| Type: | Package |
| Title: | Describe Image Patterns in Natural Structures |
| Version: | 1.3 |
| Date: | 2020-04-22 |
| Author: | Carlos Biagolini-Jr. |
| Maintainer: | Carlos Biagolini-Jr.<c.biagolini@gmail.com> |
| Depends: | stats, utils |
| Imports: | jpeg, png |
| Description: | A computational tool to describe patterns in black and white images from natural structures. 'bwimage' implemented functions for exceptionally broad subject. For instance, 'bwimage' provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2020-04-22 18:47:59 UTC; biago |
| Repository: | CRAN |
| Date/Publication: | 2020-04-22 20:24:10 UTC |
Describe Image Patterns in Natural Structures
Description
A computational tool to describe patterns in black and white images from natural structures. 'bwimage' implemented functions for exceptionally broad subject. For instance, 'bwimage' provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure.
Details
The DESCRIPTION file:
| Package: | bwimage |
| Type: | Package |
| Title: | Describe Image Patterns in Natural Structures |
| Version: | 1.3 |
| Date: | 2020-04-22 |
| Author: | Carlos Biagolini-Jr. |
| Maintainer: | Carlos Biagolini-Jr.<c.biagolini@gmail.com> |
| Depends: | stats, utils |
| Imports: | jpeg, png |
| Description: | A computational tool to describe patterns in black and white images from natural structures. 'bwimage' implemented functions for exceptionally broad subject. For instance, 'bwimage' provide examples that range from calculation of canopy openness, description of patterns in vertical vegetation structure, to patterns in bird nest structure. |
| License: | GPL (>= 2) |
Index of help topics:
aggregation_index Aggregation index calculator altitudinal_profile Highest black pixel by sections bwimage-package Describe Image Patterns in Natural Structures compress Compress square to circle denseness_column Denseness in column sections denseness_row Denseness in row sections denseness_sample Denseness in samples denseness_total Denseness for whole image heigh_maximum Height of the highest black pixel in the image heigh_propotion Cumulative denseness for each line heigh_propotion_test Cumulative denseness test hole_columm Holes description in columns sections hole_row Holes description in row sections hole_section Hole finder hole_section_data Summary of holes information image_information Summary of image information light_gap Light gap plot_samples Plot samples from denseness_sample stretch stretch circle to square threshold_color Image to matrix - Single threshold_image_list Image to matrix - List topline Top line
A computational tool to describe patterns in black and white images from natural structures.
Author(s)
Carlos Biagolini-Jr.
Maintainer: Carlos Biagolini-Jr.<c.biagolini@gmail.com>
References
Biagolini-Jr C, Macedo RH (2019) bwimage: A package to describe image patterns in natural structures. F1000Research 8 Lambers M (2016) Mappings between sphere, disc, and square. Journal of Computer Graphics Techniques Vol 5:1-21 Nobis M, Hunziker U (2005) Automatic thresholding for hemispherical canopy-photographs based on edge detection. Agricultural and forest meteorology 128:243-250 Shirley P, Chiu K (1997) A low distortion map between disk and square. Journal of graphics tools 2:45-52 Zehm A, Nobis M, Schwabe A (2003) Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants 198:142-160
Examples
bush<-system.file("extdata/bush.JPG", package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional",compress_rate = 0.1)
aggregation_index(bush_imagematrix)
Aggregation index calculator
Description
The function aggregation_index calculate the aggregation index. It works for matrix with and without transparent pixel. The aggregation index is a standardized estimation of the average proportion of same-color pixels around each image pixel. First, the proportion of same-color neighboring pixels (SCNP) is calculated (marginal lines and columns are excluded). Next, the SCNP for all pixels are averaged; then, given the proportion of black and white pixels, number of pixels in height and width, and location of transparent pixels (when present), the maximum and minimum possible aggregation indexes are calculated. Finally, the observed aggregation is standardized to a scale where the minimum possible value is set at zero and the maximum value is set at one.
