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-boun...@r-project.org [mailto:r-help-bounces@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@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.
