Labels with Coordinates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pillow is installed but import PIL due to backward dependency\n",
    "from PIL import Image\n",
    "from tesserocr import PyTessBaseAPI, RIL\n",
    "from IPython.display import Image as Imagex \n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "base = '/Users/macbook/Documents/RAVI/Notebook/images/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(x1, y1, x2, y2):\n",
    "    return (x2-x1)**2 + (y2-y1)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Label:\n",
    "    \"\"\"Class for the identified labels\"\"\"\n",
    "    \n",
    "    def __init__(self, x, y, w, h, confidence, text):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "        self.w = w\n",
    "        self.h = h\n",
    "        self.confidence = confidence\n",
    "        self.text = text\n",
    "        \n",
    "    def getX(self):\n",
    "        return self.x\n",
    "    \n",
    "    def getY(self):\n",
    "        return self.y\n",
    "    \n",
    "    def getW(self):\n",
    "        return self.w\n",
    "    \n",
    "    def getH(self):\n",
    "        return self.h\n",
    "    \n",
    "    def getConfidence(self):\n",
    "        return self.confidence\n",
    "    \n",
    "    def getText(self):\n",
    "        return self.text\n",
    "    \n",
    "    def updateText(self, newtext):\n",
    "        self.text = newtext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(x):\n",
    "    x = re.sub(' +',' ',x)\n",
    "    return \" \".join(x.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(776, 29, 185, 27, 84, u'Delivery tube')\n",
      "(1124, 146, 291, 28, 84, u'Burning of hydrogen')\n",
      "(1123, 181, 302, 27, 85, u'gas with a pop sound')\n",
      "(1091, 269, 249, 21, 64, u's\\u2014Candle')\n",
      "(896, 414, 521, 51, 71, u'@\\u2014 5021p bubble \\ufb01lled')\n",
      "(1163, 463, 203, 28, 87, u'with hydrogen')\n",
      "(116, 380, 135, 27, 87, u'sulphuric')\n",
      "(194, 414, 57, 21, 86, u'acid')\n",
      "(488, 322, 191, 28, 95, u'')\n",
      "(544, 361, 46, 23, 85, u'gas')\n",
      "(59, 455, 192, 28, 84, u'Zinc granules')\n",
      "(1165, 633, 68, 27, 86, u'Soap')\n",
      "(1165, 666, 113, 22, 88, u'solution')\n",
      "(62, 768, 1366, 34, 84, u'Figure 2. 1 Reaction of zinc granules with dilute sulphuric acid and testing hydrogen')\n",
      "(261, 811, 247, 32, 83, u'gas by burning')\n",
      "\n",
      "\n",
      "\n",
      "-------------------------------------------------\n",
      "There are in total 12 text labels.\n",
      "-------------------------------------------------------\n",
      "-------------------------------------------------------\n",
      "Image Caption : gas by burning\n",
      "-------------------------------------------------------\n",
      "-------------------------------------------------------\n",
      "----------------------LABELS---------------------------\n",
      "Delivery tube\n",
      "Burning of hydrogen\n",
      "gas with a pop sound\n",
      "sCandle\n",
      "@ 5021p bubble lled\n",
      "with hydrogen\n",
      "sulphuric\n",
      "acid\n",
      "gas\n",
      "Zinc granules\n",
      "Soap\n",
      "solution\n",
      "-------------------------------------------------------\n",
      "-------------------------------------------------------\n",
      "-----------------RELATIVE POSITIONS--------------------\n",
      "Burning of hydrogen is on the right of Delivery tube\n",
      "acid is on the left of Delivery tube\n",
      "@ 5021p bubble lled is on the bottom of Delivery tube\n",
      "gas is on the left of Burning of hydrogen\n",
      "gas with a pop sound is on the bottom of Burning of hydrogen\n",
      "Delivery tube is on the left of gas with a pop sound\n",
      "sCandle is on the bottom of gas with a pop sound\n",
      "@ 5021p bubble lled is on the left of sCandle\n",
      "Burning of hydrogen is on the top of sCandle\n",
      "with hydrogen is on the bottom of sCandle\n",
      "with hydrogen is on the right of @ 5021p bubble lled\n",
      "gas is on the left of @ 5021p bubble lled\n",
      "gas with a pop sound is on the top of @ 5021p bubble lled\n",
