Morph-M

h1. Morph-M Python - Launch some examples

h3. Micro aneurysm detection

| !retina2.png! |/4.<pre><code class="ruby">

#Structuring Element

nl = SquareSE

imIn = fileRead(images_dir+"/Gray/retina2.png")

imOut = getSame(imIn)

imTmp = getSame(imIn)

# ..............................

# open and then build

# ..............................

ImOpen(imIn,HomotheticSE(nl,4),imTmp)

ImUnderBuild(imTmp,imIn,nl,imOut)

fileWrite(imOut,"imBuildOpen.png")

# ..............................

# all in one step

# ..............................

#ImEroBuildOpen(imIn, HomotheticSE(nl, 4), nl, imOut)

#fileWrite(imOut,"imEroBuildOpen.png")

</code></pre> |

|=.*_Original Image_* |

| !imBuildOpen.png!|

|=.*_Result_* |

h3. Holes detection

| !gruyere.png! |/4.<pre><code class="ruby">

##################################################

# Fill Holes

##################################################

nl = SquareSE

imIn = fileRead(images_dir+"/Bin/gruyere.png")

imMark = getSame(imIn)

imOut = getSame(imIn)

imTmp = getSame(imIn)

# ..............................

#  with underbuild

# ..............................

arithInvertImage(imIn,imIn)

ImSetConstant(imMark,0)

DrawBorder(imMark,255)

ImUnderBuild(imMark,imIn,nl,imOut)

arithInvertImage(imIn,imIn)

arithInvertImage(imOut,imOut)

fileWrite(imOut,"imFillUnder.png")

</code></pre> |

|=.*_Original Image_* |

| !imFillUnder.png! |

|=.*_Result_* |

h3. Coffee segmentation

| !coffee.png! |/4.<pre><code class="ruby">

im=fileRead(images_dir+"/Bin/"+Image+ext)

    ##Initialisation des images intermédiaires    

    imStrech,imDistance,imDistanceFilter,imDisInvert,imWs,imWsShowLinesOnInitImage=getSame(im),getSame(im),getSame(im),getSame(im),getSame(im),getSame(im)

    ##Choix Connexité du graphe

    nl=NeighborList.neighborsHex2D

    ##Affichage de l'image des grains

    fileWrite(im,Image+".png")

    ##Calcule de la fonction distance

    print "Compute Distance function:"

    print

    Distance(im,nl,imDistance)

    ImStretchHistogram(imDistance,0,255,imStrech)

    fileWrite(imDistance,Image+"_DistanceFunction.png")

    fileWrite(imStrech,Image+"_DistanceFunction_0_255.png")

##Filtage par HMaxima de la fonction distance

    ##Parameters HMaxima

    TreshHeightMaxima=2

    print "Erase lowest maxima, dynamic <=",TreshHeightMaxima

    print

    ImHMaxima(imDistance,nl,TreshHeightMaxima,imDistanceFilter)

    ##Calule du watershed de la fonction distance

    print "Compute watershed in inverse Filter Distance function:"

    print

    arithInvertImage(imDistanceFilter,imDisInvert)

    ImWatershed(imDisInvert,nl,imWs)

    ImCompare_ssi(imWs,compareOp.Equal,255,127,im,imWsShowLinesOnInitImage)

    fileWrite(imWsShowLinesOnInitImage,Image+"_WsOfDistanceFunctionFilter.png")

</code></pre> |

|=.*_Original Image_* |

| !coffee_WsOfDistanceFunctionFilter.png! |

|=.*_Result_* |

h3. Road Detection

| !road.png! | !road_ws.png! |/8.<pre><code class="ruby">

im = fileRead(images_dir+"/Gray/road.png")

imgra = getSame(im)

imtmp = getSame(im)

imws0 = getSame(im)

imws1 = getSame(im)

nl=HexSE

# Segmentation with the waterfall algo.

ImMorphoGradient(im,nl,imgra)

ImWatershed(imgra,nl,imws0)

fileWrite(imws0,"road_ws.png")

for i in range(3):

	ImWaterfalls(imgra,imws0,nl,imws1,imtmp)

	fileWrite(imws1,"road_wf"+str(i)+".png")

	ImCopy(imws1,imws0)

</code></pre> |

|=.*_Original Image_* |=.*_road_watershed_* |

| !road_wf0.png! | !road_wf1.png! |

|=.*_road_waterFall 0_* |=.*_road_waterFall 1_* |