To get distances to objects, there are one of two options, either use sizes of known objects such as car widths or license plates or use a disparity mapping. I decided to start working on the disparity approach which gives a new image of far and near objects, without an exact distance.
Since performance is critical to the application of our project, I thought it’d be important to log our current sampling frame rate by writing on top of the window.
I decided to start off developing the algorithmic part of the car collision project. After a few weeks of debating whether to use C++ or Python, I decided to use Python since there are around 10 weeks left to get this project up and running. I also decided to use OpenCV as it has many of the functions we need to perform and also its highly optimised. Although through my research into OpenCV shows that there is no built in way to get a Depth Map that returns exact distances for each pixel, I decided we can worry about that later after we get the Depth Map working. In a worst case scenario, we can map depth map values to distances or find an alternative. I just wanted to get our feet wet with the project.