4_Prog_CV_DrawingOnImage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20e3fdba610>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAGiCAYAAABd6zmYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAbBElEQVR4nO3dbWzV9f3/8Vdr2wO1nlOueo4EOkkkEsLFtGh35g2T0VBd47zghiHcIGpmwLKAGhK6RYy7UzITN90cW2Im3ljs0mXoZEBsWi0zHgvUdpQLO2+gbZTTTknPqQi9fP9u+Of792hFCrTn3fp8JJ9Evp/POedzPpI+PfRLzTEzEwAADuVmewMAAHwbIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwK2uReuGFF3TDDTdoxowZKi8v16FDh7K1FQCAU1mJ1N/+9jc9/vjjeuqpp/Tee+9p5cqVqqysVG9vbza2AwBwKicbP2C2vLxct956q/7whz9IkkZHR7Vw4UL94he/0Pbt2yd7OwAAp/Im+wUHBwfV2tqqmpqa4Fpubq4qKiqUSCTGfMzAwIAGBgaCX4+OjurMmTOaM2eOcnJyJnzPAICry8zU39+v+fPnKzf32/9Qb9Ij9emnn2pkZETRaDTjejQa1fvvvz/mY2pra/X0009PxvYAAJOou7tbCxYs+Nb5KXF3X01NjVKpVDC6urqyvSUAwFVw3XXXXXR+0j9JzZ07V9dcc416enoyrvf09CgWi435mFAopFAoNBnbAwBMou/6ls2kf5IqKChQWVmZGhsbg2ujo6NqbGxUPB6f7O0AAByb9E9SkvT4449rw4YNWrVqlW677Tb97ne/09mzZ/Xggw9mYzsAAKeyEqkHHnhA//vf/7Rjxw4lk0n98Ic/1IEDB75xMwUA4PstK39P6kql02lFIpFsbwMAcIVSqZTC4fC3zk+Ju/sAAN9PRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4Ne5IHTx4UHfffbfmz5+vnJwcvfrqqxnzZqYdO3bo+uuv18yZM1VRUaEPPvggY82ZM2e0fv16hcNhFRcX6+GHH9bnn39+RW8EADD9jDtSZ8+e1cqVK/XCCy+MOf+b3/xGzz//vP70pz+ppaVF1157rSorK3X+/Plgzfr163X8+HE1NDRo7969OnjwoB555JHLfxcAgOnJroAk27NnT/Dr0dFRi8Vi9swzzwTX+vr6LBQK2SuvvGJmZidOnDBJdvjw4WDN/v37LScnxz7++ONLet1UKmWSGAwGgzHFRyqVuujX+6v6PalTp04pmUyqoqIiuBaJRFReXq5EIiFJSiQSKi4u1qpVq4I1FRUVys3NVUtLy5jPOzAwoHQ6nTEAANPfVY1UMpmUJEWj0Yzr0Wg0mEsmkyopKcmYz8vL0+zZs4M1X1dbW6tIJBKMhQsXXs1tAwCcmhJ399XU1CiVSgWju7s721sCAEyCqxqpWCwmSerp6cm43tPTE8zFYjH19vZmzA8PD+vMmTPBmq8LhUIKh8MZAwAw/V3VSC1atEixWEyNjY3BtXQ6rZaWFsXjcUlSPB5XX1+fWltbgzVNTU0aHR1VeXn51dwOAGCqG8fNfGZm1t/fb21tbdbW1maS7Nlnn7W2tjb76KOPzMxs586dVlxcbK+99podPXrU7rnnHlu0aJGdO3cueI4777zTbr75ZmtpabG3337bFi9ebOvWrbvkPXB3H4PBYEyP8V139407Um+++eaYL7RhwwYz+/I29CeffNKi0aiFQiFbvXq1dXZ2ZjzHZ599ZuvWrbOioiILh8P24IMPWn9/P5FiMBiM79n4rkjlmJlpikmn04pEItneBgDgCqVSqYveZzAl7u4DAHw/ESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDg1rgiVVtbq1tvvVXXXXedSkpKdO+996qzszNjzfnz51VdXa05c+aoqKhIa9euVU9PT8aarq4uVVVVqbCwUCUlJdq2bZuGh4ev/N0AAKaVcUWqublZ1dXVevfdd9XQ0KChoSGtWbNGZ8+eDdY89thjev3111VfX6/m5mZ98sknuv/++4P5kZERVVVVaXBwUO+8845efvll7d69Wzt27Lh67woAMD3YFejt7TVJ1tzcbGZmfX19lp+fb/X19cGakydPmiRLJBJmZrZv3z7Lzc21ZDIZrNm1a5eFw2EbGBi4pNdNpVImicFgMBhTfKRSqYt+vb+i70mlUilJ0uzZsyVJra2tGhoaUkVFRbBmyZIlKi0tVSKRkCQlEgktX75c0Wg0WFNZWal0Oq3jx4+P+ToDAwNKp9MZAwAw/V12pEZHR7V161bdfvvtWrZsmSQpmUyqoKBAxcXFGWuj0aiSyWSw5quBujB/YW4stbW1ikQiwVi4cOHlbhsAMIVcdqSqq6t17Ngx1dXVXc39jKmmpkapVCoY3d3dE/6aAIDsy7ucB23evFl79+7VwYMHtWDBguB6LBbT4OCg+vr6Mj5N9fT0KBaLBWsOHTqU8XwX7v67sObrQqGQQqHQ5WwVADCFjeuTlJlp8+bN2rNnj5qamrRo0aKM+bKyMuXn56uxsTG41tnZqa6uLsXjcUlSPB5XR0eHent7gzUNDQ0Kh8NaunTplbwXAMB0M567+TZt2mSRSMTeeustO336dDC++OKLYM3GjRuttLTUmpqa7MiRIxaPxy0ejwfzw8PDtmzZMluzZo21t7fbgQMHbN68eVZTU3PJ++DuPgaDwZge47vu7htXpL7tRV566aVgzblz5+zRRx+1WbNmWWFhod133312+vTpjOf58MMP7a677rKZM2fa3Llz7YknnrChoSEixWAwGN+z8V2Ryvl/8ZlS0um0IpFItrcBALhCqVRK4XD4W+f52X0AALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHBrXJHatWuXVqxYoXA4rHA4rHg8rv379wfz58+fV3V1tebMmaOioiKtXbtWPT09Gc/R1dWlqqoqFRYWqqSkRNu2bdPw8PDVeTcAgGllXJFasGCBdu7cqdbWVh05ckQ/+clPdM899+j48eOSpMcee0yvv/666uvr1dzcrE8++UT3339/8PiRkRFVVVVpcHBQ77zzjl5++WXt3r1bO3bsuLrvCgAwPdgVmjVrlr344ovW19dn+fn5Vl9fH8ydPHnSJFkikTAzs3379llubq4lk8lgza5duywcDtvAwMAlv2YqlTJJDAaDwZjiI5VKXfTr/WV/T2pkZER1dXU6e/as4vG4WltbNTQ0pIqKimDNkiVLVFpaqkQiIUlKJBJavny5otFosKayslLpdDr4NDaWgYEBpdPpjAEAmP7GHamOjg4VFRUpFApp48aN2rNnj5YuXapkMqmCggIVFxdnrI9Go0omk5KkZDKZEagL8xfmvk1tba0ikUgwFi5cON5tAwCmoHFH6qabblJ7e7taWlq0adMmbdiwQSdOnJiIvQVqamqUSqWC0d3dPaGvBwDwIW+8DygoKNCNN94oSSorK9Phw4f13HPP6YEHHtDg4KD6+voyPk319PQoFotJkmKxmA4dOpTxfBfu/ruwZiyhUEihUGi8WwUATHFX/PekRkdHNTAwoLKyMuXn56uxsTGY6+zsVFdXl+LxuCQpHo+ro6NDvb29wZqGhgaFw2EtXbr0SrcCAJhuxnMn3/bt2625udlOnTplR48ete3bt1tOTo698cYbZma2ceNGKy0ttaamJjty5IjF43GLx+PB44eHh23ZsmW2Zs0aa29vtwMHDti8efOspqZmPNvg7j4Gg8GYJuO77u4bV6Qeeugh+8EPfmAFBQU2b948W716dRAoM7Nz587Zo48+arNmzbLCwkK777777PTp0xnP8eGHH9pdd91lM2fOtLlz59oTTzxhQ0ND49kGkWIwGIxpMr4rUjlmZppi0um0IpFItrcBALhCqVRK4XD4W+f52X0AALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3iBQAwC0iBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHDriiK1c+dO5eTkaOvWrcG18+fPq7q6WnPmzFFRUZHWrl2rnp6ejMd1dXWpqqpKhYWFKikp0bZt2zQ8PHwlWwEATEOXHanDhw/rz3/+s1asWJFx/bHHHtPrr7+u+vp6NTc365NPPtH9998fzI+MjKiqqkqDg4N655139PLLL2v37t3asWPH5b8LAMD0ZJehv7/fFi9ebA0NDXbHHXfYli1bzMysr6/P8vPzrb6+Plh78uRJk2SJRMLMzPbt22e5ubmWTCaDNbt27bJwOGwDAwOX9PqpVMokMRgMBmOKj1QqddGv95f1Saq6ulpVVVWqqKjIuN7a2qqhoaGM60uWLFFpaakSiYQkKZFIaPny5YpGo8GayspKpdNpHT9+fMzXGxgYUDqdzhgAgOkvb7wPqKur03vvvafDhw9/Yy6ZTKqgoEDFxcUZ16PRqJLJZLDmq4G6MH9hbiy1tbV6+umnx7tVAMAUN65PUt3d3dqyZYv++te/asaMGRO1p2+oqalRKpUKRnd396S9NgAge8YVqdbWVvX29uqWW25RXl6e8vLy1NzcrOeff155eXmKRqMaHBxUX19fxuN6enoUi8UkSbFY7Bt3+1349YU1XxcKhRQOhzMGAGD6G1ekVq9erY6ODrW3twdj1apVWr9+ffDP+fn5amxsDB7T2dmprq4uxeNxSVI8HldHR4d6e3uDNQ0NDQqHw1q6dOlVelsAgGlhnDf2fcNX7+4zM9u4caOVlpZaU1OTHTlyxOLxuMXj8WB+eHjYli1bZmvWrLH29nY7cOCAzZs3z2pqai75Nbm7j8FgMKbH+K67+8Z948R3+e1vf6vc3FytXbtWAwMDqqys1B//+Mdg/pprrtHevXu1adMmxeNxXXvttdqwYYN+/etfX+2tAACmuBwzs2xvYrzS6bQikUi2twEAuEKpVOqi9xnws/sAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuESkAgFtECgDgFpECALhFpAAAbhEpAIBbRAoA4BaRAgC4RaQAAG4RKQCAW0QKAOAWkQIAuEWkAABuTclImVm2twAAuAq+6+v5lIzUZ599lu0tAACugv7+/ovO503SPq6q2bNnS5K6uroUiUSyvBu/0um0Fi5cqO7uboXD4Wxvxy3O6dJwTpeGc7o0Zqb+/n7Nnz//ouumZKRyc7/8ABiJRPhNcAnC4TDndAk4p0vDOV0azum7XcqHjCn5x30AgO8HIgUAcGtKRioUCumpp55SKBTK9lZc45wuDed0aTinS8M5XV05xv3cAACnpuQnKQDA9wORAgC4RaQAAG4RKQCAW1MyUi+88IJuuOEGzZgxQ+Xl5Tp06FC2tzSpDh48qLvvvlvz589XTk6OXn311Yx5M9OOHTt0/fXXa+bMmaqoqNAHH3yQsebMmTNav369wuGwiouL9fDDD+vzzz+fxHcxsWpra3XrrbfquuuuU0lJie699151dnZmrDl//ryqq6s1Z84cFRUVae3aterp6clY09XVpaqqKhUWFqqkpETbtm3T8PDwZL6VCbVr1y6tWLEi+Iun8Xhc+/fvD+Y5o7Ht3LlTOTk52rp1a3CNs5ogNsXU1dVZQUGB/eUvf7Hjx4/bz3/+cysuLraenp5sb23S7Nu3z371q1/ZP/7xD5Nke/bsyZjfuXOnRSIRe/XVV+0///mP/exnP7NFixbZuXPngjV33nmnrVy50t59913797//bTfeeKOtW7dukt/JxKmsrLSXXnrJjh07Zu3t7fbTn/7USktL7fPPPw/WbNy40RYuXGiNjY125MgR+9GPfmQ//vGPg/nh4WFbtmyZVVRUWFtbm+3bt8/mzp1rNTU12XhLE+Kf//yn/etf/7L//ve/1tnZab/85S8tPz/fjh07Zmac0VgOHTpkN9xwg61YscK2bNkSXOesJsaUi9Rtt91m1dXVwa9HRkZs/vz5Vltbm8VdZc/XIzU6OmqxWMyeeeaZ4FpfX5+FQiF75ZVXzMzsxIkTJskOHz4crNm/f7/l5OTYxx9/PGl7n0y9vb0myZqbm83syzPJz8+