annotation_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import join, isfile\n",
    "from os import listdir\n",
    "\n",
    "from xmljson import badgerfish as bf\n",
    "from xml.etree.ElementTree import fromstring\n",
    "from pprint import pprint\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import numpy as np\n",
    "import numpy as np              # (pip install numpy)\n",
    "from skimage import measure                        # (pip install scikit-image)\n",
    "from shapely.geometry import Polygon, MultiPolygon # (pip install Shapely)\n",
    "from uuid import uuid4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = \"data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 91758 .xml files\n"
     ]
    }
   ],
   "source": [
    "XMLS = [\n",
    "    join(base_path, f) for f in listdir(base_path)\n",
    "    if f[-3:] == \"xml\"\n",
    "]\n",
    "IMGS = [\n",
    "    join(base_path, f) for f in listdir(base_path)\n",
    "    if f[-3:] != \"xml\"\n",
    "]\n",
    "\n",
    "print(f\"There are {len(XMLS)} .xml files\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
      "<Image>\n",
      "  <ObservationId>15168</ObservationId>\n",
      "  <MediaId>102424</MediaId>\n",
      "  <Vote>4</Vote>\n",
      "  <Content>Flower</Content>\n",
      "  <ClassId>3384</ClassId>\n",
      "  <Family>Gentianaceae</Family>\n",
      "  <Genus>Gentianopsis</Genus>\n",
      "  <Species>Gentianopsis ciliata (L.) Ma</Species>\n",
      "  <Author>liliane roubaudi</Author>\n",
      "  <Date>2011-9-25</Date>\n",
      "  <Location>Le Mollard de Don</Location>\n",
      "  <Latitude />\n",
      "  <Longitude />\n",
      "  <YearInCLEF>ImageCLEF2013</YearInCLEF>\n",
      "  <ObservationId2014>11337</ObservationId2014>\n",
      "  <ImageId2014>41057</ImageId2014>\n",
      "  <LearnTag>Train</LearnTag>\n",
      "</Image>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(XMLS[0], 'r') as F:\n",
    "    print(F.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(XMLS[0], 'r') as F:\n",
    "    X = bf.data(fromstring(F.read()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_dict(fpath):\n",
    "    with open(fpath, 'r') as F:\n",
    "        X = bf.data(fromstring(F.read()))\n",
    "    DATA = dict(X['Image'])\n",
    "    data = {}\n",
    "    for key in DATA:\n",
    "        if DATA[key].get(\"$\"):\n",
    "            data[key] = DATA[key]['$']\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 91758/91758 [00:14<00:00, 6417.27it/s]\n"
     ]
    }
   ],
   "source": [
    "ImageData = []\n",
    "\n",
    "for xml in tqdm(XMLS):\n",
    "    ImageData.append(to_dict(xml))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91758\n"
     ]
    }
   ],
   "source": [
    "ImageData = sorted(ImageData, key=lambda x:x.get(\"Content\"))\n",
    "print(len(ImageData))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Content = {}\n",
    "for img in ImageData:\n",
    "    content = img['Content']\n",
    "    if content not in Content:\n",
    "        Content[content] = [img]\n",
    "    else:\n",
    "        Content[content].append(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Branch', 'Entire', 'Flower', 'Fruit', 'Leaf', 'LeafScan', 'Stem'])\n"
     ]
    }
   ],
   "source": [
    "print(Content.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12605\n"
     ]
    }
   ],
   "source": [
    "print(len(Content['LeafScan']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ObservationId': 19664, 'MediaId': 67989, 'Vote': 3, 'Content': 'LeafScan', 'ClassId': 3279, 'Family': 'Fagaceae', 'Genus': 'Castanea', 'Species': 'Castanea sativa Mill.', 'Author': 'annick larbouillat', 'Date': '2011-10-16', 'Location': 'La Queue-en-Brie', 'Latitude': 48.75675, 'Longitude': 2.58162, 'YearInCLEF': 'ImageCLEF2012', 'ObservationId2014': 3823, 'ImageId2014': 51815, 'LearnTag': 'Train'}\n"
     ]
    }
   ],
   "source": [
    "LeafScan = Content['LeafScan']\n",
    "print(LeafScan[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6c5865d0f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpath = join(base_path, f\"{LeafScan[2]['MediaId']}.jpg\")\n",
