Render Convolution — render_convolution • rayimage

Takes an image and applys a convolution operation to it, using a user-supplied or built-in kernel. Edges are calculated by limiting the size of the kernel to only that overlapping the actual image (renormalizing the kernel for the edges).

render_convolution(
  image,
  kernel = "gaussian",
  kernel_dim = 11,
  kernel_extent = 3,
  absolute = TRUE,
  min_value = NULL,
  filename = NULL,
  preview = FALSE,
  gamma_correction = FALSE,
  progress = FALSE
)

Arguments

image: Image filename or 3-layer RGB array.
kernel: Default gaussian. By default, an 11x11 Gaussian kernel with a mean of 0 and a standard deviation of 1, running from -kernel_extent to kernel_extent. If numeric, this will be the standard deviation of the normal distribution. If a matrix, it will be used directly as the convolution kernel (but resized always to be an odd number of columns and rows).
kernel_dim: Default 11. The dimension of the gaussian kernel. Ignored if user specifies their own kernel.
kernel_extent: Default 3. Extent over which to calculate the kernel.
absolute: Default TRUE. Whether to take the absolute value of the convolution.
min_value: Default NULL. If numeric, specifies he minimum value (for any color channel) for a pixel to have the convolution performed.
filename: Default NULL. The filename of the image to be saved. If this is not given, the image will be plotted instead.
preview: Default TRUE. Whether to plot the convolved image, or just to return the values.
gamma_correction: Default TRUE. Controls gamma correction when adding colors. Default exponent of 2.2.
progress: Default TRUE. Whether to display a progress bar.

Value

3-layer RGB array of the processed image.

Examples

if(run_documentation()){
#Perform a convolution with the default gaussian kernel
plot_image(dragon)
}

if(run_documentation()){
#Perform a convolution with the default gaussian kernel
render_convolution(dragon, preview = TRUE)
}

if(run_documentation()){
#Increase the width of the kernel
render_convolution(dragon, kernel = 2, kernel_dim=21,kernel_extent=6, preview = TRUE)
}

if(run_documentation()){
#Perform edge detection using a edge detection kernel
edge = matrix(c(-1,-1,-1,-1,8,-1,-1,-1,-1),3,3)
render_convolution(render_bw(dragon), kernel = edge, preview = TRUE, absolute=FALSE)
}

if(run_documentation()){
#Perform edge detection with Sobel matrices
sobel1 = matrix(c(1,2,1,0,0,0,-1,-2,-1),3,3)
sobel2 = matrix(c(1,2,1,0,0,0,-1,-2,-1),3,3,byrow=TRUE)
sob1 = render_convolution(render_bw(dragon), kernel = sobel1)
sob2 = render_convolution(render_bw(dragon), kernel = sobel2)
sob_all = sob1 + sob2
plot_image(sob1)
plot_image(sob2)
plot_image(sob_all)
}




if(run_documentation()){
#Only perform the convolution on bright pixels (bloom)
render_convolution(dragon, kernel = 5, kernel_dim=24, kernel_extent=24,
                  min_value=1, preview = TRUE)
}

if(run_documentation()){
#Use a built-in kernel:
render_convolution(dragon, kernel = generate_2d_exponential(falloff=2, dim=31, width=21),
                  preview = TRUE)
}

if(run_documentation()){
#We can also apply this function to matrices:
volcano |> image()
volcano |>
 render_convolution(kernel=generate_2d_gaussian(sd=1,dim=31)) |>
 image()
}


if(run_documentation()){
#Use a custom kernel (in this case, an X shape):
custom = diag(10) + (diag(10)[,10:1])
plot_image(custom)
render_convolution(dragon, kernel = custom, preview = TRUE)
}