Filter Image (deprecated)

../../../../_images/image_filtering.svg

Some of the docstrings for this module have been extracted from the scikit-image library and are covered by their respective licenses.

class node_filtering.ImageFiltering_deprecated[source]

Filters one image into another using one of a number of predefinedfiltering operations. The implemented filters are to a large extentbased on scikit-image, but some filters are not from this package.

This node is DEPRECATED, use instead the more specific nodes such as:Edge Detection, Corner Detection, Threshold, etc.

Algorithms:
  • adjust, adaptive histogram

    Improves contrast by stretching and equalizing the histogramin a sliding window over the image

    sigma:

    Clipping limit (normalized between 0 and 1). Higher values give more contrast. (default 1.0)

    adaptive kernel size:

    Size of the sliding window. Must evenly divide both image width and height.

    bins:

    Number of bins in computed histogram (default 256)

  • adjust, gamma correction

    Applies the correction: Vout = scale Vin^gamma

    Processes each channel separately

    scale:

    Constant scale factor applied after gamma correction

    gamma:

    Gamma factor applied to image.

    <1 increases intensities of mid-tones,

    >1 decreases intensities of mid-tones

  • adjust, histogram equalization

    Improves contrast by stretching and equalizing the histogram

    bins:

    Number of bins in computed histogram (default 256)

  • adjust, log correction

    Applies the correction: Vout = scale log(1 + Vin)

    Processes each channel separately

    scale:

    Constant scale factor applied after gamma correction

    inverse:

    Perform inverse log-correction instead (default false):

    Vout = scale (2^Vin - 1)

  • adjust, sigmoid

    Performs Sigmoid correction on input image. Also known as contrast adjustment.

    Vout = 1/(1+exp(gain*(cutoff-Vin)))

    Processes each channel separately

    inverse:

    Perform negative sigmoid correction instead (default false)

    cutoff:

    Shifts the characteristic curve for the sigmoid horizontally(default 0.5)

    gain:

    Gain of sigmoid, affects rise time of curve (default 10.0)

  • canny

    Canny edge detection.

    sigma:

    Standard deviation of gaussian kernel (default 1.0)

  • center image

    Shifts image so that center of mass lies in center of image

  • clamp

    Restricts the output values to a given maximum/minimum

    maximum:

    The maximum output value that can be passed through

    minimum:

    The minimum output value that can be passed through

  • color, generic

    Generic conversion from one colour space to another. Requires secondary input (right click to add) with table.Each row row of table corresponds to one output colour. Number of columns must match number of channels in input image.

  • color, grey2cmap

    Converts greyscale values after normalization and scaling from 0 - 255 into a matplotlib colourmap.

    cmap:

    The colormap to use in conversion

  • color, hsv2rgb

    Interprets input channels as Hue-Saturation-Value (HSV) and outputs Red-Green-Blue (RGB) channels.

  • color, rgb2hsv

    Interprets input channels as Red-Green-Blue (RGB) and outputs Hue-Saturation-Value (HSV) channels.

  • color, rgb2xyz

    Interprets input channels as sRGB and outputs CIE XYZ channels.

  • color, xyz2rgb

    Interprets input channels as CIE XYZ and outputs sRGB channels.

  • convex hull, image

    Computes the convex hull of a binary image.

    The convex hull is the set of pixels included in the smallest convexpolygon that surround all white pixels in the input image.

  • convex hull, objects

    Computes the convex hull of each object in a binary image.

    The convex hull is the set of pixels included in the smallest convexpolygon that surround all white pixels in the input image.

    This function uses labeling to define unique objects, findsthe convex hull of each using convex_hull_image,

    and combines these regions with logical OR. Be aware the convexhulls of unconnected objects may overlap in the result

  • convolution

    Convolves the input image with a kernel given by second input as a table (right-click on node to add second operand input)

    border mode:

    Method for handling border of image

    k:

    Value to give as input at borders when mode=constant

  • corner, FAST

    Corner detection using the FAST (Feature from Accelerated Segment Test) method.

    n:

    Number of points out of 16 that should be all brighter ordarker than test point. (default 12)

    threshold:

    Threshold used in determining wheter the pixels are darker orbrighter (default 0.15).

    Decrease threshold when more corners are desired

  • corner, KR

    Compute Kitchen-Rosenfeld corner measure response image

    border mode:

    Method for handling values outside the borders

    k:

    Value outside image borders when method constant is used.

  • corner, ST

    Compute Shi-Tomasi (Kanade-Tomasi) corner measure responseimage. Uses information from auto-correlation matrix

    sigma:

    Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix.

  • corner, harris

    Compute corner harris response image.

    sigma:

    Standard deviation used for the Gaussian kernel, which is used as weighting function for the auto-correlation matrix.

    eps:

    Normalisation factor (Nobles corner measure)

    k:

    Sensitivity factor to separate corners from edges, typically in range [0, 0.2]. Small values of k result in detection of sharp corners.

    harris method:

    Method to compute response image from auto-correlationmatrix

  • corner, moravec

    Compute Moravec corner measure response image.

