01_images_2d.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><center>SimpleITK Tutorial</center></h1>\n",
    "\n",
    "# 2D Images with SimpleITK\n",
    "\n",
    "Author: José D. Tascón-Vidarte \\\n",
    "Email: jota@di.ku.dk \\\n",
    "University of Copenhagen\n",
    "\n",
    "Objectives:\n",
    "- Learn how to create images with SimpleITK\n",
    "- Reading images and show its properties\n",
    "- Plotting images with numpy and matplotlib\n",
    "\n",
    "The documentation of SimpleITK is available at https://itk.org/SimpleITKDoxygen100/html/index.html\n",
    "\n",
    "This tutorial is based on the Notebooks provided by the Insight Software Consortium. http://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Cloning files\n",
    "\n",
    "Open the files to be used from github."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !git clone https://github.com/josetascon/registration_tutorial.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries\n",
    "\n",
    "Import the libraries to be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install simpleitk           # install simpleitk in colabs\n",
    "\n",
    "import SimpleITK as sitk         # simpleitk library\n",
    "import numpy as np               # array and matrix library\n",
    "import matplotlib.pyplot as plt  # plot library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the actual version of SimpleITK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimpleITK Version: 1.2.0.dev298-gaffed (ITK 4.13)\n",
      "Compiled: Oct 26 2018 13:47:38\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sitk.Version())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Image Class\n",
    "\n",
    "The most important class in SimpleITK is the Image Class. Several algorithms (functions) use this class as an input parameter.\n",
    "\n",
    "The constructor of the image class is: <b>sitk.Image(...)</b>\n",
    "\n",
    "- A monochromatic 2D image is created with three inputs: width, height, PixelIDValueEnum.\n",
    "\n",
    "- An RGB or any other multichannel image is created with the imputs: [width, height] , PixelIDValueEnum, channels\n",
    "\n",
    "An example of this is:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_2d = sitk.Image(400, 300, sitk.sitkFloat32)\n",
    "image_rgb = sitk.Image([256,128], sitk.sitkVectorUInt16, 3) # multichannel image, RGB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PixelIDValueEnum refers to the pixel type. The options for monochromatic images are presented in left, while the multichannel options in right:\n",
    "\n",
    "<table>  \n",
    "    <thead><tr><th>Pixel Type</th><th>Description</th><th>|</th>\n",
    "        <th>Multichannel Type</th><th>Description</th></tr>\n",
    "    </thead>\n",
    "    <tr><td>sitkUInt8</td><td>Unsigned 8 bit integer</td><td>|</td>\n",
    "        <td>sitkVectorUInt8</td><td>Multi-component of unsigned 8 bit integer</td>\n",
    "    </tr>\n",
    "    <tr><td>sitkInt8</td><td>Signed 8 bit integer</td><td>|</td>\n",
    "        <td>sitkVectorInt8</td><td>Multi-component of signed 8 bit integer</td>    \n",
    "    </tr>\n",
    "    <tr><td>sitkUInt16</td><td>Unsigned 16 bit integer</td><td>|</td>\n",
    "        <td>sitkVectorUInt16</td><td>Multi-component of unsigned 16 bit integer</td>\n",
    "    </tr>\n",
    "    <tr><td>sitkInt16</td><td>Signed 16 bit integer</td><td>|</td>\n",
    "        <td>sitkVectorInt16</td><td>Multi-component of signed 16 bit integer</td>\n",
    "    </tr>\n",
    "    <tr><td>sitkUInt32</td><td>Unsigned 32 bit integer</td><td>|</td>\n",
    "        <td>sitkVectorUInt32</td><td>Multi-component of unsigned 32 bit integer</td>\n",
    "    </tr>\n",
    "    <tr><td>sitkInt32</td><td>Signed 32 bit integer</td><td>|</td>\n",
    "        <td>sitkVectorInt32</td><td>Multi-component of signed 32 bit integer</td>\n",
    "    </tr>\n",
    "    <tr><td>sitkUInt64</td><td>Unsigned 64 bit integer</td><td>|</td>\n",
    "        <td>sitkVectorUInt64</td><td>Multi-component of unsigned 64 bit integer</td>\n",
    "    </tr>\n",
    "    <tr><td>sitkInt64</td><td>Signed 64 bit integer</td><td>|</td>\n",
    "        <td>sitkVectorInt64</td><td>Multi-component of signed 64 bit integer</td>\n",
    "    </tr>\n",
    "    <tr><td>sitkFloat32</td><td>32 bit float</td><td>|</td>\n",
    "        <td>sitkVectorFloat32</td><td>Multi-component of 32 bit float</td>\n",
    "    </tr>\n",
    "    <tr><td>sitkFloat64</td><td>64 bit float</td><td>|</td>\n",
    "        <td>sitkVectorFloat64</td><td>Multi-component of 64 bit float</td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Documentation\n",
    "SimpleITK classes and functions have the Docstrings derived from the C++ definitions and the Doxygen documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class Image in module SimpleITK.SimpleITK:\n",
      "\n",
      "class Image(builtins.object)\n",
      " |  Image(*args)\n",
      " |  \n",
      " |  The main Image class for SimpleITK.\n",
      " |  \n",
      " |  C++ includes: sitkImage.h\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  CopyInformation(self, srcImage)\n",
      " |      CopyInformation(Image self, Image srcImage)\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Copy common meta-data from an image to this one.\n",
      " |      \n",
      " |      \n",
      " |      Copies the Origin, Spacing, and Direction from the source image to\n",
      " |      this image. The meta-data dictionary is not copied.\n",
      " |      \n",
      " |      It is required for the source Image's dimension and size to match, this image's attributes, otherwise an\n",
      " |      exception will be generated.\n",
      " |  \n",
      " |  EraseMetaData(self, key)\n",
      " |      EraseMetaData(Image self, std::string const & key) -> bool\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Remove an entry from the meta-data dictionary.\n",
      " |      \n",
      " |      \n",
      " |      Returns true, when the value exists in the dictionary and is removed,\n",
      " |      false otherwise.\n",
      " |  \n",
      " |  GetDepth(self)\n",
      " |      GetDepth(Image self) -> unsigned int\n",
      " |  \n",
      " |  GetDimension(self)\n",
      " |      GetDimension(Image self) -> unsigned int\n",
      " |  \n",
      " |  GetDirection(self)\n",
      " |      GetDirection(Image self) -> VectorDouble\n",
      " |  \n",
      " |  GetHeight(self)\n",
      " |      GetHeight(Image self) -> unsigned int\n",
      " |  \n",
      " |  GetITKBase(self, *args)\n",
      " |      GetITKBase(Image self) -> itk::DataObject\n",
      " |      GetITKBase(Image self) -> itk::DataObject const *\n",
      " |  \n",
      " |  GetMetaData(self, key)\n",
      " |      GetMetaData(Image self, std::string const & key) -> std::string\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Get the value of a meta-data dictionary entry as a string.\n",
      " |      \n",
      " |      \n",
      " |      If the key is not in the dictionary then an exception is thrown.\n",
      " |      \n",
      " |      string types in the dictionary are returned as their native strings.\n",
      " |      Other types are printed to string before returning.\n",
      " |  \n",
      " |  GetMetaDataKeys(self)\n",
      " |      GetMetaDataKeys(Image self) -> VectorString\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      get a vector of keys in from the meta-data dictionary\n",
      " |      \n",
      " |      \n",
      " |      Returns a vector of keys to the key/value entries in the image's meta-\n",
      " |      data dictionary. Iterate through with these keys to get the values.\n",
      " |  \n",
      " |  GetNumberOfComponentsPerPixel(self)\n",
      " |      GetNumberOfComponentsPerPixel(Image self) -> unsigned int\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Get the number of components for each pixel.\n",
      " |      \n",
      " |      \n",
