Package 'OpenImageR'

Title: An Image Processing Toolkit
Description: Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
Authors: Lampros Mouselimis [aut, cre] , Sight Machine [cph] (findHOGFeatures function of the SimpleCV computer vision platform), Johannes Buchner [cph] (average_hash, dhash and phash functions of the ImageHash python library), Mohammad Haghighat [cph] (Gabor Feature Extraction), Radhakrishna Achanta [cph] (Author of the C++ code of the SLIC and SLICO algorithms (for commercial use please contact the author)), Oleh Onyshchak [cph] (Author of the Python code of the WarpAffine function)
Maintainer: Lampros Mouselimis <[email protected]>
License: GPL-3
Version: 1.3.0
Built: 2024-10-10 06:37:50 UTC
Source: CRAN

Help Index

image augmentations of a matrix, data frame, array or a list of 3-dimensional arrays (where the third dimension is equal to 3)

Description

image augmentations of a matrix, data frame, array or a list of 3-dimensional arrays (where the third dimension is equal to 3)

Usage

Augmentation(
  image,
  flip_mode = NULL,
  crop_width = NULL,
  crop_height = NULL,
  resiz_width = 0,
  resiz_height = 0,
  resiz_method = "nearest",
  shift_rows = 0,
  shift_cols = 0,
  rotate_angle = 0,
  rotate_method = "nearest",
  zca_comps = 0,
  zca_epsilon = 0,
  image_thresh = 0,
  padded_value = 0,
  verbose = FALSE
)

Arguments

image

a matrix, data frame, array or list of 3-dimensional arrays where the third dimension is equal to 3
flip_mode

a character string ('horizontal', 'vertical')
crop_width

an integer specifying the new width of the image, after the image is cropped. Corresponds to the image-rows.
crop_height

an integer specifying the new height of the image, after the image is cropped. Corresponds to the image-columns.
resiz_width

an integer specifying the new width of the image, after the image is resized. Corresponds to the image-rows.
resiz_height

an integer specifying the new height of the image, after the image is resized. Corresponds to the image-columns.
resiz_method

a string specifying the interpolation method when resizing an image ('nearest', 'bilinear')
shift_rows

a positive or negative integer specifying the direction that the rows should be shifted
shift_cols

a positive or negative integer specifying the direction that the columns should be shifted
rotate_angle

an integer specifying the rotation angle of the image
rotate_method

a string specifying the interpolation method when rotating an image ('nearest', 'bilinear')
zca_comps

an integer specifying the number of components to keep by zca whitening, when svd is performed
zca_epsilon

a float specifying the regularization parameter by zca whitening
image_thresh

the threshold parameter, by image thresholding, should be between 0 and 1 if the data is normalized or between 0-255 otherwise
padded_value

either a numeric value or a numeric vector of length equal to N of an N-dimensional array. If it's not equal to 0 then the values of the shifted rows or columns will be filled with the user-defined padded_value. Applies only to the shift_rows and shift_cols parameters.
verbose

a boolean (TRUE, FALSE). If TRUE, then the total time of the preprocessing task will be printed.

Details

This function takes advantage of various methods to accomplish image augmentations. The order of the preprocessing steps, in case that all transformations are applied to an image, is : 1st flip image, 2nd crop image, 3rd resize image, 4th shift rows or columns, 5th rotate image, 6th zca-whitening and 7th image-thresholding.

Value

the output is of the same type with the input (in case of a data frame it returns a matrix)

Author(s)

Lampros Mouselimis

Examples

## Not run: 

# a matrix
object = matrix(1, 10, 10)

res = Augmentation(object, resiz_width = 8, resiz_height = 8, rotate_angle = 40)


# an array
object = array(0, dim = c(10, 10, 3))

res = Augmentation(object, resiz_width = 8, resiz_height = 8, rotate_angle = 30)


# an array (multiple matrices)
object = array(0, dim = c(10, 10, 10))

res = Augmentation(object, resiz_width = 8, resiz_height = 8, rotate_angle = 20)


# a list of 3-dimensional arrays (where the third dimension is equal to 3)
object = list(array(0, dim = c(10, 10, 3)), array(0, dim = c(10, 10, 3)))

res = Augmentation(object, resiz_width = 8, resiz_height = 8, rotate_angle = 40)

## End(Not run)

calculation of the 'average hash' of an image

Description

This function calculates the average hash of an image

Usage

average_hash(gray_image, hash_size = 8, MODE = "hash", resize = "nearest")

Arguments

gray_image

a (2-dimensional) matrix or data frame
hash_size

an integer specifying the hash size (should be less than number of rows or columns of the gray_image)
MODE

one of 'hash' (returns the hash of the image), 'binary' (returns binary identifier of the image)
resize

corresponds to one of 'nearest', 'bilinear' (resizing method)

Details

The function is a modification of the 'average_hash' function of the imagehash package [ please consult the COPYRIGHT file ]. The average hash works in the following way : 1st convert to grayscale, 2nd, reduce the size of an image (for instance to an 8x8 image, to further simplify the number of computations), 3rd average the resulting colors (for an 8x8 image we average 64 colors), 4th compute the bits by comparing if each color value is above or below the mean, 5th construct the hash.

Value

either a hash-string or a binary vector

Examples

image = readImage(system.file("tmp_images", "1.png", package = "OpenImageR"))

image = rgb_2gray(image)

res_hash = average_hash(image, hash_size = 8, MODE = 'hash')

res_binary = average_hash(image, hash_size = 8, MODE = 'binary')

convolution

Description

convolution

Usage

convolution(image, kernel, mode = "same")

Arguments

image

either a matrix, data frame or array
kernel

a kernel in form of a matrix
mode

the convolution mode (one of 'same', 'full')

Details

This function performs convolution using a kernel matrix. When mode 'same' the output object has the same dimensions with the input, whereas when mode 'full' the rows and columns of the output object equals : ROWS = nrow(image) + nrow(kernel) - 1 and COLUMNS = ncol(image) + ncol(kernel) - 1

Value

either a matrix or an array, depending on the input data

Author(s)

Lampros Mouselimis

Examples

# kernel
x = matrix(1, nrow = 4, ncol = 4) / 16   # uniform


# matrix
image_matrix = matrix(runif(100), 10, 10)

res = convolution(image_matrix, x, "same")


# array
image_array = array(runif(300), dim = c(10, 10, 3))

res = convolution(image_array, x, "same")

crop an image

Description

crop an image

Usage

cropImage(image, new_width, new_height, type = "equal_spaced")

Arguments

image

matrix or 3-dimensional array where the third dimension is equal to 3
new_width

Corresponds to the image-rows. If 'equal_spaced' then the new_width should be numeric of length 1. If 'user_defined' then the new_width should be a sequence of numeric values.
new_height

Corresponds to the image-columns. If 'equal_spaced' then the new_height should be numeric of length 1. If 'user_defined' then the new_height should be a sequence of numeric values.
type

a string specifying the type ('equal_spaced' or 'user_defined'). If 'equal_spaced' the image will be cropped towards the center (equal distances horizontaly and verticaly). If 'user_defined' the user specifies the cropped region.