3+vr6YM3JkydNkiUSCTP78j8GcnNzLZlMBmt27dpl4XDYBgYGJvcNTKJZs2bZiy++yBmNob+/3xYvXmwNDQ12xx13BJHirCbOlPrjvsHBQbW2tqqioiK4lpubq4qKCiUSiSzuzI9Tp04pmUxmnFEkElF5eXlwRolEQsXFxVq1alWwpqKiQrm5uWppaZn0PU+GVCol6f//cOLW1lYNDQ1lnNOSJUtUWlqacU7Lly9XNBoN1lRWViqdTuv48eOTuPvJMTIyorq6Op09e1bxeJwzGkN1dbWqqqoyzkTi99NEmlI/YPbTTz/VyMhIxr9kSYpGo3r//feztCtfksmkJI15RhfmksmkSkpKMubz8vI0e/bsYM10Mjo6qq1bt+r222/XsmXLJH15BgUFBSouLs5Y+/VzGuscL8xNFx0dHYrH4zp//ryKioq0Z88eLV26VO3t7ZzRV9TV1em9997T4cOHvzHH76eJM6UiBVyO6upqHTt2TG+//Xa2t+LSTTfdpPb2dqVSKf3973/Xhg0b1NzcnO1tudLd3a0tW7aooaFBM2bMyPZ2vlem1B/3zZ07V9dcc8037pjp6elRLBbL0q58uXAOFzujWCym3t7ejPnh4WGdOXNm2p3j5s2btXfvXr355ptasGBBcD0Wi2lwcFB9fX0Z679+TmOd44W56aKgoEA33nijysrKVFtbq5UrV+q5557jjL6itbVVvb29uuWWW5SXl6e8vDw1Nzfr+eefV15enqLRKGc1QaZUpAoKClRWVqbGxsbg2ujoqBobGxWPx7O4Mz8WLVqkWCyWcUbpdFotLS3BGcXjcfX19am1tTVY09TUpNHRUZWXl0/6nieCmWnz5s3as2ePmpqatGjRooz5srIy5efnZ5xTZ2enurq6Ms6po6MjI+gNDQ0Kh8NaunTp5LyRLBgdHdXAwABn9BWrV69WR0eH2tvbg7Fq1SqtX78++GfOaoJk+86N8aqrq7NQKGS7d++2EydO2COPPGLFxcUZd8xMd/39/dbW1mZtbW0myZ599llra2uzjz76yMy+vAW9uLjYXnvtNTt69Kjdc889Y96CfvPNN1tLS4u9/fbbtnjx4ml1C/qmTZssEonYW2+9ZadPnw7GF198EazZuHGjlZaWWlNTkx05csTi8bjF4/Fg/sItw2vWrLH29nY7cOCAzZs3b1rdMrx9+3Zrbm62U6dO2dGjR2379u2Wk5Njb7zxhplxRhfz1bv7zDiriTLlImVm9vvf/95KS0utoKDAbrvtNnv33XezvaVJ9eabb5qkb4wNGzaY2Ze3oT/55JMWjUYtFArZ6tWrrbOzM+M5PvvsM1u3bp0VFRVZOBy2Bx980Pr7+7PwbibGWOcjyV566aVgzblz5+zRRx+1WbNmWWFhod133312+vTpjOf58MMP7a677rKZM2fa3Llz7YknnrChoaFJfjcT56GHHrIf/OAHVlBQYPPmzbPVq1cHgTLjjC7m65HirCYG/6sOAIBbU+p7UgCA7xciBQBwi0gBANwiUgAAt4gUAMAtIgUAcItIAQDcIlIAALeIFADALSIFAHCLSAEA3CJSAAC3/g8kFCYeAwd78wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blank_img = np.zeros(shape=(500,500,3), dtype=np.int16)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw rectangles on the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20e42057490>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv2.rectangle(blank_img, pt1=(300,10), pt2=(450,200), color=(255,0,0), thickness=5)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw a filled shaped on