Class: Image

Image

new Image()

Image utility.
Source:

Methods

<static> blur(pixels, width, height, diameter) → {array}

Computes gaussian blur. Adpated from https://github.com/kig/canvasfilters.
Parameters:
Name Type Description
pixels pixels The pixels in a linear [r,g,b,a,...] array.
width number The image width.
height number The image height.
diameter number Gaussian blur diameter, must be greater than 1.
Source:
Returns:
The edge pixels in a linear [r,g,b,a,...] array.
Type
array

<static> computeIntegralImage(pixels, width, height, opt_integralImage, opt_integralImageSquare, opt_tiltedIntegralImage, opt_integralImageSobel)

Computes the integral image for summed, squared, rotated and sobel pixels.
Parameters:
Name Type Description
pixels array The pixels in a linear [r,g,b,a,...] array to loop through.
width number The image width.
height number The image height.
opt_integralImage array Empty array of size `width * height` to be filled with the integral image values. If not specified compute sum values will be skipped.
opt_integralImageSquare array Empty array of size `width * height` to be filled with the integral image squared values. If not specified compute squared values will be skipped.
opt_tiltedIntegralImage array Empty array of size `width * height` to be filled with the rotated integral image values. If not specified compute sum values will be skipped.
opt_integralImageSobel array Empty array of size `width * height` to be filled with the integral image of sobel values. If not specified compute sobel filtering will be skipped.
Source:

<static> grayscale(pixels, width, height, fillRGBA, The)

Converts a color from a colorspace based on an RGB color model to a grayscale representation of its luminance. The coefficients represent the measured intensity perception of typical trichromat humans, in particular, human vision is most sensitive to green and least sensitive to blue.
Parameters:
Name Type Description
pixels pixels The pixels in a linear [r,g,b,a,...] array.
width number The image width.
height number The image height.
fillRGBA boolean If the result should fill all RGBA values with the gray scale values, instead of returning a single value per pixel.
The Uint8ClampedArray grayscale pixels in a linear array ([p,p,p,a,...] if fillRGBA is true and [p1, p2, p3, ...] if fillRGBA is false).
Source:

<static> horizontalConvolve(pixels, width, height, weightsVector, opaque) → {array}

Fast horizontal separable convolution. A point spread function (PSF) is said to be separable if it can be broken into two one-dimensional signals: a vertical and a horizontal projection. The convolution is performed by sliding the kernel over the image, generally starting at the top left corner, so as to move the kernel through all the positions where the kernel fits entirely within the boundaries of the image. Adpated from https://github.com/kig/canvasfilters.
Parameters:
Name Type Description
pixels pixels The pixels in a linear [r,g,b,a,...] array.
width number The image width.
height number The image height.
weightsVector array The weighting vector, e.g [-1,0,1].
opaque number
Source:
Returns:
The convoluted pixels in a linear [r,g,b,a,...] array.
Type
array

<static> separableConvolve(pixels, width, height, horizWeights, vertWeights, opaque) → {array}

Fast separable convolution. A point spread function (PSF) is said to be separable if it can be broken into two one-dimensional signals: a vertical and a horizontal projection. The convolution is performed by sliding the kernel over the image, generally starting at the top left corner, so as to move the kernel through all the positions where the kernel fits entirely within the boundaries of the image. Adpated from https://github.com/kig/canvasfilters.
Parameters:
Name Type Description
pixels pixels The pixels in a linear [r,g,b,a,...] array.
width number The image width.
height number The image height.
horizWeights array The horizontal weighting vector, e.g [-1,0,1].
vertWeights array The vertical vector, e.g [-1,0,1].
opaque number
Source:
Returns:
The convoluted pixels in a linear [r,g,b,a,...] array.
Type
array

<static> sobel(pixels, width, height) → {array}

Compute image edges using Sobel operator. Computes the vertical and horizontal gradients of the image and combines the computed images to find edges in the image. The way we implement the Sobel filter here is by first grayscaling the image, then taking the horizontal and vertical gradients and finally combining the gradient images to make up the final image. Adpated from https://github.com/kig/canvasfilters.
Parameters:
Name Type Description
pixels pixels The pixels in a linear [r,g,b,a,...] array.
width number The image width.
height number The image height.
Source:
Returns:
The edge pixels in a linear [r,g,b,a,...] array.
Type
array

<static> verticalConvolve(pixels, width, height, weightsVector, opaque) → {array}

Fast vertical separable convolution. A point spread function (PSF) is said to be separable if it can be broken into two one-dimensional signals: a vertical and a horizontal projection. The convolution is performed by sliding the kernel over the image, generally starting at the top left corner, so as to move the kernel through all the positions where the kernel fits entirely within the boundaries of the image. Adpated from https://github.com/kig/canvasfilters.
Parameters:
Name Type Description
pixels pixels The pixels in a linear [r,g,b,a,...] array.
width number The image width.
height number The image height.
weightsVector array The weighting vector, e.g [-1,0,1].
opaque number
Source:
Returns:
The convoluted pixels in a linear [r,g,b,a,...] array.
Type
array