A modular and versatile Python package for Computer Vision object detection pipelines Tailored for robotics applications


Keywords
computer-vision, first-robotics-competition, image-processing, object-det, opencv, opencv-python, ovl, ovl-python, python, robotics, robotics-programming
License
Apache-2.0
Install
pip install ovl==0.2.2

Documentation

Ovl-Python

ovl logo

Python Module for Computer Vision Object Tracking and Detection mainly for the FIRST® Robotics Competition Program You can follow up on changes in for the current version in the changelog folder There have been significant changes from the previous version - changelog

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)

  • sklearn (scikit-learn) for HSVCalibration creation (not needed for use in Vision)

  • PySerial (pyserial) for SerialConnection

OVL is officially supported for python 3.5+ only!

Installation:

Using pip:
python -m pip install ovl

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

# filter contours that are larger than 200 pixels
# and are approximately a circle and then sort by size

contour_filters = [ovl.area_filter(min_area=200),
                   ovl.circle_filter(min_area_ratio=0.7),
                   ovl.dec_area_sort()]

image_filters = [ovl.gaussain_blur()]

threshold = ovl.YELLOW_HSV  # Define the wanted color to detect 

yellow_circle = ovl.Vision(threshold=threshold,
                           contour_filters=contour_filters,
                           camera=0,  # open the first connected camera
                           image_filters=image_filters)

while True:
    image = yellow_circle.get_filtered_image()
    contours, filtered_image = yellow_circle.detect(image)
    directions = yellow_circle.get_directions(contours, filtered_image)

    print(directions)  # prints out the (x, y) coordinates of the largest target


There are more code examples and usages here

Documentation

The Hebrew and English documentation have been removed for being outdated all functions and classes now have in code documentation. This includes code examples, recommended usage reference to further documentation and more.

There are multiple code examples here

Simply use the built-in help on a class/function:

help(ovl.Vision)

help(ovl.contour_filter)

help(ovl.area_filter)