OpenCV-Python接口、cv和cv2的性能比较

问题描述

几天前，我开始使用新的 OpenCV-Python 接口，cv2.

A few days back, I started using new OpenCV-Python interface, cv2.

我的问题是关于 cv 和 cv2 接口的比较.

My question is regarding the comparison of cv and cv2 interface.

在易用性方面，新的 cv2 界面有了很大的改进，使用 cv2 真的很简单有趣.

Regarding the ease of use, new cv2 interface has improved far greater, and it is really easy and fun to work with cv2.

但是速度呢?

我制作了两个小代码片段，一个在 cv 中，另一个在 cv2 中，以检查性能.两者都执行相同的功能，访问图像的像素，对其进行测试，进行一些修改等.

I made two small code snipplets, one in cv and another in cv2, to check the performances. Both does the same function, access pixels of an image, test it, make some modifications, etc.

下面是代码:

cv2 接口:

import time
import numpy as np
import cv2

gray = cv2.imread('sir.jpg',0)
width = gray.shape[0]
height = gray.shape[1]
h = np.empty([width,height,3])
t = time.time()
for i in xrange(width):
    for j in xrange(height):
        if gray[i,j]==127:
            h[i,j]=[255,255,255]
        elif gray[i,j]>127:
            h[i,j]=[0,0,255-gray[i,j]]
        else:
            h[i,j]=[gray[i,j],0,0]
t2 = time.time()-t
print "time taken = ",t2

======================================================

=====================================================

结果是:

所用时间 = 14.4029130936

time taken = 14.4029130936

=======================================================

======================================================

简历界面:

import cv,time

gray = cv.LoadImage('sir.jpg',0)
h = cv.CreateImage(cv.GetSize(gray),8,3)

t=time.time()

for i in xrange(gray.width):
    for j in xrange(gray.height):
        k = cv.Get2D(gray,j,i)[0]
        if k==127:
            cv.Set2D(h,j,i,(255,255,255))
        elif k>127:
            cv.Set2D(h,j,i,(0,0,255-k))
        else:
            cv.Set2D(h,j,i,(k,0,0))

t2 = time.time()-t
print "time taken = ",t2
cv.ShowImage('img',h)
cv.WaitKey(0)

=======================================================

======================================================

结果是:

所用时间 = 1.16368889809

time taken = 1.16368889809

========================================================

=======================================================

看，这里旧的 cv 比 cv2 快 12 倍.结果图像是相同的.(输入图像大小为 720x540)

See, here old cv is about 12 times faster than cv2. And resulting images are same. (input image is of size 720x540)

为什么会这样?

cv2 比 cv 慢吗?

Is cv2 slower compared to cv?

或者我在这里犯了什么错误?以上代码在cv2中有更快的方法吗?

Or am I making any mistake here? Is there a faster method in cv2 for the above code?

推荐答案

cv2.imread()返回的图片是NumPy的数组对象.所以你可以使用 NumPy 的函数来加速计算.

The image returned by cv2.imread() is an array object of NumPy. So you can use NumPy's functions to speedup calculation.

下面的程序展示了如何使用 ndarray 对象的 item(), itemset() 方法来加速你的 origin for 循环版本.

The following program shows how to speedup your origin for loop version by using item(), itemset() method of ndarray object.

import time
import numpy as np
import cv2

gray = cv2.imread('lena_full.jpg',0)
height, width = gray.shape
h = np.empty((height,width,3), np.uint8)

t = time.time()
for i in xrange(height):
    for j in xrange(width):
        k = gray.item(i, j)
        if k == 127:
            h.itemset(i, j, 0, 255)
            h.itemset(i, j, 1, 255)
            h.itemset(i, j, 2, 255)
        elif k > 127:
            h.itemset(i, j, 0, 0)
            h.itemset(i, j, 1, 0)
            h.itemset(i, j, 2, 255-k)
        else:
            h.itemset(i, j, 0, k)
            h.itemset(i, j, 1, 0)
            h.itemset(i, j, 2, 0)
print time.time()-t

下面的程序展示了如何首先创建调色板，并使用 NumPy 的数组索引来获取结果:

And the following program show how to create the palette first, and use NumPy's array index to get the result:

t = time.time()
palette = []
for i in xrange(256):
    if i == 127:
        palette.append((255, 255, 255))
    elif i > 127:
        palette.append((0,0,255-i))
    else:
        palette.append((i, 0, 0))
palette = np.array(palette, np.uint8)

h2 = palette[gray]

print time.time() - t

print np.all(h==h2)

输出是:

0.453000068665
0.0309998989105
True

cv 版本输出为:

0.468999862671

注意:0轴的长度是图片的高度，1轴的长度是图片的宽度

这篇关于OpenCV-Python接口、cv和cv2的性能比较的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持html5模板网！