keras_retinanet checking random_transform

check_augumentation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T02:00:26.047854Z",
     "start_time": "2017-12-14T02:00:25.991474Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/isisilon/.pyenv/versions/3.6.0/envs/jupyter3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['seed']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "from keras_retinanet.utils.image import random_transform\n",
    "import keras.preprocessing.image\n",
    "import keras_retinanet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T01:54:24.835627Z",
     "start_time": "2017-12-14T01:54:24.832440Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T02:00:26.371527Z",
     "start_time": "2017-12-14T02:00:26.367467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T02:00:26.546137Z",
     "start_time": "2017-12-14T02:00:26.542752Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create image data generator objects\n",
    "train_image_data_generator = keras.preprocessing.image.ImageDataGenerator(\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T02:00:26.751903Z",
     "start_time": "2017-12-14T02:00:26.743468Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 3, 4]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test data\n",
    "image = np.zeros((10,10,3))\n",
    "image[1,1]=1\n",
    "image[3,2]=0.5\n",
    "image[4,1]=0.25\n",
    "\n",
    "x,y = np.argwhere(image.mean(-1)==1).T\n",
    "boxes = np.array([x-1,y-1,x+2,y+3]).T\n",
    "boxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T02:00:27.085147Z",
     "start_time": "2017-12-14T02:00:26.951477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f487607b668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot test data\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.collections import PatchCollection\n",
    "\n",
    "def plot_boxes(image, boxes, ax=None):\n",
    "    if ax is None:\n",
    "        ax=plt.gca()\n",
    "    ax.imshow(image, origin='lower')\n",
    "    \n",
    "    polygons = []\n",
    "    for x1,y1,x2,y2 in boxes:\n",
    "        p = mpatches.Rectangle(\n",
    "                    xy=[x1,y1],\n",
    "                    width=x2-x1,\n",
    "                    height=y2-y1,\n",
    "                )  \n",
    "        polygons.append(p)\n",
    "    collection = PatchCollection(patches=polygons, facecolor='none', edgecolor='blue')\n",
    "    ax.add_collection(collection)\n",
    "\n",
    "plot_boxes(image, boxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T02:00:28.626204Z",
     "start_time": "2017-12-14T02:00:27.183770Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875c77c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875ee3b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f487617fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875bf50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875f36a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875e73438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f487bb08e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875e56128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875d65518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875dbaef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4876103d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f487bb08940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for seed in range(12):\n",
    "    image2, boxes2 = random_transform(image.copy(), boxes.copy(), train_image_data_generator, seed=seed)\n",
