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A ipython notebook: Skeletons from 2D binary shapes are used to produce a features vector depending on the evolution of the number of endpoints after successive skeleton prunings.
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{
"metadata": {
"name": "Shape"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Shape analysis of 2D binary images by counting endpoints after successive skeleton pruning "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"image dataset from http://www.lems.brown.edu/vision/researchAreas/SIID/"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"version 0.2 24/05/2013"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Dependance: mahotas, pymporph, skimage, numpy"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\"\n",
"Created on Sat Apr 20 12:57:37 2013\n",
"\n",
"@author: Jean-Patrick Pommier\n",
" http://www.dip4fish.blogspots.fr\n",
"\"\"\"\n",
"\n",
"#supposed to use mahotas 0.97\n",
"\n",
"import mahotas as mh\n",
"from mahotas import polygon\n",
"import pymorph as pm\n",
"import numpy as np\n",
"\n",
"from scipy import ndimage as nd\n",
"import skimage.transform as transform\n",
"import skimage.io as sio\n",
"import scipy.misc as sm\n",
"\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import os\n",
"import math"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def branchedPoints(skel):\n",
" branch1=np.array([[2, 1, 2], [1, 1, 1], [2, 2, 2]])\n",
" branch2=np.array([[1, 2, 1], [2, 1, 2], [1, 2, 1]])\n",
" branch3=np.array([[1, 2, 1], [2, 1, 2], [1, 2, 2]])\n",
" branch4=np.array([[2, 1, 2], [1, 1, 2], [2, 1, 2]])\n",
" branch5=np.array([[1, 2, 2], [2, 1, 2], [1, 2, 1]])\n",
" branch6=np.array([[2, 2, 2], [1, 1, 1], [2, 1, 2]])\n",
" branch7=np.array([[2, 2, 1], [2, 1, 2], [1, 2, 1]])\n",
" branch8=np.array([[2, 1, 2], [2, 1, 1], [2, 1, 2]])\n",
" branch9=np.array([[1, 2, 1], [2, 1, 2], [2, 2, 1]])\n",
" br1=mh.morph.hitmiss(skel,branch1)\n",
" br2=mh.morph.hitmiss(skel,branch2)\n",
" br3=mh.morph.hitmiss(skel,branch3)\n",
" br4=mh.morph.hitmiss(skel,branch4)\n",
" br5=mh.morph.hitmiss(skel,branch5)\n",
" br6=mh.morph.hitmiss(skel,branch6)\n",
" br7=mh.morph.hitmiss(skel,branch7)\n",
" br8=mh.morph.hitmiss(skel,branch8)\n",
" br9=mh.morph.hitmiss(skel,branch9)\n",
" return br1+br2+br3+br4+br5+br6+br7+br8+br9\n",
"\n",
"def endPoints(skel):\n",
" endpoint1=np.array([[0, 0, 0],[0, 1, 0],[2, 1, 2]])\n",
" endpoint2=np.array([[0, 0, 0],[0, 1, 2],[0, 2, 1]])\n",
" endpoint3=np.array([[0, 0, 2],[0, 1, 1],[0, 0, 2]])\n",
" endpoint4=np.array([[0, 2, 1],[0, 1, 2],[0, 0, 0]])\n",
" endpoint5=np.array([[2, 1, 2],[0, 1, 0],[0, 0, 0]])\n",
" endpoint6=np.array([[1, 2, 0],[2, 1, 0],[0, 0, 0]])\n",
" endpoint7=np.array([[2, 0, 0],[1, 1, 0],[2, 0, 0]])\n",
" endpoint8=np.array([[0, 0, 0],[2, 1, 0],[1, 2, 0]])\n",
" ep1=mh.morph.hitmiss(skel,endpoint1)\n",
" ep2=mh.morph.hitmiss(skel,endpoint2)\n",
" ep3=mh.morph.hitmiss(skel,endpoint3)\n",
" ep4=mh.morph.hitmiss(skel,endpoint4)\n",
" ep5=mh.morph.hitmiss(skel,endpoint5)\n",
" ep6=mh.morph.hitmiss(skel,endpoint6)\n",
" ep7=mh.morph.hitmiss(skel,endpoint7)\n",
" ep8=mh.morph.hitmiss(skel,endpoint8)\n",
" ep = ep1+ep2+ep3+ep4+ep5+ep6+ep7+ep8\n",
" return ep\n",
"\n",
"def pruning(skeleton, size):\n",
" '''remove iteratively end points \"size\" \n",
" times from the skeleton\n",
" '''\n",
" for i in range(0, size):\n",
" endpoints = endPoints(skeleton)\n",
" endpoints = np.logical_not(endpoints)\n",
" skeleton = np.logical_and(skeleton,endpoints)\n",
" return skeleton\n",
"\n",
"def CountEndPoints(skel):\n",
" ep = endPoints(skel)\n",
" lab, n = mh.label(ep)\n",
" return n\n",
"\n",
"def EndPointsSpectrum(skel):\n",
" #if no end points: its a closed curved :end\n",
" #count end points\n",
" #prune skeleton\n",
" #do it again until skeleton vanish, or has no endpoints\n",
" spectrum = []\n",
" steps = []\n",
" iteration = 0\n",
" epn = CountEndPoints(skel)\n",
" skellab, n = mh.label(skel)\n",
" skelsize = mh.labeled.labeled_size(skellab)\n",
" #print 'skelsize',skelsize[1]\n",
" if epn == 0 :\n",
" spectrum.append(epn)\n",
" return steps, spectrum\n",
" while skelsize[1] >= 1:\n",
" skel = pruning(skel, 1)\n",
" epn = CountEndPoints(skel)\n",
" if epn == 0 : #loop(s) in the skeleton\n",
" steps.append(iteration)\n",
" spectrum.append(epn)\n",
" return steps, spectrum\n",
" spectrum.append(epn)\n",
" steps.append(iteration)\n",
" iteration = iteration + 1\n",
" skellab, n = mh.label(skel)\n",
" skelsize = mh.labeled.labeled_size(skellab)\n",
" #print iteration, skelsize[1], epn\n",
" return steps, spectrum\n",
"\n",
"def normedEPS(binImg):\n",
" sk = mh.thin(binImg)\n",
" lab, n = mh.label(sk)\n",
" size = mh.labeled.labeled_size(lab)\n",
" #print size, n\n",
" size = size[1]\n",
" steps, spectrum = EndPointsSpectrum(sk)\n",
