imagerExtra: Extra Image Processing Library Based on Imager

Description

imagerExtra is built on imager. imager by Simon Simon Barthelme provides an interface with CImg that is a C++ library for image processing. imager makes functions of CImg accessible from R and adds many utilities for accessing and working with image data from R. imagerExtra provides advanced functions for image processing based on imager.

Balance color of image by Simplest Color Balance

Description

Balance color of image by Simplest Color Balance

Usage

BalanceSimplest(im, sleft, sright, range = c(0, 255))

Arguments

Value

a grayscale image of class cimg

Author(s)

Shota Ochi

References

Nicolas Limare, Jose-Luis Lisani, Jean-Michel Morel, Ana Belen Petro, and Catalina Sbert, Simplest Color Balance, Image Processing On Line, 1 (2011), pp. 297-315. https://doi.org/10.5201/ipol.2011.llmps-scb

Examples

dev.new()
par(mfcol = c(1,2))
boats_g <- grayscale(boats)
plot(boats_g, main = "Original")
BalanceSimplest(boats_g, 1, 1) %>% plot(., main = "Simplest Color Balance")

Two Dimensional Discrete Cosine Transformation and Inverse Cosine Transformation

Description

DCT2D computes two dimensional discrete cosine transformation. IDCT2D computes two dimensional inverse discrete cosine transformation.

Usage

DCT2D(imormat, returnmat = FALSE)

IDCT2D(imormat, returnmat = FALSE)

Arguments

Value

a grayscale image of class cimg or a numeric matrix

Author(s)

Shota Ochi

References

Makhoul, J. (1980). A fast cosine transform in one and two dimensions. IEEE Transactions on Acoustics, Speech, and Signal Processing. 28 (1): 27-34.

Examples

g <- grayscale(boats)
layout(matrix(1:2, 1, 2))
plot(g, main = "Original")
gg <- DCT2D(g) %>% IDCT2D() %>% plot(main = "Transformed")
mean((g - gg)^2)

denoise image by DCT denoising

Description

denoise image by DCT denoising

Usage

DenoiseDCT(im, sdn, flag_dct16x16 = FALSE)

Arguments

Value

a grayscale image of class cimg

Author(s)

Shota Ochi

References

Guoshen Yu, and Guillermo Sapiro, DCT Image Denoising: a Simple and Effective Image Denoising Algorithm, Image Processing On Line, 1 (2011), pp. 292-296. https://doi.org/10.5201/ipol.2011.ys-dct

Examples

dev.new()
par(mfcol = c(1,2))
boats_g <- grayscale(boats)
boats_noisy <- imnoise(dim = dim(boats_g), sd = 0.05) + boats_g 
plot(boats_noisy, main = "Noisy Boats")
DenoiseDCT(boats_g, 0.05) %>% plot(., main = "Denoised Boats")

Adaptive Double Plateaus Histogram Equalization

Description

compute the paramters, t_down and t_up, and then apply double plateaus histogram equalization.

Usage

EqualizeADP(im, n = 5, N = 1000, range = c(0, 255),
  returnparam = FALSE)

Arguments

Value

a grayscale image of class cimg or a numericvector

Author(s)

Shota Ochi

References

Kun Liang, Yong Ma, Yue Xie, Bo Zhou ,Rui Wang (2012). A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization. Infrared Phys. Technol. 55, 309-315.

Examples

g <- grayscale(dogs)
layout(matrix(1:2, 1, 2))
plot(g, main = "Original")
EqualizeADP(g) %>% plot(main = "Contrast Enhanced")

Double Plateaus Histogram Equalization

Description

enhance contrast of image by double plateaus histogram equalization.

Usage

EqualizeDP(im, t_down, t_up, N = 1000, range = c(0, 255))

Arguments

Value

a grayscale image of class cimg

Author(s)

Shota Ochi

References

Kun Liang, Yong Ma, Yue Xie, Bo Zhou ,Rui Wang (2012). A new adaptive contrast enhancement algorithm for infrared images based on double plateaus histogram equalization. Infrared Phys. Technol. 55, 309-315.

