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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