" normSteps = [100-100.0*s/size for s in steps]\n",
" return normSteps, spectrum\n",
"\n",
"def CountBranchedPoints(bIm):\n",
" sk = mh.thin(bIm)\n",
" bp = branchedPoints(sk)\n",
" lab, n = mh.label(bp)\n",
" return n\n",
"\n",
"def CountHoles(bIm):\n",
" noHoles = mh.close_holes(bIm)\n",
" anti = np.logical_not(bIm)\n",
" holes = np.logical_and(noHoles, anti)\n",
" lab, n = mh.label(holes)\n",
" return lab,n\n",
"\n",
"def segmentHolesBellow(bimg, thresh=5):\n",
" bimg = bimg>0\n",
" #filled convexhull\n",
" cvxf = polygon.fill_convexhull(bimg)\n",
" area= np.sum(bimg)\n",
" cvxf_areaF = np.sum(cvxf > 0)\n",
" #print area_wheel, areaF_wheel, 100.0*area_wheel/areaF_wheel\n",
" #holes size \n",
" holes_map, nbh = CountHoles(bimg)\n",
" holes_size_map = np.copy(holes_map)\n",
" holes_size_map = np.float32(holes_size_map)\n",
" for hole in range(1, nbh+1):\n",
" hole_size = np.sum((holes_map==hole)>0)\n",
" holes_size_map[holes_size_map==hole]=100.0*hole_size / 1.0*areaF_wheel \n",
" holes_size_map[holes_size_map>=thresh] = 0\n",
" smallHoles = holes_size_map>0\n",
" smallHolesFilled = np.logical_or(wheel<150, smallHoles)\n",
" return smallHolesFilled\n",
"\n",
"def EPSpectrumToFeatures(steps, spectrum):\n",
" '''\n",
" eptest = [4,3,3,1,1,1,1,0]\n",
" pruntest = [0,1,2,3,4,5,6,7]\n",
"the returned vec:[7,6,-1,2,0] which means:\n",
" 0 enpoints -> 7 prunings necessary\n",
" 1 endpoint -> 6 prunings\n",
" 4 endpoints-> 0 pruning (initial skeleton size)\n",
"'''\n",
" #feat_vect = [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1]\n",
"\n",
" values = set(spectrum)\n",
" feat_vect = [-1]*(len(values)+1)\n",
" spec = np.asarray(spectrum)\n",
" print 'EPS spec to feat func'\n",
" print 'values',values\n",
" print spec\n",
" print feat_vect\n",
" for epn in values:\n",
" indexmin = np.where(spec == epn)[0][-1]\n",
" feat_vect[epn] = steps[indexmin]\n",
" #print epn,np.where(spec == epn),np.where(spec == epn)[0],np.where(spec == epn)[0][-1], feat_vec\n",
" return feat_vect\n",
"\n",
"def step(endpoints):\n",
" '''ep = [8,7,4,4,3,3,3,2,0]\n",
"'''\n",
" \n",
" values = set(endpoints)\n",
" feat = [-1]*(max(values)+1)\n",
" \n",
" endpoints = np.asarray(endpoints)\n",
" #print endpoints\n",
" #print values\n",
" for ep in values:\n",
" elements = np.where(endpoints == ep)[0]\n",
" last = elements[-1]\n",
" feat[ep] = last\n",
" return feat\n",
"\n",
"#essai eps to feat\n",
"eptest = [4,4,3,3,1,1,1,1,0]\n",
"\n",
"pruntest = [0,1,2,3,4,5,6,7,8]\n",
"normedprun = [100*(1-i/25.0) for i in pruntest]\n",
"print 'normesSteps', normedprun\n",
"print 'feat vector',EPSpectrumToFeatures(normedprun,eptest)\n",
"##\n",
"### Essai2\n",
"p1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87]\n",
"ep =[13, 13, 12, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,6, 6,6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 5, 5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2,2,2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0]\n",
"print len(ep)\n",
"print len(p1)\n",
"#print 'feat vector',EPSpectrumToFeatures(p1, ep)\n",
"#print 'normed feat vector',EPSpectrumToFeatures(normedprun,eptest)\n",
"#structuring elements\n",
"disk7 = pm.sedisk(3)#size 7x7: 7=3+1+3\n",
"disk5 = pm.sedisk(2)\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"normesSteps [100.0, 96.0, 92.0, 88.0, 84.0, 80.0, 76.0, 72.0, 68.0]\n",
"feat vector EPS spec to feat func\n",
"values set([0, 1, 3, 4])\n",
"[4 4 3 3 1 1 1 1 0]\n",
"[-1, -1, -1, -1, -1]\n",
"[68.0, 72.0, -1, 88.0, 96.0]\n",
"88\n",
"88\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"particle is a class to analyse particle anisotropy"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class particle(object):\n",
" def __init__(self,arrayIm):\n",
" self.particuleImage=arrayIm\n",
" def orientation(self,step):\n",
" \"\"\" Performs successive rotations of a binary particle from 0 to 180 by \"step\"\n",
" Then at each angle performs:\n",
" *successives erosions\n",
" *pixel counts\n",
" Find relationship between angle and the minimal erosion to destroy the particle \n",
" An anisotropic particle such a long chromosome should have a \n",
" principal orientation, overlapping chromosomes should have at least two\n",
" orientation.\n",
" \"\"\"\n",
" def erodeVParticle(im):\n",
" '''Counts the number of vertical erosion neccessary \n",
" to destroy a particule\n",
" vline is a 3X1 structuring element\n",
" DestroyParticle returns the scalar number:nb_erosion\n",
" necessary to erode totally a binary particle'''\n",
" \n",
" def countTruePix(bim):\n",
" '''bim must be a flat binary (True,False) array \n",
" counts the occurence of True (pixel) in a binary array/image ''' \n",
" return np.sum(bim[:,:]==True) \n",
" vline=np.array([[0,1,0],\n",
" [0,1,0],\n",
" [0,1,0]])\n",
" #threshold the image\n",