      "gas is on the left of with hydrogen\n",
      "gas with a pop sound is on the top of with hydrogen\n",
      "Soap is on the bottom of with hydrogen\n",
      "acid is on the right of sulphuric\n",
      "Zinc granules is on the bottom of sulphuric\n",
      "gas is on the right of acid\n",
      "Zinc granules is on the left of acid\n",
      "sCandle is on the right of gas\n",
      "sulphuric is on the left of gas\n",
      "Delivery tube is on the top of gas\n",
      "gas is on the right of Zinc granules\n",
      "@ 5021p bubble lled is on the left of Soap\n",
      "sCandle is on the top of Soap\n",
      "solution is on the bottom of Soap\n",
      "@ 5021p bubble lled is on the left of solution\n",
      "with hydrogen is on the top of solution\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAFpCAYAAACvaj13AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n51TWChmGiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a187f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 7\n",
    "path = base + str(index) + '.png'\n",
    "image = Image.open(path)\n",
    "\n",
    "img = mpimg.imread(path)\n",
    "plt.figure(figsize=(10,50))\n",
    "imgplot = plt.imshow(img)\n",
    "\n",
    "# ----------------------------------------------------------------\n",
    "\n",
    "api = PyTessBaseAPI()\n",
    "api.SetImage(image)\n",
    "boxes = api.GetComponentImages(RIL.TEXTLINE, True)\n",
    "labels = []\n",
    "caption = ''\n",
    "b = False\n",
    "\n",
    "# ----------------------------------------------------------------\n",
    "\n",
    "\"\"\"im is a PIL image object\"\"\"\n",
    "\"\"\"box is a dictionary with keys x, y, w and h\"\"\"\n",
    "\n",
    "for i, (im, box, _, _) in enumerate(boxes):\n",
    "    api.SetRectangle(box['x'], box['y'], box['w'], box['h'])\n",
    "    ocrResult = api.GetUTF8Text()\n",
    "    conf = api.MeanTextConf()\n",
    "\n",
    "    if conf < 5: continue\n",
    "\n",
    "    print (box['x'], box['y'], box['w'], box['h'], conf, ocrResult.strip())\n",
    "\n",
    "    if b:\n",
    "        caption = caption + \" \" + ocrResult.strip()\n",
    "        caption = fn(caption)\n",
    "        continue\n",
    "\n",
    "    if ocrResult.strip().lower().startswith('fig'):\n",
    "        b = True\n",
    "#         for r in range(len(ocrResult.strip())):\n",
    "        continue\n",
    "    \n",
    "    ocrResult = ocrResult.encode('ascii', 'ignore').decode('ascii')\n",
    "    text = fn(str(ocrResult.strip()))\n",
    "    if len(text) < 3: continue\n",
    "    newLabel = Label(box['x'], box['y'], box['w'], box['h'], conf, text)\n",
    "    labels.append(newLabel)\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------------\n",
    "pairs = set()\n",
    "\"\"\" my_tuple = (3,5)\"\"\"\n",
    "\"\"\" pairs.add(my_tuple)\"\"\"\n",
    "\n",
    "descriptions = []\n",
    "\n",
    "\n",
    "for i,label in enumerate(labels):\n",
    "    my_x = label.getX()\n",
    "    my_y = label.getY()\n",
    "\n",
    "    ### maintaining info for all the 8 regions\n",
    "    bx = [False]*8\n",
    "    minx = [(-1,1000000000)]*8 \n",
    "    \"\"\"array of tuples (index,value)\"\"\"\n",
    "\n",
    "\n",
    "    for j in range(0,len(labels)):\n",
    "\n",
    "        # First check  if the pair already exists or not\n",
    "        if i == j: continue\n",
    "        if (i,j) in pairs or (j,i) in pairs: continue\n",
    "\n",
    "        # Get the cordinates for the other point\n",
    "        his_x = labels[j].getX()\n",
    "        his_y = labels[j].getY()\n",
    "\n",
    "        #print (\"\\nPair: \" + labels[i].getText() + \", \" + labels[j].getText())\n",
    "        \"\"\" \n",
    "        If its in the range of 20 degress above or below the direct direction\n",
    "        x increases on the right and y increases towards bottom\n",
    "        tan(20*) = 0.36 \n",
    "\n",
    "\n",
    "        Go through all the labels only once\n",
    "        For each label calculate in which of the 8 regions does it lie\n",
    "\n",
    "        For each region maintain the closest one you have obtained till now\n",
    "        Also if you have at all obtained any label from that region or not.\n",