      " |      For scalar images this methods returns 1. For vector images the number\n",
      " |      of components for each pixel is returned.\n",
      " |  \n",
      " |  GetNumberOfPixels(self)\n",
      " |      GetNumberOfPixels(Image self) -> uint64_t\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Get the number of pixels in the image.\n",
      " |      \n",
      " |      \n",
      " |      To Calculate the total number of values stored continuously for the\n",
      " |      image's buffer, the NumberOfPixels should be multiplied by\n",
      " |      NumberOfComponentsPerPixel in order to account for multiple component\n",
      " |      images.\n",
      " |  \n",
      " |  GetOrigin(self)\n",
      " |      GetOrigin(Image self) -> VectorDouble\n",
      " |  \n",
      " |  GetPixel(self, *idx)\n",
      " |      Returns the value of a pixel.\n",
      " |      \n",
      " |      This method takes 2 parameters in 2D: the x and y index,\n",
      " |      and 3 parameters in 3D: the x, y and z index.\n",
      " |  \n",
      " |  GetPixelAsComplexFloat64(self, idx)\n",
      " |      GetPixelAsComplexFloat64(Image self, VectorUInt32 idx) -> std::complex< double >\n",
      " |  \n",
      " |  GetPixelID(self)\n",
      " |      GetPixelID(Image self) -> itk::simple::PixelIDValueEnum\n",
      " |  \n",
      " |  GetPixelIDTypeAsString(self)\n",
      " |      GetPixelIDTypeAsString(Image self) -> std::string\n",
      " |  \n",
      " |  GetPixelIDValue(self)\n",
      " |      GetPixelIDValue(Image self) -> itk::simple::PixelIDValueType\n",
      " |  \n",
      " |  GetSize(self)\n",
      " |      GetSize(Image self) -> VectorUInt32\n",
      " |  \n",
      " |  GetSpacing(self)\n",
      " |      GetSpacing(Image self) -> VectorDouble\n",
      " |  \n",
      " |  GetWidth(self)\n",
      " |      GetWidth(Image self) -> unsigned int\n",
      " |  \n",
      " |  HasMetaDataKey(self, key)\n",
      " |      HasMetaDataKey(Image self, std::string const & key) -> bool\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Query the meta-data dictionary for the existence of a key.\n",
      " |  \n",
      " |  MakeUnique(self)\n",
      " |      MakeUnique(Image self)\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Performs actually coping if needed to make object unique.\n",
      " |      \n",
      " |      \n",
      " |      The Image class by default performs lazy coping and assignment. This method\n",
      " |      make sure that coping actually happens to the itk::Image pointed to is only pointed to by this object.\n",
      " |  \n",
      " |  SetDirection(self, direction)\n",
      " |      SetDirection(Image self, VectorDouble direction)\n",
      " |  \n",
      " |  SetMetaData(self, key, value)\n",
      " |      SetMetaData(Image self, std::string const & key, std::string const & value)\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Set an entry in the meta-data dictionary.\n",
      " |      \n",
      " |      \n",
      " |      Replaces or creates an entry in the image's meta-data dictionary.\n",
      " |  \n",
      " |  SetOrigin(self, origin)\n",
      " |      SetOrigin(Image self, VectorDouble origin)\n",
      " |  \n",
      " |  SetPixel(self, *args)\n",
      " |      Sets the value of a pixel.\n",
      " |      \n",
      " |      This method takes 3 parameters in 2D: the x and y index then the value,\n",
      " |      and 4 parameters in 3D: the x, y and z index then the value.\n",
      " |  \n",
      " |  SetPixelAsComplexFloat64(self, idx, v)\n",
      " |      SetPixelAsComplexFloat64(Image self, VectorUInt32 idx, std::complex< double > const v)\n",
      " |  \n",
      " |  SetSpacing(self, spacing)\n",
      " |      SetSpacing(Image self, VectorDouble spacing)\n",
      " |  \n",
      " |  TransformContinuousIndexToPhysicalPoint(self, index)\n",
      " |      TransformContinuousIndexToPhysicalPoint(Image self, VectorDouble index) -> VectorDouble\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Transform continuous index to physical point\n",
      " |  \n",
      " |  TransformIndexToPhysicalPoint(self, index)\n",
      " |      TransformIndexToPhysicalPoint(Image self, VectorInt64 index) -> VectorDouble\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Transform index to physical point\n",
      " |  \n",
      " |  TransformPhysicalPointToContinuousIndex(self, point)\n",
      " |      TransformPhysicalPointToContinuousIndex(Image self, VectorDouble point) -> VectorDouble\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Transform physical point to continuous index\n",
      " |  \n",
      " |  TransformPhysicalPointToIndex(self, point)\n",
      " |      TransformPhysicalPointToIndex(Image self, VectorDouble point) -> VectorInt64\n",
      " |      \n",
      " |      \n",
      " |      \n",
      " |      Transform physical point to index\n",
      " |  \n",
      " |  __GetPixelAsComplexFloat32__(self, idx)\n",
      " |      __GetPixelAsComplexFloat32__(Image self, VectorUInt32 idx) -> std::complex< float >\n",
      " |  \n",
      " |  __GetPixelAsDouble__(self, idx)\n",
      " |      __GetPixelAsDouble__(Image self, VectorUInt32 idx) -> double\n",
      " |  \n",
      " |  __GetPixelAsFloat__(self, idx)\n",
      " |      __GetPixelAsFloat__(Image self, VectorUInt32 idx) -> float\n",
      " |  \n",
      " |  __GetPixelAsInt16__(self, idx)\n",
      " |      __GetPixelAsInt16__(Image self, VectorUInt32 idx) -> int16_t\n",
      " |  \n",
      " |  __GetPixelAsInt32__(self, idx)\n",
      " |      __GetPixelAsInt32__(Image self, VectorUInt32 idx) -> int32_t\n",
      " |  \n",
      " |  __GetPixelAsInt64__(self, idx)\n",
      " |      __GetPixelAsInt64__(Image self, VectorUInt32 idx) -> int64_t\n",
      " |  \n",
      " |  __GetPixelAsInt8__(self, idx)\n",
      " |      __GetPixelAsInt8__(Image self, VectorUInt32 idx) -> int8_t\n",
      " |  \n",
      " |  __GetPixelAsUInt16__(self, idx)\n",
      " |      __GetPixelAsUInt16__(Image self, VectorUInt32 idx) -> uint16_t\n",
      " |  \n",
      " |  __GetPixelAsUInt32__(self, idx)\n",
      " |      __GetPixelAsUInt32__(Image self, VectorUInt32 idx) -> uint32_t\n",
      " |  \n",
      " |  __GetPixelAsUInt64__(self, idx)\n",
      " |      __GetPixelAsUInt64__(Image self, VectorUInt32 idx) -> uint64_t\n",
      " |  \n",
      " |  __GetPixelAsUInt8__(self, idx)\n",
      " |      __GetPixelAsUInt8__(Image self, VectorUInt32 idx) -> uint8_t\n",
      " |  \n",
      " |  __GetPixelAsVectorFloat32__(self, idx)\n",
      " |      __GetPixelAsVectorFloat32__(Image self, VectorUInt32 idx) -> VectorFloat\n",
      " |  \n",
      " |  __GetPixelAsVectorFloat64__(self, idx)\n",
      " |      __GetPixelAsVectorFloat64__(Image self, VectorUInt32 idx) -> VectorDouble\n",
      " |  \n",
      " |  __GetPixelAsVectorInt16__(self, idx)\n",
      " |      __GetPixelAsVectorInt16__(Image self, VectorUInt32 idx) -> VectorInt16\n",
      " |  \n",
      " |  __GetPixelAsVectorInt32__(self, idx)\n",
      " |      __GetPixelAsVectorInt32__(Image self, VectorUInt32 idx) -> VectorInt32\n",
      " |  \n",
      " |  __GetPixelAsVectorInt64__(self, idx)\n",
      " |      __GetPixelAsVectorInt64__(Image self, VectorUInt32 idx) -> VectorInt64\n",
      " |  \n",
      " |  __GetPixelAsVectorInt8__(self, idx)\n",
      " |      __GetPixelAsVectorInt8__(Image self, VectorUInt32 idx) -> VectorInt8\n",
      " |  \n",
      " |  __GetPixelAsVectorUInt16__(self, idx)\n",
      " |      __GetPixelAsVectorUInt16__(Image self, VectorUInt32 idx) -> VectorUInt16\n",
      " |  \n",
      " |  __GetPixelAsVectorUInt32__(self, idx)\n",
      " |      __GetPixelAsVectorUInt32__(Image self, VectorUInt32 idx) -> VectorUInt32\n",
      " |  \n",
      " |  __GetPixelAsVectorUInt64__(self, idx)\n",
      " |      __GetPixelAsVectorUInt64__(Image self, VectorUInt32 idx) -> VectorUInt64\n",
      " |  \n",
      " |  __GetPixelAsVectorUInt8__(self, idx)\n",
