CV_PS_22_23.ipynb

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  "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": "3.7.3"
    },
    "colab": {
      "name": "CV-PS_22-23.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhipraay/CVI_Projects_PS/blob/main/CV_PS_22_23.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3HspUFJBejkn"
      },
      "source": [
        "#Computer Vision and Intelligence Group, CFI- IIT Madras\n",
        "\n",
        "#Common CV Problem Statement \n",
        "## Lane Detection using Computer Vision\n",
        "\n",
        "Write a python program such that, given an input image of a road with lanes, will give an output image highlighting the lanes on the road, using conventional CV techniques like Colour Spaces, Convolution Operators, Edge Detection and Hough Transform.\n",
        "\n",
        "Sample input : \n",
        "\n",
        "![test_img02.jpeg](data:image/jpeg;base64,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)\n",
        "\n",
        "Sample Output : \n",
        "\n",
        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)\n",
        "\n",
        "The following cells shall brush-up your knowledge about the above mentioned techniques and the task to be done is at the end. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UymZ5qiIytv"
      },
      "source": [
        "Cloning the github repo, and making the current Directory as CV_PS, which has two repositories with images - LD_test_imgs, sample_imgs. \n",
        "\n",
        "LD_test_imgs - \n",
        "*   test_img01.jpeg \n",
        "*   test_img02.jpeg \n",
        "*   test_img03.jpeg \n",
        "\n",
        "sample_imgs - \n",
        "\n",
        "*   hat_lady.png  \n",
        "*  sample.jpg\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D43EIqXaIXuP"
      },
      "source": [
        "!git clone https://github.com/shreyesss/CVI-Project-apps.git\n",
        "%cd CVI-Project-apps/CV_PS_imgs/\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5UFPYQpejk0"
      },
      "source": [
        "## Representing Images in a Computer\n",
        "\n",
        "<p align=\"justify\"> Consider a pixel, the smallest constituent of an image, such that each pixel displays one particular colour.The image can be represented as a matrix of pixels with each element of the matrix denoting the colour to be displayed by that pixel.</p>\n",
        "\n",
        "\n",
        "\n",
        "An image is represented by a matrix of the following dimension: (image height) x (image width) x (number of channels).\n",
        "\n",
        "**For Grayscale Images:**\n",
        "<p align=\"justify\">Each pixel has one channel represented by an 8-bit number (from 0 to 255). A pixel value of 0 corresponds to Black, while 255 corresponds to white. The values in between correspond to different shades in between.</p>\n",
        "\n",
        "**RGB Images:** \n",
        "\n",
        "Each pixel has three associated channels-\n",
        "\n",
        "Red- from 0 to 255 \n",
        "\n",
        "Green- from 0 to 255\n",
        "\n",
        "Blue- from 0 to 255\n",
        "\n",
        "(0, 0, 0) => Black\n",
        "\n",
        "(255, 255, 255) => White\n",
        "\n",
        "The colour of an RGB image, coming from any pixel, is a mixture of the colours from the three channels."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tz-xm6pxejk0"
      },
      "source": [
        "### Reading, Displaying and Writing an Image to a Path\n",
        "\n",
        "Functions to be used:\n",
        "\n",
        "1.   cv2.imread(file_path, flag)\n",
        "\n",
        "  ```\n",
        "  flag = 0: read image in Grayscale \n",
        "  flag = 1: read image as a BGR image\n",
        "  ```\n",
        "\n",
        "2.   cv2.imwrite(file_path, variable_name)\n",
        "\n",
        "  ```\n",
        "    variable_name: name of variable holding the image that is to be saved\n",
        "    ```\n",
        "\n",
        "3.   cv2.waitKey(delay)\n",
        "\n",
        "  ```\n",
        "    delay: waits for a keypress event for 'delay' ms before proceeding to next line\n",
        "    ```\n",
        "\n",
        "4.   cv2.imshow(title, variable_name)\n",
        "  \n",
        "  However, Google colab crashes if you try to display image using cv2.imshow() instead the following is imported : \n",
        "\n",
        "  ```\n",
        "  from google.colab.patches import cv2_imshow \n",
        "  ``` \n",
        "  And images can be displayed using cv2_imshow(variable_name),  alternatively plt.imshow(variable_name) can also be used. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3iKMWczejk2"
      },
      "source": [
        "#Importing a few relevant packages\n",
        "#The numpy library contains optimised functions that are used to operate on multi-dimensional arrays \n",
        "#cv2 is an OpenCV library containing functions used in Computer Vision\n",
        "#matplotlib is a python plotting library\n",
        "\n",
        "import numpy as np  \n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from google.colab.patches import cv2_imshow "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDfFSNQtejk4"
      },
      "source": [
        "#EXERCISE: GETTING STARTED WITH IMAGES- READING, DISPLAYING AND WRITING AN IMAGE \n",
        "\n",
        "\"\"\"\n",
        "Using the functions described above, write a program to read the image 'sample.jpg' in BGR and Grayscale separately\n",
        "display them and save the Grayscale image as 'sample_BW.jpg'\n",
        "\"\"\"\n",
        "#NOTE: Path of sample.jpg is \"sample_imgs/sample.jpg\"; write sample_BW.jpg to the path \"sample_imgs/sample_BW.jpg\" \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdtQTcAqejk5"
      },
      "source": [
        "## Colour Spaces\n",
        "\n",
        "**A colour space is a map from a certain colour to a set of numbers (usually 8-bit in size). Different colour spaces (functions) may be advantageous for different purposes.**\n",
        "\n",
        "Let's look at a few colorspaces used commonly, a few of which we'll use for lane detection.\n",
        "\n",
        "1. Grayscale\n",
        "\n",
        "\n",
        "2. BGR : <p align=\"justify\">Various colours are obtained by a linear combination of Blue, Green and Red values. (2^24 = 16,777,216 possibilities). The three channels are correlated by the amount of light hitting the surface.  In BGR colour space, chrominance (colour related information) and luminance (intensity related information) tend to get mixed. This makes colour based segmentation (separation of components of the image) difficult in BGR colourspace. Also, a difference in lighting would alter the values of the three channels quite drastically.</p>\n",
        "\n",
        "\n",
        "3. HSV- HSV images have three components:\n",
        "        a. Hue (Dominant Wavelength Information)\n",
        "        b. Saturation (Purity or shades of the colour)\n",
        "        c. Value (Intensity)\n",
        "    Since there is only one variable giving the information about the colour, it is easier to segment images based on colour in the HSV space. \n",
        "    \n",
        "4. CMYK- This colour space is mainly used in printing. The components are Cyan, Magenta, Yellow and Black\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O5jARmUjejk6"
      },
      "source": [
        "### Converting between Colour Spaces in OpenCV\n",
        "\n",
        "Often, we would need to use different colour spaces during image processing as certain features may be easier to extract in one colour space over another. \n",
        "\n",
        "Function:\n",
        "\n",
        "cv2.cvtColor(input_image, flag)\n",
        "\n",
        "```\n",
        "flag: Specifies the type of conversion\n",
        "cv2.COLOR_BGR2GRAY- Converts BGR image to Grayscale, the Grayscale channel value is the average of the B, G and R channel values\n",
        "cv2.COLOR_GRAY2BGR- Converts Grayscale image to BGR. The Grayscale value is simply copied to all the three channels\n",
        "cv2.COLOR_BGR2HSV\n",
        "cv2.COLOR_HSV2BGR\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Conversion from RGB to HSV\n",
        "\n",
        "![Screenshot from 2019-07-15 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)\n",
        "\n",
        "Try to write a code to convert an input image in BGR format to Grayscale and HSV, and display them, using OpenCV functions. Read the image 'hat_lady.png' from the folder 'sample_imgs'. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MslDy5Nejk6"
      },
      "source": [
        "#EXERCISE: CHANGING COLOUR SPACES \n",
        "\n",
        "\"\"\"\n",
        "Using the functions described above, write a program to read the image 'hat_lady.png' in BGR convert them to\n",
        "GRAY and HSV, use numpy slicing to store hue, saturation and value channels separately in variables, display \n",
        "all the images (except the input) in separate windows. \n",
        "\"\"\"\n",
        "#NOTE: Path of hat_lady.png is \"sample_imgs/hat_lady.png\"\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsdVtUjP089x"
      },
      "source": [
        "## The  2D Convolution Operation\n",
        "\n",
        "A 1D Convolution Operation on two discrete functions f(n) and g(n) may be defined as:\n",
        "\n",
        "$$ y(n) =  \\sum \\limits _{k=-\\infty} ^{+\\infty} f(k)g(n-k)  $$\n",
        "\n",
        "This operation may be summarised in the following steps:\n",
        "\n",
        "1. Express each function in terms of a dummy variable k\n",
        "\n",
        "2. Reflect the function g(k) about the y-axis. g(k)->g(-k)\n",
        "\n",
        "3. Shift the function g(-k) by n units to the right, where n is the value at which we wish to compute y\n",
        "\n",
        "4. <p align=\"Justify\">Take an element-wise product of g(n-k) and f(k). Now sum over the products for all values of k from -inf to +inf. The sum is equal to the value of y at n.</p>\n",
        "\n",
        "\n",
        "<p align=\"Justify\">For our purposes, we may *convolve* our image matrix with a **filter matrix** in 2 Dimensions. The filters may be used for specific purposes such as blurring, sharpening, edge detection, corner detection etc.</p>\n",
        "\n",
        "Consider the following filter used for sharpening an image:\n",
        "\n",
        "$$\n",
        "\\begin{bmatrix}\n",
        "0 & -1 & 0 \\\\\n",
        "-1 & 5 & -1\\\\\n",
        "0 & -1 & 0\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "\n",
        "**2D convolution of the image matrix with the filter consists of the following steps:**\n",
        "\n",
        "1. Flip the filter both horizontally and vertically.\n",
        "\n",
        "2. <p align=\"Justify\">Put the first element of the kernel at every pixel of the image (element of the image matrix). Then each element of the kernel will stand on top of an element of the image matrix.</p>\n",
        "\n",
        "3. To calculate the value of convolution output at pixel (2,2), center the kernel at that position on the image matrix.\n",
        "\n",
