Lab_02_QR_Bar_Code_Detector_Webcam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick Recap about the Basic\n",
    "\n",
    "Use functions in python to makes everything looks formated.\n",
    "\n",
    "There are many QR generator website you can use to obtain samples:\n",
    "- https://www.the-qrcode-generator.com/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import pyzbar.pyzbar as pyzbar\n",
    "import numpy as np\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt \n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "def decode(im) : \n",
    "    # Find barcodes and QR codes\n",
    "    decodedObjects = pyzbar.decode(im)\n",
    "    # Print results\n",
    "    for obj in decodedObjects:\n",
    "        print('Type : ', obj.type)\n",
    "        print('Data : ', obj.data,'\\n')     \n",
    "    return decodedObjects\n",
    " \n",
    "# Display barcode and QR code location  \n",
    "def display(im, decodedObjects):\n",
    "    # Loop over all decoded objects\n",
    "    for decodedObject in decodedObjects: \n",
    "        points = decodedObject.polygon\n",
    "        # If the points do not form a quad, find convex hull\n",
    "        if len(points) > 4 : \n",
    "          hull = cv2.convexHull(np.array([point for point in points], dtype=np.float32))\n",
    "          hull = list(map(tuple, np.squeeze(hull)))\n",
    "        else : \n",
    "          hull = points\n",
    "        # Number of points in the convex hull\n",
    "        n = len(hull)\n",
    "        # Draw the convext hull\n",
    "        for j in range(0,n):\n",
    "          cv2.line(im, hull[j], hull[ (j+1) % n], (255,0,0), 3)\n",
    "    # Display results \n",
    "    #cv2.imshow(\"Results\", im);\n",
    "    #cv2.waitKey(0);\n",
    "    return im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type :  CODE128\n",
      "Data :  b'LifeIsShortUsePython' \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAABYCAYAAADlegyqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAACbFJREFUeJzt3U/MHHUdx/H3x9qCESIgaJrSSEs4yMGU2iAJhoN/oZdKwqEclINJjUoCiR6qJAaPGsHExEBKaIKGCAoYuRglivFkoYVSWpvCA2IoNDTE8O8iAl8P81tYlpnZmZ3d2Wd+/bySJ8/uzG9mvt/5Pc93Z34zu6uIwMzMhu9Dyw7AzMzmwwXdzCwTLuhmZplwQTczy4QLuplZJlzQzcwy0amgS7pS0jFJK5J2zysoMzNrT7Pehy5pDfAU8GXgOPAocG1E/HN+4ZmZWVNdjtAvBVYi4tmIeBO4B9gxn7DMzKytD3dYdgPw/Njz48DnJhtJ2gXsSk8/22F7Zmanqpcj4rxpjboUdJVM+8D4TUTsAfYASAp/1ICZWXOSAP7dpG2XIZfjwMax5+cDL3ZYn5mZddCloD8KXCRpk6R1wE7gwfmEZWZmbc085BIRb0m6HvgTsAbYGxFH5haZmZm1MvNtizNtzGPoZmatpDH0AxGxbVpbv1PUzCwTLuhmZplwQTczy4QLuplZJk6dgq6y90GZmeWjyztFB0HSe29flWDsLhtJTN51MzlNYy8EEVH6vGwdVW1Gj0dtqpad1r5su2XLV+U0uU5NvOCVbaftvqjal1X7vG7fl22nbL9WLVvWvmm+VbnWxTVt3ZN517Wry6fseZt5TfZNVZuqv4vJuGft27r1VcVQtWzd/qhrU7b+1Sz7I/QP7H7JR+tmlqXsC7qZ2anCBd3MLBMu6GZmmXBBNzPLhAu6mVkmXNDNzDLhgm5mlgkXdDOzTLigm5llwgXdzCwTLuhmZpmYWtAlbZT0sKSjko5IuiFNv1nSC5IOpp/tiw/XzMyqNPm0xbeA70XEY5LOBA5IeijN+3lE/Gxx4ZmZWVNTC3pEnABOpMevSzoKbFh0YGZm1k6rMXRJFwCXAPvSpOslHZK0V9LZFcvskrRf0v5OkZqZWa3GBV3SGcD9wI0R8RpwG3AhsIXiCP6WsuUiYk9EbIuIbXOI18zMKjQq6JLWUhTzuyPiAYCIeCki3o6Id4A7gEsXF6aZmU0zdQxdxXcv3QkcjYhbx6avT+PrAFcDhxcTYkcR7/+GorGvkCr7OqnJaW2fT2szelz1VVZVyzbZbtt4yuKqW7brvpm2jS7t2/ZL23zbrH8RfTvr31mTeU3m17Wp+1q2efRt2xhm+b+e1ma1f/XcSJO7XC4Hvg48KelgmvZD4FpJWyi+5e054FsLiXAeRkV9IJ1iZjYL9fnKIymG8kpnZrYapC+oPtDkOmSTI3QzoP7b3ZvM79tQvql9NfC+yoPf+m9mlgkfoRvQ/QhtfHlJSGq0rvGj+nFN45hcvq8jzLqzkcl9Oc+j37L91Wa9PhLPmwu6Nda0CLQpFl2KXlnhPFUK1mTOuedrzbigWyNdjoTndVQ5bdmq6VVH03VxtZnX5cWoSVxtz1bK4hydOU2LeZa4up5l2fx4DN0aqfvnLCsUZc8jYmoRmFx2vPBUHc2P2tUVlslly9ZbFlfddsvadM2pbl7depqYvK+66l7sLnG17WObLx+hW2fjR39NC3/T9Y6WGw0r1L35Y5FDLvNaX5OcRvP7iMfy4oJuvZm1CC3i4uKyNTnqN2vLQy7Wu7rhkbJ2VWPDTdfTh3nlNOt6J9fVdkjL8uAjdHufsjHkqjHc0fw648uXXZysu8BYVaDGP++jat40ZcvOsvxkzNPiWmROk22rxrhnHX/vEpf1wwXdgGYfsLSI5ftcd5thjUXG1WXZeRTRqouh06a1ubvIxX45XNDNMtL1FlEbNhd0s4y4eJ/afFHUzCwTLuhmZplwQTczy4QLuplZJlzQzcwy4YJuZpYJF3Qzs0z0fh+6PzvCzGwx+i7obwDHet5mH84FXl52EAvgvIbFeQ1Lm7w+1aRR3wX9WERs63mbCydpv/MaDuc1LM6rOY+hm5llwgXdzCwTfRf0PT1vry/Oa1ic17A4r4bkT2czM8uDh1zMzDLhgm5mloneCrqkKyUdk7QiaXdf210ESc9JelLSQUn707RzJD0k6en0++xlxzmNpL2STko6PDatNA8VfpH675CkrcuLvF5FXjdLeiH12UFJ28fm/SDldUzSV5cT9XSSNkp6WNJRSUck3ZCmD7rPavIadJ9JOl3SI5KeSHn9OE3fJGlf6q97Ja1L009Lz1fS/Atab3T0ZbyL/AHWAM8Am4F1wBPAxX1se0H5PAecOzHtp8Du9Hg38JNlx9kgjyuArcDhaXkA24E/AgIuA/YtO/6Wed0MfL+k7cXp7/E0YFP6O12z7Bwq8loPbE2PzwSeSvEPus9q8hp0n6X9fkZ6vBbYl/rht8DONP124Nvp8XeA29PjncC9bbfZ1xH6pcBKRDwbEW8C9wA7etp2X3YAd6XHdwFfW2IsjUTE34H/TEyuymMH8Kso/AM4S9L6fiJtpyKvKjuAeyLivxHxL2CF4u911YmIExHxWHr8OnAU2MDA+6wmryqD6LO0399IT9emnwC+ANyXpk/216gf7wO+qJafldJXQd8APD/2/Dj1HbbaBfBnSQck7UrTPhkRJ6D4AwU+sbTouqnKI4c+vD4NPewdGxIbZF7pdPwSiqO+bPpsIi8YeJ9JWiPpIHASeIjibOKViHgrNRmP/d280vxXgY+32V5fBb3sVWbI90teHhFbgauA70q6YtkB9WDofXgbcCGwBTgB3JKmDy4vSWcA9wM3RsRrdU1Lpq3a3EryGnyfRcTbEbEFOJ/iLOLTZc3S78559VXQjwMbx56fD7zY07bnLiJeTL9PAr+n6KiXRqez6ffJ5UXYSVUeg+7DiHgp/XO9A9zBe6fog8pL0lqKond3RDyQJg++z8ryyqXPACLiFeBvFGPoZ0kafY7WeOzv5pXmf4zmQ4dAfwX9UeCidHV3HcWA/4M9bXuuJH1U0pmjx8BXgMMU+VyXml0H/GE5EXZWlceDwDfSnROXAa+OTvOHYGLs+GqKPoMir53pDoNNwEXAI33H10QaT70TOBoRt47NGnSfVeU19D6TdJ6ks9LjjwBforg+8DBwTWo22V+jfrwG+GukK6SN9XjFdzvF1etngJv6vNo85zw2U1xhfwI4MsqFYqzrL8DT6fc5y461QS6/oTiV/R/F0cE3q/KgOB38Zeq/J4Fty46/ZV6/TnEfSv8468fa35TyOgZctez4a/L6PMUp+CHgYPrZPvQ+q8lr0H0GfAZ4PMV/GPhRmr6Z4gVoBfgdcFqafnp6vpLmb267Tb/138wsE36nqJlZJlzQzcwy4YJuZpYJF3Qzs0y4oJuZZcIF3cwsEy7oZmaZ+D9qA4wf4Uw3mAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bea1ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Main \n",
    "if __name__ == '__main__': \n",
    "    im = cv2.imread('Bar_Images/code128_1.png')\n",
    "    decodedObjects = decode(im)\n",
    "    img = display(im, decodedObjects)\n",
    "    plt.imshow(img, cmap = 'gray')    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type :  QRCODE\n",
      "Data :  b'https://www.latrobe.edu.au/' \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1240b9860>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12407fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = cv2.imread('QR_Images/QR_latrobe.png')\n",
    "decodedObjects = decode(im)\n",
    "img = display(im, decodedObjects)\n",
    "\n",
    "plt.imshow(img, cmap = 'gray')    \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type :  QRCODE\n",
      "Data :  b'https://www.latrobe.edu.au/' \n",
      "\n",
      "Type :  QRCODE\n",
      "Data :  b'https://www.melbourne.vic.gov.au/Pages/home.aspx' \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12411b4a8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123f44908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = cv2.imread('QR_Images/QR_complex.png')\n",
    "decodedObjects = decode(im)\n",
    "img = display(im, decodedObjects)\n",
    "plt.imshow(img, cmap = 'gray')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Webcam to Detect QR codes\n",
