jLM.ImageAnalysis Namespace Reference

Classes
class	SvgContext

Functions
	_makeMaskPath (binarySlice)
	mplBoundaryPath (binarySlice, faceColor='none', edgeColor='r', extent=None)
	_processImgToDisplay (img, cmap, scl, vmin, vmax, mode='rgba')
	imshow (img, scl=None, cmap="inferno", vmin=None, vmax=None, xlines=[], ylines=[])
	alphaBlend (rgbaBottom, rgbaTop)
	_interpretColor (c)
	markup (imgIn, scl=4, cmap="inferno", vmin=None, vmax=None, markerColor="blue", markerWeight=1, markerSize=2, markers=None, mask=None, maskColor="green", maskWeight=1, svgFile=None)

Detailed Description

Image analysis tools.

Function Documentation

_interpretColor()

jLM.ImageAnalysis._interpretColor ( c )

protected

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_makeMaskPath()

jLM.ImageAnalysis._makeMaskPath ( binarySlice )

protected

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_processImgToDisplay()

jLM.ImageAnalysis._processImgToDisplay ( img, cmap, scl, vmin, vmax, mode = 'rgba' )

protected

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alphaBlend()

jLM.ImageAnalysis.alphaBlend	(		rgbaBottom,
			rgbaTop )

Alpha blend two rgba images

Arguments:
    rgbaBottom:
        Image of dimensions (N,M,4) to use as base
    rgbaTop:
        Image of dimensions (N,M,4) to blend atop rgbaBottom
Returns:
    Blended rgba image

imshow()

jLM.ImageAnalysis.imshow	(		img,
			scl = None,
			cmap = "inferno",
			vmin = None,
			vmax = None,
			xlines = [],
			ylines = [] )

Quick display of array in notebook

Arguments:
    img (array):
        A grayscale image of shape (M,N) or a rgb image of shape (M,N,3)

Keyword Arguments:
    scl (int):
        Factor to upscale image

    cmap (str):
        Matplotlib colormap

    vmin (float):
        Minimum value

    vmax (float):
        Maximum value

    xlines (list):
        List of lines of constant x

    ylines (list):
        List of lines of constant y

Returns:
   PIL.Image: PIL image which is displayed in Jupyter

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markup()

jLM.ImageAnalysis.markup	(		imgIn,
			scl = 4,
			cmap = "inferno",
			vmin = None,
			vmax = None,
			markerColor = "blue",
			markerWeight = 1,
			markerSize = 2,
			markers = None,
			mask = None,
			maskColor = "green",
			maskWeight = 1,
			svgFile = None )

Markup an image

Markup generates nearest-neighbor interpolated images with binary mask outlines,
and labeled points. Colors are specified as an RGB triple, a hex code, or a named
color.

Arguments:
    imgIn:
        Image data, either mono (N,M), rgb (N,M,3), or rgba (N,M,4)
Keyword arguments:
    scl (int):
        Upscale image by factor
    cmap (str):
        Matplotlib colormap name:
    vmin (float):
        Minimum value
    vmax (float):
        Maximum value
    markers:
        Markers to place. Either a single x,y pair, a list of x,y pairs, or a dictionary
        where keys are the marker color and the values are the x,y pair
    markerColor:
        Color of + markers.
    markerWeight:
        Linewidth of + markers in pixels
    markerSize:
        Diameter of + markers
    mask:
        Binary mask(s) to trace. Either a single numpy array, or a dictionary with color 
        keys mapping to binary arrays. The outline is placed around the True elements
    maskColor:
        Color of mask outline
    maskWeight:
        Linewidth of mask outline in pixels
    svgFile:
        Also write the SVG output to this file
        
Returns:
    IPython.core.display.SVG

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mplBoundaryPath()

jLM.ImageAnalysis.mplBoundaryPath	(		binarySlice,
			faceColor = 'none',
			edgeColor = 'r',
			extent = None )

Generate a Matplotlib patch defining the boundaries of a binary image.

Arguments:
    binarySlice:
        Binary 2D image
Keyword Arguments:
    faceColor:
        Matplotlib color of interior (True)
    edgeColor:
        Matplotlib color of boundary
Returns:
    matplotlib.patches.PathPatch: patches

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