https://maxkostka.github.io/catflap/
The catflap for detecting prey in the cats snout. Based on a raspberry pi 2 with the raspicam and the catflap sureflap. The raspberry runs ubuntu with ros indigo, python 2.x opencv. The system has 4 sensors, two ir sensors to detect the presence of a cat in the catflap tunnel, a sensor to check, whether the status of the flap and a camera to take pictures of the cats profile.
I further think that the original idea was not created by the three youngsters or at least they were not the only ones. I was further inspired by joakim soderberg and the catcierge https://joakimsoderberg.github.io/catcierge/ (Nice work Joakim!) and the flow control project which can no longer be found on the web (origial url: http://www.quantumpicture.com/Flo_Control/flo_control.htm) but we can find some remaints of its impact on others http://www.openscience.org/blog/?p=109.
At least four years after the flo control project, three germans participated in a contest called 'jugend forscht' http://www.neumarktonline.de/art.php?newsid=59507). The price was patent application http://www.freepatentsonline.com/DE202009002677.html which is currently owned by the Feldberglicht GmbH. They are still developing a commercial application - check it out http://www.mouse-lock.de/. But don't expect a working catflap before a working fusion reactor ;)
usage so far
roslaunch catflap_training.launch < to start the ros nodes to aquire images, when a cat enters passes the flap
i added the startup.sh via
sudo crontab -e < just add a line like @reboot /home/max/projects/catflap/ros_catkin_ws/src/startup.sh >>/home/max/projects/catflap/log_cronrun 2>&1 <
to start it at at reboot
How detection and the door opening logic works:
I trained a haar classifier on my cats snout.
catsnout.xml is the haar classifier for catsnouts trained with opencv.
The classifier is used to look for catsnouts in each image taken.
If a catsnout is found: The area of the catsnout (rectangle) and below (two times the catsnout rectangles height) is the region of interest for detection. The background is partly removed from the roi, then it is blurred and then thresholded with OTSUs method to get the biggest contour in this area (usually the catsnout and pray). If this contour is reaching down below the catsnout rectangle and takes up more pixels than a configurable threshold a pray is detected.
If the contour does not reach down, or the area taken up is below the threshold, no pray is detected.
Or there can be no catsnout found at all.
The doorlock is operated on a trust value. The thre possible detection outcomes each have a configurable trust factor assigned. e.g.
- catsnout detected, no pray - factor 2.0
- catsnout detected, pray detected - factor 0.33
- no catsnout detected - factor 0.95
The momentary trust value is multiplied with the detection outcome factor. Starting value is 1.0 The door opens if the trust value is above or equal to 4.0