    "\n",
    "img = cv2.imread(fpath)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def create_mask(img, show_plot:bool=False):\n",
    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    _, thresh = cv2.threshold(gray, 210, 255, cv2.THRESH_BINARY)\n",
    "    \n",
    "    if False:\n",
    "        thresh = cv2.adaptiveThreshold(\n",
    "            gray,\n",
    "            255,\n",
    "            cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\n",
    "            cv2.THRESH_BINARY_INV,\n",
    "            11,\n",
    "            2\n",
    "        )\n",
    "\n",
    "    kernel = cv2.getStructuringElement(\n",
    "        cv2.MORPH_ELLIPSE,\n",
    "        (30,30)\n",
    "    )\n",
    "    res = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel)\n",
    "    if show_plot:\n",
    "        plt.imshow(res, 'gray')\n",
    "    return res\n",
    "\n",
    "fpath = join(base_path, f\"{LeafScan[2]['MediaId']}.jpg\")\n",
    "img = cv2.imread(fpath)\n",
    "mask = create_mask(img, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpath = join(base_path, f\"{LeafScan[2]['MediaId']}.jpg\")\n",
    "\n",
    "for leafscan in tqdm(LeafScan):\n",
    "    fpath = join(base_path, f\"{leafscan['MediaId']}.jpg\")\n",
    "    img = cv2.imread(fpath)\n",
    "    mask = create_mask(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sub_mask_annotation(sub_mask, \n",
    "                               image_id, \n",
    "                               category_id, \n",
    "                               annotation_id, \n",
    "                               is_crowd):\n",
    "    # Find contours (boundary lines) around each sub-mask\n",
    "    # Note: there could be multiple contours if the object\n",
    "    # is partially occluded. (E.g. an elephant behind a tree)\n",
    "    contours = measure.find_contours(sub_mask, 0.5, positive_orientation='low')\n",
    "\n",
    "    segmentations = []\n",
    "    polygons = []\n",
    "    for contour in contours:\n",
    "        # Flip from (row, col) representation to (x, y)\n",
    "        # and subtract the padding pixel\n",
    "        for i in range(len(contour)):\n",
    "            row, col = contour[i]\n",
    "            contour[i] = (col - 1, row - 1)\n",
    "\n",
    "        # Make a polygon and simplify it\n",
    "        poly = Polygon(contour)\n",
    "        poly = poly.simplify(1.0, preserve_topology=False)\n",
    "        polygons.append(poly)\n",
    "        segmentation = np.array(poly.exterior.coords).ravel().tolist()\n",
    "        segmentations.append(segmentation)\n",
    "\n",
    "    # Combine the polygons to calculate the bounding box and area\n",
    "    multi_poly = MultiPolygon(polygons)\n",
    "    x, y, max_x, max_y = multi_poly.bounds\n",
    "    width = max_x - x\n",
    "    height = max_y - y\n",
    "    bbox = (x, y, width, height)\n",
    "    area = multi_poly.area\n",
    "\n",
    "    annotation = {\n",
    "        'segmentation': segmentations,\n",
    "        'iscrowd': is_crowd,\n",
    "        'image_id': image_id,\n",
    "        'category_id': category_id,\n",
    "        'id': annotation_id,\n",
    "        'bbox': bbox,\n",
    "        'area': area\n",
    "    }\n",
    "\n",
    "    return annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12605/12605 [04:45<00:00, 44.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There were 8358 Exceptions!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "Annotations = []\n",
    "exceptions = 0\n",
    "Exceptions = []\n",
    "\n",
    "for leafscan in tqdm(LeafScan):\n",
    "    fpath = join(base_path, f\"{leafscan['MediaId']}.jpg\")\n",
    "    img = cv2.imread(fpath)\n",
    "    mask = create_mask(img)\n",
    "    try:\n",
    "        annotation = create_sub_mask_annotation(\n",
    "            mask,\n",
    "            fpath,\n",
    "            1,\n",
    "            f\"{uuid4()}\",\n",
    "            False\n",
    "        )\n",
    "        Annotations.append(annotation)\n",
    "    except Exception as e:\n",