Details

This function crops an image in two different ways.

Value

depending on the input, either a matrix or an array

Author(s)

Lampros Mouselimis

Examples

path = system.file("tmp_images", "2.jpg", package = "OpenImageR")

image = readImage(path)

# IF 'equal_spaced':
crop1 = cropImage(image, new_width = 20, new_height = 20, type = 'equal_spaced')

# IF 'user_defined':
crop2 = cropImage(image, new_width = 5:20, new_height = 5:20, type = 'user_defined')

calculation of the 'dhash' of an image

Description

This function calculates the dhash of an image

Usage

dhash(gray_image, hash_size = 8, MODE = "hash", resize = "nearest")

Arguments

gray_image

a (2-dimensional) matrix or data frame
hash_size

an integer specifying the hash size (should be less than number of rows or columns of the gray_image)
MODE

one of 'hash' (returns the hash of the image), 'binary' (returns binary identifier of the image)
resize

corresponds to one of 'nearest', 'bilinear'

Details

The function is a modification of the 'dhash' function of the imagehash package [ please consult the COPYRIGHT file ]. In comparison to average_hash and phash, the dhash algorithm takes into consideration the difference between adjacent pixels.

Value

either a hash-string or a binary vector

Examples

image = readImage(system.file("tmp_images", "3.jpeg", package = "OpenImageR"))

image = rgb_2gray(image)

res_hash = dhash(image, hash_size = 8, MODE = 'hash')

res_binary = dhash(image, hash_size = 8, MODE = 'binary')

Dilation or Erosion of an image

Description

this function performs dilation or erosion to a 2- or 3- dimensional image

Usage

dilationErosion(image, Filter, method = "dilation", threads = 1)

Arguments

image

a matrix, data frame or 3-dimensional array where the third dimension is equal to 3
Filter

a vector specifying the dimensions of the kernel, which will be used to perform either dilation or erosion, such as c(3,3)
method

one of 'dilation', 'erosion'
threads

number of cores to run in parallel ( > 1 should be used if image high dimensional )

Details

This function utilizes a kernel to perform dilation or erosion. The first value of the vector indicates the number of rows of the kernel, whereas the second value indicates the number of columns.

Value

a matrix or 3-dimensional array where the third dimension is equal to 3

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

res_dilate = dilationErosion(image, Filter = c(3,3), method = 'dilation')

res_erode = dilationErosion(image, Filter = c(5,5), method = 'erosion')

downsampling an image ( by a factor ) using gaussian blur

Description

downsampling an image ( by a factor ) using gaussian blur

Usage

down_sample_image(
  image,
  factor,
  gaussian_blur = FALSE,
  gauss_sigma = 1,
  range_gauss = 2
)

Arguments

image

matrix or 3-dimensional array where the third dimension is equal to 3
factor

a positive number greater or equal to 1.0
gaussian_blur

a boolean (TRUE,FALSE) specifying if gaussian blur should be applied when downsampling
gauss_sigma

float parameter sigma for the gaussian filter
range_gauss

float number specifying the range of values for the gaussian filter

Details

This function downsamples an image with the option to use gaussian blur for optimal output.

Value

depending on the input, either a matrix or an array

Author(s)

Lampros Mouselimis

Examples

path = system.file("tmp_images", "2.jpg", package = "OpenImageR")

image = readImage(path)

dsamp = down_sample_image(image, factor = 2.0, gaussian_blur = TRUE)

edge detection (Frei_chen, LoG, Prewitt, Roberts_cross, Scharr, Sobel)

Description

edge detection (Frei_chen, LoG, Prewitt, Roberts_cross, Scharr, Sobel)

Usage

edge_detection(
  image,
  method = NULL,
  conv_mode = "same",
  approx = F,
  gaussian_dims = 5,
  sigma = 1,
  range_gauss = 2,
  laplacian_type = 1
)

Arguments

image

matrix or 3-dimensional array and the third dimension must be equal to 3
method

the method should be one of 'Frei_chen', 'LoG' (Laplacian of Gaussian), 'Prewitt', 'Roberts_cross', 'Scharr', 'Sobel'
conv_mode

the convolution mode should be one of 'same', 'full'
approx

if TRUE, approximate calculation of gradient (applies to all filters except for 'LoG')
gaussian_dims

integer specifying the horizontal and vertical dimensions of the gaussian filter
sigma

float parameter sigma for the gaussian filter
range_gauss

float number specifying the range of values for the gaussian filter
laplacian_type

integer value specifying the type for the laplacian kernel (one of 1, 2, 3, 4)

Details

This function takes either a matrix or a 3-dimensional array (where the third dimension is equal to 3) and it performs edge detection using one of the following filters : 'Frei_chen', 'LoG' (Laplacian of Gaussian), 'Prewitt', 'Roberts_cross', 'Scharr', 'Sobel'

Value

depending on the input, either a matrix or an array

Author(s)

Lampros Mouselimis

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

res = edge_detection(image, method = 'Frei_chen', conv_mode = 'same')

flip image horizontally or vertically

Description

flip an image row-wise (horizontally) or column-wise (vertically)

Usage

flipImage(image, mode = "horizontal")

Arguments

image

a matrix, data frame or 3-dimensional array where the third dimension is equal to 3
mode

one of 'horizontal', 'vertical'

Details

This function flips an image row-wise or column-wise

Value

a matrix or 3-dimensional array where the third dimension is equal to 3

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

im = readImage(path)

flp = flipImage(im, mode = 'vertical')

Gabor Feature Extraction

Description

Gabor Feature Extraction

Gabor Feature Extraction

Usage

# init <- GaborFeatureExtract$new()

Details

In case of an RGB image (3-dimensional where the third dimension is equal to 3) one can use the rgb_2gray() to convert the image to a 2-dimensional one

I added the option downsample_gabor to the original matlab code based on the following question on stackoverflow : https://stackoverflow.com/questions/49119991/feature-extraction-with-gabor-filters

Methods

GaborFeatureExtract$new()
--------------
gabor_filter_bank(scales, orientations, gabor_rows, gabor_columns, plot_data = FALSE)
--------------
gabor_feature_extraction(image, scales, orientations, gabor_rows, gabor_columns, downsample_gabor = FALSE, plot_data = FALSE, downsample_rows = NULL, downsample_cols = NULL, normalize_features = FALSE, threads = 1, vectorize_magnitude = TRUE)
--------------
gabor_feature_engine(img_data, img_nrow, img_ncol, scales, orientations, gabor_rows, gabor_columns, downsample_gabor = FALSE, downsample_rows = NULL, downsample_cols = NULL, normalize_features = FALSE, threads = 1, verbose = FALSE)
--------------
plot_gabor(real_matrices, margin_btw_plots = 0.15, thresholding = FALSE)
--------------
plot_multi_images(list_images, par_ROWS, par_COLS)
--------------

