| Title: | 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. |
| Authors: | Carlos Biagolini-Jr. |
| Maintainer: | Carlos Biagolini-Jr.<[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.3 |
| Built: | 2024-11-10 04:45:08 UTC |
| Source: | https://github.com/biagolini/bwimage |
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.
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.<[email protected]> |
| 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) |
| URL: | https://github.com/biagolini/bwimage |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| RoxygenNote: | 6.1.1 |
| Imports: | jpeg, png |
| Depends: | stats, utils |
| Suggests: | knitr, rmarkdown, covr, testthat (>= 2.1.0) |
| Config/pak/sysreqs: | libjpeg-dev libpng-dev |
| Repository: | https://biagolini.r-universe.dev |
| RemoteUrl: | https://github.com/biagolini/bwimage |
| RemoteRef: | HEAD |
| RemoteSha: | 441f3ca3905e5b0ae8521942b23f825adf20ad61 |
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.
Carlos Biagolini-Jr.
Maintainer: Carlos Biagolini-Jr.<[email protected]>
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
bush<-system.file("extdata/bush.JPG", package ="bwimage") bush_imagematrix<-threshold_color(bush, "jpeg", "proportional",compress_rate = 0.1) aggregation_index(bush_imagematrix)bush<-system.file("extdata/bush.JPG", package ="bwimage") bush_imagematrix<-threshold_color(bush, "jpeg", "proportional",compress_rate = 0.1) aggregation_index(bush_imagematrix)
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.
aggregation_index(imagematrix)aggregation_index(imagematrix)
imagematrix |
The matrix to be analysed. |
# Functions to be imported
adjusted_aggregation |
Standardized aggregation. |
non_adjusted_aggregation |
Observed aggregation. |
Carlos Biagolini-Jr.
threshold_color
# 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)# 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)
Break the original matrix in a number of section ( n_sections), then find the higher black pixel in each image section.
altitudinal_profile(imagematrix, n_sections, height_size)altitudinal_profile(imagematrix, n_sections, height_size)
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..). |
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. |
Carlos Biagolini-Jr.
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.
threshold_color
# 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.# 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 data from square image to circular in binary matrix
compress(imagematrix, method = "radial", background = NA)compress(imagematrix, method = "radial", background = NA)
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. |
A matrix of 0, 1 and NA representing white, black and transparent pixels, respectively.
Carlos Biagolini-Jr.
Lambers 2016 Mappings between Sphere, Disc, and Square. Journal of Computer Graphics Techniques, 5(2): 1-21.
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")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")
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.
denseness_column(imagematrix, n_sections = "all")denseness_column(imagematrix, n_sections = "all")
imagematrix |
The matrix to be analysed. |
n_sections |
Break the image in this number of columns. |
Denseness |
Denseness of each column section. |
Mean |
Mean of column sections denseness. |
SD |
standard deviations of column sections denseness. |
Carlos Biagolini-Jr.
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.
denseness_total threshold_color
# 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)# 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)
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.
denseness_row(imagematrix, n_sections = "all")denseness_row(imagematrix, n_sections = "all")
imagematrix |
The matrix to be analysed. |
n_sections |
Break the image in this number of rows. |
Denseness |
Denseness of each row section. |
Mean |
Mean of row sections denseness. |
SD |
standard deviations of row sections denseness. |
Carlos Biagolini-Jr.
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.
denseness_total threshold_color
# 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)# 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)
Calculate the denseness (proportion of black pixel in relation to the total number of pixels) for a given number of samples.
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")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")
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". |
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. |
Carlos Biagolini-Jr.
plot_samples
# 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)# 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)
Proportion of black pixels in relation to all pixels. It do not take into account transparent pixels (when present).
denseness_total(imagematrix)denseness_total(imagematrix)
imagematrix |
The matrix to be analysed. |
Proportion of black pixels in relation to all pixels. It do not take into account transparent pixels (when present).
Carlos Biagolini-Jr.
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.
threshold_color
# 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# 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
Find the higher black pixel in the whole image.
heigh_maximum(imagematrix, height_size)heigh_maximum(imagematrix, height_size)
imagematrix |
The matrix to be analysed. |
height_size |
Real size of image width (in mm, cm, m, etc..). |
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.
