diatoms_detector/Create True - False data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy\n",
    "import pickle\n",
    "import skimage\n",
    "from scipy.misc import imread\n",
    "from xmljson import badgerfish as bf\n",
    "from xml.etree.ElementTree import fromstring\n",
    "from matplotlib import pyplot, rcParams\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def negative_regions(positive):\n",
    "    \"\"\"Estimate some negative rectangular regions, where positive is a bitmap marking diatomenes.\n",
    "    First extract 200 to 800 (randomly) consequtive rows from positive.  From that extract all column\n",
    "    sequences without positive marks. Yield those as a list of 2 slices.\"\"\"\n",
    "    for _ in range(3):\n",
    "        height = numpy.random.randint(200,800)\n",
    "        ymin = numpy.random.randint(0, positive.shape[0]-width)\n",
    "        ymax = ymin + height\n",
    "        has_diatomeen = positive[ymin:ymax].sum(0) == 0\n",
    "\n",
    "        for cont in cv2.findContours(\n",
    "            has_diatomeen[:,None].astype(numpy.uint8),\n",
    "            cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE\n",
    "        )[1]:\n",
    "            if len(cont) != 2:\n",
    "                continue\n",
    "            xmin, xmax = cont[0][0][1], cont[1][0][1]\n",
    "            if xmax - xmin > 200 and xmax - xmin < 800:\n",
    "                region = [slice(ymin, ymax), slice(xmin, xmax)]\n",
    "                assert positive[region].sum() == 0, \"Due to a bug, some positive regions were selected as negative.\"\n",
    "                yield region\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "negatives = []\n",
    "positives = []\n",
    "\n",
    "for annotation in os.listdir('annotations/'):\n",
    "    with open('annotations/' + annotation) as f:\n",
    "        data = bf.data(fromstring(f.read()))['annotation']\n",
    "        filename = data['filename']['$']\n",
    "        try:\n",
    "            image = imread('images/' + filename)\n",
    "        except FileNotFoundError:\n",
    "            continue\n",
    "        positive = numpy.zeros(image.shape[:2]).astype(numpy.bool)\n",
    "        for o in data['object'] if type(data['object'])==list else [data['object']]:\n",
    "            region = [\n",
    "                slice(o['bndbox']['ymin']['$'], o['bndbox']['ymax']['$']),\n",
    "                slice(o['bndbox']['xmin']['$'], o['bndbox']['xmax']['$']),\n",
    "            ]\n",
    "            positive[region] = 1\n",
    "            positives.append(image[region])\n",
    "        for region in negative_regions(positive):\n",
    "            negatives.append(image[region])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = negatives + positives\n",
    "y = [False] * len(negatives) + [True] * len(positives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/herbert/.virtualenvs/medical/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
