I decided to build onto the doodlenet sketch that was demonstrated during class. I attempted to make the doodle net classify the webcam, but to be completely honest, I am not sure if I did it right, since the confidence scores were all below 30%, and it was not an accurate guess (but I guess a lot of doodlenet guesses are inaccurate).
In addition to this, I kept running into the problem of my web browser freezing. every time I opened the camera, the browser froze before it could classify anything (this happened on October 19th).
However, on October 20th, I tried again, and although it was slow, my web browser did not freeze on me.
Perhaps to make the classification more accurate, I thought maybe putting black and white filter would be helpful, since all of the doodlenet samples are black and white, and drawn with a big stroke.
I thought that maybe I should just use filter (), but that did not have enough contrast. Then, I used PixelLoaded() to create this pixelated look on the webcam.
With the black and white, pixelated image, the top guesses from the classifier were the following:
- lion
- mona lisa
- mustache
I still wasn’t sure if the classifications were correct because the confidence scores were still all below 30 percent.
I think Mona Lisa, if anything, is the most accurate classification of myself, but I could see how all of these classifications were derived from my image: they’re all somewhat related to facial features.
Lion has two eyes, a nose, and a mouth, and so does the Mona Lisa. A Mustache may not have these individual facial features, but it’s on a face, so people might’ve drawn the eyes and the nose to go along with the mustache.
I commented out a lot of things that may slow down the sketch, since my laptop couldn’t handle too many codes. The below is the link to the sketch: