Logitech C600 Camera - PanTilt Rig with $16 BBB Arduino Clone - not super fancy but it works..
Template Matching is now available through MRL.
Template Matching is a process of matching a small sub image within a larger global image. As an exercise I chose a wall socket since this could be the goal for a self charging robot.
When the target is locked and centered, an event will fire off. If this were a mobile platform and the goal was to mate with the socket, the next behavior would be to move closer to the socket and avoid obstacles. Since, this is not a mobile platform, I have chosen to send the event to a Text To Speech service with the appropriate verbiage.
The interface for creating a template can be programmed with coordinate numbers, or selected through the video feed. To select a new template the Matching Template filter should be high-lighted, then simply select the top left and bottom right rectangle of the new template. You will see the template image become visible in the Photo Reel section of the OpenCV gui.
Currently, I am using the Face Tracking service in MRL. The Face Tracking service will soon be decomposed into a more generalized Tracking Service, which can be used to track a target with any sort of appropriate sensor data. Previously I found tracking problematic. The pan/tilt platform would bounce back and forth and overcompensate (Hysterisis). The lag which video processing incurs makes the tracking difficult. In an attempt to compensate this issue, I have recently combined a PID controller into the Tracking service, and have been very pleased with the results. The tracking is bounces around much less, although there is still room for improvement.
PID is a method (and artform) which allows error correction in complex systems. Initially a set of values must be chosen for the specific system. There are 3 major values.
The video will show the initial setup. This involves connecting an Arduino to a COM port, then connecting 2 Servos to theArduino (1 for pan & another for tilt). After this is done, I begin selecting different templates to match as the test continues. The template match value in the upper left corner represents and represents the quality of matching.
The states which can occur
More to Come
In the video you can see when I switch off the lights the lock is lost. Template matching is sensitive to scale, rotation, and light changes. Haar object detection is more robust and less sensitive to scale and lighting changes. The next step will be taking the template and proceeding with Haar training.
I was on vacation for a week, however, when I got back I wanted to make sure the latest (mrl 13) was cross platform compatible.
I had some problems with Fedora Core 15 / GNOME 4 desktop / Java & OpenCV
FC15 can install opencv version 2.2 but 2.3 is available for download.
I removed the 2.2 version - and did a clean install of mrl 13
The desktop is still acting a little "goofy" but after :
I got tempate matchin on the MAAHR brain going at 78 ms per frame running with debug logging. (almost 13 frames per second!)
it says 93ms because the screen-capture slows the process down.
MAAHR is currently running on a 12V SLA
The CPU and power supply are cool
None of the CPUs are over 60% and this should drop off significantly if the video feed was not being displayed.
MRL-13 has now been tested on Windows XP & 7, Fedora Core 13 & 15 - The next version should come with opencv binaries compatible with an ARM-7, although Ro-Bot-X's Chumby appears down for some reason.....