    "\n",
    "Find more information about [openCV](https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_gui/py_video_display/py_video_display.html)\n",
    "\n",
    "\n",
    "- Type `q` to quit the webcamera\n",
    "- Type `s` to take a snapshoot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Tue Oct 7 11:41:42 2018\n",
    "\n",
    "@author: Caihao.Cui\n",
    "\"\"\"\n",
    "from __future__ import print_function\n",
    "\n",
    "import pyzbar.pyzbar as pyzbar\n",
    "import numpy as np\n",
    "import cv2\n",
    "import time\n",
    "\n",
    "# get the webcam:  \n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "cap.set(3,640)\n",
    "cap.set(4,480)\n",
    "#160.0 x 120.0\n",
    "#176.0 x 144.0\n",
    "#320.0 x 240.0\n",
    "#352.0 x 288.0\n",
    "#640.0 x 480.0\n",
    "#1024.0 x 768.0\n",
    "#1280.0 x 1024.0\n",
    "time.sleep(2)\n",
    "\n",
    "def decode(im) : \n",
    "    # Find barcodes and QR codes\n",
    "    decodedObjects = pyzbar.decode(im)\n",
    "    # Print results\n",
    "    for obj in decodedObjects:\n",
    "        print('Type : ', obj.type)\n",
    "        print('Data : ', obj.data,'\\n')     \n",
    "    return decodedObjects\n",
    "\n",
    "\n",
    "font = cv2.FONT_HERSHEY_SIMPLEX\n",
    "\n",
    "while(cap.isOpened()):\n",
    "    # Capture frame-by-frame\n",
    "    ret, frame = cap.read()\n",
    "    # Our operations on the frame come here\n",
    "    im = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "         \n",
    "    decodedObjects = decode(im)\n",
    "\n",
    "    for decodedObject in decodedObjects: \n",
    "        points = decodedObject.polygon\n",
    "     \n",
    "        # If the points do not form a quad, find convex hull\n",
    "        if len(points) > 4 : \n",
    "          hull = cv2.convexHull(np.array([point for point in points], dtype=np.float32))\n",
    "          hull = list(map(tuple, np.squeeze(hull)))\n",
    "        else : \n",
    "          hull = points;\n",
    "         \n",
    "        # Number of points in the convex hull\n",
    "        n = len(hull)     \n",
    "        # Draw the convext hull\n",
    "        for j in range(0,n):\n",
    "          cv2.line(frame, hull[j], hull[ (j+1) % n], (255,0,0), 3)\n",
    "\n",
    "        x = decodedObject.rect.left\n",
    "        y = decodedObject.rect.top\n",
    "\n",
    "        print(x, y)\n",
    "\n",
    "        print('Type : ', decodedObject.type)\n",
    "        print('Data : ', decodedObject.data,'\\n')\n",
    "\n",
    "        barCode = str(decodedObject.data)\n",
    "        cv2.putText(frame, barCode, (x, y), font, 1, (0,255,255), 2, cv2.LINE_AA)\n",
    "               \n",
    "    # Display the resulting frame\n",
    "    cv2.imshow('frame',frame)\n",
    "    key = cv2.waitKey(1)\n",
    "    if key & 0xFF == ord('q'):\n",
    "        break\n",
    "    elif key & 0xFF == ord('s'): # wait for 's' key to save \n",
    "        cv2.imwrite('Capture.png', frame)     \n",
    "\n",
    "# When everything done, release the capture\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a caputer of myself holding a printed paper with two QR code:     \n",
    "![webcam QR detection](Capture.png)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**END**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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