        "4. Multiply each element of the kernel with its corresponding element of the image matrix (the one which is overlapped with it)\n",
        "\n",
        "5. Sum up all product outputs and put the result at the same position in the output matrix as the center of kernel in image matrix.\n",
        "\n",
        "6. <p align=\"Justify\">For the pixels on the border of image matrix, some elements of the kernel will stand out of the image matrix and therefore does not have any corresponding element from the image matrix. In this case, we may apply 0-padding to the input matrix (based on the size of the kernel we might need one or more pixels padding, in our example we just need 1 pixel padding).</p>\n",
        "\n",
        "If after the convolution operation, an output element happens to exceed 255 or is less than 0, it is thresholded to 255 or 0 respectively.\n",
        "\n",
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        "\n",
        "**Parameters of a 2D convolution operation:**\n",
        "\n",
        "1. Size of the kernel (F)\n",
        "\n",
        "2. <p align=\"Justify\">Stride (S)- Number of steps by which the centre of the kernel is slid, after every iteration. This need not always be one, especially when the output matrix is smaller in dimension when compared to the input matrix (image)</p>\n",
        "\n",
        "3. Padding (P)- Size of the zero-padding.\n",
        "\n",
        "<p align=\"Justify\">The size of the output matrix ($N_o$) is dependent on these parameters as well as the size of the input matrix ($N_i$, \n",
        "assuming all matrices are square), as:</p>\n",
        "\n",
        "$ N_o = (N_i - F + 2P)/S +1 $\n",
        "\n",
        "**Implementing 2D convolution on a Matrix:**\n",
        "\n",
        "Now, let's implement a convolution operation on an image.\n",
        "Functions to be used:\n",
        "\n",
        "1.   **numpy.zeros(shape, dtype, order)- creates a numpy array with all elements 0**\n",
        "\n",
        "  ```\n",
        "  shape: shape of matrix to be created\n",
        "  dtype: data type of elements to be held (if the matrix is to be manipulated)\n",
        "  order: whether the multi-dimensional data is to be stored in row-major or column-major form\n",
        "  ```\n",
        "\n",
        "2.  **cv2.filter2D(src, ddepth, kernel)**\n",
        "\n",
        "  ```\n",
        "  src: source, input matrix\n",
        "  ddepth: Depth of the output matrix (for 2D convolution on 3D matrices), if ddepth = -1, the output image has the same depth as the input image\n",
        "  kernel: filter matrix\n",
        "  ```\n",
        "\n",
        "\n",
        "\n",
        "<p align=\"Justify\">Once you implement the code, experiment with the values of the kernel and observe how that changes the output image. Remember that when the kernel element values are high  in magnitude (positive or negative), you are more likely to cause the pixel value of the output to get thresholded at 255(positive) or 0(negative).</p>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Yi0-MzFejk7"
      },
      "source": [
        "#EXERCISE : 2D CONVOLUTION OPERATION \n",
        "\n",
        "\"\"\"\n",
        "Write a program that performs a 2D convolution on the image 'hat_lady.png' with a 3x3 kernel, create a \n",
        "randomkernel with element values of your choice. Remember that if the kernel weights are too high or too \n",
        "low, the pixels get thresholded to 255 or 0 respectively. \n",
        "\"\"\"\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vGDPyK2ejk8"
      },
      "source": [
        "If you want to sharpen, blur the image or detect edges in an image, you may try the following kernels:\n",
        "\n",
        "![conv_kernels.png](data:image/png;base64,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)\n",
        "\n",
        "\n",
        "## Edge Detection\n",
        "\n",
        "<p align=\"Justify\">Gradients provide the direction of greatest ascent of a function. Therefore, regions where there is a drastic change in intensity would have gradients of strong magnitude. In order to detect edges, we compute the gradients of the intensity with respect to x and y (image co-ordinates) and look for regions where the magnitude of the gradient is above a certain threshold.</p>\n",
        "\n",
        "<p align=\"Justify\">In order to compute the gradients and find regions of high gradient magnitude, we may use the following filters. These filters are convolved with the input image.</p> \n",
        "\n",
        "1. <p align=\"Justify\">Sobel Operator: The input is convolved with two filters of odd size- one to obtain Gradients in x ($G_x$) and another to obtain Gradients in y ($G_y$):</p>\n",
        "\n",
        "$$\n",
        "G_x = \n",
        "\\begin{bmatrix}\n",
        "-1 & 0 & 1 \\\\\n",
        "-2 & 0 & 2\\\\\n",
        "-1 & 0 & 1\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "\n",
        "$$\n",
        "G_y = \n",
        "\\begin{bmatrix}\n",
        "-1 & -2 & -1 \\\\\n",
        "0 & 0 & 0\\\\\n",
        "1 & 2 & 1\n",
        "\\end{bmatrix}\n",
        "$$\n",
        "\n",
        "The magnitude of the gradient may be computed as:\n",
        "\n",
        "(L2 Distance)\n",
        "$$G = \\sqrt{G_x^2 + G_y^2} $$ \n",
        "\n",
        "or\n",
        "\n",
        "(L1 Distance)\n",
        "$$G = |G_x| + |G_y| $$ \n",
        "        \n",
        "\n",
        "2. <p align=\"Justify\">GScharr Operator: This is a more accurate version of the Sobel operator, with different weights for the kernel.</p>\n",
        "\n",
        "3. <p align=\"Justify\">Laplace Operator: Instead of computing the first-order derivative, the Laplace operator checks for the double derivative to be zero. </p>\n",
        "\n",
        "<p align=\"Justify\">For our purposes, we are going to use a method known as the Canny Edge Detection. The OpenCV function for Canny detection (cv2.Canny()) uses the Sobel Kernel.</p>\n",
        "\n",
        "### Canny Edge Detection\n",
        "\n",
        "Canny Edge Detection involves the following steps:\n",
        "\n",
        "1. Gaussian Blurring to remove noise \n",
        "        The presence of noise in the image will give erroneous edges\n",
        "        Weights are sampled from a Gaussian distribution\n",
        "\n",
        "2. Applying the Sobel Filter\n",
        "\n",
        "3. Non-max suppression of Gradients \n",
        "        Suppresses all gradient values in the direction of the gradient except the local maxima\n",
        "        This ensures that the edges are thin\n",
        "        Also, notice that this complies with the fact that the gradient at every point is normal to the edge\n",
        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        "        \n",
        "\n",
        "4. Double Thresholding\n",
        "        Selects two threshold values for gradient magnitude: upper and lower\n",
        "        If the magnitude is greater than the upper threshold, the gradient is considered to be a STRONG GRADIENT\n",
        "        If the magnitude lies between the two thresholds, then it is a WEAK GRADIENT\n",
        "        Gradients will magnitude lesser than the lower threshold are suppressed\n",
        "        This is done in order to remove erroneous edges, the threshold values are parameters for the Canny() function\n",
        "        Maintain an upper: lower ratio between (2:1) and (3:1)\n",
        "\n",
        "5. Hysteresis\n",
        "        The strong edges are classified as true edges\n",
        "        Weak edges that are somehow connected to the strong edges are also considered as true edges\n",
        "        Weak edges that are not connected to the strong edges are non-edges, they are removed\n",
        "\n",
        "<p align=\"Justify\">Note:  Gaussian Blurring must be done before calling the cv2.Canny() function. cv2.Canny() is defined for images with one channel; therefore the input image must be in Grayscale</p>\n",
        "\n",
        "#### Edge Detection with the cv2.Canny() Function:\n",
        "\n",
        "**Functions to be used:**\n",
        "\n",
        "1. **cv2.GaussianBlur(img, ksize, sigmaX, sigmaY)**\n",
        "\n",
        "  ```\n",
        "  img: input image\n",
        "  ksize: size of the kernel\n",
        "  sigmaX: standard deviation in X-direction\n",
        "  sigmaY: standard deviation in Y-direction, if sigmaY is not specified, the value of sigmaX is copied to sigmaY; if both are zero, a default sigma value defined based on kernel size is used\n",
        "  ```\n",
        "\n",
        "2. **cv2.Canny(src, minVal, maxVal, ksize, L2gradient)**\n",
        "\n",
        "  ```\n",
        "  src: input matrix\n",
        "  minVal: lower threshold value for double thresholding\n",
        "  maxVal: upper threshold value for double thresholding\n",
        "  ```\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ys0vkTBgejk-"
      },
      "source": [
        "#EXERCISE : EDGE DETECTION \n",
        "\n",
        "\"\"\"\n",
        "write a Program that performs Gaussian Bluriing and Canny edge Detection on 'hat_lady.png',\n",
        "Display the Canny Output.\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1ciaFGMejk-"
      },
      "source": [
        "## Motivation for Lane Detection \n",
        "\n",
        "<p align=\"Justify\">Let's come to our lane detection problem. Starting with the image, we are now well-equipped to get a contour of the edges in the image.</p>\n",
        "\n",
        "<p align=\"Justify\">Since most lanes are either white or yellow, we may simplify our problem by selecting the white and yellow components in the image. Note that, yellow is more distinct in the HSV colour space than the RGB colour space; and we may use the grayscale version of the image to filter the whites in the image.</p>\n",
        "\n",
        "Thus, the steps to get a contour of the edges are:\n",
        "1. Create a mask that picks the yellow and white pixels in the image\n",
        "\n",
        "2. Blur the image using a Gaussian filter\n",
        "\n",
        "3. Apply Canny edge detector\n",
        "\n",
        "<p align=\"Justify\">The contour of edges obtained may contain breaks and may be noisy. Also, usually the lane markings on road are not continuous, while what we require are continuous curves that act as boundaries between one lane and another.</p>\n",
        "\n",
        "<p align=\"Justify\">Our next task would be to approximate the edges corresponding to the lanes using appropriate functions. We shall stick to finding straight lines today, since that deals with most cases we would come across. Finding functions like arcs (for curved roads) is merely an extension of the straight line case. We will find a straight line representing the lanes using a technique known as the Hough Transform.</p>\n",
        "\n",
        "<p align=\"Justify\">But before that, let's reduce the complexity of the problem by selecting regions of interest. Most images that we will deal with will contain foreground objects like roads and lanes, and background objects like buildings, trees and sky.A rudimentary way to select the Region of Interest (RoI) would be to arbitrarily define coordinates for the region of interest.</p> \n",