Methods

Public methods

Method new()

Usage
GaborFeatureExtract$new()

Method gabor_filter_bank()

Usage
GaborFeatureExtract$gabor_filter_bank(
  scales,
  orientations,
  gabor_rows,
  gabor_columns,
  plot_data = FALSE
)
Arguments
scales

a numeric value. Number of scales (usually set to 5) ( gabor_filter_bank function )

orientations

a numeric value. Number of orientations (usually set to 8) ( gabor_filter_bank function )

gabor_rows

a numeric value. Number of rows of the 2-D Gabor filter (an odd integer number, usually set to 39 depending on the image size) ( gabor_filter_bank function )

gabor_columns

a numeric value. Number of columns of the 2-D Gabor filter (an odd integer number, usually set to 39 depending on the image size) ( gabor_filter_bank function )

plot_data

either TRUE or FALSE. If TRUE then data needed for plotting will be returned ( gabor_filter_bank, gabor_feature_extraction functions )

Method gabor_feature_extraction()

Usage
GaborFeatureExtract$gabor_feature_extraction(
  image,
  scales,
  orientations,
  gabor_rows,
  gabor_columns,
  downsample_gabor = FALSE,
  plot_data = FALSE,
  downsample_rows = NULL,
  downsample_cols = NULL,
  normalize_features = FALSE,
  threads = 1,
  verbose = FALSE,
  vectorize_magnitude = TRUE
)
Arguments
image

a 2-dimensional image of type matrix ( gabor_feature_extraction function )

scales

a numeric value. Number of scales (usually set to 5) ( gabor_filter_bank function )

orientations

a numeric value. Number of orientations (usually set to 8) ( gabor_filter_bank function )

gabor_rows

a numeric value. Number of rows of the 2-D Gabor filter (an odd integer number, usually set to 39 depending on the image size) ( gabor_filter_bank function )

gabor_columns

a numeric value. Number of columns of the 2-D Gabor filter (an odd integer number, usually set to 39 depending on the image size) ( gabor_filter_bank function )

downsample_gabor

either TRUE or FALSE. If TRUE then downsampling of data will take place. The downsample_rows and downsample_cols should be adjusted accordingly. Downsampling does not affect the output plots but the output gabor_features ( gabor_feature_extraction function )

plot_data

either TRUE or FALSE. If TRUE then data needed for plotting will be returned ( gabor_filter_bank, gabor_feature_extraction functions )

downsample_rows

either NULL or a numeric value specifying the factor of downsampling along rows ( gabor_feature_extraction function )

downsample_cols

either NULL or a numeric value specifying the factor of downsampling along columns ( gabor_feature_extraction function )

normalize_features

either TRUE or FALSE. If TRUE then the output gabor-features will be normalized to zero mean and unit variance ( gabor_feature_extraction function )

threads

a numeric value specifying the number of threads to use ( gabor_feature_extraction function )

verbose

either TRUE or FALSE. If TRUE then information will be printed in the console ( gabor_feature_extraction, gabor_feature_engine functions )

vectorize_magnitude

either TRUE or FALSE. If TRUE the computed magnitude feature will be returned in the form of a vector, otherwise it will be returned as a list of matrices ( gabor_feature_extraction function )

Method gabor_feature_engine()

Usage
GaborFeatureExtract$gabor_feature_engine(
  img_data,
  img_nrow,
  img_ncol,
  scales,
  orientations,
  gabor_rows,
  gabor_columns,
  downsample_gabor = FALSE,
  downsample_rows = NULL,
  downsample_cols = NULL,
  normalize_features = FALSE,
  threads = 1,
  verbose = FALSE
)
Arguments
img_data

a numeric matrix specifying the input data (gabor_feature_engine function)

img_nrow

an integer specifying the number of rows of the input matrix (gabor_feature_engine function)

img_ncol

an integer specifying the number of columns of the input matrix (gabor_feature_engine function)

scales

a numeric value. Number of scales (usually set to 5) ( gabor_filter_bank function )

orientations

a numeric value. Number of orientations (usually set to 8) ( gabor_filter_bank function )

gabor_rows

a numeric value. Number of rows of the 2-D Gabor filter (an odd integer number, usually set to 39 depending on the image size) ( gabor_filter_bank function )

gabor_columns

a numeric value. Number of columns of the 2-D Gabor filter (an odd integer number, usually set to 39 depending on the image size) ( gabor_filter_bank function )

downsample_gabor

either TRUE or FALSE. If TRUE then downsampling of data will take place. The downsample_rows and downsample_cols should be adjusted accordingly. Downsampling does not affect the output plots but the output gabor_features ( gabor_feature_extraction function )

downsample_rows

either NULL or a numeric value specifying the factor of downsampling along rows ( gabor_feature_extraction function )

downsample_cols

either NULL or a numeric value specifying the factor of downsampling along columns ( gabor_feature_extraction function )

normalize_features

either TRUE or FALSE. If TRUE then the output gabor-features will be normalized to zero mean and unit variance ( gabor_feature_extraction function )

threads

a numeric value specifying the number of threads to use ( gabor_feature_extraction function )

verbose

either TRUE or FALSE. If TRUE then information will be printed in the console ( gabor_feature_extraction, gabor_feature_engine functions )

Method plot_gabor()

Usage
GaborFeatureExtract$plot_gabor(
  real_matrices,
  margin_btw_plots = 0.65,
  thresholding = FALSE
)
Arguments
real_matrices

a list of 3-dimensional arrays (where the third dimension is equal to 3). These arrays correspond to the real part of the complex output matrices ( plot_gabor function )

margin_btw_plots

a float between 0.0 and 1.0 specifying the margin between the multiple output plots ( plot_gabor function )

thresholding

either TRUE or FALSE. If TRUE then a threshold of 0.5 will be used to push values above 0.5 to 1.0 ( similar to otsu-thresholding ) ( plot_gabor function )

Method plot_multi_images()

Usage
GaborFeatureExtract$plot_multi_images(
  list_images,
  par_ROWS,
  par_COLS,
  axes = FALSE,
  titles = NULL
)
Arguments
list_images

a list containing the images to plot ( plot_multi_images function )

par_ROWS

a numeric value specifying the number of rows of the plot-grid ( plot_multi_images function )

par_COLS

a numeric value specifying the number of columns of the plot-grid ( plot_multi_images function )

axes

a boolean. If TRUE then the X- and Y-range of values (axes) will appear in the output images ( plot_multi_images function )

titles

either NULL or a character vector specifying the main-titles of the output images. The length of this vector must be the same as the length of the input 'list_images' parameter ( plot_multi_images function )

Method clone()

The objects of this class are cloneable with this method.

