1. The document discusses various image processing techniques in R using the EBImage library. It loads images from URLs and locally, and performs operations like brightness/contrast manipulation, gamma correction, grayscale conversion, cropping, flipping, rotating, resizing, and low-pass/high-pass filtering. 2. It also demonstrates combining two images through addition and weighted addition, and saves the processed image as a new file. 3. The key functions used include readImage(), display(), print(), hist(), colorMode(), filter2(), flip(), flop(), rotate(), resize(), makeBrush(), combine(), and writeImage().

1. IMAGE PROCESSING IN R
SARAVANAKUMAR SEKAR
14 May 2018
library(EBImage)
#image1=readImage("~//pp//pic1.jpg")
#image2=readImage("~//pp//pic2.jpg")
#print(image1)
#display(image1)
image3=readImage("http://all4desktop.com/data_images/1920%20x%201080/4166784-
ms-dhoni-HD.jpg")
Inference
Here I get image from internet using link of that image
print(image3)
## Image
## colorMode : Color
## storage.mode : double
## dim : 1920 1080 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.4235294 0.4078431 0.4274510 0.4235294 0.4235294 0.4392157
## [2,] 0.4196078 0.4039216 0.4078431 0.4156863 0.4274510 0.4431373
## [3,] 0.4156863 0.4078431 0.3960784 0.4039216 0.4156863 0.4274510
## [4,] 0.4117647 0.4156863 0.4000000 0.4000000 0.4000000 0.4000000
## [5,] 0.4000000 0.4156863 0.4039216 0.4078431 0.4078431 0.4000000
Inference
In this print statement , it tell about overview of the image
In this image colorMode is denoted the it color or not ?
And it tell dim size ,frame.total,frames.render and image clarity weight

2. display(image3)
Inference
Using display for view the image
image4=readImage("http://www.olympicsupdate.com/wp-content/uploads/2016/02/MS
-Dhoni-wallpapers.jpg")
print(image4)
## Image
## colorMode : Color
## storage.mode : double
## dim : 1920 1080 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.2705882 0.2666667 0.2666667 0.2627451 0.2627451 0.2666667
## [2,] 0.2745098 0.2745098 0.2705882 0.2666667 0.2666667 0.2666667
## [3,] 0.2823529 0.2784314 0.2745098 0.2705882 0.2705882 0.2705882

3. ## [4,] 0.2784314 0.2784314 0.2745098 0.2705882 0.2705882 0.2705882
## [5,] 0.2705882 0.2705882 0.2705882 0.2705882 0.2705882 0.2705882
display(image4)
#histogram for image
hist(image3)

4. Inference
In this chart based we can tell about the intensity range and pixels of the image .
If any changes will occur in manipulating this chart intensity will change based on the
manipulating weight.
hist(image4)

5. #manipulating brightness
b1=image4+0.2
Inference
In this image I just increase the brightness 0.2 for image 4
print(b1)
## Image
## colorMode : Color
## storage.mode : double
## dim : 1920 1080 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.4705882 0.4666667 0.4666667 0.4627451 0.4627451 0.4666667
## [2,] 0.4745098 0.4745098 0.4705882 0.4666667 0.4666667 0.4666667
## [3,] 0.4823529 0.4784314 0.4745098 0.4705882 0.4705882 0.4705882
## [4,] 0.4784314 0.4784314 0.4745098 0.4705882 0.4705882 0.4705882
## [5,] 0.4705882 0.4705882 0.4705882 0.4705882 0.4705882 0.4705882
Inference

6. Using print statement we can see the weight difference before weight is 0.27 but now 0.47i
n the category of brightness
display(b1)
b2=image4-0.4
print(b2)
## Image
## colorMode : Color
## storage.mode : double
## dim : 1920 1080 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.1294118 -0.1333333 -0.1333333 -0.1372549 -0.1372549 -0.1333333
## [2,] -0.1254902 -0.1254902 -0.1294118 -0.1333333 -0.1333333 -0.1333333
## [3,] -0.1176471 -0.1215686 -0.1254902 -0.1294118 -0.1294118 -0.1294118
## [4,] -0.1215686 -0.1215686 -0.1254902 -0.1294118 -0.1294118 -0.1294118
## [5,] -0.1294118 -0.1294118 -0.1294118 -0.1294118 -0.1294118 -0.1294118

7. Inference
Here I reduced the brightness 0.4 now we can see the difference of the image in print state
ment.
display(b2)
hist(b2)

8. #manipulating contrast
c1=image4*0.3
Inference
Here I increase the image contrast0.3
display(c1)

9. c2=image4*1
Inference
last image contrast is very low so again I increase the contrast from 0.3 to 1 .using * symbol
we can modify the image contrast
display(c2)

10. #gamma corrections
g1=image4>0.3
display(g1)

11. g2=image4>0.6
display(g2)
g3=image4<0.5
display(g3)

12. Inference
Gamma is used for manipulate the darkness of the picture. Here, I just increase the darknes
s from 0.3 to 0.6 using >and < symbols
#color
colorMode(image4)=Grayscale
print(image4)
## Image
## colorMode : Grayscale
## storage.mode : double
## dim : 1920 1080 3
## frames.total : 3
## frames.render: 3
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.2705882 0.2666667 0.2666667 0.2627451 0.2627451 0.2666667
## [2,] 0.2745098 0.2745098 0.2705882 0.2666667 0.2666667 0.2666667
## [3,] 0.2823529 0.2784314 0.2745098 0.2705882 0.2705882 0.2705882
## [4,] 0.2784314 0.2784314 0.2745098 0.2705882 0.2705882 0.2705882
## [5,] 0.2705882 0.2705882 0.2705882 0.2705882 0.2705882 0.2705882

13. #to return to color
colorMode(image4)=Color
Inference
ColorMode is use for manipulate color. Here I changed the colorMode from color to graysca
le and again I changed to color
#cropping
k=image4[60:860,360:960,]
display(k)
Inference
Cropping is again important manipulating operating in image processing . Here,I just crop
in this range of 60:860,360:960.
#new image file
writeImage(k,"MS.jpg")
Inference
In EBimage have write operation for save your all updates pictures in your local machine u
sing writeImage.
#flip,flop,rotate resize

14. l=flip(image4)
display(l)
m=flop(image4)
display(m)

15. r=rotate(image4,45)
display(r)

16. o=resize(image4,360)
display(o)
Inference
Using Flip,flop,rotate and resize commands we can flip and flop the image ,rotate the image
, and resize the image, which size you want?
#low-pass filter
low=makeBrush(21,shape = "disc",step = FALSE)
low=low/sum(low)
image.low=filter2(image4,low)
display(image.low)

17. low=makeBrush(81,shape = "disc",step = FALSE)
low=low/sum(low)
image.low=filter2(image3,low)
display(image.low)

18. #high-pass filter
high=matrix(1,nc=3,nr=3)
high[2,2]=-5
image.high=filter2(image4,high)
display(image.high)

19. Inference
Low-pass filter and high-pass filter is used for just blur the image whatever you want?
# combine
c=combine(image3,image4)
display(c)
## Only the first frame of the image stack is displayed.
## To display all frames use 'all = TRUE'.

20. Inference
We can merge the image one by one using combine command. Here I combine image3 and i
mage4
#put two image into one
i1=image3+image4
display(i1)

21. hist(i1)

22. i2=image3/3+image4
display(i2)
hist(i2)

23. Inference
Here I put two image into one image
If you want second image is more thickness compare with first image just increase the first
image thickness using / symbol