11. Contours - Drawing, Hierarchy and Modes.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"11. Contours - Drawing, Hierarchy and Modes.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyMZhjYGiR2DUICKa8Tiu1wR"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"oQXrIyd1H9OH"},"source":["![](https://github.com/rajeevratan84/ModernComputerVision/raw/main/logo_MCV_W.png)\n","\n","# **Contours**\n","\n","####**In this lesson we'll learn:**\n","1. Using findContours\n","2. Drawing Contours\n","3. Hierachy of Contours\n","4. Contouring Modes (Simple vs Approx) "]},{"cell_type":"code","metadata":{"id":"Vwex3q_7G6ME","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1636998419086,"user_tz":0,"elapsed":10126,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"eac0e5b4-0a6a-4aab-8542-550e8dcf62b8"},"source":["# Our Setup, Import Libaries, Create our Imshow Function and Download our Images\n","import cv2\n","import numpy as np\n","from matplotlib import pyplot as plt\n","\n","# Define our imshow function \n","def imshow(title = \"Image\", image = None, size = 10):\n","    w, h = image.shape[0], image.shape[1]\n","    aspect_ratio = w/h\n","    plt.figure(figsize=(size * aspect_ratio,size))\n","    plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n","    plt.title(title)\n","    plt.show()\n","\n","# Download and unzip our images\n","!wget https://moderncomputervision.s3.eu-west-2.amazonaws.com/images.zip\n","!unzip -qq images.zip"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1O2uCujErifjvK1ziRGssaQO9khI15g6q\n","To: /content/images.zip\n","100% 29.6M/29.6M [00:00<00:00, 138MB/s]\n","replace images/castara.jpeg? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\n"]}]},{"cell_type":"markdown","metadata":{"id":"zfEgMODosUoY"},"source":["## **What are Contours?**\n","Contours are continous lines or curves that bound or cover the full boundary of an object."]},{"cell_type":"code","metadata":{"id":"TZLOlABfG6Ob","colab":{"base_uri":"https://localhost:8080/","height":108},"executionInfo":{"status":"ok","timestamp":1636998444082,"user_tz":0,"elapsed":235,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"ee822d10-09f8-4874-d689-cb63a5412f8e"},"source":["# Let's load a simple image license plate image\n","image = cv2.imread('images/LP.jpg')\n","imshow('Input Image', image)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 146.25x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"47lMgJ2nxWS1"},"source":["#### **Applying cv2.findContours()**\n","\n","```cv2.findContours(image, Retrieval Mode, Approximation Method)```\n","\n","**Retrieval Modes**\n","- **RETR_LIST** - Retrieves all the contours, but doesn't create any parent-child relationship. Parents and kids are equal under this rule, and they are just contours. ie they all belongs to same hierarchy level.\n","- **RETR_EXTERNAL** - eturns only extreme outer flags. All child contours are left behind.\n","- **RETR_CCOMP** - This flag retrieves all the contours and arranges them to a 2-level hierarchy. ie external contours of the object (ie its boundary) are placed in hierarchy-1. And the contours of holes inside object (if any) is placed in hierarchy-2. If any object inside it, its contour is placed again in hierarchy-1 only. And its hole in hierarchy-2 and so on.\n","- **RETR_TREE** -  It retrieves all the contours and creates a full family hierarchy list. \n","\n","**Approximation Method Options**\n","- cv2.CHAIN_APPROX_NONE – Stores all the points along the line(inefficient!)\n","- cv2.CHAIN_APPROX_SIMPLE – Stores the end points of each line\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"50V08ycKIhA9","colab":{"base_uri":"https://localhost:8080/","height":195},"executionInfo":{"status":"ok","timestamp":1636998614906,"user_tz":0,"elapsed":1078,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"89299e5d-4a45-4c12-e205-e5266aa0c762"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","_, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n","imshow('After thresholding', th2)\n","\n","# Finding Contours\n","# Use a copy of your image e.g. edged.copy(), since findContours alters the image\n","contours, hierarchy = cv2.findContours(th2, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 146.25x720 with 1 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\n","text/plain":["<Figure size 146.25x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Number of Contours found = 38\n"]}]},{"cell_type":"code","metadata":{"id":"gPlVs7kzCwiM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1605647428991,"user_tz":240,"elapsed":411,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhBYJnwdIGYSJdvGmzIt64rYSF1dDuiFyfRw4Rpeg=s64","userId":"08597265227091462140"}},"outputId":"770e80d6-3e1f-4b82-b80b-897b7f213f9e"},"source":["contours[0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[[564, 112]],\n","\n","       [[563, 113]],\n","\n","       [[562, 113]],\n","\n","       [[561, 113]],\n","\n","       [[560, 113]],\n","\n","       [[559, 113]],\n","\n","       [[558, 113]],\n","\n","       [[557, 113]],\n","\n","       [[556, 113]],\n","\n","       [[555, 113]],\n","\n","       [[554, 113]],\n","\n","       [[553, 113]],\n","\n","       [[552, 113]],\n","\n","       [[551, 113]],\n","\n","       [[550, 113]],\n","\n","       [[549, 113]],\n","\n","       [[548, 113]],\n","\n","       [[547, 113]],\n","\n","       [[546, 113]],\n","\n","       [[545, 113]],\n","\n","       [[544, 113]],\n","\n","       [[543, 113]],\n","\n","       [[542, 113]],\n","\n","       [[541, 113]],\n","\n","       [[540, 113]],\n","\n","       [[539, 113]],\n","\n","       [[538, 113]],\n","\n","       [[537, 113]],\n","\n","       [[536, 113]],\n","\n","       [[535, 113]],\n","\n","       [[534, 113]],\n","\n","       [[533, 113]],\n","\n","       [[532, 113]],\n","\n","       [[531, 114]],\n","\n","       [[530, 114]],\n","\n","       [[529, 114]],\n","\n","       [[528, 114]],\n","\n","       [[527, 114]],\n","\n","       [[526, 114]],\n","\n","       [[525, 114]],\n","\n","       [[524, 114]],\n","\n","       [[523, 114]],\n","\n","       [[524, 114]],\n","\n","       [[525, 114]],\n","\n","       [[526, 114]],\n","\n","       [[527, 114]],\n","\n","       [[528, 114]],\n","\n","       [[529, 114]],\n","\n","       [[530, 114]],\n","\n","       [[531, 114]],\n","\n","       [[532, 114]],\n","\n","       [[533, 114]],\n","\n","       [[534, 114]],\n","\n","       [[535, 114]],\n","\n","       [[536, 114]],\n","\n","       [[537, 114]],\n","\n","       [[538, 114]],\n","\n","       [[539, 114]],\n","\n","       [[540, 114]],\n","\n","       [[541, 114]],\n","\n","       [[542, 114]],\n","\n","       [[543, 114]],\n","\n","       [[544, 114]],\n","\n","       [[545, 114]],\n","\n","       [[546, 114]],\n","\n","       [[547, 114]],\n","\n","       [[548, 114]],\n","\n","       [[549, 114]],\n","\n","       [[550, 114]],\n","\n","       [[551, 114]],\n","\n","       [[552, 114]],\n","\n","       [[553, 114]],\n","\n","       [[554, 114]],\n","\n","       [[555, 114]],\n","\n","       [[556, 114]],\n","\n","       [[557, 114]],\n","\n","       [[558, 114]],\n","\n","       [[559, 114]],\n","\n","       [[560, 114]],\n","\n","       [[561, 114]],\n","\n","       [[562, 114]],\n","\n","       [[563, 114]],\n","\n","       [[564, 114]],\n","\n","       [[565, 114]],\n","\n","       [[566, 113]],\n","\n","       [[566, 112]],\n","\n","       [[565, 112]]], dtype=int32)"]},"metadata":{"tags":[]},"execution_count":56}]},{"cell_type":"markdown","metadata":{"id":"ncMUDtL_-ABm"},"source":["#### **What happens if we don't threshold? Bad things..**"]},{"cell_type":"code","metadata":{"id":"2EQ4V7AoRG-8","colab":{"base_uri":"https://localhost:8080/","height":195},"executionInfo":{"status":"ok","timestamp":1636998700887,"user_tz":0,"elapsed":864,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"ed9607b7-ae12-438c-9dd6-f9eee69e827d"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","imshow('After Grayscaling', gray)\n","\n","# Finding Contours\n","contours, hierarchy = cv2.findContours(gray, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","#cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 146.25x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Number of Contours found = 1\n"]}]},{"cell_type":"markdown","metadata":{"id":"dCOMYIvuz_-1"},"source":["# **NOTE: For findContours to work, the background has to be black and foreground (i.e. the text or objects)** \n","\n","#### Otherwise you'll need to invert the image by using **cv2..bitwise_not(input_image)**"]},{"cell_type":"markdown","metadata":{"id":"6v88ae8cACPN"},"source":["#### **We can use Canny Edges instead of Thresholding**"]},{"cell_type":"code","metadata":{"id":"PkQ7P_WQw2R5","colab":{"base_uri":"https://localhost:8080/","height":195},"executionInfo":{"status":"ok","timestamp":1636998771214,"user_tz":0,"elapsed":1144,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"969c51e4-420a-40eb-b314-a1cdce616e15"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","# Canny Edges\n","edged = cv2.Canny(gray, 30, 200)\n","imshow('Canny Edges', edged)\n","\n","# Finding Contours\n","contours, hierarchy = cv2.findContours(edged, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 146.25x720 with 1 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\n","text/plain":["<Figure size 146.25x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Number of Contours found = 77\n"]}]},{"cell_type":"markdown","metadata":{"id":"B-a_luCqAHst"},"source":["## **Remember these Countouring Steps**\n","\n","1. Grayscale\n","2. Threshold or Canny Edge Detection to Binarize image\n","\n","**Note:** Blurring before Step 2 is recommended to remove noisy contours"]},{"cell_type":"markdown","metadata":{"id":"AtX6Ym0wAsAX"},"source":["# **Retreival Modes** \n","\n","Official Doc - https://docs.opencv.org/master/d9/d8b/tutorial_py_contours_hierarchy.html\n","\n","**Hierachry**\n","\n","This array stores 4 values for each contour:\n","- First term is the index of the Next contour\n","- Second term is the index of the Previous contour\n","- Third term is the index of the parent contour\n","- Forth term is the index of the child contour"]},{"cell_type":"markdown","metadata":{"id":"fItsrDH-BRkv"},"source":["### **RETR_LIST**\n","Retrieves all the contours, but doesn't create any parent-child relationship. Parents and kids are equal under this rule, and they are just contours. ie they all belongs to same hierarchy level."]