Usage
GaborFeatureExtract$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

References

https://github.com/mhaghighat/gabor

https://stackoverflow.com/questions/20608458/gabor-feature-extraction

https://stackoverflow.com/questions/49119991/feature-extraction-with-gabor-filters

Examples

library(OpenImageR)

init_gb = GaborFeatureExtract$new()

# gabor-filter-bank
#------------------

gb_f = init_gb$gabor_filter_bank(scales = 5, orientations = 8, gabor_rows = 39,

                                 gabor_columns = 39, plot_data = TRUE)


# plot gabor-filter-bank
#-----------------------

plt_f = init_gb$plot_gabor(real_matrices = gb_f$gabor_real, margin_btw_plots = 0.65,

                           thresholding = FALSE)


# read image
#-----------

pth_im = system.file("tmp_images", "car.png", package = "OpenImageR")

im = readImage(pth_im) * 255


# gabor-feature-extract
#----------------------

# gb_im = init_gb$gabor_feature_extraction(image = im, scales = 5, orientations = 8,

#                                          downsample_gabor = TRUE, downsample_rows = 3,

#                                          downsample_cols = 3, gabor_rows = 39, gabor_columns = 39,

#                                          plot_data = TRUE, normalize_features = FALSE,

#                                          threads = 6)


# plot real data of gabor-feature-extract
#----------------------------------------

# plt_im = init_gb$plot_gabor(real_matrices = gb_im$gabor_features_real, margin_btw_plots = 0.65,

#                             thresholding = FALSE)


# feature generation for a matrix of images (such as the mnist data set)
#-----------------------------------------------------------------------

ROWS = 13; COLS = 13; SCAL = 3; ORIEN = 5; nrow_mt = 500; im_width = 12; im_height = 15

set.seed(1)
im_mt = matrix(sample(1:255, nrow_mt * im_width * im_height, replace = TRUE), nrow = nrow_mt,

                      ncol = im_width * im_height)

# gb_ex = init_gb$gabor_feature_engine(img_data = im_mt, img_nrow = im_width, img_ncol = im_height,

#                                      scales = SCAL, orientations = ORIEN, gabor_rows = ROWS,

#                                      gabor_columns = COLS, downsample_gabor = FALSE,

#                                      downsample_rows = NULL, downsample_cols = NULL,

#                                      normalize_features = TRUE, threads = 1, verbose = FALSE)


# plot of multiple image in same figure
#---------------------------------------

list_images = list(im, im, im)

plt_multi = init_gb$plot_multi_images(list_images, par_ROWS = 2, par_COLS = 2)

Gamma correction

Description

Gamma correction

Usage

gamma_correction(image, gamma)

Arguments

image

matrix or 3-dimensional array where the third dimension is equal to 3
gamma

a positive value

Details

This function applies gamma correction to a matrix or to a 3-dimensional array where the third dimension is equal to 3. The gamma correction controls the overall brightness of an image.

Value

depending on the input, either a matrix or an array

Author(s)

Lampros Mouselimis

Examples

path = system.file("tmp_images", "2.jpg", package = "OpenImageR")

image = readImage(path)

filt = gamma_correction(image, gamma = 0.5)

Get Affine Transform

Description

Get Affine Transform

Usage

getAffineTransform(original_points, transformed_points)

Arguments

original_points

a matrix object that corresponds to the original points
transformed_points

a matrix object that corresponds to the transformed points

Value

a matrix

References

https://github.com/OlehOnyshchak/ImageTransformations/blob/master/AffineTransformation.ipynb

Examples

require(OpenImageR)

r = 600
c = 600
offset = 50

original_points = matrix(data = c(0, 0, r, 0, 0, c),
                         nrow = 3,
                         ncol = 2,
                         byrow = TRUE)

transformed_points = matrix(data = c(offset, 0, r, offset, 0, c-offset),
                            nrow = 3,
                            ncol = 2,
                            byrow = TRUE)

M_aff = getAffineTransform(original_points = original_points,
                           transformed_points = transformed_points)
M_aff

calculate the binary or the hexadecimal hash for a matrix, array or a folder of images for the average_hash, phash or dhash functions

Description

This function takes either a matrix, array or a folder and returns either the binary hash features or the hashes (as a character vector)

Usage

hash_apply(
  object,
  rows = 28,
  columns = 28,
  hash_size = 8,
  highfreq_factor = 3,
  method = "phash",
  mode = "binary",
  threads = 1,
  resize = "nearest"
)

Arguments

object

a matrix, a data frame, a 3-dimensional array (where the third dimension is equal to 3) or a path to a folder of files (images)
rows

a number specifying the number of rows of the matrix
columns

a number specifying the number of columns of the matrix
hash_size

an integer specifying the hash size. IF method = 'phash' : the hash_size * highfreq_factor should be less than number of rows or columns of the gray_image. IF method = 'dhash' or 'average_hash' : the hash_size should be less than number of rows or columns of the gray_image
highfreq_factor

an integer specyfing the highfrequency factor (IF method = 'phash' : the hash_size * highfreq_factor should be less than number of rows or columns of the gray_image)
method

one of 'phash', 'average_hash', 'dhash'
mode

one of 'binary', 'hash'
threads

the number of cores to run in parallel
resize

corresponds to one of 'nearest', 'bilinear' (resizing method)

Details

This function calculates the binary hash or the hexadecimal hash for various types of objects.

Value

If the input is a matrix, data frame or array this function returns a matrix (if mode = 'binary') or a character vector (if mode = 'hex_hash'). If the input is a path to a folder the function returns a list of length 2, the 1st sublist is a vector with the names of the image files (the order of the files in the vector corresponds to the order of the rows of the output matrix), the 2nd sublist is a matrix (if mode = 'binary') or a character vector (if mode = 'hex_hash').

Examples

path = paste0(system.file("tmp_images", "same_type", package = "OpenImageR"), '/')

res_phash = hash_apply(path, method = 'phash', mode = 'binary')

calculate the HOG (Histogram of oriented gradients) for an image

Description

The function is a modification of the 'findHOGFeatures' function of the SimpleCV package [ please consult the COPYRIGHT file ] The function takes either an RGB (it will be converted to gray) or a gray image and returns a vector of the HOG descriptors. The main purpose of the function is to create a vector of features, which can be used in classification tasks.

Usage

HOG(image, cells = 3, orientations = 6)

Arguments

image

matrix or 3-dimensional array where the third dimension is equal to 3
cells

the number of divisions ( cells )
orientations

number of orientation bins

Details

This function takes either a matrix, a data frame or a 3-dimensional array (where the third dimension is equal to 3) and returns a vector with the HOG-descriptors (histogram of oriented gradients).

