Python 3 for the FIRST Robotics Competition (FRC)
This fixes two major bugs in the original RobotPy release:
All RobotPy users should upgrade to this latest version of RobotPy.
It’s a little late, but we’re happy to announce the initial release of RobotPy for the 2017 season. This release contains most new changes for WPILib in 2017. Here’s what this package provides you:
This release will only work correctly on a RoboRIO that has been reimaged for the 2017 season.
There’s a couple of important things to note here:
Additionally, the documentation site has been restructured significantly, so that all RobotPy projects essentially share the same set of documentation – no more need to remember five different sites!
Thanks to everyone who contributed to this release, but particularly @james-ward @auscompgeek @Twinters007 and @ArthurAllshire
Installation instructions can be found on our documentation site, and are also included as a README file with this zipfile. We expect there to be some bugs at first as teams start playing with their new toys, so please report them on our issue tracker as you find them!
As some of you may be aware, for a number of years we’ve had an official IRC channel for RobotPy. However, IRC is a bit obscure and isn’t always the friendliest to beginners, so I’m switching the official robotpy support channel to use gitter instead.
If you’re interested in helping with the ongoing RobotPy 2017 WPILib updates, please join the room to find out how you can contribute!
pynetworktables has been rewritten in the style of ntcore, and now fully supports all of the NT3 features that are available in ntcore. For the most part…. it should all work. There are a few breaking changes I can think of:
I haven’t had the opportunity to try this on a real robot yet, BUT the unit tests have 75% coverage and it works on my machine, so it’s probably good to go if you’re using this on a driver station or coprocessor. Try it out, let me know how it works!
Installation is super easy if you already have python and pip installed:
pip install --pre pynetworktables
Also, if you’re using pynetworktables2js, there’s an alpha release of that available too, which accommodates some of the NT3 changes. However, more work needs to be done to fully support all of the NT3 features in pynetworktables2js.
I’m happy to announce the release of an OpenCV input plugin for mjpg-streamer, which allows you to write simple little filter plugins that can process the image from a webcam, and change what is streamed out via HTTP. You can install the mjpg-streamer-cv or mjpg-streamer-py packages using the instructions on our github repo. Here’s an example filter plugin:
import cv2
import numpy as np
class MyFilter:
def process(self, img):
'''
:param img: A numpy array representing the input image
:returns: A numpy array to send to the mjpg-streamer
output plugin
'''
# silly routine that overlays a really large crosshair over the image
h = img.shape[0]
w = img.shape[1]
w2 = int(w/2)
h2 = int(h/2)
cv2.line(img, (int(w/4), h2), (int(3*(w/4)), h2), (0xff, 0, 0), thickness=3)
cv2.line(img, (w2, int(h/4)), (w2, int(3*(h/4))), (0xff, 0, 0), thickness=3)
return img
def init_filter():
'''
This function is called after the filter module is imported.
It MUST return a callable object (such as a function or
bound method).
'''
f = MyFilter()
return f.process
If you scp’ed this to the roborio, you could use the following command line to run it:
mjpg_streamer -i 'input_opencv.so -r 320x240 --fps 15 --quality 30 --filter /usr/local/lib/mjpg-streamer/cvfilter_py.so --fargs /home/admin/example_filter.py'
Our team used the OpenCV plugin on our robot this weekend with a python script to do image processing and NetworkTables operations (Lifecam 3000, 320x240, 15fps, 30 quality), and it seemed to be about 20% CPU usage. Not too shabby. In theory, you could use this on a RPi or other platform too, as I’ve pushed the changes (plus some significant build system improvements) to mjpg-streamer upstream.