template_matching.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2b61fdeff60>"
      ]
     },
     "execution_count": 91,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import cv2\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "ff3 = pickle.load(open('myo_mask_100.pkl','rb'))\n",
    "ff3 =  ff3.T\n",
    "\n",
    "I = ff3[4]\n",
    "I8 = (((I - I.min()) / (I.max() - I.min())) * 255.9).astype(np.uint8)\n",
    "\n",
    "fgf = Image.fromarray(I8)\n",
    "fgf.save('a4.png')\n",
    "\n",
    "#plt.imshow(ff3[0])\n",
    "\n",
    "#import PIL\n",
    "\n",
    "#fgf = PIL.Image.fromarray(ff3[0])\n",
    "\n",
    "#fgf.save('a2.png')\n",
    "\n",
    "plt.imshow(fgf , cmap=plt.cm.bone)\n",
    "\n",
    "#cv2.imwrite('alpha.png',np.float32(fgf))\n",
    "\n",
    "#plt.savefig('abc.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import copy\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "img = cv2.imread('a4.png',0)\n",
    "#img = np.float32(ff3[0])\n",
    "img2 = copy.copy(img)\n",
    "template = cv2.imread('ggg.PNG',0)\n",
    "w, h = template.shape[::-1]\n",
    "\n",
    "# All the 6 methods for comparison in a list\n",
    "#methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',\n",
    "            #'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']\n",
    "\n",
    "methods = ['cv2.TM_CCOEFF_NORMED']\n",
    "\n",
    "for meth in methods:\n",
    "    img = copy.copy(img2)\n",
    "    method = eval(meth)\n",
    "\n",
    "    # Apply template Matching\n",
    "    res = cv2.matchTemplate(img,template,method)\n",
    "    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\n",
    "\n",
    "    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum\n",
    "    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:\n",
    "        top_left = min_loc\n",
    "    else:\n",
    "        top_left = max_loc\n",
    "    bottom_right = (top_left[0] + w, top_left[1] + h)\n",
    "\n",
    "    cv2.rectangle(img,top_left, bottom_right, 255, 2)\n",
    "\n",
    "    plt.subplot(121),plt.imshow(res,cmap = 'gray')\n",
    "    plt.title('Matching Result'), plt.xticks([]), plt.yticks([])\n",
    "    plt.subplot(122),plt.imshow(img,cmap = 'gray')\n",
    "    plt.title('Detected Point'), plt.xticks([]), plt.yticks([])\n",
    "    plt.suptitle(meth)\n",
    "\n",
    "    plt.show()\n",
    "    #x=[min_loc[0],max_loc[0],min_loc[1],max_loc[1]]\n",
    "    #arr.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ff3[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109, 98)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 102)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=[min_loc[0],max_loc[0],min_loc[1],max_loc[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[82, 93, 47, 94]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trial():\n",
    "    print(\"hi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trial2():\n",
