Yesterday, we were playing around with Arduino again. This time Javier and me went to bigger project. A led that turns on when the computer detects a face with the webcam. After solving issues with the Internet connection we divided the work. He went into the task of manipulating the Arduino from Processing and I install the opencv and the respective libraries for processing.


Here are the steps:

1. Install processing.  Just download, uncompress and run.

2. Install opencv. Just open Terminal and execute the next command

sudo apt-get install libcv2.1 libcvaux2.1 libhighgui2.1

3. Find the sketchbook folder. To find the Processing sketchbook location on your computer, open the Preferences window from the Processing application and look for the "Sketchbook location" item at the top. So, for example, my sketchbook was located in /home/roberto/sketches.

4. Find or create the libraries folder. Inside the sketchbook folder (/home/roberto/sketches) there should be another folder called libraries. If you don't find  it, just create it.

5. Download the library. Click here or look for the last version in the official web page.

6. Uncompress the tar.gz

7. Copy the whole uncompressed folder into the libraries folder. In my case /home/roberto/sketches/libraries. Normally the installation finish here, however it does not work because some of the files are named differently (different version of opencv)

8. Open a Terminal an create the following symbolic links with these commands:

sudo ln -s /usr/lib/ /usr/lib/
sudo ln -s /usr/lib/ /usr/lib/
sudo ln -s /usr/lib/ /usr/lib/
sudo ln -s /usr/lib/ /usr/lib/
sudo ln -s /usr/lib/ /usr/lib/

9. Open processing and paste the code. Just past this code in the processing window.

10. A small change in the previous code.  Just change this line

opencv.cascade( OpenCV.CASCADE_FRONTALFACE_ALT );    // load the FRONTALFACE description file

for this one

opencv.cascade( "/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml" );    // load the FRONTALFACE description file

Actually, I found more than one file. So it probably worth these others files. I just went to try them! The second one is much faster.


11. Run the code and enjoy.

It's amazing how easily you can run very cool libraries to let your imagination fly. Don't miss Javier's blog who is going to post about the whole mini-project with Arduino included during this week.


I trained an Adaboost classifier to distinguish between two artistic styles. A tecnichal report of my results can be found on my account. This sort of tutorial - or more precisely collection of blog posts - explains the steps and provides the code to create an image classifier from histograms of oriented edges, colors and intensities. Therefore you can replicate my methodology to any other problems.

There are two main steps on this: (1) produce the features of the images, and (2) train and use the classifier. I started the blog sequence from the classifier that I used (Adaboost), and then continue explaining how to produce features for big collections. Probably this is a weird way of viewing the problem because I am starting from the last step,however I found that most of the decisions I took in the process were justified by the input I wanted to reach. I also recommend to check the comments where I have answered multiple questions during the time of existance of this posts.