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
There are multiple code examples here
Documentation is available here
The following python module dependencies are needed:
The following python modules are optional for certain features:
NetworkTables (pyNetworkTables) for
Requests (requests) for
PySerial (pyserial) for
OVL is officially supported for python 3.5+ only!
python -m pip install ovl
The library uses simple yet highly customizable syntax to create
a vision pipeline using the
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.area_sort()] threshold = ovl.YELLOW_HSV # Define the wanted color to detect 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_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
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