Examples

g <- grayscale(dogs)
layout(matrix(1:2, 1, 2))
plot(g, main = "Original")
EqualizeDP(g, 20, 186) %>% plot(main = "Contrast Enhanced")

Piecewise Affine Histogram Equalization

Description

enhance contrast of image by piecewise affine histogram equalization

Usage

EqualizePiecewise(im, N, smax = 255, smin = 0, range = c(0, 255))

Arguments

Value

a grayscale image of class cimg

Author(s)

Shota Ochi

References

Jose-Luis Lisani, Ana-Belen Petro, and Catalina Sbert, Color and Contrast Enhancement by Controlled Piecewise Affine Histogram Equalization, Image Processing On Line, 2 (2012), pp. 243-265. https://doi.org/10.5201/ipol.2012.lps-pae

Examples

dev.new()
par(mfcol = c(1,2))
boats_g <- grayscale(boats)
plot(boats_g, main = "Original")
EqualizePiecewise(boats_g, 10) %>% plot(., main = "Piecewise Affine Equalization")

store hue of color image

Description

store hue of color image

Usage

GetHue(imcol)

Arguments

Value

a color image of class cimg

Author(s)

Shota Ochi

Examples

GetHue(boats)

compute average of RGB channels

Description

compute average of RGB channels

Usage

Grayscale(imcol)

Arguments

Value

a grayscale image of class cimg

Author(s)

Shota Ochi

Examples

Grayscale(boats) %>% plot

Optical Character Recognition with tesseract

Description

OCR and OCR_data are wrappers for ocr and ocr_data of tesseract package. You need to install tesseract package to use these functions.

Usage

OCR(imorpx, engine = tesseract::tesseract("eng"), HOCR = FALSE)

OCR_data(imorpx, engine = tesseract::tesseract("eng"))

Arguments

Author(s)

Shota Ochi

Examples

hello <- DenoiseDCT(papers, 0.01) %>% ThresholdAdaptive(., 0.1, range = c(0,1))
if (requireNamespace("tesseract", quietly = TRUE))
{
  OCR(hello) %>% cat
  OCR_data(hello)
}

restore hue of color image

Description

restore hue of color image

Usage

RestoreHue(im, hueim)

Arguments

Value

a color image of class cimg

Author(s)

Shota Ochi

Examples

g <- Grayscale(boats)
hue <- GetHue(boats)
layout(matrix(1:2, 1, 2))
plot(g, main = "Original")
RestoreHue(g, hue) %>% plot(main="Resotred")

Correct inhomogeneous background of image by solving Screened Poisson Equation

Description

Correct inhomogeneous background of image by solving Screened Poisson Equation

Usage

SPE(im, lamda, s = 0.1, range = c(0, 255))

Arguments

Value

a grayscale image of class cimg

Author(s)

Shota Ochi

References

Jean-Michel Morel, Ana-Belen Petro, and Catalina Sbert, Screened Poisson Equation for Image Contrast Enhancement, Image Processing On Line, 4 (2014), pp. 16-29. https://doi.org/10.5201/ipol.2014.84

Examples

dev.new()
par(mfcol = c(1,2))
boats_g <- grayscale(boats)
plot(boats_g, main = "Original")
SPE(boats_g, 0.1) %>% plot(main = "Screened Poisson Equation")

Chan-Vese segmentation

Description

iterative image segmentation with Chan-Vese model

Usage

SegmentCV(im, mu = 0.25, nu = 0, lambda1 = 1, lambda2 = 1,
  tol = 1e-04, maxiter = 500, dt = 0.5, initial, returnstep)

Arguments

Value

a pixel set or a list of lists of numeric and pixel set

Author(s)

Shota Ochi

References

Pascal Getreuer (2012). Chan-Vese Segmentation. Image Processing On Line 2, 214-224.