Value

a numeric vector

Examples

## Not run: 

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

res = HOG(image, cells = 3, orientations = 6)

## End(Not run)

calculate the HOG (Histogram of oriented gradients) for a matrix, array or a folder of images

Description

calculate the HOG (Histogram of oriented gradients) for a matrix, array or a folder of images

Usage

HOG_apply(
  object,
  cells = 3,
  orientations = 6,
  rows = NULL,
  columns = NULL,
  threads = 1
)

Arguments

object

a matrix, a data frame, a 3-dimensional array (where the third dimension is equal to 3) or a path to a folder of files (images)
cells

the number of divisions ( cells )
orientations

number of orientation bins
rows

a value specifying the number of rows of each image-row of the matrix (required if object is a matrix)
columns

a value specifying the number of columns of each image-row of the matrix (required if object is a matrix)
threads

the number of parallel cores to use

Details

This function takes as input either a matrix, a data frame, a 3-dimensional array (where the third dimension is equal to 3) or a character path to a folder of files (images). It returns the HOG-descriptors (histogram of oriented gradients) for each row (if matrix or data frame), for each array-slice (if array) or for each file (if path to a folder of images).

Value

If the input is a matrix, data frame or array it returns a matrix of the hog descriptors. If the input is a path to a folder it returns a list of length 2, the 1st sublist is a vector with the names of the image files (the order of the files in the vector corresponds to the order of the rows of the output matrix), the 2nd sublist is the matrix of the hog descriptors.

Examples

## Not run: 

MATR = matrix(runif(75), ncol = 25, nrow = 5)

res = HOG_apply(MATR, cells = 3, orientations = 5, rows = 5, columns = 5, threads = 1)


ARRAY = array(5, dim = c(10, 10, 3))

res = HOG_apply(ARRAY, cells = 3, orientations = 6, threads = 1)


FOLDER_path = paste0(system.file("tmp_images", "same_type", package = "OpenImageR"), '/')

res = HOG_apply(FOLDER_path, cells = 3, orientations = 6, threads = 1)

## End(Not run)

image thresholding

Description

image thresholding

Usage

image_thresholding(image, thresh)

Arguments

image

matrix or 3-dimensional array where the third dimension is equal to 3
thresh

the threshold parameter should be between 0 and 1 if the data is normalized or between 0-255 otherwise

Details

This function applies thresholding to a matrix or to a 3-dimensional array where the third dimension is equal to 3.

Value

a matrix

Author(s)

Lampros Mouselimis

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

filt = image_thresholding(image, thresh = 0.5)

display an image

Description

This function displays an image

Usage

imageShow(file_path, clear_viewer = FALSE)

Arguments

file_path

if file_path is a character string, then a shiny application is utilized. If file_path is a matrix, data.frame OR a 3-dimensional array (where the third dimension is equal to 3) then the grid.raster function of the base grid package is used.
clear_viewer

a boolean. If TRUE then the previous image will be removed in the viewer before displaying the next one

Details

This function displays an image using either a character path, a 2- or a 3-dimensional object where the third dimension is equal to 3

Value

displays an image

Examples

# path = system.file("tmp_images", "1.png", package = "OpenImageR")

# imageShow(path)

invariant hashing (caclulation of the hamming or the levenshtein distance when the image is flipped, rotated or cropped)

Description

flip-rotate-crop an image and caclulate the hamming or the levenshtein distance for phash, average_hash, dhash

Usage

invariant_hash(
  image,
  new_image,
  method = "phash",
  mode = "binary",
  hash_size = 8,
  highfreq_factor = 4,
  resize = "nearest",
  flip = T,
  rotate = T,
  angle_bidirectional = 10,
  crop = T
)

Arguments

image

a 2-dimensional matrix or data frame (only gray-scale images are valid)
new_image

a new image to be compared with the previous input image
method

one of 'phash', 'average_hash', 'dhash'
mode

one of 'binary', 'hash'
hash_size

an integer specifying the hash size. IF method = 'phash' : the hash_size * highfreq_factor should be less than number of floor(rows * 0.8) or floor(columns * 0.8) of the gray_image IF method = 'dhash' or 'average_hash' : the hash_size should be less than number of floor(rows * 0.8) or floor(columns * 0.8) of the gray_image
highfreq_factor

an integer specyfing the highfrequency factor (IF method = 'phash' : the hash_size * highfreq_factor should be less than number of floor(rows * 0.8) or floor(columns * 0.8) of the gray_image)
resize

corresponds to one of 'nearest', 'bilinear' (resizing method)
flip

if TRUE the new_image will be flipped both horizontal and vertical
rotate

if TRUE the new_image will be rotated for a specified angle (see angle_bidirectional)
angle_bidirectional

a float specifying the angle that the images should be rotated in both directions. For instance, if angle_bidirectional = 10 then the image will be rotated for 10 and 350 (360-10) degrees.
crop

if TRUE the new_image will be cropped 10 or 20 percent (equally spaced horizontally and vertically)

Details

This function performs the following transformations : flips an image (no-flip, horizonal-flip, vertical-flip), rotates an image (no-angle, angle_bidirectional, 360-angle_bidirectional) and crops an image (no-crop, 10-percent-crop, 20-percent-crop). Depending on the type of mode ('binary', 'hash'), after each transformation the hamming or the levenshtein distance between the two images is calculated.

Value

If flip, rotate and crop are all FALSE then the function returns either the hamming distance (if mode = 'binary') or the levenshtein distance (if mode = 'hash') for the two images. If any of the flip, rotate, crop is TRUE then it returns the MIN, MAX of the hamming distance (if mode = 'binary') or the MIN,MAX of the levenshtein distance (if mode = 'hash').

Examples

## Not run: 

path1 = system.file("tmp_images", "1.png", package = "OpenImageR")

path2 = system.file("tmp_images", "2.jpg", package = "OpenImageR")

image1 = rgb_2gray(readImage(path1))

image2 = rgb_2gray(readImage(path2))

res1 = invariant_hash(image1, image2, hash_size = 3, flip = TRUE, crop = FALSE)

res2 = invariant_hash(image1, image2, mode = 'hash', hash_size = 3, angle_bidirectional = 10)

## End(Not run)

convert a list of matrices to an array of matrices

Description

convert a list of matrices to an array of matrices

Usage

List_2_Array(data, verbose = FALSE)

Arguments

data

a list of matrices
verbose

if TRUE then the time taken to complete the task will be printed

Details

This is a helper function mainly for the HOG and hash functions. In case that matrices are stored in a list, this function converts the list to an array of 2-dimensional data.

Value

an array

Author(s)

Lampros Mouselimis

Examples

lst = list(matrix(0, 100, 100), matrix(1, 100, 100))

arr = List_2_Array(lst, verbose = FALSE)

loads either 2- or 3-dimensional data (where the third dimension is equal to 3) from a binary file

Description

loads either 2- or 3-dimensional data (where the third dimension is equal to 3) from a binary file

Usage

load_binary(path, type)

Arguments

path

a character string specifying a file path ( where the binary data is saved )
type

a character string. Either '2d' or '3d' to indicate what kind of data data will be loaded from the specified path

Details

This function can be used to load either 2- or 3-dimensional data (where the third dimension is equal to 3) from a binary file. It is used in combination with the superpixels function in case that the write_slic parameter is not an empty string ("").

