Lecture 4.3 - Approximating Contours and Convex Hull .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approximating Contours and Convex Hull \n",
    "\n",
    "***cv2.approxPolyDP(contour, Approximation Accuracy, Closed)***\n",
    "- **contour** – is the individual contour we wish to approximate\n",
    "- **Approximation Accuracy** – Important parameter is determining the accuracy of the approximation. Small values give precise-  approximations, large values give more generic approximation. A good rule of thumb is less than 5% of the contour perimeter\n",
    "- **Closed** – a Boolean value that states whether the approximate contour should be open or closed \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "# Load image and keep a copy\n",
    "image = cv2.imread('images/house.jpg')\n",
    "orig_image = image.copy()\n",
    "cv2.imshow('Original Image', orig_image)\n",
    "cv2.waitKey(0) \n",
    "\n",
    "# Grayscale and binarize\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)\n",
    "\n",
    "# Find contours \n",
    "_, contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n",
    "\n",
    "# Iterate through each contour and compute the bounding rectangle\n",
    "for c in contours:\n",
    "    x,y,w,h = cv2.boundingRect(c)\n",
    "    cv2.rectangle(orig_image,(x,y),(x+w,y+h),(0,0,255),2)    \n",
    "    cv2.imshow('Bounding Rectangle', orig_image)\n",
    "\n",
    "cv2.waitKey(0) \n",
    "    \n",
    "# Iterate through each contour and compute the approx contour\n",
    "for c in contours:\n",
    "    # Calculate accuracy as a percent of the contour perimeter\n",
    "    accuracy = 0.03 * cv2.arcLength(c, True)\n",
    "    approx = cv2.approxPolyDP(c, accuracy, True)\n",
    "    cv2.drawContours(image, [approx], 0, (0, 255, 0), 2)\n",
    "    cv2.imshow('Approx Poly DP', image)\n",
    "    \n",
    "cv2.waitKey(0)   \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convex Hull\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "image = cv2.imread('images/hand.jpg')\n",
    "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "cv2.imshow('Original Image', image)\n",
    "cv2.waitKey(0) \n",
    "\n",
    "# Threshold the image\n",
    "ret, thresh = cv2.threshold(gray, 176, 255, 0)\n",
    "\n",
    "# Find contours \n",
    "_,contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n",
    "    \n",
    "# Sort Contors by area and then remove the largest frame contour\n",
    "n = len(contours) - 1\n",
    "contours = sorted(contours, key=cv2.contourArea, reverse=False)[:n]\n",
    "\n",
    "# Iterate through contours and draw the convex hull\n",
    "for c in contours:\n",
    "    hull = cv2.convexHull(c)\n",
    "    cv2.drawContours(image, [hull], 0, (0, 255, 0), 2)\n",
    "    cv2.imshow('Convex Hull', image)\n",
    "\n",
    "cv2.waitKey(0)    \n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}