image = minPropagation2dImg (inImg)

Detailed Description

propagation of minima in a 2d image

Minima propagation algorithm allows to propagate minimum grayscale values inside particules separated by zero grayscale pixel values with respect to a given neighborhood 2d policy (see 2d neighborhood models).

Two versions of this algorithm are implemented which can be selected using attribute InOptOptimizationPolicy which associated to enumerate ipsdk::imaproc::attr::eProcessingOptimizationPolicy :

a 'fast' version using a binary and a label intermediate image (which can optionally be provided via OutOptWk1BinImg and OutOptWk1LabelImg attributes)
a low memory consumption iterative algorithm which propagate minima until convergence.

The following figure illustrate algorithm effect on synthetic data :

Here is an example of minima propagation applied to a grayscale input image (with 8 connexity neighborhood type) :

Example of Python code :

Example imports

import PyIPSDK

import PyIPSDK.IPSDKIPLAdvancedMorphology as advmorpho

Code Example

    # opening of input image
    inImg = PyIPSDK.loadTiffImageFile(inputImgPath)
    
    # minima 2d propagation computation
    outImg = advmorpho.minPropagation2dImg(inImg)

Example of C++ code :

Example informations

Header file

#include <IPSDKIPL/IPSDKIPLAdvancedMorphology/Processor/MinPropagation2dImg/MinPropagation2dImg.h>

Code Example

        // opening input image
        ImagePtr pInImg = loadTiffImageFile(inputImgPath);
        // compute minimum propagation on input image
        ImagePtr pOutImg = minPropagation2dImg(pInImg);