" binaryIm=(im>0)\n",
" pixcount=countTruePix(binaryIm)\n",
" nb_erosion=1\n",
" while pixcount>0:\n",
" erodedIm=nd.binary_erosion(binaryIm,structure=vline,iterations=nb_erosion)\n",
" nb_erosion=nb_erosion+1\n",
" pixcount=countTruePix(erodedIm)\n",
" #erodedParticle work should be done here\n",
" return nb_erosion\n",
" #\n",
" #Analysis of the particle orientation starts here\n",
" #\n",
" maxRotationNubr=180/step\n",
" compassTable=np.zeros((2,maxRotationNubr+1),dtype=np.uint8)#rotation angle from 0 to 180 by step of 5\u00b0\n",
" #Store \n",
" #print compassTable.shape\n",
" i=0\n",
" angle=i*step \n",
" rotatedIm=self.particuleImage\n",
" #print self.particuleImage.shape\n",
" while i<=maxRotationNubr:\n",
" maxErosion=erodeVParticle(rotatedIm)\n",
" compassTable[0,i]=angle\n",
" compassTable[1,i]=maxErosion\n",
" #print \"i=\",i,\" angle=\",angle,\" -erosion max:\",maxErosion\n",
" rotatedIm=nd.rotate(self.particuleImage,angle)\n",
" i=i+1 \n",
" angle=i*step\n",
" return compassTable\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Let's use a class to handle shapes and to produce a feature vector"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Shape(object):\n",
" def __init__(self, silhouette, neg = False):\n",
" if neg == True:\n",
" self.silhouette = np.logical_not(silhouette > 0)\n",
" if neg == False:\n",
" self.silhouette = silhouette > 0\n",
" \n",
" self.skeleton = mh.thin(self.silhouette)\n",
" self.skelsize = np.sum(self.skeleton > 0)\n",
" self.endPointSpectrum = [EndPointsSpectrum(self.skeleton)]\n",
" self.prunedSkeletons = {}\n",
" self.prunedSkeletons[0] = self.skeleton\n",
" \n",
" def End_Points(self):\n",
" return endPoints(self.skeleton)\n",
" \n",
" def Branched_Points(self):\n",
" return branchedPoints(self.skeleton)\n",
" \n",
" def End_Points_Spectrum(self):\n",
" '''From skeleton image return end points spectrum\n",
" '''\n",
" return EndPointsSpectrum(self.skeleton)\n",
" \n",
" def Normed_EPS(self):\n",
" return normedEPS(self.skeleton)\n",
" \n",
" def End_Points_Features(self, normed = True):\n",
" ''''return transitions of the endpoints spectrum as the number of \n",
" pruning steps if normed = False or \n",
" as a % of skeleton size if normed = True (100*(1-Pn/skelsize), n=0,1,...).\n",
" -1 value(s) are missing endpoints value for a given bumber of pruning\n",
" '''\n",
" eps = self.End_Points_Spectrum()\n",
" #neps = h0.NormedEPS()\n",
" feat = step(eps[1])\n",
" if normed == False:\n",
" #print 'raw'\n",
" return feat\n",
" elif normed == True:\n",
" #print 'normed'\n",
" size = self.skelsize\n",
" nfeat =[100*(1-f/(1.0*size)) for f in feat]\n",
" nfeat =np.asarray(nfeat)\n",
" nfeat[np.where(nfeat>100)]=-1\n",
" return nfeat\n",
" \n",
" def feat(self):\n",
" print type(self.endPointSpectrum)\n",
" endpoints = self.endPointSpectrum[1]\n",
" return step(self, endpoints)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Load images"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Load shape images (pgm format)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hands = ['hand','hand-10','hand-90','hand-bent1','hand-bent2','sketch-hand']\n",
"user=os.path.expanduser(\"~\")\n",
"workdir=os.path.join(user,\"Images\",\"silhouette-database\",\"hands\")\n",
"handsImg = []\n",
"for img in hands:\n",
" image = sio.imread(os.path.join(workdir,img+'.pgm'))\n",
" handsImg.append(np.logical_not(image>0))\n",
"\n",
"i = 1\n",
"title = iter(hands)\n",
"print len(handsImg)\n",
"for im in handsImg:\n",
" plt.subplot(int('16'+str(i)),frameon=False, xticks=[], yticks=[])#\n",
" plt.title(title.next(), fontsize = 8)\n",
" plt.imshow(im>0)\n",
" i = i+1 \n",
"#plt.imshow(handsImg[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"6\n"
]
},
{
"output_type": "display_data",
"png": 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}
],
"prompt_number": 5
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"load fish images"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fishes = \"/home/simon/Images/silhouette-database/fish\"\n",
"print os.listdir(fishes)\n",
"fishesImg=[]\n",
"for im in os.listdir(fishes):\n",
" image = sio.imread(os.path.join(fishes,im))\n",
" fishesImg.append(np.logical_not(image>0))\n",
"plt.imshow(fishesImg[2])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['sketch.pgm', 'puffers.pgm', 'cardinalfishes.pgm', 'dogfish-sharks.pgm', 'goatfishes.pgm', 'whale-sharks.pgm', 'livebearers2.pgm', 'mullets.pgm', 'bonefishes.pgm', 'swordfishes.pgm', 'herrings.pgm', 'sketch2b.pgm', 'sand-tigers.pgm']\n"
]
},
{
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<matplotlib.image.AxesImage at 0xbb4f7ec>"
]
},
{
"output_type": "display_data",
"png": 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EQIJACIEEgRACCQIhBBIEQggkCIQQSBAIIZAgEEIgQSCEQIJACIEEgRACCQIh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\nYII=\n"
