than

ptr []

and

ptr

, and 2000~4000x times faster than

at

and

iterator

.

code

code here

Python

pure python

# import the necessary packages
import matplotlib.pyplot as plt
import cv2
print(cv2.__version__)

%matplotlib inline

3.4.2

# load the original image, convert it to grayscale, and display
# it inline
image = cv2.imread("cat.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print(image.shape)
#plt.imshow(image, cmap="gray")

(360, 480)

%load_ext cython

The cython extension is already loaded. To reload it, use:
%reload_ext cython

%%cython -a

def threshold_python(T, image):
# grab the image dimensions
h = image.shape[0]
w = image.shape[1]

# loop over the image, pixel by pixel
for y in range(0, h):
for x in range(0, w):
# threshold the pixel
image[y, x] = 255 if image[y, x] >= T else 0

# return the thresholded image
return image

%timeit threshold_python(5, image)

263 ms ± 20.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

cython

%%cython -a

import cython

@cython.boundscheck(False)
cpdef unsigned char[:, :] threshold_cython(int T, unsigned char [:, :] image):
# set the variable extension types
cdef int x, y, w, h

# grab the image dimensions
h = image.shape[0]
w = image.shape[1]

# loop over the image
for y in range(0, h):
for x in range(0, w):
# threshold the pixel
image[y, x] = 255 if image[y, x] >= T else 0

# return the thresholded image
return image

numba

%timeit threshold_cython(5, image)

150 µs ± 7.14 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

from numba import njit

@njit
def threshold_njit(T, image):
# grab the image dimensions
h = image.shape[0]
w = image.shape[1]

# loop over the image, pixel by pixel
for y in range(0, h):
for x in range(0, w):
# threshold the pixel
image[y, x] = 255 if image[y, x] >= T else 0

# return the thresholded image
return image

%timeit threshold_njit(5, image)

43.5 µs ± 142 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

numpy

def threshold_numpy(T, image):
image[image > T] = 255
return image

%timeit threshold_numpy(5, image)

111 µs ± 334 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

conclusions

image = cv2.imread("cat.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print(image.shape)

%timeit threshold_python(5, image)
%timeit threshold_cython(5, image)
%timeit threshold_njit(5, image)
%timeit threshold_numpy(5, image)

(360, 480)
251 ms ± 6.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
143 µs ± 1.19 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
43.8 µs ± 284 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
113 µs ± 957 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

image = cv2.imread("big.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print(image.shape)

%timeit threshold_python(5, image)
%timeit threshold_cython(5, image)
%timeit threshold_njit(5, image)
%timeit threshold_numpy(5, image)

(2880, 5120)
21.8 s ± 460 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
12.3 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
3.91 ms ± 66.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
10.3 ms ± 179 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

60,480

• python: 251 ms
• cython: 143 us
• numba: 43 us
• numpy: 113 us

2880, 5120

• python: 21 s
• cython: 12 ms
• numba: 4 ms
• numpy: 10 ms

Reference

• Part1: OpenCV访问Mat图像中每个像素的值 4种对比
• Part2: OpenCV访问Mat图像中每个像素的值 13种对比
• parallel-pixel-access-in-opencv-using-foreach
• fast-optimized-for-pixel-loops-with-opencv-and-python
• python performance tips

History

• 20180823: created.

Copyright

• Post author: kezunlin
• Post link: https://kezunlin.me/post/61d55ab4/
• Copyright Notice: All articles in this blog are licensed under CC BY-NC-SA 3.0 unless stating additionally.

• 点赞
• 收藏
• 分享
• 文章举报

kezunlin 发布了72 篇原创文章 · 获赞 0 · 访问量 1168 私信 关注