},{"cell_type":"code","metadata":{"id":"mIZimn_-_-ZR","colab":{"base_uri":"https://localhost:8080/","height":864},"executionInfo":{"status":"ok","timestamp":1636998891585,"user_tz":0,"elapsed":268,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"e229280c-a5e9-4926-bdc7-06ce658a152e"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","_, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n","imshow('After thresholding', th2)\n","\n","# Use a copy of your image e.g. edged.copy(), since findContours alters the image\n","contours, hierarchy = cv2.findContours(th2, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))\n","print(hierarchy)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 146.25x720 with 1 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\n","text/plain":["<Figure size 146.25x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Number of Contours found = 38\n","[[[ 1 -1 -1 -1]\n","  [ 2  0 -1 -1]\n","  [ 3  1 -1 -1]\n","  [ 4  2 -1 -1]\n","  [ 5  3 -1 -1]\n","  [ 6  4 -1 -1]\n","  [ 7  5 -1 -1]\n","  [ 8  6 -1 -1]\n","  [ 9  7 -1 -1]\n","  [10  8 -1 -1]\n","  [11  9 -1 -1]\n","  [12 10 -1 -1]\n","  [13 11 -1 -1]\n","  [14 12 -1 -1]\n","  [15 13 -1 -1]\n","  [16 14 -1 -1]\n","  [17 15 -1 -1]\n","  [18 16 -1 -1]\n","  [19 17 -1 -1]\n","  [20 18 -1 -1]\n","  [21 19 -1 -1]\n","  [22 20 -1 -1]\n","  [23 21 -1 -1]\n","  [24 22 -1 -1]\n","  [25 23 -1 -1]\n","  [26 24 -1 -1]\n","  [27 25 -1 -1]\n","  [28 26 -1 -1]\n","  [29 27 -1 -1]\n","  [30 28 -1 -1]\n","  [31 29 -1 -1]\n","  [32 30 -1 -1]\n","  [33 31 -1 -1]\n","  [34 32 -1 -1]\n","  [35 33 -1 -1]\n","  [36 34 -1 -1]\n","  [37 35 -1 -1]\n","  [-1 36 -1 -1]]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"G4wHneN2BMKC"},"source":["### **RETR_EXTERNAL**\n","\n","Returns only extreme outer flags. All child contours are left behind."]},{"cell_type":"code","metadata":{"id":"4GptFLEfA9XI","colab":{"base_uri":"https://localhost:8080/","height":510},"executionInfo":{"status":"ok","timestamp":1605647272428,"user_tz":240,"elapsed":687,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhBYJnwdIGYSJdvGmzIt64rYSF1dDuiFyfRw4Rpeg=s64","userId":"08597265227091462140"}},"outputId":"c5f9cae3-59a8-4691-dbb5-91879f172530"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","_, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n","imshow('After thresholding', th2)\n","\n","# Use a copy of your image e.g. edged.copy(), since findContours alters the image\n","contours, hierarchy = cv2.findContours(th2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image, size = 10)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))\n","print(hierarchy)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAATgAAABmCAYAAABfjI1ZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO2deVwUV7bHf6ehm0VRFBBR3AF3jcYxbnHfIm4ZicJogkbHLZtGx5jEcV40MWZiNqPRwWDMU2NMoo5LxhhjcNcoKOBCXDAiiAsgq9Bsdd4fXfAa6Kpe6AWY+n4+50NX1b3nnrpUnbr7JWaGgoKCQl1E5WgDFBQUFGyF4uAUFBTqLIqDU1BQqLMoDk5BQaHOojg4BQWFOovi4BQUFOosioP7L4OI+hPRDSLKI6KJdkpzMBGl2CGd1kTERORsQVxZG4loCxG9K/5+moiuVcdWBfugOLg6ChEdJaJMInKpdGkFgHXMXJ+Z/y06hAArp211nTUJZj7BzO0dbYeCcRQHVwchotYAngbAAMZXutwKwBUrpWN2SclEvU620Kvw34fi4OomLwA4C2ALgPCyk0SUCKAtgP1iFfWMeClOPJ4ihhtLRLFElEVEp4mom56O20T0BhHFA3hc2ckR0XFDOsVri4joIRHdI6IZeue3ENEGIvoPET0GMISImhHRLiJKI6I/iOhVvfC9iSiaiHKI6AERfVzp/qcS0R0iSieit/XiuRDRp0SUKsqnBkq4ZWF7ENEFIsolop0AXPWuVajOinmymIjiiSibiHYSkX74JeI9pxLRrLpewq1RMLMidUwA3AQwH8CTAIoB+Opduw1guN4xAwjQO+4B4CGApwA4QecgbwNw0YsfC6AFADeJ9CvrHAygBLrqsRrAGAD5ABqJ17cAyAbQH7qPrjuAGADLAWigc8q3AIwSw58B8Lz4uz6APuLv1mLamwC4AegOoBBAR/H6CugcfxMAPgBOA1ipZ2OK+FsDIAnAQtHeEDEf360cVi9PzgFoBqAxgAQAc8VrowHcB9BZvK9tlfNHEduJUoKrYxDRAOiqod8xcwyARAB/MUPFbAD/YubfmLmUmb+Gzkn00QuzlpmTmbnADL3FAFYwczEz/wdAHgD9dqy9zHyKmQUAXQH4MPMKZi5i5lvQOa1QPV0BROTNzHnMfLZSWu8wcwEzxwGIg87RAcBU0YaHzJwG4B0AzxuwtQ90ju1T0d4fAJw3cn9rmTmVmR8B2A/gCfH8ZABfMfMVZs4H8D9G9ChYEcXB1T3CAfzMzOni8TfQq6aaQCsAi8TqaRYRZUFXWmumFybZArsymLlE7zgfutKXIZ2tADSrZMNbAHzF6zMBBAH4nYjOE9HYSmndl0inGXQlszKSUPG+oBfuLotFML2wcsilqX9vluSdgoXYpJFYwTEQkRt0JQYnIip74VwAeBJRd7FEY4xkAO8x83syYWyxBI2+zmQAfzBzoMGAzDcAhBGRCsCfAfxARF4mpJGKip0sLcVzlbkHoDkRkZ6Tawldadhc7gHw1ztuYYEOBQtRSnB1i4kASgF0gq6K9ASAjgBOQNfxYIgH0LVxlbEJwFwieop01COiYCLyMMOOyjrN5RyAXLEzw42InIioCxH9CQCIaBoR+YjV2SwxjmCC3h0AlhGRDxF5Q9fGt81AuDPQtRm+SkRqIvozgN4W3st3AGYQUUcicgfwdwv1KFiA4uDqFuHQtffcYeb7ZQJgHXQ9i4ZK7P8D4GuxKjiZmaMB/FWMkwldh8V0M+2ooNPcm2DmUgBjoXPQfwBIB/AlgIZikNEArhBRHoDPAISa2B74LoBoAPEALgG4IJ6rnH4RdCXD6QAeAZgCYLe59yHqOghgLYAo6PKyrL2w0BJ9CuZBFZsZFBQUbAkRdQRwGbpe6RJj4RWqh1KCU1CwMUT0rDgGrxGADwDsV5ybfbCJgyOi0UR0jYhuEtFSW6ShoFCLmAPd2MJE6NpI5znWnP8erF5FFafZXAcwAkAKdOOHwpj5qlUTUlBQUDCCLUpwvQHcZOZbYmPttwAm2CAdBQUFBVls4eCao+JgxhTxnIKCgoJdcdhAXyKaDd20IEA3Z1JBQUHBIpiZDJ23hYO7i4qjtf3Fc5UNigAQAejWD7OBHQoKCv/l2KKKeh5AIBG1ISINdBOk99kgHQUFBQVZrF6CY+YSInoZwCHoltvZzMxWWWDR2gQFBaFx48bV1pOXl4fLly9bwSIFBQVrUiNmMjiiitq6dWucPn0aTZs2rbau3NxcnD9/Hjdv3sTOnTuRmpqKa9dss2R//fr10atXL7Rr1w5hYWEVruXk5GDjxo149OgRYmJiUJ3/7auvvorx4ysvBqzj2LFjWL16NYqLiy3S3atXL3h4GJ7ampmZidjYWIv0mptWdcnPz8f58+chCKZMg5WnY8eOaNq0KZgZgYGByMnJwcOHD5GUlIRbt25Zwdq6jVQbnMMXpBNfQra3vPfeeywIAlsTQRBYEAROTk7mkJAQdnV1tZq97u7uHBYWxjExMeXpSNmQl5fHp06d4nnz5nGjRo0sSm/9+vWS95mWlsbe3t4W6VWpVBwdHc23bt0qvw99iYqKslqeTZ06lXNzcw2mYw3Jz8/n06dP886dO9nLy8tiOwcOHMipqaksCAK/9tprHBERwYIgsFar5UmTJnH79u158ODB7OTkZLP3gYi4bdu2PGbMGP7kk0/41KlTFWTTpk08ZswY7tSpE4sFkholkr5F6oI9xREZ8tVXX5npvsyjuLiYT58+zd27d6+WnRqNhqdMmcLnz5/nkpISs2wQBIGvXr3Kc+bMYQ8PD7PStaWD++abb/jq1asGdVvTwR07dsys/LIUQRB4yJAhZtvn7u7OH3/8MT98+LBc1/Xr1