Examples

## Not run: 

library(OpenImageR)

#------------------------------------------
# assuming the saved data are 2-dimensional
#------------------------------------------

path = "/my_dir/data.bin"

res = load_binary(path, type = '2d')


## End(Not run)

minimum and maximum values of vector, matrix, data frame or array

Description

minimum and maximum values of vector, matrix, data frame or array

Usage

MinMaxObject(x)

Arguments

x

either a vector, matrix, data frame or array

Details

This helper function returns the minimum and maximum values of a vector, 2-dimensional or 3-dimensional objects (where the third dimension is equal to 3). In case of a vector, matrix or data frame it returns a single value for the minimum and maximum of the object. In case of an array it returns the minimum and maximum values for each slice of the array.

Value

a list

Author(s)

Lampros Mouselimis

Examples

# vector
x = 1:10

res = MinMaxObject(x)


# matrix
x = matrix(runif(100), 10, 10)

res = MinMaxObject(x)


# data frame
x = data.frame(matrix(runif(100), 10, 10))

res = MinMaxObject(x)


# array
x = array(runif(300), dim = c(10, 10, 3))

res = MinMaxObject(x)

Normalize a matrix to specific range of values

Description

Normalize a matrix to specific range of values

Usage

norm_matrix_range(data, min_value = -1, max_value = 1)

Arguments

data

a matrix
min_value

the new minimum value for the input data
max_value

the new maximum value for the input data

Value

a matrix

Examples

set.seed(1)
mt = matrix(1:48, 8, 6)

res = norm_matrix_range(mt, min_value = -1, max_value = 1)

normalize a vector, matrix or array (in the range between 0 and 1)

Description

normalize a vector, matrix or array (in the range between 0 and 1)

Usage

NormalizeObject(x)

Arguments

x

either a vector, matrix, data frame or array

Details

This is a helper function which normalizes all pixel values of the object to the range between 0 and 1. The function takes either a vector, matrix, data frame or array as input and returns a normalized object of the same type (in case of data frame it returns a matrix).

Value

either a normalized vector, matrix, or array

Author(s)

Lampros Mouselimis

Examples

# vector
x = 1:10

res = NormalizeObject(x)


# matrix
x = matrix(runif(100), 10, 10)

res = NormalizeObject(x)


# data frame
x = data.frame(matrix(runif(100), 10, 10))

res = NormalizeObject(x)


# array
x = array(runif(300), dim = c(10, 10, 3))

res = NormalizeObject(x)

Padding of matrices or n-dimensional arrays with a user specified value

Description

Padding of matrices or n-dimensional arrays with a user specified value

Usage

padding(input_data, new_rows, new_cols, fill_value = 0)

Arguments

input_data

either a matrix or a 3-dimensional array where the third dimension is equal to 3
new_rows

an integer specifying the new rows of the output matrix or array
new_cols

an integer specifying the new columns of the output matrix or array
fill_value

a numeric value to fill the extended rows / columns of the initial input data

Details

The padding function returns a list, where data is the padded / extended matrix or array and padded_start, padded_end, padded_left and padded_right are integer values specifying how many rows or columsn in up-, down-, left- or right-direction the input matrix or array was padded / extended with the specified fill-value.

Value

a list

Examples

library(OpenImageR)


#-------
# matrix
#-------

set.seed(1)
mt = matrix(runif(100), 10, 10)

res_mt = padding(mt, 15, 20, fill_value = -1)


#------
# array
#------

lst = list(matrix(1, 10, 10), matrix(2, 10, 10))

arr = List_2_Array(lst, verbose = FALSE)

res_arr = padding(arr, 15, 20, fill_value = mean(as.vector(mt)))

calculation of the 'phash' of an image

Description

This function calculates the phash of an image

Usage

phash(
  gray_image,
  hash_size = 8,
  highfreq_factor = 4,
  MODE = "hash",
  resize = "nearest"
)

Arguments

gray_image

a (2-dimensional) matrix or data frame
hash_size

an integer specifying the hash size (hash_size * highfreq_factor should be less than number of rows or columns of the gray_image)
highfreq_factor

an integer specyfing the highfrequency factor (hash_size * highfreq_factor should be less than number of rows or columns of the gray_image)
MODE

one of 'hash' (returns the hash of the image), 'binary' (returns binary identifier of the image)
resize

corresponds to one of 'nearest', 'bilinear' (resizing method)

Details

The function is a modification of the 'phash' function of the imagehash package [ please consult the COPYRIGHT file ]. The phash algorithm extends the average_hash by using the discrete cosine transform.

Value

either a hash-string or a binary vector

Examples

image = readImage(system.file("tmp_images", "2.jpg", package = "OpenImageR"))

image = rgb_2gray(image)

res_hash = phash(image, hash_size = 6, highfreq_factor = 3, MODE = 'hash')

res_binary = phash(image, hash_size = 6, highfreq_factor = 3, MODE = 'binary')

this function reads various types of images

Description

Reads images of type .png, .jpeg, .jpg, .tiff

Usage

readImage(path, ...)

Arguments

path

a character string specifying the path to the saved image
...

further arguments for the readPNG, readJPEG and readTIFF functions

Details

This function takes as input a string-path and returns the image in a matrix or array form. Supported types of images are .png, .jpeg, .jpg, .tiff. Extension types similar to .tiff such as .tif, .TIFF, .TIF are also supported

Value

the image in a matrix or array form

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

resize an image using the 'nearest neighbors' or the 'bilinear' method

Description

resize an image using the 'nearest neighbors' or the 'bilinear' method

Usage

resizeImage(image, width, height, method = "nearest", normalize_pixels = FALSE)

Arguments

image

matrix or 3-dimensional array where the third dimension is equal to 3
width

a number specifying the new width of the image. Corresponds to the image-rows.
height

a number specifying the new height of the image. Corresponds to the image-columns.
method

one of 'nearest', 'bilinear'
normalize_pixels

a boolean. If TRUE, then the output pixel values will be divided by 255.0

Details

This function down- or upsamples an image using the 'nearest neighbors' or the 'bilinear' method

Value

depending on the input, either a matrix or an array

Author(s)

Lampros Mouselimis

Examples

path = system.file("tmp_images", "2.jpg", package = "OpenImageR")

image = readImage(path)

resiz = resizeImage(image, width = 32, height = 32, method = 'nearest')

convert an RGB image to Gray

Description

convert an RGB image to Gray

Usage

rgb_2gray(RGB_image)

Arguments

RGB_image

a 3-dimensional array where the third dimension is equal to 3

Details

This function converts an RGB image to gray

Value

a matrix

Author(s)

Lampros Mouselimis

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

gray = rgb_2gray(image)

Conversion of RGB to HSV colour type

Description

Conversion of RGB to HSV colour type

Usage

RGB_to_HSV(input_data)

Arguments

input_data

a 3-dimensional array (RGB image) where the third dimension is equal to 3

Details

Meaning: RGB (Red-Green-Blue) to HSV (Hue, Saturation, Value) colour conversion

Examples

library(OpenImageR)

set.seed(1)
array_3d = array(sample(1:255, 675, replace = TRUE), c(15, 15, 3))

res = RGB_to_HSV(array_3d)

