![\"\"](\"http:\/\/course.ece.cmu.edu\/~ece500\/projects\/s21-teamc8\/wp-content\/uploads\/sites\/139\/2021\/03\/3-different-masks-300x87.png\")
<\/p>\nFor this week, I looked over the feedback given by peers from the design review presentation and discussed some plan changes with my group. I have decided to train my gesture detection ML model in Python and then export it as a json or txt file, since training is done separately and doesn’t affect application speed. Then, in our actual application, we will be using c++ and using that to run a classifier with our model.<\/p>\n
In terms of coding, I have been able to do 3 different types of maskings, one which does threshing by detecting pixels within a skintone range of colors, one that contours areas of skintone, and one that does dynamic background subtraction, essentially detecting changes in motion. I have taken a screenshot below for demo purposes:<\/p>\n
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For next week, I will be working on figuring out a way to combine information from the three to 1) filter out noise and 2) only focus on the hand and ignore the face.<\/p>\n","protected":false},"excerpt":{"rendered":"
For this week, I looked over the feedback given by peers from the design review presentation and discussed some plan changes with my group. I have decided to train my gesture detection ML model in Python and then export it as a json or txt file, since training is done separately and doesn’t affect application … <\/p>\n