    This is one of the simplest corner detectors and is comparatively fast but has several limitations (e.g. not rotation invariant).

    window size:

    Size of window used during calculations

  • edges, laplace

    Find edges using the Laplace operator.

    kernel size:

    Kernel size of the discrete Laplacian operator

  • edges, prewitt

    Find edges using the Prewitt transform as one of, or combination of horizontal and vertical prewitt convolutions

    horizontal/vertical:

    Select orientation for transform, if both then mean square of both will be used

  • edges, roberts

    Find edges using Robert’s cross operator.

    positive/negative diagonal:

    Select orientation for transform

  • edges, scharr

    Find edges using the Scharr transform as one of, or combination of horizontal and vertical prewitt convolutions.

    The Scharr operator has a better rotation invariance than other edge filters such as the Sobel or the Prewitt operators

    horizontal/vertical:

    Select orientation for transform, if both then mean square of both will be used

  • edges, sobel

    Find edges using the Sobel transform as one of, or combination of horizontal and vertical prewitt convolutions.

    horizontal/vertical:

    Select orientation for transform, if both then mean square of both will be used

  • gaussian

    Two-dimensional Gaussian filter

    sigma-x:

    Standard deviation of gaussian filter along X-axis

    sigma-y:

    Standard deviation of gaussian filter along Y-axis

    border mode:

    Determines how the array borders are handled

    k:

    Value outside image borders when method constant is used.

  • greyscale

    Transforms RGB images into greyscale

    luminance preserving:

    Use weighted average based on separate luminosity of red-green-blue receptors in human eye.

    Only works for three channel images

    preserve alpha:

    Passes through channel 4 (alpha), otherwise it is treated as another channel affecting output

  • hessian determinant

    Computes an approximation of the determinant of the hessian matrix for each pixel.

    sigma:

    Standard deviation of gaussian kernel (default 3.0) used for calculating Hessian.

    Approximation is not reliable for sigma < 3.0

  • integral image

    Creates the integral image of the input.

    An integral image contains at coordinate (m,n) the sum of all values above and to the left of it.

    S(m,n) = sum(im[0:m, 0:n])

  • morphology, labeling

    Creates a unique integer label for each connected component in an integer valued or binary image.

    diagonal neighborhood:

    If true then also consider diagonals for connectivity

  • morphology, remove small holes

    Removes small holes from an integer or boolean image.

    n:

    Maximum size in pixels of areas to remove (default 64)

    diagonal neighborhood:

    If true then also consider diagonals for connectivity

  • morphology, remove small objects

    Removes connected components smaller than the given size.

    n:

    Maximum size in pixels of areas to remove (default 64)

    diagonal neighborhood:

    If true then also consider diagonals for connectivity

  • morphology, skeletonize

    Returns the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton.

  • normalize

    Adds a (positive) scale and offset so that smallest/highest value in image becomes 0 and 1 respectively.

    Operates on each channel separately

  • scale/offset

    Adds a scale and/or an offset to each channel equally

    scale:

    Scale factor applied to image before offset

    offset:

    Offset applied to image after scale

  • threshold, adaptive

    Applies an adaptive threshold to an array.

    Also known as local or dynamic thresholding where the threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant.

    sigma:

    Standard deviation of gaussian kernel when method gaussian is used.

    offset:

    Constant subtracted from weighted mean of neighborhood to calculate the local threshold value. (default 0.0)

    kernel size:

    Size of blocks used during threshold check.

    Must be an odd number. (default 3)

    threshold method:

    Method used for calculating adaptive threshold

  • threshold, automatic

    Performs global thresholding based a selection of automatic algorithms with none or few parameters

    auto threshold method:

    Method used for calculating threshold

  • threshold, basic

    Compares each channel with a threshold

    threshold:

    Threshold value to compare with

  • transform, crop

    Crops the image to the given rectanglular area

    x:

    Left edge of image

    y:

    Top edge of image

    width:

    Width of image

    height:

    Height of image

  • transform, padding

    Adds a padding to an image

    x:

    If positive, amount of padding added on the left side. If negative the amount of padding added on the right side.

    y:

    If positive, amount of padding added on the top.If negative the amount of padding added on the bottom.

    k:

    Constant value used in padded areas

    add alpha:

    Adds an alpha with value 1.0 inside image, 0.0 outside

  • transform, rescale

    Rescales an image by a given factor

    interpolation degree:

    Degree of polynomial (0 - 5) used for interpolation.

    0 - no interpolation, 1 - bi-linear interpolation, 3 - bi-cubic interpolation

    scale x:

    Scale factor along X direction (horizontal)

    scale y:

    Scale factor along Y direction (vertical)

  • transform, resize

    Resizes an image to match the given dimensions

    width:

    The new width of the image

    height:

    The new height of the image

    interpolation degree:

    Degree of polynomial (0 - 5) used for interpolation.

    0 - no interpolation, 1 - bi-linear interpolation, 3 - bi-cubic interpolation

    aspect:

    Preserve aspect ratio (gives smaller size on one axis)

    padding:

    Adds padding to fill out full width/height after aspect-correct scaling

  • transform, rotate

    Rotates an image

    angle:

    Angular degrees to rotate clockwise

    resize:

    If true new image dimensions are calculated to exactly fit the image

  • transform, to integer

    Converts all channels into integer data

Inputs:

source : image

source image to filter

operand : table

Filter operands

Outputs:

result : image

result after filtering