Virtual Mouse using Colour Segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPERIMENTATION\n",
    "Before starting off with the project a brief experiment is done to demonstrate the process of colour segmentation\n",
    "\n",
    "\n",
    "\n",
    "# STEP1:Read the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import cv2 \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "img=cv2.imread('RED_DOT.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP2: Display the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Open' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-4c317a81375c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'img'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mOpen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdestroyAllWindows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Open' is not defined"
     ]
    }
   ],
   "source": [
    "cv2.imshow('img',Open)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP3: Convert color space and create image mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)\n",
    "lower_red=np.array((160,100,100))\n",
    "upper_red=np.array((179,255,255))\n",
    "mask = cv2.inRange(hsv,lower_red,upper_red)\n",
    "\n",
    "kernel = np.ones(shape=(3,3))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Close = cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernel,iterations=3)\n",
    "\n",
    "res=cv2.bitwise_and(img,img,mask=Close)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP4: Display the resulting image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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E4fSkvfj90bSKZ6n3Tp/u3vLHiwcX4zSEUolGczNjldvP+H9ghlPPN2NFZAz4G+D9qnrq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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(res)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP5: Load Video Source and apply the above filter to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lower_red = np.array((160,100,100))\n",
    "upper_red = np.array((179,255,255))\n",
    "lower_yellow = np.array((25,50,50))\n",
    "upper_yellow = np.array((32,255,255))\n",
    "lower_blue = np.array((50,90,90))\n",
    "upper_blue = np.array((60,110,110))\n",
    "kernel = np.ones((3,3))\n",
    "cap = cv2.VideoCapture(0)\n",
    "while True:\n",
    "    ret,frame = cap.read()\n",
    "    hsv = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)\n",
    "    mask1 = cv2.inRange(hsv,lower_red,upper_red)\n",
    "    mask2 = cv2.inRange(hsv,lower_blue,upper_blue)\n",
    "    mask3 = cv2.inRange(hsv,lower_yellow,upper_yellow)\n",
    "    \n",
    "    \n",
    "    #med = cv2.medianBlur(mask,3)\n",
    "    #maskOpen = cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernel)\n",
    "    \n",
    "    maskClose1 = cv2.morphologyEx(mask1,cv2.MORPH_CLOSE,kernel,iterations =3)\n",
    "    maskClose2 = cv2.morphologyEx(mask2,cv2.MORPH_CLOSE,kernel,iterations =3)\n",
    "    maskClose3 = cv2.morphologyEx(mask3,cv2.MORPH_CLOSE,kernel,iterations =3)\n",
    "    mask_temp = cv2.bitwise_or(mask1,mask2)\n",
    "    mask_final = cv2.bitwise_or(mask_temp,mask3)\n",
    "    res1=cv2.bitwise_and(frame,frame,mask=mask_final)\n",
    "    res2=cv2.bitwise_and(frame,frame,mask=maskClose2)\n",
    "    res3=cv2.bitwise_or(res2,res2,mask=maskClose3)\n",
    "    \n",
    "    \n",
    "    \n",
    "    #cv2.findContours(res,cv2.RETR_EXTERNAL)\n",
    "    cv2.imshow('frame',res2)\n",
    "    if(cv2.waitKey(1) & 0xff == ord('q')):\n",
    "        break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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