Image segmentation can be useful for edge detection. You saw how to segment images with k-Means awhile ago. Now, expand upon that by thinking through the process with the eventual goal being able to perform edge detection on objects and detect their shape.
The step you may want to take after that is image recognition, but we'll leave that for homework.
Libraries needed: jpeg (could replace with png), grid and gridExtra (if not installed general installation of packages is outline in 01.Installing stuff)
PART 1. Think through a general process of finding edges or borders in an image. If it's helpful think of it in the context of these pictures:
These pictures live in the following folders. Don't worry, these personal photos are not copyrighted. Their file names are (including folder) are:
Bonus points to those who also figure out how to rotate the middle image clockwise by 90 degrees.
Code example to follow taken from Ryan Walker wrote a marvelous blog post on color quantization and segmentation in R.
PART 2. Perform image processing task such as segmentation (we saw k-Means earlier)
library(jpeg)
myimg <- readJPEG('images/05_Expanded_Image_Analysis_Lab/hummingbird_mharris.jpg')
# copy the image three times
myimg.R = myimg
myimg.G = myimg
myimg.B = myimg
# zero out the non-contributing channels for each image copy
myimg.R[,,2:3] = 0
myimg.G[,,1]=0
myimg.G[,,3]=0
myimg.B[,,1:2]=0
...
In [12]:
library(jpeg)
library("grid")
library("gridExtra")
myimg <- readJPEG('images/05_Expanded_Image_Analysis_Lab/hummingbird_mharris.jpg')
In [7]:
### EX 3: show the 3 channels in separate images
# copy the image three times
myimg.R = myimg
myimg.G = myimg
myimg.B = myimg
# zero out the non-contributing channels for each image copy
myimg.R[,,2:3] = 0
myimg.G[,,1]=0
myimg.G[,,3]=0
myimg.B[,,1:2]=0
In [8]:
# build the image grid
img1 = rasterGrob(myimg.R)
img2 = rasterGrob(myimg.G)
img3 = rasterGrob(myimg.B)
grid.arrange(img1, img2, img3, nrow=1)
In [9]:
# Now let’s segment this image. First, we need to reshape the
# array into a data frame with one row for each pixel and three columns for the RGB channels:
# reshape image into a data frame
df = data.frame(
red = matrix(myimg[,,1], ncol=1),
green = matrix(myimg[,,2], ncol=1),
blue = matrix(myimg[,,3], ncol=1)
)
In [10]:
# Now, we apply k-means to our data frame. We’ll choose k=4 to break the image into 4 color regions.
### compute the k-means clustering
K = kmeans(df,4)
df$label = K$cluster
### Replace the color of each pixel in the image with the mean
### R,G, and B values of the cluster in which the pixel resides:
# get the coloring
colors = data.frame(
label = 1:nrow(K$centers),
R = K$centers[,"red"],
G = K$centers[,"green"],
B = K$centers[,"blue"]
)
# merge color codes on to df
# IMPORTANT: we must maintain the original order of the df after the merge!
df$order = 1:nrow(df)
df = merge(df, colors)
df = df[order(df$order),]
df$order = NULL
In [11]:
# Finally, we have to reshape our data frame back into an image:
# get mean color channel values for each row of the df.
R = matrix(df$R, nrow=dim(myimg)[1])
G = matrix(df$G, nrow=dim(myimg)[1])
B = matrix(df$B, nrow=dim(myimg)[1])
# reconstitute the segmented image in the same shape as the input image
myimg.segmented = array(dim=dim(myimg))
myimg.segmented[,,1] = R
myimg.segmented[,,2] = G
myimg.segmented[,,3] = B
# View the result
grid.raster(myimg.segmented)
Create a new notebook with the emphasis on a document for reproducible research.