contours_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features of Contours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zawster/.local/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['box', 'plt']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Moments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'m00': 95748.0, 'm10': 64523721.5, 'm01': 69713456.83333333, 'm20': 43986758871.166664, 'm11': 47245339834.0, 'm02': 51953384858.5, 'm30': 30322909076193.152, 'm21': 32384203086395.7, 'm12': 35396872258949.47, 'm03': 39568305323695.35, 'mu20': 504799600.89927673, 'mu11': 266064303.27067566, 'mu02': 1195498911.5034103, 'mu30': 265503332.265625, 'mu21': -848651645.579895, 'mu12': -1488649206.0310059, 'mu03': 536010483.8671875, 'nu20': 0.055062963389920526, 'nu11': 0.02902199004170987, 'nu02': 0.1304036545978506, 'nu30': 9.359350215917031e-05, 'nu21': -0.00029916114025831006, 'nu12': -0.0005247689039906978, 'nu03': 0.00018895091805842741}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2 as cv\n",
    "img = cv.imread('images/P0147.png',0)\n",
    "ret,thresh = cv.threshold(img,127,255,0)\n",
    "im2,contours,hierarchy = cv.findContours(thresh, 1, 2)\n",
    "cnt = contours[0]\n",
    "M = cv.moments(cnt)\n",
    "print( M )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### From this moments, you can extract useful data like area, centroid etc. Centroid is given by the relations, Cx=M10/M00 and Cy=M01/M00. This can be done as follows: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cx = int(M['m10']/M['m00'])\n",
    "cy = int(M['m01']/M['m00'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area of Contour is: 95748.0\n"
     ]
    }
   ],
   "source": [
    "### Contour Area\n",
    "area = cv.contourArea(cnt)\n",
    "print(\"Area of Contour is:\",area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Circles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "img11 = cv2.imread('images/P0147.png',0)\n",
    "img = cv2.resize(img11, (400,400))\n",
    "ret,thresh = cv2.threshold(img,127,255,0)\n",
    "im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)\n",
    "cnt = contours[0]\n",
    "M = cv2.moments(cnt)\n",
    "(x,y),radius = cv2.minEnclosingCircle(cnt)\n",
    "center = (int(x),int(y))\n",
    "radius = int(radius)\n",
    "radius += 5\n",
    "# for i in range(4,0,-1):\n",
    "img2 = cv2.circle(img,center,radius,(0,255,0),2)\n",
    "# print(radius)\n",
    "# img2 = cv2.imread('H0017.jpg',0)\n",
    "# img1 = cv2.resize(img2, (400,400))\n",
    "# fimg = cv2.bitwise_or(img1,img)\n",
    "##--------\n",
    "# cv2.imshow('detected circles',img2)\n",
    "# cv2.waitKey(0)\n",
    "# cv2.destroyAllWindows()\n",
    "img  = cv2.cvtColor(img2,cv2.COLOR_BAYER_BG2BGR)\n",
    "imshow(img) # showing image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ellipse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 400, 3)\n"
     ]
    },
    {
     "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": [
    "img1 = cv2.imread('images/mainImage.png',0)\n",
    "img = cv2.resize(img1, (400,400))\n",
    "ret,thresh = cv2.threshold(img,127,255,0)\n",
    "im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)\n",
    "cnt = contours[0]\n",
    "cnt = contours[0]\n",
    "ellipse = cv2.fitEllipse(cnt)\n",
    "fimg = cv2.ellipse(img,ellipse,(0,255,0),2)\n",
    "imga  = cv2.cvtColor(fimg,cv2.COLOR_BAYER_BG2BGR)\n",
    "print(imga.shape)\n",
    "# cv2.imshow('detected ellips',fimg)\n",
    "# cv2.waitKey(0)\n",
    "# cv2.destroyAllWindows()\n",
    "imshow(img) # showing image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Straight Rectangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "img11 = cv2.imread('images/P0147.png',0)\n",
    "img = cv2.resize(img11, (400,400))\n",
    "ret,thresh = cv2.threshold(img,127,255,0)\n",
    "im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)\n",
    "cnt = contours[0]\n",
    "cnt = contours[0]\n",
    "\n",
    "x,y,w,h = cv2.boundingRect(cnt)\n",
    "straigt_rect = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,127),2)\n",
    "imga  = cv2.cvtColor(straigt_rect,cv2.COLOR_BAYER_BG2BGR)\n",
    "# cv2.imshow('Detected Straight Rectangle',imga)\n",
    "# cv2.waitKey(0)\n",
    "# cv2.destroyAllWindows()\n",
    "# print(imga.shape)\n",
    "imshow(imga) # showing image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rotated Rectangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "img11 = cv2.imread('images/P0147.png',0)\n",
    "img = cv2.resize(img11, (400,400))\n",
    "ret,thresh = cv2.threshold(img,127,255,0)\n",
    "im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)\n",
    "cnt = contours[0]\n",
    "rect = cv2.minAreaRect(cnt)\n",
    "box = cv2.boxPoints(rect)\n",
    "box = np.int0(box)\n",
    "rectangle_image = cv2.drawContours(img,[box],0,(0,0,255),2)\n",
    "# cv2.imshow('Detected Rotated Rectangle',rectangle_image)\n",
    "# cv2.waitKey(0)\n",
    "# cv2.destroyAllWindows()\n",
    "imshow(rectangle_image) # showing image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contour Approximation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[193 147]]\n",
      "\n",
      " [[206 160]]\n",
      "\n",
      " [[212 169]]\n",
      "\n",
      " [[219 183]]\n",
      "\n",
      " [[222 191]]\n",
      "\n",
      " [[227 206]]]\n"
     ]
    }
   ],
   "source": [
    "img11 = cv2.imread('images/P0147.png',0)\n",
    "img = cv2.resize(img11, (400,400))\n",
    "ret,thresh = cv2.threshold(img,127,255,0)\n",
    "im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)\n",
    "cnt = contours[0]\n",
    "epsilon = 0.1*cv2.arcLength(cnt,True)\n",
    "approx = cv2.approxPolyDP(cnt,epsilon,True)\n",
    "print(hull[-6:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting a Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "img11 = cv2.imread('images/P0147.png',0)\n",
    "img = cv2.resize(img11, (400,400))\n",
    "ret,thresh = cv2.threshold(img,127,255,0)\n",
    "im2,contours,hierarchy = cv2.findContours(thresh, 1, 2)\n",
    "cnt = contours[0]\n",
    "\n",
    "rows,cols = img.shape[:2]\n",
    "[vx,vy,x,y] = cv2.fitLine(cnt, cv2.DIST_L2,0,0.01,0.01)\n",
    "lefty = int((-x*vy/vx) + y)\n",
    "righty = int(((cols-x)*vy/vx)+y)\n",
    "line_image = cv2.line(img,(cols-1,righty),(0,lefty),(0,255,0),2)\n",
    "# cv2.imshow('Fitted Straight Line',line_image)\n",
    "# cv2.waitKey(0)\n",
    "# cv2.destroyAllWindows()\n",
    "imshow(line_image) # showing image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}