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Detecting end-points or branched-points after skeleton pruning"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if __name__ == \"__main__\":\n",
" handsSkel = []\n",
" #\n",
" prunedHandskel = {}\n",
" fishesSkel = []\n",
" # Skeleton+Pruning\n",
" for im in handsImg:\n",
" skel = mh.thin(im)\n",
" handsSkel.append(skel)\n",
" for im in fishesImg:\n",
" skel = mh.thin(im)\n",
" fishesSkel.append(skel)\n",
" \n",
" hand0 = Shape(handsImg[0])\n",
" \n",
" \n",
" #Example with a hand\n",
" skel = hand0.skeleton\n",
" endpts = hand0.End_Points()\n",
" endpts = mh.morph.dilate(endpts, disk5)\n",
" brpts = hand0.Branched_Points()# \n",
" brpts = mh.morph.dilate(brpts, disk5)\n",
" \n",
" #Example with a fish\n",
" fish0 = Shape(fishesImg[2])\n",
" skel_f = fish0.skeleton\n",
" endpts_f = fish0.End_Points()\n",
" endpts_f = mh.morph.dilate(endpts_f, disk5)\n",
" brpts_f = fish0.Branched_Points()# \n",
" brpts_f = mh.morph.dilate(brpts_f, disk5)\n",
" \n",
" \n",
" display_H0 = pm.overlay(hand0.silhouette, red = skel>0, blue = endpts > 0, green = brpts > 0)\n",
" display_F0 = pm.overlay(fish0.silhouette, red = skel_f>0, blue = endpts_f > 0, green = brpts_f > 0)\n",
" \n",
" plt.subplot(121)\n",
" plt.imshow(display_H0)\n",
" plt.subplot(122)\n",
" plt.imshow(display_F0)\n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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3GjdunHmloCDYsEHOyrt1exWiNo9gZmZGWFiY3sII5xVybFoZO3Ys3bp148aN\nG1y+fJkaNWoYTabxSZMmsWrVqrQn6tSRtuSGDTO81tTUlGXLlqUf69sYKV8efv1VzcYVDB8+HJfM\nUrM9eAA//SSz5dSpkyddVJs1a8b48eMNLYbRkaMZ+dOnTzl58mTSI465uTlFixZl165dHH+5Hd7D\nw4O2bdsaRJnfuXMn/awqhQpBrVpyN9qePdIPfOzYFNELTUxMqFy5MkOHDqVu3bqYmZkZzJ0rU9av\nl3kJCxSAGjWUp4oWMeanzVyxb59MOlK5slTo9vY67S6kBMQWfAoEYYUVxdDO+2VhYYGdnR2PHz8m\nPh0PtNeRHClyPz8/SpUqxZAhQ7h06RKNGjXi22+/1VumcU24ceMGhw4don379mmzAbVtC1u3wurV\n0q7drp2MvZyMZs2aGU2gq3Q5dEh6rPToAVZWhpZGYxwcHGiVwdqBjw+kzHXxN/CvPsRKQeLT5s8/\n/0xcXBwRERHMnj0bFxcXJk2axLx58/D09DTYE2euqF9fKnQ9/Aj1+xmuWE0GZtKd7qxnvVbarVu3\nLn/99RctWrTg/v37WmkzzyNywLlz54S5ubk4e/asEEKIsWPHiunTp4tixYqlqFe8ePE01wJ6K2+8\n8YaIiopK/yYWLhTC1lZGFN++XYjnz3PyVqTlyRMh6tUTYsmSLKtGikgRGHRRBL5ZWQRuWijCRFjW\n7cfFCfHwoRB9+ggxcmS2xfPx8RE2NjZ6/RxMTExEiRIlhL29vXBzc0sjU0KCEI8eCTFsWGKA98Qy\nRUCxHPWZU8LCwkTlypXTHHdychJBQUFCCCECAwOFk5NTmjr6fE/TKzt27Mj4xlasEMLJSYg338zx\ne6Mp98V90VF0FIVFYcHLv5KipHATbiJBJGilj7i4OHH69GnRpEkTg7/v+iwZkaMZebly5ShXrhxN\nmjQBoF+/fsydOxd7e3u9ZxrPMR4e8tFy0CBpXpk8WQac0iOHOcz4Eh/Dj/eg+Hx6EsgCFmR+0b17\nMjHuxYuvgv0bOQUKFOB///sfdevWxdraOs3558+lZ+iJE6nPjAPsgU/0IKUkLzxt5ghfX2kT//RT\nnXcVSSQnOUk00UnHHvOYc8G7EV99gInIIhFLz55pEsGkxszMjDfeeIOZM2eyaNEijh49qg3R8yw5\nUuT29vaUL1+eW7du4ejoyKFDh6hVqxa1atXC29ubyZMn6y3TeGb4+voyZswYxo8fT61atVKeLFFC\nLhCuXi2WlTCMAAAgAElEQVT9vvUccH4jG/HCi1vmvshMW8HsYAemmDKRiZQkncBYp07Bd99JG8SA\nAVKhGzF9+vShS5cumJub06ZNG0qVSj8edUKCzBkdFpb6TGkgnXC9OiQuLg4fHx+WLVtGkyZN+OST\nT9KYUExMTFJmo8or1KwJffqkOXzt2jWOHTtGwYIFGTBggA6Dzpm8XI/K5L3bu1dOVszM5N6PLOje\nvTvR0dE4OzsTGBjIL7/8oj1x8xA5dj/87rvvGDhwIDExMVStWhUvLy/i4+MNkmk8Ix4+fMi6detw\ndXVNq8hBzsiHD5cxWC5fhoMHpR+4HviDPzhCyt2Y//Ivm198z6gDdpR8no7SO3FCDvQuXWDIEGje\nXC+yZocyZcrQ8eUX0NXVle7du2daP4AA9vAnT+gEFNeDhJmTL542s4mvry/ff/89fn5+2NraUqxY\nMRwdHdMPlawBBSlIa1pzmtOEI8PHlqQkTe26YfLdMjJV5DNmSPfI+fNfhWNu2jTTdaC+ffvSt29f\nrly5QnBwMOfOnSMmk5j8+ZEcK/J69epxLp2dYakDTxkDjx49IiwsLGMvg9KlZZqRoCAZ99jOTnqD\nGIKnT2Vi27sZfDQNGsCyZTr3OMgJRYsWpX379mzYsEHja3zw4X3TkWC/DXybw/MiOpQwa/LK06Y2\n6dGjByVLlqRHjx707duXhIQE5s2bx5gxYyhcuHC223PAgQMcoAMduPL8DzA14y3r7njhlfXFX30l\nlfaSJfLp4cUL+SRat26WeyXq1KnDb7/9RsuWLbl16xaxsbHZlj3PopWVh2yAARYI7OzsxPz58zMW\nKjBQiLFjhbCyEqJKFSFeLuLmCA0XO98X7yctBCX/Kx9bRvwXcFwIP7/0y717csEzh+hysXPChAlJ\nC4KaskPsEMSbCu47CAZ7CUi94LlVq4tCmnDx4kXRuHFjUbduXdG7d28RFhYmQkJCRIcOHUT16tWF\ni4uLePLkSZrrDDG2k5dMFzs//VSIoUMzPB0dHS2uX78uSpYsKQBRpEgRMW3atFy9j4/FYxE0uq8I\nmvuJeCLSvl8Z8vy5EEFBQhw8KISJiRAlSwpx4IBGl8bHx4uHDx+Kli1bGvzz0EXJiHy1RT8jgoOD\nCUtrgH2Fvb3crmxlJe3lUVE56ucCF1hSaAF8dReqbaIRJkl5C1Pj4Q0mL2Dl6FfHmtOccebjKFWu\nAaD9memBAwdYtmxZ9nesZoGpqSlTpkyhf//+OUtuYJoADg/AJr30c02BpYAn8CB3gmpIXnra1BaW\nlpaUKlUqyVX3+fPnbNiwgTNnzmBra8u6desoUiR7Y7IEJeCZFUQUhuz4kBcuLEuhQnK/x9ChcpH2\ns8/A1TXTS01NTSlVqhRLly7lyZMn7Ny5k6VLl2ZL7rzIa6HINaJ+fQgNhQUL4Pff5bb3+vU1vvwv\n/mIjG/G23Aw9AM5yhThssaUb3Sieyv77xhkwfVqFmNHtXh3jDfrRT0s3lJbr169rPbRnmTJleOut\ntxg8eLBmQccCAmRogUQqXoRM17QqAa7ASvSlyBWSe/fuce/ePaytrZkwYQJTpkyhSpUq+hOgSBG5\nZuXpKW3mS5fKeEkDB2Z5aYMGDQAZP0kIwXfffadraQ2KUuTJKVRI7npbulQmMM6GfXBbydUsK7Yu\nxTEffBjLWOpR75Uij4+H+/fh6VOa0oymfK/NO9Artra2dOjQgTVr1mh2QUiI3Izy/vsyMXaBAlh3\ngCodq3Cf+8mc1RQ5Yf/+/VSoUCFJiWWHwMBAfvzxx3Sf1iIiIlizZg0eHh76VeQgvVfc3WVsmNWr\nZUrFQoVk6GYNqFOnDh9++GEKe/nx48fTj5Cah1GKPDkNGsDRo/DOO/D993L3p6Z8/gxGalAvNFQO\nzPPnpb9sHmbIkCFMnTpV8wtWroTFi+UXcfVqqFuXllbwO8/oS1/yf2ZF3bJy5UrKly+fI0V++/Zt\nPtW2j/mFC/DwoZwc5ZYxY6Tr4rffwvTp0iGhUSONnBIcHR1ZuXJl0usFCxYkPZlGR0fz999/5/mt\n/q+NIt+xYwcWFhbMmDEDMzOz9CsVKCDjin/9tUyknB0abgT2ZF0vIUEO7nfflba/PMqMGTNwdXWl\nRIkSmVe8dAnmzZP/v3gRKlaEadOkS5mtLVZAFUqykIU8ca/COTOp619xBpgPqK3YuiI6Oprn6cTo\nzzXDhsn0g23aaKe90aNlTKH335cbhk6fll5mFhbZambixIlMnDgRgAcPHvDmm2+muP/Y2FjCw8O1\nI7OeeG0U+fXr1zl06BDTp0/PvKKJiUYbEVLTHEuuk8A+9iUdq+IH3Y5FcejtbZy0fZloouBzePcJ\nbdo1xtkI/cCzokyZMrz99tsMGjQIR0fHzCufOSN99H/6Cd5+W8a4qV0b+qVcB7DEki504VTkKY5F\nHE7ViA/wqzZvQZGKn376iXHjxumm8Xnz5BOotujVSyrzHj3k93TLllyFjbazs+Ovv/5KkbT98OHD\nuGtTZj3w2ihyXdOXvhSnOHd5lfC12X/PGPD9PQa3/IrbiRFDbYCZ8DlBjOA+ZSmrNxlLlChBlSpV\n8Pf3TzFwNaVUqVJ07NiRFStWZL6zUQi5VXPtWvlFq1MHPv9c/psO8fHx3Llzh1WrVrFx48Zsy6XI\nHRERETx+/Fg3jRcrJhcttUWhQtCyJezYITfzTZggTS0a2sxTY2Zmhn2qPRldu3blQPIF+Zf8+eef\nfPHFFznqR9coRa5FWtKS4xxPen2w9e/02D2Yp+lEmF3IQgIJZBXpxE3XEf369aNcuXK8/fbbOXqU\n9vDwYPr06VlvT4+NlY/Bx4/Dm2/Ctm2ZhtkNDw9n6NChnD17NtsyKVLy+++/U6FCBQZq4NkB8Ouv\nv/LTTz/pWKrMCQwMZP78+QAMHTqUOhn84CdRtKjc3Tx3rnQXXrJEjrksXBM1pWTJkunGda9SpQpF\nk43jDRs2cOHCBa30mVuUItcillimiJFSwLIIoRnE7g8nnOfowC6ZCQULFqRYsWI5ihPy+eef4+rq\nmmIgp2HzZti+Xa4DnD0rc4i+/76Ma5MJQgjCwsK07t/+OvLHH39QpUoVOnceyI4dr443uwx1yqWs\nu3fvXpYvX87hw6nNWfolISGBZ8+esX37dmJiYhg5ciT16tXL/CJLS2mDf/5cPvklerNkERIiN1St\nWpVPPnkVwK1gwYKcP38egCdPnrB9+3ad9Z0VSpErNKJVq1Y4OztnXun6dZnQt3dvGdCre3do0SLD\n6idPnsTHx4fIyEjdPdq/hjx6JNPGjhjx6tiHVOSTocWpmqze4sWLs6XEr127RvXq1bUeZ6Zs2bKs\nXr2aP/74gxUrVpCQkIC7uzvW1tbUrVs384sTFet338kNQ3Z2cv9HNhdAc8KoUaMYNWoUIKNmPngg\n9zncvHmT0NBQnfefAs33zWoHDLi9tUWLFiI2NlZv93pYHBa1RW1hJazSbMUvJ8qJKWKK3mRJ5MaN\nG6JFixaifv36wsHBIcv3zNraWtSrV0/89ddfGTeakCDE7dtCDB8uhItLljLEx8eLmzdvikGDBul9\nK7MuMeTYTlncBGlCHbzaoZ+QkCCeP38u2rVrl+22V69erfkb0qCBEBs3alQ1Li5OODk5peirXr16\n4unTpyI+Pj7rBry8hLC2FqJ4cSFu3RJCj9/z1AwcOFDY2NgIGxsbYW1trZdxrRS5DokVscJP+Ima\nomYaRb5YLBYvxAu9yZJIXFycePbsmXj69Kn4+uuvs3zP3njjDRESEpL5+xYXJ0TnzkIUKKCRIn/+\n/Llo3bq1sLS01NlnbQgMr8ATS+aK/NmzZ6JJkybCysoq223rU5FbWlqKatWqiTt37mTdQHi4TBBj\naiqEg4MQf/yhuZxa5tGjR+Lu3bvi7t27Yt++fXoZ1zlOvqzIGnPMccCBFaxgV+Jf2AZ2jSlP731W\nFERXcZ8zxszMjCJFimBjY0OBLDZTvPPOO3zzzTcUL14cc/MMrHCXL0uXsHPn5EaqWbMybfPcuXO4\nurpy6dKl1y7UqDFw48YN+vTpw5UrV4jKQUyhJUuWsHr1ah1IlpaYmBh8fX1xc3PLeiHc2lqmbNyy\nBSIjpcmla1dpO9czJUuWpHz58pQvX57mzZuze/duypTRcVx9Pf5QCSFerxl5ugQFCVGzphArVxpW\nDiHEsWPHhJubm7CwsEjzXvXr10/s3bs38wZOnRJi1CgZoW7AACEOHcqyz927d+vlszYEhhzbKYuz\ngG8FyWbj3bsLMWvWWfHee+/luv3x48dr9oZoOCN/8OCBGD9+vLC1tc2wz969e4ujR49m3WdkpExr\nt2CBEM2bC1G/vsG/a3FxcWLNmjViwYIFOTJnaTKu1WLna0ybNm2wtrbml19+SRO7efjw4XTOIt0W\nBw9KT5VmzWDSpCyDjAUEBHDr1q3ciq3IkuvAt8hNCxJra/D3P8WPP/5oMKky4vHjxyxatCjTOr/+\n+isWFhaYm5vTsmXLjCtaWUnXV5BRTZcvl+6JxYrJRXgD5BkwMzNj+PDhAFSsWJG4uDhOnjyp1T6U\nIlfkjoYNZdaiLIL+g4wFsnDhQj0IlTvmzp3Lxo0bMTU1pU6dOnh5eREREUH//v25c+dOUvarDBOV\nGAX+wKsQENkJG5QVQUFB3Lx5Eycnp1y3FRoaytWrVzWqu23bNl68eIGtrW3WHlQg8/FaWcmsQx98\nIN1gixaVsV8ySDuYKU+ewO3b6Z8rWDDDDW/JeeeddyhUqBChoaFcu3Yt+zJkRE4fF+bMmSOcnZ1F\n7dq1xYABA0RUVJQICQkRHTt2NNrg+0ZlWnF2FmLuXMPKIoR4+vSpOHjwoNi3b1+K8ujRo8wvnDhR\niKpVhWjfPss+oqOjxccffywqVaqkt886p/j5+YnKlSuLqKgoIYQQrq6uYv369WLixIli3rx5Qggh\nPD09xeTJk9Nca8ixre/SpUuXrN/MLEwr0dHRwtvbO9t9N27cWERERIiEhATNPtTjx6VHi7W1EGZm\nQnz3nRDR0ZpdK4QQUVFyMXXLFrmYmthWYilQQH6nw8MzLZHhcUkvT506LwoVKiRMTEy0Mq5zNOLz\n6mA3CkX+/LkQ334rRMOG0q6c17hzR4jZs4WoXFmI1q2FWLs2y0tevHghmjZtqtfPOqeEhIQIR0dH\nERoaKmJjY0X37t3FgQMHhJOTU1L2o8DAQOHk5JTmWkMrV30WbSjyxYsXi9KlS2e77wIFCojatWuL\nBw8eaPahRkYK4esrS+PGMuPQrFmaXSuEEOPGyfFuZyf/vX37VXu+vkIsWyaEpaU8l0kZXvlQ0ssu\nXaLEtWvXNHIB1mRc58i0YmNjg4WFBS9evMDMzIwXL17g4ODA3LlzOX5cblH38PCgbdu2aTKQv/YU\nLgxjx8I//8C//8odkHXqyEczYycgQO40mT5dBr8aPFgmgc6EJ0+e4OPjo5voejrA1taWCRMmUKFC\nBQoWLEjnzp1xcXEhODg4KfuRnZ0dwcHBBpbUsPz9998MHTqUFStWYJVJYuTMCAsL4+HDh9m+Ljo6\nmn/++Qc3Nzfmz59Pw4YNM7/AyupVKN0lS6RnlZcXnD3L4xLwwQp4UUie7kMfhpyrndL76sIFmejc\n3V1+f6tWlcH1EunTR0b1zIC4OBg9BnYH1iZx1Dx5UoAJEyry5ImWrNua/yylZNWqVaJw4cKiVKlS\nYtCgQUIIIYoVK5Z0PiEhIcXrRDDgLMIoZuSJvP++9PYoV05uYMgLzJolHy1BiCNH5EagLNi3b58w\nNTXV+2edU/79919Rs2ZN8fjxYxEbGyt69eolfvjhhzRjuXjx4mmuNeTYNkSxsbER48aNE3fv3k3/\nzcxkRr5x40bRokWLXMvg6uoqTp48mb0Ped8+ITw9xS3PYWLMcoRlNEn7O5qKpsLrt35C2NoK8fXX\nQnh6ynL8ePb6SEZ0tLRCksa3P1xABa2M6xyN+Lw62I1Kkd+8KcT06XLzQl5Q5BMmyGfCunWlEk9n\n/SM1q1evFvXq1TPIZ51TtmzZIoYNG5b0esOGDWLMmDGiRo0aIjAwUAgh3eVed9NK8pJ61+8z8Uxs\nFBuF94yKwnveRuHtLYS3txA7dsjf/p07d2rV1DZw4EBx+vTpbH/W+8X+dBOgv3WxrBDjx2fPjp4J\n+lDkOdoQdP78eZo3b06JEiUwNzenT58+nD59Gnt7e4KCggAZ0UzbMRnyFY6O4Owsg/589x34+Bha\nosy5eBEqVJAbLdq1k+5cGRAfH8/atWvZsGEDly5d0qOQuadGjRqcOXOGyMhIhBAcOnQIZ2dnevTo\ngbe3NwDe3t706tXLwJIaD//88w8hISEAPH0KR/5+ytC/l+Hxdkk8jtni4QEeHnLo/P03jB//mVYj\nXW7atIn58+dz8+ZN7TRYrz4sXKiRJ5YmmJhIS2TKeHMRwAVAS5vicvILc/HiRVGrVi3x4sULkZCQ\nINzd3cWyZcvExIkThaenpxBCiLlz56rFzqw4fFiIFi2EKFRIiDVrDC1N+oSFyRl4w4ZCzJyp0SXR\n0dHizTffNOhnnRvmzZuX5JHl7u4uYmJiREhIiOjQoYPRemQZuqxZs0ZERcWK7dvTm3mmLrV1IkPz\n5s1FdHS0xt4sB8VBYSksU8zGzYSZ6CV65Wr8JCchIUFERUWJqKgo8dZbUQISyx9aHdc5HvF5cbAb\nnSIXQoj794WoXl2I1auF0CQ4kD5JSBDixAkhzM2lbfzzzzW