Usage
aggregation_index(imagematrix)
Arguments
imagematrix |
The matrix to be analysed. |
Value
adjusted_aggregation |
Standardized aggregation. |
non_adjusted_aggregation |
Observed aggregation. |
Author(s)
Carlos Biagolini-Jr.
See Also
threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
# Using aggregation_index to estimate vegetation agregation
bush<-system.file("extdata/bush.JPG", package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
aggregation_index(bush_imagematrix)
# Using aggregation_index to estimate aggregation of nest wall holes
nestwall<-system.file("extdata/bird_nestwall.png", package ="bwimage")
nestwall_imagematrix<-threshold_color(nestwall, "png", "width_fixed", target_width=300)
aggregation_index(nestwall_imagematrix)
Highest black pixel by sections
Description
Break the original matrix in a number of section ( n_sections), then find the higher black pixel in each image section.
Usage
altitudinal_profile(imagematrix, n_sections, height_size)
Arguments
imagematrix |
The matrix to be analysed. |
n_sections |
Break the image in this number of columns. |
height_size |
Real size of image height (in mm, cm, m, etc..). |
Value
Mean |
Height mean of the highest black pixel in sections. |
SD |
Standard deviations of the highest black pixel in sections. |
Size |
Height of the highest black pixel in sections. |
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Profile of highest black pixels on sections of the bush image matrix
altitudinal_profile(bush_imagematrix,n_sections = 10, height_size=100)
# Conclusions:
# i) the mean height of the highest black pixel is 45.28 cm.
# ii) standard deviation of highest black height is 21.54.
Compress square to circle
Description
Compress data from square image to circular in binary matrix
Usage
compress(imagematrix, method = "radial", background = NA)
Arguments
imagematrix |
The matrix to be compressed. |
method |
Compress algorithm. Four algorithms (radial, shirley, squircle, and elliptical) are available to stretch the image. The algorithms were adapted from Lambers 2016. |
background |
Code for background cell value. When compressing a squared matrix, corners of the transformed matrix will no have corresponding pixel from original matrix. Thus, the background value will be the value of transformed matrix corners. |
Value
A matrix of 0, 1 and NA representing white, black and transparent pixels, respectively.
Author(s)
Carlos Biagolini-Jr.
References
Lambers 2016 Mappings between Sphere, Disc, and Square. Journal of Computer Graphics Techniques, 5(2): 1-21.
Examples
img_location <- system.file("extdata/chesstable.png",package ="bwimage")
image_matrix<- threshold_color(img_location,"png", "frame_fixed",target_width = 50,target_height=50)
compress(image_matrix,method="radial")
Denseness in column sections
Description
Calculate the denseness (proportion of black pixel in relation to the total number of pixels) for a given number of sections (n_sections). n_sections should be set as a number, in this situation denseness_column will break the original matrix in slices, and apply denseness_total function for each section. For instance, in a matrix of 1000x1000 if n_sections = 10, it will break to 10 sections of 1000x100 and analyze it. In other words, the sections will be the following sections of the original matrix [1:1000, 1:100] ,[ 1:1000,101:200] ,[ 1:1000,201:300] ,[ 1:1000,301:400] ,[ 1:1000,401:500] ,[ 1:1000,501:600] ,[ 1:1000,601:700] ,[ 1:1000,701:800] ,[ 1:1000,801:900] ,[ 1:1000,901:1000]. The default for parameter n_sections is "all", it will calculate denseness for each column of pixel. In other words, it will break the image in a number of section equal to the image pixel width.