    "\n",
    "        In the end just add the details of those closest ones from each region.\n",
    "        \"\"\"\n",
    "\n",
    "\n",
    "\n",
    "        if his_x > my_x  and abs(his_y-my_y)/(his_x-my_x) <  0.36:    \n",
    "            \"\"\" Right \"\"\"\n",
    "            k = 0\n",
    "            d = dist(my_x, my_y, his_x, his_y)\n",
    "            if bx[k]:\n",
    "                if d < minx[k][1]:\n",
    "                    minx[k] = (j,d)\n",
    "            else:\n",
    "                bx[k] = True\n",
    "                minx[k] = (j,d)\n",
    "\n",
    "            #print (\"Region \" + str(k))\n",
    "\n",
    "        elif his_x < my_x and abs(his_y-my_y)/(my_x-his_x) <  0.36:\n",
    "            \"\"\" Left \"\"\"\n",
    "            k = 1\n",
    "            d = dist(my_x, my_y, his_x, his_y)\n",
    "            if bx[k]:\n",
    "                if d < minx[k][1]:\n",
    "                    minx[k] = (j,d)\n",
    "            else:\n",
    "                bx[k] = True\n",
    "                minx[k] = (j,d)\n",
    "\n",
    "            #print (\"Region \" + str(k))\n",
    "\n",
    "        elif his_y < my_y and abs(his_x-my_x)/(my_y-his_y) <  0.36:\n",
    "            \"\"\" Top \"\"\"\n",
    "            k = 2\n",
    "            d = dist(my_x, my_y, his_x, his_y)\n",
    "            if bx[k]:\n",
    "                if d < minx[k][1]:\n",
    "                    minx[k] = (j,d)\n",
    "            else:\n",
    "                bx[k] = True\n",
    "                minx[k] = (j,d)\n",
    "\n",
    "            #print (\"Region \" + str(k))\n",
    "\n",
    "        elif his_y > my_y and abs(his_x-my_x)/(his_y-my_y) <  0.36:\n",
    "            \"\"\" Bottom \"\"\"\n",
    "            k = 3\n",
    "            d = dist(my_x, my_y, his_x, his_y)\n",
    "            if bx[k]:\n",
    "                if d < minx[k][1]:\n",
    "                    minx[k] = (j,d)\n",
    "            else:\n",
    "                bx[k] = True\n",
    "                minx[k] = (j,d)\n",
    "\n",
    "            #print (\"Region \" + str(k))\n",
    "\n",
    "        elif his_x > my_x and his_y < my_y:\n",
    "            \"\"\" Top Right \"\"\"\n",
    "            k = 4\n",
    "            d = dist(my_x, my_y, his_x, his_y)\n",
    "            if bx[k]:\n",
    "                if d < minx[k][1]:\n",
    "                    minx[k] = (j,d)\n",
    "            else:\n",
    "                bx[k] = True\n",
    "                minx[k] = (j,d)\n",
    "\n",
    "            #print (\"Region \" + str(k))\n",
    "\n",
    "        elif his_x > my_x and his_y > my_y:\n",
    "            \"\"\" Bottom Right \"\"\"\n",
    "            k = 5\n",
    "            d = dist(my_x, my_y, his_x, his_y)\n",
    "            if bx[k]:\n",
    "                if d < minx[k][1]:\n",
    "                    minx[k] = (j,d)\n",
    "            else:\n",
    "                bx[k] = True\n",
    "                minx[k] = (j,d)\n",
    "\n",
    "            #print (\"Region \" + str(k)) \n",
    "\n",
    "        elif his_x < my_x and his_y < my_y:\n",
    "            \"\"\" Top Left \"\"\"\n",
    "            k = 6\n",
    "            d = dist(my_x, my_y, his_x, his_y)\n",
    "            if bx[k]:\n",
    "                if d < minx[k][1]:\n",
    "                    minx[k] = (j,d)\n",
    "            else:\n",
    "                bx[k] = True\n",
    "                minx[k] = (j,d)\n",
    "\n",
    "            #print (\"Region \" + str(k)) \n",
    "\n",
    "        elif his_x < my_x and his_y > my_y:\n",
    "            \"\"\" Bottom Left \"\"\"\n",
    "            k = 7\n",
    "            d = dist(my_x, my_y, his_x, his_y)\n",
    "            if bx[k]:\n",
    "                if d < minx[k][1]:\n",
    "                    minx[k] = (j,d)\n",
    "            else:\n",
    "                bx[k] = True\n",
    "                minx[k] = (j,d)\n",
    "\n",
    "            #print (\"Region \" + str(k))\n",
    "\n",
    "\n",
    "    ### Add all the 8 regions best ones into the pairs set.\n",
    "    for j in range(8):\n",
    "#         print (\"j is \" + str(j))\n",