4JEG8ePFCvPHGGwb9rA2BoZWpIUvp\n0qVFtWrzRZkyhlPkVlZWolGjRuLhw4cafV4RIkKcFCdFYVE4SZEPF8PFA6GhN4wGXL9+XVSrVk1U\nq1ZNWFtXE5BYyml1XJsIIQR6JCexsLVFixYtOHbsWMZxQwzBgwcyVZUQMvTrzJmGlugVP/wACxbA\n1asy6W2vXtK8kgnnzp1j7NixXL161aCeKnoe1oBhx7ZxMBnQxEutDqDZJqDsYmtry7Vr19Jk/cmI\nSCI5ylFiJ42Dzl2o1mEktailcX9bt25l8+bNGZ5/+vQpx44d07i9rMhoXBuRRntNsbGB8eNlcPyM\ndo0ZgvXrwdsbnj2Trlg9e2apxA8ePIiXlxenT5/Wj4yKPMYjYAmgO9fNyMhI5s6dy9ixY6lSpUqW\n9QtSkG50gxOzoGZ90FCJL126lODgYE6ePKn17fY5QSlyQ1O4sMyi8/ff4OsLx47JDPOFChlGnrAw\n6du+bJnckty3L0ydCmZmWV564sSJTGcnivzOdeA40CbZsT+R4QIAAoE5SCuBboiMjGTp0qUUKVIE\nDw8PqlevrpN+vv/+e65cuaKTtnOCUuTGgoUF8adPETPSDfbvh8qVMcMMCyyIRjq6AphgQgEKYIJ8\njBeIFOdNMcUSy6Tz2SIuDi5dkrNvIWDKFPjyS40ujY6OThN4S/G6sRt4hpx1JzIb2KN3SWbPnk1C\nQgIffPABZctqmOA8IAACA0HXIWd1gdas+hqCARdkjHKxMxE/P7Hn7CzR5KKlaBJVVzQRTcSX4kvx\nQDwQPUQP0eTlX2/RWzwUrxZz/IW/6CK6JJ13Fa4iVITmTAZvbxkzwsREiKVLhQgI0Oiy+Ph4MWLE\niGxvN9ZlMQSGvmdV0pbRo0eLuLi4rD+85s3lgv7EiRp91nXq1DHI/WTEazUj9/f3Z/LkyYwePZpq\n1aoZWpwU/FTpHOsrneEcMcBlAF7wgnvc4zjHecYzAG5zmxnMYCQjiSGGNazhJCeJIAIAX3yZznRG\nbShE3ew++Z09Cy9eyOzkPXpAuXJZXvLvv/+ycuVK9u7dm5SzUKEwFjZv3kxERETSHoAM+fFHuVaV\nR3mtFPn9+/dZtGgR3bp1MzpFfoAD7En1CHrt5V9ywghjFato5WNNhFk06+qtS3E+hBBWsIJ2gY2p\ne8she0IUKybt8+PHa5y89u7du1nGklYoDEVYWBgBAQFZV6xYMfWOnTzFa6XI8xUr/wfWQuYPSI/J\nk4F++pRIoTBK/Pz8mD9/Ph9++CGFsnIiOHUKNm6UsczzECpnZ15lxnT49FNDS6FQGD3+/v7MmTOH\nzZs38/jx44wrNmkiTYuLFsHvv0N8fIZVO3fuTPny5XUgbQ7RdCFHW2AECyCHNMgtqW8O/ThcuG0g\nxXbhZqKZ+Fx8LmyFbdKxkqKkmCVmiX/EP+KKuCI+E58JG2GTdN5e2Is5Yo64LW7rXOY9e/aI3r17\nG/zzTK8YAkPfsypZlw0bNojQ0EycAdatE6JiRRkg7sWLTD/v/v37613+jFCmFUMTFQXHjtFhrR8W\nZSoQ6dY06VRb2uKKK8EE8/jcXjA1wb5Rdz7gA2yxBWAMYwgmmDDC4L//KHfhER9gh82bBUFDr6uc\ncPz4cby8vPj1119114lCoWXc3d1ZtWoVI0aMSH8n7pAhULo0fPQRGGB3cI7J4eQjx2AEv8rbt28X\n4eHh+r719EmMtVKkiBCjR2dcz91diGT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qde7cWZw+fTrb7Sfnl19+EQMHDsxx+8q0olDk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\nBhb9Z0iftwAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Comparing the evolution end-points number of a skeleton with pruning steps in two shapes (endpoints spectrum)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.subplot(111)\n",
"S= hand0.endPointSpectrum\n",
"pruning_steps= S[0][0]\n",
"end_points = S[0][1]\n",
"F= fish0.endPointSpectrum\n",
"pruning_steps_f= F[0][0]\n",
"end_points_f = F[0][1]\n",
"#print S[0]\n",
"#plt.gca().invert_xaxis()\n",
"plt.step(pruning_steps, end_points)\n",
"plt.scatter(pruning_steps, end_points)\n",
"plt.step(pruning_steps_f, end_points_f)\n",
"plt.xlabel('pruning steps')\n",
"plt.ylabel('end-points')\n",
"plt.legend(['hand','fish'])\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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IIUTjVuPlo6lTp/L999+zevVqANzc3Jg2bZrNAxNCCNHwajxT+P7770lM/Ol+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\nIXSSFIQQQuj+HzFT1SA9D7taAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Effect of image size on endpoints spectrum stability"
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Reduce image size successively: 100%, 81%, 49%, 25%, 9% (from initial size). The curve is more or less stable as the image resolution decreases."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"im100 = pm.asf(handsImg[0], seq='CO', n=0)\n",
"scales =[1, 0.9, 0.7, 0.5, 0.3]\n",
"images =[]\n",
"for s in scales:\n",
" images.append(transform.rescale(im100, (s,s)))\n",
" #images.append(sm.imresize(im100, (s,s), interp='bilinear', mode=None)\n",
"\n",
"i = 1\n",
"title = iter(scales)\n",
"for im in images:\n",
" plt.subplot(int('15'+str(i)),frameon=False, xticks=[], yticks=[])\n",
" plt.title(title.next())\n",
" display = pm.overlay(im>0, red = mh.thin(im>0)>0)\n",
" plt.imshow(display)\n",
" i = i+1 \n",
"print len(images) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5\n"
]
},
{
"output_type": "display_data",
"png": 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OPV63jgfxUlI46mrKFMDTE0+ePMGa2Fj8/McfSNfnDbYIY/0vRYuPj4/efhT6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\nREQyuhISEhIiIhldCQkJCRH5P80wpJPNIcfyAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Show the end-points spectrum"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scales =[1, 0.9, 0.7, 0.5, 0.3]\n",
"for im in zip(images, scales):\n",
" plt.xlim(75, 101)\n",
" plt.gca().invert_xaxis()\n",
" normstp, spec = normedEPS(im[0]>0)\n",
" plt.title('End-points Spectrum')\n",
" plt.step(normstp, spec)\n",
" #print 'scale:',im[1],' feat vect:',EPSpectrumToFeatures(normstp, spec)\n",
"#a = '100\\left(1-\\frac{pruning}{skel \\; size} \\right)'\n",
"plt.xlabel(r'normed pruning steps $ 100 \\left(1-\\frac{pruning}{skel \\; size} \\right)$', fontsize=16)#\n",
"plt.ylabel('end-points')\n",
