Tour of Notebooks and Short Image Analysis Lab

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## Turn this cell into a markdown cell and render


    

In [22]:

    

# Keep as code cell and indent the part in braces with indent shortcut
library(foreach)

x <- foreach(i = seq(0, 1, by = 0.25)) %do% {
y <- i^2 # indent me!
}

x


    

    
Out[22]:





	
0
	
0.0625
	
0.25
	
0.5625
	
1

In [23]:

    

IRdisplay::display_html("<img width = 200 src = 'http://pages.discovery.wisc.edu/~avelten/images/zebrafish_brain_10x.jpeg'>")


    

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# code up your solution here...


    

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IRdisplay::display_html("<img width = 500 src = 'images/05_Expanded_Image_Analysis_Lab/a_query.jpg'>")


    

In [32]:

    

R.version.string


    

    
Out[32]:




'R version 3.2.2 (2015-08-14)'

In [40]:

    

if ("jpeg" %in% rownames(installed.packages()) == FALSE) {
    install.packages("jpeg"), 
}


    

In [1]:

    

# load the package jpeg
library(jpeg)

# Andreas Velten Lab - confocal images of zebrafish CNS
url <- "http://pages.discovery.wisc.edu/~avelten/images/zebrafish_brain_10x.jpeg"

# A different zebrafish - entire body
# url <- "http://pages.discovery.wisc.edu/~avelten/images/zebrafish_stitched.png"

# download the file and save it as "Image.jpg" in the current directory
image.down <- download.TAB(urTAB, "image.jpg")

# read the image
img <- readJPEG(TAB  "image.jpg") # Read the image


    

    
Out[1]:





	
1120
	
1120
	
3

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# load the package jpeg
library(jpeg)

# Andreas Velten Lab - confocal images of zebrafish CNS
url <- "http://pages.discovery.wisc.edu/~avelten/images/zebrafish_brain_10x.jpeg"

# A different zebrafish - entire body
# url <- "http://pages.discovery.wisc.edu/~avelten/images/zebrafish_stitched.png"

# download the file and save it as "Image.jpg" in the current directory
image.down <- download.(url, "image.jpg")

# read the image
img <- readJPEG("image.jpg") # Read the image


    

In [8]:

    

img <- readJPEG("images/03_Image_Analysis_with_k-Means/zebrafish_brain_10x.jpeg") # Read the image

# Obtain the dimension
imgDm <- dim(img)

# Assign RGB channels to data frame
imgRGB <- data.frame(
  x = rep(1:imgDm[2], each = imgDm[1]),
  y = rep(imgDm[1]:1, imgDm[2]),
  R = as.vector(img[,,1]),
  G = as.vector(img[,,2]),
  B = as.vector(img[,,3])
)


    

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library(ggplot2)

# customize some ggplot settings
plotTheme <- function() {
  theme(
    panel.background = element_rect(
      size = 3,
      colour = "black",
      fill = "white"),
    axis.ticks = element_line(
      size = 2),
    panel.grid.major = element_line(
      colour = "gray80",
      linetype = "dotted"),
    panel.grid.minor = element_line(
      colour = "gray90",
      linetype = "dashed"),
    axis.title.x = element_text(
      size = rel(1.2),
      face = "bold"),
    axis.title.y = element_text(
      size = rel(1.2),
      face = "bold"),
    plot.title = element_text(
      size = 20,
      face = "bold",
      vjust = 1.5)
  )
}


    

In [27]:

    

# Plot the image - this is a re-rendering of original by the way
ggplot(data = imgRGB, aes(x = x, y = y)) + 
  geom_point(color = rgb(imgRGB[c("R", "G", "B")])) +
  labs(title = "Original Image: Zebrafish Brain") +
  xlab("x") +
  ylab("y") +
  plotTheme()


    

In [14]:

    

kClusters <- 4
kMeans <- kmeans(imgRGB[, c("R", "G", "B")], centers = kClusters)
kColors <- rgb(kMeans$centers[kMeans$cluster,])


    

In [15]:

    

head(kMeans$centers)


    

    
Out[15]:





R
G
B

	
1
0.1542233
0.5705637
0.2478567
	
2
0.1544659
0.2991689
0.5766785
	
3
0.1286115
0.1478875
0.2035499
	
4
0.02084664
0.01701807
0.02323046

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head(kMeans$cluster)


    

    
Out[16]:





	
4
	
4
	
4
	
4
	
4
	
4

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ggplot(data = imgRGB, aes(x = x, y = y)) + 
  geom_point(color = kColors) +
  labs(title = paste("k-Means Clustering of", kClusters, "Colors")) +
  xlab("x") +
  ylab("y") + 
  plotTheme()


    

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