    "#         print (\"bx[j] is \" + str(bx[j]))\n",
    "        if bx[j]:\n",
    "            pairs.add((i,minx[j][0]))\n",
    "\n",
    "            \"\"\"For each case, add the Description\"\"\"\n",
    "            if j==0:\n",
    "                s = labels[minx[j][0]].getText() + \" is on the right of \" + labels[i].getText()\n",
    "                descriptions.append(s)\n",
    "            elif j==1:\n",
    "                s = labels[minx[j][0]].getText() + \" is on the left of \" + labels[i].getText()\n",
    "                descriptions.append(s)\n",
    "            elif j==2:\n",
    "                s = labels[minx[j][0]].getText() + \" is on the top of \" + labels[i].getText()\n",
    "                descriptions.append(s)\n",
    "            elif j==3:\n",
    "                s = labels[minx[j][0]].getText() + \" is on the bottom of \" + labels[i].getText()\n",
    "                descriptions.append(s)\n",
    "            elif j==4:\n",
    "                s = labels[minx[j][0]].getText() + \" is on the top right of \" + labels[i].getText()\n",
    "                descriptions.append(s)\n",
    "            elif j==5:\n",
    "                s = labels[minx[j][0]].getText() + \" is on the bottom right of \" + labels[i].getText()\n",
    "                descriptions.append(s)\n",
    "            elif j==6:\n",
    "                s = labels[minx[j][0]].getText() + \" is on the top left of \" + labels[i].getText()\n",
    "                descriptions.append(s)\n",
    "            elif j==7:\n",
    "                s = labels[minx[j][0]].getText() + \" is on the bottom left of \" + labels[i].getText()\n",
    "                descriptions.append(s)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ----------------------------------------------------------------\n",
    "# ----------------------------------------------------------------\n",
    "\n",
    "\n",
    "print '\\n\\n\\n-------------------------------------------------'\n",
    "print 'There are in total {} text labels.'.format(len(labels))\n",
    "print '-------------------------------------------------------'\n",
    "\n",
    "print '-------------------------------------------------------'\n",
    "print ('Image Caption : ' + caption)\n",
    "print '-------------------------------------------------------'\n",
    "\n",
    "print '-------------------------------------------------------'\n",
    "print '----------------------LABELS---------------------------'\n",
    "\n",
    "for label in labels:\n",
    "    print label.getText()\n",
    "\n",
    "print '-------------------------------------------------------'\n",
    "print '-------------------------------------------------------'\n",
    "print '-----------------RELATIVE POSITIONS--------------------'\n",
    "for description in descriptions:\n",
    "    print description\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Delivery tube...\n",
      "Burning of hydrogen...\n",
      "gas with a pop sound...\n",
      "@ 5021p bubble lled...\n",
      "with hydrogen...\n",
      "sulphuric...\n",
      "acid...\n",
      "gas...\n",
      "Zinc granules...\n",
      "Soap...\n",
      "solution...\n"
     ]
    }
   ],
   "source": [
    "for label in labels:\n",
    "    print (label.getText() + \"...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 1), (0, 2), (1, 2), (2, 3), (3, 1)}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally output all the things one by one: \n",
    "- The No. of labels\n",
    "- The Caption\n",
    "- The Labels itself\n",
    "- The Relative positions pairwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[False, False, False, False, False, False, False, False, False]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [False]*9\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-1, 1000000000),\n",
       " (-1, 1000000000),\n",
       " (-1, 1000000000),\n",
       " (-1, 1000000000),\n",
       " (-1, 1000000000),\n",
       " (-1, 1000000000),\n",
       " (-1, 1000000000),\n",
       " (-1, 1000000000)]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mix = [(-1,1000000000)]*8\n",
    "mix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}