Usage
denseness_column(imagematrix, n_sections = "all")
Arguments
imagematrix |
The matrix to be analysed. |
n_sections |
Break the image in this number of columns. |
Value
Denseness |
Denseness of each column section. |
Mean |
Mean of column sections denseness. |
SD |
standard deviations of column sections denseness. |
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
denseness_total threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Calculate vegetation denseness in 20 column sections
denseness_column(bush_imagematrix,20)
Denseness in row sections
Description
Calculate the denseness (proportion of black pixel in relation to the total number of pixels) for a given number of sections (n_sections). n_sections should be set as a number, in this situation denseness_row will break the original matrix in slices, and apply denseness_total function for each section. For instance, in a matrix of 1000x1000 if n_sections = 10, it will break to 10 sections of 100x1000 and analyze it. In other words, the sections will be the following sections of the original matrix [1:100, 1:1000] , [101:200, 1:1000] , [201:300, 1:1000] , [301:400, 1:1000] , [401:500, 1:1000] , [501:600, 1:1000] , [601:700, 1:1000] , [701:800, 1:1000] , [801:900, 1:1000] , [901:1000, 1:1000] .The default for parameter n_sections is "all", it will calculate denseness for each row of pixel. In other words, it will break the image in a number of section equal to the image pixel height.
Usage
denseness_row(imagematrix, n_sections = "all")
Arguments
imagematrix |
The matrix to be analysed. |
n_sections |
Break the image in this number of rows. |
Value
Denseness |
Denseness of each row section. |
Mean |
Mean of row sections denseness. |
SD |
standard deviations of row sections denseness. |
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
denseness_total threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional",compress_rate = 0.1)
# Calculate vegetation denseness in 20 row sections
denseness_row(bush_imagematrix, n_sections = 20)
Denseness in samples
Description
Calculate the denseness (proportion of black pixel in relation to the total number of pixels) for a given number of samples.
Usage
denseness_sample(imagematrix, width_size, height_size, sample_width,
sample_height, method = "random", sample_shape = "rectangle",
n_samples = 10, n_sample_horizontal = 10, n_sample_vertical = 1,
proportion_horizontal = 1, proportion_vertical = 1,
aligin_horizontal = "center", aligin_vertical = "bottom")
Arguments
imagematrix |
The matrix to be analysed. |
width_size |
Real size of image width (in mm, cm, m, etc..). |
height_size |
Real size of image height (in mm, cm, m, etc..). |
sample_width |
Width of sample area. |
sample_height |
Height of sample area. |
method |
Method for sample ("random" or "uniform"). |
sample_shape |
The shape of sample unity ("rectangle" or "ellipse"). See plot_samples function. |
n_samples |
Defines the number of samples, when sample_shape="random". |
n_sample_horizontal |
Defines the number of samples column, when sample_shape=" uniform". |
n_sample_vertical |
Defines the number of samples lines, when sample_shape=" uniform". |
proportion_horizontal |
Range from 0 to 1. Represent the proportion of horizontal plane to be sample. If proportion_horizontal=1 (default) all columns beacome potentially sample. |
proportion_vertical |
Range from 0 to 1. Represent the proportion of vertical plane to be sample. If proportion_vertical=1 (default) all lines become potentially sample. |
aligin_horizontal |
Define horizontal align. Three options are available: "center", "left" or "right". |
aligin_vertical |
Define vertical align. Three options are available: "middle","bottom" or "top". |
Value
Sample_denseness |
Proportion of black pixels in samples. It do not take into account transparent pixels (when present).. |
Height |
Height of each sample (in mm, cm, m, etc. ..). Central point used as reference. |
Distance(left) |
Distance ti the left side of each sample (in mm, cm, m, etc. ..). Central point used as reference. |
Matrix(line) |
Imagem matrix line coordinates. |
Matrix(column) |
Imagem matrix column coordinates. |
Author(s)
Carlos Biagolini-Jr.
See Also
plot_samples
Examples
# Get a matrix from your image. Here examples provided by bwimage package.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush<-threshold_color(bush, "jpeg", "proportional",compress_rate = 0.1)
denseness_sample(bush, width_size=100, height_size=100, sample_width=5, sample_height=5)
Denseness for whole image
Description
Proportion of black pixels in relation to all pixels. It do not take into account transparent pixels (when present).
Usage
denseness_total(imagematrix)
Arguments
imagematrix |
The matrix to be analysed. |
Value
Proportion of black pixels in relation to all pixels. It do not take into account transparent pixels (when present).