the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20e43183fa0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AwFlECgDgLCIFAHAWkQIAOItIAZJ0WFKF30M47vuSOv0eAsMNkQIk6aikX/s9hOO2SOr2ewgMN0QKAOAsIgX0sDML5+O6wCdECuhRLulDv4dw1PckfeD3EBiOiBTQ47ikLr+HcNQJsWkCviBSwNlWSzrt9xCOqZf0E7+HwHBFpICz/VLSb/0ewiEmaZfioQJ8QKSAsx2XtNLvIRzSJWmp30NgOCNSwLk+kLTR7yEcsVrSSb+HwHBGpIBzfSapStIXfg/is3+WtF18jQ6+IlJAb56X9Hu/h/DZTyXt9HsIDHdECriQxzR8v4n1t5Je8HsIgEgBF7ZXw/P1/Ezxpzsb/B4EIFLAhcUkfU1Sjd+DJNmPJZX6PQQQR6SAi6lXfLffcNk80C7pH8/8E3AAkQIu5XENj1dcOC5phaRNPs8BnIVIAZdiij/99X/8HmQQmaT/IekVvwcBEhEpoC+OKx6qf9DQe+rvC0lfl/S//R4EOF+G3wMAKaNNUomkNEl/5fMsA+ULxZ/OJFBwFJEC+mvZmX8+oHiwUlXPU3wECg7j6T6gv1oVf9HV/6vUfervuOKxfdnvQYCL45EUcDlO6M9P/T3g8yz91S7pv4tNEkgJPJICLpcpvuHgPyr+Y+ddfwklU/wbdf+TCBRSBo+kgCvRovg3v74n6ReSCiRN8HGeC/mt4i91VCq+URcphUgBA+FzSbMkFUtaqPiL047wdaK4f5b0/xR/sdjh/qruSElEChhIv5D0T5LekfSwpHsU/68smbsATfGfqLta8Ud4w+21BzGk9OtrUmvXrtX06dMVDAYVDAYViUS0efNm73h7e7tKS0s1btw4ZWdna9GiRWpqakp4Hw0NDVq4cKFGjx6tCRMm6Mknn1RXV9fAfDSAC04rHqsHJF2n+Gv/NV30LQZOveIv4TRW0v8SgULqs354++237Z133rGPPvrI6uvr7amnnrLMzEzbv3+/mZktWbLECgoKrLKy0nbv3m1z5861efPmeW/f1dVl06ZNs6KiItu7d69VVFRYXl6elZWV9WcMi8Vipvj/L7JYqbHulunvZHpLNuC/umV67sz7n+zAx8pi9WPFYrGLfr7vV6R6M2bMGHv11VetpaXFMjMzbcOGDd6xQ4cOmSSrrq42M7OKigpLT0+3aDTqnbN27VoLBoPW0dHR5/skUqyUXRNk+jdnrV/IdKKX9YVMXRc4dkKmvz/n/WQ68LGxWJexBi1SXV1d9vrrr1tWVpYdOHDAKisrTZL96U9/SjivsLDQnnvuOTMzW716tc2YMSPh+O9+9zuTZHv27LngfbW3t1ssFvNWY2Oj7xeWxRqQlSFToJd1tUzfucCxgEzpDszOYg3AulSk+v19Uvv27VN2drYCgYCWLFmijRs3aurUqYpGo8rKylJubm7C+aFQSNFoVJIUjUYVCoXOO95z7ELKy8uVk5PjrYKCgv6ODbipS1JHL6tN0t9c4FiHpG4/hgWSr9+Rmjx5surq6lRTU6OlS5eqpKREBw8eHIzZPGVlZYrFYt5qbGwc1PsDALih31vQs7KydMMNN0iSZs6cqV27dun555/Xfffdp1OnTqmlpSXh0VRTU5PC4bAkKRwOa+fOnQnvr2f3X885vQkEAgoEAv0dFQCQ4q74ZZG6u7vV0dGhmTNnKjMzU5WVld6x+vp6NTQ0KBKJSJIikYj27dun5uZm75ytW7cqGAxq6tSpVzoKAGCo6c9miVWrVllVVZUdOXLEPvzwQ1u1apWlpaXZu+++a2bxLeiFhYW2bds22717t0UiEYtEIt7b92xBX7BggdXV1dmWLVts/PjxbEFnsVisYboGdHffQw89ZNddd51lZWXZ+PHjbf78+V6gzMxOnjxpjz32mI0ZM8ZGjx5t9957rx09ejThfXzyySd255132qhRoywvL89WrlxpnZ2d/RmDSLFYLNYQWZeKVJqZmVJMa2urcnJy/B4DAHCFYrGYgsHgBY/zozoAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOOuKIvXMM88oLS1NK1as8G5rb29XaWmpxo0bp+zsbC1atEhNTU0Jb9fQ0KCFCxdq9OjRmjBhgp588kl1dXVdySgAgCHosiO1a9cuvfLKK5o+fXrC7Y8//rh+/vOfa8OGDaqqqtIf/vAHffWrX/WOnz59WgsXLtSpU6f0q1/9Sj/+8Y/12muvac2aNZf/UQAAhia7DG1tbTZp0iTbunWr3X777bZ8+XIzM2tpabHMzEzbsGGDd+6hQ4dMklVXV5uZWUVFhaWnp1s0GvXOWbt2rQWDQevo6OjT/cdiMZPEYrFYrBRfsVjsop/vL+uRVGlpqRYuXKiioqKE22tra9XZ2Zlw+5QpU1RYWKjq6mpJUnV1tW666SaFQiHvnOLiYrW2turAgQO93l9HR4daW1sTFgBg6Mvo7xusX79ee/bs0a5du847Fo1GlZWVpdzc3ITbQ6GQotGod87Zgeo53nOsN+Xl5Xr66af7OyoAIMX165FUY2Ojli9frp/85CcaOXLkYM10nrKyMsViMW81NjYm7b4BAP7pV6Rqa2vV3NysW265RRkZGcrIyFBVVZVeeOEFZWRkKBQK6dSpU2ppaUl4u6amJoXDYUlSOBw+b7dfz597zjlXIBBQMBhMWACAoa9fkZo/f7727dunuro6b82aNUuLFy/2fp+ZmanKykrvberr69XQ0KBIJCJJikQi2rdvn5qbm71ztm7dqmAwqKlTpw7QhwUAGBL6ubHvPGfv7jMzW7JkiRUWFtq2bdts9+7dFolELBKJeMe7urps2rRptmDBAqurq7MtW7bY+PHjraysrM/3ye4+FovFGhrrUrv7+r1x4lK+973vKT09XYsWLVJHR4eKi4v10ksvecdHjBihTZs2aenSpYpEIrrqqqtUUlKib33rWwM9CgAgxaWZmfk9RH+1trYqJyfH7zEAAFcoFotddJ8Br90HAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHAWkQIAOItIAQCcRaQAAM4iUgAAZxEpAICziBQAwFlECgDgLCIFAHBWSkbKzPweAQAwAC71+TwlI/X555/7PQIAYAC0tbVd9HhGkuYYUGPHjpUkNTQ0KCcnx+dp3NXa2qqCggI1NjYqGAz6PY6zuE59w3XqG65T35iZ2tralJ+ff9HzUjJS6enxB4A5OTn8S9AHwWCQ69QHXKe+4Tr1Ddfp0vryICMln+4DAAwPRAoA4KyUjFQgENA3v/lNBQIBv0dxGtepb7hOfcN16huu08BKM/ZzAwAclZKPpAAAwwORAgA4i0gBAJxFpAAAzkrJSL344ou6/vrrNXLkSM2ZM0c7d+70e6Sk2rFjh+6++27l5+crLS1Nb731VsJxM9OaNWt0zTXXaNSoUSoqKtLHH3+ccM6xY8e0ePFiBYNB5ebm6uGHH9bx48eT+FEMrvLyct166626+uqrNWHCBN1zzz2qr69POKe9vV2lpaUaN26csrOztWjRIjU1NSWc09DQoIULF2r06NGaMGGCnnzySXV1dSXzQxlUa9eu1fTp071vPI1EItq8ebN3nGvUu2eeeUZpaWlasWKFdxvXapBYilm/fr1lZWXZj370Iztw4IA98sgjlpuba01NTX6PljQVFRX2jW98w958802TZBs3bkw4/swzz1hOTo699dZb9utf/9q+/OUv28SJE+3kyZPeOXfccYfNmDHDPvjgA/vlL39pN9xwg91///1J/kgGT3Fxsa1bt872799vdXV1dtddd1lhYaEdP37cO2fJkiVWUFBglZWVtnv3bps7d67NmzfPO97V1WXTpk2zoqIi27t3r1VUVFheXp6VlZX58SENirffftveeecd++ijj6y+vt6eeuopy8zMtP3795sZ16g3O3futOuvv96mT59uy5cv927nWg2OlIvU7NmzrbS01Pvz6dOnLT8/38rLy32cyj/nRqq7u9vC4bA9++yz3m0tLS0WCATs9ddfNzOzgwcPmiTbtWuXd87mzZstLS3NPv3006TNnkzNzc0myaqqqswsfk0yMzNtw4YN3jmHDh0ySVZdXW