jeanpat/HMT-SE.ipynb

## HMT-SE.ipynb
{
 "metadata": {
  "name": "Untitled5"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import mahotas as mh\nfrom mahotas import polygon\nimport pymorph as pm\n\nimport networkx as nx\n\nfrom scipy import ndimage as nd\nimport skimage.transform as transform\nimport skimage.io as sio\nimport scipy.misc as sm\n\nimport os\nimport math",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def branchedPoints(skel, showSE=True):\n    X=[]\n    #cross X\n    X0 = np.array([[0, 1, 0], \n                   [1, 1, 1], \n                   [0, 1, 0]])\n    X1 = np.array([[1, 0, 1], \n                   [0, 1, 0], \n                   [1, 0, 1]])\n    X.append(X0)\n    X.append(X1)\n    #T like\n    T=[]\n    #T0 contains X0\n    T0=np.array([[2, 1, 2], \n                 [1, 1, 1], \n                 [2, 2, 2]])\n            \n    T1=np.array([[1, 2, 1], \n                 [2, 1, 2],\n                 [1, 2, 2]])  # contains X1\n  \n    T2=np.array([[2, 1, 2], \n                 [1, 1, 2],\n                 [2, 1, 2]])\n    \n    T3=np.array([[1, 2, 2],\n                 [2, 1, 2],\n                 [1, 2, 1]])\n    \n    T4=np.array([[2, 2, 2],\n                 [1, 1, 1],\n                 [2, 1, 2]])\n    \n    T5=np.array([[2, 2, 1], \n                 [2, 1, 2],\n                 [1, 2, 1]])\n    \n    T6=np.array([[2, 1, 2],\n                 [2, 1, 1],\n                 [2, 1, 2]])\n    \n    T7=np.array([[1, 2, 1],\n                 [2, 1, 2],\n                 [2, 2, 1]])\n    T.append(T0)\n    T.append(T1)\n    T.append(T2)\n    T.append(T3)\n    T.append(T4)\n    T.append(T5)\n    T.append(T6)\n    T.append(T7)\n    #Y like\n    Y=[]\n    Y0=np.array([[1, 0, 1], \n                 [0, 1, 0], \n                 [2, 1, 2]])\n    \n    Y1=np.array([[0, 1, 0], \n                 [1, 1, 2], \n                 [0, 2, 1]])\n    \n    Y2=np.array([[1, 0, 2], \n                 [0, 1, 1], \n                 [1, 0, 2]])\n    \n    Y2=np.array([[1, 0, 2], \n                 [0, 1, 1], \n                 [1, 0, 2]])\n    \n    Y3=np.array([[0, 2, 1], \n                 [1, 1, 2], \n                 [0, 1, 0]])\n    \n    Y4=np.array([[2, 1, 2], \n                 [0, 1, 0], \n                 [1, 0, 1]])\n    Y5=np.rot90(Y3)\n    Y6 = np.rot90(Y4)\n    Y7 = np.rot90(Y5)\n    Y.append(Y0)\n    Y.append(Y1)\n    Y.append(Y2)\n    Y.append(Y3)\n    Y.append(Y4)\n    Y.append(Y5)\n    Y.append(Y6)\n    Y.append(Y7)\n    \n    bp = np.zeros(skel.shape, dtype=int)\n    for x in X:\n        bp = bp + mh.morph.hitmiss(skel,x)\n    for y in Y:\n        bp = bp + mh.morph.hitmiss(skel,y)\n    for t in T:\n        bp = bp + mh.morph.hitmiss(skel,t)\n        \n    if showSE==True:\n        fig = plt.figure(figsize=(4,5))\n        tX =['X0','X1']\n        tY =['Y'+str(i) for i in range(0,8)]\n        tT =['T'+str(i) for i in range(0,8)]\n        ti= tX+tY+tT\n        SE=X+Y+T\n        print len(SE), len(ti)\n        n = 1\n        ti = iter(ti)\n        for se in SE:\n            #print next(ti)\n            #print se\n            mycmap = mpl.colors.ListedColormap(['black','blue','red'])\n            ax = fig.add_subplot(4,5,n,frameon=False, xticks=[], yticks=[])\n            title(str(next(ti)))\n            imshow(se, interpolation='nearest',vmin=0,vmax=2,cmap=mycmap)\n            n = n+1\n        fig.subplots_adjust(hspace=0.1,wspace=0.08)\n        #ax_cb = fig.add_axes([.9,.25,.1,.3])#\n        color_vals=[0,1,2]\n        #cb = mpl.colorbar.ColorbarBase(ax_cb,cmap=mycmap, ticks=color_vals)\n        #cb.set_ticklabels(['back', 'hit', 'don\\'t care'])\n        \n        plt.show()\n    return bp\n\ndef endPoints(skel):\n    endpoint1=np.array([[0, 0, 0],\n                        [0, 1, 0],\n                        [2, 1, 2]])\n    \n    endpoint2=np.array([[0, 0, 0],\n                        [0, 1, 2],\n                        [0, 2, 1]])\n    \n    endpoint3=np.array([[0, 0, 2],\n                        [0, 1, 1],\n                        [0, 0, 2]])\n    \n    endpoint4=np.array([[0, 2, 1],\n                        [0, 1, 2],\n                        [0, 0, 0]])\n    \n    endpoint5=np.array([[2, 1, 2],\n                        [0, 1, 0],\n                        [0, 0, 0]])\n    \n    endpoint6=np.array([[1, 2, 0],\n                        [2, 1, 0],\n                        [0, 0, 0]])\n    \n    endpoint7=np.array([[2, 0, 0],\n                        [1, 1, 0],\n                        [2, 0, 0]])\n    \n    endpoint8=np.array([[0, 0, 0],\n                        [2, 1, 0],\n                        [1, 2, 0]])\n    \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\ndef 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",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 149
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a = np.array([[0,0,0,0,0,0],\n              [0,0,1,0,1,0],\n              [0,1,1,0,1,0],\n              [0,0,0,1,0,0],\n              [0,0,1,0,1,0],\n              [0,1,0,0,0,0]])\nlab,_ = mh.label(a>0)\nsk =mh.thin(a)\nprint a.dtype, sk.dtype\nbp = branchedPoints(a>0)\nh = mh.labeled.labeled_size(bp)\nep = endPoints(a)\nsubplot(141)\ntitle('skeleton')\nimshow(a,interpolation='nearest')\n\nsubplot(142)\ntitle('label')\nimshow(lab,interpolation='nearest')\nsubplot(143)\ntitle('junction')\nimshow(bp,interpolation='nearest')\nsubplot(144)\ntitle('end-points')\nimshow(ep,interpolation='nearest')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "int32 int32\n18 18\n"
      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "pyout",
       "prompt_number": 158,
       "text": "<matplotlib.image.AxesImage at 0x17191b2c>"
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 158
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "mh.__version__",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 159,
       "text": "'1.0'"
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}