      "  max_open_warning, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6909a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c42fbac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c41531d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74bf146780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74bf83d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74bff936a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c64ab978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c66424a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c7352e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c64cbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c71a1780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c4b91a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6727eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6eb2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6bee748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c69d5b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6df80f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c726a400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c78db828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c441ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABHEAAAElCAYAAABjz34HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzsvVmPZEl25/c7ZnYXX2PPraqytu5is8iGyOGQ4vBBggaad30IAQPoSQPMg6AHYQA9CfoCepA+gh4EPQoYYCSIJDQzzeHSZHd1V9ealZEZkREe4cvdzOzo4brHllmVlVXZXZU99ks4It3v9et3czezv/3POaKqJBKJRCKRSCQSiUQikUgkvt+Y73oHEolEIpFIJBKJRCKRSCQSzyeJOIlEIpFIJBKJRCKRSCQSrwBJxEkkEolEIpFIJBKJRCKReAVIIk4ikUgkEolEIpFIJBKJxCtAEnESiUQikUgkEolEIpFIJF4BkoiTSCQSiUQikUgkEolEIvEKkEScRCKRSCQSiUQikUgkEolXgCTiJBKJRCKRSCQSiUQikUi8AiQRJ5FIJBKJRCKRSCQSiUTiFSCJOIlEIpFIJBKJRCKRSCQSrwDuu96BRCKRSCQSiUQikUh8M2QoyvZ3vReJROJb85BjVT143mpJxEkkEolEIpFIJBKJV5Vt4J9/1zuRSCS+Nf+KT77OaimcKpFIJBKJRCKRSCQSiUTiFSA5cRKJxCtHsg0nEq8+w7MRy+VCvuv9SCQSiUQikXiVSCJOIpF49Ui24UTilWf3f937rnchkUgkfiv5H07/G0QEYwwx+mvLjLkeiGH1+ntjjNeeBzGo6sXj2rpqn3rv1XWj6z9LFAQw2v8f+nAQNQERuVj/6v9VFdRhjLl4fbN9EUFEiFzuz9XXb+7n11n+orRcP09Xz6uLN9d+MW7un8j1+Y64fro5n6KKoT82UehM96Xbbtz199orfwG8fLU8IM85dWpawIAawKzPuyGG/v5wrgTjwFqwhmgsxmaIdTgMxnryLJBlATEtooHoA74NNFGwEhha2B04HELbeaom4KMiq5YmGlbR0qolaH+tRQPECEaIMT7zPthcv82ym+dcQn9RI6ACWHNx96k8fc1vvv/L7rn/6fb/8tUn9EtIIk4ikUgkEolEIpFI/JagAghElELs+jUlmEikQ42CEUTA+uyacGIsN0QbB+jF47ro4vv/x/49xjgQg0aDophniBm6HtsGII8AigqEtSgTBdT0B5CpouqJF/u2EUrWA+J4XZC6KjQ5+ywhRC/e64M8Y/kV4lerFblc/2zRy/PClwzgv66IFM1XCwBOL5ergIqsJaW1WvYVQ/yse1rMUsCvN+nN5niuC28XZ147ohWiEYIBif11tmshyGv+1GeqRsSANdAaizGCkX6bViNWPSZGFIcQiRKIRJw1aADtQL3gAhijOG3JQiATg0YhBANeUOMh9ufZiKHrwlrEiYhqf3rW1+aqqLcRPK9dgytij4iAs5sFoIq5ekm+5HJuhEfg4jM2rz3rM1+EJOIkEolEIpH4jWMlpeVLJBKJXwdXB5+ydsOoKBZBoiKiwFrEsYIqFw/g2nODXhN1RC7FEq/XP+9F2ThKNn9FwfKlY+LnHvN1rm/laXfL0wLQteXfYB++DGvtxTkLIXyrwfvL5mlB6eVGOT/laLl6b14Rk1QVvXGvxRjRGInrRwgKXIoi/evCRmLq38fFtjafLUh/VF+xL8/j6nl6kfW/yffi65BEnEQi8crz3y//BSH01lGlAxQbBWuUzARCe44RyIsCKUqWforJ7tBtz8l9QRECQ1cxLluKbJd3X/8B793ZYuAiwzqjmj3k3z74lE+WNQbLD967y/u//zqT7QIOP+cn/+cv+dePWprJiN95/23+sz9+j1tblupnn/KLn3zIr8yQ0daIybAk88rSL2l9xAeICLk6TOboYoDM4rRX551zF41UZYuLxj/GiIkdEUsblS6A1jUiQttEghfatkV9pAsdQVuwYPIWZ3NUBRFL1II8GGIH3hhMDABr+3UkhEAdI973M21d12G7SIgNqrG3aBeOruuIMdJ1HcYFjAg2gq8btPXE3GBGgilgUgxQDfgVxDqHzjBjwtbBHr//u29x33V0jz2taXjUHvI4NGzfvsM//eN3uP87BZHbdESGv/gr/uYv/pL/90mO3R3yj//Q8o9+8IeE4RhL5OzP/5r/7X//B/7hzFFMRvz4R/f4T//oDj9+902kUx58