lobrien

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

# Trains a neural net that recognizes the digit displayed by a 7-segment LED:
# "Lit segments" are indexed as:
#  -- 0 --
# |        |
# 1        2

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

from sklearn.cluster import KMeans
import numpy as np

data = np.array([101, 107, 106, 199, 204, 205, 207, 306, 310, 312, 312, 314, 317, 318, 380, 377, 379, 382, 466, 469, 471, 472, 557, 559, 562, 566, 569])

kmeans = KMeans(n_clusters=5).fit(data.reshape(-1,1))
kmeans.predict(data.reshape(-1,1))

# array([4, 4, 4, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 3,
#       3, 3, 3, 3], dtype=int32)

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

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

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

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

# 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

uti	id	title	platforms	packages
com.xamarin.workbook	7e650a9b-4216-4b28-945c-217cd85530b2	FlexLayout Options	Android	id version Xamarin.Forms 3.0.0.482510

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))