"plt.legend(scales, title=\"scale factor\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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kopgAiIhMFBMAEZGJYgIgIjJRTABERCaKCYCIyEQxARARmSgmACIiE6XXBDB27Fg4Ojqq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\n"
}
],
"prompt_number": 10
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"comparison of several hands endpoints spectrum"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hands = ['hand','hand-10','hand-90','hand-bent1','hand-bent2','sketch-hand']\n",
"for h in handsImg:\n",
" hand = Shape(h)\n",
" plt.xlim(75, 101)\n",
" plt.gca().invert_xaxis()\n",
" spec = hand.Normed_EPS()\n",
" normstp = spec[0]\n",
" ep = spec[1]\n",
" plt.title('End-points Spectrum')\n",
" plt.step(normstp, ep)\n",
"plt.legend(hands)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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hxhM3+WLTdGW3BxRv/3Ivy7O1L+lGpGQcT3zwYHk2IiJiiBMRKRlDnIhIwRRX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\n"
}
],
"prompt_number": 11
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"\n",
" "
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Extraction of feature vector"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"plt.imshow(handsImg[0])\n",
"h0 = Shape(handsImg[0])\n",
"pr = h0.End_Points_Spectrum()[0]\n",
"ep = h0.End_Points_Spectrum()[1]\n",
"print h0.skelsize\n",
"eps = h0.End_Points_Spectrum()\n",
"neps = h0.Normed_EPS()\n",
"feat = step(eps[1])\n",
"nfeat =[100*(1-f/(1.0*h0.skelsize)) for f in feat]\n",
"nfeat =np.asarray(nfeat)\n",
"nfeat[np.where(nfeat>100)]=-1\n",
" \n",
"plt.subplot(121)\n",
"plt.xlim((-2,100))\n",
"plt.step(eps[0],eps[1])\n",
"plt.scatter(eps[0],eps[1])\n",
"plt.scatter(feat, [-0.5]*len(feat),c='red',marker='o')\n",
"\n",
"plt.subplot(122)\n",
"plt.xlim((80,101))\n",
"plt.gca().invert_xaxis()\n",
"plt.step(neps[0],neps[1])\n",
"plt.scatter(nfeat, [-0.5]*len(nfeat),c='red',marker='o')\n",
"plt.scatter(neps[0],neps[1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"490\n"
]
},
{
"output_type": "pyout",
"prompt_number": 16,
"text": [
"<matplotlib.collections.PathCollection at 0xb83a44c>"
]
},
{
"output_type": "display_data",
"png": 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njFMreUAKkA2cAFKB477HnTFOPc4AbiMuLoGdOzfRvHnzSOxGuVU0x7SYm8T+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\nc6WUMoH/H/u3+fgQ4duwAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print h0.End_Points_Features(normed=False)\n",
"print h0.End_Points_Features(normed=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[87, 86, 85, 69, 59, 57, 54, 30, 12, 4, -1, -1, 2, 1]\n",
"[ 82.24489796 82.44897959 82.65306122 85.91836735 87.95918367\n",
" 88.36734694 88.97959184 93.87755102 97.55102041 99.18367347 -1. -1.\n",
" 99.59183673 99.79591837]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Load silhouette images\n",
" "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import defaultdict\n",
"import skimage.io as io\n",
"silhouette216 = ['bird','Bone','brick','camel','car','children','classic','elephant','face',\n",
"'fork','fountain','Glas','hammer','Heart','key','Misk','ray','turtle']\n",
"\n",
"user=os.path.expanduser(\"~\")\n",
"workdir=os.path.join(user,\"Images\",\"shapes216\")\n",
"\n",
"filesname =defaultdict(list)\n",
"for files in os.listdir(workdir):\n",
" for silhouette in silhouette216:\n",
" if files.startswith(silhouette):\n",
" filesname[silhouette].append(files)\n",
"#print filesname['fork']\n",
"\n",
"images_collections =defaultdict(list)\n",
"for silhouette in silhouette216:\n",
" collection = io.ImageCollection(os.path.join(workdir,silhouette+'*'))\n",
" print silhouette,':%s images added'%len(collection),collection[0].shape, collection[0].dtype\n",
" images_collections[silhouette].append(collection)\n",
"\n",
"print type(images_collections['bird'])\n",
"print type(images_collections['bird'][0]), len(images_collections['bird'][0])\n",
"print type(images_collections['bird'][0][0])\n",
" \n",
" \n",
"\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"bird :12 images added "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(108, 115) uint8\n",
"Bone :12 images added (127, 141) uint8\n",
"brick :12 images added "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(57, 113) uint8\n",
"camel :12 images added "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(108, 115) uint8\n",
"car :12 images added "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(63, 119) uint8\n",
"children :12 images added (123, 126) uint8\n",
"classic :12 images added "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(52, 115) uint8\n",
"elephant :12 images added (115, 115) uint8\n",
"face :12 images added (130, 109) uint8\n",
"fork :12 images added (42, 115) uint8\n",
"fountain :12 images added "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(90, 109) uint8\n",
"Glas :12 images added (100, 100) uint8\n",
"hammer :12 images added (47, 115) uint8\n",
