bhive01

#base packages
import os, sys
import errno 
import datetime
import subprocess
from time import gmtime, strftime, sleep

#rPi packages
from picamera import PiCamera
import RPi.GPIO as GPIO

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library(tidyverse)

df <- data.frame(Group = c(rep("A", 7), rep("B", 7), rep("C", 7)),
                 Time = c(rep(c(1:7), 3)),
                 Result = c(100, 96.9, 85.1, 62.0, 30.7, 15.2, 9.6, 
                            10.2, 14.8, 32.26, 45.85, 56.25, 70.1, 100,
                            100, 55.61, 3.26, -4.77, -7.21, -3.2, -5.6))

df %>%
filter(Group != "B") %>% # filter out B because fails NLS fitting

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# from https://stackoverflow.com/questions/32328179/opencv-3-0-python-lineiterator#
def createLineIterator(P1, P2, img):
    """
    Produces and array that consists of the coordinates and intensities of each pixel in a line between two points

    Parameters:
        -P1: a numpy array that consists of the coordinate of the first point (x,y)
        -P2: a numpy array that consists of the coordinate of the second point (x,y)
        -img: the image being processed

bhive01

import argparse
import sys
import os.path
import trans #pip install trans
import time
import cv2
import math
import skimage

import numpy as np

bhive01

import sys
import os.path
import trans #pip install trans
import time
import datetime
import cv2
import math
import pandas as pd
import numpy as np

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# Brandon Hurr
# Assumptions
#	1. Color card is Xrite Passport
#	2. Card is not mirrored
#	3. Only color calibration half of card is visible
#	4. When remove edges is selected at command line, the card is not at the edge of the image.
#	3. The scale factor is based upon median width of found objects (contours) that should be xrite squares, but could not be
#		could be problematic with lots of squares objects in image other than color card

import argparse

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import argparse
import trans #pip install trans
import time
import cv2
import math
import pandas as pd
import numpy as np
import zbar # sudo apt-get install libzbar-dev \ pip install zbar
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import squareform, pdist

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library(tidyverse)

dt <- tibble(V1=c(1,2,4), V2=c("a","a","b"), V3=c(2,3,1))

swap_if<- function (condition, val1, val2, missing = NA) 
{
    if (!is.logical(condition)) {
        stop("`condition` must be logical", call. = FALSE)
    }
    out1 <- val1[rep(NA_integer_, length(condition))]

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---
output:
  html_document:
    mathjax: null
    theme: null
    highlight: null
    pandoc_args: [
      "+RTS", "-K64m",
      "-RTS"
    ]

bhive01

---
title: "testforcarson"
author: "Brandon Hurr"
date: "September 15, 2016"
output: html_document
---

```{r setup, eval = TRUE, echo = FALSE, warning = FALSE, message = FALSE, results='asis', out.width = 1000, out.height = 1000}
library(ggplot2)
library(dplyr)