College CV
This repository contains the implementation of a system to detect yield signs from images and videos. It is a proof of concept realization based on a course of machine vision at RheinMain University of Applied Sciences in 2017. The scope of the task is to implement some operations on images including but not restricted to color segmentation, morphological operations, hough transform for lines and highlighting found yield signs.
Installation and Execution
The least stressful way to set this up is probably to create a virtualenv with python 3.5+ and then
run pip install -r requirements. You find two executables: main.py for
the graphical user interface where you can play around with parameters for the different algorithms
and video.py, a script to process video data.
> ./main.py --help
usage: main.py [-h] [--fname FNAME]
optional arguments:
-h, --help show this help message and exit
--fname FNAME open a file directly
> ./video.py --help
usage: video.py [-h] [--config CONFIG] [--binary] [--edges] [--save-all]
f_in f_out
positional arguments:
f_in input file
f_out output file
optional arguments:
-h, --help show this help message and exit
--config CONFIG configuration file
--binary only apply segmentation and morphology
--edges only apply --binary and edge detection
--save-all save not only the result but all intermediate steps