    "    plot_boxes(image2, boxes2)\n",
    "    plt.title(str(seed))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T02:00:28.649129Z",
     "start_time": "2017-12-14T02:00:27.380Z"
    }
   },
   "outputs": [],
   "source": [
    "boxes, boxes2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T01:59:48.073884Z",
     "start_time": "2017-12-04T01:59:48.071640Z"
    }
   },
   "source": [
    "# How to get bboxes to be the same side before and after?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T01:57:12.007110Z",
     "start_time": "2017-12-14T01:57:12.004134Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create image data generator objects\n",
    "train_image_data_generator = keras.preprocessing.image.ImageDataGenerator(\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T01:57:12.397574Z",
     "start_time": "2017-12-14T01:57:12.347612Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def random_transform(\n",
    "    image,\n",
    "    boxes,\n",
    "    image_data_generator,\n",
    "    seed=None\n",
    "):\n",
    "    if seed is None:\n",
    "        seed = np.random.randint(10000)\n",
    "\n",
    "    image = image_data_generator.random_transform(image, seed=seed)\n",
    "\n",
    "    # set fill mode so that masks are not enlarged\n",
    "    fill_mode = image_data_generator.fill_mode\n",
    "    image_data_generator.fill_mode = 'constant'\n",
    "\n",
    "    for index in range(boxes.shape[0]):\n",
    "        # generate box mask and randomly transform it\n",
    "        mask = np.zeros_like(image, dtype=np.uint8)\n",
    "        b = boxes[index, :4].astype(int)\n",
    "\n",
    "        assert(b[0] < b[2] and b[1] < b[3]), 'Annotations contain invalid box: {}'.format(b)\n",
    "        assert(b[2] < image.shape[1] and b[3] < image.shape[0]), 'Annotation ({}) is outside of image shape ({}).'.format(b, image.shape)\n",
    "\n",
    "        mask[b[1]:b[3], b[0]:b[2], :] = 255\n",
    "        mask = image_data_generator.random_transform(mask, seed=seed)[..., 0]\n",
    "        mask = mask.copy()  # to force contiguous arrays\n",
    "\n",
    "        # find bounding box again in augmented image\n",
    "        [i, j] = np.where(mask == 255)\n",
    "        boxes[index, 0] = float(min(j))\n",
    "        boxes[index, 1] = float(min(i))\n",
    "        boxes[index, 2] = float(max(j))\n",
    "        boxes[index, 3] = float(max(i))\n",
    "\n",
    "    # restore fill_mode\n",
    "    image_data_generator.fill_mode = fill_mode\n",
    "\n",
    "    return image, boxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-14T01:57:13.459272Z",
     "start_time": "2017-12-14T01:57:12.876287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875c8aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4875cfc080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAEICAYAAAByNDmmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACiJJREFUeJzt3UHIZXd5x/Hvz3lj40QbrYJoEswsqm0QJPJSoimmmAha\ng+miiwgKdTMuWh2lRVppibPoTopZFGFIKgVHRSZZiKh1oYRuOjiZBOPM2GJjTCZGTBuqQaRJyOPi\nvZYxdu4977zneO593u9nNe+d/z3zEN5vzrnnnntuqgpJPb1o7gEkTcfApcYMXGrMwKXGDFxqzMCl\nxgxcaszA96kkv5Xk7iQ/SPJ0kgeTvGvuuTQuA9+/toDHgJuAK4G/Bb6Y5NoZZ9LI4pVs+qUk3waO\nVtU9c8+icbgHFwBJXg28Hjgz9ywaj3twkeQy4KvAf1bVB+eeR+Mx8H0uyYuAzwG/DdxWVc/OPJJG\ntDX3AJpPkgB3A68G/ti4+zHw/e3TwO8Dt1TVz+ceRuPzEH2fSvI64BHgf4HnLvirD1bV8VmG0ugM\nXGrMt8mkxgxcaszApcYMXGpskrfJknjmTppYVWXVGvfgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMG\nLjVm4FJjBi415h1d1Nx/A78z9xADPQW8ctQtTnLDB69F1/ooYOUl22tid7N6Lbq0zxm41JiBS40Z\nuNSYgUuNGbjUmIFLjRm41JiBS415qaq0MNXXeO18ies83INLjRm41JiBS40ZuNSYgUuNGbjUmIFL\njQ0KPMlHk5xJ8p0kn09y+dSDSdq7lYEnuQr4MLBdVW8EDgC3Tz2YpL0beoi+BbwkyRZwEPjhdCNJ\nGsvKwKvqceCTwKPAE8BPqurrL1yX5HCSU0lOjT+mpEsx5BD9FcBtwCHgtcAVSd73wnVVdayqtqtq\ne/wxJV2KIYfotwDfr6onq+pZ4F7grdOOJWkMQwJ/FLghycHsfCzmZuDctGNJGsOQ1+AngRPAaeCh\nxXOOTTyXpBH4zSZqbvi3hcz/eXC/2UTSLhi41JiBS40ZuNSYgUuNeVdVaWHOu59OxT241JiBS40Z\nuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm4\n1JiBS41500VtpDvuuGPQuqNHd7P26F5GWkvuwaXGDFxqzMClxgxcaszApcYMXGrMwKXGBgWe5OVJ\nTiT5bpJzSd4y9WCS9m7ohS53Al+rqj9N8mLg4IQzSRrJysCTXAm8DfgzgKp6Bnhm2rEkjWHIHvwQ\n8CTwmSRvAu4HjlTVzy5clOQwcHj8EaVLd/nlP+fo0U8MXD103VSeGn2LqarlC5Jt4N+AG6vqZJI7\ngZ9W1d8tec7yjUp7NPT68t3YtGvRqyqr1gw5yXYeOF9VJxc/nwDevJfBJP1mrAy8qn4EPJbkDYuH\nbgbOTjqVpFEMPYv+IeD44gz6w8AHphtJ0lgGBV5VDwLbE88iaWReySY1ZuBSYwYuNWbgUmMGLjW2\n8kq2S9qoV7JpA910002TbPe+++6bZLtjXckmaUMZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuN\nGbjUmIFLjRm41JiBS40ZuNSYgUuNGbjUmIFLjRm41JiBS40ZuNSYgUuNeVdVaUN5V1VpnzNwqTED\nlxozcKkxA5caM3CpMQOXGhsceJIDSR5I8uUpB5I0nt3swY8A56YaRNL4BgWe5Grg3cBd044jaUxD\n9+CfAj4GPH+xBUkOJzmV5NQok0nas5WBJ7kV+HFV3b9sXVUdq6rtqtoebTpJezJkD34j8J4kjwBf\nAN6e5LOTTiVpFLv6NFmSPwL+qqpuXbHOT5NJE/PTZNI+5+fBpQ3lHlza5wxcaszApcYMXGrMwKXG\nDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYM\nXGrMwKXGDFxqzMClxgxcaszApcYMXGrMwKXGDFxqzMClxgxcaszApcYMXGpsZeBJrknyzSRnk5xJ\ncuQ3MZikvUtVLV+QvAZ4TVWdTvIy4H7gT6rq7JLnLN+opD2rqqxas3IPXlVPVNXpxZ+fBs4BV+19\nPElT29rN4iTXAtcDJ/+fvzsMHB5lKkmjWHmI/n8Lk5cC9wF/X1X3rljrIbo0sVEO0QGSXAbcAxxf\nFbek9THkJFuAfwaeqqqPDNqoe3BpckP24EMC/0PgX4GHgOcXD3+8qr6y5DkGLk1slMAvhYFL0xvt\nNbikzWTgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJj\nBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMGLjVm4FJjBi41ZuBSYwYuNWbgUmMG\nLjVm4FJjBi41NijwJO9M8u9Jvpfkr6ceStI4UlXLFyQHgP8A3gGcB74FvLeqzi55zvKNStqzqsqq\nNUP24H8AfK+qHq6qZ4AvALftdThJ0xsS+FXAYxf8fH7x2K9IcjjJqSSnxhpO0t5sjbWhqjoGHAMP\n0aV1MWQP/jhwzQU/X714TNKaGxL4t4DfTXIoyYuB24EvTTuWpDGsPESvqueS/AXwL8AB4J+q6szk\nk0nas5Vvk13SRn0NLk1urLfJJG0oA5caM3CpMQOXGjNwqbHRrmR7gf8CfjBg3asWazfFJs27SbPC\nZs27DrO+bsiiSd4mGyrJqaranm2AXdqkeTdpVtiseTdpVg/RpcYMXGps7sCPzfzv79YmzbtJs8Jm\nzbsxs876GlzStObeg0uakIFLjc0W+KbcqTXJNUm+meRskjNJjsw90xBJDiR5IMmX555lmSQvT3Ii\nyXeTnEvylrlnWibJRxe/B99J8vkkl8890zKzBL64U+s/Au8CrgPem+S6OWYZ4DngL6vqOuAG4M/X\neNYLHQHOzT3EAHcCX6uq3wPexBrPnOQq4MPAdlW9kZ37I9w+71TLzbUH35g7tVbVE1V1evHnp9n5\nBfy1m06ukyRXA+8G7pp7lmWSXAm8DbgboKqeqar/mXeqlbaAlyTZAg4CP5x5nqXmCnzQnVrXTZJr\ngeuBk/NOstKngI8Bz889yAqHgCeBzyxeTtyV5Iq5h7qYqnoc+CTwKPAE8JOq+vq8Uy3nSbaBkrwU\nuAf4SFX9dO55LibJrcCPq+r+uWcZYAt4M/Dpqroe+BmwzudjXsHOkeYh4LXAFUneN+9Uy80V+Ebd\nqTXJZezEfbyq7p17nhVuBN6T5BF2Xvq8Pcln5x3pos4D56vql0dEJ9gJfl3dAny/qp6sqmeBe4G3\nzjzTUnMFvjF3ak0Sdl4jnquqf5h7nlWq6m+q6uqqupad/67fqKq13MtU1Y+Ax5K8YfHQzcBFvxJr\nDTwK3JDk4OL34mbW+KQgTPdx0aU27E6tNwLvBx5K8uDisY9X1VdmnKmTDwHHF/+jfxj4wMzzXFRV\nnUxyAjjNzrsrD7Dml616qarUmCfZpMYMXGrMwKXGDFxqzMClxgxcaszApcZ+AeohdlQC0QRrAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f48760a7e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     },
     "metadata": {},
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    },
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f487609c828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for seed in range(5):\n",
    "    image2, boxes2 = random_transform(image.copy(), boxes.copy(), train_image_data_generator, seed=seed)\n",
    "    plot_boxes(image2, boxes2)\n",
    "    plt.title(str(seed))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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