MahShaaban/test-reproduce_app.R

## test-reproduce_app.R


library(imager)

# download data
if(!file.exists('./stats.csv')) {
  download.file('https://raw.githubusercontent.com/MahShaaban/colocr/master/inst/colocr_app/stats_18.09.02_05.15.01.csv',
                destfile = './stats.csv')
}
if(!file.exists('./inputs.csv')) {
  download.file('https://raw.githubusercontent.com/MahShaaban/colocr/master/inst/colocr_app/inputs_18.09.02_05.15.08.csv',
                destfile = './inputs.csv')
}
if(!file.exists('./Image0001_.jpg')) {
  download.file('https://github.com/MahShaaban/colocr/blob/master/inst/extdata/Image0001_.jpg?raw=true',
                destfile = './Image0001_.jpg')
}
if(!file.exists('./Image0003_.jpg')) {
  download.file('https://github.com/MahShaaban/colocr/blob/master/inst/extdata/Image0003_.jpg?raw=true',
                destfile = './Image0003_.jpg')
}

  # read input and output from the app
  stats <- read.csv('./stats.csv')
  inputs <- read.csv('./inputs.csv')

  # read images
  lst <- as.list(inputs)
  num <- list()
  for(i in 1:2) {
    fl <- paste0('./', lst$image[i])
    img <- load.image(fl)

    # transform to gray scale
    img.g <- grayscale(img)

    # apply threshold
    img.t <- threshold(img.g, paste0(lst$threshold[i], '%'))

    # change to pixset
    px <- as.pixset(1-img.t)

    # apply shrink
    px.m <- shrink(px, lst$shrink[i])

    # apply grow
    px.m <- grow(px.m, lst$grow[i])

    # apply fill
    px.m <- fill(px.m, lst$fill[i])

    # apply clean
    px <- clean(px.m, lst$clean[i])

    # add labels
    px.labs <- label(px, tolerance = lst$tolerance[i])
    value <- as.data.frame(px.labs)$value

    ids <- reorder(value, value, length)

    k <- levels(ids)

    k <- k[(length(k)-1):(length(k)-lst$roi_num[i])]

    new.ids <- ifelse(value %in% as.numeric(k), value, 0)
    f <- as.numeric(factor(new.ids))

    new.px <- as.data.frame(px.labs)
    new.px$value <- f - 1
    new.px <- as.cimg(new.px, dim = dim(px))
    labs <- as.data.frame(new.px)$value

    # extract first two channels
    img1.g <- channel(img, ind = 1)
    img2.g <- channel(img, ind = 2)

    # subset and change images to numeric
    img1.num <- as.numeric(img1.g[as.pixset(new.px)])
    img2.num <- as.numeric(img2.g[as.pixset(new.px)])
    num[[i]] <- list(img1.num,
                     img2.num,
                     labs[labs != 0])
  }

  # test pcc
  corr <- list()
  for(i in 1:2) {
    lab <- (num[[i]][[3]])
    res <- list()
    for(j in unique(lab)) {
      c1 <- num[[i]][[1]][lab == j]
      c2 <- num[[i]][[2]][lab == j]

      rx <- c1 - mean(c1)
      gx <- c2 - mean(c2)

      nom <- sum(rx * gx)
      den <- sqrt(sum(rx**2) * sum(gx**2))


      res[[j]] <-nom/den
    }
    corr[[i]] <- res
  }
  print(
    all.equal(round(stats$pcc, 5), round(unlist(corr), 5))
  )

  # test moc
  corr <- list()
  for(i in 1:2) {
    lab <- (num[[i]][[3]])
    res <- list()
    for(j in unique(lab)) {
      c1 <- num[[i]][[1]][lab == j]
      c2 <- num[[i]][[2]][lab == j]

      nom <- sum(c1 * c2)
      den <- sqrt(sum(c1**2) * sum(c2**2))

      res[[j]] <- nom/den
    }
    corr[[i]] <- res
  }

  print(
    all.equal(round(stats$moc, 5), round(unlist(corr), 5))
  )
unlink('./*.csv')
unlink('./*.jpg')


	library(imager)