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
threshold_color
Examples
# Get a matrix from your image. Here examples provided by bwimage package.
# I) Calculate vegetation denseness
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional",compress_rate = 0.1)
denseness_total(bush_imagematrix)
# II) Calculate canopy openness
# Convert image into binary matrix
canopy<-system.file("extdata/canopy.JPG",package ="bwimage")
canopy_matrix<-threshold_color(canopy,"jpeg", compress_method="proportional",compress_rate=0.1)
1-denseness_total(canopy_matrix) # canopy openness
Height of the highest black pixel in the image
Description
Find the higher black pixel in the whole image.
Usage
heigh_maximum(imagematrix, height_size)
Arguments
imagematrix |
The matrix to be analysed. |
height_size |
Real size of image width (in mm, cm, m, etc..). |
Value
Height of the highest black pixel. It is scaleted for the real size (in mm, cm, m, etc..) based in the information from argument height_size.
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Calculate height of the highest black pixel in the bush image matrix
heigh_maximum(bush_imagematrix,height_size=100)
# Conclusions: The highest vegetation unit ,i.e. highest black pixel, is 84.4 cm above ground.
Cumulative denseness for each line
Description
Proportion of black pixel below each matrix line.
Usage
heigh_propotion(imagematrix)
Arguments
imagematrix |
The matrix to be analysed. |
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Proportion of black pixel below each matrix line.
heigh_propotion(bush_imagematrix)
Cumulative denseness test
Description
Find the height which a given proportion of black pixel is found.
Usage
heigh_propotion_test(imagematrix, proportion, height_size)
Arguments
imagematrix |
The matrix to be analysed. |
proportion |
Proportion of denseness to test. |
height_size |
Real size of image height (in mm, cm, m, etc..). |
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# See the proportion of black pixels (1) below each bush image matrix row
heigh_propotion_test(bush_imagematrix,0.75,100)
# Conclusion: in this imagem, 75 percent of the vegetation is hold below 31.2 cm.
Holes description in columns sections
Description
Summary information of holes in a given number of columns (n_sections). n_sections must be set as a number, in this situation hole_columm will sample columns, and apply hole_section_data function for each section. Next, all results will be display on hole_columm output. Example of how column sample works: in a matrix of 250x250 if n_sections = 5 , it will sample columns 1,51,101,151, and 201 and analyze it. In other words, the sections will be following sections of the original matrix [1:250,1] , [1:250,51], [1:250,101], [1:250,151], [1:250,201]. The default for parameter n_sections is "all", it will calculate hole_section_data for each column of pixel. In other words, it will break the image in a number of section equal to the image pixel width.
Usage
hole_columm(imagematrix, color = 0, n_sections = "all")
Arguments
imagematrix |
The matrix to be analysed. |
color |
Color of the hole (0 or 1). |
n_sections |
Sample this number of columns. |
Value
N |
Number of sections. |
Mean |
Mean sections size. |
SD |
standard deviations of sections size. |
Min |
Minimum sections size sections size. |
Max |
Maximum sections size. |
LH |
Stratum with largest hole count. |
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
hole_section_data threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Information of white (i.e. 0s in matrix) holes in 5 columns uniformly sample among matrix.
hole_columm(bush_imagematrix, n_sections=5 )
# Information of black (i.e. 1s in matrix) holes in 20 columns uniformly sample among matrix.
hole_columm(bush_imagematrix, n_sections=20 )
Holes description in row sections
Description
Summary information of holes in a given number of rows (n_sections). n_sections must be set as a number, in this situation hole_row will sample rows, and apply hole_section_data function for each section. Next, all results will be display on hole_columm output. Example of how row sample works: in a matrix of 250x250 if n_sections = 5 , it will sample rows 1,51,101,151, and 201 and analyze it. In other words, the sections will be following sections of the original matrix [1,1:250] , [51,1:250] , [101,1:250] , [151,1:250] , [201,1:250]. The default for parameter n_sections is "all", it will calculate hole_section_data for each row of pixel. In other words, it will break the image in a number of section equal to the image pixel height.
