🦉 Python Module for real-time computer vision pipelines
OTHER License
Python Module for Computer Vision Object Tracking and Detection mainly for the FIRST® Robotics Competition Program
Ovl support complex yet modular computer vision pipelines that are easy to create and modify.
Easy to create and setup for beginners and flexible for pros
You can follow up on changes in for the current version in the changelog folder
There are multiple code examples here
Documentation is available here
The following python module dependencies are needed:
OpenCV
numpy
The following python modules are optional for certain features:
NetworkTableConnection
(installed automatically)OVL is officially supported for python 3.7+
Installation:
Using pip
:
python -m pip install ovl[cv]
For the full installation of all features use:
python -m pip install ovl[full]
For the frc related features use the frc option:
python -m pip install ovl[frc]
Note that ovl doesn't come with the precompiled version of opencv for python automatically. If you wish to compile opencv for yourself - simply refrain from using the cv flag during installation.
python -m pip install ovl
The library uses simple yet highly customizable syntax to create a vision pipeline using the Vision
object
A pipeline that detects a yellow circle:
import ovl
target_filters = [ovl.percent_area_filter(min_area=0.005),
ovl.circle_filter(min_area_ratio=0.7),
ovl.area_sort()]
threshold = ovl.Color([20, 100, 100], [55, 255, 255])
yellow_circle = ovl.Vision(threshold=threshold,
target_filters=target_filters,
camera=0, # open the first connected camera
image_filters=[ovl.gaussian_blur()])
while True:
image = yellow_circle.get_image()
targets, filtered_image = yellow_circle.detect(image)
directions = yellow_circle.get_directions(targets, filtered_image)
print(directions) # prints out the (x, y) coordinates of the largest target
There are more code examples and usages here