      " |      __GetPixelAsVectorUInt8__(Image self, VectorUInt32 idx) -> VectorUInt8\n",
      " |  \n",
      " |  __SetPixelAsComplexFloat32__(self, idx, v)\n",
      " |      __SetPixelAsComplexFloat32__(Image self, VectorUInt32 idx, std::complex< float > const v)\n",
      " |  \n",
      " |  __SetPixelAsDouble__(self, idx, v)\n",
      " |      __SetPixelAsDouble__(Image self, VectorUInt32 idx, double v)\n",
      " |  \n",
      " |  __SetPixelAsFloat__(self, idx, v)\n",
      " |      __SetPixelAsFloat__(Image self, VectorUInt32 idx, float v)\n",
      " |  \n",
      " |  __SetPixelAsInt16__(self, idx, v)\n",
      " |      __SetPixelAsInt16__(Image self, VectorUInt32 idx, int16_t v)\n",
      " |  \n",
      " |  __SetPixelAsInt32__(self, idx, v)\n",
      " |      __SetPixelAsInt32__(Image self, VectorUInt32 idx, int32_t v)\n",
      " |  \n",
      " |  __SetPixelAsInt64__(self, idx, v)\n",
      " |      __SetPixelAsInt64__(Image self, VectorUInt32 idx, int64_t v)\n",
      " |  \n",
      " |  __SetPixelAsInt8__(self, idx, v)\n",
      " |      __SetPixelAsInt8__(Image self, VectorUInt32 idx, int8_t v)\n",
      " |  \n",
      " |  __SetPixelAsUInt16__(self, idx, v)\n",
      " |      __SetPixelAsUInt16__(Image self, VectorUInt32 idx, uint16_t v)\n",
      " |  \n",
      " |  __SetPixelAsUInt32__(self, idx, v)\n",
      " |      __SetPixelAsUInt32__(Image self, VectorUInt32 idx, uint32_t v)\n",
      " |  \n",
      " |  __SetPixelAsUInt64__(self, idx, v)\n",
      " |      __SetPixelAsUInt64__(Image self, VectorUInt32 idx, uint64_t v)\n",
      " |  \n",
      " |  __SetPixelAsUInt8__(self, idx, v)\n",
      " |      __SetPixelAsUInt8__(Image self, VectorUInt32 idx, uint8_t v)\n",
      " |  \n",
      " |  __SetPixelAsVectorFloat32__(self, idx, v)\n",
      " |      __SetPixelAsVectorFloat32__(Image self, VectorUInt32 idx, VectorFloat v)\n",
      " |  \n",
      " |  __SetPixelAsVectorFloat64__(self, idx, v)\n",
      " |      __SetPixelAsVectorFloat64__(Image self, VectorUInt32 idx, VectorDouble v)\n",
      " |  \n",
      " |  __SetPixelAsVectorInt16__(self, idx, v)\n",
      " |      __SetPixelAsVectorInt16__(Image self, VectorUInt32 idx, VectorInt16 v)\n",
      " |  \n",
      " |  __SetPixelAsVectorInt32__(self, idx, v)\n",
      " |      __SetPixelAsVectorInt32__(Image self, VectorUInt32 idx, VectorInt32 v)\n",
      " |  \n",
      " |  __SetPixelAsVectorInt64__(self, idx, v)\n",
      " |      __SetPixelAsVectorInt64__(Image self, VectorUInt32 idx, VectorInt64 v)\n",
      " |  \n",
      " |  __SetPixelAsVectorInt8__(self, idx, v)\n",
      " |      __SetPixelAsVectorInt8__(Image self, VectorUInt32 idx, VectorInt8 v)\n",
      " |  \n",
      " |  __SetPixelAsVectorUInt16__(self, idx, v)\n",
      " |      __SetPixelAsVectorUInt16__(Image self, VectorUInt32 idx, VectorUInt16 v)\n",
      " |  \n",
      " |  __SetPixelAsVectorUInt32__(self, idx, v)\n",
      " |      __SetPixelAsVectorUInt32__(Image self, VectorUInt32 idx, VectorUInt32 v)\n",
      " |  \n",
      " |  __SetPixelAsVectorUInt64__(self, idx, v)\n",
      " |      __SetPixelAsVectorUInt64__(Image self, VectorUInt32 idx, VectorUInt64 v)\n",
      " |  \n",
      " |  __SetPixelAsVectorUInt8__(self, idx, v)\n",
      " |      __SetPixelAsVectorUInt8__(Image self, VectorUInt32 idx, VectorUInt8 v)\n",
      " |  \n",
      " |  __abs__(self)\n",
      " |  \n",
      " |  __add__(self, other)\n",
      " |  \n",
      " |  __and__(self, other)\n",
      " |  \n",
      " |  __del__ lambda self\n",
      " |  \n",
      " |  __div__(self, other)\n",
      " |  \n",
      " |  __eq__(self, other)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __floordiv__(self, other)\n",
      " |  \n",
      " |  __ge__(self, other)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattr__ lambda self, name\n",
      " |  \n",
      " |  __getitem__(self, idx)\n",
      " |      Get an pixel value or a sliced image.\n",
      " |      \n",
      " |      This operator implements basic indexing where idx is\n",
      " |      arguments or a squence of integers the same dimension as\n",
      " |      the image. The result will be a pixel value from that\n",
      " |      index.\n",
      " |      \n",
      " |      Multi-dimension extended slice based indexing is also\n",
      " |      implemented. The return is a copy of a new image. The\n",
      " |      standard sliced based indices are supported including\n",
      " |      negative indices, to indicate location relative to the\n",
      " |      end, along with negative step sized to indicate reversing\n",
      " |      of direction.\n",
      " |      \n",
      " |      If the length of idx is less than the number of dimension\n",
      " |      of the image it will be padded with the defaults slice\n",
      " |      \":\".\n",
      " |      \n",
      " |      A 2D image can be extracted from a 3D image by providing\n",
      " |      one argument being an integer instead of a slice.\n",
      " |  \n",
      " |  __gt__(self, other)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __iadd__(self, other)\n",
      " |      # NOTE: the __i*__ methods are not implemented because there\n",
      " |      # currently in no way to make the underlying filters run\n",
      " |      # inplace. But python will implement a default version based\n",
      " |      # on the standard method\n",
      " |  \n",
      " |  __init__(self, *args)\n",
      " |      __init__(itk::simple::Image self) -> Image\n",
      " |      __init__(itk::simple::Image self, Image img) -> Image\n",
      " |      __init__(itk::simple::Image self, unsigned int width, unsigned int height, itk::simple::PixelIDValueEnum valueEnum) -> Image\n",
      " |      __init__(itk::simple::Image self, unsigned int width, unsigned int height, unsigned int depth, itk::simple::PixelIDValueEnum valueEnum) -> Image\n",
      " |      __init__(itk::simple::Image self, VectorUInt32 size, itk::simple::PixelIDValueEnum valueEnum, unsigned int numberOfComponents=0) -> Image\n",
      " |  \n",
      " |  __invert__(self)\n",
      " |  \n",
      " |  __iter__(self)\n",
      " |  \n",
      " |  __le__(self, other)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self)\n",
      " |  \n",
      " |  __lt__(self, other)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __mod__(self, other)\n",
      " |  \n",
      " |  __mul__(self, other)\n",
      " |  \n",
      " |  __ne__(self, other)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __neg__(self)\n",
      " |  \n",
      " |  __or__(self, other)\n",
      " |  \n",
      " |  __pos__(self)\n",
      " |  \n",
      " |  __pow__(self, other)\n",
      " |  \n",
      " |  __radd__(self, other)\n",
      " |  \n",
      " |  __rand__(self, other)\n",
      " |  \n",
      " |  __rdiv__(self, other)\n",
      " |  \n",
      " |  __repr__ = _swig_repr(self)\n",
      " |  \n",
      " |  __rfloordiv__(self, other)\n",
      " |  \n",
      " |  __rmul__(self, other)\n",
      " |  \n",
      " |  __ror__(self, other)\n",
      " |  \n",
      " |  __rpow__(self, other)\n",
      " |  \n",
      " |  __rsub__(self, other)\n",
      " |  \n",
      " |  __rtruediv__(self, other)\n",
      " |  \n",
      " |  __rxor__(self, other)\n",
      " |  \n",
      " |  __setattr__ lambda self, name, value\n",
      " |  \n",
      " |  __setitem__(self, idx, value)\n",
      " |      Sets the pixel value at index idx to value.\n",
      " |      \n",
      " |      The dimension of idx should match that of the image.\n",
      " |  \n",
      " |  __str__(self)\n",
      " |      __str__(Image self) -> std::string\n",
      " |  \n",
      " |  __sub__(self, other)\n",
      " |  \n",
      " |  __truediv__(self, other)\n",
      " |  \n",
      " |  __xor__(self, other)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __swig_destroy__ = delete_Image(...)\n",
      " |      delete_Image(Image self)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __hash__ = None\n",
      " |  \n",
      " |  __swig_getmethods__ = {}\n",
      " |  \n",
      " |  __swig_setmethods__ = {}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(sitk.Image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Attributes\n",
    "\n",
    "An image object has a string casting method. This can be used with the print() python built-in function. With this all the stored information of the image is shown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image (0x55e4486784c0)\n",
      "  RTTI typeinfo:   itk::Image<float, 2u>\n",
      "  Reference Count: 1\n",
      "  Modified Time: 779\n",
      "  Debug: Off\n",
      "  Object Name: \n",
      "  Observers: \n",
      "    none\n",
      "  Source: (none)\n",
      "  Source output name: (none)\n",
      "  Release Data: Off\n",
      "  Data Released: False\n",
      "  Global Release Data: Off\n",
      "  PipelineMTime: 0\n",
      "  UpdateMTime: 0\n",
      "  RealTimeStamp: 0 seconds \n",
      "  LargestPossibleRegion: \n",
      "    Dimension: 2\n",
      "    Index: [0, 0]\n",
      "    Size: [400, 300]\n",
      "  BufferedRegion: \n",
      "    Dimension: 2\n",
      "    Index: [0, 0]\n",
      "    Size: [400, 300]\n",
      "  RequestedRegion: \n",
      "    Dimension: 2\n",
      "    Index: [0, 0]\n",
      "    Size: [400, 300]\n",
      "  Spacing: [1, 1]\n",
      "  Origin: [0, 0]\n",
      "  Direction: \n",
      "1 0\n",
      "0 1\n",
      "\n",
      "  IndexToPointMatrix: \n",
      "1 0\n",
      "0 1\n",
      "\n",
      "  PointToIndexMatrix: \n",
      "1 0\n",
      "0 1\n",
      "\n",
      "  Inverse Direction: \n",
      "1 0\n",
      "0 1\n",
      "\n",
      "  PixelContainer: \n",
      "    ImportImageContainer (0x55e4493ed220)\n",
      "      RTTI typeinfo:   itk::ImportImageContainer<unsigned long, float>\n",
      "      Reference Count: 1\n",
      "      Modified Time: 780\n",
      "      Debug: Off\n",
      "      Object Name: \n",
      "      Observers: \n",
      "        none\n",
      "      Pointer: 0x55e4494b8100\n",
      "      Container manages memory: true\n",
      "      Size: 120000\n",
      "      Capacity: 120000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(image_2d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Image Class has several methods to write or read its attributes.\n",
    "\n",
    "The following methods are used to get the image size and length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "400\n",
      "300\n",
      "(400, 300)\n",
      "(1.0, 1.0)\n"
     ]
    }
   ],
   "source": [
    "print(image_2d.GetDimension()) # return integer with dimension\n",
    "print(image_2d.GetWidth())     # return the width\n",
    "print(image_2d.GetHeight())    # return the height\n",
    "print(image_2d.GetSize())      # return tuple with pixel size (width, depth)\n",
    "print(image_2d.GetSpacing())   # return tuple with length per pixel. Typical units [mm/pixel]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following methods are used to get the image information related to the channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "vector of 16-bit unsigned integer\n",
      "15\n"
     ]
    }
   ],
   "source": [
    "print(image_rgb.GetNumberOfComponentsPerPixel()) # return integer with number of channels\n",
    "print(image_rgb.GetPixelIDTypeAsString())        # return pixel type as string\n",
    "print(image_rgb.GetPixelIDValue())               # return integer with pixel coded value (ITK enum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next function is declared to read the main attributes of any image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_info( image ):\n",
    "    # Return a string with image information details. Pixel type, dimensions, scale.\n",
    "    # Input: sitk.Image\n",
    "    # Output: string\n",
    "    info = '\\n===== Image Information ====='\n",
    "    info += '\\nPixel type: \\t\\t' + str(image.GetPixelIDTypeAsString())\n",
    "    info += '\\nPixel channels: \\t' + str(image.GetNumberOfComponentsPerPixel()) # channels\n",
    "    info += '\\nDimensions: \\t\\t' + str(image.GetDimension())\n",
    "    info += '\\nSize: \\t\\t\\t' + str(image.GetSize())\n",
    "    info += '\\nLength (mm): \\t\\t' + str(image.GetSpacing())\n",
    "    info += '\\nTotal Elements: \\t' + str(image.GetNumberOfPixels())\n",
    "    info += '\\n'\n",
    "    return info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function can be called to summarize the information of an image. Observe the difference between the single channel and the multichannel 2D images.\n",
    "\n",
    "<b>Note:</b> This function is stored in a local file called [local_utils.py](./local_utils.py) for further usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===== Image Information =====\n",
      "Pixel type: \t\t32-bit float\n",
      "Pixel channels: \t1\n",
      "Dimensions: \t\t2\n",
      "Size: \t\t\t(400, 300)\n",
      "Length (mm): \t\t(1.0, 1.0)\n",
      "Total Elements: \t120000\n",
      "\n",
      "\n",
      "===== Image Information =====\n",
      "Pixel type: \t\tvector of 16-bit unsigned integer\n",
      "Pixel channels: \t3\n",
      "Dimensions: \t\t2\n",
      "Size: \t\t\t(256, 128)\n",
      "Length (mm): \t\t(1.0, 1.0)\n",
      "Total Elements: \t32768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(image_info(image_2d))\n",
    "print(image_info(image_rgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a 2D Image\n",
    "\n",
    "\n",
    "The method to read images with SimpleITK is: <b>sitk.ReadImage(...)</b>\n",
    "\n",
    "SimpleITK support different image formats such as: jpg, png, tiff, among others.\n",
    "\n",
    "An example of reading images is presented above. The images are found in the local folder [images/](./images/)\n",
    "\n",
    "Credits:\n",
    "* thorax_ct.jpg:  [www.klinikum.uni-heidelberg.de](https://www.klinikum.uni-heidelberg.de/fileadmin/radiologie/radiodiagnostik/Fallsammlungen/cases/Fall20/inhalt17.html)\n",
    "* brain.tif:      http://slicer.kitware.com/midas3/item/241320\n",
    "* simpleitk.png:  https://itk.org/Wiki/SimpleITK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "prefix = './images/'\n",
    "# prefix = \"/content/registration_tutorial/images/\"\n",
    "\n",
    "image_jpg = sitk.ReadImage(prefix + 'thorax_ct.jpg')  # www.klinikum.uni-heidelberg.de\n",
    "image_tif = sitk.ReadImage(prefix + 'brain.tif')      # http://slicer.kitware.com/midas3/item/241320\n",
    "image_png = sitk.ReadImage(prefix + 'simpleitk.png')  # https://itk.org/Wiki/SimpleITK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, it comes usefull the function image_info() to print the attributes of the 2D images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===== Image Information =====\n",
      "Pixel type: \t\t8-bit unsigned integer\n",
      "Pixel channels: \t1\n",
      "Dimensions: \t\t2\n",
      "Size: \t\t\t(381, 259)\n",
      "Length (mm): \t\t(0.26458333333333334, 0.26458333333333334)\n",
      "Total Elements: \t98679\n",
      "\n",
      "\n",
      "===== Image Information =====\n",
      "Pixel type: \t\t16-bit unsigned integer\n",
      "Pixel channels: \t1\n",
      "Dimensions: \t\t2\n",
      "Size: \t\t\t(221, 257)\n",
      "Length (mm): \t\t(1.0, 1.0)\n",
      "Total Elements: \t56797\n",
      "\n",
      "\n",
      "===== Image Information =====\n",
      "Pixel type: \t\tvector of 8-bit unsigned integer\n",
      "Pixel channels: \t4\n",
      "Dimensions: \t\t2\n",
      "Size: \t\t\t(300, 300)\n",
      "Length (mm): \t\t(1.0, 1.0)\n",
      "Total Elements: \t90000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(image_info(image_jpg))\n",
    "print(image_info(image_tif))\n",
    "print(image_info(image_png))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing Pixels\n",
    "\n",