"Heart :12 images added (95, 113) uint8\n",
"key :12 images added (60, 116) uint8\n",
"Misk :12 images added "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(129, 129) uint8\n",
"ray :12 images added (100, 138) uint8\n",
"turtle :12 images added (80, 115) uint8\n",
"<type 'list'>\n",
"<class 'skimage.io.collection.ImageCollection'> 12\n",
"<class 'skimage.io._io.Image'>\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Make a feature vector for each silhouette\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"feature_vectors_collection =defaultdict(list)\n",
"for silhouette in silhouette216:\n",
" print silhouette\n",
" for image in images_collections[silhouette][0]:\n",
" shape = Shape(image)\n",
" feature_vectors_collection[silhouette].append(shape.End_Points_Features(normed = True))\n",
"\n",
"print type(feature_vectors_collection['bird'])\n",
"print type(feature_vectors_collection['bird'][0]), len(feature_vectors_collection['bird'][0])\n",
"print type(feature_vectors_collection['bird'][0][0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"bird\n",
"Bone"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"brick"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"camel"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"car"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"children"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"classic"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"elephant"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"face"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"fork"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"fountain"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Glas"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"hammer"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Heart"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"key"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Misk"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"ray"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"turtle"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"<type 'list'>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"<type 'numpy.ndarray'> 12\n",
"<type 'numpy.float64'>\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#f = feature_vectors_collection['bird'][0]\n",
"#plt.scatter(f, [-0.5]*len(f),c='red',marker='o')\n",
"import itertools\n",
"y = 0\n",
"#silh = reversed(silhouette216)\n",
"colors = itertools.cycle([\"r\", \"b\", \"g\",\"c\",\"m\",\"y\"])\n",
"\n",
"plt.figure(num=None, figsize=(8, 16), dpi=80, facecolor='w', edgecolor='k')\n",
"plt.subplot(111,yticks=[])\n",
"plt.ylim((-1,220))\n",
"plt.xlim((85,102))\n",
"plt.gca().invert_xaxis()\n",
"plt.title('Aligned feature vectors from 18 family of shapes')\n",
"h=1\n",
"\n",
"for silhouette in silhouette216:\n",
" n=1\n",
" col=next(colors)\n",
" #plt.legend(silhouette216, loc='center left', bbox_to_anchor=(1, 0.5))\n",
" #print silhouette, 6+12*(h-1)\n",
" plt.text(101.5,6+12*(h-1), silhouette)\n",
" h = h+1\n",
" for feat in feature_vectors_collection[silhouette]:\n",
" plt.scatter(feat, [y]*len(feat),s=8,c=col,marker='x')\n",
" n=n+1\n",
" y = y+1\n",
" \n",
"print y, y/12.0"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"216 18.0\n"
]
},
{
"output_type": "display_data",
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LdPQkhg17OWK739/I3r2XoNUm4vXWMH78Ng4evJ5g0IPXW8mYMZ9iMo2MKFNb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OAAAg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cDud5hvtUK8DezB4F8gK0WtUK/+v+P5gbmpc7ZpjbMHTd2BUA+S/NDc0xdOdQLA5YjJMTTtb1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uD9nBx8Ex2NhwMDscISizonLw/TIyLwbGgolhQVYVN8PP4sK8O4wEB8HBmJz3JzsS4uDlla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\n"
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"birds = images_collections['bird'][0]\n",
"print type(birds), len(birds),type(birds[0])\n",
"i=1\n",
"#for l in range(1,4):\n",
" # for c in range (1,5):\n",
" # plt.subplot(l,c,i)\n",
" # plt.imshow(birds[i-1])\n",
" # i = i+1\n",
"index = [i for i in range(1,13)]\n",
"\n",
"for bird, im in zip(birds, index):\n",
" plt.subplot(3,4,im, frameon=False, xticks=[], yticks=[])\n",
" plt.imshow(bird<128)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class 'skimage.io.collection.ImageCollection'> 12 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class 'skimage.io._io.Image'>\n"
]
},
{
"output_type": "display_data",
"png": 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parSEYhnBTPqylL4spQn7DNPh0UHaGrpJwcr9WKKKIEFBNtKRVuygGmeMllIs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\nlTnv1piurNVIjcBFD1bxJB8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}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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