Drawing on an image-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "blank_ig = np.zeros(shape=(512, 512, 3), dtype=np.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 512, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blank_ig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff3850b4550>"
      ]
     },
     "execution_count": 4,
     "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(blank_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]]], dtype=int16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.rectangle(blank_ig, pt1=(384, 0), pt2=(510, 150), color=(0,255,0), thickness= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff38504dcf8>"
      ]
     },
     "execution_count": 6,
     "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(blank_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0],\n",
       "        [  0, 255,   0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]]], dtype=int16)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.rectangle(blank_ig, pt1=(200, 200), pt2=(300, 300), color=(0,0,255), thickness= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff384fb1f28>"
      ]
     },
     "execution_count": 8,
     "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(blank_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff384f8fbe0>"
      ]
     },
     "execution_count": 9,
     "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": [
    "cv2.circle(img=blank_ig, center=(100, 100), radius=50, color=(255,0,0), thickness= 10)\n",
    "plt.imshow(blank_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff384eeda58>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HgJ1tz+/FjHbiHJ5xTacb5OnlGQ0NbgeOV9VHhlpvkivaSIEkLwLeAhwH7gNuPkOdy/XfDNxbbcN4mqrq1qraXlVXM/o7vLeq3j20OmGdLoWwnjufzrAT5UZGe9S/A/zpjGv5LPAU8H+MUnYfo+3GI8AT7f6y1jfA37S6vwnsWuda38hoOPgI8FC73Ti0eoFfAx5sdT4K/FlrfwXwdUan5/8jcElrv7TNn2jLXzGDv4M38cKvEoOrs9X0cLs9trzeTPKz98hHSZ1Zb0pIGiCDQVLHYJDUMRgkdQwGSR2DQVLHYJDUMRgkdf4flgMznwyOoaEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv2.circle(img=blank_ig, center=(300, 100), radius=10, color=(255,0,0), thickness= -10)\n",
    "plt.imshow(blank_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff384ec7978>"
      ]
     },
     "execution_count": 11,
     "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": [
    "cv2.circle(img=blank_ig, center=(300, 400), radius=30, color=(255,0,0), thickness= -5)\n",
