Linaro + opencv + exercise

Exercises with Ubuntu-Linaro

1º Load an image

2º Contours

	libjpeg	libjpeg-turbo	Linaro
Load	199	63	126
2Gray	61	45	57
Trheshold	12	12	12
Create	32	11	10
find	53	31	71
Draw	47	47	87
Total	404	209	363

 3º Search one pattern

	libjpeg-turbo	Linaro
Load Source	11	6
Load Pattern	11	6
Search	488	487
Total	510	499

4º Haar-Features

The time is quite similar

Conclusion 

The ubuntu-linaro is a little bit more fast, but it'snt a big difference.

I prefer to use raspbian cause it has a big community, and the information it's easy to find, and may be it has more things that I need, but I feel comfortable  in raspbian than in ubuntu-linaro.

But I know that I'm not taking all the power of the cubieboard, I've in mind to improve the compilation using cross compilation, and take advantage of the NEON acceleration, but I'll do latter when I've more knowledge 

libjpeg vs libjpeg-turbo

With the first exercise I've the opportunity to compare libjpeg vs libjpeg-turbo.

Before to install libjpeg-turbo I made a backup with the standard one, so it's a great moment to compare both

The code used is the same in both cases (Contours) and here are the results

libjpeg-turbo

libjpeg

	libjpeg	libjpeg-turbo
Load	199	63
2Gray	61	45
Trheshold	12	12
Create	32	11
find	53	31
Draw	47	47
Total	404	209

Finally the libjpeg-turbo is faster than libjpeg, we will check in the future what happen with Linaro vs Raspbian.

By the moment I'll be working with Raspbian with libjpe-turbo

Contours in openCV

Detecting contours.

The process of detecting contours is one of the simples.

Load the image -> Change to gray --> use the threshold function --> find the contour --> draw the contours

The code

//Load the image in colour
        flag=(double) getTickCount();
        imagen=imread(argv[1],CV_LOAD_IMAGE_UNCHANGED);
        flagLoad=getTick(flag);
//Change the image to gray
        flag=(double)getTickCount();
        cvtColor(imagen,imgGris,CV_BGR2GRAY);
        flag2Gray=getTick(flag);
//use the threshold to separate few objects
        flag=(double)getTickCount();
        threshold(imgGris,imgContorno,122,255,THRESH_BINARY);
        flagThreshold=getTick(flag);
//we need a place to leave the new iamge
        flag=(double)getTickCount();
        Mat dst = Mat::zeros(imgGris.rows,imgGris.cols,CV_8UC3);
        flagCreate=getTick(flag);

        namedWindow("gris",CV_WINDOW_NORMAL);
        namedWindow("contorno",CV_WINDOW_NORMAL);
        namedWindow("binario",CV_WINDOW_NORMAL);

        imshow ("gris",imgGris);
        imshow ("contorno",imgContorno);
        cvMoveWindow("gris",300,50);
        cvMoveWindow("contorno",600,50);

vector< vector<Point> > vecContornos;
vector<Vec4i>jerarquia;
//Find the contours
        flag = (double)getTickCount();
        findContours(imgContorno,vecContornos,jerarquia,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE);
        flagFind =getTick(flag);
        flag = (double)getTickCount();
        for (int idx=0;idx >=0;idx=jerarquia[idx][0])
        {
                drawContours(dst,vecContornos,idx,WHITE,5,8,jerarquia);
        }

The results 

And finally we have this results from the original image

Original image

Result image

 All the values are in milliseconds 

This exercise give us a useful information

The main information is the threshold to separate the right image (12 ms) and find the contours (31 ms) around 50 ms to detect objects, it's not really bad for a dive which could use this information.

But the structural analysis could be take more time, we will what happen in the next exercises 

Any comment will be appreciated