A Laplacian edge filter written with Keras/TensorFlow.
I wrote this as an exploration of Convolution filtering, while learning about computer vision from first-principles. As such, convolution kernels were manually hard-coded to effect the desired function.
In this case, I convert the video input to greyscale before sending into the convolution stack, then the first convolution layer applies a Guassian blur to the image. We then apply a Laplacian convolution and feed the result into a binary mask. The output is a black and white image of the detected edges in the source image. This is looped for each frame in the video stream, and is very performant at ~10ms per frame.
Install dependencies with Conda
conda env create -f environment.yml
conda activate tensorflowRun the script
./edge_filter.pyOr use the included Jupyter Notebook
jupyter notebook edge_filter.ipynb
