# Filtering and Convolution

## IceMan Reference Guide |

#### Filtering and Convolution

`ice.Image Blur(x, y, blurType)`*x:*Width of blur in x. In pixels (int).*y:*Width of blur in y. In pixels (int).*blurType:*Type of blurring kernel (int). Default Box.Available blur types from

*IceBlurType*:- ice.constants.BLUR_BOX
- ice.constants.BLUR_GAUSSIAN
- ice.constants.BLUR_BICUBIC
- ice.constants.BLUR_CIRCULAR

Fast, general-purpose blurring operation. Suitable for most purposes, this operation is essentially constant-time with kernel width. The different types of kernels yield different visual results. Note that each of the examples below produces a result image of a different size: as mentioned before, the rectangle occupied by a result is guaranteed to contain all non-zero contributions from the original image.

Box filtering employs an incremental moving window algorithm which makes it extremely fast. Note that IceMan allows the filter width to be non-integral, and that such widths yield the expected "weighted-sum" result.

*blurType = ice.constants.BLUR_BOX result = m1_1.Blur(10, 1, blurType)**blurType = ice.constants.BLUR_GAUSSIAN result = m1_1.Blur(10, 1, blurType)**blurType = ice.constants.BLUR_BICUBIC result = m1_1.Blur(10, 1, blurType)*`ice.Image Convolve(x, y, kernel)`*x:*Size in x of kernel (int).*y:*Size in y of kernel (int).*kernel:*Convolution kernel (list).General convolution with arbitrary kernel. The kernel must have x * y entries.

*// DiscKernel is a function that generates a circular convolution // kernel 30 pixels in diameter. disc = DiscKernel(30) result = fruit.Convolve(30, 30, disc)**Note: This is full-fledged, no-magic-tricks convolution. For large kernels, this can be***very***time-consuming.*`ice.Image ConvolveSelective(kernelImage, pointList)`*kernelImage:*Image to be used as convolution kernel (ice.Image)*pointList:*List of points at which convolution is to be performed (list of lists of 2 ints)This is not a true convolution. Effectively, the operation is a multiplication of the kernel by the channel value at the center of the kernel, and subsequent addition with all the pixels under the kernel. This operation is performed for all points in the specified point list. Note that the points may be non-integral.

Useful for generating "sparkle" or "starburst" effects.

`ice.Image ConvolveTrig(kernelImage, triggerImage)`*kernelImage:*Image to be used as convolution kernel (ice.Image)*triggerImage:*"Trigger" image (ice.Image)This is not a true convolution. Effectively, the operation is a multiplication of the kernel by the channel value at the center of the kernel, and subsequent addition with all the pixels under the kernel. This operation is performed for all points in the trigger image that are not zero.

Useful for generating "sparkle" or "starburst" effects.

*# Create a starburst "kernel" sparkleType = ice.constants.SPARKLE_FRAUNHOFERSLITS size = [128, 128] center = [64, 64] kernel = ice.Sparkle(sparkleType, size, center, ice.constants.FRACTIONAL, 23) # Translate the kernel so that it's centered at the origin kernel = kernel.Translate((-64, -64)) # Perform selective convolution .. result = image.ConvolveTrig(kernel, image.Shuffle([0]).Gt( ice.Card(ice.constants.FLOAT,[7.9]))) // É and add a fraction of the result back into the original // image result = result.Add(result.Multiply( ice.Card(ice.constants.FLOAT,[0.05]) ))**Note:**The trigger image is typically generated by thresholding an input image.*`ice.Image Dilate(pixels)`*pixels:*Amount in pixels to dilate or erode (int).Morphological dilation/erosion operation. Positive

*pixel*values result in dilation and negative ones in erosion. Values may be non-integral, and results vary as expected.*Note: Dilation is done by filling all values in a neighborhood with the maximum value, and erosion by filling with the minimum value. It is particularly useful for tweaking matte edges.*`ice.Image DirectionalBlur(width, angle, blurType)`*width:*Blur width in pixels (int).*angle:*Blur direction in degrees (float).*blurType:*Type of blur (int).Directional blur operation.

*blurType = ice.constants.BLUR_GAUSSIAN result = cdev.DirectionalBlur(20, 20, blurType)*`ice.Image GaussianBlur(blurWidth)`*blurWidth:*Width of blur in pixels (list).Simple Gaussian blur operation. The operation is separable, and is thus performed in one dimension at a time, but there's no other attempt at optimization. Intended for use only in special cases where

*Blur*is not suitable.`ice.Image Gradient()`- Compute the gradient of an image using center-differencing. Each channel in the original image results in two channels in the result, corresponding respectively to the horizontal and vertical components of the gradient vector field.
`ice.Image Lowpass(freq)`*freq:*Spatial threshold frequency in x and y (list).This operation performs convolution with a bicubic (which approximates a sinc function) such that the image is band-limited to the specified spatial frequency. The frequency is expressed as a fraction of the maximum spatial frequency representable in the image.

`ice.Image VariableBlur(width, widthRange, blurType)`*width:*Blur width image (ice.Image).*widthRange:*Blur width scale (list)*blurType:*Blur type (int).Variable-width blur operation. The extent of blur at each pixel is specified by an image. The

*widthRange*argument specifies the blur widths corresponding to 0 and 1 in the blur-width image.