        "\n",
        "## Hough Transform \n",
        "\n",
        "<p align=\"Justify\">Given a set of points that seem to fall on a certain curve, which can be represented mathematically, one may use the Hough transform to obtain the equation of the curve. Here, we will consider the Hough Transform for straight lines.</p>\n",
        "\n",
        "A straight line may be represented parametrically as:\n",
        "\n",
        "$$ \\rho = x.\\cos\\theta + y.\\sin\\theta $$\n",
        "\n",
        "<p align=\"Justify\">where $\\rho$ is the perpendicular distance to the line from the origin and $\\theta$ is the angle subtended by the perpendicular and the x-axis</p>\n",
        "\n",
        "![r_theta.GIF](data:image/gif;base64,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)\n",
        "\n",
        "<p align=\"Justify\">Thus, any line in the x-y plane may be represented using the two parameters ($\\rho$, $\\theta$). The Hough Transform finds those values of $\\rho$ and $\\theta$ that pass through a large number of points in the image (above a certain threshold).</p>\n",
        "\n",
        "**Accumulator (or Hough Space):**\n",
        "\n",
        "<p align=\"Justify\">The Transform creates a 2D accumulator array with rows representing values of $\\rho$ and columns representing values of $\\theta$. The size of the array varies according to the accuracy required.\n",
        "The maximum possible value of $\\rho$ is the diagonal length of the image, therefore for one pixel accuracy, the number of rows is equal to the diagonal length of the image. For a $\\theta$ accuracy of 1 degree, there will be 360 columns in the accumulator.</p>\n",
        "\n",
        "<p align=\"Justify\">Now consider the grayscale image of the edges of the image. Every white point in this image belongs to an edge. For every white point in the image, the Hough Transform finds all the pairs ($\\rho$, $\\theta$) that result in lines which pass through the point. For every ($\\rho$, $\\theta$) pair obtained, the corresponding element in the accumulator array is incremented by 1.</p>\n",
        "\n",
        "![Hough_diag.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAF2CAYAAACWOPyrAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAABmJLR0QA/wD/AP+gvaeTAAAAB3RJTUUH4gseDAYUFdSLCwAAgABJREFUeNrsnXdgVFX2xz/3zaRRVBDsogIikFA0kJlgAwXbyrpKZrBSrFsVu67uGnXtDd11d9FVipWZoLviT90FBQtkJoAoJCAK2LtgoYQkM+/8/nhvyBADJCFlkpzPP3Bf3rxy7333nu8t54CiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqitF8KwXoWjgjBbzQ3FEVRlFTG0ixQFEVp/WTD7RYsAf4egh6aI4qiKIoKEEVRFKXJEHgz6f8jNUcURVEUFSCKoijtXSQUYhWH/RdGinxjGvvaW2A+UOk27CpAFEVRFBUgiqIo7Vp8CCba3zfPII8iPDgvNLxTY15/HGwCogACowq1fVcURVFUgCiKorRfjEHEmJfc5P4drM3XNbrIgTnuf7v1h4Ga64qiKIoKEEVRlHbMbrLhAeADVy1ctTCU37tRRQ7MTUqO0hxXFEVRVIAoiqK0Y7KDZZXGyDWO/jAZFvadjXyLEuB79/+6D0RRFEVRAaIoipKSCKa5buUrKPk3wn8BMIyJhHyNNlMRhDjwups8JgRZWriKoihKaxAgu2m2KIrSAniAjs1908FLB++RV5L3li/iO60Zb3sFUOWKkAfmzRvubTwttXUZVqaBYVqtFKVOtHrbZ/i84Zm+iO8eX8TXat5l8ezcDpEi383FYf/JWgVVgNyj2aIoSgswCgg05w1zF+emZVRkvGrEDANCedG8U5vjvv5gdIUYM8VNZndYt/nCRhQgc5L+r8uwFKVutGrb5+SXTs4ozyovAq4CXjvqzaO6pPozl4Wy0+MVnmUIfzZGJpeFstO1GrZfAdIJmAgcrFmjKEoz8zvgoua84ZIhS6qAhwEbSDdiZjXXTEiswvtnYB2AiLkt+lzeno1x3TPhfeAjN6kCRFF2Tqu3fT7f/3Mv1TPIuVVpVXNSXYRkB8sqEYoAEPpsMJ0u1arYfgXIacBm4JeaNYqiNDN7AB2a+6ZRf/RxV/jYQDoQag4RcvQ5b30vmJvdZFeJmxsb69oGXnP/e8TT0E2rlqLskFZv+ywbtGxTeVb5qTgBSVuNCKlKT7sN+NJN/nnxrNx9tTq2TwHyb+A74DHNGkVRmhEDPA3MAtLaiwj5VA78O7DcTf5+0XNDchrp0ollWFYajNDqpSg7pE3YPq1RhBx12oINwA1usnPc9tzSks+jjjtaToBsqvGvoihKcyBAJY4Xp6qWeICWECHBYDhuRCa5SW885nmgMa5b5WxEt92M1WVYirJj2ozt0xpFiK8sOh3HhTiCOb9k1tChLSA8CkKwAPh2dgvMxKsAURRFace0hAjxBUteA/4DgGFkNJT3i1295tnOaO4yN3mClqyitB9amwgxhdjuQIwAlthmsjSja3SXrjheAztuhqO1FqkAURRFafMixGPZVxikAkCMmfzSSydnNMJlE8uwDn4WemnJKoqKkFQVIb5gSbFBngYQzLBI2B9szvvH4X9JhrHOGqsAURRFaTERcmFziZChYxatBSa7yd5dN63//a5e01THA8E4Lo4VRVERkrIiJC6ea3GXwRkj9747Y2CzxYU6y/EcuNYRQNpeqgBRFEVpOREytTlFSA1vMDeVhIbusyvX2whvAOWuANERPUVREZLSImRYsPhz4C43ecCWrMyrmvP+STGUBoZgH609KkAURVHavAg56rQFGzAkXPF2to21S95gJsIWYKGbPD7kRJpXFEVFSMqKEFusezFOHCPBXLtg1rCDmuveSbPGBjhOa04bFiDPwMEz4aIwXKNFoShKexchvtLoNFxvMMAF0aK8Ibt4yUSHuocNR2hpKoqKkFQWIcOCxeWCudZNZnns+B3Nde8qeBXHE6N6D2zrAsQDDxt4RODGKS3g+19RFCWVREhNbzAID+6iN5g5W6+t65oVRUVIKxAh+QWRkEFed5Nnlcwaekxz3Pcc+B54220v1XtgWxEgC0P5XRcW5fdNPibVo3Odu4BPi0NRlPYuQnzBkmIx5hmnjTTDIrP8gYZeawUsxXHJq/tAFEVpNSLEHYhxZiNsM1kKm8deTbJL9y+CvlpjWrkAKQ75f2MZ+wNL7GdCoYAnqaDnJJ2mo3OKoqgIcTrca0h4g0HuXTw7t0GBsQqd55znJo+cAR21FBVFRUiqi5C84KJ3ME5kesEcHs3xTWimW2+1S20dtGn9AsQYycEJ8jL4IPPxhYnjY6GMaq8vWtCKoqgIwfUGI9ztqBEOjJV7d8UbTGJELz1TA2wpirIDEZK/ML9rqjxjpZ1+I/CD2w7esTiUu3uT26tONPRNape2EQFii/UnYJ2bvHXp84P3cAtajLPpByAvBLtrkSiK0hpFSF4071eN2m5i3ZPwBoPhuoZ6g0kOsKUbKxVF2ZEIsS07ZUTIMcE3vxUxt7rJvapM2h+b+p5BqATecpPH6f7kViZA8qJ5gSPfOrJzIj0sWLxejLnF6QBN98p4+p+qO9mto3NeA8O1SBRFaY0ixIiZ2ZgiZFiwuFxsc52bzLLEvr0h13EDbK1xdIwudVUUZYci5IhUEiFp31f9FVjltF8yKfpsXp+mvqepXobVuSsM1VrSSgSIL+K72IiZGfPG/uuL+HZLHN+yZ+bfgeUAIubSkqKh2a4AmYPj8UVH5xRFURGSRH4wMjPhDcaInBUN5zVoCVVShzpAA2wpitJaRMiQS5ZUGZEr3WS6eMw9TX3PePXAOOigTesQIMPnDfeKkfFOf0e+GHk5MRMyYsT8mIV9uXuq1xbrAYCz4AtgpRa0oigqQmoRD9XeYAzwYEO8wdgaYEtRlFYqQnzBkv8DXnaTv1wYyj+pKe83FpYBX4EOjLcaATJ/xPzYlswtJwBvOB2nGZY8E5IXWPSqEZmdEBuRIt8p7v8To3OHPQMHarEoiqIixCEvuOgd4HGnMzSHR7N94+t7jfSkAFvoQI+iKK1MhNjGugKoArAs+/7FU3KbbG+GcVblvOYm/U/Cblo7UlyAJFXgUxIiBMgHXkmIkJjtvdwgFTi96YMvvXRyhkma7vKq2lQUpR4MHPj4ETk500ODB089uK2KkLjHU+0Nhvp7gznd+e3bKkAURWmNImRYQfF7gvmbazv2q+qa9uumvF+SXepNg2O1ZqSoABkwYPpIKLTqIkKOPHPhGjHmIfd47y4bv/9dOryeULbaOSqKUh9s23oAJBCLmVU5OdMePOywxzq3NRFy5BkLvwH+4ib3jhvP9Q3oUBMzzRpgS1GUVidCTIUU4i6NMsjNi5/O7dZU95Ik74GWDoynpgDJzn78OBGZk5Nz8KKBA6ceXRcREkvz3oob/8MYuWnf0NCOQNQt9FGFzRihXVGU1svgwVP3MMbE3GQ6cGlammdlTs70cx0v321HhHSWjVu9wQjm8vp6g0naB4LoQI+iKK1MhPjPjf5kkEI32SWW5i1sqnsF4XPgPbe9VAGSigLEGOuWrZXTNq9nZ09/auDAJw9IFiFiHC8uCRFy5d4xY0QSrnh3s41VSPXoXLe+MECLRlGUnfHOOxN/WL58/PEi8ktcV7PA/iBP5ORML8nJeXxYWxEh2cGySoMkAhKmi8fcXa+2OinAlnaoiqK0RhGSV1byKLDETf46UuQb2IS3Swza9J8FB2iNSDEBEouZ04CHcL20GCNn23ZsVXb21Duzs0Odlg1atmlL5pZf1BQhd3SvnCViFrnHLtptZPdPkzpKHZ1TFKXOlJVNnJ2RUdEPzCTgJ/fwELDeysmZHsrOntGjLYgQX6DkRaq9wZwWLco7sa6/dQNsvekmR2iALUVRWpsIMYXYCJNwNop7EB5osnslzRrH4HitDSkmQN57b/y60tIJl9m2GUr1cqsOxphrjdn8Xnb2tHHLBr27uaYI+TIr46UvvXKDW4msLhccfCHwo1voOjqnKEq9WLLkkqrS0vEPxuOeviI84hr+BiRgjL0yJ2dq4cEHT81s7SIk2RuMYB6ojzeYpA618x6Qp7VGURqHAQOmTsjJmfbggAFPdVER0rT4g9G3gFlu8rhoKO/0prhPuuMJq8o1ltUuTTUBkmDFivFLS0snHOsuhfjQPby/MUzPyZke4bzLB9QUIXftx80xw3MA4rWGeffOfN/92zEhyNLiURSlvqxced6XZWUTLjGGPJxlRwAdwNzUqZN5Pzt72rjWLEKGFRS/BzyMo0D6xbt4LqmHAJmT1Phrh6oojcBhhz3WWcTcAVwqUrUmJ2f6ZcOHz/O2UREyLxVEiBWzrwA2A4gx986bOrzRB5dOgw3AIqepZZQ4cZSUlhYga8bIkA8D8rOIumVlE2d37twh2xhzHU7hAeTZtllgn3PlP8qnHHd+sgi5Yx8OFrcSdTpxr97u8SycpVqKoigNYvnyCUtKS8cfbYwEwXzsHj7QGRSZNi87+7FBLShCQrsiQjK8FTcb5Fu3872lrt5gxsByqgNs6VJXRWkE0tLSDgC+c5NdQCZ/993Hbw8cOK1NLdtZNmjZppg3lhIiJO+sRZ8C97vJnlkdyyc1xX2ketBm7zDkaG1PAQGCxRM2fLk2IGvWBmTK2gIJrAnI7gDFxcHy5cvH3+XxmH5gnsBdZgVynpT0Xbb58okLxHbWIn/nJff1TqwHyMrt0iWp0HV0TlGUXcTI8uUTwxkZW/qD3AyUu38Ybozn7Zyc6TMGDpyxVwuIkLRdESGHn/7ODxj+nDB4qtLTbqpTbmwbYMunAbYUZdcpLR23srT0o0EijAe+dQ8PsG3m5uRMm52TM61XW3nXJUOWbE4VEeLNjN0hmE/cxu2Gt545cr9G70G29R6odmlLC5BVAdkf14+8QE+Bi8UQAr5bE5DFawrkzjVBGfmYb9w3paXjx4mIDyh2f96Rn7L+uPnS8/eXnzqsAHhxDw74ySMVaftl4t0zXdxC19E5RVEap9Nccsnm0tKJhZbl7VNzUMS27VUDBky/tnfvhzKaQ4QYMRc0hgj52D7oUYR3HZklv1kYyq+r98DEiJ4G2FKURqPQLiubMMOYtMNE5C4cpw8ApwIrc3KmPdi795NtQvCniggZMnrJZmPkBjfZyZsWu62x79EdIriOTdQuTQEB0nkL3yOMAf4BfJAsSIFcDNcizOnyPd+sCchzL/SfMCR86PgJ7ujAVwBsyei5+fqz+9ufdPu+ysDze5gMgIyBuyfW2B3xNHTTIlIUpbFYtuzcz0pLx4+zbSvfGIm6h/cQkTszMnZblpMz/dSmfoZIfmRaY4iQYDAcx8PlbtJjGXtyPQWI7gNRlEZm+fJzvi8rm3idZcUHgnnJPZwGXJqZGXtvwIBpFwcCIY+KkMbBNyb6FPAWAML4haH8RnWuMQJiOMGyAY59CTK0lregANlvttncq8g81ytsftsrbPp44uxnhKCBRwS+SDp1D+B04O/pXla90H/8XU/3Pav4iE77v2iggoo0yu/6VZf4B/vK2x1gdQZkDtx967N5YbgWkaIojc2KFeOiy5d/PMwdFPkawBj6gMzOyZk2p3//admtQYT4x0TnIUneYIp2fo0aAbZ0RE9RmmSw44JVpaXjf2GMGQWscA/vK8KUlSs3l2RnzzhKRciuYwziuuW1AWNhPyjSuJvFpXoZVoeN4Nfa3YICpCYHP2e+7Flkwj3D5pLeYbN/3KaXwCUYwlRvRAfYp6NJP/2mA0ee+s9eZ2QM7XTgJirS2DL5Fyb+/n481wXSB++Oqa462jkqitJEOEsmRDr0dveHVLh/GGlZLM3JmfZgbu6U3VNdhHiJVXuDEXPfSy+dvNMRuqSNlf2egQO1LihK07B8+fi5GRkVg90YRT8mDHVj7DdzcqbN7tfvXwepCNk1/MHoEnGW1oLBH53lO6dRRU7SPhC1Sxs1X3/GaqB3Y91g3nDx9uiOH+FUDCOBw5OFz7JNX/LY14v4iG/IvOwlzun+BXtcvIyFn5aTkW5t/Ful3R3YokWlKG2eC4C9gDta4ub9+k091OOxbtu4cVmgouJrOnY8jMzMA9eBubVfv6y/hcPBeJN0nsX+CWLkMbddrBIjwRJfyb/rc41oUd5fRMwNjrgw1+UHInft6PwQ/BL4j5ucGIRpWv2Udk6j2j610bfv9D29Xvkz8DsgsQxrs4j8FTr+paws2Bnn29wAPNeabJ/cxbkdvDHvi8AI99Dblm2NKh5WvL457h952rc3abyP41jj83LJ6jsiOH9jY10/BJ/iREOPBnUWpHUIkJqsPF32zPBwHIaR7vT/IbYIr/+0lsd+KMZ74bNsvGEuP37rDEbuvlfOiuePXX7qwWHzoRaXoqgAaWLOt6yO16Wl7XGAZaVn7b//BDIzDwJ4xxhr0vLl415PRRGyeHZuh6otaSsN0gPY4LVihw0Zs+TL7Z3/H+hcAeuANIGnxsK5Wv0UFSBNK0ASDBz4+BHxuDXZGI5OHIvHN32xevWf11dVrUvMxEZdodJqaHEREvZdD9zuJv/iD0T/1FjXDsM0gfFAHNgrCOv1k9k1rOa+Yb/nzbrEcq1eYdMzbtPLGHPJiD16hf9xYPD7Qx49lW881fuzvrNX939g/f/WvntGxYcJd7+fBqSrFp2iKE3AcNve9FNFxRcrY7H1H2/cWLbJPT5YxJ6fkzNt9sCBMw5p9I5zF5djDRm9ZLMRudFNdo7Z3h16g3EDbJUAGBipAbYUpflYtuz8t8vKJhwjIr8U4SOAiorP9/N6O+dkZOzX17IyOwI+oENreq+WXo7VWTbeR7VjpKuKn/Ef3FjXtquXYXmM7k9unQKkJn1mmbW9w+aRXiETHDQgvVt/6XBKbGOXbxLd4X62zQcX/JU/fBE6+L8/vH9x3EioEr5Ndvf74QTJ1KJUFKURWO38I1JV9eN369e/fgHwEM6oF8Cptm2/l5Mz7cHDDnuscyqJEF8g+iSyNfL7Tr3BJG2s3HsWDNCiV5Tmpaxs4uzdduvQ3xhzXXp6t03gwbLSO2Zk7NO3Q4fee+XkPNq5tb1TS4qQ7GBZpRG51k1mGi93N9a1486+OXHbTvUe2Ag0+xKsOnJcfro1a49Ke49fZHlIC+Xyr817seW+0fS09+PCvfPo32Gb2GHlwAIMc0WY2yubpabQ2Fq8itKqSIUlWLsBk4DDgPnAvwDJyZnRzxj7fhFO2tp4Gr4Abl6+/KN/QWGjtTe7shwrEvLlYihxf1vsK4geaYzTadZkJhxpEu4r4cpgdVRhRWmvgw8tZvsMGjR9//Xri6du2LB8lMeTxZ57jiI9fe+NIPdt2bLhjtWrL61oTZnZksuxImHff4ETHLFgRuQHIvMb47ohWIYzWLM6CIfqJ9M2BQhhuF7ctXx735HD4/kdWfF1d7bcNxo2ZzC004GbL9onr8NeaZ1q+/m3GOaLMNeK89+ez5mPtagVRQXIrpKdPXW0MWYy0DPp8CKwJ5WWnr8wJURI2DcdGOd2vufmByJP1XbePPB+6+wD2Q14OQinaBVUVIC0dBszPc8YmQzkJx3+wBi5YfnyiWEVIXVoA0O+/hjeBbwI735Cj9xgMLzLTkRCcB9whZvsGQTdm7wLWKn6YCYpWFbF8h85/QfwHvQtmVfNFjpUULLx0w4Xr36u/MaP//vfDfHK54Efkn7eHSFgYIp4+GhtQNYk9o98+CvZQ4tdUZSGUFY2cbZIh36uS82f3MNDwXorJ2d6KDt7Ro9G6UB3ZTlWFdewNXKv3PXujIEdaztNA2wpSiq2MeNLSkvHH7lN4GY4VMSEsrOnzR04cEZOa3mXllqO5Q9GVwCPuMbkoIPMxxc2kl06N+n/x2ttbaMCpBTeBr4D2LLsR/apgiM3gtXjW5N1/XMbTYcKBMlavvmrE899/5mhY9574vIqm6EYrsOpJJWJawn0FLhYDCE7je+S94+UBSRdq4GiKHU3EIKVpaXjH4zHPX1FeMQVCQYkYIy9IidnauHBB0/d5X1pDRUh/rOjXxsk4YZ3//IOWdfsoENNDPR02LTtiKuiKC2GkbKyCTMsy0rEKNoCYAzH27a9NDt72pQ+fZ7upiJk+9hi/QlnhhfB3L4wtOv3y3QGbCpcu1L3gey6oPsZKTENCRCCMFCA13DAtCFUZVmxm/fFu8kCNmeu3nTd2elsztg64miMRONxz2UrVoyLfnWedNxUQT42I934I0ds5303AcVb94+EedtgRKuGojQ7Kb8EqzZycmYMAXsycGTS4U9FuLGsbMKMXb2+L+I7E3gSJ25AnZZjlYWy0zeYTmVuW14uMdM//6zIRzXPexb6WdVRmm8Lwo1aDZV2SsrYPjXJzn6itzH27SCBpMPrwdzSrVuPh+fPHxFL9cxtieVYxUX+S43Ig64IeSA/ELmiEezS+cCxwLoVsFehM0CktEEBcjEwBaD7DX3JGrw7Szry+Yyu7A8gtllaeeM5L8S+6XwlkNgMYoN5CiqvLi296OvEtdaeJXtTxTFu/JGT2X70368xvCHCXDy83PtZ86lWE0VRAbJjxAwYMK1AxLoHJDmy8Twwk0pLxy9rbhESKfKNQShyGnoJ+QIlY7fTzn7itoclQcf1p6KoAElJIfL4ccZYk9nWa917InJ5WdnEV1SEbMu8ecO9Wd+Vv+3mV8wy9uC8gkVlu2iX3gD8xTU2c890VusobU2APAMHe9xNPp1P2OvbLhcd0h2IPbAX73yUwRD3tCU8ddy4TfP6XgdybtI77dB7xPtjpKdlMdIYRiKcAOy+nQxaC8xFmCuG//UKmx+12iiKCpBaO9jcKR0qKtKvAXMtkJk8KGJZ5qply8Z906wiJOz7H07AV2yxhg8LFr9eS4c6FZiABthSVID0TvWHHD58nnfduo/PF+EvQPekP71ojH3Z8uXnr1URUk1JeOjxNlZi78YcfyB6wq5crwh8NkQABK4bC3fpp9MGBYjbOa4BelpZnjUHzBjSy+nNzfxJPWxjxBybECFpVWmjvv/1RYcaIw8C/qRL7NR7xLzh4j2wO4NcMTISZ3otrZZTY8C7CHOxmPv9Hrw+5BFTpdVIURpfgHx8tnTp8TQ/tMYlkQMHPnmAbcdvrzEo8oMx5k7bznqgrCxY2RwipKRoaLYt1juAF3jnE+kxpKY3mDCcI841ESgYC7O0KioqQFKbAQOe6iJSVQj81v2+wdn7+s8tW7x/Wr363J9UhDhEQ3kviDGjXav3F/6C6Eu7YJN6gG+ArsDcoDvAo7RBATIT/mngEkD2fXjwK2l7ZZzs9OT22JsOsH5DdUTKJWlVaaPeOvrNH7Kzp59nDHcB+ySuI8KrHo81admycaU7u2dZQDplGvxJ+0dyt3PqRiCS2D/SO2yWaJVSlMYRIKsD8oqBI1qzS+0BA2YcKyIPggxKaoveN4YrSksn/F9ziJBoUd7fRcxvAMSYi/MLIo8m//1p2NsLXwLGwD8D8ButiooKkNYy2PHYYbbtvR8k2Y32l8ZQ2NgxilqrCFnw7LBeXk+sTDAZwOr1HbvmnHLKyw2OqxJyBmnOwHEO0DXoxKJT2poACUEBzmZ00np2vHzfu3LuADKNkTWP7OfJK7XsWT8XIW99P3DgjI62Hb8azHVUu5eMifB4VVX6De+/f/Z3dX2GDwOyjwhHYxhpw6kG9tvOqV9heFOEuXH4v8PC5nOtYopSfwFSFpD0TGcpUMcaDdbWJZEmxpxD/m1+SP3XKrSysw8+1xjuBvZO+sNcEfuysrLzVzSlCFkYyu9qGft9YE/gmwxvxWGHn/7ODzXa2XeBgWiALUUFSKtkwIDpI0XkIaBf0uElItaksrJxb7UGESJGlnrinpFNIUIiRb67Ea4GEDFX5gcjDQ68GnIGaf7uJk8IJoWNUNqQAHkKuqTBt4BHYOpBYd9XwPXOw8u1F/XZ8nBWedaLtYkQaBrvETX2j5wEdN5O5m41lioreaXvC2aDVjlF2bkA+eAc2c2q4gp3SaSP6iUGycSAKMIcsZj76TdER8w3KesNZvDgqXtUVXGdMWZS0qBIFfCPjIyKPy9Zckm99pfVR4REQr7LMEx2k/f5A9GranSoGmBLUQHSigUIQG7ulLSKiszfgtyCE2AUQMAUxeNVV69ceWHKzSA3lwh56z9HdvZWxlYB+wI/WWIflhdc9FUDBUhv4AM3c+8eC9fq59MGBYhb2CXAUODz7qHhfbNM+SqcWYgNXit22Pm9q37akQhxhEjTeI9I2j8yGuFU4HBqj6+yzf6RT75hfiobS4rSkgIk+WBbc6k9aND0PvG43AecmnR4HZhb+/XL+ls4HKxzxN5aRMjYEl/J8z9rp+YN92Z+u2WpMZIDVHkkPmBocPGqpDb2JOBlt0O9eCw8qtVRUQHSOunT5+lu6emVfwJ+57YNAJtB7uncueNdxcXBlFoy1FwiJBrKu0CM+Zdr/T7iL4hesgt26VrgEODt4PaX6SutXYDMhNuNO+sB9D2oKM8vYqa56cf9gegFA98d2HFnIqQ5vEesPF32zPBwnOvud5RbQWvL+HVieE2EuV6Yc3DY6IijogKkDl6warjUPgU4YDunbnWpLXFeOvQ581kqveyAAdNHgkwWIXtru2BYCkxavnzCG40tQopn+UcaWxJLBf7PH4huFUCzoUO5s+Qtw0AoAGO1OioqQFo3Awc+fkQ8bk02hqOTDn8mwg1lZeOfIIUGaJpDhEghVqS/P2KMDAVshDx/MNqgvbsz4VEDF+LEAdkn6KzUUdqgABlh4DU3+YeA8HC0yFeMszTDtizbnzdm0aK6iBCn428+7xE1lmsdj+M5obaC2LpcK83w6oFho64wFRUg9f/GTqR66cF2v7F4Bv899CnT4l5icnOnpFVWZkwU4TYgObLxi5ZlXbps2bg6DUzUWYSE/bMNciqAYE7JD0ReTvwt5EQqHo4G2FJUgLQpsrOnjgbzkDEcnGSOvy7CpLKyie+0JxESDeXlizELAIOwwBeIHm0M9RZiMyFoYKabPDNY/X+lLQmQEKRTvSH1P0H4VWSWz4/NQvcdin0F0SONQeoqQhwhMrWvbZv7jeHkpMNN5j1CCsVaU8bhSe5+j6I6VkAyNrA0sVzL6sBbh0wzW7S6KipAdkxrdamdnf2vrsZ4b2LbJRPlIvJQLGbftmrVBTvdP1YXEVLDG8x73vWxgUMuWVLltrNbA2wBQ4KgXv0UFSBthNzcKR0qKzP/ICI3spPAzW1dhETCvmeAMx0jWIK+QEm4vtd4DvaMOe54LYF/jYWL9BNqgwLE7RxfAU4ENnwPe14CVZGw7wngXABj5GxfQckzAPURIYnRAWPMA0CvpMNLRLisrGzCgqZ6p08DklVpOLIOa9vLgQVb17Zns9QUGh2dVFSA7ITW5lI7J2dGP4g/AObEpMOfi/DHuiyZqIsIiYR99wCJTeiX+wPRyW4bmwdEQQNsKSpA2iqDBk3fPx7njvoEbm5rIiQyy3cANu8BHTF86s2I9R0yesnmBtilS1y77eMgybNLSpsSIDPhKgP3uA99ZAAWLgzl728Z+z1XzX+WVV7ed9C4ZZsaIkJSwXvEBwHp7hGGu2vbTwQO2s6p37bm2AiK0lwCpCYfnSH72hZH1cGl9pcY3hJhbrqHF3s8a75ozoxwB0UmAz2TDi8yxly2fPn44l0RITW8wfxQKel9jgm++W2NAFuvBmGkVklFBUjbJDt7el5DAje3FRFSHPYXGuQmN/lnfyB6a32vEYY7xfWAZUGfAtczltLGBMizMMiCd9yHLgzAzW4lutEgtzpqwdycH4gUJn5TXxECqeU9osba9lHAHtspxK1r2z3pzD3oafO9Vm1FBUg9vjEYjWNwZ+7sG2sul9rZ2aF0Y8p/g9O+dU4aFHlSxL6mrGziVw0VIcVh/4UGedRtN/+ZH4j8BiAERcAYNMCWogKkHbA1RtE2gZuBuZZlXV6XwM2tVYQsDOVnWcZeiTPQW+6VWN8hwSWf1OcaM2GkcWOAGPhdoDo2iNKWBIiACcMX7kfyVhDHq8O8qcMzszqVr8DxNlUuMdM//6zIR7siQsDxHmHb1oM4+zQStJj3CAmIZw0MTlrbfgzO3piaxIF3Emvb7Q28eejLpkKruqICZMfUsiRyRy61oxhmN8eSyMMOe2w/r9dzkzFcmPQ8m0Du3biROz/6aOKW+ooQKcSKZvuiwBCSvMHMhF8b+Id7CQ2wpagAaQdsJ3BzlQhT6xu4uTWJkEjYdybwjGMMy1O+QMm59fn9VMjs6OxPzgKeDzrR0ZW2JkAAQvAUcDYQq4Q9z4WfACIhXwHGiZaOYaa/IHrmNh9XA0UIJJZCWH8FSVoO1fLeI+oRG2EzsDDVYyMoSksLkJqsOku6pVUxYmcutYHvMMxrapfaOTkzhoA8CDIsWTeJcGNZ2YQZ9RUhC8P5wyzst9x24y1fQfSYsKEX1csI7gnCNVotFRUg7YMdBW6ub4yiViNCQr7XMRwDCMIx/mC0XlHjQ/A/nP7hB6Bb0BkEVtqaAAnDRIHH3QcfHYAXk5RsohJgWfaxeWMWvdFYIqQ1eI+oERvhZODA7Zy6NTYCHl7u/az5VD8DRQXIzkkNl9piBgyYViBi7gV6JP1hHphJpaXjl9VHhESKfM8ibswPIeAPRouSAmwtDToDG4qiAqQdMXDgtONtmwfYNnDzShHr8rKycf9tQREyGziuMUXIwqL8wy2xFwEehLd9K6JDTWHdXZCH4RpxHXZY4C9wHXkobUyAhGB/4DP3wR8KwGWJv5UUDc22xXoH8Bpk6cdy0NBgMBxvLBHifJRPHmDb8dtreI/4wRhzZ3n5j5NTwXvEdoylE4Ddd2YsieF/vcLmR/0sFBUgO5EBP18SeTTVSxeSaZIlkbm5UzpUVKRfA+Zaqvet2GCeSkuLX7l06fnf1kWE1OYN5sPRSyaL41JSvLDPGc7GdEVRAdKOaI7AzakiQoqL/P8yIhcAiJiJ+cHItLr+9lk4wnJdlhv4U6DalbnSlgSIK0JWAn2BFUGqIwgDRMK+fwKXAAjmovxA5F8/ExG7KEIA+vef4bMsezIp6j2iJq01NoKiAqS18MVo6VCexbDmXhKZk/P4geC5DeS8pMPfG2Pusu2sB8rKgpU7EyHJ3mBEzI2fBCMfJAJsGTgrAM9q1VRUgLRPkmIUNXng5pYSIQueG7aXJx5/H2eg9msq6OM/N1qn9yoEqz985Yq014PVtqXSBgXIX4Hf4/SiB45xZ0QAFobyu1rG/gBnacQ3Xon1GRJc8mNTiJAk7xF3A3sn/WGubTNpxYoJZamah60tNoKiAqS10dwutXNypg4HazLIoKTmfZUx9hXLl098aUciZPLHnleSvMFs3jz3m2HfTfnwbZwN748F4UKtmooKkPZNbYGbjeEL4OamCNzc3CIkGsq7Woy527WM7/QXRK+vh136LDDWFWZ7Bh3bSWlrAiQMpwn8G0BgwliYvk0lKsq7XMTc7ybv9QeiV9d2ncYRIantPaKufBiQfUQ4ug6xEb7C8KYIc+Pwf4eFzef6CSkqQHZOQ1xqmxhzDvm3+aEBgyL3uPm4dVBExL6srOz8FUki5Amc0cwqMTL2r5+SKWKedh/iiU8Kov3dgYlPgtsXT4qiAqSdsZ3AzYsdxzxNF7i5qUVIWSg7fYPVaTlCH6ASQ46/IFqnuB5huFDAdWvOL8bCS1pT2qAA+Q90roB1QJqBJwOQvPSAefOGe7O+K38HZ3lWpUfiA4cGF69qShEC0K/f1EM9Huu2VPMe0QjG0klUxyDYrrHUXLERFBUgrZ2GutTeIryRHTaVO7v+4MFT96iq4jpjzOVJ160C/pGRUfHnJUsu+bGmCDGWfebkj61JOHtZ5Msrlz1V9Un5uQA2HHYmvK/VU1EBosCOAzeLmKvKysZ90izP0cgipLjIP9qIvOC+zb/9wejpdfndLDgoDh+5dtEDAbhCa0kbFCAAIVgADAO+DsC+BrZZQ108yz/S2DLHqUPmxfxAZPT2rtWYIgS2eo+YDOQkHW5R7xG7Qo39I6OBfLYfG2Hr/pFPvmH+iPkmpp+YogJkx9TDpfYmoLiu+0cGDZreJx6X+4BTkw6vA3Nrv35Zf/vkyvsCySLk6M3muoJ1cg9gbVn8/fvf3PV+H7eT0ABbigoQ5Wf06/fEvpYVLzSGC6gRuHlHMYpSWYREw3kvC+YkN3miPxD9Xx3t0veBQ4HS4Lbew5S2JEBmQqGBmxI2fxCW1zwnEva9CPwCwBbr5GHB4leaS4QkeY+4DeiW9KcW8x7RWKw8XfbM8HBcHWIjbABeF5jdlLERFBUgbY0aLrVPAQ7YzqlbXWpLnJcOfc58VttJAwZMHwkyWWQbpx1vW5ZMynrkn/sni5DrvzJv7FMlx0uVzWfnLq4UW9KBfwfhdK2eSnsQIPOGi3d4d8SEjcZzqCMDBkzLFWEy2wZudmMUNX3g5sYUIcXP+vsZj7yL46hnRXm3rEEjRszf6WDqTHjYOBv1xQP7j4EvtWa0QQEScir5mwACV4yFB2qeszCU39tj4qWCycCw0rsuNmjIJUuqmkuEwI69R1RVxW9cteqCVr9kKTViIygqQNouNb6xE6le8rDdb6ymS+2kJRM3s61L7hc73BT+nznwu/sBbyebqlu+oMojdPi6cGVlRdlP6cCP3aHbCGeWU1HatABZUyBnYJiOOmCpJ4kYRdY92wZuZr5IfFJZ2QXvthYREgn7HgQudRvW3/sLog/XwS49HXjOtUvPG+s4+1DamgCZB95vnX0guwEvB51Rwp8RLcq7T8Q4a/GESf5g9MEdXbc2EWLZ1gm76mN6wICpfcE8IM5+CifjW8h7RJM2P4VirSnj8KS17UdRHaMgGRtYmliuZXXgrUOmmS36OaoAUQGyk7ZvF11q9+07fU+vV/4M/I7qJRPlaaMXz0k/bdEpgHfkT8RH/4jnpxe+5IcnPkl8sPlnQkRLQGnzAiQgfwd+U+NvX2J4S4S56R5e7PGs+UKzaztCYAeBmy3LXLVs2bgmiytUmwgBRpX4StbV5zpvPnVUl7T0qvdxVrB8bzxyqO+MHV/jedijCr4DPAIzxsJ4rQ1tUIC4avMFYDSwuRN0PQV+Ftwr8qRvNzJYBewDfO+tivUZcvaS7+opQt62bGvUrooQ2Oo9YjLQM+nwYrAvKy09f2Fbq1yfBiSr0nBk0tr2w6l9/0g5sGDr2vZslppCY+vnqQJE2TENdan9y7IZtmXJZJwN8AB4h72/PmPia7t7Ec91XyN7vL/JfHlNqTt+w5/Hwq2a40qbFyBBOR/hLHY8gPYOhrnAnHRhwYFhU67ZV8OWaqHAzY0lQiJh32+Bhx1DWf7qC5RcWge7NAL4gC8CcEDN/clKGxEgM+FSA4kZjeFBeL3WShTyXYxhCoAx8g9fQclvd/rhNKkICaUbU/6blvYe0RI0d2wERQVIe+OjM2Rf2+KoOrjU/hLDW0988/Y3z31XOtpGegB481aTcdFcydki5qJv4bOL3sb+sQrgjaAz26IobVqAJBL1GECLAVEMs3UA7ef07z/D5/HEHxQxvsQxEd43xlxZWjr+xVQVIaFQwNPDfPI2MBCI22IdPixYvHwndumtBm5MmHtBWKE1oA0KkBD0BxLB/m4LVhf6NkghVjTbFwWGAHHjkVzfGSU7XYvYlCIEtvEecWFSo9as3iNamhpr20cCXbZTSbeubfekM/egp833+umqAFHq/Y3V6lK7UuLMWle6/rl1yztX2vE0b95qMi6cy6/XCd3vXM3mBeswhliFsOe58JPmqtIeBEhNVp0l3dKqGFEHByzfYZgnwlx1wJKg+QM3N4YIiczyjcDmNTf5mj8QPX4ndumxwHw3OSlYPUiutCUB4hb2pzgeYqJB8G/vvOJZ/iONLW8CBmGePxg9ri7Xb2oRAlu9RzwIHJl02PUeMWFG0rGhOC7eFuL6m25L1BIb4WiqAzsms01sBHsDbx76sqnQT1kFiLJjdjaiu65qM89+9y5zf/wAa+gH9Bj/Kn949ms+/8da3gVWWdbNL9n2re43qCjtSoDsRNyrA5Y6kJ0d6mTMpqtqBm4G/uH1yk3vvDPxh1p+1mDbp1FESNg3CzgDQMSclh+MvLADmzQdZ39yJ+DFoLNNQGmLAiQM08TZ6BMH9grCdj/saDhvpmCCAIIZkx+IPJcqIqQO3iMOA65xj1UCE4FVbblifjFaOpRnMawOsRE2AwvrGhtBUQGiOGxvRPeD8u/419eLWD1gIWflzyZ00bt8D8Q6ZsQr490e/WrL57/R3FPauwDZpgfXAbR6UY/AzcFdtX12VYREQ3mHiDErgExjZM26Dntmn3LKyxU7ECH/h+MYacP3sOcljsBS2poAmQnnGseHPQbGBFwXaLVR8szQA22v9R7QAfiwfGNW/xET59dpmVPziBDHe0RFRfo1YK4BstzD9kcf3fvRpk3vbRCJJdxgzgAeak8VtUZshJOBA7dz6jcYXhdhLh5e7v2s+VQ/cxUgys5JHtG1heOLN3zU9eGsW/juxelIlY1Jt/i+y35MH1L8eXaHA/5PR3QVFSC1owNodaMOgZunwzbxixpk++yqCImEfbcD1zu2plzrC5TcvQO79HID97t26TEBN2SE0sYEyNOwt9cJ9mKAfwSdeBs7qkS3AH9yK9ENvkDJ7XX+UJpJhDgf5bbeI77+uogNG5bFY7ENX8ViP3wNchs7EFvtzVhCOIFtYxskV/DtxkZQVIAotZMY0X3jm9fOuWXxqZOsTeUGIH3QbmTlXsqI90/hvL0OZzdP5jYutXVJpKIC5OfUcMByEtBjO6e2OwcsOwrcvGrV5Z9WVX2fl3Ts9obaPrsiQuaFhnfKMuWrcJx6bPBascOGjFlSa6DBZyHHqg6OfUuwOmi20pYECEAIluGEvV8ddNYJbpeFofwsY+Q943h82RiLeQ876qwFdfbn3ZwiBGDAgBnHitiTY7ENg7/99j9UVHxNhw49v+ve/RcXlJVd/IJWX7dx2MXYCJqDKkCU7XO8Zd39g21fnQmMO+tAXvntfnw4dRSZS/pzZrfB/KJrX6zqrkSXRCoqQHZCjQG0UcAe2zHQtg6gmRhzDvm3+aGtZn5tgZtjsZ8qP//88ZUbNy7/ViQ+F7iXXdiDtisiJFqUN17ETHOTj/sD0QtqHbwBE4bPgX2BhcFt9/cqbUyA3Adc4SZ7BmGHHieKw/5zDJKIUDnDH4jWK1hMc4uQlvAe0dppaGwEjXarAkSptY3dHcM6BE/HY7vx3VW9+Hs3Q8WjI4mV9GaftM5VF++Tl5bb6YDafq4utRUVIDuglv0jx+BsZq7JNvtHtghvZIdNZVsrhKYO3NxQESKCiRb5inHifNi2WPnDgsUltZ0bhicEzsUZ9OwWBF150RYFSBhOFnjJfZmLAvCvOlSi13E2iYkYk59fEImmtgjZsfeIjIyKPy9ZcolW8O3wYUD2EeFod/r7F8D+2zn1KwxvijA3Dv93WNh8rrmnAkSBEBQDfs8eaez/yBE8sheUphsq/+WIEIC90jqtvGL/Yz7pl9Xdh47oKipAGsRX50nHTRXkYzPSGE4QGLwdW20TUNxWZxubMnBzQ0VIZJbPj81CtzyKfQXRI435ebDBMIwTZ/8KBn4VgP/o59UGBchs6FDueL/KAGYG4cyd/aZ4lv8IY8siHBeUS3xl0TxTSL1UdUuIENiu94h1YG6t4T1C2Q51iY1Q01iqrOSVvi+YDZp7KkDaI2G4Rdz9c/veP5CfembKbfsYExOk8l8j47Hood7EoIjB/PPOHicX9e3UPV9HdBUVIA1nbUBeMV7ukCr2cgfQTsEJPVAbX2N4Q4S5EuelQ58zn7V+EdJ0gZtrihDgHTEycqcipMg3A+E8AGPkbF9ByTM1z3kG9vPAZ66N/bcg/EE/rzYoQABCTuCXY4F1K2CvQnYuJiJh3+M4Lt1AGOcPRp+o731bSoQADBgwfaSIPECS9whjWApMWr58whtatetGLftHhuOuP63BNvtHPvmG+SPmm5jmoAqQ9sBMOMbA6wBdJhxE51/swwu7i/3qbsYC4hVPH7Us9tqAw5N+8h2Yv/Trl/W3v2YGMhMjujvxCNSmR3QVFSD1YU2B/NbA3+Iexh4604QTx2sMoJ2YZJjXNO7ajAOW7QRu3gRy764Ebm6ICFkYyt/fMvZ7OLE+PssqL+87aNyyTbXYpWU4AbNXBaGvfl5tV4DcAPwFwIbcM+HtnQqQp317k8b77sf7FRUc5j83Wu8ov64ImQ2MaG4RsiPvEZZlXbps2TiNwFpPVp4ue2Z4OK4O0W43AK8LzLZt5vaZZdZq7qkAaatMgbQu8B2wW9bA3bZ0/1O/TBvKC/cn7UcLLxCPL+5115Z/nvArt9Nla3toyaRlyyZudUVZw6V2nUZ01aW20p4EyPtjpKfH4nXgAAxX9AqZB2o7r705YKlH4OYmFSHFIf8NxshfAARzc34gUliLXToZuAwgDoec1QYDSKsAAYrAZ0PEqQxcNxbuqsvvIiHfdRjH2DFGbvMVlNy4CxX4xZYQIbCN94jfAR73cLmIPBSL2betWnWBLh1qIB+MlV6WMMpt3EewnWi3wKrvuzBAvWqpAGmrhOBF4BfGYyoOfGJIBmkWX6TLa3ftbY7BmTWMm5h3/MZfX9wN5Ga2dY39otcrf3jnnYkf1WZsqUttRQWIgxSK9WEZr7gDYAC39AqbOrlybR8OWHYauPndphYhL710ckbXTetL3fpSLjHTP/+syDZtWxhOFZjt2qUXjIXH9RNrgwIk5Bjd37jG4dxg9Ye74481lJ2+wXQqBQ41SEVcPDnDgsWrW6MIAcjJmdEP4g+AOXFrATei94j2jhSKtaaMw5NGm44CMt0/L+wVNupuTwVImyUMkwQeANjrj4etzDx8j36A/XwX+dP8TubmhAgRI+f9NP53//N65c81BkU2i8hfoeNfysqCG2u7h7rUVtq7AFlbILeI4USgn4EOCA/3LDKXNeRaH50h+9oWR2EYacOpxoljURtfYnhLhLnpHl7s8az5ItULa3uBm8E8ZVnmqmXLxn3TlCIkEvIVYAi7lvRMf0F0m/3H/4HOFbAOSDPwTADO1k+sDQoQV4TMAs4AtgBdg1Bel99Fi/J+JWKedyxMZvmD0YIGfxApIEJgu94jFoE9aVe9RyjVfDFaOmzOdJaSILzfO2we0VxRAdJWKYJsG0oB0g/qMHWfewdMcMY4ZOHlB5q/xuGJZBFS4it5pn//6YdblkzG2Yie4HMR/lhWNv4JdrLHQ11qK+1NgLj1Pj0LVsaFOwwc1bvITGiM67ZFByw1Aze7h38wxtxp21kPlJUF6+zYot4iJOz7H+6Aty3W8GHB4tdr2KVv4Hhc/W4F7F0IOgjcRgXIb4C/u8kTgjCnrr+NhHyv4Iw4gHCCPxid09DnSB0R0nTeIxRFBUj7JOR4dtkfKD4onLdWMOe4fzpr0gFGxMiTNUVI0qDIg2y7p6rEsuSyZcsmRup6/3qM6G51qd1aRnQVFSAJPiiQ4Zbhol5hc8684eJtCocnnwYkq9JwZJK4P5zqDd7JxIAohtkizO2VzVJTaFLOkE4EbsZxW+wYPML7xnBFaemE/2sKEVJSNDTbFusdt8175xPpMSQYDMeT2ss/AzcD2DD4THhXP7O2KUB6Ax+4VvbdY+HaegiQ/hjewZnqLyvvljV4xIj5Df7gU0WEABx22GP7eb2emxrbe4SiqABplwJkBnAeEO96Se+BnUbuWQJ0xPUGc8mhHU7dngjJzw9lbdxYfqmI3ED1yKsN5ikR+5qysolf1fd51KW20hYFyOqA/MUIa3oVmanNdc9VZ0m3tCpG7MwBi4F1YnhNhLlemHNw2KSQs5vGCdxcHxFSHPb/wyC/dmxPc1F+IPKvpPYyH1jo2qVXj3UiuSttTYC4hb3W/WjeDm5/qr5Wiov8fzUivwcwRn7jKyj55648SyqJEICcnBlDwJ5MI3qPUBQVIO1OgJyHI0IQOP2gsG8AcIvTmUihL1Bys7/YP3Z7IgRg0KDp+8fj3FFjycQmkHu3bNlwx+rVl1Y05NnmDRdvj+74EU6tw4juuxheFGF2qo7oKu1XgKwJSFQ8FLSk57ca4v54tuOAJVncpxlePTBs1rd0/g0ePHWPqiquM8ZMooGBm+sqQhaG8rtaxn4f2BP4xiuxPkOCS34EmAfebx3vgbsD/w1SHd1daWMCZCY8auBCnHV2+wTh27r+9s2njuqSll71gVuJ1huP9PGdseOANK1NhFR7jzD3Aj2S+24wk0pLxy/TT0JRAaLsQIDsA3wBGAMPd5s6/KqszuUrEQ4GyuOWp9+RYxZ+vDMRApCdPXWouywrP7n/MUb+uHz5xPCuPmtdXWqn9oiu0t4EyIe/kj3sNN7sFTYDUiVjJCCeNTA4yTnE0UmGfTI2sDThHMLewJuHvmwqWuq5dzVwc11FSCTsm4TroAO41x+IXp3UZv4bOA1nX/Kedd2frAKk9QmQoIGZbvLMYPX/60Rx2P8HgzzkJif7A9HLd/WZUk+EbOM94lqqvTg12HuEoqgAaVcipBTIBt4PwmHRcF5AMCH3z8/4A9GzAeoiQkBMdvb084zhLhxx4xwVXrUsuXz58onLG+u5W/OIrtJ+BMiaAjnDWAzvGTKXpmpGfTFaOpRnMawOwUU3AwtbOrjorgRurosImTdvuDfru/J33Hax0iPxgUODi1e57eXvgb+6px4fhNf0U2uDAuQ52DPmuOO1BP41Fi6qV8caCnh6mE+WAgOAmMcTP3zoGYtL26IIAcjJefxA8NxWYynE98aYu+rrPUJRVIC0D2bCAwYmQXWArWg4b75gjgWwLPvYvDGL3qi7CIGBA2d0tO341TUGRWIiPJ6ebt+4dOn53zbmOzR0RNfqwFuHTDO6b04FSNMJkID83cArPcPmhdaScR8EpLtHGO7ONp4IHLSdU7/FMF+EuVac//Z8znzcXM+YmzslrbIyY2JDAjfnLs7tkFaV9oIYOX57IqR4ln+ksWUOgGBezA9ERrsC5DDgPec4d4yFP+qn1gYFiFvYS1w1/nEQDq7v76OhvOPEmFedXoq5/mB0VKNU/hQVIY4QmTocrMkgg7Z20A3wHqEoKkDaPiH4BU5Qwq0BtkpCQwfbxloMeAyyNK+sZIgpdFxO1lWEOG3RtF5g7qixZGI9mFu6devx8Pz5I2JN8U71GNEtBxa09Iiu0nYFyOqArDCQ35oDbdaYbRwF7LEdA3TrbKOJMeeQf5sfmvrZGhq4uS4iJBL2vYjTPiKYU/IDkZfdNvNDHHt0cRCG6qfWRgVIGO4U1wOWBX0KXM9Y9SES9v0bZ80eRuRUX7CkUYzwVBYhSd4j7nGNwARzRezLysrOX6Gfi6ICRJkBHTOdAFsZAs+OhbMAIkW+KQgXu+3mhb5gyWOJ39RHhDhGwuPHGWM9AAxMOvyeMeaK5cvHv9zU79gaRnSVtidA1p4hB4mHp3qFzVFtJVNrmW08Bkiv5dQ48E5itnGL8EZ22DTZKoztBW62ba7fXoyinYmQhaH83h4TLxVMBvCed31s4JBLllSF4TGB8wE7Bnuf7WxMVwHS1gTITBhp3BggBn4XqI4NUmcWzRra07bNCrcSrV7fsWvOKae83CgbqVJbhDSO9whFUQHStgnB664hsTXA1huho7unm8r3cUY7t/EG0xARkjQoci/QPekPLwKTSksnrGmu903lEV2lDQmQoFwksH+vkClsq5n81XnScVMF+XWYbdwEFDf1bOP2AjcbYy5bvnx8cX1FSCTsuxe40lFfTPIHow/OhLMMPO2eHwhCkX5ubVCATIXMjrAeyAKeDzrR0etNNJx3pzjrkQGu8gei9zXWM6a6CAEYNGh6n3hc7gNOTTpcZ+8RiqICpO0yE/5kXPe7wOFBeAegOOS/whi5z+1d7vEXRK9J/l39RQgMGPBUF9uuvNYYcznVI6dVwD+2bPH+afXqc39qzndP1RFdpfULkNUFMtMY/torbN5qL5m+9izZmyqOcWcbTwEO2M6pX2N4Q4S5eHi5MV0UJwVuvpXqWEICpgjiV5aWnv9pXUXIW/85srO3MvY+jlON771VsT7vn70EL3yN4xb8kSBcop9bGxQgACH4H47bxR+AbkGnI6gXbiVaBewLbLDE7pMXXPRVYz1jaxAhTuc/fSTIZBGyt1aaOnqPUBQVIG2T5ABbwDVBuAdg8ZTctFhX73KcjZeVJi4DfGeWvL+rIgRg4MDHDrNt7/0gpyQd/tIYCvv27fBYSw2KpNqIrtI6BYgUivVhGR+s70LfIY+YqvZaCDVmG08EdtuO8bp1tlEM/2uMPTP1Cdy8IxFSXOS/yIg84thL8g9fQclvQ7AUJ0r7R8HtuARXAdIGBEgYrhG4C6cG+Qsg2pDrRIp85yMk1jE/6g9EL27M52wtIiQ3d0paRUXmb0EK2XbpwU69RyiKCpA2KUA8OHGWugD/Czr7JJx2M+QbheF/bvIFfyB6Ws3f+xb4zsXD1PqKkMSgiIg8CPRPOrxExJpUVjauxUeOa4zongwcuJ1Tm2xEV2l9AmR1QHIN3NQrbH6pReEwb7h4D+zOoKTZxmOBtFpOdYKLurON3+/B67si4uoauHl7IiT6csn30WxfFBgCxI1Hcj85o+RcgasAPNBrjCOgVIC0NQFSBIfb8Lb7gn8KwF8aNiKBFc32FQN5gG2M+HwFJYvbowiBhnuPUBQVIG1ShDwP/ArYAnRNDrAVCftewjG+scU6eViw+BWA/IX5XW3Lvl1E8owxDwCPN0SEJA2K3IwTZRjcJRPxeNXVK1demDKbwWuM6J6Q9Lw1O+NGH9FVWo8AWROUawXKe4fMQ1oUtVMWkE6ZBn/SbGPudk7dCEQSs429w2ZJAyzAOgVu3p4IefAzq6+x5U3AIMz7OBi9y8Ar7rf+6wBMUQHSBgWIgAk76+26A68HYXhDrxUN5eWLMQsAY5CFeQUlRxlDo06btyYR4o4O/Mx7BPC5CH/cnvcIRVEB0raYCb8z8De3IxkZgFcTf1sYyu9tGbsMSMew8oa9KNiYzu8QzsCwD/BE1B8d5y/2B8XIUw0RIQB9+07f0+uVP9cYFNksIn+Fjn8pKwtuTKU8a6kRXaUVCJCAzPFYXHrwTLNSi6JufHSG7GtbHIVhpA2nGthvO6d+heFNEeame3ixx7PmizrbZ3UI3Lw9EfLXT/i7YIIA9g+VZ3920dLH3WsUBSGgAqQNChCAEDwLjAUqgT2DjiJuEMVF/qeMyNmOJjZj8wsiocZ+3tYmQmCr94gH2XY943a9RyiKCpC2w7PQx4JVbvLOIFy/TbsZ9t9vkMuXZsGze7J+i9kaefzryvTKAUuPWPotwK6KEICBAx8/Ih63JhvD0UmHPxPhhlQeFPnwV7KH7eU4dzR3JHDodk79AZhnpzPh0KfMT1r72pYA+XCCZNqbWN4rbA7VYmg4NWYbT6J6Q3lNw3frbGNlJa/0fcHsdPVGUuDm85IObw3cnDn9Lm9NETL+a8YfUUkx0AH48NNgycciMhz4HujekP3JKkBaAWG4UOBRAIFfjIWXGnqthaH8/S1jrwI6YvjUmxHrO2T0ks0qQnboPeJJEfuasrKJX6EoKkDaJEkBtpYEnfXOW4k86duNDFYB+6zMYsOUbngFsrB4JJoX3cYLTGOIkMSgCJiHjKkOQmuMRONxz2UrVoyLpnp+7mRE9/NeYXOA1rq2J0DWBmSUwNm9wmaiFkPjMG+4eHt0x49wqivwD6d6Y3kyzmyj4UURZvfKZqkpNPb2hcjPAzeDWWWMfUX61L/NrylC7viCOR3iXA2wbvLq/21asO4EABvyzoRF7bmMrDb8YnOS/j9yV641LFj8OcLdrml9YKzce1VTPPOSIUs2x7yxU4F57qEjbMuek78wv2uq5nNZWbCytHT8g1VV8b4iPOJ8VxiQ84wxq3NyphYefPDUTG0OFaVNklh2dfjT0G0bUXFu9CeDFH6eBs/tQeez1stc4GPb2HfVvEgkPxIyYs5xjQGPEfNEXjTvrPq3RxNn77Zbh/7GmOtwZ71FjM+y7IU5OdNn5OQ8uncqZ+bBz5kvexaZcM+wuaRXNgfaFrkiXAvMQZit1a1tIs6I/VzNicZjxHwT6xU2b/UqMtf1CpshMS97GyFo4BGcgZMEXiAX4SYDiz8s45s1QQmtDsjFHwXkZ96qSksnzi8t/fAIEcYD37gleJiI+b+KCX/4z+bbz7jGiEm0i4Nv2ocTxcinAJ1P3eeYxHU8u2iXtgXa7AwIQAjex5nSLg3CgF251sJQfpZl2SsQDgbK45an35FjFjbJRseaMyFiZKkn7hmZyjMh1aMDM4aAPAgyLOnwGmPk+uXLJ4a1WVR2gM6AtDLCcKbAM25nEgzANt/48LfyhmRh3rz4OzL3rSL+9+7W9TOGF9+zves11kwIwKBB0/ePx7kD5Nykvm4jyH1btmy4Y/XqSyu0BJVUsH3WBGSJJ86pBz9nvtRiaB5qLNc6Hui6HSN563KtNMOrB4bNVjtsu4GbO255tNP907PFYx8LcORGPg5+z0Ei8Pm4xRX2lngGMC8Ix6kAaaMCZCY8bOC3gHhg/zGwSx93ccg/1hh51hEF5un8gsg5TfXsrVmEbM97hDHmNREuT3iPUBQVIK2bp6FbIsCWgUcDsNVVuS/iywWePOUnbj/xR6a7/c1r/kD0+B1dszFFCEB29vQ8Y+RBwJ90+ANj5AYdFFFa2vZZdZZ088aY2ytsBmsRtJDF8vPgokcnCYpkbGBpwjmE1YG3DplmttQauDmjan2HwtB3pvtPfQxwxdds6lFJx+/u+4DNkfUAlVug6zgnPpAKkLYmQEJwOvAcgMB5Y+HJXb1mNJw3XzDHAmKQY32BkjdVhNTOwIEzOtp2/OravEekpcWvXLr0/G+16VNUgLT6djYRYOvjoLMfZKv4sC27YFHeorJI2BcGCgCMyBm+YMnzzSlCQEx29vTzjOEunAjFzlHhVY/HmrRs2bhSLUkVIC1h+6wukDONYWivsLlSiyA1+GK0dCjPYlgdgouWAwsS7n4LVj7ZNW7iD2wN3JweI+uK2Rus3l91PqAKrvwKNs/9hvVTnBVgAieNhf+213y22vLLpTl7KWJuclSjKDaRSTieCwzwoBQ2XR7W3BNixBwe98TnpvKekGSWLRu3qbR0YiHYfcA8UV3n5LyqKmvVgAHTr83ODqVrc6corZrEfruDnoVe+Qvzh+EsyxqzKG9RGYAVs68ANjtSwNw7b+rwHe4La6w9Icktd1nZhBmWZfV2Y4dUABjD8bZtL83OnjalT5+nu2lRKs2NMYwU3f+RUuw322zuFTJzE/tHbLbZP5K89D4LGIlwp4HFs/qd+1So77nvHdX5kJDB/ESll/L7R3e2Vx7AZ2mwqCNkDto92QBv1/tA2rQAOd1xXbgYRy2MlNoVbL3ICy56Byd4FoI5PJrtG9+U79DaRQhAaen5n5aWjh8nYo4D8657uIuI3GlM+bIBA6aeok2eorRSA4pq4+ndgn1+bVv2VCPmtKg/umJru3nWok+B+91kz6xO5Zfv7LrbESFnN8agiIgnxwkXBYDXGC5OT69clZMz/bLhw+d5tVSVZuToCsObmg2py6Fh8+1W5xBhc3DcppfAJRjCOHZmgu7peMZcfcAxwSf6jN3txC59fjSVaVL+15OxVx7A7N2hau8MvHs7q7vEMie1877jZ7SZJVgAM+FWAze6yewgrNjVay54bthennh8FbAH8LVXYocNCS5p0qi1rX05VjWFVnb2wecawz0ge/3001K2bPmU9PS9l+6+e965ZWXnr0Bpr+gSrFbIVMjsCOs/H9Q566Vbev+0sXtmbvGw4tU1z1s8O7dD1Za0lQbpAWyMxbyHHXXWgp0GBKtlOda4El/J043w6Ad06HDonzp1GjC6a9cR+3q9W0MGvCcil5eVTXxFS7dd0ey2z5oxcqixeKRn2IzQ7G+d1LJ/5Bhg68qOtVvW89jXJZTGPifrDy8z6oDPOPLuD5k/5xs+AhbB2R+5jjzaG1Zbf8Hk0TkaaRnWkWcs/Ab4i5vcO2481zf1e7SFmRBXgNhlZRNmeL1y2Ndf/3v2t9/+RzZsWMq6da8cvnFj6bs5OdMezM2dsrs2a4rSOpgIW9YctceKOdcdQsHvV3L5sOIPaztvyOglm42RG9xkJ29a7La6XL+WmZAZuzoTghMY7NHNmz/I/eab57/88MM73gZJ7Enra4x5OSdn2uwBAx7vqSWsNKGBMgp0+VWrLsKwifcOmyW9QuauXmEzqmMmXTGMQrgLWNIzs6vcdtBJ3LDXCXT+ZwHzPtufuRkWs4AlQGXWHo+efEDBHwQx7a/6p8AoQFMScpToOqAT8GIQRjfGdRdPyU2LdfUuBw4DKk1cBvjOLHm/qd+n7cyEAPCQMenHpaV1PcDjydy9c+dB7LXX6TjlZW7t1y/rb+FwsF1HCm1n6AxIK2RoydBjOq2Lh8+6sGyvPT6vAPAFoaS2c0Uw0SLfG8BRgNhi+YcFi0vqcp9GngnJBqYnH+ja9ehx++13wSU4nhMTy7AqgX9u2eL90+rV52oE8rZN88+ABOQ5A3f2DJsSzf62ydqzZG+qOAbDyErbPuXFTcsPmFbxSzbP+8RpEzunsedeQf4x8JFvu6Z1mC/CXDy83PtZ82lbz5s2PwMSdDqQN9zksVMgrTGuO+SSJVUGSQQkTBePubs53qftzIQAUCpSuaWy8qvVFRXffpCe3j3xwe0JMnnlys0lAwdOPVqbMEVJTXwR3wmWbU0ZNvXzC13xgexgY6UxiFjmMtyApRb2gyJ125vXyDMhnwDJguKb9evffL+0dMJllhXPEeHlRNsOXJqZGXtvwIBpF0OhpaWuNAYSEA8w+BBnIFxpo/R8xnyd2D/Sd5bnwF92HNRr/0+P/Q8ZHscIL4/R4bRSfvP1M92f+eadQJUdn2LifLI2IGvWBmTK2gIJrAlIm1wV0uZnQABmwuXG3QBp4JgAjbfhKxL2vQSc7HSucpKvoKRZXKq1kZmQNGAiMAiI9u79h5mZmUN+7XqpSf7gXvR65Q/vvDPxI23O2jQ6A9KKyIvmnWjETBYjp5ziK/m4vxMPpBswP+i2S9ujOOyfZpDxbi90nr8gWmcX6Y04E5Lttj9VwKM4Aceq/5g9dbQx5gGgV9LhJSJcVlY2YYHWgDZHo9k+a4JyCsLdwCHAGgOv2B7+mjyq/eFY8ds2V/cKmzGa9e2OtDGdvfM3bogN6w0c97dB3L3XAWy+fzT7VHbnvL2O4MjOByefHwPeTcQf+X4PXh/yiKlSAdIKeBZyLFjuJm8Jwk2Nde2FRfl9LbGXAWkYVnrXxQYNuWRJs1SMNrYcayt9+07f0+uVPwO/Azzu4XIReQg6/qWsLLhR2y8VIErLkb8w/yTbsu8GTon6o58BhCAEBKhDgK3I0769SeN9YDfg83LJ6jsiOL/O33UTbkzfto3NnZJWUZH5W5Bb3GcFEDBF8XjV1StXXvix1gYVIMl8dZ503LSFj4xhTOUWlmak0d+2CALjsDil10yzCGB1UP5k4NteIfNPzfr2x0wYYeA1gC4XHMzLZ+3Nm+u7seX+0cjGTA7r0H3Db/fJ73xwRpfafr4RiCTij/QOm1Y5i9YuppPHQhnVUdBHNea1hxUUvwc87HZL/eJdPJc013u1seVYW3nvvfHrSksnXGbbZijVy+eyjDHXGrP5vezsaeNohxu2FCUV8Bf7T7Yt+y7Ltk5OiA/XKk/EA0nPdCIJb/8aZ0e/Bu50k/tnmfJr6/MMTbQx/edt7JJLqkpLxz9YWZneC3iIrTGgJODxeFfk5EwtzM8PZWmtUBJs2MJBwHc9Q+aNjAx6xS1+1ytsrhThRoSrE+cZ0fgf7RkDC4wbG2nLsh859UfofMB3ZF452zadtrBq87edL1v7QuyyNS/M/Ta2YYZAssfATiTFH1kTkC/XBCW0OiAXf3Km7KcCJLUKWqj2NJH3vOM+t9HI8FbcbFwPKmLMLYufzm22gFZJIuS1tiRCAFasGL+0tHTCsSLySyDhWWd/Y5iekzM9MnDgVL82Y4rSfPgivlPEyJ3emPfk4mHFnyf/zQv/SxIjOw2w1Vk23gd84CavKn7Gf3AqihCA998/+ztnf4idB7zlHu4A5qYNGza/r4MiSoIfu/AB0Hl1QK6mio/E4mbXDsnCZgM4syTAPr3DZrXmWPskCJXibgfYUvoTWVXCiT+BdeB3VtaNs9abTlsAvB9Vfj/ywg+eG336ezMe+rhiQ9+k+CMbki63D0LAwJSqOJ8n7x9575fSWQVIy4uQhADxxODYxrz24ae/8wOGP7vJLlXpaTc157u5ImR0WxQhAGVlE2d37twh2xhzXdJHl2fbZmFOzvQZ2dlT99HmTFGalrxo3qnA7d6Y9+QFR/08fscYJ0LwGre93elMc3awrNKIJGY+Mo2XejvyaE4RArBs2flvl5ZOOFpEfinCR+7hA5xBkWnzsrOnDtaa0r4Z8oipEhgOHGWn8bFlM3VNQF7CcL3x8BDApgqOFeF1za32jbh2qZTHqVq9kWM2IPtVgen2U9cOdzy92uy2uSxhV4rInZeuff4/p7837bNeIRP85Fu6CgzBcDOOIwM76bo9BS4WQygtg/VrArJ4TYHcuSYgR0mhpIzd3y72gAA8A/t54DPAGHg4AL9vzOuHQgFPDz5ZgmEQELfFOnxYsHh5c76juydkNnActJ09Ickcdthj+6Wlee8EOTep/m4CuXfLlg13rF59aYU2a60W3QOSovgivgLgzzFv7MQlQ5Z8ub3zwvAPgV8D4oH9x1Qvfd2+iAj7/guc4HScZkR+IDK/vs/XXHtCtmlvc6d0qKzM/IOI3IizJALHCDBPQeXVpaUXfa01p1XR6LbPh7+SPWwvuQLpVXFK+j1v1gGsCcoDBop7hkxIs7398iwMsuAdgN2DB7B7YH8+SWP9ffuQGDx+p7xw7KP2Z11vcvvGBHPBurS0dNzKxIGVp8ueGR6OwzBSnAGgQ7Zj9K8Tw2sizPXCnIPD5kMVIM1AyNkL0h9YFYS+jX39yCzfCGxnFgJ4zR+IHt/c79geRAhAdvbUocaYB4H85LprjPxx+fKJYW3aVIAojUNeNC9gxNxoW/aJi/IWfbWjc2fCGANFbudybgCe2mm7GfL1x/Au4EV49xN65AaD4XrH/2kJEQIwaND0/eNx7qgxKPKDMebO8vIfJ+ugSPsVINtjTUDetWHkoWHzrWZ7+0XAhJ29Hfuk9eiwbt/7BuwJ8OSe8s6iDmZwQoTY87LHbH7y6IuNMZdTHWW9CvhHRkbFn5csueTHmtd+f4z0tCxGuhHajwe6bkcErAXmIsxNM7x6YNg0m63Y3gTIZOAygDgcchZbp9AbT4SEfEUYxgAYI6f7Ckr+rSKkyT5fM2DAtAIRcx9w4NZKbcxrYE9avnzichQVIEqDcY36a2Le2ElLhiz5bmfnPw97VMF3ON7rpgUdN7c7bzfDvodxAgBikF/7AiVTduF5m12EOIMi0/OMkQeB5L1pHxgjN+igiAqQBB8GZB8bZvcKm6Ga5UrIGaQ5WyB+4PTcmNXBm4GRtVceYD6KuTYc8I4YGVlx8e/2jMflfuAXSZfYaeBmKRRrTRmHu2JkJE4g2MxaTrWBpQl3v1YH3jpkmtnSVO/eroIqJe0Dwaou2EbFS+wKXM8GIua+l146OaO537Ot7wlJKlFZvnxi2LKsfm7skC1OvstxIubt7OxpUw4//PHu2sQpSoOM+bFi5Oq6ig+A0+EHqgOr1dnjoC3Wn5yOFARz+8JQw9qq5t4TkkxZ2fiS0tKPjhRhPE5MFIBDRUwoJ2fanIEDZ+RorWrbrA3IDasDMnbVWdINYG1Qzl17hhy0jTEIoxBe1dxSku1SA57Nr30726kkpucdnzOXapt1sBEz1/vYQ+tLSyecaowZZQyJ/SE7DdxsCo3dO2yW9AqZu3qFzaiOmXQTJ37d/cAyp1pu1QS5GK5FmGNvYt3agLy8OigjVIDsIunwOs60FVYdvLQ0hCHBJZ8YIw+4yZ5dNn0/qSXetf2IEFi2bNym0tKJhWD3AfNEQgsaw8VVVdaqnJzplw0fPs+rTZ2i1A1fxHcmcKUYqbP4qNmhAvs/C/3q8pthweL1YswtbrKrMXJjQ5+9JUUIFNplZRNmWJbVyx0USSy/Gmnb9tvZ2dOm9OnzdDetYW0TG84x8HtvjNVrArJUhL/aHkZ8OEEykz6QkVS7rFbaOZLkPfD7Zz/7DnffXLrN9Vd+IxcnixBvzDs3d3Fut+XLx89NT684HMwkILH86gjbNm/k5EybPXjw1IN3dM99njCbeofNK73C5speYTPIhr2NEDTwCI4zkQQdBE4yQgcVILvIaY4HpUhiFKKwid7fkxG/XTCfOG2N3LB4Vu6+KkKantLS8z8tLR0/TsQc56p6gC4gk7/77uPlAwZMP1mbO0XZqfiYCFxpxJxU4itZ14BLbDWuPPUY6NmyZ+bfcQPGGuQPJUVDs1unCKkeFBHx5DjLvAFIM4aL09MrV+XkTL8sEAh5tLa1LTw2ZwA/boEDEO4FqgxcZG/iva0GpzAs3bBQc0sBCMLn4NQPqbCPAW5w/9T5oAr+aNnWL5NEyKCECEnEKIrFTHKMIoBTYzGzIjt76p3Z2aFOdXmGQ8Pm255FJtwzbC7pFTYHx216Jbn7/WYLTeOxzWqH5Z0oyG79YWBT3GDI6CWbjWwdwescs723tdTLtjcRAlBWNn5eaelHh7tLIb5xD/cVkZdycqbNzsmZ1kubPUWpVXycb8T8xrKtExu6Z0xgIW4UdKmHABkxYn7Mwr7cTXptsR7YlXdpaRHitEXnrS4tHR+0LEYmxBXQ1V0ysTw7e8aJWuvaDofMMu8h/DMTpgPdMTzXK2yO3AJ9AD4YI9nAhweGTbnmllKLXdr/23HRV4ESp/0050/+Ip69PRECTRO4uc8ss7Z32DzSK2SCPbPZNztsNqoAaRzmJHWUI5vqJr5A9EmEBW5y/MJQfl6qiZC8aN6ebbeYnaUQxqT1FZG7gMrE6ACwMidn2oO9ez+5m7Z7iuLgL/ZfCEwUI7vksCLofGtvJnTFFEir62/zAoteBV5wk6MiRb5TWrsIAVi2bMKrGRkVuThOUL53D/czxn5lwIBp4cMOe2w/rYFtg15F5kWExRhuT9gb2WFTCWBZGv1c+TnJ+5M3lzPCiExyTFQshAcX5hdv2ZEIgaYL3GwKjd1U790eBUgJzkbJOgXLanCFMojb2diAZRl7sggtFim3NhECzGnbIgSWLz/n+7KyiddZVnwgmJfcw2nApZmZsTW6FEJRwF/sv0iMjAd+EfVHf2qESyYGejrvAfUafInHPVcYxNk7ITy4q448UkWEOEsmJjwkEuuNs2QiBiDCiZaVLloL25QIuQPDefxcbIwS0f0fyrakO3bZ1v3JvmBJsRjzjNMEmmGRsD9YPKy4fGciBFpX4OZ2J0CCzjq5xHq2Y0KQ1WSdejC6BHjSTeZHivxnt+S7t1cRArBs2QWrSkvH/0KE04AP3MPd3KUQk7QJVNoredG8S8TIed6Y95RGEh9YbGN41Wug58gzF64RYx5yk727bPz+d7v6PKkiQhwD4cL1paUTLjNGBojwMsitK1ee96XWxDYmQkJmVq+w2RqfYd5w8QL9ew/gXc0dJRl3f/IiR3AwSsCIba7BXcpqjNz77oyBHesqQoqLg