    "plt.imshow(blank_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff384e24400>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAD35JREFUeJzt3VusXFd9x/Hvr3EutFBMQhJFttsE4Qd4aENqQRCoogGqkCKShyCFImGhSJZ6kUBUok4rtULqQ+kDQagV1GpQTcUt5aJEES2NkqD2hRCbXEhwQ0xFyakjLJQLVEhtA/8+zDpw8Dr2meMzlz1zvh9pNHuvWTPzPx6v36y9z977pKqQpLV+Yd4FSBoeg0FSx2CQ1DEYJHUMBkkdg0FSZyrBkOTaJI8nOZ7k4DTeQ9L0ZNLHMSQ5B/gW8GZgBXgAeEdVfXOibyRpaqYxY3g1cLyq/qOq/hf4DHD9FN5H0pTsmMJr7gKeXLO+ArzmTE9I4uGXmq/fmHcBM3CU71fVxeN0nUYwZJ22buAnOQAcmML7S5t3ZN4FzED4z3G7TiMYVoA9a9Z3AydO7VRVh4BD4IxBGpppBMMDwN4kVwD/BdwE/O4U3kearvXmvovmLL9yJx4MVfV8kj8EvgycA3y8qh6b9PtImp6J/7ryrIpwU0Lztt7/wGWbMYSjVbVvnKd55KOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkzjSux7B0Tnfq5zKcfCetx2BYY7Pnfi/rmbqSmxLNpC4IURN8LWletv2MYVqDePV1nUFoEW3bYJjVt7oBoUW0LYNh3FAYdzCP83q1ideT5m3bBcNGg/hsBu/qczZ6bcNBi2Jb7Xw808ANWx+047yGOya1CLZNMGwUCpO0UUAYDhq6bREMswyFWb22NE1LHwxnOmpxFgP3dO/hrEFDtvTBMASGgxbNUgfDkM5xcLNCi2Spg2E98xyg6723swYN0dIGgyc4SWdvKYNhyN/Czhq0CJYyGNbjbEEa37YJhqFz1qAhMRjmwNmLhm5bBIMDUdqcbXd2pYZhSMeYqLfhjCHJx5OcTPLomrYLk9yd5Il2/5LWniQfSXI8ySNJrppm8etZlG317TwAzvQZLcrnt+zG2ZT4e+DaU9oOAvdU1V7gnrYO8BZgb7sdAD46mTIlzdKGwVBV/wo8fUrz9cDhtnwYuGFN+ydq5KvAziSXTapYSbNxtjsfL62qpwDa/SWtfRfw5Jp+K62tk+RAkiNJjpxlDZKmZNI7H8c+sK+qDgGHAJK4aSkNyNnOGL63uonQ7k+29hVgz5p+u4ETZ1+epHk422C4E9jflvcDd6xpf1f77cTVwHOrmxyzsih7+7fzFOl0F8mZ1cVztLENNyWSfBp4A/DSJCvAnwN/Cdye5Gbgu8DbW/cvAdcBx4EfAe+eQs1aEobAcKVq/t9dk97HsAinXC9CjdvKsn4ga3+ucLSq9o3ztG1xSPTQzD+KpTPbNsEw9MG4DF9OWh5LGQwOMmlrljIYTmcIs4Zl3ZTVclnaYBjiYBtCMEnjWNpgOJ15DU5PM9YiWepg8A+9SGdnqYPhTGYZDs4WtGiWPhjm+Ven6wzvYShoyJY+GGA+4TCvv7AtTcK2CAbYOBwmGRDuw9Ci21YXgw3jX29ws9/q44SBMwUtim0VDPCzwbnRQB4nJDYzMzAUtEi2XTCs2mj2sNZWNw0MhQW1jbcJt20wwPizh62+vrRots3OxzOZ9JWDvBLRAvID+znbesZwqq3MIPx/pWViMKzDQb5N+cH/lJsSkjoGg6SOwSCpYzBI6hgMkjoGg6SOwSCpYzBI6hgMkjoGg6SOwSCpYzBI6hgMkjoGg6SOwSCps2EwJNmT5L4kx5I8luQ9rf3CJHcneaLdv6S1J8lHkhxP8kiSq6b9Q0iarHFmDM8Df1RVrwCuBv4gySuBg8A9VbUXuKetA7wF2NtuB4CPTrxqbaAW8KYh2TAYquqpqvp6W/4hcAzYBVwPHG7dDgM3tOXrgU/UyFeBnUkum3jlOo1FHWSLWvdy2tQ+hiSXA68C7gcuraqnYBQewCWt2y7gyTVPW2ltkhbE2Nd8TPJC4PPAe6vqB8lpL5C33gPd10GSA4w2NSQNzFgzhiTnMgqFT1bVF1rz91Y3Edr9yda+AuxZ8/TdwIlTX7OqDlXVvqrad7bFS5qOcX4rEeA24FhVfWjNQ3cC+9vyfuCONe3var+duBp4bnWTQ/OSgd40VKk6806fJK8H/g34BvCT1vwnjPYz3A78CvBd4O1V9XQLkr8GrgV+BLy7qo5s8B7ueZqY9f4phzoIT611qHUujaPjztA3DIZZMBgmyWDQaY0dDB75KKljMEjqGAySOgaDpI7BIKljMEjqGAySOgaDpI7BIKljMEjqGAySOgaDpI7BIKljMEjqGAySOgaDpI7BIKljMEjqGAySOgaDpI7BIKljMEjqGAySOgaDpI7BIKljMEjqGAySOgaDpI7BIKljMEjqGAySOgaDpI7BIKljMEjqbBgMSS5I8rUkDyd5LMkHWvsVSe5P8kSSzyY5r7Wf39aPt8cvn+6PIGnSxpkx/A9wTVX9OnAlcG2Sq4EPArdW1V7gGeDm1v9m4Jmqejlwa+unuaqB3jRUGwZDjfx3Wz233Qq4Bvhcaz8M3NCWr2/rtMffmCQTq1jS1I21jyHJOUkeAk4CdwPfBp6tqudblxVgV1veBTwJ0B5/Drhondc8kORIkiNb+xEkTdpYwVBVP66qK4HdwKuBV6zXrd2vNzvo5o1Vdaiq9lXVvnGL1TgWdXK2qHUvpx2b6VxVzyb5CnA1sDPJjjYr2A2caN1WgD3ASpIdwIuBpydXsjbmINPWjPNbiYuT7GzLLwDeBBwD7gNubN32A3e05TvbOu3xe6vKPU3SAhlnxnAZcDjJOYyC5PaquivJN4HPJPkL4EHgttb/NuAfkhxnNFO4aQp1S5qiDOHLPMn8i5CW39Fx9+l55KOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6m/rbldJWjfOXhfzLm/PnjEEzM+6fG6tN9NV0OGPQ1J3tIF99njOI2XPGoKmaxDe/s4fZMxg0NZMc0IbDbBkMmoppDGTDYXYMBkkdg0ETN81