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))
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))
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)) > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code.