OpenCV Python通过图像数据绑定令人难以置信的慢速迭代 [英] OpenCV Python binds incredibly slow iterations through image data

问题描述

我最近使用了一些基于 OpenCV c ++ 中的颜色跟踪对​​象的代码，并在python绑定中重写。

结果和方法都相同减去语法明显。但是，当我对视频的每个帧执行以下代码，需要几乎2-3秒来完成其中，作为c ++变体，也在下面，是即时比较，我可以在帧之间的快速，我的手指可以按

任何想法或意见？

  cv.PyrDown（img，dsimg）
 for i in range（0，dsimg.height）：$ b对于范围（0，dsimg.width）中的j，$ b：
 
如果dsimg [i，j] [1] （_RED_DIFF + dsimg [i，j] [2]）和dsimg [i，j] [1] （_BLU_DIFF + dsimg [i，j] [0]）：
 res [i，j] = 255 
 else：
 res [i，j] = 0 
  





---

  for（int i = 0;对于（int j = 0; j <（width）; j ++）
 {
 if（（（data [i （data [i * step + j * channels + 1]）（步骤+ j * channel + 1]）>（RED_DIFF + data [i * step + j * channels + 2] ] +>（BLU_DIFF + data [i * step + j * channels]）））
 data_r [i * step_r + j * channels_r] = 255; 
 else 
 data_r [i * step_r + j * channels_r] = 0; 
} 
} 
  

感谢

解决方案

尝试使用numpy来进行计算，而不是嵌套循环。

例如，你的嵌套for循环可以用几个numpy表达式来代替...

我不太熟悉opencv，但我认为python绑定现在有一个numpy数组接口，所以上面的例子应该是简单的：

  cv.PyrDown（img，dsimg）
 
 data = np.asarray（dsimg）
 blue， green，red = data.T 
 
 res =（green>（_RED_DIFF + red））& （绿色>（_BLU_DIFF +蓝色））
 res = res.astype（np.uint8）* 255 
 
 res = cv.fromarray（res）
  

（完全未经测试，当然...）再次，我真的不是很熟悉opencv，但嵌套python for循环不是无论... ...

希望帮助一下，无论如何！

I recently took some code that tracked an object based on color in OpenCV c++ and rewrote it in the python bindings.

The overall results and method were the same minus syntax obviously. But, when I perform the below code on each frame of a video it takes almost 2-3 seconds to complete where as the c++ variant, also below, is instant in comparison and I can iterate between frames as fast as my finger can press a key.

Any ideas or comments?

    cv.PyrDown(img, dsimg)
    for i in range( 0, dsimg.height ):
        for j in range( 0, dsimg.width):

            if dsimg[i,j][1] > ( _RED_DIFF + dsimg[i,j][2] ) and dsimg[i,j][1] > ( _BLU_DIFF + dsimg[i,j][0] ):
                res[i,j] = 255
            else:
                res[i,j] = 0

---

    for( int i =0; i < (height); i++ ) 
    {
        for( int j = 0; j < (width); j++ )
        {
            if( ( (data[i * step + j * channels + 1]) > (RED_DIFF + data[i * step + j * channels + 2]) ) &&
                ( (data[i * step + j * channels + 1]) > (BLU_DIFF + data[i * step + j * channels]) ) )
                data_r[i *step_r + j * channels_r] = 255;
            else
                data_r[i * step_r + j * channels_r] = 0;
        }
    }

Thanks

解决方案

Try using numpy to do your calculation, rather than nested loops. You should get C-like performance for simple calculations like this from numpy.

For example, your nested for loops can be replaced with a couple of numpy expressions...

I'm not terribly familiar with opencv, but I think the python bindings now have a numpy array interface, so your example above should be as simple as:

cv.PyrDown(img, dsimg)

data = np.asarray(dsimg)
blue, green, red = data.T

res = (green > (_RED_DIFF + red)) & (green > (_BLU_DIFF + blue))
res = res.astype(np.uint8) * 255

res = cv.fromarray(res)

(Completely untested, of course...) Again, I'm really not terribly familar with opencv, but nested python for loops are not the way to go about modifying an image element-wise, regardless...

Hope that helps a bit, anyway!

这篇关于OpenCV Python通过图像数据绑定令人难以置信的慢速迭代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IT屋！