Conversion of RGB to Lab colour type

Description

Conversion of RGB to Lab colour type

Usage

RGB_to_Lab(input_data)

Arguments

input_data

a 3-dimensional array (RGB image) where the third dimension is equal to 3

Details

Meaning: RGB (Red-Green-Blue) to LAB (Lightness, A-colour-dimension, B-colour-dimension) colour conversion

References

https://www.epfl.ch/labs/ivrl/research/snic-superpixels/

Examples

library(OpenImageR)

set.seed(1)
array_3d = array(sample(1:255, 675, replace = TRUE), c(15, 15, 3))

res = RGB_to_Lab(array_3d)

Rotate an image by 90, 180, 270 degrees

Description

Rotate an image by 90, 180, 270 degrees

Usage

rotateFixed(image, angle)

Arguments

image

matrix, data frame or 3-dimensional array where the third dimension is equal to 3
angle

one of 90, 180 and 270 degrees

Details

This function is faster than the rotateImage function as it rotates an image for specific angles (90, 180 or 270 degrees).

Value

depending on the input, either a matrix or an array

Examples

path = system.file("tmp_images", "3.jpeg", package = "OpenImageR")

image = readImage(path)

r = rotateFixed(image, 90)

Rotate an image using the 'nearest' or 'bilinear' method

Description

Rotate an image by angle using the 'nearest' or 'bilinear' method

Usage

rotateImage(image, angle, method = "nearest", mode = "same", threads = 1)

Arguments

image

matrix, data frame or 3-dimensional array where the third dimension is equal to 3
angle

specifies the number of degrees
method

a string specifying the interpolation method when rotating an image ( 'nearest', 'bilinear' )
mode

one of 'full', 'same' (same indicates that the ouput image will have the same dimensions with initial image)
threads

the number of cores to run in parallel

Details

This function rotates an image by a user-specified angle

Value

depending on the input, either a matrix or an array

Examples

path = system.file("tmp_images", "2.jpg", package = "OpenImageR")

image = readImage(path)

r = rotateImage(image, 75, threads = 1)

Bounding box for the superpixel labels

Description

Bounding box for the superpixel labels

Usage

superpixel_bbox(superpixel_labels, non_overlapping_superpixels = FALSE)

Arguments

superpixel_labels

a matrix. The superpixel_labels parameter corresponds to the output labels of the superpixels function
non_overlapping_superpixels

either TRUE or FALSE. If TRUE then besides the (x,y) coordinates of each superpixel-segment (matrix), the overlapping indices for each superpixel will be returned (list). See the details section for more information

Details

If the non_overlapping_superpixels parameter is set to FALSE then : the superpixel_bbox function returns the bounding box for the labels of the superpixels function. The output is a matrix which contains the min and max indices of the x-y-coordinates and the corresponding unique superpixel labels.

If the non_overlapping_superpixels parameter is set to TRUE then : the superpixel_bbox function returns besides the previously explained matrix also the overlapping indices for each superpixel. These indices can be used to overwrite pixels with a specific value (say 0.0), which might appear in two superpixels simultaneously. This feature might be useful in case a user intends to use an algorithm and the separability of superpixel-segments is of importance.

Therefore in both cases overlapping superpixels will be computed, however if the non_overlapping_superpixels parameter is set to TRUE then also a list of overlapping indices will be returned.

Examples

library(OpenImageR)


#-----------
# read image
#-----------

path = system.file("tmp_images", "slic_im.png", package = "OpenImageR")

im = readImage(path)

im = im[,, 1:3]


#--------------------
# compute superpixels
#--------------------

res = superpixels(input_image = im, method = "slic", superpixel = 200,

                  compactness = 20, return_labels = TRUE)


#-------------------------
# compute the bounding box
#-------------------------

bbox = superpixel_bbox(res$labels, non_overlapping_superpixels = FALSE)


#-------------------------------------------
# plot the bounding boxes of the superpixels ( for illustration purposes )
#-------------------------------------------


graphics::plot(1:ncol(im), type='n', xlim = c(ncol(im), 1), ylim = c(1, nrow(im)))

graphics::rasterImage( flipImage(im), 1, 1, ncol(im), nrow(im))


for (i in 1:nrow(bbox)) {

  # the order of the bounding box is c('xmin', 'ymin', 'xmax', 'ymax')
  graphics::rect(bbox[i,3], bbox[i,1], bbox[i,4], bbox[i,2], border = "red", lwd = 2)
}

Bounding box for a subset of superpixel labels

Description

Bounding box for a subset of superpixel labels

Usage

superpixel_bbox_subset(superpixel_labels, superpixel_subset)

Arguments

superpixel_labels

a matrix. The superpixel_labels parameter corresponds to the output labels of the superpixels function
superpixel_subset

a numeric or integer vector specifying the subset of superpixel segments.

Details

This function should be utilized to return the bounding box for a subset of superpixel segments. To compute the bounding box for all superpixels use the superpixel_bbox function.

Examples

library(OpenImageR)


#-----------
# read image
#-----------

path = system.file("tmp_images", "slic_im.png", package = "OpenImageR")

im = readImage(path)

im = im[,, 1:3]


#--------------------
# compute superpixels
#--------------------

res = superpixels(input_image = im, method = "slic", superpixel = 200,

                  compactness = 20, return_labels = TRUE)


#-------------------------
# compute the bounding box   ( for subset of superpixels )
#-------------------------

bbox = superpixel_bbox_subset(res$labels, superpixel_subset = c(0, 10, 30))

SLIC and SLICO superpixel implementations

Description

SLIC and SLICO superpixel implementations

Usage

superpixels(
  input_image,
  method = "slic",
  superpixel = 200,
  compactness = 20,
  return_slic_data = FALSE,
  return_lab_data = FALSE,
  return_labels = FALSE,
  write_slic = "",
  verbose = FALSE
)

Arguments

input_image

either a 2-dimensional or a 3-dimensional input image where the third dimension is equal to 3 (the range of the pixel values should be preferably in the range 0 to 255)
method

a character string specifying the method to use. Either "slic" or "slico"
superpixel

a numeric value specifying the number of superpixels to use
compactness

a numeric value specifying the compactness parameter. The compactness parameter is needed only if method is "slic". The "slico" method adaptively chooses the compactness parameter for each superpixel differently.
return_slic_data

a boolean. If TRUE then the resulted slic or slico data will be returned
return_lab_data

a boolean. If TRUE then the Lab data will be returned ( the Lab-colour format )
return_labels

a boolean. If TRUE then the labels will be returned
write_slic

a character string. If not an empty string ("") then it should be a path to the output file with extension .bin ( for instance "/my_dir/output.bin" ). The data will be saved in binary format.
verbose

a boolean. If TRUE then information will be printed in the R session

References

https://www.epfl.ch/labs/ivrl/research/slic-superpixels/

Examples

library(OpenImageR)

#-------------------
# 3-dimensional data
#-------------------

path = system.file("tmp_images", "slic_im.png", package = "OpenImageR")

im = readImage(path)

res = superpixels(input_image = im, method = "slic", superpixel = 200,

                  compactness = 20, return_slic_data = TRUE)


#-------------------
# 2-dimensional data
#-------------------

im_2d = im[,,1]

res_mt = superpixels(input_image = im_2d, method = "slic", superpixel = 200,

                     compactness = 20, return_slic_data = TRUE)

image translation

Description

shift the position of an image by adding/subtracting a value to/from the X or Y coordinates

Usage

translation(image, shift_rows = 0, shift_cols = 0, padded_value = 0)

Arguments

image

a matrix, data frame or 3-dimensional array where the third dimension is equal to 3
shift_rows

a positive or negative integer specifying the direction that the rows should be shifted
shift_cols

a positive or negative integer specifying the direction that the columns should be shifted
padded_value

either a numeric value or a numeric vector of length 3 (corresponding to RGB). If it's not equal to 0 then the values of the shifted rows or columns will be filled with the user-defined padded_value

Details

If shift_rows is not zero then the image will be sifted row-wise (upsides or downsides depending on the sign). If shift_cols is not zero then the image will be sifted column-wise (right or left depending on the sign).

Value

a matrix or 3-dimensional array where the third dimension is equal to 3

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

res_tr = translation(image, shift_rows = 10, shift_cols = -10)

uniform filter (convolution with uniform kernel)

Description

uniform filter (convolution with uniform kernel)

Usage

uniform_filter(image, size, conv_mode = "same")

Arguments

image

matrix or 3-dimensional array where the third dimension is equal to 3
size

a 2-item vector specifying the horizontal and vertical dimensions of the uniform kernel, e.g. c(3,3)
conv_mode

the convolution mode should be one of 'same', 'full'

Details

This function applies a uniform filter to a matrix or to a 3-dimensional array where the third dimension is equal to 3

Value

depending on the input, either a matrix or an array

Author(s)

Lampros Mouselimis

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

filt = uniform_filter(image, c(4,4), conv_mode = "same")

Verify that the input image extension is valid

Description

Verify that the input image extension is valid

Usage

verify_image_extension(image_path, regex_img = "jpe?g|png|tif$|tiff$")

Arguments

image_path

a character string specifying the path to the saved image
regex_img

a character string specifying the regex used to verify if the image extension is valid

Details

The OpenImageR package uses the 'readPNG', 'readJPEG' and 'readTIFF' R packages in the background. Thus, only image file path extensions that can be processed from these R packages should be used as input to the 'readImage' function

Value

either the image path extension or an error

References

https://github.com/mlampros/OpenImageR/issues/25

Examples

vec_img_ext = c('png', 'PNG', 'jpg', 'JPG', 'jpeg', 'JPEG', 'tif', 'TIF', 'tiff', 'TIFF')

vec_valid = sapply(vec_img_ext, function(x) {
  ext_iter = paste(c('example_image', x), collapse = '.')
  verify_image_extension(image_path = ext_iter)
})

all(vec_img_ext == vec_valid)

Warp Affine

Description

Warp Affine

Usage

warpAffine(img, M, R, C, threads = 1, verbose = FALSE)

Arguments

img

either a matrix or a 3-dimensional array (where the third dimension is equal to 3) with a range of values between 0 and 255
M

a matrix corresponding to the transformation matrix
R

a value corresponding to the destination number of rows
C

a value corresponding to the destination number of columns
threads

an integer specifying the number of threads to run in parallel. This parameter applies only if the input "img" parameter is of type matrix.
verbose

a boolean. If TRUE then information will be printed in the console

Value

either a matrix or a 3-dimensional array (where the third dimension is equal to 3)

References

https://github.com/OlehOnyshchak/ImageTransformations/blob/master/AffineTransformation.ipynb

Examples

require(OpenImageR)

path = system.file("tmp_images", "landscape.jpg", package = "OpenImageR")
img = readImage(path)
img = img * 255

#.............................
# compute the affine transform
#.............................

r = ncol(img)
c = nrow(img)
offset = 50

original_points = matrix(data = c(0, 0, r, 0, 0, c),
                         nrow = 3,
                         ncol = 2,
                         byrow = TRUE)

transformed_points = matrix(data = c(offset, 0, r, offset, 0, c-offset),
                            nrow = 3,
                            ncol = 2,
                            byrow = TRUE)

M_aff = getAffineTransform(original_points = original_points,
                           transformed_points = transformed_points)

#..............
# 2-dimensional
#..............

img_2d = rgb_2gray(img)

res_2d = warpAffine(img = img_2d,
                    M = M_aff,
                    R = r,
                    C = c,
                    threads = 1,
                    verbose = TRUE)

# imageShow(res_2d)

#..............
# 3-dimensional
#..............

res_3d = warpAffine(img = img,
                    M = M_aff,
                    R = r,
                    C = c,
                    verbose = TRUE)

# imageShow(res_3d)

This function writes 2- or 3-dimensional image (where the third dimension is equal to 3) data to a file

Description

This function writes 2- or 3-dimensional image (where the third dimension is equal to 3) data to a file. Supported types are .png, .jpeg, .jpg, .tiff (or .tif, .TIFF, .TIF)

Usage

writeImage(data, file_name, ...)

Arguments

data

a 2- or 3-dimensional object (matrix, data frame or array where the third dimension is equal to 3)
file_name

a string specifying the name of the new file
...

further arguments for the writePNG, writeJPEG and writeTIFF functions

Details

This function takes as input a matrix, data frame or array and saves the data in one of the supported image types ( .png, .jpeg, .jpg, .tiff ). Extension types similar to .tiff such as .tif, .TIFF, .TIF are also supported

Value

a saved image file

Examples

# path = system.file("tmp_images", "1.png", package = "OpenImageR")

# im = readImage(path)

# writeImage(im, 'new_image.jpeg')

zca whiten of an image

Description

this function performs zca-whitening to a 2- or 3- dimensional image

Usage

ZCAwhiten(image, k, epsilon)

Arguments

image

a matrix, data frame or 3-dimensional array where the third dimension is equal to 3
k

an integer specifying the number of components to keep when svd is performed (reduced dimension representation of the data)
epsilon

a float specifying the regularization parameter

Details

Whitening (or sphering) is the preprocessing needed for some algorithms. If we are training on images, the raw input is redundant, since adjacent pixel values are highly correlated. When using whitening the features become less correlated and all features have the same variance.

Value

a matrix or 3-dimensional array where the third dimension is equal to 3

References

http://ufldl.stanford.edu/wiki/index.php/Whitening

Examples

path = system.file("tmp_images", "1.png", package = "OpenImageR")

image = readImage(path)

res = ZCAwhiten(image, k = 20, epsilon = 0.1)