2_Drawing_functions_on_images.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/JaledMC/Learning-Artificial-Vision/blob/master/Import_images.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Drawing functions on images\n",
    "\n",
    "It is possible to realize arithmetic operations on images using *OpenCV*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read image and create a list of images\n",
    "\n",
    "Read image and create a list of images containing the original picture and its copies.\n",
    "\n",
    "\n",
    "**_IMPORTANT_** : There are some methods in `cv2` that modifies the original image. So it is important to create a copy using *Python* method  `copy()` in order to mantaing the original image and aporate in copies.\n",
    "\n",
    "Exercise:\n",
    "\n",
    "\n",
    "Generate multiple image clones of the image in a list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = cv2.imread('imgs/windows.png')\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Generate multiple image clones of the image in a list\n",
    "photos = [image.copy() for i in range(10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Operate on images\n",
    "\n",
    "* Draw a line.\n",
    "* Draw a circle.\n",
    "* Draw a rectangle.\n",
    "* Draw a polygon.\n",
    "* Insert text\n",
    "\n",
    "\n",
    "**_Good practice_**: Use relative drawing, this is using formulas like: \n",
    "    $line_{width} = (width*height)/100$\n",
    "\n",
    "\n",
    "**_Note_**: Reload images (*code block below*) every time you want to draw again as if e.g.:\n",
    "   \n",
    "   <span style=\"color:red\">**WRONG**</span>:\n",
    "   1. Draw a 50px width line.\n",
    "   2. Draw a 20px width line (no effect because there is a 50px width line from last time.\n",
    "   \n",
    "   \n",
    "   <span style=\"color:green\">**RIGHT**</span>:\n",
    "   1. Draw a 50px width line.\n",
    "   3. Reload images.\n",
    "   2. Draw a 20px width line (no effect because there is a 50px width line from last time.\n",
    "  \n",
    "**_SEE ALL OPERATIONS BELOW_**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw a line\n",
    "\n",
    "To draw a line it is possible to use `cv2`'s method `line`. \n",
    "\n",
    "`cv2.line(image, (start coordinate, finish coordinate), color, width);`\n",
    "\n",
    "Exercise:\n",
    "\n",
    "Draw a line on a photo of the previous list and plot the original and the new one with a line\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line width: 50\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.imread('imgs/windows.png')\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Generate multiple image clones of the image in a list\n",
    "photos = [image.copy() for i in range(10)]\n",
    "width, height, dimension = photos[1].shape\n",
    "line_width = int(width*height/1000)\n",
    "\n",
    "# check line_width\n",
    "print(\"line width:\",line_width)\n",
    "\n",
    " # If you want to see output and check that there are 250 250 250 value pixels, delete ;\n",
    "fig, axs = plt.subplots(1, 2, figsize=(15, 15))\n",
    "axs[0].imshow(photos[1])\n",
    "axs[0].set_title(\"Original img\")\n",
    "axs[1].imshow(cv2.line(photos[1], (0, 0), (500, 450), (250, 250, 250), line_width))\n",
    "axs[1].set_title(\"Converted to gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw a circle\n",
    "\n",
    "To draw a circle it is possible to use `cv2`'s method `circle`. \n",
    "\n",
    "`cv2.circle(image, centre_coordinate, radius, color, width)`\n",
    "**_note_**: If `width` value is negative, then the circle if filled: (*negative == fill*)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line width: 50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.imread('imgs/windows.png')\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Generate multiple image clones of the image in a list\n",
    "photos = [image.copy() for i in range(10)]\n",
    "width, height, dimension = photos[1].shape\n",
    "line_width = int(width*height/1000)\n",
    "\n",
    "# check line_width\n",
    "print(\"line width:\",line_width)\n",
    "\n",
    " # If you want to see output and check that there are 250 250 250 value pixels, delete ;\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 15))\n",
    "axs[0].imshow(photos[1])\n",
    "axs[0].set_title(\"Original img\")\n",
    "axs[1].imshow(cv2.circle(photos[1], (round(width/2), round(height/2)), 100, (250, 250, 250), 10))\n",
    "axs[1].set_title(\"Not filled circle\")\n",
    "photos1 = [image.copy() for i in range(10)]\n",
    "axs[2].imshow(cv2.circle(photos1[1], (round(width/2), round(height/2)), 100, (250, 250, 250), -10))\n",
    "axs[2].set_title(\"FIlled circle\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw a rectangle\n",
    "\n",
    "To draw a rectangle it is possible to use `cv2`'s method `rectangle`.\n",
    "\n",
    "`cv2.rectangle(image, border_coor_1, border_coor_2, color, width)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "line width: 50\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.imread('imgs/windows.png')\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Generate multiple image clones of the image in a list\n",
    "photos = [image.copy() for i in range(10)]\n",
    "width, height, dimension = photos[1].shape\n",
    "line_width = int(width*height/1000)\n",
    "\n",
    "# check line_width\n",
    "print(\"line width:\",line_width)\n",
    "\n",
    " # If you want to see output and check that there are 250 250 250 value pixels, delete ;\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 15))\n",
    "axs[0].imshow(photos[1])\n",
    "axs[0].set_title(\"Original img\")\n",
    "axs[1].imshow(cv2.rectangle(photos[1], (100, 100), (200,150), (250, 250, 250), 10))\n",
    "axs[1].set_title(\"Not filled rectangle\")\n",
    "photos1 = [image.copy() for i in range(10)]\n",
    "axs[2].imshow(cv2.rectangle(photos1[1], (100, 100), (200,150), (250, 250, 250), -10))\n",
    "axs[2].set_title(\"Filled rectangle\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw a polygon\n",
    "\n",
    "To draw a polygon it is possible to use `cv2`'s method `polylines`. \n",
    "\n",
    " set of points. Params: \n",
    " \n",
    " `cv2.polycicles(image, points, close-shape, color, width)`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Not filled rectangle')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.imread('imgs/windows.png')\n",
    "image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# Generate multiple image clones of the image in a list\n",
    "photos = [image.copy() for i in range(10)]\n",
    "# first select some points in form of a numpy array\n",
    "points = np.array([[10, 10], [20, 106], [130, 200], [58, 20], [10, 30]], np.int32)\n",
    "fig, axs = plt.subplots(1, 2, figsize=(15, 15))\n",
    "axs[0].imshow(photos[1])\n",
    "axs[0].set_title(\"Original img\")\n",
    "axs[1].imshow(cv2.polylines(photos[1], [points], True, (255, 255, 255), 10))\n",
    "axs[1].set_title(\"Not filled rectangle\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Insert text in image\n",
    " \n",
    " to put text on the image it is possible to use `cv2`'s method `putText`.\n",
    " \n",
    " `cv2.putText(image, text, origin, font, fontScale, color, thickness, lineType)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "\n",
    "text = 'OpenCV'\n",
    "cv2.putText(photos[0], text, (int(height/2)-60, int(width/2)-60), font, 1, (200, 255, 155), 3, cv2.LINE_AA);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrap up\n",
    "\n",
    "Following points need to be clear in your mind to be able to advance in `cv2`:\n",
    "\n",
    "* Create a copy of the image in order to not rewrite the original:\n",
    "    * *idea:* list of images.\n",
    "    \n",
    "* Reload images: \n",
    "    *  Reset list of images.\n",
    "   \n",
    "* Draw on images:\n",
    "    *  Use relative drawing so it is simple to refactor when you change images.\n",
    "    *  OpenCV has different shapes to draw including text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "Import_images.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}