    "    img = cv2.imread('a4.png',0)\n",
    "    #img = np.float32(ff3[0])\n",
    "    img2 = copy.copy(img)\n",
    "    template = cv2.imread('ggg.PNG',0)\n",
    "    w, h = template.shape[::-1]\n",
    "\n",
    "# All the 6 methods for comparison in a list\n",
    "#methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',\n",
    "            #'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']\n",
    "\n",
    "    methods = ['cv2.TM_CCOEFF_NORMED']\n",
    "\n",
    "    for meth in methods:\n",
    "        img = copy.copy(img2)\n",
    "        method = eval(meth)\n",
    "\n",
    "    # Apply template Matching\n",
    "        res = cv2.matchTemplate(img,template,method)\n",
    "        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\n",
    "\n",
    "    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum\n",
    "        if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:\n",
    "            top_left = min_loc\n",
    "        else:\n",
    "            top_left = max_loc\n",
    "        bottom_right = (top_left[0] + w, top_left[1] + h)\n",
    "\n",
    "        cv2.rectangle(img,top_left, bottom_right, 255, 2)\n",
    "\n",
    "        plt.subplot(121),plt.imshow(res,cmap = 'gray')\n",
    "        plt.title('Matching Result'), plt.xticks([]), plt.yticks([])\n",
    "        plt.subplot(122),plt.imshow(img,cmap = 'gray')\n",
    "        plt.title('Detected Point'), plt.xticks([]), plt.yticks([])\n",
    "        plt.suptitle(meth)\n",
    "\n",
    "        #plt.show()\n",
    "        x=[top_left[0],bottom_right[0],top_left[1],bottom_right[1]]\n",
    "        arr.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishw\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:31: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "C:\\Users\\vishw\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:33: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in list(range(20)):\n",
    "    I = ff3[i]\n",
    "    I8 = (((I - I.min()) / (I.max() - I.min())) * 255.9).astype(np.uint8)\n",
    "    fgf = Image.fromarray(I8)\n",
    "    fgf.save('a4.png')\n",
    "    trial2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[105, 169, 94, 165],\n",
       " [106, 170, 96, 167],\n",
       " [109, 173, 98, 169],\n",
       " [113, 177, 93, 164],\n",
       " [103, 167, 102, 173],\n",
       " [74, 138, 94, 165],\n",
       " [107, 171, 98, 169],\n",
       " [105, 169, 98, 169],\n",
       " [105, 169, 98, 169],\n",
       " [94, 158, 94, 165],\n",
       " [90, 154, 96, 167],\n",
       " [85, 149, 94, 165],\n",
       " [87, 151, 84, 155],\n",
       " [89, 153, 76, 147],\n",
       " [75, 139, 74, 145],\n",
       " [87, 151, 97, 168],\n",
       " [95, 159, 94, 165],\n",
       " [95, 159, 94, 165],\n",
       " [93, 157, 102, 173],\n",