dMT/9f99yL//Dx8xW4EZjnjnvS3+9Mf3+IN3d7m7O+XhgxP+4i8/5Ce/OOKjx56mDtx+7YA33h7yT//sdf7knTvUxys+Ozzh3/7DA/79zx/wyScrglqmexP+7J/c5r/4z3/Ie7cmrD474y//3Sf8H//333B4UrO0U954c5f/6r98lz/7vVtk3YC/+Dd/z7/+85/y6cMnPAkl5d6YP/qD3+f1W7fp2gWL81Nmx484enzI4XHF+dLzxrvv8oMfvcnd6QF1s8SHCh9WtG1F260I0eCyHJUM5xx5hOgDtVGcvz47JGQXzzcdJs/lbJW1liC9td3a9czuupOSZRkhBKy15CESxNARESwSFGMM/+35f33te+uy38CPQyKRSPxHyCYESUTo1nqBWTsrMmsxFwNcxbirAo5cGQivn3tAhBh7h0k/6O6Xd37jVug/JL5gGNHFvrF2e2yMJM822nwlzxssPzdE6cbOy0sUM15UAPhN8l2KOFfFrBgjyNMiTgyhf0QlRkUkXuxzCAFVexGydVPE2ThfRLW/Ob+FiHPTmfNcN9WN9W/eX9+WJOIkEolXHu1qnA3ECL5TVAWTFYTYMXCBrXIbEzMsNefVEJPfYvfWu9weBcZVydm0ZTX/GeNyhutm6OJjgr7NYHsPtg2DxzX3Ppnz6cdP+DAbsSynDMcrfve2wfk75PunDL74jO6843TZ8MEn55wOhhT1EPYPKGJLGzpOV0oMBtWMKBAtNL4jQxHv+06XV3JvEWvwKngrqFiy0Ass62kzMCUAQ1WCCVSuJEYoSkvwStN0VFUvzNT1is434Bsa9evBc0A0Y4XSEokoVW6Q1pPVHukC2nlsbEGVGALqPU2ptNRglBBa7KqlbgKdWqwbMnZCbgQTOoZjwEeWXctyYWj9PZbDt3BdywqhnFoye8zt3T3ef/NH/LM/2WccjjDzPThfcvhx4N/MPubQHKJhiNU9rHQY7sDegK7Z5ejzQ+qZZaK7uHrG776bY7MJZ/IagY9pqsDcez54VHPnC8vrOwv2txyv3S754a0JD0YFoVGenEcePDT83XaLjGp29/e4uz/mrdcOeDJbcvzkiDrCo9OGfBQ5/PSI2diyvbfLvW7EW7s7PNxe8fjBMYu5YfYw46cfPGHnrW1uuRHb21N++NYeP/rZgHCmdMs5Tw4tf/5XM046w2vTXY6jYXRrwiQsqWYNNjoOj48I1pJJhmDJtweMGXGQGyaNZzyGtm05PTvtZ1NRYsxAlMKURCJGDdZYfFDaGIlqyLAY29DHrTuIlkzj2tKudBoJRIL2M4hiDF4V6yNY6IwSMyD0He/YKg5L8JHWCC4GdvOcIrO4ssBYhfPr39vvWT82kUgkfivxGvvfaRSrEedMH8ay9ieo+XIXDoCYdSiTuZK3Zv3vKpsJoBchmnUbsnbgwKWYs1n+dXlREcc8J2TpZTZSIYSLwby19qUP6L8NLyM/0FfxPCfORUiX9qFu10QcjdcFnRsizrPyNF119VzVo0Tkmdf064oy39RZ801das8jiTiJROKVR2nxoe07HRiKcoBtM5yNlIUlKyB0FT6UFC5jZ9/y43+0z/tbDePTgvNY0z2e8reHj/j4uOZ05mB4SMOKgyZSdoG9gzG3q3PmC2i7Bb98/IQuBl7LBzTlhO07t2lqT7cKPPj8iHluyENL7DrU9jMEIQRi6C3MUaCLoR90a7xQ6Y0xLDILBOg8Wey7NX49W3E5a9FcHL91QmEzYlSsyVAVyi6jKIWmDjhbUNctbawIoabtWpBIrVXfkYi9w2fQtRAVu06MJyI0pu0dQBrwxlPVHnxDJlAQCW1JphVZpki+oLUOshKhYNVFxBQgW+howjtvvMWPXr9DvlziF4LXhi/aFXEE4+EZ3Y7QDkd
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c680de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6f6a6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6da4a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6a148d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c4506208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c6ac1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c662ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f74c4b3f898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (20,5)\n",
    "axes = (axis for _ in range(10000) for axis in pyplot.subplots(1,4)[1])\n",
    "\n",
    "for image, class_ in zip(X, y):\n",
    "    if numpy.random.rand() < 0.05:\n",
    "        axis = next(axes)\n",
    "        axis.imshow(image)\n",
    "        axis.set_xticks([])\n",
    "        axis.set_yticks([])\n",
    "        h, w, _ = image.shape\n",
    "        axis.plot([w, 0, 0, w, w], [0, 0, h, h, 0], 'g' if class_ else 'r', linewidth=8)\n",
    "        axis.set_xlim(0, w)\n",
    "        axis.set_ylim(0, h)\n",
    "#         axis.set_ylabel('negative' if not class_ else 'positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('true_false_data.p3', 'wb') as file:\n",
    "    pickle.dump((X,y), file)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}