Usage
hole_row(imagematrix, color = 0, n_sections = "all")
Arguments
imagematrix |
The matrix to be analysed. |
color |
Color of the hole (0 or 1). |
n_sections |
Sample this number of rows. |
Value
N |
Number of sections. |
Mean |
Mean sections size. |
SD |
standard deviations of sections size. |
Min |
Minimum sections size sections size. |
Max |
Maximum sections size. |
LH |
Stratum with largest hole count. |
Author(s)
Carlos Biagolini-Jr.
See Also
hole_section_data threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Information of white (i.e. 0s in matrix) holes in 10 rows uniformly sample among matrix.
hole_row(bush_imagematrix, n_sections=10)
# Information of black (i.e. 1s in matrix) holes in 15 rows uniformly sample among matrix.
hole_row(bush_imagematrix, n_sections=15)
Hole finder
Description
Description of when a sequence of same color pixel start and end.
Usage
hole_section(section)
Arguments
section |
Section to be analysed. |
Value
Description of start and end of each same color sequence
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
hole_section_data threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Find pixel hole sections in the column 200 of bush image
hole_section(bush_imagematrix[,200])
# Find pixel hole sections in the row 250 of bush image
hole_section(bush_imagematrix[250,])
Summary of holes information
Description
Summary information of holes of a given color in a given section. Result unit is the number of cell.
Usage
hole_section_data(section, color = 0)
Arguments
section |
Section to be analysed. |
color |
Color of the hole (0 or 1). |
Value
N |
Number of hole sections |
Mean |
Mean size of hole sections |
SD |
Standard deviation of hole sections size |
Min |
Minimum size of hole sections |
Max |
Maximum size of hole sections |
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
hole_section threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Detail information of white (0) holes sections in the column 200 of bush image
hole_section_data(bush_imagematrix[,200], color = 0)
# Detail information of black (1) holes sections in the row 250 of bush image
hole_section_data(bush_imagematrix[250,], color = 1)
Summary of image information
Description
Provide the information of: number of black, white and transparent pixels, total number of pixels, height and width size.
Usage
image_information(imagematrix)
Arguments
imagematrix |
The matrix to be analysed. |
Value
Black |
Number of black pixels |
White |
Number of white pixels |
Transparent |
Number of transparent pixels |
Total |
Total number of pixels |
Height |
Size in height |
Width |
Size in width |
Author(s)
Carlos Biagolini-Jr.
See Also
threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional",compress_rate = 0.1)
image_information(bush_imagematrix)
Light gap
Description
Left and right distances from first black pixel to image edge.
Usage
light_gap(imagematrix, width_size = NA, scale = TRUE)
Arguments
imagematrix |
The matrix to be analysed |
width_size |
Real size of image width (in mm, cm, m, etc..). |
scale |
If FALSE do not ajust the output for real size. |
Value
Distances without black pixel in each side of the picture
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# Calculate vegetation Light gap in the bush image matrix
light_gap(bush_imagematrix,width_size=100)
# Conclusion: there is no light gap on both sides of bush image.
Plot samples from denseness_sample
Description
Plot samples from denseness_sample.
Usage
plot_samples(imagematrix, central_lines, central_collumns, width_size,
height_size, sample_width, sample_height, sample_shape)
Arguments
imagematrix |
The matrix to be analysed. |
central_lines |
Lines data (i.e. "Matrix(line)") provided by denseness_sample |
central_collumns |
Collumns data (i.e. "Matrix(column)") provided by denseness_sample |
width_size |
Real size of image width (in mm, cm, m, etc..). |
height_size |
Real size of image height (in mm, cm, m, etc..). |
sample_width |
Width of sample area. |
sample_height |
Height of sample area. |
sample_shape |
Inform the shape of sample unity used ("rectangle" or "ellipse"). See denseness_sample function. |
Value
Plot of the analysed matrix (black and white) and sample locations (red).
Author(s)
Carlos Biagolini-Jr.
