A modular and versatile Python package for Computer Vision object detection pipelines Tailored for robotics applications
Project description
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) -
Requests (requests) for
HTTPConnection
-
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.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
Project details
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