Finding+Centers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Generate seg and flow images\n",
    "\n",
    "Same code as TimeLapseData2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "import time\n",
    "import copy\n",
    "import numpy as np\n",
    "from skimage.filters import gaussian\n",
    "import cv2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "\n",
    "%matplotlib inline\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (15, 10)\n",
    "\n",
    "from tifffile import imread, imsave\n",
    "import cPickle as pickle\n",
    "\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from netsurface2d import NetSurf2d\n",
    "from netsurface2dt import NetSurf2dt\n",
    "from data3d import Data3d\n",
    "\n",
    "from shapely.geometry import Point\n",
    "from shapely.geometry.polygon import Polygon\n",
    "\n",
    "# filename = '/Users/jug/Repositories/GIT/BobSeg/TestLapseData2d_151119-nmy2GFP-tagRfpPH-wt-04_w1LMM_488+488_LP_myTestCrop_bgrem.tif'\n",
    "# filename = '/Users/jug/Dropbox/ProjectGoldstein/4MyosinMembraneWholeEmbryo.tif'\n",
    "filename = '/Users/jug/Repositories/GIT/BobSeg/data/MAX_LP320-06-08_Concat-51-100.tif'\n",
    "#filename = '/home/hp/git/BobSeg/data/MAX_LP320-06-08_Concat-1-50.tif'\n",
    "\n",
    "\n",
    "stack = imread(filename)\n",
    "\n",
    "segchannel = stack[:,1,:,:]\n",
    "flowchannel = stack[:,0,:,:]\n",
    "sigma = [0.,2.,2.]\n",
    "segchannel_smooth = gaussian(segchannel,sigma)\n",
    "\n",
    "max_rs = (segchannel[0].shape[1] / 4,\n",
    "          segchannel[0].shape[1] / 4)\n",
    "\n",
    "min_rs = (25,25)\n",
    "\n",
    "data = Data3d( segchannel_smooth, pixelsize=(1.,1.), silent=False )\n",
    "data.set_seg_params(num_columns=72,K=100,max_delta_k=4)\n",
    "objects = []\n",
    "\n",
    "cell = data.init_object(\"cell 1\")\n",
    "objects.append(cell)\n",
    "\n",
    "data.add_object_at( cell, min_rs, max_rs, frame=0, seed=(170,250), segment_it=True )\n",
    "data.track(cell, seed_frame=0, target_frames=range(1,len(data.images)), recenter_iterations=1)\n",
    "\n",
    "data.segment2dt( cell, max_radial_delta=2 );\n",
    "\n",
    "segimgs = data.create_segmentation_image()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Display segmented boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14efd28d0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cxfMfBK6fT3MDnwR+Amyi9u6hxVP/vgCPA9eU7cXlOFUwaz8wClwHPFr+Q9O2mau+xF0N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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14eeecc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (4, 5)\n",
    "imshow(segimgs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import flow and segment images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ". . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...done!\n"
     ]
    }
   ],
   "source": [
    "data.compute_flow( flowchannel );\n",
    "\n",
    "seg = segimgs\n",
    "flow = np.swapaxes(np.swapaxes(data.flows,1,3),2,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Dispalying Flow in X and Y for first frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14e34ea90>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ji6ORb7VVwXnBeULeN8Z4oFP3vebVeLko8X1oMYTxdlR/x+XbRbH+k9CJnbHW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\nfL+OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14eeec990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow_x = np.swapaxes(flow,0,1)[0]\n",
    "flow_y = np.swapaxes(flow,0,1)[1]\n",
    "\n",
    "pylab.rcParams['figure.figsize'] = (6, 4)\n",
    "fig = plt.figure()\n",
    "fig.suptitle('flows in X and Y', fontsize=16)\n",
    "ax = fig.add_subplot(121)\n",
    "ax.imshow(flow_x[1], plt.get_cmap('gray'))\n",
    "ax = fig.add_subplot(122)\n",
    "ax.imshow(flow_y[1], plt.get_cmap('gray'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting boolean array of interior points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cell_interior(fid, oid=0):\n",
    "    in_points = []\n",
    "    polygon = Polygon(data.get_result_polygone_2dt(oid, fid))\n",
    "    size_x, size_y = shape(flow_x[0])[0], shape(flow_x[0])[1]\n",
    "    flag = numpy.full((size_x, size_y), False, dtype=bool)\n",
    "\n",
    "    for i in range(size_x):\n",
    "        for j in range(size_y):\n",
    "            point = Point(j,i)\n",
    "            flag[i][j] = polygon.contains(point)\n",
    "            if flag[i][j]:\n",
    "                in_points.append((j,i))\n",
    "    return in_points, flag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Random points and recording their trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_start_end(fig, points):\n",
