OpenCV-2 (Basic Shapes).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_1 = np.zeros(shape=(512,512,3), dtype=np.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 512, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d5194a12b0>"
      ]
     },
     "execution_count": 7,
     "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(img_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rect = cv2.rectangle(img_1,pt1=(100,100), pt2=(400,400),color=(255,0,255),thickness=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d51adbe8d0>"
      ]
     },
     "execution_count": 10,
     "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(img_rect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_circle = cv2.circle(img_1,(300,250),20,(255,0,0),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d51a7bfe10>"
      ]
     },
     "execution_count": 15,
     "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(img_circle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Put Text on Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = np.zeros((512,512,3),dtype=np.int16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d51acc1390>"
      ]
     },
     "execution_count": 17,
     "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(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = cv2.putText(img,text='HEY',org=(100,500),fontFace = cv2.FONT_HERSHEY_COMPLEX,fontScale=3,color=(255,0,255),thickness=2,lineType=cv2.LINE_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d51afa4e80>"
      ]
     },
     "execution_count": 19,
     "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(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img = np.zeros((512,512,3), dtype=np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d5192df358>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(new_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## On using dtype=np.int8, the background of an image look a little more gray than the case of using dtype=np.int16"
   ]
  },
  {
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   "execution_count": null,
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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