zkvL6/8eM+ePbxr1y7WarX8888/c6tWrazmYIiI/f39efny5RwTE8PJyclG7/X+/fscExPD77//Po8cOZLVarXd311DwoqDqyi2dnBlXLt2jSdPnszOzs5m26jRaPif//wnFxUVVcuG0tJS3rZtG3t6epqctpyDS09PZ19fX4vy3dnZmS9evCip+7/FwfXo0YOjo6O5uLjYrLTu3LnD8+bNq7aT8/X15WXLlnFSUpJF97xmzRr+9ttvOSoqitu2bevwUh0rDq6i2MvBMTMXFhbyxIkTzbKPiHjVqlXVdm5llJaWckJCAg8aNMik9OUcXElJCS9cuNCifB89ejRrtVpJ3dZycH5+fnzjxg2r5J0xTHVwbm5u/Nxzz/GwYcOqZVt+fj6vXLmS/fz8zM4XIuJRo0ZxVFRUtZpo0tPTOTs7m+Pj4zk1NZVffvllVqlUdn+Py4QVB+c4B8fMnJCQwJ07dzbZvh49evD9+/etbkdKSgoPHDiwWg6OmfnGjRvcuHFjs/K8ZcuWfOnSJVm91nJwAwYMsHobqxSmODgnJyd+7733+JtvvuGcnByrpBkVFWV2G2tQUBBnZGRY4a515OTklLdHzp8/32FOjiV8i7Jckp3o0KEDPv/8c2g0GqNhAwMD8f3338PX19doWHNp3rw5vvjiCzRo0EA23OPHj8s+PgZp06YN1qxZA2dn00YaqdVqzJkzB507dzaabm0jOTnZaE+nt7c3YmJi4OTkZJVeXSLCgAEDsHz5cjRs2NB4BABNmjTBunXr0KhRI7PSYmbk5eUZvObh4QEigpubG9asWYPw8HCzdNscKc9nT0ENLcEVFhby7du3+fbt2/zgwQNLP3LlFBcX8+jRo43a9uGHH1Y7LTlKSkp41qxZ1f7SZ2Zm8siRI03K7yFDhhitbmu1Wh4+fLhV/r/BwcF2K8GdOnVK1hYvLy/u06cPX79+3SL9xcXFnJSUxLm5uVWuFRYW8l/+8hej+aFSqXj16tVm54kgCHz69Gl+/fXXTQp/8eJFbtWqVY0pwSmbzkiQlpaGBQsWYO/evRAEAc2bN8fgwYMlw0+aNAkDBw6Eu7u7ZBgnJyfMnz8fP/30k2SYgIAATJ061ah9jx8/RkpKCq5cuYKDBw+iT58+GDBgAHx9feHp6Skb18nJCe+++y6io6Mlx5zduHEDCxcuxObNm+HkZHijeU9PT3zyySfo168fsrOzJdNr1qwZNmzYYLS0t2bNGkRFRcmGMZXZs2fLXk9NTUVubq7kdV9fX7i6uuLhw4do0aIFiAwPswKAR48eSV5Tq9XYsGEDxo0bBxcXg3tMl1NQUIA7d+4A0JXQWrdujYsXL+LQoUM4ceIE1q9fDy8vLzRu3LjcHo1Gg5deegn79u2TLGUBQIsWLTB9+nTZ+zDE3bt3ce7cOQQEBGDfvn3o378/vLyk9/fp3r07XnrpJSxZssSsdGyGlOezp6AGluASExPNGsfm7OzMc+fO5fz8fFm9KSkp3Lp1a0k9H3zwgdGvbF5eHk+fPp3VanX52CiVSsVqtZr79OnDO3bs4OzsbFkdzMwzZ86UvSdPT08+d+6crI7S0lL+8ssvJXVoNBpetWoVl5aWyuq5cOGCVb/8P/74o2Rad+/e5aCgIFar1ZLSp08ffuaZZ7hZs2YcFhYmKy1atJC0o0WLFpyamip771qtlletWsWjRo1ijUbDarWaXV1dedy4cbx27Vp+/PgxFxYW8p07dzg0NLRKXmq1Wp48ebJsfixcuNDoc/Xw4UM+deoUz5gxo8K9leXJU089xY8ePZLVwcwcExPDDRs2rBElOIc7N64jDg7Q9VDNmzdP1smVlpby2LFjDcZ3d3fnEydOyNqVl5fHM2bMMGrHpEmTOCsrS1ZXXFwcN2jQQFbXxIkTOTMzU1bPo0eP+JlnnjEYv1+/flxQUCAbv6CggMePH2+1/227du343r17kun9+uuvFg3bsVS2bdsmaUthYSEvXbpUchCvRqPhjz76iEtKSjg+Pp779etn8GPx2WefSepwcXHhqKgoSRtKS0t5y5Yt3KVLF9lOAmdnZx4xYgR/+umnssNbMjMzuWnTpnZ9n1lxcBVlzJgxsgNnLXFwZQ/BoUOHJPUKgsA7d+40GLdHjx6yNgmCwG+88YbJtnz11VeyX21ThzdERkYa/frHxMRUcZZdu3bl69evG7Xhww8/tGrvW8+ePXn9+vWSjtlY+6O1xMXFhcePHy85gLaoqIiXLVtmdIaCi4sLf/rppxwbGyvp4NLS0jgwMNBg/Hr16smOd7t79y63bNnS5PvSaDS8Y8cOyf9rUVGR3fK4TFhxcBWlc+fOso3eljo4ADxz5kxJvcws6eAmTZokW5VLSkoya+yTp6cnX7lyRVKfIAgmPYjdu3c3WjURBIHXr19fHketVvPXX39tNE5MTIxsld0SefLJJ3nUqFF89OhRg+na6+Vr0aKF7JCQyMhIk0uSrq6u3L9/f965c6ekY3n55ZcNxjXm4Hbs2GH2vfXt21e2ZP7OO+/YJY/LhCV8izJMpAYxefJkqFTS/5L4+HikpaWZrC8rKwvR0dGS14kIU6ZMMaonLi4O06dPR1ZWlmQYIkJYWBjGjRsHAHjhhReqLAZQmezsbDz//PO4ffu2URvMITw8HB07dsSWLVsMXg8KCoJarYaTkxOcnZ0xdOhQTJo0CePHj0e9evXg5ORkdmO8ISZPnizb6fTgwQOUlJi2uZZWq0V2djb+/Oc/S9oWGBgo+/xYE3d3d6vkka1RHJyVUalU6NSpk9nx2rRpg6FDh8qG2b9/v8kvRBl79uwx2xZDHDhwAPv375ddOaNRo0b46quvsHjxYrzxxhtQq9WSYYuLi/Huu+/aZNUVX19fzJ07V3LM3auvvorY2FjExcUhNjYWBw4cwA8//IDdu3cjJiYGcXFxWLNmDcaNG2dw3KKpTsTHx0eyB7q4uNjq9x4cHGzSOMvKtGzZ0ui4yMqY0itcI5Aq2tlTYEIRtEuXLly/fn2rNQ7bqooaFBQk28AtCAIvXbq0Srz27dtzYWGhZLycnBwePHiw2faMHTuWc3NzJWXVqlUm62rcuLHFY7kq58HZs2ctXunEmOzcubPaNjLreiePHz/OEyZMYBcXFwZ07U+mLKDg6urKe/fuldSdnZ3NzZs3N+u+/Pz8+NKlS5IN/Ddv3jT4zGo0Gj5w4ICkLcXFxTx16lST55P269dP9hnXarX83HPP2eR/KyVcm9vg2rdvz0OHDuUVK1ZwcHCwVTLEFg6uTZs2vHXrVkmdzLoeq549e1aJ2717d1kHl5SUxPXq1TP7Pl1dXdnf319Syl5cUyU4OLjaU43y8vL4iSeesMmD3rhxY75w4UK17CujuLiYf//9d46Pj+fNmzezu7u7yXZ4e3tzenq6pO4zZ84Y7cE2JF5eXvzzzz8b1Hnr1i1JnYsXL+bS0lLJecCZmZn81ltvsZ+fn+QzUTYsKSwsTHY+8YMHD7hJkybV/l8GBgZyz549uUePHkYLNizhW2rFQN+kpCRcv34d586ds3iRRXNxcnKCq6srtFqt0bANGjRAmzZt8Le//c3oIN2bN2/i3r17Vc7PmDFDtkqXn59f9jEwC61Wi5SUFLPjSXH48GHs27cPYWFhFrX3CIKA5cuX49KlS1azSZ8mTZoYnQ5mKjExMZg4cSLefvttPP/882jSpAkiIyPLB3/L4ebmBq1WC0EQKuSTIAjYtm0bUlJSkJOTY7ZNGRkZuHDhAoYPH16lDaxly5Z45plnsHPnzirxLly4gDt37iAuLg4TJlTdxdPT0xMrV67EwoULceLECcTHx1cJExAQgMzMTEyZMkWyesrM2LZtG9LT082+t8okJyeX5525TTNl1AoHV+Zk5EZqWxt/f398/fXXuH79utGww4YNQ2BgIOrVqycbjpkRGxtr0MEZa7TdunUr8vPzjRtuY4qKirBo0SIMGDAArVq1MisuM+O3337D1q1bUVpaaiMLrUNiYiKWLVuGefPmYc6cOUhOTsbOnTuxYsUKAMbbNidPnowDBw5g6tSpqF+/fvl5QRCQnJxsdpuXPj179jT4rJR9lA1x6tQpnDx5EiEhIZJ6VSoVvL298eyzz+LZZ5812y5mxs8//4zVq1dbZZVjUwoXxqgVDs4RODk5SS7ZbSlarRafffaZRXGLioqsakt1ePDgAebPn48ffvgBbm5uJscrKCjAwoULzeoJNpcRI0bgyJEjGDVqlMU9isXFxfjXv/6FNWvWoHPnznB2dkabNm2waNEirF69GiEhIThz5gzu378vqUOj0aCoqMhgqZuIqjUlLTMzE8xsVi9mYWEhlixZgsGDB8Pf39/itOXIysrCggULbPr/NRelF9WO/OMf/8CZM2ccbYZViIqKwvfff2/ylzo/Px8LFy7EuXPnbGpXt27dEB8fb1F1vrS0FAkJCbh06RKGDBmCLl26lM+fJSJ069YNb731Frp27YpXXnnFqAP18vKqEkalUmH06NEGq4CmsmnTJovi3bt3D3PnzrXJx1IQBCxcuNBme5FYilKCsxMnT57E//7v/1r04tVECgoKsGzZMgwbNgzNmzc3Gv7w4cPYtGmTXe6/b9++uH37Ntq1aycbLjc3FwkJCeXHjx8/xuuvvw4/Pz9ERERUGeJBROVDgNq2bYuTJ0/i4MGDkvpDQ0OrlLIsHUakT5MmTSyOe/XqVZs1D1y6dKnGPd9KCc7GMDNOnz6NKVOm4MGDB442x6qkpaWZXBLRarU2f/hdXFzQpEkTPP3002jbtq3BMImJiTh+/Di+++47jBgxAn369CmXoUOHIjs7Gx06dMCECRNw9OhRybTq1auHvn37ylYTVSqVTQbDhoeHW6RXo9Fg7ty5Fo2VM4apg8btjlT3qj0FNhguYEyMDROxBlqtlnft2sX+/v5G7YmIiJDVtXjxYrvnkTFZunSpyRvhJCQkWLxRjanSqlWrChu2MP//TmdlYs5S64MGDZJdVTk5OVlyJZE333xTMl5BQQG3a9fO4vuUGibCzBweHi4Zr3fv3lXyx5qkpqY6ZC04QHqYiFKCswGCIOD69etYvHgxJk+ebNVhGjWF3r17Y/78+ZIj9SvToUMHrF271qY25efnIzk5GaWlpSgpKUFJSQmKi4tx/PhxfPnll0hKSsL+/ftN1nfs2DFs375dsuTp7++PAQMGWMt8m9KwYUNERkYa7ekHAGaukIclJSUmlb59fX3xzjvvyE5PszdKG5wMJSUlVdorNBpNlepBcXFxeWP7L7/8gmPHjmHr1q2yvWyVSUhIQGlpqckOw5G4ublh8eLFaNGihVnxxo0bh6lTp2L79u02sSstLQ1TpkxBQEBAleE9Xbt2RUREhNmbKG/cuBFTp06VXD6+U6dOICKTq9+CIODq1atm2WAqzCzZ6TNp0iR06NBBNn5paSni4uJw5swZREZGlo85JSKEh4ejb9++6N27t+TCpSqVCqGhodixYwcOHTpUvZuxFlJFO3sKHFCkNVZFzc7O5pCQEA4MDCyX9u3b86pVq3jjxo0VJDg4mAMDAzkgIMDsmQFlYmyqlqHpXY6SV155xew9WsuIjY21+1ph1RW5KuHFixcNLvUkVUUtKSnhvXv3WlxF9fHxkVwhJikpiX18fKrEcXZ2Nrr+YUFBAb/22muyGwk1aNCAX3jhBc7IyJBdAmvt2rV2/x+xlG+RumBPccRDa8zBff/993ZdFNGYg7t8+bJZU4X0pX79+tywYUODYu70r/79+5u0QbAUgiDw5s2bHbrFnLkSEhIieT9SDm7s2LGSC58WFBRwp06dLLKld+/ekktqSc1F7dWrl+xyV2XOzdi6dIBuMdVu3bpV2Ky6MmlpaVbbW8NUYaUNzjyOHz9u8fQQS8jIyMDvv/8ueb1s9yJzcXJywv79+3Ht2jWDsn37dpN3xvLw8MDGjRurNVCUiBASElLtoRK2wM3NzWD7kSVTqs6ePSs588TFxQXTp083W6cxCgoKDFaVe/bsKbmTliAI+Pvf/45169aZNHyEmXH58mUsWbJEcjydt7c3RowYUSOWU1IcXA0hPT1dtm3G09MTPXr0sEi3l5cXfH19DUpgYKBJ28gREVavXo2OHTvKhivbDEfu41C/fn188cUXRjfHsSdqtRrz5s2z2jxWOYjI4oZ4uS0Ht2zZgsLCQrP0MTOOHDli1tg4QRCwe/dunD9/XjJMaGhotaajWQvFwdUgDh8+LNlI3KBBA/Tr189snV27dkWzZs0kr6emppo0tWbgwIGYPHmybCdIbm4u5s2bhy5duiAiIkLypSEi9OnTB1OnTq0xnSqCIOCPP/7A5cuXq1yzxBHn5eXh1KlTktc9PDzMvnc/Pz98/vnnkiUjc50boBv4m5ycbHa8nJycCoOkK+Pi4lIjSnBKL2oN4uLFi7K9cX/9618RGRmJjIwMk3W+/PLLstu8mUK9evXw0UcfwdvbWzIMM+Nvf/sbtm3bBmbGsmXLMHz4cAQFBRkMr1arsWLFChw+fNikBQ1MwdvbG6tWraqy0kV8fDwOHjyItLQ0SWdeWloqOYFebgtCqVUztFotEhISJOczjx8/Hr6+vkhNTZXUXZlp06ahffv2Bh1HZmYmTp48abKuMu7cuWOVlT9qLFKNc/YUOKDh2FgnwyuvvGJ3mzw9Pfn8+fOSNgmCwN98843JCxN269ZNdvNmQRB43rx5RvWsXbvW6KYz8fHxVXrgFixYYHQTnePHj7Obm5tV8s/QQF9m3Rp8xcXF/Pnnn5vUkK4v7du3l21Ql9t0edCgQZIdDUVFRUa3bdQXX19f2cVGL168KNlhNHv2bMl4d+/e5TZt2pid1z4+PpyQkCCp9/79++zp6Wm3d4eVToaaT1ZWFmJiYiRLcUSE4OBghIeHG23f8PDwwLJly9C4cWPJML///ju+++47WT3Dhg1DWFiYbHUjNzcX7733XpUNkDdu3Ii9e/dKxiMi/OlPf8LMmTNtupeASqWCs7MzZs2ahUmTJpkcr23btoiMjJQsuRYUFMhW7xMTEyXbItVqNfr162dyB09QUBBatmwpef2HH37A48ePDV5LS0uTrL76+flh1qxZJtlQhlqtxoIFCxAYGGhWPIcg5fnsKbCTl9eXmliCK7NLrtTFrCv57Nq1i7t162ZQR5cuXfjbb781Wuravn27rC1t27aVnapUZsurr74qqaNXr16yOzox66b4WGNnLRcXF967d6/sfUdHR5uUVv/+/fns2bOyug4dOiQ7lMjFxYV37dolGd/UbQN79OjBiYmJkrZkZ2dLPguA8V21UlNTOSQkxKQVrNVqNa9YscLoNMeIiAi7DrNiZRxcVUci90+Se2ltLZs2bZJ9eMrIyMjgiIgIHj9+fLls2LCB09LSjMbNysri3r17S9pQv3593rp1q1En+dNPP7GXl5fs/bz99tucnp7OJ0+elNR3+PBhq7wQgwcPNvryJSYm8tKlS7lp06YVqvsqlYrd3Nx4ypQpRsf65efn85gxY4zas2jRItlqemFhIa9cudLgnFaNRsMTJkzgGzduyNpy4cIF2eXPXVxc+Ndff5X9X5aUlPDp06c5LCyM27VrxyqVqoL4+/vzpEmT+JdffjGav0VFRTx9+nS7vjOsODjzHNzp06ctnpVQXTE2kLK6CILA27dvl23LW7Fihewercw6J2nKRjguLi48bdo0HjJkiORLVlBQYJWPiouLC3/88cdGHTOzbg+DPXv2cGhoKD///PN87Ngxvnz5suyu7WUcPnzYpOejadOmJm3Uk5SUxMuXL+fQ0FAODQ3llStXcnx8vOzgb2bT2/LatWtntGZQRkpKCsfGxlaQ27dvmxRXEAQ+f/48N2zY0K7vDCsOzjwHt3z5cpMb820hI0eONLrZsqXs27dPtgHY19eXU1JSZHUIgsBvvvmmWXk0ePBgo9VHa5TiWrZsyTdv3jTJyVlCTk4Ojxs3zmR7Fi1aZJLTNBdBEPjcuXMm7U7m7u7Ov/zyi83ypIxLly5xQECA3d8XVhxcRampbXBlolKpODIy0mgpylzS09N50KBBsmk3b95cdues0tJS3r9/v9GqaWUZMGCA7Iuu1Wr56aeftkr+PfHEE5yYmGjVvGNmzs3N5fDwcLOmmqnVat6/f7/Vncvly5fNmtMaEBAgu9NXddFqtTxnzhyHvC+sOLiKUtMdHAD28PDgrcmoOVMAAAibSURBVFu3Ws3JZWRk8Pjx442Wuow5uLt375q0xl1l0Wg0fPToUVkbR44cabX8K2uctxZ5eXn84osvWjSPtkOHDpKT5C2hoKCA586da5YNKpWKZ8yYwbm5uVazg1lXkszMzORFixaZPQzHWsKWOjgAmwE8BHBZ71xjAIcB3BD/NhLPE4C1AG4CiAfQ05h+VhycrHh4ePCSJUtM6jiQewDv3LnDEydONKlKaczBffzxxxZX3+UmrjNb18EB4J49e8pulmxq/t2/f59nzpxZrUUCrOXk0tLSeMGCBRY5E5VKxS+++CLfu3fPaiXKuLg47tSpk8OcG1A9BzcQQE9UdHD/BLBU/L0UwAfi7zEADkLn6PoA+M2Yfq6hDm737t2sVqsd9g/TFyLiQYMG8Z49e8z++ubl5fG///3vKj2GcmLMwb3//vsW30twcLCsvdZ2cIBumZ/PPvvM5EZ2fQoKCvjIkSPcunVrq6yA0r59e16/fr1Fq0kXFhbyqVOnOCgoqFq2qFQqbtWqFR85coQLCgrMtkPfnrVr1zqkza2ycHWqqABao6KDuwbAT/ztB+Ca+PtfAMIMhTOi3+4ZEhAQINuIv2HDBod+kQyJs7MzDxkyhPft22d0bNmdO3f4P//5Dw8fPtxsRy3n4O7fv2+0DU9OWrRowUePHpXcGd0WDg4AOzk58ZNPPslff/21SR0QqampHBUVxePGjbP6h06tVnNYWBhHR0ebVLIsKiriM2fOcEhIiFV79tVqNQcHB/Pq1as5ISHBZKf78OFDXrduHT/77LN2HesmJyzhW4h1DkYWImoN4AAzdxGPs5jZU/xNADKZ2ZOIDgBYzcwnxWtHALzBzNEGdM4GUDbJ70mjRtiA/v37w8/Pz+C12NhY3Lx5084WmYaTkxN8fX3LJ9/36tULI0aMAAD89NNPuHjxIs6ePYt79+5ZtIOSj48Pdu/ebXDFi127dmHVqlXVst/V1RU7duyoMDL/xx9/RHx8PE6cOGHTzXlUKhUaNGiAYcOGoWXLlpg2bVr5tcLCQkRERCAvLw+xsbH4448/bLpBtbu7O4YPH46mTZti1qxZBiffFxQUYO3atdi3b59VNkI2BBFBo9Fg+PDh8PHxwezZsw3uXF9cXIxNmzbht99+w5UrV2CK77AXzGxwqk21HZx4nMnMjcxxcJX015ycqqWUTaWy1kMnNzXLGmlU1u+ol6Wm2qGPvW2qSbaYipSDs3Q1kQdE5MfM94jID7pOCAC4C0B/oX5/8ZyCjbH2g2frB7mmvCiKHVWpSbZUF0tnOO8DEC7+DgewV+/8C6SjD4BsZr5XTRsVFBQULMJoFZWIdgAYDMAbwAMA/wDwbwDfAWgJIAnAZGZ+JLbHrQMwGkA+gBnGqqdiGnXnk6GgoGB3qtUGZ2sMObjmzZtjyJAhVXbhFgShwtIvLi4uKCoqglarxY8//mjRqqYKCgr2ozqr/Q4aNAgAqmxLaO02OJuTmZmJjIwMjB49usL59PT08lVjiQihoaE4ePAgMjIyyvdxVFBQqBk0bNgQEyZMKF/vT6VSYdq0aRavMt2kSRNs2rTJ5H1Xa2wJTkFBofbj5OQEHx8fq+7PkJGRUWVHr5peRc2FblBwbcUbQG1d2F6x3TEotluPVszsY+hCTamiXmPmXo42wlKIKLq22q/Y7hgU2+2DsieDgoJCnUVxcAoKCnWWmuLgIhxtQDWpzfYrtjsGxXY7UCM6GRQUFBRsQU0pwSkoKChYHYc7OCIaTUTXiOgmES11tD2VIaLNRPSQiC7rnWtMRIeJ6Ib4t5F4nohorXgv8UTU03GWA0TUgoiiiOgqEV0hotdqi/1E5EpE54goTrT9HfF8GyL6TbRxJxFpxPMu4vFN8XprR9leBhE5EdFFcZWd2mb7bSK6RESxRBQtnqvxz01lHOrgiMgJwHoAzwDoBCCMiDo50iYDbIFubq0+SwEcYeZAAEfEY0B3H4GizAawwU42SlECYBEzd4JuheWXxPytDfYXAhjKzN0BPAFgtLiAwwcAPmHmAACZAGaK4WdCty5hAIBPxHCO5jUACXrHtcl2ABjCzE/oDQmpDc9NRaRWwrSHAOgL4JDe8ZsA3nSkTRJ2toYNVzS2433sBTCittkPwB3ABQBPQTfA1Lny8wPgEIC+4m9nMRw50GZ/6JzAUAAHoFvGv1bYLtpxG4B3pXO16rlhZodXUZsDSNY7ThHP1XR8+f+XgboPwFf8XWPvR6z29ADwG2qJ/WIVLxa69QYPA0gEkMXMJQbsK7ddvJ4NwLIJj9bhUwBLAAjisRdqj+2Abinwn4kohnSrbwO15LnRp6bMZKi1MHPZZi41FiKqD2AXgAXMnKM/L7Am28/MpQCeICJPAHsAdHCwSSZBRGMBPGTmGCIa7Gh7LGQAM98loiYADhPR7/oXa/Jzo4+jS3C1dQXgB+JKxqjpKxoTkRo657admXeLp2uN/QDAzFkAoqCr1nkSUdmHWd++ctvF6w0BZNjZ1DL6AxhPRLcBfAtdNfUz1A7bAQDMfFf8+xC6j0tv1LLnBnC8gzsPIFDsXdIACIVuVeCaTq1Y0Zh0RbVIAAnM/LHepRpvPxH5iCU3EJEbdG2HCdA5uhAxWGXby+4pBMCvLDYI2RtmfpOZ/Zm5NXTP9K/MPBW1wHYAIKJ6RORR9hvASACXUQuemyo4uhEQur1Ur0PXvvK2o+0xYN8OAPcAFEPXtjATuvaRI9BtfP0LgMZiWIKuVzgRwCUAvRxs+wDo2lLiAcSKMqY22A+gG4CLou2XASwXz7cFcA66zcW/B+AinncVj2+K19s6+tkR7RoM3YZNtcZ20c44Ua6UvZe14bmpLMpMBgUFhTqLo6uoCgoKCjZDcXAKCgp1FsXBKSgo1FkUB6egoFBnURycgoJCnUVxcAoKCnUWxcEpKCjUWRQHp6CgUGf5P7LoUxfXZb0cAAAAAElFTkSuQmCC\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 