R: Render Convolution

render_convolution {rayimage} | R Documentation

## 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

    #Perform a convolution with the default gaussian kernel

    plot_image(dragon)

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

    #Increase the width of the kernel

    render_convolution(dragon, kernel = 2, kernel_dim=21,kernel_extent=6, preview = TRUE)

    #Perform edge detection using a edge detection kernel

    edge = matrix(c(-1,-1,-1,-1,8,-1,-1,-1,-1),3,3)
    render_convolution(dragon, kernel = edge, preview = TRUE, absolute=FALSE)

    #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(dragon, kernel = sobel1)
    sob2 = render_convolution(dragon, kernel = sobel2)
    sob_all = sob1 + sob2
    plot_image(sob_all)

    #Only perform the convolution on bright pixels (bloom)

    render_convolution(dragon, kernel = 5, kernel_dim=24, kernel_extent=24,
                      min_value=1, preview = TRUE)

    #Use a built-in kernel:

    render_convolution(dragon, kernel = generate_2d_exponential(falloff=2, dim=31, width=21),
                      preview = TRUE)

    #We can also apply this function to matrices:

    volcano %>% image()
    volcano %>%
     render_convolution(kernel=generate_2d_gaussian(sd=1,dim=31)) %>%
     image()

    #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)

[Package rayimage version 0.5.1 Index]