```
# Copyright 2007 Walter Alini, Matías 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: Edge Detection Operations
#
#
# Function: imgHomogeneityEdgeDetection
# Enhace image's edge by the homogeneity method. It uses a 3x3 matrix to determine the
# current pixel value (by getting the maximum value between the distances of the pixel
# and its neighbors)
#
# Parameters:
# imgdata - The input image
# bias - Value to be added to each pixel after method is applied (used to correct some expected
# behaviour). This value is optional (default = 32)
#
# Returns:
# The image whose edges has been enhaced
#
# See also:
# <imgDifferenceEdgeDetection>
#
imgHomogeneityEdgeDetection <- function(imgdata, bias=32){
.edgeDetection(imgdata, bias, "homogeneityEdgeDetection")
}
#
# Function: imgDifferenceEdgeDetection
# Enhace image's edge by the difference method. It uses a 3x3 matrix to determine the
# current pixel value (by getting the maximum value between the distances of matrix's
# opposite neighbors)
#
# Parameters:
# imgdata - The input image
# bias - Value to be added to each pixel after method is applied (used to correct some expected
# behaviour)
#
# Returns:
# ret - The image whose edges has been enhaced
#
# See also:
# <imgHomogeneityEdgeDetection>
#
imgDifferenceEdgeDetection <- function(imgdata, bias=32){
.edgeDetection(imgdata, bias, "differenceEdgeDetection")
}
.edgeDetection <- function(imgdata, bias=32, method){
.error_range ("bias", bias, 0, 255)
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(method, image=as.integer(imgmatrix), width=as.integer(width), height=as.integer(height),
depth=as.integer(depth), bias=as.integer(bias), ret=integer(width * height * depth), 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$ret, dim=imgdim) # build the matrix from linear result
imagedata(img, type=imgtype) # build the imagedata
}
```

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