    "There are the member functions ``GetPixel`` and ``SetPixel`` which provides an ITK-like interface for pixel access."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(image_jpg.GetPixel(0, 0))\n",
    "image_jpg.SetPixel(0, 0, 1)\n",
    "print(image_jpg.GetPixel(0, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pixel values can also be accesed with array index. In this case the sintax is: sitk.Image[x,y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "44\n"
     ]
    }
   ],
   "source": [
    "print(image_jpg[0,0]) # get pixel\n",
    "image_jpg[0,0] = 44   # set pixel\n",
    "print(image_jpg[0,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy Conversion\n",
    "\n",
    "There is a built-in interface of SimpleITK and NumPy. NumPy is a well-known Python library that support multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. \n",
    "\n",
    "Several mathematical and image processing libraries support numpy arrays. Therefore a good way to integrate SimpleITK to other libraries is through numpy.\n",
    "\n",
    "A numpy array can be obtained with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_npa = sitk.GetArrayFromImage(image_jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here, the numpy array can be used.\n",
    "\n",
    "The function sitk.GetArrayFromImage( ) creates a full copy of the array representing the image.\n",
    "\n",
    "The resulting numpy array is inverted (transposed) with regards to the image in SimpleITK. The numpy attributes can be verified with:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8\n",
      "(259, 381)\n",
      "98679\n"
     ]
    }
   ],
   "source": [
    "print(image_npa.dtype)\n",
    "print(image_npa.shape)\n",
    "print(image_npa.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A numpy array can be converted to an sitk.Image. The opposite method is sitk.GetImageFromArray().\n",
    "\n",
    "In the following example a numpy array with zeros is created, and next transformed to sitk.Image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "npa = np.zeros((100,200), dtype=np.int8)\n",
    "image_itk = sitk.GetImageFromArray(npa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the attributes of this new image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===== Image Information =====\n",
      "Pixel type: \t\t8-bit signed integer\n",
      "Pixel channels: \t1\n",
      "Dimensions: \t\t2\n",
      "Size: \t\t\t(200, 100)\n",
      "Length (mm): \t\t(1.0, 1.0)\n",
      "Total Elements: \t20000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(image_info(image_itk))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot  the image\n",
    "\n",
    "Transforming sitk.Image to NumPy also enable plotting with Matplotlib. Matplotlib provides an object-oriented API for embedding plots into applications.\n",
    "\n",
    "The function imshow_2d() is defined to plot a sitk.Image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow_2d(image_itk, title = '', show = True, axis = False):\n",
    "    # Function to show a 2D image with matplotlib\n",
    "    # Inputs: sitk.Image, string with title\n",
    "    # Output: None\n",
    "    channels = image_itk.GetNumberOfComponentsPerPixel() # get the number of channels\n",
    "    image_npa = sitk.GetArrayFromImage(image_itk) # get a copy as numpy array with the image data\n",
    "    if channels == 1: # set the color map according to the channels\n",
    "        plt.imshow(image_npa, cmap = plt.cm.gray) # 1 channel for monochrome image\n",
    "    else:\n",
    "        plt.imshow(image_npa)   # default color map of pyplot\n",
    "    plt.title(title)\n",
    "    if not axis: plt.axis('off') # disable to see axis\n",
    "    if show: plt.show()         # stop show, useful when subplotting\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note:</b> This function is stored in a local file called [local_utils.py](./local_utils.py) for further usage.\n",
    "\n",
    "Now, the previous images can be plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow_2d(image_jpg, 'thorax_ct.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow_2d(image_tif, 'brain.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VaRkTofKapKIGovnapjo3L5JgOmMVsMOHAL+TY6K1vTM1vPrSbsEMcCGQMThSGfvLsDiY4OBC9JyX229p8AziTW9Ny56n/c0rn9V2PnAN5wKWJaAbHOkMR8YQMAynD2f8nNv9EUkgM4zWDX+5faBr0L7j1bkSNOQ0E5F98wr4PNw+5pWvfyBYAowLWIwjnRFIahypNEdGZ2CWAGM983AkJ2SYaNvwwIpCPgsZJnK2Kud6Wkwgo1vwFY8syH2dCW8Jtb94xmYYYQD2/WRYHKkMQ0vERGOHiR0tBrY3GdjWZKC1myGSZDB0Zwmc/RwEA4tvGV3IOQeLISssuFKCuMbAWU6jtm8Y5x8yMZgdq5Z9nufsqntrGjciownYI2NJCIG0bqG4avJbpVUT896kmci2+RIYssKGhJ2f4lUAvWPd6Mjb12ymTAeYE33aGWFoCzO0hjliKQ5dF7kHSzhqBkHAoyjQW1+/KHseq/n5ZQQO3eBIpjm64xxtYYa2sIVw0l4ycL7n8lUigiIriO1+dfFA1yHZ8cliWZi2Jpd7UAGPBBSVVKHzlfPWse41cxkDNAOIJhmauy00dVpo7LQQTnCkdAYBsseRc3vb44hvf+TmQj4Pj0eG5Kyq7Fh9wLIALcMQqpgwoO0o3flRdeSd727m0Q1TiWxNUbeApMbQHuHY0WRic4OBzfUGNjcY2NJooq7VRFuEIaYxsF7XkQiQCdj54jVPFDL2wWDILkNmHHvJta8/8uPbR1dYKAvJ8KlkZ37C0bEJkFgarQ9PERQYExtx8sul/+o5o/VvrBDJhhJRbCv3cPJPdFOgK6phzpHnfzdf23DTutFI7pqqqhxCSMi+SmUSUMtGIfza2Y2Cc2iWQF2LhfaYhZ3NJtK6/fqaMtqLscMVVFcoKA/JzgNir/clEkDX6xcmNwXDyU133GIxgVSSY2uzhY6ohXCcIZq04PVKGFYk47hZAZQGJXg9Pe5GAUCSJEg8NfB12LnyP4IiAcAOrwfs/BiSCL7yCdA635ttmgK72010RDnaIxbq2w0YFgcJYMJIL2qGezBrIuBTJRCzk/Fs7QQwIxtrNj516UvTT737hHxjiDS8WxvwenLLBnKWIqkMQ3csg/knDxzmHX75qy2KRCAJYMyOz9jVYqKh00JTh4Gw5m05bM6xLx41Z8k/R46f81Yi0jT+0f+99u8t3dGKQ8b5i0uDMjxKbj0JiQiJ+hdO2/j8TX+cftJP+81cHgyGrLCYc/zlv3v+H7+8vStuObkeMiTK3nS5JAEQWUBqd0nnaxe+VTzz+mXe8kNjn/WcZqp5GqwEiALoUcE5BAe8wRFQvcWd+drG2z8+UYIBmXqltMO+yZnWDsYYMqZANM2xq81Ae9jC7tY0ysrKEPAH0NTVhaJAAKVBARG0J5cNRCMisOTu8tTme25hnCOeYuiKc+xo1tERtRBNGKisHAGdG2jpjiOV8SHoJUdY9AhYwTmIZMTat/tLhtdq+S9EtFqW7bZ7GNgJMMIfwzA5EimO+nYLLd0m2iMWgiXDXxw/avxuWZb4zk2rjvF6aLo+RoVHFlBkybapZOU8N5GJ1s3p77PgRnSEJMv28gm2y5RzAd1gUPyVUP1l/bpNm1+54nkp60VzYjM0k6MlbKGx3UA4xeO/ebR51J6t5nTedMSpEx77zamPaOH3j2VcVMqiR70XArCMDGLN67/c37kHiyErLABg1oKvPty86cGzZBIoCkhQJOSMgraCLnLfzfbV88Ovb4p6a77ym/LDf5hXA+gPpnXWKEq2hgUAELiwc1FC5TUxxduPvaJj8yIZBnqsiz0IbmfGxpIcrWGG+nYDsTTh5//3fsWIsdPDAPDiX65+on7tA6dVlxWBc25b5vdwGQOMZcAZoTPO0dRlorHTwIjRtTjvymsvmH3c+X8FgM0fPLms5dVvP2uG7ECmrLDJqtNCAJybKoC8wkIRes770BuCnaqf1ATaIwy72wx0RM2Ns4449vVLb3r6quxxG995+Mjn7r/qD7opjgz4JAiyzZzZz0uSCdxM9muhTEd2HWEbNh0h43zTMgz+0lGa6i/Km2navvZ3Nxrtby8NKJKzdCVYlkAiDTR36rCkUMPVtzx4XL72Z1z31Nee/dW0jyXKVFKPawkcgGVZSMbDB6TtYkgLi2Urbjv7P878y1n17QY0XaCyREZRUELIJ0GSbYnvLBhsVd0MQ9/+p+uadz58nTp66UOV839dcNBWw7q/rLDiO6cVeZSs9g8hBCxmq741M0+/tXR4/pyQVNPrK4qlDCQoOWMsAFvYWAK72y3UtZnY3W7i1gc/7fI78Ru/P33DiFH/1bHm1zcIUZyLKXFcDLAsgbTBEY4zvLMpCa4Ox++fbPtUP4cccdrK1lU/gGkmwXhPDQg7+QrQTY7eykJfyDwOScIedg8hACY40rrAhjod9a0mrvyv1cHK0Yek924//eiz3p9+9FnzVv92tAj6JNhail06gHMBiSSYqbxyF/H2zSXdO164cphjXRaOd4VxQjiWwfiFy35XNXZma19t61/57j+s5qfP8ioWuKCcoTqS4mhoN3H6d/4yt3bW8rX9XwHg0NknPCl1Pj1dcAGSHK2CCYTjHBHO5IHaDwZD1sCZ5YKrfntBe5hhV5uJ3Z0mOqIMmsHBLNuQZ1ev6vkCBIglodc/dm7L00vbCz2PHmuaBjOGbEUmCGFHC3IOwxKoGD3zhf7ay9AhS5Sr1el0YavOJkdb2EJrN0N1zYy8Zf3Khk96j+CY4EEQvOe7aXEk0gxdUQYpMApfWfGTvJmvXn/QFhI5bcJ5MwrAsARAMsvXFgAIBiRyKlM5/zEhYFoCyTRDe7cF7il/uS9B0RtmWQA4OLPnxB2PBhcCnOcfgqVHq41Yw4ScscCZA2McmiFQVn3o6/na6h3vniFDd4zM2XNxZAyOZDqDQgQFAIjEJ7PtnCS7j2yJgpgmAG9p3ozlwWTIC4s5x57/1/95Okrd5gi8/pGGl9alsHabji2N9rpf0x0XnUPWzUpCQCS3V+1++CgR3vrYgDEZWuf6ZTJPQZKlnGbAhXODJHQEikds76+9RyThUexFEZDVSjjSOkdrhKGuQ8ElN78w5sqfv1aUrw9JIubzZJdAPf1wDnTHGepaTWxtEfjB/2ykGQvOyVt8x+uR4ZF76lUCduRjxuCIJ02UV+evQ9q65YWFXqUnMCm7BNANjs4ow44WE0efeuPpP7pnW17jZBZb8KFHgDouWMYFdMPM2y7RuvYU0ptAUk98BeMCuilQPGKWFiob02ecSKzt4yo50yArsm0g5jx7TkJAMTFvyYUDGkVTOx9b2PjQIZvUzI7lcPpgDAjH7eVjY7QI85Ze9fWB+hkMhrywyPKTuzfQkuXfeLIjBuxoMbGr1XbTJTT7zZ99KIgo9wUhwNONiHx85wMD9c/1yGiPIu1xwTm3b9K0ZqJsRD8GQQASrNyLMOtuZFwgneGIJhmOWXbJ70aMm9OvUc5MtdWqquwEaPXElVgciKc5OqMGykb1rX73hkQmZwjuPRaTAWkt/0MK2G5Toj1dpkLY9S4TaY5w3ML8k699cqAxALarsbfAysoezgEu8t/aqa5t81XF1v175mB/FqHKCWuqamb1eQ26tvzz+4oig6jn5ZENaA2oFsbMOud/+xtvuuGl6d3v/OefieuHEmwBazL7M2wLMzR2MIQqJrbWzl5+QNb8HNI2i7356mW/P33Wgq/O/tUNp69LpBniaQV+lTCsREZlCdnLgF6qK4igKCr0+I7QQH1zI1wiSz0PqW3cJBimGPABAwCJGIh6L2XJudEEwgmGr5w5cG