    "    ax=fig.add_subplot(1,1,1)\n",
    "    st_en = []\n",
    "    ax.imshow(flag, plt.get_cmap('gray'))\n",
    "\n",
    "    st_en.append((points[0][0], points[0][1]))\n",
    "    st_en.append((points[-1][0], points[-1][1]))\n",
    "                \n",
    "    ax.scatter(points[0][0], points[0][1])\n",
    "    ax.scatter(points[-1][0], points[-1][1], c='r', s= 40, marker= 'o' )\n",
    "    \n",
    "    ax.plot(*zip(*st_en))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_trajectory(fig, points, color='#00FF00'):\n",
    "    ax=fig.add_subplot(1,1,1)\n",
    "    segment = []\n",
    "    ax.imshow(flag, plt.get_cmap('gray'))\n",
    "\n",
    "    for i in range(len(points)-1):\n",
    "        segment.append((points[i][0], points[i][1]))\n",
    "        segment.append((points[i+1][0], points[i+1][1]))\n",
    "                \n",
    "        if i==0:\n",
    "            ax.scatter(points[i][0], points[i][1], color=color, s=20, marker= 'o')\n",
    "        #ax.scatter(points[i+1][0], points[i+1][1], color=color, s=10, marker= 'x' )\n",
    "        if i==len(points)-2:\n",
    "            ax.scatter(points[i+1][0], points[i+1][1], c='r', s=40, marker= 'o' )\n",
    "    \n",
    "        ax.plot(*zip(*segment), color=color, linewidth=.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_trajectories(flow_x, flow_y, rand_p, epochs=100):\n",
    "    traj  = []\n",
    "    for k in range(len(rand_p)):\n",
    "        pos_x = in_points[rand_p[k]][1]\n",
    "        pos_y = in_points[rand_p[k]][0]\n",
    "        int_x = int(round(pos_x))\n",
    "        int_y = int(round(pos_y))\n",
    "        epoch = 0\n",
    "        temp = []\n",
    "        while flag[int_x][int_y] and epoch<epochs:\n",
    "            int_x = int(round(pos_x))\n",
    "            int_y = int(round(pos_y))\n",
    "            temp.append( ( int_y, int_x ) )\n",
    "            pos_x += flow_x[int_x][int_y]\n",
    "            pos_y -= flow_y[int_x][int_y]\n",
    "            epoch=epoch+1\n",
    "        traj.append(temp)\n",
    "        plot_trajectory(fig, temp, color=col)\n",
    "    return traj\n",
    "\n",
    "frame = 45\n",
    "in_points, flag = compute_cell_interior(frame)\n",
    "np.random.seed(79)\n",
    "rand_p = np.random.randint(0,len(in_points),20)\n",
    "traj = compute_trajectories(flow_x[frame], flow_y[frame], rand_p, epochs=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8JNuyLbuyy/nXgHKCC9aK/fuT++9PvvSl0eXMKcJXbkl27EhyLMmRjKYVk5x//mj56Y5W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\nABQTXAAAxf5/Z0W9q40MNpkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14ccf3ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (10, 16)\n",
    "fig=plt.figure()\n",
    "\n",
    "colors=[\"#FF00FF\", \"#00FF00\", \"#FF0066\", \"#00FF66\"]\n",
    "\n",
    "for i in range(len(traj)):\n",
    "    col = colors[i%len(colors)]\n",
    "    plot_trajectory(fig, traj[i][:], color=col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking whether interior points are correctly captured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14bb7ccd0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TFpGoAqbds3d9Pj/f93fdrnB/OPJeePTBPHXrtRN/POaT8AJgZh3Yc2+eTnLapR9LHbe2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14c951f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pylab.rcParams['figure.figsize'] = (10, 8)\n",
    "fig=plt.figure()\n",
    "ax=fig.add_subplot(1,2,1)\n",
    "ax.imshow(flag, plt.get_cmap('gray'))\n",
    "\n",
    "ax=fig.add_subplot(1,2,2)\n",
    "ax.imshow(flag, plt.get_cmap('gray'))\n",
    "ax.scatter(*zip(*in_points))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting start and end points of trajectory of random points\n",
    "\n",
    "start => blue in color \n",
    "end => red in color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1088a4810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.rcParams['figure.figsize'] = (40, 80)\n",
    "fig=plt.figure()\n",
    "for k in range(len(rand_p)):\n",
    "   \n",
    "    ax=fig.add_subplot(5,4,k+1)\n",
    "    st_en = []\n",
    "    ax.imshow(flag, plt.get_cmap('gray'))\n",
    "    temp = traj[k]\n",
    "    # put a blue dot at (10, 20)\n",
    "    st_en.append((temp[0][0], temp[0][1]))\n",
    "    st_en.append((temp[-1][0], temp[-1][1]))\n",
    "                \n",
    "    ax.scatter(temp[0][0], temp[0][1])\n",
    "    \n",
    "    # put a red dot, size 40, at len(temp) locations:\n",
    "    #plt.scatter(*zip(*temp), c='r', s=40)\n",
    "    \n",
    "    ax.scatter(temp[-1][0], temp[-1][1], c='r', s= 40, marker= 'o' )\n",
    "    \n",
    "    ax.plot(*zip(*st_en))\n",
    "    #ax.plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}