Set Up
###########Set Up########################################
knitr::opts_chunk$set(echo = TRUE)
memory.size(max=T)
## Warning: 'memory.size()' is no longer supported
## [1] Inf
library(doParallel)
## Loading required package: foreach
## Loading required package: iterators
## Loading required package: parallel
library(foreach)
library(jpeg)
library(EBImage)
files=list.files(path='c:/users/lfult/documents/footjoy/Images/jpg',pattern="\\.jpg")
#########################################################
View Shoes
###################Set Adj. Parameters##########################
height=1200
width=2500
scale=20
plot_jpeg = function(path, add=FALSE) #initialize function
{
require('jpeg')
jpg = readJPEG(path, native=T) # read the file
res = dim(jpg)[2:1] # get the resolution, [x is 2, y is 1]
if (!add) # initialize an empty plot area if add==FALSE
plot(1,1,xlim=c(1,res[1]),ylim=c(1,res[2]), #set the X Limits by size
asp=1, #aspect ratio
type='n', #don't plot
xaxs='i',yaxs='i',#prevents expanding axis windows +6% as normal
xaxt='n',yaxt='n',xlab='',ylab='', # no axes or labels
bty='n') # no box around graph
rasterImage(jpg,1,1,res[1],res[2]) #image, xleft,ybottom,xright,ytop
}
################################################################
Load the Data into an Array
###################Load#########################
#initialize array with zeros.
im=array(rep(0,length(files)*height/scale*width/scale*3),
#set dimension to N, x, y, 3 colors, 4D array)
dim=c(length(files), height/scale, width/scale,3))
for (i in 1:length(files)){
#define file to be read
tmp=paste0("c:/users/lfult/documents/FootJoy/Images/jpg/", files[i])
#read the file
temp=EBImage::resize(readJPEG(tmp),height/scale, width/scale)
#assign to the array
im[i,,,]=array(temp,dim=c(1, height/scale, width/scale,3))
}
#################################################
Generate Principal Components
###################Generate Variables###########################
height=1200
width=2500
scale=20
newdata=im
dim(newdata)=c(length(files),height*width*3/scale^2)
mypca=princomp(t(as.matrix(newdata)), scores=TRUE, cor=TRUE)
sum(mypca$sdev^2/sum(mypca$sdev^2)) #verify that sum of variance=1
## [1] 1
mycomponents=mypca$sdev^2/sum(mypca$sdev^2)
sum(mycomponents[1:19]) #first 19 components account for 80% of variability
## [1] 0.802521
sum(mycomponents[1:79]) #first 79 components account for 90% of variability
## [1] 0.8921594
################################################################
Eigenshoes
###################Eigenshoes###################################
mypca2=t(mypca$scores)
dim(mypca2)=c(length(files),height/scale,width/scale,3)
par(mfrow=c(5,5))
par(mai=c(.001,.001,.001,.001))
for (i in 1:373){ #plot the first 81 Eigenshoes only
plot_jpeg(writeJPEG(mypca2[i,,,], quality=1,bg="white"))
}
