ovl 2022.2.2

A modular and versatile Python package for computer vision object detection pipelines tailored for robotics applications

OVL - Object Vision Library

Python Module for Computer Vision Object Tracking and Detection mainly for the FIRSTֲ® 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

Documentation

There are multiple code examples here

Documentation is available here

Dependencies:

The following python module dependencies are needed:

• OpenCV

• numpy

The following python modules are optional for certain features:

• NetworkTables (pyNetworkTables) for NetworkTableConnection (installed automatically)

OVL is officially supported for python 3.7+ only!

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.

Usage:

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