RRtoolbox.tools package

Submodules

RRtoolbox.tools.lens module

RRtoolbox.tools.segmentation module

RRtoolbox.tools.segmentation.find_optic_disc_watershed(img, P)

Find optic disk in image using a watershed method.

Parameters:

• img – BGR image
• P – gray image

Returns:

optic_disc, Crs, markers, watershed

RRtoolbox.tools.segmentation.get_beta_params_Otsu(P)

Automatically find parameters for alpha masks using Otsu threshold value.

Parameters:P – gray image Returns:beta1 for minimum histogram value, beta2 for Otsu value

RRtoolbox.tools.segmentation.get_beta_params_hist(P)

Automatically find parameters for bright alpha masks using a histogram analysis method.

Parameters:P – gray image Returns:beta1 for minimum valley left of body, beta2 for brightest valley right of body where the body starts at the tallest peak in the histogram.

RRtoolbox.tools.segmentation.get_bright_alpha(backgray, foregray, window=None)

Get alpha transparency for merging foreground to background gray image according to brightness.

Parameters:

• backgray – background image. (as float)
• foregray – foreground image. (as float)
• window – window used to customizing alfa. It can be a binary or alpha mask, values go from 0 for transparency to any value where the maximum is visible i.e a window with all the same values does nothing. A binary mask can be used, where 0 is transparent and 1 is visible. If not window is given alfa is not altered and the intended alpha is returned.

Returns:

alfa mask

RRtoolbox.tools.segmentation.get_layered_alpha(back, fore)

Get bright alpha mask (using Otsu method)

Parameters:

• back – BGR background image
• fore – BGR foreground image

Returns:

alpha mask

RRtoolbox.tools.segmentation.layeredfloods(img, gray=None, backmask=None, step=1, connectivity=4, weight=False)

Create an alpha mask from an image using a weighted layered flooding algorithm,

Parameters:

• img – BGR image
• gray – Gray image
• backmask – background mask
• step – step to increase upDiff in the floodFill algorithm. If weight is True step also increases the weight of the layers.
• connectivity – pixel connectivity of 4 or 8 to use in the floodFill algorithm
• weight – Increase progressively the weight of the layers using the step parameter.

Returns:

alpha mask

RRtoolbox.tools.segmentation.retina_markers_thresh(P)

Retinal markers thresholds to find background, retinal area and optic disc with flares based in the histogram.

Parameters:P – gray image Returns:min,b1,b2,max

where:

black background < min b1 > retina < b2 flares > max

RRtoolbox.tools.segmentation.retinal_mask(img, biggest=False, addalpha=False)

Obtain the mask of the retinal area in an image. For a simpler and lightweight algorithm see retinal_mask_watershed().

Parameters:

• img – BGR or gray image
• biggest – True to return only biggest object
• addalpha – True to add additional alpha mask parameter

Returns:

if addalpha:

binary mask, alpha mask

else:

binary mask

RRtoolbox.tools.segmentation.retinal_mask_watershed(img, parameters=(10, 30, None), addMarkers=False)

Quick and simple watershed method to obtain the mask of the retinal area in an image. For a more robust algorithm see retinal_mask().

Parameters:

• img – BGR or gray image
• parameters – tuple of parameters to pass to filterFactory()
• addMarkers – True to add additional Marker mask. It contains 0 for unknown areas, 1 for background and 2 for retinal area.

Returns:

if addMarkers:

binary mask, Markers mask

else:

binary mask

RRtoolbox.tools.selectors module

RRtoolbox.tools.sticher module

Module contents