[R] picture processing

[PIKAL Petr]

OK. I uploaded files to 

https://drive.google.com/?tab=wo&authuser=0#folders/0B-hUB8Dm7X_VYWhwWFNmVkl0cEE

and I hope you are able to download it.

Sorry for the inconvenience.

Best regards
Petr


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of PIKAL Petr
> Sent: Monday, January 21, 2013 2:37 PM
> To: r-help
> Subject: Re: [R] picture processing
> 
> I am sorry, pictures did not went through. Using dput is not a way to
> go, the output file has over 3 MB. I try to find a suitable way where
> to put those pictures (they have about 100 kB) for you to be able to
> use the code.
> 
> Petr
> 
> 
> > -----Original Message-----
> > From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> > project.org] On Behalf Of PIKAL Petr
> > Sent: Monday, January 21, 2013 10:01 AM
> > To: r-help
> > Subject: [R] picture processing
> >
> > Dear all
> >
> > I try to evaluate pictures and find positions, size and amount of
> > dropouts (I enclose two pictures to play with and a code I used
> below).
> > You can see that the first picture is smooth with only few dropouts
> in
> > entire area. The second picture has many dropouts and even a scratch,
> > with number of dropouts increasing to the bottom of the picture.
> >
> > I go through the picture row by row and find a differences from
> supsmu
> > or linear model, compare the differences to some threshold and count
> > number of positive differences in the row, but here I am stuck.
> >
> > I could find position of overall increased dropouts by smoothing
> > rowSums data and evaluate this smoothed value but I do not know
> >
> > 1	How to distinct between big and small dropouts
> > 2	Distinct between scratch and dropout and find starting position
> > of a scratch
> > 3	Find number of small dropouts and find position where this number
> > of small dropouts surpass some given threshold
> >
> > I tried to play with rle but had not been successful yet.
> >
> > If anybody has some clever idea how to proceed with those three tasks
> > I will be grateful.
> >
> > Best regards
> > Petr
> >
> > library(ReadImages)
> > # read and plot those 2 images
> > temp1<-read.jpeg("temp1.jpg")
> > temp1<-rgb2grey(temp1)
> > temp1<-normalize(temp1)
> > temp2<-read.jpeg("temp2.jpg")
> > temp2<-rgb2grey(temp2)
> > temp2<-normalize(temp2)
> > par(mfrow=c(1,2))
> > plot(temp1)
> > plot(temp2)
> >
> > # one possible way to evaluate dropouts suma<-NA for(i in
> > 1:nrow(temp1)) suma[i]<-sum(((temp1[i,]-
> > supsmu(1:ncol(temp1),temp1[i,])$y)^2>.0005))
> > plot(suma)
> >
> > suma<-NA
> > for(i in 1:nrow(temp2)) suma[i]<-sum(((temp2[i,]-
> > supsmu(1:ncol(temp2),temp2[i,])$y)^2>.0005))
> > plot(suma)
> >
> > # the other way to evaluate dropouts
> > x<-1:ncol(temp1)
> > rozdil<-temp1
> > for( i in 1:nrow(temp1)) {
> > y<-temp1[i,]
> > fit<-lm(y~x)
> > rozdil[i,] <- (predict(fit)-y)^2}
> > plot(rowSums(rozdil>.01))
> >
> > x<-1:ncol(temp2)
> > rozdil<-temp2
> > for( i in 1:nrow(temp2)) {
> > y<-temp2[i,]
> > fit<-lm(y~x)
> > rozdil[i,] <- (predict(fit)-y)^2}
> > plot(rowSums(rozdil>.01))
> 
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