02-Drawing-on-Images.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.pieriandata.com\"><img src=\"../DATA/Logo.jpg\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drawing on Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "blank_img = np.zeros(shape=(512,512,3),dtype=np.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 512, 3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blank_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9cf3f0b70>"
      ]
     },
     "execution_count": 37,
     "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_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "# Shapes\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rectangles\n",
    "\n",
    "* img Image.\n",
    "* pt1 Vertex of the rectangle.\n",
    "* pt2 Vertex of the rectangle opposite to pt1 .\n",
    "* color Rectangle color or brightness (grayscale image).\n",
    "* thickness Thickness of lines that make up the rectangle. Negative values, like #FILLED,mean that the function has to draw a filled rectangle.\n",
    "* lineType Type of the line. See #LineTypes\n",
    "* shift Number of fractional bits in the point coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pt1 = top left\n",
    "# pt2 = bottom right\n",
    "cv2.rectangle(blank_img,pt1=(384,0),pt2=(510,128),color=(0,255,0),thickness=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9cf80edd8>"
      ]
     },
     "execution_count": 41,
     "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.rectangle(blank_img,pt1=(384,0),pt2=(510,128),color=(0,255,0))\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's practice by drawing a blue rectangle in the middle of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9cf86f320>"
      ]
     },
     "execution_count": 42,
     "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": [
    "# pt1 = top left\n",
    "# pt2 = bottom right\n",
    "cv2.rectangle(blank_img,pt1=(200,200),pt2=(300,300),color=(0,0,255),thickness=5)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9cf8c82b0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dps6T9d8E3Fdtw3iWquqWqtpRVVcw+n94X1W9d2h1wgZdCmEjdz6dZifK9Yz2qH8X+LM51/J54Gng/xil7F5G2433Ak+2+4tb3wB/2+r+FrB7g2t9M6Ph4KPAw+12/dDqBX4DeKjV+Rjw5639VcA3GJ2e/0/Aha39ojZ/rC1/1Rz+H7yFF7+VGFydraZH2u3xk+vNNN97j3yU1Jn3poSkATIYJHUMBkkdg0FSx2CQ1DEYJHUMBkkdg0FS5/8BHPQk3DM9orcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv2.circle(img=blank_img, center=(100,100), radius=50, color=(255,0,0), thickness=5)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filled In"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9cf922320>"
      ]
     },
     "execution_count": 44,
     "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_img, center=(400,400), radius=50, color=(255,0,0), thickness=-1)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9cf9d3c50>"
      ]
     },
     "execution_count": 46,
     "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": [
    "# Draw a diagonal blue line with thickness of 5 px\n",
    "cv2.line(blank_img,pt1=(0,0),pt2=(511,511),color=(102, 255, 255),thickness=5)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9d0a59f60>"
      ]
     },
     "execution_count": 48,
     "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_SIMPLEX\n",
    "cv2.putText(blank_img,text='Hello',org=(10,500), fontFace=font,fontScale= 4,color=(255,255,255),thickness=2,lineType=cv2.LINE_AA)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polygons\n",
    "\n",
    "To draw a polygon, first you need coordinates of vertices. Make those points into an array of shape ROWSx1x2 where ROWS are number of vertices and it should be of type int32. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "blank_img = np.zeros(shape=(512,512,3),dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "vertices = np.array([[100,300],[200,200],[400,300],[200,400]],np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pts = vertices.reshape((-1,1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[100, 300]],\n",
       "\n",
       "       [[200, 200]],\n",
       "\n",
       "       [[400, 300]],\n",
       "\n",
       "       [[200, 400]]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a9d0cd3ba8>"
      ]
     },
     "execution_count": 69,
     "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.polylines(blank_img,[pts],isClosed=True,color=(255,0,0),thickness=5)\n",
    "plt.imshow(blank_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Play around with this! Add shapes. "
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}