	# download data
	if(!file.exists('./stats.csv')) {
	download.file('https://raw.githubusercontent.com/MahShaaban/colocr/master/inst/colocr_app/stats_18.09.02_05.15.01.csv',
	destfile = './stats.csv')
	}
	if(!file.exists('./inputs.csv')) {
	download.file('https://raw.githubusercontent.com/MahShaaban/colocr/master/inst/colocr_app/inputs_18.09.02_05.15.08.csv',
	destfile = './inputs.csv')
	}
	if(!file.exists('./Image0001_.jpg')) {
	download.file('https://github.com/MahShaaban/colocr/blob/master/inst/extdata/Image0001_.jpg?raw=true',
	destfile = './Image0001_.jpg')
	}
	if(!file.exists('./Image0003_.jpg')) {
	download.file('https://github.com/MahShaaban/colocr/blob/master/inst/extdata/Image0003_.jpg?raw=true',
	destfile = './Image0003_.jpg')
	}

	# read input and output from the app
	stats <- read.csv('./stats.csv')
	inputs <- read.csv('./inputs.csv')

	# read images
	lst <- as.list(inputs)
	num <- list()
	for(i in 1:2) {
	fl <- paste0('./', lst$image[i])
	img <- load.image(fl)

	# transform to gray scale
	img.g <- grayscale(img)

	# apply threshold
	img.t <- threshold(img.g, paste0(lst$threshold[i], '%'))

	# change to pixset
	px <- as.pixset(1-img.t)

	# apply shrink
	px.m <- shrink(px, lst$shrink[i])

	# apply grow
	px.m <- grow(px.m, lst$grow[i])

	# apply fill
	px.m <- fill(px.m, lst$fill[i])

	# apply clean
	px <- clean(px.m, lst$clean[i])

	# add labels
	px.labs <- label(px, tolerance = lst$tolerance[i])
	value <- as.data.frame(px.labs)$value

	ids <- reorder(value, value, length)

	k <- levels(ids)

	k <- k[(length(k)-1):(length(k)-lst$roi_num[i])]

	new.ids <- ifelse(value %in% as.numeric(k), value, 0)
	f <- as.numeric(factor(new.ids))

	new.px <- as.data.frame(px.labs)
	new.px$value <- f - 1
	new.px <- as.cimg(new.px, dim = dim(px))
	labs <- as.data.frame(new.px)$value

	# extract first two channels
	img1.g <- channel(img, ind = 1)
	img2.g <- channel(img, ind = 2)

	# subset and change images to numeric
	img1.num <- as.numeric(img1.g[as.pixset(new.px)])
	img2.num <- as.numeric(img2.g[as.pixset(new.px)])
	num[[i]] <- list(img1.num,
	img2.num,
	labs[labs != 0])
	}

	# test pcc
	corr <- list()
	for(i in 1:2) {
	lab <- (num[[i]][[3]])
	res <- list()
	for(j in unique(lab)) {
	c1 <- num[[i]][[1]][lab == j]
	c2 <- num[[i]][[2]][lab == j]

	rx <- c1 - mean(c1)
	gx <- c2 - mean(c2)

	nom <- sum(rx * gx)
	den <- sqrt(sum(rx*2) sum(gx**2))


	res[[j]] <-nom/den
	}
	corr[[i]] <- res
	}
	print(
	all.equal(round(stats$pcc, 5), round(unlist(corr), 5))
	)

	# test moc
	corr <- list()
	for(i in 1:2) {
	lab <- (num[[i]][[3]])
	res <- list()
	for(j in unique(lab)) {
	c1 <- num[[i]][[1]][lab == j]
	c2 <- num[[i]][[2]][lab == j]

	nom <- sum(c1 * c2)
	den <- sqrt(sum(c1*2) sum(c2**2))

	res[[j]] <- nom/den
	}
	corr[[i]] <- res
	}

	print(
	all.equal(round(stats$moc, 5), round(unlist(corr), 5))
	)
	unlink('./*.csv')
	unlink('./*.jpg')