# Copyright 2007 Walter Alini, MatÃas Bordese
#
# This file is part of biOps.
#
# biOps is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# biOps is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with biOps; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
#
#
# Title: Noise Generation Operations
#
# Function: imgSaltPepperNoise
# Adds salt and pepper noise to an image
#
# Parameters:
# imgdata - The image data.
# percent - Percent of noise to add
#
# Returns:
# The image with salt and pepper noise.
#
imgSaltPepperNoise <- function(imgdata, percent){
imgmatrix <- array(imgdata) # get linear array image representations
depth <- if (attr(imgdata, "type") == "grey") 1 else dim(imgdata)[3] # get images depth
width <- dim(imgdata)[2]
height <- dim(imgdata)[1]
# call the C function for image operation
res <- .C("saltPepperNoise", image=as.integer(imgmatrix),
width=as.integer(width), height=as.integer(height), depth=as.integer(depth),
percent=as.double(percent), PACKAGE="biOps")
imgtype <- if (depth == 1) "grey" else "rgb" # type of the result
imgdim <- c(height, width, if (depth == 3) depth else NULL) # dim of the result
img <- array(res$image, dim=imgdim) # build the matrix from linear result
imagedata(img, type=imgtype) # build the imagedata
}
# Function: imgGaussianNoise
# Adds gaussian noise to an image
#
# Parameters:
# imgdata - The image data.
# mean - Gaussian mean
# variance - Gaussian variance
#
# Returns:
# The image with gaussian noise.
#
imgGaussianNoise <- function(imgdata, mean, variance){
imgmatrix <- array(imgdata) # get linear array image representations
depth <- if (attr(imgdata, "type") == "grey") 1 else dim(imgdata)[3] # get images depth
width <- dim(imgdata)[2]
height <- dim(imgdata)[1]
# call the C function for image operation
res <- .C("gaussianNoise", image=as.integer(imgmatrix),
width=as.integer(width), height=as.integer(height), depth=as.integer(depth),
mean=as.double(mean), variance=as.double(variance), PACKAGE="biOps")
imgtype <- if (depth == 1) "grey" else "rgb" # type of the result
imgdim <- c(height, width, if (depth == 3) depth else NULL) # dim of the result
img <- array(res$image, dim=imgdim) # build the matrix from linear result
imagedata(img, type=imgtype) # build the imagedata
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.