vdmcNs2EwSOqMHQxJzknyYJK72voVSe5P8kSSzyY5r7Wf39aPt8cvn07pGqJZfKM7a5i+zcwY3gMcW7P+QeDWqtoLPAPc3NpvBp6pqpcDt7Z+khbIWMGQZDfwO8DftfUA1wCfa10OAze05evbOu3xN7b+khbEuDOGDwPvB37S1i8Cnq2q59v6CrCrLe8CngRojz/X+v+cJAeSHEly5CxrlzQlGwZDkrcCJ6vq6Nrmdbqe6QjWbrOwqg5V1b6q2jdWpZJmZpxzJV4HvC3JdcAFwC8zmkHsTLKjzQp2Ayda/xVgD7CSZAfwYuDpiVcuaWo2nDFU1S1VtbuqLgduAu6tqncC9wE3tm77gTva8p1tnfb4vVXljmRpgWzlOIY/Bt6X5DijfQi3tfbbgIta+/uAg1srUYtkFnuZ3ZM9fRnCl3mS+RehiZn2h2kwnLWj4+7T88hHTdw0B66hMBsGg6SOwaCpmMY3u7OF2TEYNDWTHMiGwmwZDJqqSQxoQ2H2vBispm51YG/2txUGwvw4Y9DMjDvQs4m+mg5nDJopB/xicMYgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOoYDJI6BoOkjsEgqWMwSOqMFQxJvpPkG0keSnKktV2Y5O4kT7T7l7T2JPlIkuNJHkly1TR/AEmTt5kZw29V1ZVVta+tHwTuqaq9wD1tHeAtwN52OwB8dFLFSpqNrWxKXA8cbsuHgRvWtH+iRr4K7Exy2RbeR9KMjRsMBfxLkqNJDrS2S6vqKYB2f0lr3wU8uea5K63t5yQ5kOTI6qaJpOHYMWa/11XViSSXAHcn+fcz9F3vL51X11B1CDgEkKR7XNL8jDVjqKoT7f4k8EXg1cD3VjcR2v3J1n0F2LPm6buBE5MqWNL0bRgMSX4pyYtWl4HfBh4F7gT2t277gTva8p3Au9pvJ64Gnlvd5JC0GMbZlLgU+GKS1f6fqqp/TvIAcHuSm4HvAm9v/b8EXAccB34EvHviVUuaqlTNf/M+yQ+Bx+ddx5heCnx/3kWMYVHqhMWpdVHqhPVr/dWqunicJ4+783HaHl9zfMSgJTmyCLUuSp2wOLUuSp2w9Vo9JFpSx2CQ1BlKMByadwGbsCi1LkqdsDi1LkqdsMVaB7HzUdKwDGXGIGlA5h4MSa5N8ng7Tfvgxs+Yai0fT3IyyaNr2gZ5enmSPUnuS3IsyWNJ3jPEepNckORrSR5udX6gtV+R5P5W52eTnNfaz2/rx9vjl8+izjX1npPkwSR3DbzO6V4KoarmdgPOAb4NvAw4D3gYeOUc6/lN4Crg0TVtfwUcbMsHgQ+25euAf2J0bsjVwP0zrvUy4Kq2/CLgW8Arh1Zve78XtuVzgfvb+98O3NTaPwb8Xlv+feBjbfkm4LMz/nd9H/Ap4K62PtQ6vwO89JS2iX32M/tBTvPDvRb48pr1W4Bb5lzT5acEw+PAZW35MkbHXAD8LfCO9frNqe47gDcPuV7gF4GvA69hdPDNjlP/HwBfBl7blne0fplRfbsZXVvkGuCuNpAGV2d7z/WCYWKf/bw3JcY6RXvOtnR6+Sy0aeyrGH0bD67eNj1/iNGJdnczmiU+W1XPr1PLT+tsjz8HXDSLOoEPA+8HftLWLxponTCFSyGsNe8jH8c6RXugBlF7khcCnwfeW1U/aOe0rNt1nbaZ1FtVPwauTLKT0dm5rzhDLXOpM8lbgZNVdTTJG8aoZd6f/8QvhbDWvGcMi3CK9mBPL09yLqNQ+GRVfaE1D7beqnoW+Aqj7dydSVa/mNbW8tM62+MvBp6eQXmvA96W5DvAZxhtTnx4gHUC078UwryD4QFgb9vzex6jnTh3zrmmUw3y9PKMpga3Aceq6kNDrTfJxW2mQJIXAG8CjgH3ATeeps7V+m8E7q22YTxNVXVLVe2uqssZ/T+8t6reObQ6YUaXQpjlzqfT7ES5jtEe9W8DfzrnWj4NPAX8H6OUvZnRduM9wBPt/sLWN8DftLq/Aeybca2vZzQdfAR4qN2uG1q9wK8BD7Y6HwX+rLW/DPgao9Pz/xE4v7Vf0NaPt8dfNof/B2/gZ7+VGFydraaH2+2x1XEzyc/eIx8ldea9KSFpgAwGSR2DQVLHYJDUMRgkdQwGSR2DQVLHYJDU+X8BWEWof8A3WwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## cv2.line(blank_ig, pt1=(0,0), pt2= (512,512), color=(102,255,255), thickness = 5)\n",
    "plt.imshow(blank_ig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ff384c8dd30>"
      ]
     },
     "execution_count": 18,
     "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": [
    "font = cv2.FONT_HERSHEY_PLAIN\n",
    "cv2.putText(blank_ig, text=\"HI\", org=(100,500), fontFace = font,fontScale=7, color=(255,255,255), lineType=cv2.LINE_AA)\n",
    "plt.imshow(blank_ig)"
   ]
  },
  {
   "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": 2
}