1m8f8ZSE9Pt2g06p2zdu1aCwaD1tHRkdwPIInGjBljr776KteoF21tbTZp0iTbunWr3X777V6kuFaDJ6We7jt16pRqa2tVVFTk3Zaenq6ioiJVV1f7OJk7jhw5omg0mnCNcnJyNGfOHO8aVVdXKzc3V7NmzfLOKSoqUnp6umpqapI+czLEYjFJf35x4traWnV2diZcpylTpqiwsDDhOt10000KhULeOcXFxWptbdWBAweSOH1ynD59WuvXr9eJEycUiUS4Rr0oLS3VwoULE66JxL9PgymlXmD2s88+0+nTpxP+kiUpFArp8OHDPk3llmg0Kkm9XqOeY9FoVBMmTEg4npGRobFjx3rnDCXd3d1asWKFbrvtNk2bNk1S/BpkZWUpNzc34dxzr1Nv17Hn2FCxb98+RSIRtbe3Kzs7Wxs3btTUqVNVV1fHNTrL+vXrtWfPHu3ateu8Y/z7NHhSKlLA5SgtLdX+/fv1/vvv+z2KkyZPnqy6ujrFYjG98cYbKikpUVVVld9jOaWxsVHLly/X1q1bNXLkSL/HGVZS6um+vLw8jRgx4rwdM01NTQqHwz5N5Zae63CxaxQOh9Xc3JxwvKurS8eOHRty13HZsmXatGmTtm/frmuvvda7PRwO69SpU2ppaUk4/9zr1Nt17Dk2VGRlZemGG27QzJkzVV5erhkzZuj555/nGp2ltrZWzc3NuuWWW5SRkaGMjAxVVVXphRdeUEZGhkKhENdqkKRUpLKysjRz5kxVVlZ6t3V3d6uyslKRSMTHydwxceJEhcPhhGvU2tqqmpoa7xpFIhG1tLSotrbWO2fbtm3q7u7WnDlzkj7zYDAzLVu2TBs3btS2bds0ceLEhOMzZ85UZmZmwnWqr69XQ0NDwnXat29fQtC3bt2qYDCoqVOnJucD8UF3d7c6Ojq4RmeZP3++9u3bp7q6Om/NmjVLixcv9n7PtRokfu/c6K/169dbIBCw1157zQ4ePGiPPvqo5ebmJuyYGera2tps7969tnfvXpNkzz33nO3du9d+//vfm1l8C3pubq797Gc/sw8//NC+8pWv9LoF/eabb7aamhp7//33bdKkSUNqC/rSpUstJyfH3nvvPTt69Ki3vvjiC++cJUuWWGFhoW3bts12795tkUjEIpGId7xny/CCBQusrq7OtmzZYuPHjx9SW4ZXrVplVVVVduTIEfvwww9t1apVlpaWZu+++66ZcY0u5uzdfWZcq8GScpEyM/vBD35ghYWFlpWVZbNnz7YPPvjA75GSavv27SbpvFVSUmJm8W3oq1evtlAoZIFAwObPn2/19fUJ7+Pzzz+3+++/37Kzsy0YDNqDDz5obW1tPnw0g6O36yPJ1q1b551z8uRJe+yxx2zMmDE2evRou/fee+3o0aMJ7+eTTz6xO++800aNGmV5eXm2cuVK6+zsTPJHM3geeughu+666ywrK8vGjx9v8+fP9wJlxjW6mHMjxbUaHPyoDgCAs1Lqa1IAgOGFSAEAnEWkAADOIlIAAGcRKQCAs4gUAMBZRAoA4CwiBQBwFpECADiLSAEAnEWkAADOIlIAAGf9f6Cjr+oLnMD2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv2.circle(img=blank_img, center=(250,250), radius=50, color=(0,255,0), thickness=-1)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing text on the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20e431f9970>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "cv2.putText(blank_img, text='Warning', org=(10,400), fontFace=font, fontScale=2, color=(255,0,0), thickness=3, lineType=cv2.LINE_AA)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}