render_convolution: Render Convolution

In rayimage: Image Processing for Simulated Cameras

Render Convolution

Description

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).

Usage

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)
}

rayimage documentation built on Sept. 11, 2024, 8:13 p.m.