351x1728 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Number of Contours found = 16\n","[[[ 1 -1 -1 -1]\n","  [ 2  0 -1 -1]\n","  [ 3  1 -1 -1]\n","  [ 4  2 -1 -1]\n","  [ 5  3 -1 -1]\n","  [ 6  4 -1 -1]\n","  [ 7  5 -1 -1]\n","  [ 8  6 -1 -1]\n","  [ 9  7 -1 -1]\n","  [10  8 -1 -1]\n","  [11  9 -1 -1]\n","  [12 10 -1 -1]\n","  [13 11 -1 -1]\n","  [14 12 -1 -1]\n","  [15 13 -1 -1]\n","  [-1 14 -1 -1]]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"iTeiBX4jBcUW"},"source":["### **RETR_CCOMP**\n","\n","Retrieves all the contours and arranges them to a 2-level hierarchy. ie external contours of the object (ie its boundary) are placed in hierarchy-1. And the contours of holes inside object (if any) is placed in hierarchy-2. If any object inside it, its contour is placed again in hierarchy-1 only. And its hole in hierarchy-2 and so on."]},{"cell_type":"code","metadata":{"id":"hsJwfUlxBEYq","colab":{"base_uri":"https://localhost:8080/","height":864},"executionInfo":{"status":"ok","timestamp":1636999406500,"user_tz":0,"elapsed":941,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"05c892c6-14f1-4546-956d-66af13ac2675"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","_, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n","imshow('After thresholding', th2)\n","\n","# Use a copy of your image e.g. edged.copy(), since findContours alters the image\n","contours, hierarchy = cv2.findContours(th2, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))\n","print(hierarchy)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 146.25x720 with 1 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\n","text/plain":["<Figure size 146.25x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Number of Contours found = 38\n","[[[ 1 -1 -1 -1]\n","  [ 2  0 -1 -1]\n","  [ 3  1 -1 -1]\n","  [ 4  2 -1 -1]\n","  [ 5  3 -1 -1]\n","  [ 6  4 -1 -1]\n","  [ 7  5 -1 -1]\n","  [ 8  6 -1 -1]\n","  [ 9  7 -1 -1]\n","  [10  8 -1 -1]\n","  [17  9 11 -1]\n","  [12 -1 -1 10]\n","  [13 11 -1 10]\n","  [14 12 -1 10]\n","  [15 13 -1 10]\n","  [16 14 -1 10]\n","  [-1 15 -1 10]\n","  [25 10 18 -1]\n","  [19 -1 -1 17]\n","  [20 18 -1 17]\n","  [21 19 -1 17]\n","  [22 20 -1 17]\n","  [23 21 -1 17]\n","  [24 22 -1 17]\n","  [-1 23 -1 17]\n","  [32 17 26 -1]\n","  [27 -1 -1 25]\n","  [28 26 -1 25]\n","  [29 27 -1 25]\n","  [30 28 -1 25]\n","  [31 29 -1 25]\n","  [-1 30 -1 25]\n","  [35 25 33 -1]\n","  [34 -1 -1 32]\n","  [-1 33 -1 32]\n","  [36 32 -1 -1]\n","  [-1 35 37 -1]\n","  [-1 -1 -1 36]]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"8xHjHnlHBi-k"},"source":["### **RETR_TREE**\n","It retrieves all the contours and creates a full family hierarchy list."]},{"cell_type":"code","metadata":{"id":"HXOyW6kfBngk","colab":{"base_uri":"https://localhost:8080/","height":864},"executionInfo":{"status":"ok","timestamp":1636999440161,"user_tz":0,"elapsed":757,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"8303d0ef-f2d6-482d-c449-ff4149c45fb5"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","_, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n","imshow('After thresholding', th2)\n","\n","# Use a copy of your image e.g. edged.copy(), since findContours alters the image\n","contours, hierarchy = cv2.findContours(th2, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))\n","print(hierarchy)"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 146.25x720 with 1 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\n","text/plain":["<Figure size 146.25x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Number of Contours found = 38\n","[[[ 1 -1 -1 -1]\n","  [ 2  0 -1 -1]\n","  [ 3  1 -1 -1]\n","  [ 4  2 -1 -1]\n","  [ 5  3 -1 -1]\n","  [ 6  4 -1 -1]\n","  [ 7  5 -1 -1]\n","  [ 8  6 -1 -1]\n","  [ 9  7 -1 -1]\n","  [10  8 -1 -1]\n","  [17  9 11 -1]\n","  [12 -1 -1 10]\n","  [13 11 -1 10]\n","  [14 12 -1 10]\n","  [15 13 -1 10]\n","  [16 14 -1 10]\n","  [-1 15 -1 10]\n","  [25 10 18 -1]\n","  [19 -1 -1 17]\n","  [20 18 -1 17]\n","  [21 19 -1 17]\n","  [22 20 -1 17]\n","  [23 21 -1 17]\n","  [24 22 -1 17]\n","  [-1 23 -1 17]\n","  [32 17 26 -1]\n","  [27 -1 -1 25]\n","  [28 26 -1 25]\n","  [29 27 -1 25]\n","  [30 28 -1 25]\n","  [31 29 -1 25]\n","  [-1 30 -1 25]\n","  [35 25 33 -1]\n","  [34 -1 -1 32]\n","  [-1 33 -1 32]\n","  [36 32 -1 -1]\n","  [-1 35 37 -1]\n","  [-1 -1 -1 36]]]\n"]}]},{"cell_type":"markdown","metadata":{"id":"mflMwnhiFiK9"},"source":["## **Contouring Modes**\n","\n","#### **CHAIN_APPROX_NONE**"]},{"cell_type":"code","metadata":{"id":"rAczGb7QFAXq","colab":{"base_uri":"https://localhost:8080/","height":864},"executionInfo":{"status":"ok","timestamp":1636999615563,"user_tz":0,"elapsed":644,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgtO-hUyDqrPmYR3HGcaXMtwRIq1ObsdPjhiGDSWSw=s64","userId":"08597265227091462140"}},"outputId":"f259e77c-a27f-4ba6-e47a-7ecc83a4cf75"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","_, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n","imshow('After