Carlos Biagolini-Jr.
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.
threshold_color
# 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.# 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.
Proportion of black pixel below each matrix line.
heigh_propotion(imagematrix)heigh_propotion(imagematrix)
imagematrix |
The matrix to be analysed. |
Carlos Biagolini-Jr.
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.
threshold_color
# 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)# 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)
Find the height which a given proportion of black pixel is found.
heigh_propotion_test(imagematrix, proportion, height_size)heigh_propotion_test(imagematrix, proportion, height_size)
imagematrix |
The matrix to be analysed. |
proportion |
Proportion of denseness to test. |
height_size |
Real size of image height (in mm, cm, m, etc..). |
Carlos Biagolini-Jr.
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.
threshold_color
# 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.# 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.
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.
hole_columm(imagematrix, color = 0, n_sections = "all")hole_columm(imagematrix, color = 0, n_sections = "all")
imagematrix |
The matrix to be analysed. |
color |
Color of the hole (0 or 1). |
n_sections |
Sample this number of columns. |
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. |
Carlos Biagolini-Jr.
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.
hole_section_data threshold_color
# 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 )# 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 )
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.
hole_row(imagematrix, color = 0, n_sections = "all")hole_row(imagematrix, color = 0, n_sections = "all")
imagematrix |
The matrix to be analysed. |
color |
Color of the hole (0 or 1). |
n_sections |
Sample this number of rows. |
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. |
Carlos Biagolini-Jr.
hole_section_data threshold_color
# 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)# 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)
Description of when a sequence of same color pixel start and end.
hole_section(section)hole_section(section)
section |
Section to be analysed. |
Description of start and end of each same color sequence
Carlos Biagolini-Jr.
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.
hole_section_data threshold_color
# 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,])# 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 information of holes of a given color in a given section. Result unit is the number of cell.
hole_section_data(section, color = 0)hole_section_data(section, color = 0)
section |
Section to be analysed. |
color |
Color of the hole (0 or 1). |
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 |
Carlos Biagolini-Jr.
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.
hole_section threshold_color
# 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)# 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)
Provide the information of: number of black, white and transparent pixels, total number of pixels, height and width size.
image_information(imagematrix)image_information(imagematrix)
imagematrix |
The matrix to be analysed. |
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 |
Carlos Biagolini-Jr.
threshold_color
# 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)# 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)
Left and right distances from first black pixel to image edge.
light_gap(imagematrix, width_size = NA, scale = TRUE)light_gap(imagematrix, width_size = NA, scale = TRUE)
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. |
Distances without black pixel in each side of the picture
Carlos Biagolini-Jr.
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.
threshold_color
# 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.# 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.
plot_samples(imagematrix, central_lines, central_collumns, width_size, height_size, sample_width, sample_height, sample_shape)plot_samples(imagematrix, central_lines, central_collumns, width_size, height_size, sample_width, sample_height, sample_shape)
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. |
Plot of the analysed matrix (black and white) and sample locations (red).
Carlos Biagolini-Jr.
denseness_sample
# 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")# 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 data from circular image to square in binary matrix
stretch(imagematrix, method = "radial")stretch(imagematrix, method = "radial")
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. |
A matrix of 0, 1 and NA representing white, black and transparent pixels, respectively.
Carlos Biagolini-Jr.
Lambers 2016 Mappings between Sphere, Disc, and Square. Journal of Computer Graphics Techniques, 5(2): 1-21.
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")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")
Convert a single image into a matrix
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")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")
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. |
A matrix of 0, 1 and NA representing white, black and transparent pixels, respectively.
Carlos Biagolini-Jr.
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")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")
Convert two or more images into a list of matrices
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")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")
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. |
A matrix of 0, 1 and NA representing white, black and transparent pixels, respectively.
Carlos Biagolini-Jr.
threshold_color
# 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)# 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)
Line running along the crest of highest black pixel.
topline(imagematrix, height_size = NA, width_size = NA)topline(imagematrix, height_size = NA, width_size = NA)
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..). |
Top line size that cover black pixels
Carlos Biagolini-Jr.
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.
threshold_color
# 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.# 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.