Examples

layout(matrix(1:2, 1, 2))
g <- grayscale(dogs)
plot(g, main = "Original")
SegmentCV(g, lambda2 = 15) %>% plot(main = "Binarized")

Local Adaptive Thresholding

Description

Local Adaptive Thresholding

Usage

ThresholdAdaptive(im, k, windowsize = 17, range = c(0, 255))

Arguments

Value

a pixel set

Author(s)

Shota Ochi

References

Faisal Shafait, Daniel Keysers, Thomas M. Breuel, "Efficient implementation of local adaptive thresholding techniques using integral images", Proc. SPIE 6815, Document Recognition and Retrieval XV, 681510 (28 January 2008)

Examples

layout(matrix(1:4, 2, 2))
plot(papers, main = "Original")
threshold(papers) %>% plot(main = "A variant of Otsu")
ThresholdAdaptive(papers, 0, range = c(0,1)) %>% plot(main = "local adaptive (k = 0)")
ThresholdAdaptive(papers, 0.2, range = c(0,1)) %>% plot(main = "local adaptive (k = 0.2)")

Fuzzy Entropy Image Segmentation

Description

automatic fuzzy thresholding based on particle swarm optimization

Usage

ThresholdFuzzy(im, n = 50, maxiter = 100, omegamax = 0.9,
  omegamin = 0.1, c1 = 2, c2 = 2, mutrate = 0.2, vmaxcoef = 0.1,
  intervalnumber = 1000, returnvalue = FALSE)

Arguments

Value

a pixsel set or a numeric

Author(s)

Shota Ochi

References

Linyi Li, Deren Li (2008). Fuzzy entropy image segmentation based on particle swarm optimization. Progress in Natural Science.

Examples

g <- grayscale(boats)
layout(matrix(1:2, 1, 2))
plot(g, main = "Original")
ThresholdFuzzy(g) %>% plot(main = "Fuzzy Thresholding")

Multilevel Thresholding

Description

Segments a grayscale image into several gray levels. Multilevel thresholding selection based on the artificial bee colony algorithm is used when thr is not a numeric vector. Preset parameters for fast computing is used when thr is "fast". Preset parameters for precise computing is used when thr is "precise". You can tune the parameters if thr is "manual". Also you can specify the values of thresholds by setting thr as a numeric vector.

Usage

ThresholdML(im, k, thr = "fast", sn = 30, mcn = 100, limit = 100,
  intervalnumber = 1000, returnvalue = FALSE)

Arguments

Value

a grayscale image of class cimg or a numeric vector

Author(s)

Shota Ochi

References

Ming-HuwiHorng (2011). Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Systems with Applications.

Examples

g <- grayscale(boats)
ThresholdML(g, k = 2) %>% plot

Iterative Triclass Thresholding

Description

compute threshold value by Iterative Triclass Threshold Technique

Usage

ThresholdTriclass(im, stopval = 0.01, repeatnum, intervalnumber = 1000,
  returnvalue = FALSE)

Arguments

Value

a pixel set or a numeric

Author(s)

Shota Ochi

References

Cai HM, Yang Z, Cao XH, Xia WM, Xu XY (2014). A New Iterative Triclass Thresholding Technique in Image Segmentation. IEEE TRANSACTIONS ON IMAGE PROCESSING.

Examples

g <- grayscale(boats)
layout(matrix(1:4, 2, 2))
plot(boats, main = "Original")
plot(g, main = "Grayscale")
threshold(g) %>% plot(main = "A Variant of Otsu")
ThresholdTriclass(g) %>% plot(main = "Triclass")

Photograph of a dog from GAHAG

Description

This photograph was downloaded from http://gahag.net/img/201603/03s/gahag-0062116383-1.jpg. Its size was reduced by half to speed up loading and save space.

Usage

dogs

Format

an image of class cimg

Source

http://gahag.net/img/201603/03s/gahag-0062116383-1.jpg

Photograph of a paper

Description

This photograph was filmed by Shota Ochi.

Usage

papers

Format

an image of class cimg