+XLl4+/y+Mx/VzHPAIYkPOMMatzcqYW9u79UEZLv3d7nAFBqkfnMtk2kF3jU8U1wE8ABrnr3RkDO6oIaTnKyia8kJFRkZ3kPeKbjIyKf2kTqLRT8fFrI+Zsb8z7iwVHLdjQWNcd4+x3+NJp9+q/1DWW5r116++N3FQSGrrLo3apJEIAli+f+F5Z2YRT+vXreL/WxNbPmjFyaML9bm0c1I18DNGmXNKitAm7dO8w5AwLFn+OcLd77IAtWZlXAdRVhAC8++74zx3HPOIDit3DHcHclJm5W+mAAVMDKkCameT1dk25DwTAf3b0a4Pc5Sb3L++QdU1Lv397FyEJ7xFQdZgxBGqLIqoo7UB8XG7EnJVVntWo4sNtY0Xc9gXwPQn12m911GkLNhiRP7nJ3WzLurkxnivVRAjA9oKHKa2HtUH5IxYRb4zStWNl0JqAHPVBgQyvcdooo/s/lHrYpTbWPRgnYLZgrl0wa9hB9RUhAGVlExeVlo5PxCj6yj3cW8SEBgyY/uqAAVMHqABpJoKwCjcKelPuA0nQSTbdS2LZj3B18TP+g1WEtDylpRd9vXz5hDe06VPaG76I70rgtKzyrF/MHzG/STycJHWo3jQ4tt4CaUXJVBGzyG03L4yEfLlNJUL8xf5ztFYoDeGr86SjCFfGYKAtnInNVOB2y/DImgK5PumDGGk8GoBQqZ3uToiIn5Lt0mHB4nLBXOuekuWx43ckzq+vCAEjZWUTZlRVxfu4jnkqoDpwc07O9IcGDpzRrCt0rPZa2KZ6dO6Ip6FJA0NlB8sqBXNdohJZXvuuVMgDFSGK0i7Fx1XACZ645+SmEh8uyZtt6z3QYwqxLezLSHiDgQcby5FHTREiRqarCFEawobN9AE+OSxsPvd62CLQ9ZNvOc4Iv8dwEsAH58huArsf8qz5SHNMqY0RTluU2J987EuQAZBfEAkZJHH8zGg47+iGixBYteqCDWVlE68D+9DkwM0gx3ftemCFCpBmwK4uMCsNRjT1/fIDkecSHbJgggtD+cemQj6oCFGU9kNeNO8aYKRlW78qHlbcpLEI3ABbK6Fh+0AAfMGSYmCmo0g4sqQor6Cxnk9FiNIYxGw+AXp8MEayN9u8bVscP2K+iYlFT3GjSpsqjsMwX3NL2RFSbZd22Ahb3ea6bnnjTlPKg1JYbbs3RIRArYGbL58/f0RMBUgzEHfEgO0W+shmyWxjX+52dljGnhwKBVLC/auKEEVp+/iL/dcaMcOzyrOaXHzU0qH2ewYObGAvdTWJwIbG3Ld4dm6HxhQhYuRsFSFKQ+n3vFknwrkWVGaHTeWhM82a1QGZgHCTwD8AjK3xP5Sds739yXnBRe8AjzvHzeHRHN+E5N81VIRAdeDm0tIJ/2vu9223AuRs+M5VfQAnNMc98woWlRkjj7rJwT345IJUyQ8VIYrStsWHGDkmqzzrjPkj5m9piQ7VA8c36NnHRD8TzL1ur3xgbIv3ysZ8xhJfSVhFiLIr9C4yL/eaZT5IUt4ZYig4NGyKHeHMcWlpOgOi7JggrAA+c9vObZatxj2eG3Fj2CHcsTiUu3tjiRAobBHPbFZ7LuykzvHgZ6FXc9wzbntuBNYBGCN/Wfr84D1UhCiK0lT4Ir6bxcjRXdd3bVbxAZAB83ADbO3KTLOIuQt3OQtw/eJQbg8VIUoKC5IpvUNmAcAHZ8gBFvxw0NPme80ZpQ52acJRwZAQdE0cP/KMhd8Af3GTe1WZtD/W/O2uiRAVIM3N1k2SnmZahjUsWLwe4VanQzbdK2IZN6ZShqgIUZQ2JT5uBbJj3tjpL5/yckVz398NsFXidqwjhYZtIh8WLC4HtjryiBvP7Y39rCpClCYxsrycgC6/UupI0v5kj4HhyX/zro89hOPFFYNMij6b16c1i5B2LUA2whvAFkcMNI8AASjvnvWwiCl1k5cuCg05LNVFiBEzV0WIorQe8qJ5fwH6xryxs5YMWVLVUs+RtA9k71nQYH/z/kD0WYQ3nGuasyMh31EqQpRWwCgsFSBK3XD3J0ttdumQS5ZUGZHEEtR08Zh7artGaxEh7VqATHTEx0I3eXwImmVT+IgR82MeE5/kJtPixnNfquVNTRECDFYRoiitA1/Ed7slVp+s8qwWFR8uW2ea7V0c6LEtaxLb8QajIkRJNQQxRsiLbyCquaHUhbPha6B0q3it2bYHS/4PeNlN/jJalHdiaxUhlhb31s6xiw1HNNdN8wKLXhXMi27yF8Vh/8kqQhRF2UWLx/givgfESK/M8syz54+YH2vpR9oLosCP2+tQ68OwguKlYsw0AAxHRPr7xzXFM6sIURqDjwIMElh56MumQnNDqStJ+5N7h+CQmn+3jXUFW/fWmQcWT8lNa40ipN0LECvJS0tzREXfphLFrUkGqXDuLfdvrxKpCFEUpU7iI+qbDOzTYXOHc1JBfMDPAmwdkwiw1eB207L+mBA0xsidkSd9u6kIUVKROIzS/R9KA5iTZJf+zHvgsILi94CH3Xa/X1XXtF9v70KpLELavQAphbdxXPI2OFhWQznyzIVrBPNXN9k3vqfn96mYRypCFCX1xUdeSd6DQPes8qzzUkV8JHWiWwNsbYL8XWo3z1j4jRG5zU3uTSbXN9VzqwhRdrHejxSPChClfmQ6AzYVTtNeu12a4a242SDfOvVMbl789PYFRfGw4vKu67ueCvxfKomQdi9ACp1ghPMSfdsM6Nic94+le28BvgQQMYWRp317qwhRFKU+4sMX9f3ViOkY9UXPTTXxARBnGyNsl2eaO7HpQQzvu+9/RaTId6iKECWV+HCCZAKH9ppJmeaGUh9Gw2Yg4iZHFtZiqx9++js/YPizm+wSS/MW7uiaL5/yckXX9V3HpJII0T0gDonOMT0DjmrOGx912oINgklUot0kzRSmaiapCFGUlBQffzNiMqO+6EUY7FR8zDNhJfCp88i7PtOcHSyrFMxViXYbm7ub8vlVhCg1+eBk2eFSQnsjRwq8ZTCiuaU0gMQyrD37wuDaTsgrLXkEWOImfx0p8g1sTSJEBQgQh+QQ9KOa+/7+ssjjImYRgEEujoR8uSpCFEXZmfjwR/x/B9Ii/sjFqSo+kkgE2MpNDrDVUPILIrMN8gpOw/mrSNh3gooQpdmMp04UrQ3K6LVBOXftGXLQz04wjNT9H8ouGOc73Z9sCrERJuG47fUgPLCz66aSCFEBApwFHwFr3IIe2dz3N4XYYkyiElkYJos0LGCXihBFafsEQgGPP+KfKkasqC/661YgPpL3gXgERjTGNe14tTcY4IF584Z7VYQoTc3iiyUNOKbKQ7EId1qeWm2pkWJv7SMVpX5tGywG1u/MLvUHo28Bs9zkcdGivF+1FhGiAsQlKVjWwBDs09z3HxYoXogh5CaPioZ9Y1I5v1SEKErLiY9PenzyuBjZ0lrEB45KmIsbYMtqpIGe/DMjK4F/uMn+WevKL2nq9yjxlYSB83DikXjEyDR/sf8MrZnth67r8QOrM2y6A1sODpsPk/++8nTZE0g/9DnzmeaW0hCCTvsyP2EThiBru4Z8zL4CZ98IIua+eVOHZ7YGEaICpJrEejsDHNciT2C4Ctjklsz9i2fndlARoihKDfEx1YjZHPVFf4Oh1awvdwNsLcdRIY221LWqMq0Q15Mhwq3R55q+/Yn6o8+KkbNwZkLWiZH3tHa2H8RZXjUnJozC/HyZVYaH4zA6+6HsMom6lckO9ifnnbXoU2MksfyqZ1bH8kl1ufjLp7xcYdlWIMn+7Zlemd5TBUgzk+6sT467nePIlngG/5joZ4K51+1ID6zaknZFquebihBFaVbxMU2MfBvJj7Qq8bHVcKvu6HrVFmCrIRx9zlvfAze5yS7Et/6/SXFnQs6Me+LHRv3RFVpD2xX7i+F/RhiVZLxVo/s/lMZhTlLbuUO71JMRv10wn7j174a3njlyv7rcwI0TchrwnBFzcvGw4hIVIM3M6fADTkwQDJzQUs8hYu4CPnaeQ65fHMrt0VpEiBHzqooQRWka8fHpgZ/OAL4u8ZVc2VrfI2kfSKMO9HwiPaYAy5zrmt8uDOUPaI73ifqjsxYPXbxKa2j7olfYXNg7bF5DmFKb0BAYbqfzpuaUsisEYTXwodt27rC9HDJ6yWYjcqOb7ORNi91W1/sUDysuj/qjYyL5kQXN+X4qQLbtHBNqc/8i6NsSzzAsWFxujCQCa3WIWd6/tIa8WzJkyeaqtKpfqghRlMYld3Fu2ic9PgmJkc+j/uhVrfldsuAN3ABbphGXYQWD4TgWk9ykxzL2ZK05SpMLkSLzYq+w+TH52AdjpRfw9aFPmZ80h5RdRaq9Bw4OQfcdnesLRJ8E3nJ/OH5hKD8vld9NBUgSdtLonN1Cy7AAfAUlzyC84VaicyMh31GtIf9UhCjKjvEX+4+sz/nZZdnpaVVpM8XIB1F/9JrW/v5ugK1iN3lcYSP2Qf4x0XnAc4lrF4f8v9QapzS7USWMElPLsixFaQBJs8YWO9mfbAziuuW1AWNhP5jKHlVVgGxb0AtIbAJvgXgg24ghy9paiTBMlsLWUVYqQhRlu+LjN2Lkzbxo3ol1FR+dNnSaaVv2+yW+kuvaSj4k7QPZsz8c3qhtuMhVwBYAy7Lvf+mlkzO05inNXMFHiu7/UBoJryNAbLft3OnAuD8YXSKYJ1yj1h8p8p+tAqQVEIRK2Lpuc8QUSGupZxlWULwUmOYmc0ty8s5rLfmoIkRRahUf14qRm6nD5mtXfITFyDttSXw4feI2xlmjDvT4giUfghOMS8T02nPTusu09inNpj0C4gFyf+zCYs0NpTE4A9YB77htZ53aS1Ml1wI/Ob+Ru96dMbCjCpDW1Tl27gpDW/JZ4h7P9cCPbmd6V+RJ324qQhSldYoP22MfZ8S8AfTf0fknv3RyRseNHYuAxSW+kpvbYJYswQ2wRRMsdS2XrNuBLwAEc+PiWbn7ai1UmoO1FkcA7wx5xFRpbiiNaJcmZo0PKoJDd9rnnB39GrjTTe5fnpV1rQqQVkC8CUfn6suRZyz8xiC3u8m9yaBVjYSqCFEUsC070/bYxy0aumitZVvvWrbl35H4+L7L90WWbZVE/dFb22J+uAG25iWaudnQqPGORgTnbzRG/ugmO8fs1uHIQ2kDhqIwyhhdfqU0ch/SgP3JnWXjfcAHbvLq4mf8B6sASXHGOq4cv4KWiweSTCfZNBnD+27yykiR79DWlJ8qQpT2Tomv5IFFQxetBSgeVrxejGT4Ij//jnMX53ZY33X9i7ZlF0fyI23aaJakAFtbdhBgq6HkjSmZAUTd5ISSWUOHak1UmrxeCyN1A7rS2Gx2PFuVu8k6DYxnB8sqBZMYtM40Xu5WAZLqIxggVAfV8z8JLbrsKTtYVim2udpNpiNbp9VUhChKazRSjEwD/lhTfKRVpb0AvFbiK7m9zedBPQJsNagdN4jrllcAy7atlPYGo7R+vhgtHYAevWaa9zU3lMZkouNY4y03OSIEnrr8Lj8QeQ74n9vSBorD/uEqQFJfhCRG57xpcGxLP09+MPICwn/d5BmRsO+E1panKkKU9kL+wvzD/cX+PwxZNCSntr/HPfEpgC8vmjckWXyIkdlRf/SO9pBHZ8Ia3ABbNNFSV/+YaATDk4liKZmVd6bWTqWp2JzJMQiva04oTWyX7mHBkDr/ULgciAEYkcmhUMCTKu+kAqTW8kooRrBSYBkWgNjmciCxse2BefOGe1WEKEpq4Yv4CmzLfkmMHOGJe171RXx/ru07ECNHl/hKFg98d2BHb8w7G3gh6o8+2E471EHPwV5NcQ/btq4HNgKImLtT1RuMkvrMGy7enVTokckze4rSmCTvT5Z6DNr4g9EVwCNuHR3Ug08uUAGSwgThc+C9+hZ0U5J/ZmSlQf7pJvtnfVd+cWvMWxUhSpsevDBypxFTEPVHJ6ZXpucAv8yL5t1U87wSX8m6ge8O7JhVnjVbjDwfyY881A6zK2GsmfhOAmw1lGHB4s8Fc5ebPGBzVoertZYqDaFHd6avLZDAdvWHcDzxrcu3FaVRec9xxfuta5fWa2DcFutPOO58MUb+svT5wXuoAEllQ6K6c+w3Cw5IhWeqrEy/CfjOTd4afa51Gu0qQpQ2zE+2ZWfkRfP6VKZX3mtb9qlGzK+HlgzNTj4pd3Hu7plbMv8HFJX4Sv7WHjPK4+y1q3OArYbyfccu9wCrAQxyTSp6g1FaAYapYmqPDbb6dNkLsHs/b77RjFKagkKnrUwI3PwQdKrrb4cFi9eLMbc4ba3pXhlP/5MKkJRua6qnu2JwfCo809HnvPU9hkI32VXi5s+tNX9VhChtst0Qc5sR82haVdpG4KFFeYu+MmL+7Yl7TkwWH96Y92XLtp6M+qN/b6955QbYWuomm2ym+ZRTXq5AuN5NZpk0uVNrqlIfPghIf4SHgX+sCcpdq4NyZPKSLONlpIFXNaeUZrJL0wWOqc9vt+yZ+XcRUwogYi4tKdp2UEwFSAqR4fipr2rqzrG+fGL3+CeOq2CA3y56rvaNripCFKX5ifqjs8TIY1VpVW8YMZ3zF+YPECMnGjFLAAYvHbyHN+Z92Yh5JJIf+Yfm2NaZ5h7PQp+muok/GC3aei9hbMmsocdo1it15dCwWWFvZKAtnCY2MSM80KMbU5NOGYVo/A+lyQ32OUn/r9es8YgR82MeE5/kJr22WA+oAElRToMNwCJXdY4UUsOFYzAYjhuRyxOVKBb3Tm7N+ZwQIVRPLQ42YsZqDVRaA3nRvAv8xX5f8rESX8ntRsx1YuQh27LniJG7iocVvz546eA9MioyXhYjUyL5kWmae9vONJsmHuixjL3VG4zYJqW8wSitQIS8bCoOLTLzexeZG3qFTV7PHMYn/fmozIqtblIVpUkYAx/jBhdsyP7kvMCiV43I7IRojhT5TlEBkqIk7QPZOwwpM9PgC5a8hvBvp9OW44uL/KNbuwiJeWOjjZhXjZi72/OyFKWVGdBiLhQj/6kpQqL+aFHUHx0Y9Uf3KfGVTDnqzaO6uOLjHyW+kumacw4bYQGw2RUgTepxMK9gUZlg/uW07ebwA82nE7UElAZ/+4XGBvhwjPQFPt5vttmsuaI0o12aPQv2re/vY7b3coNUuBd78KWXTs5QAZKKDQzbTKmOSqVn83jsK3GC02BhP9CSlaixREhVWtXJkfzItVrzlFbTGRhZY8T8Voz8J39h/rH+Yv+FNc856s2julSlVb0MPFziK5mhuVZNjQBbx82DJnUvLmJuANY77bvctjiUu7uWgrIrxA2jQJdfKc1ul5qG7E8+8syFa8SYhNfF3ntuXvdbFSApSHeIAD+5yZGp9GxDxyxai2Gy26n22nPjuktbe34vGbKkSmud0qo6AzHvA1+LkV/blj1fjJyBVLerR79xdPfK9MpXjZi/Rv3RJzXHaiXRoe72dX0CbDWAYcHi9cbIX9zkXjHjvVGzX9lFi1DjfyjNRpqzPznuGvANsktjad5bgS9d+7GwJDR0HxUgKcYIZ71wIrLpsS9BSs0ylNtZtwFfAIgxf1o8K3dfLTVFaT7EyEoxcrQR82ucjuGIoYuG5gIMWzBsr8r0yv8B90byI09pbm2XOdW2XNPPNG/es8NfgTI3eemi0JDDtAiUhjBvuHgNDOiVvdWbm6I0KafDD8BicPaBNGR/8lGnLdhgRBKueHezjVWoAiQVDYzq0bkOmyA/pQRScP5Gd0kBQOcqSbtVS0xRmlWArABuBX7o8UmPUUbMRMu2jvVFfHvHvLFXxMgtJb6SpzWntk8A3gW+cQVIk880jxgxPyaWmeQm02PGe6+WgtIQDtwLH4bFif0gitJMdmli0Ga/MPRryDXyVpRMFTGL3ORFkZAvVwVI6hZ0kwbLaij+QGQ6UAJgRCaWzBo6VEtNUZqHRXmLysTI2T0+6XFOOBiOR/IjLwNPiJGXgVtKfCXPay7tGAMiSQG2noTdmvqe+WMic4H/c+4vpy4M5Z+kJaHUu+7ajDS6/Epp/jZzl/cnm0JsMWaSY9piAQ+KNK+3VxUgO+FMWAl8tisF3aQV0SBYXJaoRGKbyc1diRSlPVPiKwmHg+E4wNCSofsArxgxN5f4Sv6tuVPvDjUtHY5ujnvaYk1KeIOxLPv+xVNy07QklHpW3FGWxv9Qmp9iYKP7/wYPjA8LFC8EZrp1+ciSorwCFSCp1zkmAuXlhqBrqj2ff0w0YpCnAQQzLFrk0zgaitLM+CK+AyzbmmvE3BT1R/+jOVJ3PPDfxP+ba6Z5WLB4NYaH3Zv2i3Xx/lZLQqkr7/1SOgN7Hhw2H2puKM1JECqBN9zksVOg4YMnFlcDmwDEmPsWz87toAIktUhMsXoMDE/FB4yL59pEJQLueXfGwI5abIrSPAwtGXog8F8j5vpIfuQFzZH6McaZZX4fmmcj+lbhY8dvwd1/ApysJaHUlfRMhiPM15xQWoKk/cmdu4K/odfxj4l+Jph73YseWLUl7QoVIClElVPQ4hb6yFR8xmHB4s+Bu9zkAZuzOlytJacoTU/u4twenrjnJWBSJD8yW3OkYSStpc8Owf7Ncc8hwSU/ApcZZLwvEFUBotSHUVi6/EppMQHSaPuTRcxdOFHWMcj1i0O5PVSApAhnw9dA6dZGJ0Up35h1D4aP3Ep0zYJZww7S0lOUphUfaVVp/wdcHvVHdTPqLmBvu7HyuOa6rz8QfdYXKJlhjDPIpCh1M9o4Ll2YpzmhtARjHVfiX7rJXRIgw4LF5cB1brJDzHhvUwGSWiSMi94hOCQVH3DExPlbjMg1bjLLY8fv1GJTlPrji/ge8Bf7dxjcc9iCYQd5Y96X4p74pEh+REdCd5EqxxNWIhjpKM0RJVVZFZD9gU0Hhs16zQ2lJTAgSfuT80Kw+65czx+IPots3VdyTiTkO0oFSOoU9lYDI1WXYQH4AiVh2Lou9cySWUOP0dJTlPqJDyBfjMz0F/svrLWxLvYfHPfE/0+MXLoob9Grmmu7zrnwE26ALRoYYEtRmgMPjBL1fqW0PImBcW9j7E+2LWsSYDsmLw9KYdNqBBUgdSTTiYhe4WbayFR+VuORSUAcQGwzuakrkaK0NfEBnAZMFyMjkW2/H1/Ed6gYecWI+X2Jr+Q1zbVGbLuqB3r2mQX9NUeUlKynhpGi8T+UFibWyPuThxUULxVjprqN8RGR/v5xKkBSgNGwGcf3MgLHF6Zw3vnOKHkXw2POs5rDS/rnTdQSVJQdkxfNOzFZfOBENz8Hg510Th/gRcu2LonkR+ZrrjUu9rYzzboMS0k5BDEIfjY59oCitBRnwRc4seqgkdpLUyk34MxGg8GnAiR1SHSOe/aHw1P5QSvt9BuBHwDEmNsXh3J31+JTlO1T4iv5b3lW+fHJ4iMRYBBgyKIhhxkx/wbGFw8rfl1zrPH5wRnk+ckVICM1R5RU48MxDDDwwaEvmwrNDSUFSMzEHRaCXfZe5T87+jWG3xmR4/MDkd+oAEm9gk75zvGY4Jvfiphb3eReMct7gxafouyYZYOWbRIjj9UUH/kL8/t64p7njZiJUX80ojnVNFzibEJ/000OfwkyNFeUVEIsRqL7P5QUoSn2J/sLok/6gk2/vFgFSP1YAqx3Cz3lR+e2dM98CMdVGwiXRZ/N66NFqCg7psRXEk4WH/5ifz/bsp+3bGtCJD8S1Rxqtg614waadgmAojSgfo5CdP+HkhqkO/uTq1yDvlXNGqsAqQdBZ2P3fDd5VAiyUvl5R4yYH0O4fGs9tbhXS1FR6k5eNG+QGPmPbdnjiocVl2iONIuBNyepg9JlWErKUBaQdKDPIbNYrrmhpAKnwQYgAs6+ucJWZNerAKknSZ4vMoGjUv15/cHoHISXAMSY0QtD+SdpKSrKzhlaMnSwERMWI2cvylu0SHOkeShwZm0/S3SomiNKqpAuDBNDscFo0EollUjMGnfrCwNUgLRRWks8kGRsrMsM4rgQtuz7F0/JTdOSVJQdiw/Ltp4BCkp8JYs1R5oXcYISAgx9Crpojigp0f8bRmr8DyUFmZNko7aaQRsVIPUkCKuBD1tTQQ8LFq+2sf7u9uz9qvZM+42WpKLUjr/Yf4RlW89YthWM+qPLNEdawNCrHujxeOFYzRElRerlSDxo4FElpegOi4Aft9ZRFSDtonMc/Bzs1SqeuUIKga8AjEjh4qdzu2lJKsq2+CK+XDHyVNwTDxQPK9Z13i3HHNwAW+gyLCUF+PBXsgfQsfez5lPNDSWVGAExqvcnH5Pq+5NVgOwCScGyTByOaw3P7D83+pNBCt1kl1ia92YtSaWdiYtJedG8q3ckPoAnbcsuWDx0canmWMsRdAZLVkDrGtFT2nC/n87xxjBPc0JJURJ2aRZOQF0VIG2RNHjV0SEAnBCGQAiuCMEhqfzceWUlj+K4Ega4JFLkG6ilqbQHhpYMPR7YyxP3PFbb3/MX5g8DngHGLMpbVKY51vIkOfzo8wwcrDmitKwC0fgfSkrTauLUqQDZBc6AdcBSp03iXIELgO7A7CI4NFWf2xRii2Uuc+onHmwma2kq7cCStYyYi4ELKtMrf9bm+Yv9R9qWPQ34VdQfXaEZliLtVZLDDw8crzmitHCFHC5OzAVFSTmCsAr4yG07W8WyVRUgO+ElyAjB2BDcHIbTanaOBtIs+EMQrgeeseGPqfw++WMiC8AUuS8xIhrKO11LWWnjhoN90McHnQ3M88a8zwyfNzwz8SdfxHeUGHnEsq1TVHykHK8LTAXOicF/NDuUlmLtGXIQsK5X2PyouaGkble31XvgEU9Dyu/zVQGyA56E3TbCYmA8EBe4MwTnuX9Onu66JwwPAkcCp6T6e0mMa4ByADHm7lAo4NHSVtoy4WA43uOTHucA68qzyv89fN5wb14072hgiifu+WXxsOLVmkupRRA2joXzg/D02fCd5ojSYng5AaPLr5TUJml/suWF4SpAWgEhOLs2tZgGJwGfB+GUINxiYIbAfgAbYcFWI94J/PK502ey7nnYI5XfN/+syEfAfUAUi/OCwXBca4HSXkSIGHlsU8dNw4yYf3rinl8uPHLhGs0dRVG2a9jZjEzak6QoKUncqaOJ/ckpvwyr3QuQmc7sxj/S4MSaf/NCFOgzE64Nw4MClxj4L8BE2AK85Z66Zze4P8sp+Ni78FOqv7d3fewWfyDq94+JRvSzVdqTCPHEPd9YtvUP22OPVvGhKMqOkEKxLMOQH/agRHNDSWXcmeKE+/gTVICkMCH4jYHzBP5Um9eAMfCxDScZuMR2BMddwL+fhX6wzSbJ3b+DaeXwjkC4sFqBpixDLllSpZ+r0t7Ii+adaFv2P8XIqYuGLlqrOaIoyo5YU8bhAmVDHjHaZyopj6meqTt4FvRUAZK6TAvCSC+8yHbcllkQMPD4WLg2CP8A3vPAoBoFjQ37GrhgLNyqn4CipB75C/NPMmLuAUaV+Eo+1BxRFKUOBt0oEV1+pbQOkvaBYKf4Mqx2LUCC7h6OMbAWqCqCvjXPESgTuCgM94TgDWAfL7zi/u4d4Bu3kfIG4E2t/oqSeviL/Sfbln2XZVsnR/3RzzRHFEWpIyO9Ht2ArrQawfwGzoqdlI8H0iYFyFTIbEChzbVhZAjSn4EDE8fHwr+BS4BvDdwN5J4OP7i/Eal2e+YPQSet/oqSWvgivlPEyJ3emPfk4mHFn2uOKIpSFz6cIJkYcmI2j68NylWrT5e9NFeUVMYdWF/oJo8PQcp6OW0TAuRJ2C0Mp4XhbyF4rxM8Xp/fPws5NmQAfwa+s5x/kwv0FaBc4BbgvzOhKARFYec+Cb/g6QLHaPVXlNQhL5r3C+AOb8x78oKjFnyhOaIoSl2RjRyD8LLHw1m2jWW8zFtbINNWB6S35o6SqiTtT+5iwxEqQJqIZ6FfOnwu8HvgEwvOEvhNXVXfLOhpwQvGmbL6LXDwWLjoZw0RPIWzufx4A2OA0wV6euDvSaeN0qqvKKmBL+IbY8TcEfPGTlLxoShKAyy5kQhzD3nWfNS7yNzdM5sBGF40ULSmQO5/75fSWTNJSUEBsnXPkieFl2F5W3tGj4X3XoBA2/a/AAAohklEQVT9tsBAgVEG/gbkWDAMKNvZ7939Hz/zFOAKmMECI41TgAdS7V73M4F7xsJD7rnvA31Miq+3U5R2JD4KgD/Zln3CkiFLvtIcURSlvggc77F5YKthV2hsoGjecPn3Qd24LC2D4rVj5ZyeM827mltKqlAKb/d3XPJ2c/eB3JGKz9nqZ0AMSCX83cADFqQJ3Ah0L6iD+KjJLOgZgotDEALeA6418D1wQRD6GnjFwHwLTkqID7eRSkx3Zc+CfbX6K0rL4S/2B4HrYt7Y8YvyFqn4UBSl3qw6S7oB1sHPmS9r/m3EfBPrWWTuMxbn2DbPrA7KeM0xJVUodFbrzHeTR82Ajqn4nCkzAzILDrKhh+0sccoJQkFdf1sG4wsbEHsjBJ0E/BaMFjg5Dj8ZmCvw0ApYWPOaMXh+H7hlBMRqiKC5OMu3TNxRm0/oJ6AoLSI+xoqRq2Le2ElLhiz5TnNEUZSG4KliJGark5la6TnTvLvqLDnGG2Pm6gI5sHeR+YvmnJIizMGxo9Mz4CjcINoqQGqhCrwWPGbBU2UQbIDaq4vg+NmyKgOv4wiIPwerN5TXylmwtLbjaTCvyhElXlEBoigtgi/iOxPhzzb2FBUfiqLsCsYwUqBoZ+cd9oz5bvHFclLX73lhdVAu7R0yD2nuKSkiQGJASao+YMoIkDNhTQi8NiwobMRI4rOgpzsrMRI4HFhqYK4HLhwDHzfGPU6HH0KwGPAbOEHAGBCt/4rSPORF8842tplk2dbJWITzonkfl/hK/q05oyhKAzm6UyaX1eXEIY+Yqi9Gy5jyTP63tkC+7Flkwpp9SksShA//A11Pgw0qQOqo2CzoBw0P+jMDOmZAvgUjBUbEwePG+HhkL5hfc/lUY2FgjoAf2GcW9KcBe1AURak/vojvfGObXxsxJy08cuF6X8Q32oiZnxfNW1HiK3lfc0hRlPqwZqz0MTZf7POE2VTX3+w322xefbqcIV7mrw7I0t5hs1pzUmlJUll8QOptQp8j9XRlGwJPCHJnwrUhmJMJiw0EgCXACUEYEoDrxsLcphIfAHaSaLLVG5aiNAv+Yv+FwCXemPfE4mHF6wGi/ujXlm392oh5KndxbprmkqIo9cEIozDVrkzrSu/nzTfA7y14VBCjOakorUiAGHhkZyfNgn3DMM71VrUWKDTwfRwuCkK/IFwSgPDO9nQ0aoMFEWCjm9R4IIrS9OLjIjEyHhj11tFvfZ/8t+Jhxa+LkTe8Me+lmlOKotQHESf+R0N+2ztsXhP46MMgF2pOKsoO7eafsRpIqSifNZdVUb2sau4P8PolUJUKzxmCF4FfAJuArkGo1CqmKHXmAmAv6uCz3BfxXQyc6415f7HgqAW1TjMf+daRnWPe2CJP3HPMwiMXfqPZqyjKDlgN9JaAeNbCBz3hUBM28YZc6NOAdK2ESNYWBu8322zWrFWUn5OSgQgLweoPhyd5qzoAeAOYmwZ3ng4/pKiamyuOAOkoMBRYoFVMURqXvGjerxHO2pH4AFhw1IIN/mL/w7ZlXwbcoDmnKMrO+MhiKDZvN1R8ABwYNuvXBGTW5gzOA6Zsc/0zZN+4hxmADyhJ8zCux7Pmi+Z6vzUB+ZmDnF5hs81g9NqAvC2O057tn1MgF4rhNgzX9wqZx7XmKK1WgIRgHwNHC4wGjsWJLj4XuCwIK1pDZgr8R6DKwNyxsEqrl6I0uvi43Ij5VVZ51i/mj5i/caeDAmL+ZVv2u8PnDb8j+fzcxbn7emPeGYDPiCnxxD3jFhy14IuWeCdfxHcVcE/UHzU13vVtI2YbI6DmOf5i/4Vi5Dbg+qg/qkaAouwicWGUMQ13hJMgBn/zGl4R5BGD2Wr0x73cIzbvGEMBwk2Vce4ExjXX+yULiTUB+Y0R+tZiy3RL87D/joSRGCaLcIKBVwBte5TWI0BmQ4fNMCxpWZUFvCow4we4IHlZ1SzYN071iEEcxp0FzWYshKCrgQsF7grWsmwtBG/jjhYk/XGb88LO728TuH6sfqyK0hBD/UpBRtdVfAAUDysuz4vmzS7PKj+dpPg8nrjnHuCdmDdWkFaVdlPMG2tWIyBJZAxB6L8d8dTNG/PuvyNhJEYmW7Z1gm3ZagQoSiNghJG2xfm7ep3DwubztQEpW1vAcRTxapJ1f3y6l9wez5ofVwXkPk894zTUZQajLnw4RnxxGPvJd7U6zelWFefzNQGJ4Tj0uahX2CzfJp8MlwP/McIVWmuUlBYghTWWVZXD/gbeBOZWwV3nwPc7GEm4x4J3BAoM3OSB5jYW3gNu38Hfu8Vh/x2JIoHJBk5ARwsUpSHi4ypglCfuOXn+iPnl9fmtZVtPiZG/JAsQI+Z4b8ybG/VHf8xfmH+fGCmp5/P8zAioOTuxM4bPG96pXMofECO/MmIm1tauxLyxz30RXwxYYtnWRcXDipfXECmX25b9HyNGjQBF2UW+Ok86btrCPofONGsa43oihMTwK0gSINDVNe4T6Yr6XLMhYqMmiy+WNPt7/u61OHfEfBOr5R4dAD44WTI8Hfm9GB7HWVa+lZ4h8yjwqNYaJSUFyNOwdxoc40YHH4ET+G+ugZ8y4bTRUKfNWQaOj0HuWfBjCO6jniMGoVqCAgZr34BfK0FnYywheGB7AsQDn4ccN79LcLxxLa8hQJzRAnS0QFHqQ1407xqE4yzb+lXxsOLy+v4+kh952xfxHZpdlp1ell2WcAzR1TXusbERI/UyAuorNmqjPKv8YeDBEl/JOl/EV9s9OgCc/NLJGeu7rv+9bdk/MwIi+RE1AhSlkdi0heEivN5Y16uqZE5aBrfWOPytHcd/6HPmsw/OkAMsDwvrc83GmAHp8j0BYyg9eKZZuaPzDn3ZVHx1nvxz0xZu1tqhpLQA2cGyqrCBPyS8QoXg2U0wEMd1bV3o6hr4DRoxqI/YaAhB6ADwEmRsgt8LPx8tGOsYCWooKEo98Bf7r7Wxh2eVZ/1q/oj5W3bhUos7/9Q52THEt4A/6o9+5ov4DjBi6mUENMYMCM4s7riE+PBFfFLbNV4+5eWKge8O/GdWeZYaAYrSlBhGWlL/+B/bo+8LZsOagFSuPF327Pe8WecenmN5uGpNQG4SwySxeaU+12yMGRADFxmhcAciZ4kI92QY/rO5gt8ILNbKoaScAJkFPePOxvFTy6G3u6xqNnB3ENZv52dLPHBEPQTItx7wj4HPZsEBceo3YrCrMyB15RSomAH/zERHCxSlMcSHGDm2w+YOp++i+ECMLBUjA5IEyBzgqtzFuTcRY9L/t3fvQXKU973Gn7dnZi8CYsQpDkkFgkFCBiQUgyytFEMZ7CQOgiQG74qbMQjjCwaDLkCBq2wOx3ZxBEYQ4goWROFqYa8AcwiWCacqwWUQuwIpXFZAJGQu8uGgKiMZkLSXmen3/LG78mjZy4x2tBKj51M1VTvTPfP+pqf7O+/b3dMLlXUCqnEEpPQ1Bht8NLU1rQZuStLkf6ed6SUxRDsB0u4U+Vw+xw+q/KrPN2SZAr1HVkKWa2KB+4GNIfJMGvjS2L9NpncX6BhmlitCYEkPLCWyJiR8xZVDe3wA8jD89yJ8pv+0qiK8GXtPq7pmTu/pR+X4L+DkCpr9P0W4shWuK8C8pMLOwu4+AtIKqwPc1HcVrEtwb4E0Kk1tTddH4rSDNh90xi9n/7J7tK8XYhiYOdcA92cL2Y3AM3U9dV/aCxfDFcCSNEmX0nuhCzsB0m7yx41/mgG6P/FA+F2VX/r1YuDj/QOQIx8Im9jD/6x4wvKw/wjTnwImu1Zojw5AWqExwqf7Tqv6bAFCGOS0qgptjjC+3JkLcE2Wvj0G8ExkbPcYlB5B6f97wKDmith7re+lAdakdhSk0Qw+/meI4fjxW8Z/sRqDD4A0Sd9O0uSP+++3z2zf452AUoMdUWmf2W4nQBojp/zJaeNg9JffHShGNiWBg13CUhkDkL7Tqv6S3lOrpgR4lt7/8j3caVVlS2BLWsEA5FzYo52FkY6gzAE7ChppO/sS8ARjePnoj6JJP5z0V8C7+Vz+zF/O/mW+ah9AIftBmqQHuIS1D5pG74VUngCKLo7BffqQv2okVn8AQsKmFOpdwrLvs7MwSGf6uUlwzEuw/XnY/mbJ/+OolgA0QNIJqZ+P9hEfAw4CHgEuB/6vi2RnB0478JQDP3fgz9+8+c13Y/HDV3oZVebUhdBwaEO28zedeZe09jEBOBR4F/gfwB0ukg+7eur/WnPb2us/1lXsjC4Nyb6PVCt7AS4GjnJRSBpjnwLmAHUuCkn2fSRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkqSPhrnAO0AHcK6LQ5L5I8nskbS7nAs8CxzRd1sDnO1ikWT+SDJ7JO0OTwN/XXL/b4Cn9kAdq4DjgHagG3jPj0Yyf8weSfZ9pNrzHvDfSu4fDPx+D9TxDvAE8JdAI3CQH41k/pg9kuz7SLUnD+SA2HfLAYU9UEcETvPjkMwfs0eSfR+ptr3dN/Iv3QuwZQ9thPv5cUjmj9kjyb6PPkoSF0HFNgAzSu7PAF7aQ7Vs8+OQzB+zR5J9H6m2zaH36g9H0nsliOeA5j20F0CS+TPW+WP2SGaPfR9pD+i/Fvb/A760h2pwI5TMnz2RP2aPZPbY95EkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSdpXBBfBrnkIjizC+jmQKX28FR4DThs4/5zdsKyHquEhOLQItwCn0PtfQ3+VgYVfhDcBfg4HFuDW2FtnNsC9ERbMgWK5bS+Hb0b4DhACXNcCS1wrpNrOnxVQvxUWAWcBDUB7CpefDeuqlU8jGcuMlbT39X362lsA3DzY6w9Vo32fvUviIti1DTCFG4ZYfuMTmDkHQultLGsowrIIa/NwVB4mRegowr390/NwSwpvFOCYIkztG4nOq2DDvyzC2UWYUYQTIpz5M/iGa4ZU2/mzDb4fIOTh2B44HFiZwP3VzKcyjEnGStr7+j59fZAjgC9XWqN9HwcgH3lFeDzA4qG+HIHNe7iGE8bBjefBlvNgS+jdYzmtZPrpBfiHc+F358DGAiyKcG4Fzc9N4epzYOM58HaA6wPMdc2Qajt/Ipwe4YbzYMuX4P0c3AYcU+V8GnEAMhYZK2nv6/v0WTLUwGGEGu377EWyLoLKzYFJfaPhQTfCCM19hweLAR7thiu/BO+PVQ0BHu2Eb7fC4kbo6oT5ER4pnWVc76kP/Rvs1kzf65VpUgId/Xe6oaMOjnbNkGo7f+aUDDYegkN74Fp626pmPo04ABmLjJW09/V9lvd2+N9ogSdbK6/Rvs9exCMg1Tce+BgwEZiRwu9ycOtYFhDhEmA28G4nbAOaC/CtkllWFGBBKxzcCgfn4CsVrgvjXobtrX0b8muwFdjPj17aN/KnFWIRNgbYcBbcWeV82uszVtLYb5fL4JAICyNctYsvYd9nL+IRkOrvIWgoufveT+CmHKwf4zLujPCLAnyu70O+LAf3AH/XN31ehH+it67fB/gelR063XYsjOs/v3Mi7E/vhihpH8ifFkiWw2HA134Gs8+CFVXMp49Cxkoa4+0yCz8K8N0WeG8XX8K+jwOQfUeAOqB7jJudHeCi8/o2jHthcQNsKgmKzcDZ/feXw8kRnq/g9V8NvT/gWglQB8cDL/tpS7WdP62wrgAnhd48eevn8MM8bAQOqFY+fUQyVtLYb5fNfad5lWZSLPfH7vZ9HIDUtFZ4JsID3bC0AQ6IMB94eIzLeBG4qhUW90Cs6/2x1uqSGn8d4IEsLMvDhAi301vnwPcy1Ia9JMKih+Ccnt7pi/peQ1IN50+AJ3JwZSvc3APb872vv6aa+TRS/uwlGStpjLfLgXlQyeDDvs/ex9+A7NqGFvvPASz9u2+BXhzgjAZ4B1gTevcMXjuWNaRwPr1XlXm9Dt4ApgMXltQ4P8LF+d69jg8GuHEOPF5BCCwF7ivCcxloD7DkrN5TKCTVcP5EuCaFA4Hn6+AtYHoKF1Uzn8r40hqTjJW09/V9RlmjfR9JkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJNSW4CCqzYsWp9Qdt37yIyFlAA5H2kMbLm85eta5/nrblTd8EvgOEQLyuqWXVkmrW8Ny/ThtX6MreCDT31fB0SnLFX8x55jWAtoeaDiXlFuAUIBL5VTGTWfjpL658s5znl2N3v0dJe1/+lNl+HOy5M1vad3zfPNM688IQ4ncC8U8j4QUCX53Z3P5iWdkzQr5Jqs2+T6lnWmcuCCHeXJorI00v5z2U0e4uZ5d25n9Cr9BB2zZ/n0jI9+SOpZvDgZVpktzfP729dcZlwNlJIZ1RKGRPiIQz2x+c8Y1q1lDoyv4ACOT58/ps98cJrE5I79sxQ8qySFib78kdle/JTYohdGSKxXvLfv4IxuI9Str78mek9vsHGqU34AvAwyWdlL8OIV6axuQLPT11fxJD+Akpt5ZdxAj5Jqk2+z4lbR0RiF+udHo5+TXszo/RZpd2knURVOz0JKannHTeU1sA/vPnn7ytu1B/Vf/ESJhLwqUzznl2I8DK5bOuT2J6C/DjKtZwaqGQ/eyJ5z69CeA/Wk++sTF0LiyZfkKuIf83s1ratgOsbJ21KIR4ZQXPH9YYvUdJe1/+DNv+QM/967RxhW5uSPLp50sC5IqU5KK/mPPMS32P3NZ3K9dI+SapNvs+vW2FsCQS5gXif1Q4vaL8GqTzM9rskgOQXTezpf2YHaPhh5oO7S5wbQxhwY4ZApM6i40d/XeT7rSDeo6ucg07vV5D0nUOkRUlDz1a7M58e2XrrMV1jT1d+c44PxAfqeD5wxuD9yhp78ufEdsfoNCVvYTAY/2dkt6iOCKTFj/Ttrzp34BG4PE0Jpf+xZxnNpdZxrD5Jqk2+z4Abcub5gJvzGppe7JteVNF0yvNrw8ZfXZpp8WpXd0IIikbYwwbZjW33VkyadzJLz+5vf886KbX2rcC++22OlqbmkMav5HG5Os7RpWxcAmR2UlI3y10ZbeFEJt7euq+Ve7zyzCm71HS3pU/w7S/w4oVp9YDlyZpuninCZGPk5ALmXgceY6OMTwbkviP5bZdSb5Jqp3saVvWdAiwMBsLV+3K9Erya1CjzC45AKmKpub2JBsLhydJelDbg02zSyZte/LYk8f1//CpfWLT/sDWarcfI6F9+YzrCJxVqM+eXDoCL4TcnZHwi3xP7qB8T+4g4OG6XM895T6/DGPyHiXtnfkzTPs7jN+65fNEXpgx59l3BuZHw/auO5rOXPXuzHPbN4We+M8hxlPLbbucfJNUg9mT40eR8N1PzVn93i5NryC/hur7jCa7tDNPwap89L+OPCeF0L4JVr/1nz//5A+7C/UbgQN6O/bh1frQPRVYCZDWJccnpC9Xs4bW1pZM24MblyYhfWFmc3vLIMOT2Z2x8aJTzntyK8AL905d3NnYuKn85480+Nn971HS3pc/I7VfKiTxDNI//Ph8x+PEdflsrhHYBhDGxVwshp4KEmjYfJNUm30foDkQm0tPrWpb3hRLrnQ17PRK8mswo88uOQAZnSfIceWq1uk3pz3J9u4i84ms6Z+YkC6JhEVtDzWdk/SkIQ3pohDi7dUs4M/CW9+LITzd1LxqqEOHLzYmnVc91zptcaEnGzvrmEdkdQXPH2zDZizfo6S9Mn+GbX/ncQInAjcN8i1+R6Euu/C51mm3b2dcZyz2XA48Wm7+jJRvkmqz7zMwDwZmxEjTy82vIbOnzOxSeTwFq0KdsfGaQDwwDcnz1PMWkenFNHNR//SmOauWErmPlOfSbNIOLGlqXlXt0wOuDjHe0ba8KZbe+icWi5nziUwrhOzr1PNGDGF6IF5Y7vNHMkbvUdJelj8jtb/zd3U8jB5++6H8aF51T4jxg0LIrqkLPa9EwmF0U/ZVrMrIN0m12fcZs/watO8zyuySJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSpGoLLoJdN/OZmQtiiDe3z/zDf8xsamt6DDht4Lyl84zWqStOrd980OZFwFlAA9AeQ7x8VdOqdeXUMNoam9oG/6eF1XyPkvbO/Bmu/ZI6vgl8BwgxxOtWNa1aUq38GCn/JNV29gxVQxl9o0OBW4BTgAj8KlPMLFz56ZVvVjP/VJ6si2DXzGifcUSM8csDH48hjk/SZGbbrLb23dX25oM2fx8IuXzu2HwuXwTmJWlyPzCjnBpGW+PADa6prenvgS+7Vki1nz/Dtd837TIic9IknVHXU1csZAt3zWifEVY1rfpxNfJjpPyTVLvZM1wNZWTDshDDv2cL2a8BFLKFK4qZ4r3AZ6qVfypf4iLYNSGGJSGGeYM8Pj6GuHk3N396mqQ3PHXSU1vaZ7a/313ffVsM8Zhya6hmjdOemzYOuCFN0nmuFdI+kT9Dtt83bS5w9bMznt349IlPv52kyfV9j1UrP4bNP0m1mz0j5M9I2XBCPpe/8amTntry1ElPbQkxLAKmVTP/VD6PgOyCpramucAbbbPanmxqaxo4eTzQ3NTWtAAoAo8CV7bPbH+/Wu23z2w/pqSWQ+nm2hDDggpqqFqN2UL2EuCxZ2c8u9E1Q6r9/BmhfYBJjZ2NHf130iTtAI6uVn6UkX+SajB7RqqhjGx4NFPMfHvWylmLe+p6umI+zo8hPlLl/FOZPAJS+cp/CLCwkC1cNdj0GOL4GOLHCtnCxEK2MAP4HXDrbqolAhtDDBvaZrXdWW4N1arx1BWn1gOXpkm62DVDqv38Gan9PuOePPnJ7f2/9Whvat8K7Fft/Bgq/yTVZt+nzPwZMhsK2cIlwOw0Sd/NFrLbYojNdT1136p2+yqPR0Aq96MQw3dXf2r1e4NNXNW0qqHk7nsn/vrEm/K5/PrdUUh7U3sybfW0wzLFzNea2ppmt89sX1FODdWqccv4LZ8HXnh2xrPvuFpI+0T+DNt+n20nP3nyuCdPebL3ohftTfsDW6udH0Pln6Sa7fuUkz9DZkOukLszEn+Ry+c+B5DP5S/rqeu5B/i7arav8ngEpHLNMcSHmtqaYv8evqGu6tK3gtcB3VXeC7Cuqa3pEAJx9adWv9VT1/ND4Ge7WsOu1hhDPAN42FVC2mfyZ8T2Qwyvdtd3T93xJZMmxwMvVys/Ks0/SbXR9xmphpGyIRJnN3Y2Lur/DUhnY+fiEMNnd9cy0PA8AlKhQa7gEgdciu6ZEMMD28dtX1rfXX9AKIb5u6GT/gRw5fRV029O0mQ73cwH1pRbQ7k1DnxvgzgRuMm1Qto38mek9gHSJF0SYljU1NZ0TpqkIU3TRTHE2yvNj2HyZ9j8k1SbfZ8y8mekbHixs7HzqmnPTVucLWQjncwDVpebPeXkn8rnEZAqS5P04hjiGY2dje8kabImTdIDGroarq1mG42djdfEEA9M0uR54C1geqaYuajcGqpVYwzxMOC3furSvpM/I1nVtGopcB/wXJIm7cCSVU2r7qlWfoyUf5L2zewZKRsyxcz5wLRsIfs68EaIYXoM8UI/PUmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEkfVf4Hx11w3HF3XRhj+A7wp8ALEL7a0XHBi/3TJ0++55shxO8AIYRw3UsvXbBk99Vyz4IY480dHRfu9FkOV8OUKXfHwV5r4GsMZeLE2+obGg5YBOEsoAFieyaTXP7CCxesc+2Q9o38GSp7Rqrhk5+868BikVtjDKcBWeDeY44Zt2D58jnFctqdOvX+Q2Ms3BIjpwAR+FWxWFj4yisXv+naIdn3GaqGadOWjOvpqb8xRpp7+y7h6RiTK9auPf+1ctocbXZpZ/4n9ApNmXL3X8cYLg0hfiGE3J8AP4F4a8lGcRnEsyGdkc8XT4gxnjllyl3f2B21TJ167xEx8uVBNsxha+jouDCU3kIIX4DwcLntNjT80fd7t+3csV1d2cNDCCuLxXi/a4e0b+TPUNlTTg35fLglRt7o6ak7BtKpAK+8sn1euW0Xi4VlMca1IeSOCiE3CWJHJpO917VDsu8zXA09PfU/iDEGyP95Nhs/HiOrQyjeV267o80u7SzrIqhUuCKE9KKXXpr7Ut8Dt/XdAIgxzs1k4qUvvnjRxt6N9l+uh+QW4MfVriRN0yUQ50H4j9LHK6lh2rQl47q74w2Qfr6Cpk+PMZ7S0XHelr69ArcVCuEq1w1p38ifobKnnBpC4PQQ6iatW3fuFoBPfGLpolwu86/AzWUtgcAJ9fU9f7N69dztALNmtS764IPtV7puSPZ9hqshxnBqPl/87H/911c3AUye3HojbF9Y9hIYZXbJAcgoxSMg+cyUKXf/G9AIPB5j4dK1ay/e3DfDpGJxv47+ubu66joaGgpHV7uK4467ey6EN1566cInp0y5e+Dksmvo7q6/BMJjHR29G2w5OjouPOYPeyLuP7RQyF8bY1zguiHVfv6MkD3l1BAymZ4dp4EWi/Vbc7nCpAo6AY92d9d/e/Lkf17c0FDs+uCD7fOBR1w3JPs+w9XQ0XHBgHq2nRNCWFHJKGw02aWdeQpW5T6epjFXKITjIH90COFZyP1jyfRxa9e+vL3/dxavvfbaVmC/ahYwZcqdhwAL6+q6hjrqUFYNEyfeVg9cGmO6eNfquDumaWFjCMmGtWvn3umqIdV2/pSRPWXUEFbk88mC44//l4OPP/5fDq6vL3ylku+iurruS0JgdgjZd7u767cBzSHkvuWqIdn3KbeGyZPvaQ4hfCNNC1+vYPwxquySA5DR2pbJJHe8+uoF73Z0fHVTZ2fmn0OIp5ZOnzz52HH9P4yaOHHi/sDWahYQQu5HMcbvrl799feGqrGcGurr/+jzwAtr1859Z1fq6Oi4IIkxOTzGeNBxx90121VDqu38KSN7Rqwhxvy8ENJJ+XyyPp/PPBsC7wOby62hu7vhTuAXIeQO6r3Fh2PM3+OqIdn3GbmGGKZMuee6JIln5fPFk0uO4IxotNmlnXkKVuWbwLqurmwjsA0gmy3mgJ6SGV4NYetUYCVAY2N6fIy8XM0Keq/gEJpLDz9OmXJ3LLkaRLk1nAHx4UrbnzLl7nWQP6mjI2xau5a3PvnJu35YKISNwAGuH1Lt5k8Z2TNiDX1f+Gf/4fl3nQzh+QqqmJ2m4y5au3bOVoCpU+9dHGO6yXVDsu8zXA0tLa2ZV1+9e2mahhc6Oi5sqbT90WeXHICMbgO4I5fLL5w8+d7b6+oKnYVCvBzCoyWzLIFk0dSp95+Tpj0BiotiTG6vZg0DLzs3SAegrBpC4MQY403DDDTiYJfYDIEnYsxdOXnyXTd3d+e25/OF+SGwxrVDqu38KSN7RqxhypS7fx1jeCCXS5cVi2FCjNweY5xfbv4AL8L2q6ZNW7L4vff2izEW5gGrXTsk+z7D1fDKK9u/FyNPr1174bCnjA+VPeVml8rjKVgVj4AvuCcEPgghXZPPJ69APKyzM3NlyQayNATuS9PCc5C0Q1iydu0FY3p6QAU1HNbdXffbSl8/TcddAxwYQni+oaHwFjAduMi1QzJ/Rq4hmZ8k8eJCIWxKUx4MgRvXrp37eAVNnB8C07q7619vaCi8ESPTkyS50LVDMntGqOHqEMIdU6bcHUtvFXSZR5tdkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJH03BRVCZDS1x0P+aOWF5CH3THwNOG2r6bqhnAXBz6eu//bdxXGc9NxJoBhqApyNcMXF5eA3g9S/EA2OWW2PgNCAbIvceEVgQlodiNdqXVJv5s/7UWJ/szyLgLKAhQntIuHzCz8K6HfOcGQ9NMtwS4JQIEfhVKLLwyIfDm9WosZwaJNVe36eSbX+ovsmG5vhNAt8BQoxcN/HBsKTSOtZ9MR6ZSVg/YXnIuGbsuqyLoDIDV+b1zfHvk8CXSx4an6TMPOKh0L67a3mjJR5RZKe2Aehs5AdACBn+PHTSHbMsiIH7gFkAaY5bCLxRyHBMJtIYClz5emQecHM12pdUm/kT9uP7IRIydRzbEygmPcwj5X5gRv88SYZlBP49k+VrAIUCV8QM9wKfqUaN5dQgqfb6PuVu+0P1TX7TEi+LMCdmmFEHxXyRuzbMiWFCa/hxJYOPJHADkLhWjI4LcBTe/ts4LgRuiBnm9T8WYXwKm8ei/SIsSeMf2uYPRZyaS/jBkQ+ETUc8En7fGbgxwNSSOU7PZvmHTzwQfjfxp2FjNsuiGDi3au1Lqsn8CYHTQ+CGw5eFLUf9JLyf5LkNOGbAbCc0dnLj4cvClsOXhS11kUXAtGrVWGYNkmqs71Putj9U3yTC3CTh6ok/DRv/7Kfh7Ri5nsjcSmrIJDyeiSx2LRg9j4CMQlc9lyTw2JE/DRt3bCAwnkBz3+G/Yow8Guu58qifhPer2faG5jg3Rt446sHw5IaWOHBPxdGl9xsD58TIipIaQ7qdHU/qzrA1KTKpWu1Lqs38mbA87PiyX39mPDTNcm2ABQO+5R/d3sC3N7bExZkuurpgPoFHqlVjWTVIqrm+Tznb/gh9k0nbUzp2RFU9HaGHoyusYRKA/Z7R8wjILlp/aqyPgUsDHxoJjwc+BkwEZoSE3yV5bq1m2785Jx5CYGEIXDXivC2xOUa+UQdf738shRUxy4L1LfHg9S3x4CTPVypZFyppX1Jt5U/fl29MMmwksuHI1nDnThMDlyQwuwfe7WxgW4TmbJZvVbvGYWuQVJPZM9y2X0bfZNyxk9ne/3uWiUexFdjPT3XP8AjILsrsz+cjvHDE8vDOgNFxQ8nd9948N95UzLO+mm3HAj8i8t0JD4b3hpyHGH7TzHcjTMl3c/KER8MH/dPqYV4+8E8JrAd+HyLfixUcOi2nfUm1mT8ARy4n+U0Lh0X42oY5cfaE1rCiZAByJ/CLbJbPAeQLXFbIcw/wd9WscdgaJNVk9gy37ZfRN9n28lrGTe77Pctr69k/ga1+qg5APlIinBHg4ZHmK0bqUuiucvPNfYc6d9oj0P8jsdgSM69HlpLwwoTW0DLwyYctD5uBs3fs0WiOJyeB56vVvqTazJ8NLXFdyHJSeCBsYjlvvf6F+MM0x0bggJLiZnfCRZOXha0A75wfF2/rYlO1aiyrBkk11/cpY9sfqW/yan1kKrASINvD8RFe9lN1APJRc2IRbhr44Gst8RkCD+xfz9LOLg5Ii8ynjI21Eh+6rNyAzv9vAt8LgacnDHFawoaW+GsiDyQFlhVzTAhwe4T5g8w36KBipPYl1Wj+RJ5I81z5eku8uVDH9rSH+cCa0lkCvNgAV21oiYvTOuK2LuYBqyutcchcKaMGSbXX9xlp2x+pbxJgCYFF68+M54Q6QlpkUQzcXnb2qKr8DciuO4w6fvuh7SPl4iRyxrYu3klhDZEDuuHaMd5FcXWM3LGhJcbS247pKfND4OI0x6YADxK5ceLy8LgfqWT+DKcrcA0JB6bwfNLDW8D0NOGi0nmKCefTe9Wr15Me3gCmZ+DCatVYTg2Saq/vM9pt/8jlYWmE+5IMz4Ui7USWTGwN91RSQ2l/6kN9K0mSJEmSJEmSJEmSJEmSxsb/B3SGh7lsBeixAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDE4LTExLTMwVDEyOjA2OjE5KzAwOjAwi1r4YgAAACV0RVh0ZGF0ZTptb2RpZnkAMjAxOC0xMS0zMFQxMjowNjoxOSswMDowMPoHQN4AAAAASUVORK5CYII=)\n",
        "\n",
        "<p align=\"Justify\">Whenever points on a certain line ($\\rho'$, $\\theta'$) undergo this process, the element corresponding to ($\\rho'$, $\\theta'$) in the accumulator is incremented, in addition to other lines ($\\rho$, $\\theta$) which pass through the point. Finally, the accumulator holds a count of the number of pixels that belong to a certain line. Values of ($\\rho$, $\\theta$) for which this count exceeds a pre-defined threshold is considered to be a *true line*. As you can see, this method is not affected by small noises and breaks.</p>\n",
        "\n",
        "**OpenCV Functions for Hough Transform for Straight Lines:**\n",
        "\n",
        "<p align=\"Justify\">There are two functions in cv2 that performs the Hough Transform for a given image. The function cv2.HoughLines() performs the transform according to the procedure described above. The function cv2.HoughLinesP()- Probabilistic Hough Transform- is an optimised version of cv2.HoughLines(). This function reduces the computation by considering only a random subset of the points in the image; for good results, the threshold of the Probabilistic Hough Transform must be lower than that of the regular Hough Transform.</p>\n",
        "\n",
        "\n",
        "```\n",
        "cv2.HoughLinesP(edges, rho_accuracy, theta_accuracy, threshold, minLineLength, maxLineGap)\n",
        "\n",
        "minLineLength: Minimum length of line. Line segments shorter than this are rejected\n",
        "maxLineGap: Maximum allowed gap between line segments to treat them as single line\n",
        "\n",
        "```\n",
        "\n",
        "\n",
        "The function returns the two endpoints of the lines. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2W7R-smuWHo"
      },
      "source": [
        "### Structure for the Lane Detector (try coding on your own)\n",
        "\n",
        "We may define the following functions in order to make the program more modular:\n",
        "\n",
        "1. **img2edge(img):** \n",
        "<p align=\"Justify\">A function which takes the input image (img), uses a mask to pick yellow and white pixels, does Gaussian Blurring and Canny Edge Detection.</\n",
        "p> \n",
        "\n",
        "  Returns canny_output (a contour of edges)\n",
        "\n",
        "\n",
        "2. **roi_select(img, canny_output):** \n",
        "<p align=\"Justify\">Reads in the canny_output, returns canny_roi: canny_output with irrelevant regions of the image reset to 0 (blacked out). The RoI is defined using dimensions of img.</p>\n",
        "\n",
        "3. **draw_lines(canny_roi, rho_acc, theta_acc, thresh, minLL, maxLG):** \n",
        "<p align=\"Justify\">Inputs canny_roi, performs Probabilistic Hough Transform on it. Draws lines on a blank image using the values returned by HoughLinesP() and the openCV function cv2.line()</p>\n",
        "\n",
        "  Parameters for HoughLinesP() are passed in as parameters to draw_lines()\n",
        "\n",
        "  Returns img_lines: image of lines against a black background\n",
        "                         \n",
        "4. **add_weighted(img, img_lines):**\n",
        "\n",
        "  Computes a weighted sum of the original image and the image  of lines. \n",
        "\n",
        "  Returns lane_img: image with lanes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUFG54D1ejk_"
      },
      "source": [
        "# MINI-PROJECT: LANE DETECTION\n",
        "\n",
        "\"\"\"\n",
        "Input for Program: Image of a road with clearly labelled lanes\n",
        "Expected Output: Lanes highlighted with white superimposed on the original image\n",
        "Read the input images from the folder 'LD_test_imgs' and show the output images in the notebook.\n",
        "You may check the following functions out:\n",
        "1. cv2.inRange()\n",
        "2. cv2.bitwise_or() and cv2.bitwise_and\n",
        "3. cv2.fillPoly()\n",
        "4. cv2.line()\n",
        "5. cv2.addWeighted()\n",
        "In addition, you may consider the structure of the program (functions that one may define) we discussed \n",
        "in the previous cell.\n",
        "\n",
        "Fill in your code in place of 'pass'.\n",
        "\"\"\"\n",
        "\n",
        "def img2edge(img):\n",
        "    gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    hsv_image  = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
        "\n",
        "    \"\"\"\n",
        "    Define ranges for 'yellow', pixels within this range will be picked\n",
        "    \"\"\"\n",
        "    lower_yellow = pass\n",
        "    upper_yellow = pass \n",
        "\n",
        "    \"\"\"\n",
        "    cv2.inRange(): Picks pixels from the image that are in the specified range\n",
        "    \"\"\"\n",
        "    mask_y = pass      \n",
        "    mask_w = cv2.inRange(gray_image, 216, 255)\n",
        "  \n",
        "    \"\"\"\n",
        "    Compute Bitwise OR, combining both the white and yellow pixels\n",
        "    \"\"\"\n",
        "    mask_yw = pass\n",
        "\n",
        "    \"\"\"\n",
        "    Compute Bitwise AND of mask_yw with gray_img, pixels that were yellow or \n",
        "    white will have the same intensity as the original grayscale image, the \n",
        "    other pixels will be removed.\n",
        "    \"\"\"\n",
        "    mask_yw_image = pass \n",
        "\n",
        "    img_blur = cv2.GaussianBlur(mask_yw_image, (3, 3), 0)\n",
        "    img_canny = cv2.Canny(img_blur, 70, 200)\n",
        "\n",
        "    return img_canny\n",
        "    \n",
        "def roi_select(img, canny):\n",
        "    \"\"\"\n",
        "    Define the vertices of the region of interest\n",
        "    \"\"\"\n",
        "    lower_left = pass\n",
        "    lower_right = pass\n",
        "    top_left = pass\n",
        "    top_right = pass\n",
        "    vertices = [np.array([lower_left,top_left,top_right,lower_right],dtype=np.int32)]\n",
        "    \n",
        "    mask = np.zeros_like(canny)                     #creates a numpy array of the same dimensions as img\n",
        "    fill_color = 255                                #parameter for cv2.fillPoly function\n",
        "    cv2.fillPoly(mask, vertices, fill_color)        #pixels within 'vertices' in 'mask' will be made WHITE while all other pixels will be BLACK\n",
        "\n",
        "    return cv2.bitwise_and(canny, mask)\n",
        "\n",
        "def draw_lines(canny_roi, rho_acc, theta_acc, thresh, minLL, maxLG):\n",
        "    \"\"\"\n",
        "    Inputs - canny_roi \n",
        "    Parameters of HoughLinesP() are passed in as parameters to draw_lines()\n",
        "    Output - line_img (image of lines against a black background)\n",
        "\n",
        "    Perform Probabilistic Hough Transform on it, draw lines on a blank image using the values \n",
        "    returned by HoughLinesP() and the openCV function cv2.line(). \n",
        "    \"\"\"  \n",
        "    pass\n",
        "\n",
        "    return line_img\n",
        "\n",
        "def add_weighted(img, line_img):\n",
        "    return cv2.addWeighted(img, 0.8, line_img, 1, 0)\n",
        "\n",
        "\"\"\"\n",
        "The below code is for a single image, perform the same procedure for all the images in the\n",
        "directory 'LD_test_imgs' and display all of them in  output of the cell. \n",
        "\"\"\"\n",
        "img = pass                                                      #Read the input image from the directory.\n",
        "edge_img = img2edge(img)\n",
        "roi_img = roi_select(img, edge_img)\n",
        "hough_img = draw_lines(roi_img, 2, np.pi/180, 50, 50, 100)      #Change the parameters thresh, minLL, maxLG to get more accurate lines\n",
        "lane_img = add_weighted(img, hough_img)\n",
        "cv2_imshow(lane_img)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uup69P7nLUtb"
      },
      "source": [
        "**End of Problem Statement**\n",
        "\n",
        "A single .ipynb file named 'Roll_Name_CVI-CV_PS.ipynb' should be submitted. "
      ]
    }
  ]
}