Date: Thu, 28 Mar 2024 09:34:52 +0000 (UTC)
Message-ID: <1708478624.7272.1711618492630@ip-10-0-0-233.us-west-2.compute.internal>
Subject: Exported From Confluence
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ice.Image Blur(x, y, blurType)=
Fast, general-purpose blurring operation. Suitable for most purposes, th=
is operation is essentially constant-time with kernel width. The different =
types of kernels yield different visual results. Note that each of the exam=
ples 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-inte=
gral, and that such widths yield the expected "weighted-sum" result.
Parameters
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
Example
blurTyp=
e =3D ice.constants.BLUR_BOX
result =3D m1_1.Blur(10, 1, blurType)
blurTyp=
e =3D ice.constants.BLUR_GAUSSIAN
result =3D m1_1.Blur(10, 1, blurType)
blurTyp=
e =3D ice.constants.BLUR_BICUBIC
result =3D m1_1.Blur(10, 1, blurType)
ice.Image Convolve(x, y, kernel)
General convolution with arbitrary kernel. The kernel must have x =
* y entries.
Parameters
x
Size in x of kernel (int).
y
Size in y of kernel (int).
kernel
Convolution kernel (list).
Example
// Disc=
Kernel is a function that generates a circular convolution=20
// kernel 30 pixels in diameter.=20
disc =3D DiscKernel(30)=20
result =3D fruit.Convolve(30, 30, disc)
ice.Image ConvolveSelective(kern=
elImage, pointList)
This is not a true convolution. Effectively, the operation is a multipli=
cation 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.
Parameters
kernelImage
Image to be used as convolution kernel (ice.Image)
pointList
List of points at which convolution is to be performed (list of li=
sts of 2 ints)
ice.Image ConvolveTri=
g(kernelImage, triggerImage)
This is not a true convolution. Effectively, the operation is a multipli=
cation 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.
Parameters
kernelImage
Image to be used as convolution ke=
rnel (ice.Image)
triggerImage
"Trigger" image (ice.Image)
Example
# Creat=
e a starburst "kernel"=20
sparkleType =3D ice.constants.SPARKLE_FRAUNHOFERSLITS
size =3D [128, 128]
center =3D [64, 64]
kernel =3D ice.Sparkle(sparkleType, size, center, ice.constants.FRACTIONAL,=
23)=20
=09
# Translate the kernel so that it's centered at the origin=20
kernel =3D kernel.Translate((-64, -64))=20
=09
# Perform selective convolution ..=20
result =3D image.ConvolveTrig(kernel, image.Shuffle([0]).Gt( ice.Card(ice.c=
onstants.FLOAT,[7.9])))=20
=09
# and add a fraction of the result back into the original=20
# image=20
result =3D result.Add(result.Multiply( ice.Card(ice.constants.FLOAT,[0.05])=
))
ice.Image Dilate(pixels)
Morphological dilation/erosion operation. Positive pixel&n=
bsp;values result in dilation and negative ones in erosion. Values may be n=
on-integral, and results vary as expected.
Parameters
pixels: Amount in pixels to dilate or erode (int).=
span>
ice.Image DirectionalBlur(width, an=
gle, blurType)
Directional blur operation.
Parameter=
s
width
Blur width in pixels (int).
angle
Blur direction in degrees (float).
blurType
Type of blur (int).
Example
blurTyp=
e =3D ice.constants.BLUR_GAUSSIAN
result =3D cdev.DirectionalBlur(20, 20, blurType)
<=
span>ice.Image GaussianBlur(blurWidth)
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 optim=
ization. Intended for use only in special cases where Blur&nb=
sp;is not suitable.
Parameters
blurWidth: Width of blur in pixels (list).
=
ice.Image Gradient()
Compute the gradient of an image using center-differencing. Each c=
hannel in the original image results in two channels in the result, corresp=
onding respectively to the horizontal and vertical components of the gradie=
nt vector field.
ice.Image Lowpass(freq)
This operation performs convolution with a bicubic (which approximates a=
sinc function) such that the image is band-limited to the specified spatia=
l frequency. The frequency is expressed as a fraction of the maximum spatia=
l frequency representable in the image.
Parameters
freq
Spatial threshold frequency in x and y (list).
ice.Image VariableBl=
ur(width, widthRange, blurType)
Variable-width blur operation. The extent of blur at each pixel is speci=
fied by an image. The widthRange argument specifies the =
blur widths corresponding to 0 and 1 in the blur-width image.
Parameters
width
Blur width image (ice.Image). =
;
widthRange
Blur width scale (list)
blurType
Blur type (int).
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