See Also
denseness_sample
Examples
# Get a matrix from your image. Here examples provided by bwimage package.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush<-threshold_color(bush, "jpeg", "proportional",compress_rate = 0.1)
a<-denseness_sample(bush, width_size=100, height_size=100, sample_width=5, sample_height=5)
plot_samples(bush, a[,4],a[,5], 100,100, 5, 5,"rectangle")
stretch circle to square
Description
Stretch data from circular image to square in binary matrix
Usage
stretch(imagematrix, method = "radial")
Arguments
imagematrix |
The matrix to be stretched. |
method |
Stretch algorithm. Four algorithms (radial, shirley, squircle, and elliptical) are available to stretch the image. The algorithms were adapted from Lambers 2016. |
Value
A matrix of 0, 1 and NA representing white, black and transparent pixels, respectively.
Author(s)
Carlos Biagolini-Jr.
References
Lambers 2016 Mappings between Sphere, Disc, and Square. Journal of Computer Graphics Techniques, 5(2): 1-21.
Examples
img_location <- system.file("extdata/chesstable.png",package ="bwimage")
image_matrix<- threshold_color(img_location,"png", "frame_fixed",target_width = 50,target_height=50)
stretch(image_matrix,method="radial")
Image to matrix - Single
Description
Convert a single image into a matrix
Usage
threshold_color(filename, filetype = "jpeg", compress_method = "none",
compress_rate = 1, target_width = 100, target_height = 100,
threshold_value = 0.5, transparency_regulation = 0.5,
channel = "rgb")
Arguments
filename |
Name of the file to be load - ex: "Figure01.JPG". |
filetype |
Type of the file to be load. Compatible file types: ".JPGE", ".JPG" or ".PNG". |
compress_method |
For high resolution files, i.e. numbers of pixels in width and height, it is suggested to reduce the resolution to create a smaller matrix, it strongly reduce GPU usage and time necessary to run analyses. On the other hand, by reducing resolution, it will also reduce the accuracy of data description. The available methods for image reduction are: i) frame_fixed, which resamples images to a desired target width and height; ii) proportional, which resamples the image by a given ratio provided in the argument "proportion"; iii) width_fixed, which resamples images to a target width, and also reduces the image height by the same factor. For instance, if the original file had 1000 pixels in width, and the new width_was set to 100, height will be reduced by a factor of 0.1 (100/1000); and iv) height_fixed, analogous to width_fixed, but assumes height as reference. |
compress_rate |
Compress rate to by apply if compress_method=proportional. Note: it should be ser as number range from 0 to 1 . |
target_width |
Target width to be used if compress_method=frame_fixed or compress_method= width_fixed. |
target_height |
Target height to be used if compress_method=frame_fixed or compress_method= height_fixed. |
threshold_value |
For each pixel, the intensity of color channels (red, green and blue) are averaged and compared to a threshold_value (threshold). If the average intensity is less than the threshold_value (default is 0.5) the pixel will be set as black, otherwise it will be white. See channel argument. |
transparency_regulation |
For PNG images, the alpha channel is used to set transparent pixels, i.e. alpha channel values above transparency_regulation (a threshold) will set the pixel as transparent, default is 0.5. NOTE: In the data matrix the value 1 represents black pixels, 0 represents white pixels and NA represents transparent pixels. |
channel |
RGB channel to be considered in threshold. If channel=RGB (default), the intensity of red, green and blue is averaged and compared to threshold_value. If the average intensity is less than the threshold_value (default is 50 If only one channel is defined ("R" for red, "G" for green, and "B" for blue), the average intensity selected channel compared direct to the threshold_value value. |
Value
A matrix of 0, 1 and NA representing white, black and transparent pixels, respectively.
Author(s)
Carlos Biagolini-Jr.