    "        Exceptions.append(fpath)\n",
    "        pass\n",
    "\n",
    "print(f\"There were {len(Exceptions)} Exceptions!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "write_path = \"annotations.json\"\n",
    "json.dump(Annotations, open(write_path, 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    89     71    424    725 132704]\n",
      " [     0      0    589    800 338496]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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BYxHxhaGHOn2J7Ii4NSIujojNVO/JQxHxceAgcEOabXS5Bst7Q5q/U6PmmtreCaK6ZPRPgaeAv267PxP2/UNUm0+PAUfSbSfV9vcB4Eng34AL0/yiOmr3FPA4sNL2MtRYxg8D+9P99wA/pLrE9zeBc1L7uWn6RHr8PW33e143f9XELKPtTSyzojkgZhkOiFmGA2KW4YCYZTggZhkOiFnG/wFQp6VHHk6PYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def undesired_objects (image):\n",
    "    # image = image.astype('uint8')\n",
    "    nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(\n",
    "        image, \n",
    "        connectivity=8\n",
    "    )\n",
    "    sizes = stats[:, -1]\n",
    "    print(stats)\n",
    "\n",
    "    max_label = 1\n",
    "    max_size = sizes[1]\n",
    "    for i in range(2, nb_components):\n",
    "        if sizes[i] > max_size:\n",
    "            max_label = i\n",
    "            max_size = sizes[i]\n",
    "\n",
    "    img2 = np.zeros(output.shape)\n",
    "    img2[output == max_label] = 255\n",
    "    plt.imshow(img2, 'gray')\n",
    "    # cv2.imshow(\"Biggest component\", img2)\n",
    "    # cv2.waitKey(0)\n",
    "    \n",
    "undesired_objects(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'thresh' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-2dd2aef6c4be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m contours, hierarchy = cv2.findContours(\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mthresh\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRETR_EXTERNAL\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCHAIN_APPROX_SIMPLE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'thresh' is not defined"
     ]
    }
   ],
   "source": [
    "contours, hierarchy = cv2.findContours(\n",
    "    thresh,\n",
    "    cv2.RETR_EXTERNAL,\n",
    "    cv2.CHAIN_APPROX_SIMPLE\n",
    ")\n",
    "print(len(contours))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "copyImg = img.copy()\n",
    "\n",
    "\n",
    "if len(contours) != 0:\n",
    "    c = max(contours, key = lambda x: cv2.contourArea(x, False))\n",
    "\n",
    "\n",
    "drawImg = cv2.drawContours(copyImg, c, -1, (255,0,0),-1)\n",
    "polyImg = cv2.polylines(copyImg, c, True,(0,0,0),20)\n",
    "\n",
    "polyFill = cv2.fillPoly(copyImg, c, (0,255,255))\n",
    "\n",
    "\n",
    "plt.imshow(polyImg)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = cv2.cvtColor(polyImg, cv2.COLOR_BGR2GRAY)\n",
    "_, thresh = cv2.threshold(gray, 210, 255, cv2.THRESH_BINARY_INV)\n",
    "\n",
    "mask = thresh.astype(np.bool)\n",
    "\n",
    "contours, hierarchy = cv2.findContours(\n",
    "    thresh,\n",
    "    cv2.RETR_EXTERNAL,\n",
    "    cv2.CHAIN_APPROX_SIMPLE\n",
    ")\n",
    "\n",
    "if len(contours) != 0:\n",
    "    c = max(contours, key = lambda x: cv2.contourArea(x, False))\n",
    "    \n",
    "drawImg = cv2.fillPoly(\n",
    "    drawImg, c, (0,0,0)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "drawImg = cv2.fillPoly(\n",
    "    drawImg, c, (0,0,0)\n",
    ")\n",
    "\n",
    "_, thresh = cv2.threshold(drawImg, 210, 255, cv2.THRESH_BINARY_INV)\n",
    "mask = thresh.astype(np.uint16)\n",
    "\n",
    "if True:\n",
    "    h, w = thresh.shape[:2]\n",
    "    mask = np.zeros((h+2, w+2), np.uint8)\n",
    "\n",
    "    # Floodfill from point (0, 0)\n",
    "    cv2.floodFill(thresh, mask, (0,0), 255);\n",
    "\n",
    "drawImg = cv2.cvtColor(drawImg, cv2.COLOR_BGR2GRAY)\n",
    "print(np.max(drawImg))\n",
    "print(np.min(drawImg))\n",
    "\n",
    "_, thresh = cv2.threshold(drawImg, 210, 255, cv2.THRESH_BINARY_INV)\n",
    "mask = thresh.astype(np.uint16)\n",
    "\n",
    "plt.imshow(mask, 'gray')\n",
    "print(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}