C++ Development Tools

• MinGW: A collection of freely available and freely distributable Windows specific header files and import libraries combined with GNU toolsets that allow one to produce native Windows programs that do not rely on any 3rd-party C runtime DLLs. MinGW is different from Cygwin because it uses the Windows C runtime libraries(mscvrt) rather than GNU’s libc.
• Msys: A Minimal SYStem to provide POSIX/Bourne configure scripts the ability to execute and create a Makefile used by make.
• Eclipse CDT: A IDE originally made for Java but includes an extensive plugin library. Now supports C/C++ and many other languages.

The latest version can be checked on the respective website. I used MinGW 5.1.3 and Msys 1.0.10. When installing MinGW, select the G++ and other compilers. Do NOT install the make in the MinGW setup. Msys will provide it.

MinGW does not include the GDB debugger so download gdb-6.6.tar.bz2 and install it to your MinGW directory. To uncompress it, open up the msys window and type in bunzip2 gdb-6.6.tar.bz2 and then tar -xvf gdb-6.6.tar. Copy all the contents to your MinGw folder.

OpenCV

The two big open source computer Vision/Image processing libraries in C/C++ are OpenCV and the Nasa Vision WorkBench.

OpenCV is the old-school C/C++ computer vision/image processing library. It is robust and contains many functions described in computer vision textbooks. I haven’t played with the newer NASA tool but it looks like it has a decent API as well.

Download and install OpenCV.

Linking OpenCV in Eclipse

You can setup Eclipse CDT to work with the OpenCV libraries. Create a new C++ project in Eclipse CDT. Select MinGw as the toolchain.

In the project properties, go to the C/C++ Build->Settings->GCC C++ Compiler, set the directories to:

• OpenCv\cv\include
• OpenCv\cxcore\include
• OpenCv\otherlibs\highgui
• OpenCv\otherlibs\cvcam\include
• OpenCv\cvaux\include

In the C++ Linker->Libraries, set:

• cv
• highgui
• cxcore

In Library search path, set:

• OpenCV\lib

Here’s a sample file to get you started. You should be able to compile this program and see an inverted image when you run it.


////////////////////////////////////////////////////////////////////////
//
// hello-world.cpp
//
// This is a simple, introductory OpenCV program. The program reads an
// image from a file, inverts it, and displays the result.
//
////////////////////////////////////////////////////////////////////////
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <cv.h>
#include <highgui.h>
int main(int argc, char *argv[])
{
IplImage* img = 0;
int height,width,step,channels;
uchar *data;
int i,j,k;
if(argc<2){
printf("Usage: main \n\7");
exit(0);
}
// load an image
img=cvLoadImage(argv[1]);
if(!img){
printf("Could not load image file: %s\n",argv[1]);
exit(0);
}
// get the image data
height = img->height;
width = img->width;
step = img->widthStep;
channels = img->nChannels;
data = (uchar *)img->imageData;
printf("Processing a %dx%d image with %d channels\n",height,width,channels);
// create a window
cvNamedWindow("mainWin", CV_WINDOW_AUTOSIZE);
cvMoveWindow("mainWin", 100, 100);
// invert the image
for(i=0;i data[i*step+j*channels+k]=255-data[i*step+j*channels+k];
// show the image
cvShowImage("mainWin", img );
// wait for a key
cvWaitKey(0);
// release the image
cvReleaseImage(&img );
return 0;
}


Hand Tracking as Pointing Device UCSD ECE191 Poster

Check out the full report here.

Abstract

Current traffic light triggering is based on inductive loop sensors. Unfortunately, motorbikes (scooters, motorcycles, etc) have a difficult time triggering these sensors. In this paper, we propose an image processing algorithm to detect motorbikes at a traffic stop using a fixed camera. The algorithm tracks the trajectory of the objects in the footage by motion segmentation and connected component labeling. Classification can be created to categorize these objects as incoming traffic based on the object’s trajectory. To handle different lighting conditions in the motion segmentation, we take a dual approach by selecting RGB or Opponent colorspace. RANSAC is utilized to help trajectory creation. Experimental tests using real video footage exhibit robust results under varying conditions.

Video

In this video, you see the detection of all the light sources. Then we track the bike(note how it bike stays labeled #1) using RANSAC to differentiate the bike from the intersecting traffic.

Presentation

For more details about this idea, check out the project blog at Vision Based Traffic Light Triggering for Motorbikes