Examples
bush<-system.file("extdata/bush.JPG",package ="bwimage")
threshold_color(bush,"jpeg", "frame_fixed",target_width = 15,target_height=15)
# For your images, if the file is in the working directory type:
# threshold_color("FILE_NAME.EXTENSION", filetype ="FILE_EXTENSION")
# or, if the file is in the other directory:
# threshold_color("C:/PATH TO FILE FOLDER/YOUR_FILE_NAME.EXTENSION", "FILE_EXTENSION")
Image to matrix - List
Description
Convert two or more images into a list of matrices
Usage
threshold_image_list(list_names, filetype = "jpeg",
compress_method = "none", compress_rate = 1, target_width = 100,
target_height = 100, threshold_value = 0.5,
transparency_regulation = 0.5, channel = "rgb")
Arguments
list_names |
An object contains the names of the files. |
filetype |
Type of the file to be load. Compatible file types: ".JPGE", ".JPG" or ".PNG". |
compress_method |
For high resolution files, i.e. numbers of pixels in width and height, it is suggested to reduce the resolution to create a smaller matrix, it strongly reduce GPU usage and time necessary to run analyses. On the other hand, by reducing resolution, it will also reduce the accuracy of data description. The available methods for image reduction are: i) frame_fixed, which resamples images to a desired target width and height; ii) proportional, which resamples the image by a given ratio provided in the argument "proportion"; iii) width_fixed, which resamples images to a target width, and also reduces the image height by the same factor. For instance, if the original file had 1000 pixels in width, and the new width_was set to 100, height will be reduced by a factor of 0.1 (100/1000); and iv) height_fixed, analogous to width_fixed, but assumes height as reference. |
compress_rate |
Compress rate to by apply if compress_method=proportional. Note: it should be ser as number range from 0 to 1 . |
target_width |
Target width to be used if compress_method=frame_fixed or compress_method= width_fixed. |
target_height |
Target height to be used if compress_method=frame_fixed or compress_method= height_fixed. |
threshold_value |
For each pixel, the intensity of color channels (red, green and blue) are averaged and compared to a threshold_value (threshold). If the average intensity is less than the threshold_value (default is 0.5) the pixel will be set as black, otherwise it will be white. See channel argument. |
transparency_regulation |
For PNG images, the alpha channel is used to set transparent pixels, i.e. alpha channel values above transparency_regulation (a threshold) will set the pixel as transparent, default is 0.5. NOTE: In the data matrix the value 1 represents black pixels, 0 represents white pixels and NA represents transparent pixels. |
channel |
RGB channel to be considered in threshold. If channel=RGB (default), the intensity of red, green and blue is averaged and compared to threshold_value. If the average intensity is less than the threshold_value (default is 50 If only one channel is defined ("R" for red, "G" for green, and "B" for blue), the average intensity selected channel compared direct to the threshold_value value. |
Value
A matrix of 0, 1 and NA representing white, black and transparent pixels, respectively.
Author(s)
Carlos Biagolini-Jr.
See Also
threshold_color
Examples
# Image examples provided by bwimage package
bush<-system.file("extdata/bush.JPG",package ="bwimage")
canopy<-system.file("extdata/canopy.JPG",package ="bwimage")
# Convert images to a list of matrices
working_matrices<-threshold_image_list(c(bush,canopy), "jpeg", "proportional", compress_rate = 0.1)
Top line
Description
Line running along the crest of highest black pixel.
Usage
topline(imagematrix, height_size = NA, width_size = NA)
Arguments
imagematrix |
The matrix to be analysed. |
height_size |
Real size of image height (in mm, cm, m, etc..). |
width_size |
Real size of image width (in mm, cm, m, etc..). |
Value
Top line size that cover black pixels
Author(s)
Carlos Biagolini-Jr.
References
Zehm et al 2003 Multiparameter analysis of vertical vegetation structure based on digital image processing. Flora-Morphology, Distribution, Functional Ecology of Plants, 198: 142-160.
See Also
threshold_color
Examples
# First, get a matrix from your image. Here an example of a bush image is used.
bush<-system.file("extdata/bush.JPG",package ="bwimage")
bush_imagematrix<-threshold_color(bush, "jpeg", "proportional", compress_rate = 0.1)
# See the proportion of black pixels (1) below each bush image matrix row
topline(bush_imagematrix,100,100)
# Conclusion: topline size is 785.6 cm.