       " [93, 157, 102, 173]]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x2b621f5f9e8>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAANlklEQVR4nO3dX4xc5X3G8e9T/hhBQOASkDFWIZUjlVSqY60AiSqiQg1gVTK5IIKLYKWozgWoiUQvHHIRpN6kVf5IqBWSo6CYKoWiJAhf0BKwUqFKhWCQAxiX4BAKri07aRBBjUTA+fVizrbDvrPe8c7Mzix8P9LqnHnnPTM/H3sev++Zc86mqpCkfr8z7QIkzR6DQVLDYJDUMBgkNQwGSQ2DQVJjYsGQ5LokLyU5mGTHpN5H0vhlEucxJDkF+Anwp8Ah4Gng5qp6cexvJmnsJjViuBw4WFWvVNVvgAeArRN6L0ljduqEXnc98Hrf40PAFYt1Pj1r6gzOmlApkgDe4o1fVNWHh+k7qWDIgLb3zFmSbAe2A5zBmVyRayZUiiSAx+u7/zls30lNJQ4BG/oeXwwc7u9QVTuraq6q5k5jzYTKkLQckwqGp4GNSS5NcjpwE7B7Qu8lacwmMpWoqneT3A48CpwC3FtV+yfxXpLGb1LHGKiqR4BHJvX6kibHMx8lNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSY1TR9k4yavAW8Bx4N2qmkuyFvgn4BLgVeDTVfXGaGVKWknjGDH8SVVtqqq57vEOYE9VbQT2dI8lrSKTmEpsBXZ167uAGybwHpImaNRgKOAHSZ5Jsr1ru7CqjgB0ywsGbZhke5K9Sfa+w9sjliFpnEY6xgBcVVWHk1wAPJbkP4bdsKp2AjsBzsnaGrEOSWM00oihqg53y2PAQ8DlwNEk6wC65bFRi5S0spYdDEnOSnL2/DrwSeAFYDewreu2DXh41CIlraxRphIXAg8lmX+df6yqf0nyNPBgkluB14AbRy9T0kpadjBU1SvAHw1o/2/gmlGKkjRdnvkoqWEwSGoYDJIaBoOkhsEgqWEwSGoYDJIaBoOkhsEgqWEwSGoYDJIaBoOkhsEgqWEwSGoYDJIaBoOkhsEgqTHqXaK1Cuytf23a5nL1iteh1cMRg6SGI4YPAEcHOlmOGCQ1DAZJDYNBUsNgkNQwGCQ1DAZJDYNBUsNgkNQwGCQ1DAZJDYNBUsNgkNQwGCQ1lgyGJPcmOZbkhb62tUkeS/Jytzyva0+Su5McTPJcks2TLF7SZAwzYvg2cN2Cth3AnqraCOzpHgNcD2zsfrYD94ynTEkraclgqKongF8uaN4K7OrWdwE39LXfVz1PAucmWTeuYiWtjOUeY7iwqo4AdMsLuvb1wOt9/Q51bZJWkXHfwSkD2mpgx2Q7vekGZ3DmmMuQNIrljhiOzk8RuuWxrv0QsKGv38XA4UEvUFU7q2ququZOY80yy5A0CcsNht3Atm59G/BwX/st3bcTVwJvzk85JK0eS04lktwPXA2cn+QQ8GXgK8CDSW4FXgNu7Lo/AmwBDgK/Bj47gZolTdiSwVBVNy/y1DUD+hZw26hFSZouz3yU1DAYJDUMBkkNg0FSw19Rp5E8enjfsra79qJN79n22os2jaskjYEjBi3bckNh1G01eQaDTtqjh/eN/YNtUMwWpxI6KcN+gPunBn7oVx+DQWNxomMEC59bLCjm2z3eMH0Gg4Y26AO9nA/xUqOJRw/vMxymzGMMmqrFAsDpx3QZDBrKwg/qtRdtGtv/6o4OZo/BoJPmB/n9z2DQklbiRKRBr+t0YnoMBkkNg0EntJKnLQ86buGoYToMBs08w2HlGQw6IQ80fjAZDJIaBoNmjqOU6TMYJDUMBkkNg0FSw2CQ1DAYNDTPJ/jgMBgkNQwGnZRJ3O9Rs8dg0JJW+ryCQfd+0MoyGDSUaX04DYXpMBi0LE4p3t8MBkkNg0FDm/RdlhaOQpxGTI/BoJMyqZupLAwEQ2G6DAYti3daen9b8hfOJLkX+DPgWFX9Ydd2F/AXwM+7bndW1SPdc18EbgWOA39ZVY9OoG7NgPlwmA+F5UwDnDrMpmF+E9W3gb8D7lvQ/o2q+mp/Q5LLgJuAjwEXAY8n+WhVHR9DrVpFlvp1c44wZtuSwVBVTyS5ZMjX2wo8UFVvAz9LchC4HPj3ZVeombdw5NBvqQBwlDCbRvndlbcnuQXYC9xRVW8A64En+/oc6toaSbYD2wHO4MwRytCsOFFADOqn2bXcYLgH+GuguuXXgD8HMqBvDXqBqtoJ7AQ4J2sH9tHq5Ad/9VvWtxJVdbSqjlfVb4Fv0psuQG+EsKGv68XA4dFKlLTSlhUMSdb1PfwU8EK3vhu4KcmaJJcCG4EfjVaipJU2zNeV9wNXA+cnOQR8Gbg6ySZ604RXgc8BVNX+JA8CLwLvArf5jYS0+qRq+tP7c7K2rsg10y5Del97vL77TFXNDdPXMx8lNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNQwGSQ2DQVLDYJDUMBgkNZYMhiQbkvwwyYEk+5N8vmtfm+SxJC93y/O69iS5O8nBJM8l2TzpP4Sk8RpmxPAucEdV/QFwJXBbksuAHcCeqtoI7OkeA1wPbOx+tgP3jL1qSRO1ZDBU1ZGqerZbfws4AKwHtgK7um67gBu69a3AfdXzJHBuknVjr1zSxJzUMYYklwAfB54CLqyqI9ALD+CCrtt64PW+zQ51bZJWiaGDIcmHgO8BX6iqX52o64C2GvB625PsTbL3Hd4etgxJK2CoYEhyGr1Q+E5Vfb9rPjo/ReiWx7r2Q8CGvs0vBg4vfM2q2llVc1U1dxprllu/pAkY5luJAN8CDlTV1/ue2g1s69a3AQ/3td/SfTtxJfDm/JRD0upw6hB9rgI+AzyfZF/XdifwFeDBJLcCrwE3ds89AmwBDgK/Bj471oolTdySwVBV/8bg4wYA1wzoX8BtI9YlaYo881FSw2CQ1DAYJDUMBkkNg0FSw2CQ1DAYJDUMBkkNg0FSw2CQ1DAYJDUMBkkNg0FSw2CQ1DAYJDUMBkkNg0FSw2CQ1DAYJDUMBkkNg0FSw2CQ1DAYJDUMBkkNg0FSw2CQ1DAYJDUMBkkNg0FSw2CQ1DAYJDUMBkkNg0FSw2CQ1FgyGJJsSPLDJAeS7E/y+a79riT/lWRf97Olb5svJjmY5KUk107yDyBp/E4dos+7wB1V9WySs4FnkjzWPfeNqvpqf+cklwE3AR8DLgIeT/LRqjo+zsIlTc6SI4aqOlJVz3brbwEHgPUn2GQr8EBVvV1VPwMOApePo1hJK+OkjjEkuQT4OPBU13R7kueS3JvkvK5tPfB632aHGBAkSbYn2Ztk7zu8fdKFS5qcoYMhyYeA7wFfqKpfAfcAvw9sAo4AX5vvOmDzahqqdlbVXFXNncaaky5c0uQMFQxJTqMXCt+pqu8DVNXRqjpeVb8Fvsn/TxcOARv6Nr8YODy+kiVN2jDfSgT4FnCgqr7e176ur9ungBe69d3ATUnWJLkU2Aj8aHwlS5q0Yb6VuAr4DPB8kn1d253AzUk20ZsmvAp8DqCq9id5EHiR3jcat/mNhLS6pKqZ/q98EcnPgf8BfjHtWoZwPqujTlg9tVrn+A2q9feq6sPDbDwTwQCQZG9VzU27jqWsljph9dRqneM3aq2eEi2pYTBIasxSMOycdgFDWi11wuqp1TrHb6RaZ+YYg6TZMUsjBkkzYurBkOS67vLsg0l2TLuehZK8muT57tLyvV3b2iSPJXm5W5631OtMoK57kxxL8kJf28C60nN3t4+fS7J5Bmqducv2T3CLgZnarytyK4SqmtoPcArwU+AjwOnAj4HLplnTgBpfBc5f0Pa3wI5ufQfwN1Oo6xPAZuCFpeoCtgD/TO86liuBp2ag1ruAvxrQ97Lu38Ea4NLu38cpK1TnOmBzt3428JOunpnaryeoc2z7dNojhsuBg1X1SlX9BniA3mXbs24rsKtb3wXcsNIFVNUTwC8XNC9W11bgvup5Ejh3wSntE7VIrYuZ2mX7tfgtBmZqv56gzsWc9D6ddjAMdYn2lBXwgyTPJNnetV1YVUeg95cEXDC16t5rsbpmdT8v+7L9SVtwi4GZ3a/jvBVCv2kHw1CXaE/ZVVW1GbgeuC3JJ6Zd0DLM4n4e6bL9SRpwi4FFuw5oW7Fax30rhH7TDoaZv0S7qg53y2PAQ/SGYEfnh4zd8tj0KnyPxeqauf1cM3rZ/qBbDDCD+3XSt0KYdjA8DWxMcmmS0+ndK3L3lGv6P0nO6u5zSZKzgE/Su7x8N7Ct67YNeHg6FTYWq2s3cEt3FP1K4M35ofG0zOJl+4vdYoAZ26+L1TnWfboSR1GXOMK6hd5R1Z8CX5p2PQtq+wi9o7k/BvbP1wf8LrAHeLlbrp1CbffTGy6+Q+9/hFsXq4veUPLvu338PDA3A7X+Q1fLc90/3HV9/b/U1foScP0K1vnH9IbYzwH7up8ts7ZfT1Dn2PapZz5Kakx7KiFpBhkMkhoGg6SGwSCpYTBIahgMkhoGg6SGwSCp8b93UuH0cEpt1wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2b61f8fecf8>"
      ]
     },
     "execution_count": 109,
     "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": [
    "import matplotlib.lines as mlines\n",
    "ax=plt.gca()\n",
    "l = mlines.Line2D([102, 103], [98, 102])\n",
    "ax.add_line(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b6220799e8>,\n",
       " <matplotlib.lines.Line2D at 0x2b61f927240>]"
      ]
     },
     "execution_count": 116,
     "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": [
    "plt.imshow(ff3[4])\n",
    "plt.plot(109+w, 103+h, 98+w, 102+h, marker ='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.rectangle?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 102)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_left[0] + w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_left[1] + h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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": [
    "plt.plot([103,167],[167,173],marker='o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "103-h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.rectangle(img,top_left, bottom_right, 255, 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 102)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167, 173)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom_right"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}