thresholding', th2)\n","\n","# Use a copy of your image e.g. edged.copy(), since findContours alters the image\n","contours, hierarchy = cv2.findContours(th2, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))\n","for c in contours:\n","  print(len(c))"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAJkAAABGCAYAAADfNDZAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWSElEQVR4nO1de3RU1b3+fjPJDCSZkISEQHgkICg29IaSAqE0+EDboLSX6/JRBLRYRe+VpbKwIBcvcqmKsFBjyy0KgkIRA4KgvOEC15KkPEODvE0CJkACNO9MSDLJ+e4f58xxJjNJZiBDxjrfWnvNOXvv396/vc93fvs5+whJBBCAL2HoaAUC+OdHgGQB+BwBkgXgcwRIFoDPESBZAD5HgGQB+BzfS5KJyEgR+UZEakRk3C3K824RuXgL8kkQEYpI0A3ItqqjiHwsIq9r16kicvZmdPUUfk0yEfk/ESkXEXOzoHkAFpMMI7lJeyj92znvdk/Tn0ByP8k7bkVefksyEUkAkAqAAH7dLDgewMl2ysdri+FhukZfpPt9hN+SDMATAA4A+BjAk3ZPEckH0A/AZq25/JsWlKvdP6bFGysifxeRChHJFpF/cUjjgojMFJHjAKzNiSYif3WXphY2XUSuikixiEx28P9YRJaIyDYRsQK4R0TiRGSDiFwTkfMi8oJD/GEickREqkTkioi806z8E0SkUET+ISKzHeTMIpIuIpc1l+7G0tvj/kREckSkWkTWAujkEObUtGp18rKIHBeRShFZKyKO8WdoZb4sIk97ZelJ+qUDkAfgPwAkA7ABiHUIuwDgPod7AujvcP8TAFcBDAdghErSCwDMDvJ/B9AbQOcW8m+e5t0AGqE21cEAHgBQCyBSC/8YQCWAkVBf3hAARwHMAWCC+mIUAPilFv9vACZp12EAUrTrBC3vZQA6A0gCUA/gTi18HtSXrxuAGADZAP7goONF7doE4FsA0zR9H9bq8fXmcR3q5BCAOABRAE4DeE4LSwNQAiBRK9fq5vXT6rPsaDK18IB/rlVItHZ/BsA0L0i2xF7xDn5nAdzlIP9UGzq4I9l1AEEOflcdyPExgFUOYcMBFDZLcxaAj7TrvwL4b3sZHeLYSdbLwe8QgN9o1/kAHnAI+yWAC25INgrAZQDiEDe7DZJNdLhfCOB97XoFgPkOYf29IZm/NpdPAthF8h/a/Ro4NJkeIB7AdK2prBCRCqhWK84hTtEN6FVKstHhvhaqFXKXZjyAuGY6/CeAWC38dwBuB3BGRA6LyNhmeZW0kE8cVAtlx7dwLhcc4l2ixgqHuK2htTwdy+ZV3fmk03szEJHOAB4FYBQRe6HNACJEJIlkrgfJFAF4g+QbrcTxxfYTxzSLAJwnOcBtRPIbAONFxADgIQDrRaSrB3lchvPAp4/m1xzFAHqKiDgQrQ9US+gtigH0crjv7Y2wP1qycQCaAPwIwGDN3QlgP9TBgDtcgdrnsWMZgOdEZLioCBWRB0XE4oUezdP0FocAVGsDjM4iYhSRQSIyFABEZKKIxJBUAFRoMooH6X4K4FURiRGRaKh9vtVu4v0Nah/yBREJFpGHAAy7wbKsAzBZRO4UkRAA/+WNsD+S7Emo/ZZCkiV2B2Ax1BGXO+s7F8BKrVl6lOQRAM9oMuVQBxG/9VIPpzS9LQTJJgBjob4k5wH8A8CHALpoUdIAnBSRGgDvQe1zXfcg6dcBHAFwHMDXAHI0v+b5N0C1kL8FUAbgMQCfe1sOLa3tAP4IYB/UujygBdV7Ii/OTXYAAbQNEbkTwAmoo/XGtuL7oyULwA8hIv+mzdFFAlgAYLMnBAN8RDIRSRORsyKSJyKv+CKPAG45noU6ZZMPtc/8754KtntzqS2nnANwP4CLAA4DGE/yVLtmFMD3Br6wZMMA5JEs0DqfGQD+1Qf5BPA9gS9I1hPOk3UXNb8AfqDosMlYEZkCYIp2m+ylLAyGlt8PRVEQGDXfepAUd/6+INklOM8I99L8miu0FMBSQN275U0Gc+bMwVNPPdVieHFxMfbv348PP/wQ586da5FwoaGh+NWvfoUHH3wQsbGxKCsrw969e7FlyxZcvuw6iT5z5kyMHj0aALBixQpkZGS4xOnWrRumTJmCsLAwWK1WpKeno7q62m3+kZGReP755xEWFuY23I7GxkYcPHgQO3bsgM1mcwoTEaSmpiIpKQndu3fH+vXrceHCBZSXl7eaptFoRHR0NBITExEZGYmysjKcPHkSpaWlaGpqalXWa/hgcTsI6m6DvlB3AuQCSPRgMdojJyLcvn0724KiKCwuLuakSZNoMBhc0klMTGRmZiZtNhsVRXGSO3/+PCdOnMigoCAnmb/85S96vLffftutfklJSSwtLSVJXrt2jT169GixLPfddx8bGxs9KsvVq1fZvXt3J/mQkBC+/PLLLC0tZXV1NW02G/Pz85mbm8vRo0dTe3ndln3VqlW8dOkSGxoaqCgKGxoaWFhYyAULFjA5Odnj5+HoWny+7U0yjTQPQB1h5gOY7UH8dieZ/eFUVVUxLS3NKY2oqChmZmY6kas5rl+/zpkzZzo9qJUrV+rhp0+fZrdu3ZzSNRqNXLx4sZ6uO2I4uqlTp7aqgyOKi4ud8ouPj+e6devY0NDgttxlZWWcNGmSC9FSUlJ46dIllxerqamJiqLw+vXrvHjxIseMGePfJPPW3QzJKisr+f777zMjI4N79uzhnj17mJOTQ5vNpldgZmYmO3XqpKcxa9YsNjU1OT2QgwcPsqioyMmyXLt2jYMGDdLlxo8fz/r6el1u4cKFTlbyF7/4BWtqavTwzz//nCaTqcWyLFy4UM/LarWyqKiIRUVFvHjxIsvLy2m1WllXV8e6ujpmZmbqaYWHhzM9PZ3Xr18nSTY1NfHq1assKSlhXl4ez5w5Q5vNxuLiYg4YMEDPLzQ0lNnZ2TrBFEVhfX09r1y5wvXr19Nqter+Z8+eZWxs7A+XZJs3b9YfTk5ODs1mM0WEBoOBBoOBYWFhXLx4sU6kmpoaDh48WH9Ap0+f1uXPnTvH5ORkms1mxsTEcPr06frDUxTFqVns1KkTt2/frj+kyspKjho1igAYHR3NI0eO6GHFxcVMTExssRxGo5FffvklFUWhoih84403GBERwYiICEZFRfG2225jYmIik5KSmJSUxISEBF329ttvZ0VFBUmyoaGBb731Fnv27MkePXrwpZde4u7du1lQUEBFUThnzhxdbtSoUfpLUldXx0WLFnHEiBHs168fY2JiuGjRIr3sjY2NHDdu3A+TZM0tQE5Ojltr0bdvX5aXl+tkeeSRRwiAycnJrK2t1StywoQJTnLBwcFcu3atTpbc3FyGhITo4SNGjGBZWZmeblZWFqOjo/mnP/1JJ3VDQwOff/75VssQGhrKOXPm6H2ixx57jCaTiV27dm3V+okI3377bd3irl+/nmazWQ8PCgri448/rvcLjx07xs6dO+uW2LFcdn/Hsu/atUsv27x589qFZH63dhkUFNTmaMuT0U9sbCxMJhMA9UWqq6sDAKSmpqJTJ3XremVlJTIzM53kbDYbtm7dirKyMpSWlkJE9HQA4MCBA1i0aBEURYGIICUlBZs2bcLkyZNhMBhAEtu3b8fy5ctb1S8yMhJpaWlQFHV3z/z585GZmYnc3Fx89dVXePPNN3HbbbchODgYRuN3/0kxmUy4++67YTQaQRLZ2dmor/9uM0RjYyMuX76Mzp07AwDi4+MRExMDAKirq7O/1OjSpQuioqKcdLJYLOjTp49+39DQ0GoZRNzOWLiio61Yc0tmsVg4cODAVt+Y+fPn65bsxIkTjI2NZZcuXXSXnJzs1LEvKytjv379CIBLlizRZY8dO+bUV7O7kJAQxsXFMS4ujjExMS7hXbp0YVZWlttOe0lJiVM/riWXkpLCuro6F3m7FcnOzubatWuZlpZGo9Goy8XFxbGwsJAkWVRUxBEjRrikHRwczA0bNpBUBzDDhg0jAPbv35/nzp3Tm+iDBw/yxRdf5Pjx4zlp0iQuWrRI78tWVVUxJSWlVUscFRXlkSXzu52x1dXVOHPmjMfxBw4ciCNHjugWAQC6du2KkJAQiAhIYt++fSgqUhchunXrpsc7f/68buEcUVtbi9ra2hbzrKysxCuvvIItW7YgPDxc97fZbFiwYAFOnmz733qxsbGora2F2ez8RyOSuHbtGqqqqjBmzBgYjUZUVFTgwIEDulxpaSl69+6NwsJCXLzo+l9em82mz82ZTCZERkYCAAoKCrBhwwbMmDEDBoMBw4YNw9ChQ51kRQSKoiA9PR2HDh1qUX+r1Qqr1dpmOQE/3H7tLYxGI3r16uU2jCTy8vIwe/Zsl0lMAE7E9BZZWVn48ssvMXHiRN3v1KlTWLp0qd4ktYa+ffvq8Ww2G/Lz87Fjxw5cvXoVJSUleOGFFxAWFoZx48YhKioKY8aMQX19PYKCgjBggLqju0+fPi0+aHcvj6Io2LdvH6ZPn66vmLhr8hobG7Fz586bqh9HfO9J1tIDra+vx969e/H73//eyTJ6QgBP0KdPH4wcOdLJr3fv3rjjjjuQk5PTpnz37t1RXV2NwsJC5OXl4ZlnnkFFhboL22Aw4NSpU9i1axfCw8MxfPhwDBo0CEePHnXpI7qDiKB79+4u/mFhYZg9ezaCgtTHThLl5eWorKxEREQEIiIiICIIDg7Gc889h0OHDrl9Ob1GR/fHmvfJPHGOfbILFy7wiSee4OOPP84333yTCxcu5OTJk5mSkuI06rK7119/Xe9Lbd++3e1qgIjQZDLprnm4yWTiRx995NInUxSFO3bscBqNtuRCQkLYpUsXhoeHMzw83CXcaDTqUzWKovCZZ54hAA4ZMkTvy3399dcu/SJofbL9+/eTVOfffvzjHxMA77//fn0Kw2az8b333mO/fv1osVjYv39/pqen6+E1NTUcOnRou4wuO5xgN0uyTz75xCvZCRMm6FMNeXl5bh9wYmIiDx8+zGPHjnHfvn0uM/tPPvmk06RsRUWF0/TFxIkTvdLJ0UVGRjIiIoIAuGDBAr2cU6dOJQDGxsbyypUrOoHcDTIsFgu/+eYbkuSlS5d0/d966y39xdiyZYvLoMdsNusDBkVRuGTJknYhmd9NYXgLbxdzc3Jy9H5Mr169kJKS4hLn/vvvx5AhQzB48GAMGDDAKY+EhAS89tprepP19ddfY9SoUfjqq69AEsHBwZg7d67TVIA73HvvvXj22Wcxfvx4l2kDq9UKg8GAhIQEAKohsA9EKioq8O236t8nzWaz3j9zxIMPPoj4+HgA6uDGvlgeFBSk98GysrJc+m319fX6AENEEB8f7/k0RWvoaCt2s5Zs1apVXsl27tyZBw4c0N/Wo0ePctCgQfoa38CBA3UroCgKMzIy9CkEs9nMVatW6dbAarXyoYceIgDeddddrKqq0uWWLVvmssDu6D744AN9zTArK4uRkZF6mIhw7Nix+qRvTU0Nk5KS9PDFixfr+axZs8apSQ8LC+O+ffv08FdffVUPmzVrlq777t27abFYnHSKjY1ldna2XrcbNmxocZHdnWvx+XpAgBVQ93afcPCLArAbwDfar/08CIH616k8qH/ZGuJrkm3cuNGrioDW3NkXlhVFYUlJCT/88EMuWbKERUVF+oOora11WlyfMmWKUzP5/vvv6wQ0GAycNm2aTtDa2lqOHTu2RR1SU1N1UjY1NXHv3r18+OGH+bOf/Yxz5sxhWVmZPp+1ceNGJ8KmpaXpejQ0NPCDDz7g0KFDOXr0aK5bt05fDaiqqtKX0wBw+PDhrK6uJqmudmRnZ+vzZNOmTePhw4f1sttsNj711FPt0lx6QoBRAIY0I9lCAK9o168AWKBdPwBgu0a2FAAHfU2yTz/91CtZaBZpzZo1re6AaGpq4rvvvqs/3ODgYB46dEgn2IkTJ9izZ0+ndI1GI7/44gudHFu3bnWaSHV0BoOBc+fOdSJ7Y2Mj6+vrnRawCwsLXbbedOrUiRs3bnSKV1dXpy9R2fV/5513nPIPDg7m6tWrXXZg2J2j32effeay7OQzkmkkSGhGsrMAemjXPQCc1a4/gPqnEZd4viJZRkaG1yQDwJiYGK5YsYK1tbUuFVxXV8elS5c6NScmk4nHjh3T4zz99NNu03322Wf19I4cOdLqOqTJZOK8efNYVVXldqR66tQppqamupWNj4/n/v379QGHo1xDQwMzMjLcjjzj4uK4efNml310dtn6+nouW7aM0dHRXtdpe5OswuFa7PcAtgD4uUPYHgA/bW+SPfroo9y2bRu3bdvGuXPnet1cOr7Z99xzD5cvX86srCyePn2aq1ev5pgxYxgcHOwSd+vWrSwoKGBubm6LS18DBgxgVlYW8/Ly+Oc//7nVfhk0izZy5EguXbqUx48fZ35+Pnfu3MkZM2a0uuERUEeis2bN4oEDB1hQUMCCggJmZWXxkUcecbtcZnchISEcN24cV65cyVOnTrGgoID5+fnctGkTx44d2+qL0SEk0+7LvSUZ1P39RzTndYG0Q9humGDuCBcaGtpqeqGhobRYLAwLC2s1rZCQEFosFrdzcJ7ItdTEtuTMZjMtFgstFovbucHW6tBephvJ11OS3eiM/xUR6UGyWER6QB0YAB7u74eqkcsef4vFgtDQUJBEY2MjDAYDrFar23VEjajtBpvN1ubstqdrda2te/pCrr6+3mknhqcg6XGZjEaj2z/vmEwmNDQ0tFp3N0qyL6EejPKW9vuFg/9UEcmAeghcJcliTxONiorC4MGDYbPZcPbsWURERKCgoOCGKz+Am0ePHj3Qs2dP9O3b12kzgB1msxmff/45SkpK3EiraPMf5CLyKdRT+aKhHqf0GoBNUI8T6gP1YLVHSZaJOnO3GOqJNbUAJlM9YaetPKqhDhL8FdFQT+XxR/iLbvEkY9wF+MWpPiJyhORPO1qPluDP+vmzbnZ875eVAvB/BEgWgM/hLyRb2tEKtAF/1s+fdQPgJ32yAP654S+WLIB/YnQ4yTr6VEYR6S0i+0TklIicFJEXNf8oEdkt6tfodmvHWEI7TfuPmr7HRWTILdDRKCLHRGSLdt9XRA5qOqwVEZPmb9bu87TwBF/r5gk6lGTaqYz/A2AM1CPVx4vIj26xGo0AppP8EdSdI89rOrwCYA/Vc/j3aPfQdB2guSlQv37ia7wI9TM0diwA8C7J/lBP9/6d5v87qEt8/QG8q8XreHiydukrB2AEgJ0O97MAzOpgnb6AehRpu+40uQl9ekEl+b1Q14YF6uRrUPM6BLATwAjtOkiLJ77SzVPX0c2lX53KqDUvPwFwEOoHw+xLYiX47nM1t1rndAAz8N2HJLpC3aBgP3naMX9dNy28UovfoehokvkNRCQMwAYAL5Gscgyjahpu+TBc1O8tXSV59Fbn3Z7o6P9derxrw5cQkWCoBPuEpP2rHTe906QdMBLAr0XkAajfqgyH+vWSCBEJ0qyVY/523S5qX27pAqDUR7p5jI62ZIcBDNBGSyYAv4G6k+OWQVvUXw7gNEnHD5vad5oArjtNntBGmSnwcqeJNyA5i2QvkglQ62YvyQlQPz/zcAu62XV+WIvf8ROhHd0phJenMvog/59DbQqPQ/3Q6t81nbpC7XB/A+B/AURp8QXqiDgf6reN2tz520563g1gi3bdD+oHwvIAfIbvPhbbSbvP08L7dfTzJRmY8Q/A9+jo5jKAHwACJAvA5wiQLACfI0CyAHyOAMkC8DkCJAvA5wiQLACfI0CyAHyO/weGr5GkcUTWiwAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 146.25x720 with 1 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\n","text/plain":["<Figure size 146.25x720 with 1 Axes>"]},"metadata":{"needs_background":"light"}},{"output_type":"stream","name":"stdout","text":["Number of Contours found = 38\n","87\n","50\n","7\n","1\n","1\n","1\n","1\n","4\n","2\n","1\n","236\n","80\n","6\n","75\n","10\n","4\n","8\n","426\n","6\n","4\n","4\n","10\n","14\n","21\n","17\n","241\n","6\n","14\n","15\n","155\n","14\n","9\n","358\n","4\n","4\n","347\n","272\n","92\n"]}]},{"cell_type":"markdown","metadata":{"id":"thSvWAyFFuHr"},"source":["\n","#### **CHAIN_APPROX_SIMPLE**"]},{"cell_type":"code","metadata":{"id":"v2KUnN6tFo4g","colab":{"base_uri":"https://localhost:8080/","height":884},"executionInfo":{"status":"ok","timestamp":1605648366483,"user_tz":240,"elapsed":887,"user":{"displayName":"Rajeev Ratan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GhBYJnwdIGYSJdvGmzIt64rYSF1dDuiFyfRw4Rpeg=s64","userId":"08597265227091462140"}},"outputId":"670cad89-6590-4f18-9aca-665f8d232451"},"source":["image = cv2.imread('images/LP.jpg')\n","\n","# Convert to Grayscale\n","gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n","\n","_, th2 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n","imshow('After thresholding', th2)\n","\n","# Use a copy of your image e.g. edged.copy(), since findContours alters the image\n","contours, hierarchy = cv2.findContours(th2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n","\n","# Draw all contours, note this overwrites the input image (inplace operation)\n","# Use '-1' as the 3rd parameter to draw all\n","cv2.drawContours(image, contours, -1, (0,255,0), thickness = 2)\n","imshow('Contours overlaid on original image', image)\n","\n","print(\"Number of Contours found = \" + str(len(contours)))\n","for c in contours:\n","  print(len(c))"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 351x1728 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Number of Contours found = 38\n","8\n","10\n","7\n","1\n","1\n","1\n","1\n","4\n","2\n","1\n","114\n","40\n","6\n","37\n","6\n","4\n","8\n","172\n","6\n","4\n","4\n","6\n","6\n","15\n","11\n","121\n","6\n","6\n","8\n","57\n","8\n","7\n","170\n","4\n","4\n","164\n","65\n","27\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"JMMBiTRDFxwH"},"source":[""],"execution_count":null,"outputs":[]}]}