lobrien

#FROM bitnami/pytorch:latest
# Use Ubuntu as base image because Debian doesn't have g++-11, required by robotpy
FROM anibali/pytorch:2.0.1-cuda11.8
USER root

# Update the system and install basic dev packages
RUN apt-get update && apt-get install -y binutils gcc g++ make git wget libicu-dev gradle && \
apt install -y openjdk-11-jdk vim software-properties-common gnupg 

# Install additional Python packages

lobrien

import cv2
import robotpy_apriltag
from wpimath.geometry import Transform3d
import math

# This function is called once to initialize the apriltag detector and the pose estimator
def get_apriltag_detector_and_estimator(frame_size):
    detector = robotpy_apriltag.AprilTagDetector()
    # FRC 2023 uses tag16h5 (game manual 5.9.2)
    assert detector.addFamily("tag16h5")

lobrien

import numpy as np 
rs = np.array([ [1,1], [2, 11], [3, 77], [4, 111], [5, 74], [6, 20], [7.25, 6]])

vs = rs.take(indices = 0, axis=1)
ns = rs.take(indices = 1, axis=1)

mean = np.average(vs, weights = ns)
sd = np.sqrt(np.average((vs - mean)**2, weights=ns))
(mean, sd)

lobrien

# Update of the Dockerfile at https://towardsdatascience.com/detectron2-the-basic-end-to-end-tutorial-5ac90e2f90e3
FROM python:3.8-slim-buster

RUN apt-get update -y

# gcc compiler and opencv prerequisites
RUN apt-get -y install nano git build-essential libglib2.0-0 libsm6 libxext6 libxrender-dev
RUN apt-get install -y python3-opencv

# Detectron2 prerequisites

lobrien

import nltk
nltk.download('words')
from nltk.corpus import words
tetrawords = set(w for w in words.words() if len(w) == 4)

from constraint import *

wordladder = Problem()
for v in ['b','c','d','e','f','g','h'] : 
    wordladder.addVariable(v, Domain(tetrawords))

lobrien

on: push
name: Inline Python Action
jobs:
  my-job:
    name: My job
    runs-on: ubuntu-latest  
    steps:
    - uses: actions/checkout@v1
      with:
        fetch-depth: 1

lobrien

faceContainingImage.Image = UIImage.FromFile("docs.jpg");

//Put it on the screen
View.AddSubview(faceContainingImage);
faceContainingImage.TranslatesAutoresizingMaskIntoConstraints = false;
var dpConstraints = new[]
{
  faceContainingImage.LeadingAnchor.ConstraintEqualTo(View.LayoutMarginsGuide.LeadingAnchor),
  faceContainingImage.TrailingAnchor.ConstraintEqualTo(View.LayoutMarginsGuide.TrailingAnchor),
  faceContainingImage.TopAnchor.ConstraintEqualTo(View.LayoutMarginsGuide.TopAnchor, 40.0f),

lobrien

Video Capture
previewLayer = new AVCaptureVideoPreviewLayer(captureSession);
            //previewLayer.VideoGravity = AVLayerVideoGravity.ResizeAspectFill;
            previewView.Layer.AddSublayer(previewLayer);

            previewView.TranslatesAutoresizingMaskIntoConstraints = false;
            var previewConstraints = new[]
            {
                previewView.LeftAnchor.ConstraintEqualTo(View.LeftAnchor),
                previewView.RightAnchor.ConstraintEqualTo(View.RightAnchor),

lobrien

let innerXml (node : XNode ) = 
    use rdr = node.CreateReader()
    rdr.MoveToContent() |> ignore 
    rdr.ReadInnerXml() 

lobrien

import numpy as np
from keras.models import Sequential
from keras.layers.core import Activation, Dense
from keras.optimizers import SGD

# Allocate the input and output arrays
X = np.zeros((4, 2), dtype='uint8')
y = np.zeros(4, dtype='uint8')

# Training data X[i] -> Y[i]