5GsnPLQlV2EiEomzxH4JwhkjBB3krMOubMgStDmSlInp4EPHu9L5wCt7zfplp4+7wisiNBAUezEbZWEk0xxNIoeFsFWbZPby+JYMdbcJGrmZoPPVI3W1UU21jhPPiMA4YJlI455M187TId65cV0Z5enKygCpSPA/GM3whvKhacSUJwmWc6q5nWMdJM7J6c3v7gZcJKFhNJYwUEOAM0w6mOlhFo6DTR3GXikh/9cECNarBwhcVeTJh+3Pq7nokQANz/y/PWvbn++dkjyxUcNdWLkpAMr4cg9fJ1EQGKLOHjx87/4LAz/npEvn5FqglKScAxbNoPiWEKxFIWhHdEv2Pa9e4931Gdilo99gH7TdwWtmDI1QXNLbJj5WmVRaoTd2a/hk1uB3CleAWOOuX6i2cfd+F9A/Uj8zhkOdjLxezMJaGjdPScfiMf9a4Pl5V4s4IKueVYR5ShuUP/8OIfv1bwwxJQZSiSEy4iwbEhAMkMgyFK8rZLt71TU1QR7PGEADBNgUTKwvjx8/pcfm195ZbbzMimqRTs+QycqUMAYMlOtL54wY6c/9aJocn+3WICjAEZk0E3BNKmQGu3hUjSNirHzGKcds63bx09ZcEBWxzYFRb9cOH3H5zz44sPFa2RdkTTAl4vd8rgOTEOzvtRIoIea5zWsWv1tKoJx/T5YWfbZTVYgp0Kn9EtBMtr+o3dSHVtWRiUkdMIhBAQnJDRBZIaw6zF5/5ioLnEWz+qZpl2SCXlts3F0ZEEFzAswFcyrrUQQQEAnl6VsMlxPzIOaLqF4lG1eTN1Wz55dglxLWdbyMI4kNIYRoyfu6Zy7Mz8boy9UBQZjpKWU5Y4t+0fFrx528nE94gxsccgkDEYvL6+Y13S4a0LYCYAUnPnyub2WEwgrdvCQDgRuUQAYxxcECxuaxAmA2IphrTBkUgLtIUtJNICupBx3U/u/FLt7AM7+9QVFgNw872fEADcc02FUBU/VNkJEc7FCNukI/X+pk3PX1M14ZhPZT3uXvPni3yq4kQC2HABpDNAOJrG9AVf7Vf1TzWvPivkkxyjYFYz4YglLUih8Zh38sDVnOrX3P+HomAAzlIdttfALjCcSFmYsegbBaWYd+58Y7aqepwXqKP2g5BKW4jEdcyce/YN+dp2bH3mP2VmJ1tlLwQTgGFwdEb1DedcdeeVhYwBAMK7V431qgpy4RqOoZYxIJJgwLCaPtPLY+2flAQDvlwyWtajk9I5wvEM8tkrjMj2eUHFFq52ch5yy6dwQuCjXRkkMxymRU4msW2PyNqldNP29hhcBkgFyX587fxv3/qlMz6djn+g4gqLAvH4SmCaaXDRYzfo/eCblgEjk6jsq60Rb5wmOdvi9TbGmRaHZhHKq6flfaN07Fo9TRIGsmp79jsX9h4jI6ccUVA9inTnJwv9HimXdSpgq/+mJaDpDIccnd/70RsrEx4tkZSLE8kuqTImhxyoguoryRuFasR3z/AJ067pkL0WzlyCpWMbSofXFlx7NFq/+hhbePYENQnYyyqDe1GeZ68PpserZDm7oVCvdqZAxsyfrMrNNCQla2lBLurTZEBC42juFoinBQyTI1tUUQjJ0bwkSIoPkuzB4UfO3TDlsHmPLTr14Kve7gqLApl+9Fl/jmy8ewV6q7e9H3ydIZPJ9GnoNJKtU/1Sz1Oa9VxmDBO+4Ah4fEX9hHlvWiJB74nZzK6Rma32HzJpXkE1JDKRHVVFIQW9y8dxbgd09eNl/BTpyM65uS6c6EXOBTIZE8GyyZrHl7/ArTBi1T0Ztz1xIqYlUDHqsAHT2nuTbH5rQXFvyUeOd8EUMBFA5ehpr/fVjmUio2Wpx2ZCjl1BNxg8war8J2Q6JA/tsXzKfg5pzUDN1KPrU5qlMgbIisQkSWalpRXN/mCou6S8ctfUmcffM3bqgWuPKARXWBTI2EMW/Vnf+j8rpF5LASCrVQjEdRkeKfSpEOFEV52sd29aHAAco1fPOjcc0zHq0CUvjBif3+XZve25qz0sAUCy/fpw1uUmh0ElOHT+BX8uZPweHoUsB3PqOmALilSGwZTyGwN7E2n+qDq88+XLh4kedVzAjkPpjmqYeuQpv64c3feGPFte+++bJTMM1dPrje64Kw1LYPaXL+/X7dibxtW/PIci738bQb/dD5yHVhfojllQymZsmnP8JXf01bbtk39+PyBRLtM0a28Jx3UMn3TMqnznNPU44FPARXbe9jVgjEGSvbjkpqfHFTr+gxU3zqJAjETzNL8qQd7ritnLCcAUXniDwz5Vf9PUwqO5HvEYkwhMAAAgAElEQVQDvX36zo5Xmonykf0XheVa+wQFe7ojBey6C8WVkwreKcvjkT81dosBusnApUBBfVh6dIQRa6hyBpHTDCwOZCygbGTfO7ABQDqyYx7xTK8Yj2zNUwGZBKprFzYUOpfY9sdP9/my+3yIXKi0bnHE0xZG1x7e59Is1v5JiRapm53VqrKeGC4E0oaF0uFT8rpNGePge32GttuWw+cf0HP+hcAVFgWid374Zb9PtROwctGC9kOrmwIGAigZVvOpcnhWJjqC69Ee7wN6RQxaEsqqD81vr9j55gwS2qcCoCwmYFpA+ajCN+xVFaWnbkOvsWcMDijFBdkKLC1SzdIdPSuZrJGSA55gFbyB0rZ8bY1o3WziGccy3PMlSwSPLPI16xNK75jUEy5uY4eKC4QTHNPn9W0wZnq8yojVV1GvJDZbQwBMS0bpiKl5NQvLYhC8t8XC/qbIBK9X3afxH6wMGWGx8/HTt8W2PXLGZ21v7n5omdfrc1yGjnbguMKaOk0Eq+e/dtgx5z20d7to49vnKiKFbFk4IZxS84ZAxbh5sUBpdd6isC0bHr9JZjF4sgVls+0tIKlxzDnhigsLGXuifWO5x5M1zDrj50BaZ9AsH0pHz15ZSD+dW1f+R8DXE/kIR8PRDY6RtV96bdSko/KGrJPeXuXzSE44RK9KXwIIeGXE616cXMgYGl++9lqvPzhHIuTS0g3LzlLd2ZJB2KxCefXkPqNhI/XvnEt6K7ISUwgBwQDT5CgdOTtZVJ5/A2RNt6NMswZdW6sA/KqMEG8sZOgHPUNCWGx++ee/MjreqW195ZJHG5/7+gf72r57/e9v8HhDjlbQE95sMSCa5GgP65g8c/E9e7dLdNXJ6e5dRyiy1PMaRtZmAZRW1b47bOTUvHkUmeiuuZJTS7M3tjbDUVrVf4h4lq7698+QnMSnnKBzbC2cfCgfPnHAKuaR5o+qtejuOYos9Qg9YdsKTCZQNvKQvG9lACCWdtLi0aNh9fwVbW/+523ptjXD+p3HR3efmNz+t6878VcgOEVnmEB3nCGcIpRVT8v7wCc6tiz0eDw5oSuEnRbOOBCqmPDB8HGz89qOMpaMjMl76qY6QoOI4FMLKvl50POFN3B2N39S0rTuvu/VlKuQJQ/M1lVzt91dKQAZ/lHHbqic98MTJE+wWy2ZkAsQNqLb/UzrqknUv3x5ZP1vrpHVUI/LUQCGZQf+RFMc67ZrYN6RmLXo01qFFtk1N960en5lwONoBvbjYZgCSd3CpLln/ri/sfN0Y40qi1wZPe6klWsmRyRR+A6HzRsevqVKyroZnb4FEEtZYOpwNnfp1QMGdWWiu+am29aUh8qyRkUBcHsD4UTawiET5n9q/r2RrBjIq6LHBew4MZyfJTNycvPTp3QK8r5Z85Unv+KtnJULzko3vzW68amTn5Y9oVmq149sSUDdENB0gc64hU8aGcbPXv72N7775wV9nb+7/r3aaN0rSyoCau8gSzCLI21wjJ23/Lf9jT9mFsMTjqIsKEOWkYvvIAh4/SE0vXzF85Vzrj7bW37IgMWR9MiWkBHetmDnyzc+39CwE6g6vv7Uaw58A+kXXli0bV91oWWynoIvEJBlnx0b0Pz6jIYn3mxXS6doavnUNyVJMZiZLtW7Pl5oxnaCiKB4i3Ixvdn4hnRGIJZm6IhyRNMeHDF/eZ8PipHqqhFaNyjQY0C07RVARudQ+qmMBQAS1+CEZ/TyINiuTi3Tbxb4HmQi26uobM+3H2cCpkVQSwrb+DfZuX2eKvMe+wkAxjksBmR0BtX/aU9QlljnDlWRKBuR3TMGx24rYCeFSbIHQliL6p9c/qy34rCNntDIZjPRNEbv+nCW7AnNyqaFCyIIJ2oynGBoi3D4Ssfjy2d/75R8YzC17tFca4ccytby6Pk8DZPDGyjtN4HOksvRFe+CZnD4VAmyDMjOFhISAcltDy21EvWbqxf9arq3YvqnrkWma0OVlWyZEtvy4K8zHWvnmYlG6HoAXC6BkTH9/V/9A4MvvrBo/OS4dMaCYQhITk6C5LwWSFYhCQEzusVvhjcttdVKCSTJUNRAThvIei+Y4/noijN0xDhauk3UTDth0xmX9l0EJ9m1bb4iC2QNarnsRmaXb1P7iUkAAInr9p40TluQXZJft+yvQoi3f1LC0x0QpSW5eWejDy2oKC2vKSi+IdW1bb4nayQFclGM9o7zFqrGHJa3BkOs9eOluWvu/M72YHCYzP7Z6yEowv5sZMJ8o2v9fKNzLUASZEXdI5iNMQ7TAsJxCy0RhqYOE4vOXnF95ehPP6RZEh2bj/MoUi68u2cMgGEweP35jbMA4C8bv6G9Y9uMeJpBIoIKR7twXKmypwhm57rqpme+1u0pnbxL9pW2Eak6Z3qAm7FKrW3dBGHGIMleQPKA1BLoSQspHfBXVhTsCRpMvvDCYvv27fNad6bAmIISn4RQgBDySpCdh1CSARIKQIpdHs5R9YVlJwKZFofJBKIpjoQmEE0wfNKoI5WRUTtjUf0VP354er5zt215+RpV8sA0OJjzRuYAIkmOlq40ho3qX2U19BRM2QO71L79kDABdIQNlIyeV5DbdOtb99yreFSYpoDlPCcmE8gYAhrKceQRp99aSD/t219fXOpVbDuH81ZO63YSVGfM6rdt55bnrvY7dhunfhA4gM4YR1fUQsoAKktlFPsIAZ8En4dAkicnWLhlH8+5bVDtSjBEkwIb6jKIpWUcdvTpby88+Tu/7m8MzR8/c51HUWFmyw2Inj1C2rp1LBmf314BABfe8PeZ//df53/w/raVc6vLGYYVyagqtQvuSiQgO/cR1xNg7WsnCMKEbOJK7iVBRXbQmAWkdQvbmg10JP04+5zzry/kMxhsvvDCYvwhRz+86aP3rtnVaqE0KKE0KKGyWMCrEhQZ8Hoku1BvNmUbAOccjBMY50hlBDRToC3MEE0xdMcZNF6EOYuWvXb6Bdcv7+/crS27Ueazy8Vl17gWF+iMMURT/RvF6je+sDCu2W0VJxucc0C3BDqjJqYckj+VujfNuz5aShpB9fDcw2dYdi5EhgUxbtrA2yUCQEdHG1ARhG5yZO21CY2jK2aBKRX9tjUSjdP8RDnbS/ZhTWkcKV6E5u4oUrpARZGE0oBAeZGzCQ+JXP6JYdlzz+gCjZ0MXQmO7qSM6Ucuf+/i7/+pTztFb1oat4fK/ARLMCiOdqGZAokURzjZf1p9lhNOv/zC27735EZNV5DUbMEX9BI8CuDzSvZSSkKuGqhdKKmnboXFgGTGtrMk0gwNnQxKsBi1s7+c131+IPGFFxZLz/3RtQ31dXM+ePWhhUVBBSVBBfG0jIBXgk8Fgj4BRbIrRAO2uDAse/8I0xIIJxnSukBzt4V4ygCDD6d/45pfnHTOwMlbO+vbUVUeRMBrq6xC2Gp3a9iEKfe/41Xj9nWnNHQyBFQBRSZn6cChm0Bjp4GFE/oOPNqbrZs3h4IqR0KzckZa3RBIGwJUVnj5tqZOhgxj8HoYZKfCVCLF0RnTUTayNm+7WMd2vx3m3ZOlCthaQlIzMKJ22dtUoYU2vLtyRqxYQWlQgmbYpQBUp+QEZwIpA0hnONIZoK7NRCRp4qgTLlz5jf+4s1+BnWXH7nYMryyBXxV2sWAhkDLsEgFJPX+Gam9qph6zqapmJmvc+ZEcT1mwLB9CfoLfSyjyCcgKoMo9NT4sxsG5nXWa1u14nFiKI5nhSGocnXHC8qVfKSgC90DgCy8sAOCSG+5bdMkN9+Glh391y4aP1py2/uM3p+npODg3EfSpkGWCImcNdwTTYvabwGTweIsQKqvC0See9uejjjvt1tG18/otf9ebhlgp6ro0WGbaDskigiTJCJYMx/KvXdav+t/SEal+f7sFblkAGOwsBgmy4sG0w0/aNee4FQOmkzfvXD+6KRaAlsrA0hO2ZkEESVHhUf34zopvf7OQeXS318m7uoPY1q5DcAvZh8HrCyFYPBYnLbsu756wzRsevwmZDpAfe9gdTAZ0RjJYuPiKC06ceOSuVx697ea3Xn/mug0Nm0Nrt0eheiSoil3dhglAy1gwLI5AsATzT1jx5NLTvnVZ1dj8dpK9aUqWY3ckA8FMQHDbqOrxweMNYdYRx79daD8/vOMtBQBeeuS/b1756F03Jeo6YBkZ+H0KFIXgkbMBZ3ZkK2PcTt/PmJAlBSWVo1FUOlIbPXnimp/94L5FhZ73QGBIboz8zF9vua2hYdcR3R1tk6IdddW6ngIzTQjBIckKfP4QPL4QAsHy8KEz5qycOn3ew4fN/2pBb/LevPrE7Te2NO6am0gkqpjFZEmRmVf1Jo5ccOKdhfT3tz9c+2gymSw3LctLAFSPRw8VF3We++3f5C2muzcb33nilE0b3jmru6NjAudcBhErKS3pqKoauWXpOTcMqB1lee5vt97W3t4yVdO0IsaYLMsyGzlqzKbphy++e+Jhx+XdyHnNw5e+ZDSuXFIRouzeTXbGrMHxzhYT376zY4/12LN/u+W21a8+dV06EUFGi4NIgiwrKK0cg7JhozfVTp666isX7vsGPG88dcf3Gndvm59OpUsM0/QSEcrKylpHj5v89qKTr/jNvvYHAGte/ev5n2x4/4zW1qZpnU3bai0jDV1P5+IvZI8XiuKF6vOjZFhNfVlZRfORC47/49zF9rYKBxtDUli4/HuIdexQP3nqO40+fVdVUdAOiSbYsRlxjaHbt+jlk771vwdsGTmXPRkSyxCXwSHevnWRJKwqxRuApHic3xIADg4LVTWznhvM8bnsG66wcNlvpCMNMyQwKGoAktJzqwnBYELC6PFz9nlp5zJ4uMLCZb+RDu+Y5/OH4AtV9nibOMBhQGMJjJpUuLHYZfBxhYXL/oObqscbhKJ4c9GjQhbgGiFj5c2fczlAcYWFy35DEkJWvUWQVTUXyCUgYIGQ0odEwvMXCldYuOw3DJOdwv0lMCHDyIZ5c47upI6UVXRQ16McirjCwmW/EY5b4GDweGCnhDsbMLd2a/AUDd812ONz2TdcYeGyX3j3tSeXvfpBI/w+L3Kl97kAExz1jS341pU3FLShkcuBgyssXPYLw0eOX9/UFrF3bFMc+4QQsCyOI4865rFjTjijoM2PXQ4c3AhOl/3G2nffmB0Jd9Zo6WS54FxWvb5kcWlF0/xjl/Zbgs/lwMQVFv8iG9Z/MMHQdX9a00pMi8umxRTTgso4lwG7fKYsSYYig3kUyVJkiQUC/qTH40vOOvwId93uctDgCot9ZMe2LaG21uZpWkYPaAbUjoT8fCRpoalTR0pn0HQOzRBgws7MVGQJsiTg80jwqYSAV8LY4X6UBSRUBM2TvB5ifq+arh45ZkPtlPzFe11cBhtXWBTIW2++Ojel6UWJDJWs3ak/0RrWEUkKRDMSOAMgK5AlgixLKA6qCPjsKtIZk4Fzp54F7JoWlmVBcAa/oqM0KGFkmReHjZVP93uY5veq6cVLXDXd5cDDFRZ5eO/tVTPiiWTFm1vMVzc3amiJSFA8CoaV+LF47kiMqy7GqGFBjKwKQlVkFAU98MgSWjqSuPaXT2H77g7ohgG/34OigBd+n2oX1mEMybQJWfGgZmQ1gj4fiov8SJtAS2cSWsZEdYmFQ0b5cNgo41R/IBBdcoIrPFwGH1dY9MGzzzy7pCGszHh/e+K2zS0cXlXF8vljcdikYRhfXYRRVUF4PQpUT08UYntYwwP/XIunXt2AlKahorwYVeVlIFkGhASPqsCvyvD5ZPhUgUQyjXVbm+BRVIwcVoGzTpqJsuIAtjXE8MxbdUhqJg4ZITB7ou/SMjXVOX3G7BdqJ00oaJ8QF5f9gSssHF54fuXi+m552isfxv5QH5YxuaYES44Yg1OPGYdQwJNLhNqbaELHbX9ehWdf+xjlZSUwuYLuBAMTBMbhVMciSCSBJIAkgs8jw+eRMXFkESaOKUJJUMKjr67HsJJSzJkyFjd+awHSGQuvrW3Cqo9asaU+guFBE8cc4rvYy8PaOed+eo+SoU7dzq0h0zRVLWMEZs7Kv9G0y2fHFRYAXn/1xfkPrUq+9XGDCZNJ+MGKOThq+giUFnkR8PUfinLRDx/BqjXbUVk1AgkNMLmAEJItJGQZBAJJBCJn526y95sgso2dQZ+KUZV+TB6jYtWHO2FaEm6+/CQcPWMkDJMhkTKxYUc3fvPQh2DMwMwaDw4bHr/g6PkLH5owbkzhm4ccxDQ11MmZTCZkGEaAcS5rmu43TEuNJbRqzbBCac0o6ehK3J/KWCgvCeCqS84aGluE/ZsZ8kFZLz63cvGmFix8b4eBscOLcObiSVi+YBy8qjxg2/qWGN58fzvKK8rRHecQRABJtpAggiRJIKeWXG6/CqJceWvDFLCYAU23MKJ8OJbNn45VH27Hff/8EIxxLJw9Gt5SGQtnVUM3GFa+vRvr6mKoHTHsgTdfe8U/4ZsXfmrLxIOR+rodqqZppZZlqaZlqWlN9xsm80cTWrWWMUIpzaxo707+IZnKIJHW0dGdRFIzEImloRscpsWQ0U1IsoJFcycN9nS+sAxZzeK9996Z2taZmHDT37ufHVYawGO/OAklQe/e24r2STyp47b7V+G+x96Fv6gSgiTIHhUgsmtGShKIYAsLAiRHk4CTddn7khPZnhJVkSBLhHGjipDROtEVs3DfTadjfHVJz848AFKaie/+/i3saIziomPM84hknHv2Vw/YZcmzL7y2ZGt9eH5LW/fN8WQaqbSOznAKWsZELKVDN+0y/FzIEM7O6lwQBHq+iAgkKQBJziZQCsgRytnfmXoaJAwcO3sk7vvVZa5msR8YkprFB++9W9vakZxw/2uxZyePLcf3L5iD0lBh5eAB4MW3d+CBJz+A3x8CJwmyLDtLDcnRKOylh0eRIGWFhQRnzwwC53YpfJ7dfAaAyTmEIOxsiuMrx0zAG+u34G/Pb8NVZ81ESUjNnTvo9+B7X5+N+5/dgtW7uh9cMFG74C8PPrriG+edeUCWlK9rScx+8JW2m2PRbghmgAsOwZktMMkLIch58HuEgZz9WZJBJAGwf84JC9v442zSYWtvAhKIabZQcdkvDMkr29oVn3D/q7Fnt7YYeOa2ozBuZPE+tb/jwdWwhARZ8tpvN+emzgoKWSbIkgTVY++JqZAEWXY0CyFgOOXhDRO2wOD2buAgAQlAXVsGs2tr8NZH9Zg1eRhOnDcWqqdnWTRhVDGuOmsGzv3RSwD8DywYl7jgc71AnysCgaIyWEoZOLcgmAVT18A5g4C98VGPtkA5IZAVCJKjbdjS1oFy/+v1O8nZbd5VKvYXQ24Z8srLL86/4u7OtyaPKcXvv7sIE0YW7VP7j7e348uXPwBfoAiSIkNSVBBJdqSmLEGSCQGvAq+HUBRQoaoKAn4/vF5Pbp/ORDwO3WRIaCYMwxYehskAZxMeWQbKi30QvBuMB/GtU2fhvJOmfGos3TEdtz20Hqs/bMWZ09p+cPVVVwy4G/pgsLOxS+3sDNd0haM1bR2dk7bsji0KZ3zndqW9iCc1JDULGcOyo9Y+I0Y6AmJpHHNYJe79xUWuxNgPDCnNYu0H79V+3GAtDvk9uOniuRhfvW+CIpE28OLb2+H1B0CyDCLHkOkYMyWJoEiSHU/hlVBWWgQFBrob1qJl90eQFB+Ky0ehZtpxyBgmLJ6CRBwwnM1omL00kTihO6ZhxrhS7GqL49W1DX0Ki4oSLy45dRrautIoG+5vvffPD11+8Ypz7/q8rtfnxcQxw4yJY4ZtB7AdwMvvfrj9lU8+2fra6i3G3X61GEo8hc4IA+PiXxIYLvuXIaNZrP3g3drHVrXd8I/ViYvW/eUslOyDjQKwKzy98UEdLrzxMXhD5bawkGTIsgyJCF5VgapK8KkyRlQUQaU0Hrv9G9C0FCTqWUIIAJxxlA0fi1O/fTc0TUcibaI7YcA0GRiz91uViDB7cgVi8U50xBhW3n4WQn61z7GZFseiyx/HhYuLoVrdl35zxbkHjZfk8WdeOWXVRx0rNjR7z4gmDRimhc9yS7qaxf5nyBRCbG6LTHhrs3YR4xzZDW/2hXTGxPqtbUhqDJRdU9uOUWfpQPDIErweCSpp2PXB49A0DZIk22tp58s+VkK4vQ6d9R/B71Ph9ypQZYJM0h6bB7eFNRQHg5DIRCqdP6TCo0goCnrx7jYD3NIH9vkeQHz15OOfXnBYxV+Lgj74Vdm27bgckAwZYbG11VzYEjZROyrk7Ji+b2i6iU07OiB7PDljmh1oZX9XZIJHIXg9Crp2r8HH7z4FSXK2P98bIiiKig/feAjpeDv8fi88sr1zOBHZhW2FQDSpQ5Y9UGSgNZyy1fQ8TBtXhm3NGpRQlbHPkxtkzjz1xCf9HgafV8kbKesy+AyZT2blmviNuq7h+vNnf6b23WEN67e0wRcI9WgWRCCZIMkE1SPB7/OgOKTi3ef+B7F4fMA+m7Z9gPdX3gWuReDzylAV21CadQAYBkc4wRHyerF+aztMM792seLkqSBZwZtb2L2faYKDzKRK4x6fV4HXM6TMaAcVQ0ZYxFICQZ+EmuqSz9Q+oRlIZxhkWcbe2oIEO4RbkSWQMJGMx+xYgQEgiRDprIeejkCWCJKzWgGQc7NmdAuyLCOWNNCPYoHqiiCKgx50RMzPNL/BxqfwBOe8J9LV5YBjyAgLgwkEVQklRfturwAAi3FYvCcqEwRIIOfhpmxFLJBgYMwqqE9JkqGn4zAzKXtJ43wBjlOA7HGDA+mMBd6P5S/oU+D3yIhrDHW7Gw8quwVg22jsZdbQMLgfjAwZYUEEGEbGjmf4DHgUGYpHcYKDyMn5cG5sghNFKCDLChTFg0K8TJwz+ELFUP1B9DwkPe2EsD0dusXhVeV+37om40hmLHDOMf4gTDBjnKsW4+D9qU8ug8qQERZeGUikdbR1Jz5T+6Dfg6DfEQK5ZzZb/speMgjOAcmD4tJyMDbw8yq4QEVVDXzBMjv8m9vRnL3ljOACjNkeHKkfDb0zoqE7lkHAexB/pK6cOKA5iO+sfWPScL6BkR93/u2Nz9R+WFkAc6ZWI2NwZKWFXSbPDtk2nSjMdMbColOvRFXVsAH7nHDoEZi//NtQAuXIGBwG47C4cPoVEIIgOEM6Y2LW5Ko9Qr735o+PbURXVMNRUwKfaX4uLgMxZEzPk0dIb3/SHJrxzkdNsEvp7ht+nwfTJlXi6bcbndQER2CQnUnKmC0wdNNCdc0czDnmK3jhyT85rsC9ziYELMvE4Yu/Dl/xcMSTBrIqOOdOkIXThHMLFhMYVRGC3I9qseqjZoQCEiZUShfv49SwZu3ampWvrfsPcjJij18w/Z4F8+e72wu67MGQERZTx/jfrNimXb61U4FlWfAo+zb1gFfBzNpKhPYuhsMBQQIWBwyTwyMLGOTHxDmnouLNfyISCTsBXDYEQHCOUWNrMWzsTCTTOnTDhMkEGEPOiGl/E2DMQsYEioL5x8uFQEe3hrmTAxheirp9mhiAJ1766KZ7n9hwEbgOAFh+/Nzf7msfLl98hoywOO+crz0E8Qj+JFU9eOZVd+Evv7wIJcWFq+ySJGHutJH49tdm4t5ntucMmBwCghMMk9tLEm5CjaTg98o45/pHoZCOWOsWtO7eBMXjRahsBEbULoCmM3RGktAyDJrOoOm2BsGYcPomCJZByrAwuroSxXmiTjXdxOW3PI85k7y47ISSRUtPKry475MrX1l21+Nb/rRxd7qqdEQtmJmBsHR8pnhrly88Q8ZmAQDzjpjx9BE1+n3vburGr+9dCS2zbzEJRQEVS44YC93Yy3gpAMY5TIvDsDjSuoVUxkIsnkJCA/zDZ2LqMd/ApHlnY9jEhYjEUogn0khlLKQNhozJwJwaF0L0uE3BGDIm4aSjavocj25YuOPv7+P19S04+fDQFZNrJ763L/N57JUdN21qMqqKi4sg+4oh+UogqSHXzujSJ0NGswCAiZOmJE84qunuZzf6Lvq/x9ZjVGUprjh/yT71MW5UCTK6AdXjA+C8hEmAc4DAYZiAptv1KiQy4TEtaBkDsiQ5VbIEMjqHbjGkdQbTEDAsAc44uFNJK4tucYwZUYFlC8b1OZZ/vLgRdz32Efz+IsyaPOz18RMnFRzqvWXr1tBbW9LzAj4fIHtByJYAdJJTXFz2YkgJCwBY/KXj37u24cFLn1g39cbfP1lfk0g/g0vOXoyykmBB7YuDXvzmmvm48a73QJICRaZcQJEQAHGGZFpAlglp3YTsJI5JRI6Hw66KxZiAYfakpXO+Z8k94hnMmzkOv77qGAwr9e0xhlgig789ux6/eXgrZkwo2v7N48uvmnvk0VsKvQb3PfjPS+59tvGPfn8IQlKdXBk3ctKlf4bUMiTLNy88757ls+i3U0aqu359/9u48TePwLIKj2NaevR4nHviJBjmnssY22YBWEzANDkMQyBj2DaJtG4h7XzXdQbD5LAse5vDvQUFuAHOOa49ZxYqSvYUFIxx3HrPq/j5n97FjDHS9guOK/3uqacsf2Ff5v/wa023NHSasiAll+Pi4jIQQ06zyHLZRef/7jLgdxf/4N43P271L5yw5Ef4+vKZuPCMhZgyYWS/bUtCXvz0sgVYcfI0nHj1U/DIMiTJjuAkAiwuQCBY3BZARD1qve3k6ImlyP3e8X4IxlBW7MO15x2BmbU9sRo7Gjrx4DPr8deVm1BaUYmvLqh88vc3nX/6vsx5zdr1Nfc+vuZ/6zpQVVRSbNe9zGkU7tLDpX+GpGbRm28sHX/dovHJ+0qG1+Kef27G1797H3Y3tiOVzgzYduKoUnxpdrVdR9P5Xfb5z2oK2aCt7Fd2KZJtkD1GQIAzC6rCcN35R+KMxRMAAOmMgYaWML75w0dwx6MfQ/aX4mvz1FsvXFZ7/b7Mc/PmLSWPvLD+lpc+jC+VPV4QefaluYvL0MU2vKMAACAASURBVNUsshx73JfWjBr1yXXN0fen1pceMr+hpRsXXH8/zjppNpbMPxRTJo6C1E8w1M+vXIA/PPwhHnppJyTJLuffW2D02Av3NBzu6Z0UADMQ8in4/+3deXxU1d0/8M85526zZrJC2AKBQNhFUBAERUAQxKK4lKrF2mqxpbWL1lb7WPXR2qf+6kqrUjdcWxRlEwVZBBXZQRYDJCGENWSdfbnb+f0xA1WLOJkEATnv1yu+AMnNuSHzmXPvOff77dapAFePLAbAsauqBivXV2D2ku044Gco6dwWvQqtFdeNH/hAp85dm1W3Ys776+99d53/BhsSQOVjD8AJQrrO+rAAgG4lvQIv/V+vYQAwb/788a+vDL47Z3UDnnrnXSSCh9CvJA933DwaHdvlIsvtQG72f2p35mc78cBPh+In3+uNfy/ZheUbD2HPoRBsnnoKlRx9MvW/p/mM2FBlghyvgunXDMY53XNwpC6Aq375Ij4rrwNUH9rmutDO69zx/JR2P75k1OhmLY0e9eysObfPfO/Ib9xuDzSn8vVFeQThBERYfMX3rrhiESULx+2oCly8M9s1YtuB4qFbqv246Z7Z6NbJhwE92uHKS89BfrYHLqeKHJ8biszQqW0W7vzh+bhmTBAPvrAOew+HUOdPwLY4km10kpcqhAOMcjBiI9cro32BGz06ZWHr57sx818HUba3CQnuRG52AQYU0dXnFMuL+pe0XTpy5KiMguLlf82f+sbyAw+73S6Aycn+GyIohAyIsDiOiRMvXzwRWLxy5YpB8z/af+d+f961FYd9KD8SxpY9FZj34W60z3ejuIMPFw7qirZ5XnRsmwNVkaGpEh64ZRA+39OAuR9WoKYhijp/HIFwApZlgYBDgg1GbUi2hNqaIMp2VWLPEQMupxPZvgKcW8hQXEBfvXpkl4fOGzw07SXRr3r9zYVTXlu8968Vh3SHx+cBPdqbQxAyIMLiBC66aOSGiy7CdQCu271re86Bg0dKaurDxZsqwhMbQ3yKP07x0uIDiMdN+MMx6IYJ07Rg6AY4OGiqkK/EGFRVAWMMkiRB0WRoEoPDYSHLwdGns/uNO3t457Qr8Ja3L2yzp3PXHuGWjv3t+e9PvGvm5687XS54s7JBqSyWSIUWEWGRpu49+jR279FnLYC1g8q2Laurb3ykwR8p2HskNiAQIW0PN5HbYzpgWkDMQGo3JgW4DUo4NBlQGOBQCdrlSI9mu+WazoXOzbk+z+GCvJwDxSU9A6011jnz35/08rvlf3O6nGBMBRFBIbQCERYZ6N6zb213oDb126Mbon51qsbzRf9+e9G1dz697d+awwlZc4tNV0KrEWHxHfLa7IU3vP5B5cMOhxNM0r4QFCIshJY76zdlfVc89+o7015eXPG3bXujHaisgUiyCAqhVYmZxRluZ1lZ1tylm37/9Pz9v9ecLri9eaCS+uWu44LQCkRYnME2bNhU9M7Sz+59b13tzQ6nG1QWNzOFk0eExRnspXnrn1q+uXFi1KBwejQQJoNA7M4UTg4RFt+CPZW7HaFw1BdPGO6EbjjqG4NF0Vgiq74xVBRLmFmhSDy3d7e2K66/btKr6Rzvzoeef33FxiNTgoYDTMmBy6GAsNZ4MIzD5nYqcAThy0RYtFBVZblDNwwlGI7mJnTTEYnGchqawh3CkXhuUzDWPhrXfeGonlPfFL42GtUR0w34Q4lU2wALts0RTSRw+/We6nS+3pvz3p+86NPDU8IJBmeWG5QqXyoI3CKEpAoKW2dcRzPh5BNh8TXenLto8o694Yvr6+qmhyNRNPgjiMUNJHQLoZgOy0pWxzI5g2VzWDZNbcQi4JykuqwzgFBQxsCYnGqo7AClXoAQUKcMcBtgMRAqf231nQP79rAHn17y7rqyurFNEUBz58HlUEFZZq0Yj4sAlEjgVMbdTy2tNPX54LaR7HXQbDzVnU3GuKHFD935y5v/2HoDPbm2f16es/zT7bfuP9z0sKXHcPQBQH7SLu2Sxz/aicbmBJomY+ywXueNvHj4hpP0RTMiwuJr1PqN4nnrwtOj/hBMIwHDMMBtK9WRTEGyvykDZRIIo2CKBIkwEEpAaLICFaHJsDj2a3wxRJBcsbBtMJsC9Pizgy1bthQuWVP58xWfNY5NGAyK5gSVtNTxWhMBpRRcVrHrUBNg2rBNM+ObpZxbIFzHSJM4WnmgJ1VctzzbqiIPr/08hHiwDpwb4NwG+MnqCJn8/hKaLERk2yYK87Mw8ZKBsZP0BTMmwuJrEMIsT1Y+NM0FwzBgGAnYpgnLTjU+5sk9DJRSgDAQmtoARUjymv/Yr1N7Hb7Y8vBLXwjH+qcez7bdhy6d92nTPRZxQ3UxUKYe+8FqdZSCcBmaKwe2ZYDb6TV4Ph5uGeBmrPUukb4thEPVNHhy2oLLbuiJePLNIhEBse3/9LfNWKrFFScAlUAoA6ESZFkFoRR6PA5LdkCSlWbVK/k2iLD4Gj+bevmjo4dVPV3XECyubwoVbdhxaFLYUG9JUA+aQiYCoRhCUR2BsJ5sDHSSqtIRwqzioo7o2Ikm64Qer50aR6rWp4FITEckpmfY+oNCUSR4c3xwaDJcmnTC/qpfP2ggnrBRXRMAVZUzqknzoP49qwf170nKK/c6tu06MLpiX+3gww1G6b5Q1uRgOI7apghsy27W95cj+d7BKIFTk6HIDJoqw+XUIFELGtXR1h17tU2Oc0+ndjlbnZoc8Hkdh0/aSWZIhMUJdO/WJda9G3YA2NGnR/XSg4frZ2wvPzS6jDr/5nJkoSkYRjxhIWFYxzqJtTbb0plXtUAog2naOF4uURBEqA6ZEBAiIxo30uri/t+Sn+OkEXhkBT7n0crfzafLNuJeAHbzerOcLkq6do6VdO28AMCCdZs/L3590TYcYa7J4ZiMWNyEadnpHYgADASqwqDKDD6vA7IsQ6Y2CrT6BV0KnZu7dcpf26+04+IunYtO62AVYZGmrl2K9K5diraOGDpw64wX57GKWjpYycma7A/qCEV1ROOZT9lPZFD/7nPbtw8NAXCCACCIRcO+pesOTqsK+iYxGkv2TG0mzjlMm+Oa4b7bOrVrszV1iyWjH2DOwUybqDke+UAmn386OX9Arz2N/vATn2za01jn124xTBuW/c2zi+RsgkKWKLLcKjRVhlvl8Cr+pUV5bOv3Rp/3UJ+e3Ru/nbNoOREWx/HSi6/cUvb5zpFNTQ1tQ8GmAkqIJWvucElJ6ZrxE8Y8Nv1H33sEABYuWTXatHM/qG8IoepQAIbV+rOLXj17BHoBaVXJWrv9zck8AJBMrh04BzdNWHYMvbt2XzZk8HnlzT/Id9e4ked/NG7k+R/94K43bzEsCbbNoRvHyVECUEIgUQqvW4FDkyFLMjq46hcN7tNmztRrLn3h2x996xBh8RVPPjHjnvIdqyf36JY/oG+JG22yvZCZiYOH67Cj/KOhD963cvL0X9159ciRIzZcfumIpR9sWQyPxw1JCsO0zFNaUF+3bIdhpjk9Pg4OC7ZtgIOLfRZfI8tJUBMEvu4GM6MUikShKhQulwaF2MjTGlb87PuDbzyTZhHHc9aHxfJlHw5evPiD6UcO7hx03oCOpZeP7Y+Xazn+9fYHmDZ1GMYMLwW4DnAPQDlg20Wbt722/qVH/46qgwbWr65BIOGB4S4F13JB1Bwo3jbJ1RHOwbkFWBaAzF/E6TJNW9ENq2XrJLYlWoicgG4BMd2Gbf/n35OQZONsRaLI9qhgkgJN1jF9Yv6QUSPOz6h26unorA6LJx6fcc/qVR/c3Kdn2+Jb77ocbQqzIDML9/95Ni69qBuGnJsNmE0AN1MfHCAEA/rmorQ0H6GYjfGXtMO+g2G892EZ9h6KorbGRn3CByhZ0PJLobhyIDuzQRVnqkGIDW7pOBmvSI5W33whfMWhRgNxHThahjm5Kk7gUBmcigRJkpDvCKy+qH/2S9+loADO0rB47ZXXpr76yquPdu3sPXD/3ROL89tlI9fNASsCPR7C609dgWEXdEDHNhZgHkn2JARS+yEYAAccTIUjS0bBwLYY0J/gwuFFiAZ1+Jvi2LitDtUHQ/h0yweo22ehKabC1DpBdudD8eRByy5KPkYOAm7F0Vp7JiwuLh9OtvqgDcNI9oMhIGAUkCQGj1MBk2T0bx+aNWF46SMXDjl3x6kea2s7K8Ni9quPv/T2c1fC4XTlwAwDdgPgrwGsCBQ7gimXhgFzExDQ/7P0R1WASADRAMkLUAWgDgBuKFRGoVsF3Axo78HA/rmpHZkM4IBhE4RiJuJxE9FgCFu2L8eeaj8Wf3QYB+pMBCIyDvW/srS1zu8kreKe1davX1/8z/nlz4ZjJiRJAqOAy6HAoUlgTMa5HYKzLh/R+ZFhgwd850LiqLMyLHI8FJpdBYR46n5EDDAaksHADcCOI3mPwTr2yuOmgWRhsRC40QRCUp29JA/ANID5ACjJreC2IzkLITIABplKyHEzwKMA+So6dcpCwrTx/StDqK0NYVdlA16c/e8bJoz/aNSg84bMu//+39/WsjMUadHa3vig/OH1u4OjGXOBSQSaxOB2qiCEIF8Lrn34zh/cdKrHeLKdlWERioSByA7YtgkzkYBtmLAME6DJu9lMBlKPdSTvXlkcum7DNi2YJoeZiAIgIBSQ1UZICoPiSj6zAeYApFyAqABxJQPDZoClpAKEQqESFJXB08GFTu0c6NsnB4MHeFG2q6Hw0y3Lpl1/feWgH/5wym/Gjr3ko1P9vRKAOfPem/TBxoZrE7YKt1uCQ5HgUCWokoWS3Ojs743s/tCpHuO34awMi47dx869+IY3Jk0YLOHKczmozRE1KWSVglPgkbf9mDrWiwElKkJRG6bJEQ/bMBIchs6hR20cbYUsywRUJtAcIcgqgaQATg+DrFA4sxRIigyqaCByNkC1ZIhQZ/KShioAJ1BB0a0DQ7f2OZh4iRsR/dCgil33rbr66ud2vvXW6z3TPS8iit60ql8/MOutlZ/VTQ7GCTRPPrIdKrI8LijUQtfc0Nwn/qd5XezPdGdlWDz2xKNXznlr2KT5c9++Z8MrywYN7ExwaT8OmVswbIIXF4WwZF0U//vDHJzXVYVlckRDNkzDhm0BMrEhpZ4V4xZg6gShBAdjBEwmSEQ5JIUgHrUhqwkozhgcrjCYIoEyNXnPg6gAcwGEAZCSsw7OAW7ABRP9etpQza2l02792QfPzPzHmHTOS1x8tI7qqnLHirVlU95fd2SyCRWy5kKWywGnQ4ZPi+24oESaffnFFzxyqsf5bTsrwwIAJl89ee7kqyfPPfr7Z/7x9G/eW7Tw1/WV6zt8cm8byNSGaQP+QwmAA4oEmCbH54cYZm3KQY++Azd06NBxR9WeykGVlXt6e+0D6OA10bUAGFEqweMEggoDlZIzFodHh6wSyAqB5m6EJDOoDgImMRAmJYODqgCngB0EMaN47T4Dry14b/TYUSMSi5etUr/pnEwLrVjg4ux0813/XLV+p3941GJweQrhc6hwajKynHb5uUXGgj/+4prfnuoxnipnbVh81bSf3fbotJ/d9ug/Z/5z+h+fuvupwcUSivM5cl2AbXNsPUiw5QBDEwrx/Ouv5vbu3bMRAHbuqnDPnP1J6KMNe3CoYR9W796LlRVlKMkzMWkgkJdlgRgUISN5aSKrBKbOISkclkEhKTYkxYSsmsliu1QGtxKwLRuxoI1xgxjmLK8UIXCS7SzbVrDs091TP9reNBzMCVVzIsvtgKbJYJTgmqGuP0256tI3TvU4TyURFl9xy623zHA6Hf5VK1fdvKVy95BA42GHJCko7nnO2gk3D3tj1OjRT3fr1u1YrQFVVWKq0w1HYT/YWT0Rj8Wxv2kn9gYqsGXhGpTkJjC8B8d5JRy6QZCIURgGhaxQmKn7JJJC4HIDkmKDSgZs24ahW4jHODRqY8yA5tWEEEunzff825sfX7GlbgpTPJBVFzRNhcOhQGN2dWlB9KMpV11xVgcFIMLiuK6/4YZXr7/hhrSK5/LkM+OzwPlUACBMgpTXB1JeXwRxFTYAWN8QgL7hXWjRCnR2+3HrRQTtc4GYh0FSKBQnBS/gUB0UisoAcBhxIBq0EErYuKCYYOXKVQMuumjE5pN53mebivKd7hWrP7/p3bU1v/78AC+W1Gzk5XmhqTJkBvxiQtYl4y+9aMWpHufpQoRFCxV3KbKA45VK5DhauZEoXqjdvw8r3ojdjbvxp3ffRY+8KG4dRdA2H6AGYOocsvKfx9BtGzANDiPOQUwb8VjU/U1jIYS01mbQs8Lzc9bMWL6lcWqd34TmzYOUqlblYEb1ecX2XBEUXybCooWWr1g5SJblGHCCKmg8GRrMkQfWvgAJbxE2Npbh7jlL0b+jgeG9gctHAYziP1XXbA7TAAydw9S56DDWyt6Ys2DKnFW1U20iQ3V4QYkE2Bw+JVJ91ZCch340ZcI/T/UYTzciLDLw2KNP3Dv3rdfv794xAV+nicjtcRFOGBZHcRuADeouhOouRJiOwSe2jRU7a/E/C+bi2gsO4Y6fcHgkAtviiIct6BELdY02CnLz9n3T4U2LK2L99MQWvLt47B+e3fZ+VAc0T96x4sq2GUcoEMYf77h4zOCB54haHsch3q6aqaK8wrFs0cv3P/yH7vjzH9ojrrdg5m9bgG3CohqKS3pA1WRwJoESkiyuErdhGzYaQhw5uTnfWHFK5MTX27WzLOvpF9/6zcz5Fc/qXIbm8oJQOXnphmQ1cm7FUV5RPfgUD/W0JWYWadpbVcXu/M1vy7p3lkoWvj4JCG3Awb2VIHRQyw/OOWzbAqgCIilIRAOI+C1EAiYcjGN5GXBDl87fWN7uaCXtszI0SGpP21fOvnrvHvbhp59NfWDW7uc5laFqLshOTyok/hPzhEpwODTc/cQHryz/ZMetk8ed+8CE8eOWfstncVoTYZGml158aYZD9pc8cM9PgMgawGwCj+ut3IOYANxENGQhHLBAOOCPEew8cla+/JvHRvIy7wv9PT5dvaZ0yae7fv7BJv90priSNzCZ/F9BAQCESKCSA5w5sHhN9XCL2/eLsPgycRmSphVLF06761djwVx2siCOHoKRsFt59cEE0eMINZgIN5pQJWBtFUFOx76B1vwq3008WZDmC5tM/v7mhldeW35k+sEGA5LqBGFqssHTcf7RCKGgkpq8j6G4sXTt/qHvL1k6/Fs8gdOemFmk6Xe3nYPe/RjQUAau1wBGPNUURkeykHbLUoNQAiNmwL8vgZqqBLhp4/FlEgoHXLlo4ZszJrTKSXyHESYBtoJQ1MD3fvoY31EdheTIgeTIg8xYKiROdAACQiQorjZgqhd2PICf3Dt71fj3Nyy9Zvz5fxoz+pLV386ZnL5EWKShrGxXVmmxAsTqATMCAg5IFF6fhHWfLEHVkQiQd3Gq1R9BJncNCCEwdI5QkwlYNmpDgNb54s0z/iGCIh2UKeCwsX1fAuGoDs3hOtbmkTTjWpFQBiY5AA3g1IVFq6tGMwprzOhLxp3E4Z8RxGVIGhKJuNOrRYBEI2AHUy0HFeTkSHjo5zq8oUUIVK6EHQ98ITCahxDAMGxEgxZW75HxxCoPXnvjtXPT/fx91VXsbN6RRZkEyhzgzAl3Vi4kzQ2aWhZt1veFkFRgaHBmFYBTBxZ+vHfsqlUf9ztpgz9DiJlFGjweb0PN53uQn+dMbqfgVrImBZwYNyIH40Y7YJgBvLvibcxesB8bGi4Ay+0D5ipI9sZM42ENn5ti7S4PdgVKa99fvKDN75o5RtO0mGWT0Zmc33cBZRIkRQFjLhBCYJkchmVm9pwMISBMhuIuhKRlwYz5MfWe1z+bNHzNnMf+fMfVrT74M4SYWaSha9cu+ue7GwA0AWYQgJEMASIl61GYBLLKMOmqYrz65HCMaL8e2v4XYQb3wjZ0AN882ygq7jb74Uf+dt77ixe0yXScZ/OaCSGAxCgcGkOOR0WWV4HEWrb9nVAGKjsgaT5EEgwLPqmavH79+uLWG/WZRcws0vTMbAN9iwLoVUxgcQrCGCiTUt3SHYBOAEMHJRKe+9uFgEPBnt2VWLhkKeYurcVB6WIQTxcQLQdMUlKVc472nuDoWFS8deQlI4/3kEnazt6LEMAybdjMRkkbGSP7SDdtqNAnVdZ5JjUGY4gmLJiZNF9K9Y6lGkNOYSmsuB83/m5W5eRLVr9w3RUj/9ivf7/TrnnxySRmFmnqPXDM4pcXxqBHbcQiNhJRHWY8DpgBwAoAVhCwwoAdA6wEEIqiuJsXv/zVeXjtqRG4IPcT+GpeA/a9AyNSC0uPA6DH6nI25yac8N8sS0dCT8Dl0nDjtZfPGnVO1jN5PieyvQ44VKlF+2EICKjiBHNkoT5gYd6qPTe/vWjl3a03+jODCIs0ff/6H/52TZkXy9aaiEdsxMMcsSiHlYjDNoOA1fTl0OAxIBoDAlEUtnPipafG4tk/D8BPL4shu+Y10P3vwAjshRkPwDLSeK4kHWdx3nDLgKVHEY9GAQBXjB+1OEeNzM7PdiLXq4Ex0oLAICBUApVdyM5rh5qGBOatrJy+s6wsq9VO4AwgwiJNFw67YMeHa3eQebsvW1penkCk0YT/sI6mIwZCdQkkgk2wozXg8X1AYi+g7wOMGsA4AkTqACOI/j3c+NXN/bFmyeXYsWwIlj/YhMevXYfp5y7Csn8/8OCk8SNDd9/1u1dO9bmeibhtwtYjsPXwsT979Pffu25CP+vqkjaY0y7PDbdDhsQy+5EnhIAyCbK3PXI79ELIzsbUu57z//XxZx/cs+fsqGQmwqKZnnn2H2Mem6diW4UBPW4jErIQCVqIhS3EIib0mAnbDIGbfsBq+MKMI5SacUSAYBBoCqF9kQfjJpbijt9fjDf+PhJ/uK2tW018eMNvb//lO1u2bC081ed65uH46m3eayaNnTO4uzI7z+eEz6tBU1rStC25SsIUF5jmQ/m+IOZ9WHHPgvc/vLNFwz5DiBucGZi9rJw8NWPm7//2wksP3z1mLwpyKMJNDEwmyeK8LgpJ0aG6YpBkAiZRUMUNQpVkUV7mSzYjstzJFRUwuGQTg3vJGNyjHYDVkx6dOXeSrr/d9fzzB+1JZ0y2bbOzeTXkmON8E75/1bjZlrVIWVdJJzdm5Uwq39eAqG7BtjLbPEckBUTKRl7H3jgUbMD9Mz99UFWU8LSf3PhEK5zBaUvMLDL0i+m3/uWeB/563sxVPny8zUIibCIRMhHxmwg2mQg2mgg1mgg2mAg3mdBDQRjhRljRWvD4IXD9UOoypRYw65MzDzsM2CHwRBS/+YETz8/8x0vpjse0bWbzllyXf7ddf834VwcV07nZjvjcbK8DjpbOMEAgqW7Izlxk5bTFvBU773rh5TduabUBn4ZEWLTAyIsv3NBzxE0znl/XDhvLbdQ3WIiFTMT8yYAINlgINZgINJjw15rw1+kI1McQbmxArLEGRrAaVngv7GgVYBwGjDpwowmRRhPxoImyTUuGL1+2Ir36CpwzUaj3xG68dsKsgV3ogtwsJ7I9yZuemUtekiQDIwdrdxwpfGfZjntabbCnIXEZ0kKTr5r4Jzu7rzRj5c5p9Ws2wBGrwHW99qNrnoUuBRSKAjCJgDAKMICSZCMiSgFFS0CSCWSVwJ3dCAAwExzBJgvxqIXh3SwsWjD395eMGple56v0Noue1aZeN+EF87UFyuZqaTznzon+UAJxw4KV0SUJA5EpZCkPee1LsXVvfdF1t97/2bWXDfjT5CuvmPvNRziziLDIUFVVNdu4fuOkcDScpUhtprnzu4G4OyLYcBAvlS1CATuCcd2OoIPPQhtvshkzYwSMJp8wJRQwHRRMIVA0CsIICDj0OEeo0UI8ZiEcseGU5dipPtfvmh9fP/GZma/MlxrjrokcBAjFEbHMDI9GQAgBUz1QwfHhht39GCP3KrIcm3j5ZYtbdeCnmAiLDMyft3D8siULfr1r+0ejL7vq52DOdrCtBGzLhOwphNRrCpqCB/Fy+cfIIvUo9jThvPYh5DoteDUOn5NAlilcBoesEnCLQ5FJsqdqlCPcZMIyLOypsTBxYsnab+/MjlUk/8aqXGe6W2+8YsaWhxZcxn2O8eAcsYSZKjWQGSppAADFlYXl6/cPUGXpXhEWZ7Ht23YUPP7o/3tn2g05Q5/4XwnVn7vx5vqvfAstHYRKUH1FULO7wKISKgjDrkQAZkMQZtQPI7gfxAxDsZogwwAjBuxEAKOK/bi8tw6PCkTjwKAJP3/m1mk/TfsOOyEAbdENTgJu2wjH4jktOcqZ4h/3TJzw95fm37njoHyxbjjGh2I6EnpmOUkoA5NdcOd1hZkIYMmGmqE/v+uv71434YI/jBgxfGsrD/2UEGHRDH9/8sl/SdaBoYPO6wbUrUOiofHE27S5DVg6OAAqOaAoLqieDkCb3uC2BduIwbYSiMTCyIpuQMJcC24nEIgRvLWZ4O6ZU29Pd2yMUkuiHJRm3ks9eSEENPkjHQB8izOaU+fnN13xyIwX5qMm6BjPwaEbVub3fQiSD54BcHhtLPq4arzMaExR5D8MGTLkjK8YLlZD0jTl2qvLhg8iI5957gbwmnUw6nah8UAi/aVKbgGWAW7Fwa0EwE1QSYYkO8AUF/KyHLBsG2srDbztn7Ti9sc+aNO1a3Ha+8BLuvcIO2RrriRR0EymF4SAMAYQiu17Gs6qR92n33zFI33bJ57p2t6NbI8GRWYZL0ETKoEqbqieQjBPe/xrafXkH9396u7PPvvsjN9kJ8IiTQ1HqkpHDW0PhGpgx2sRDepIRHgr7WvgkIiBiiYHNkbPDfzzuWcv6devb21zj+KQrTClBDTDQREQUCph7+Fgvy2bN3XI6CCnSgv/HUYNbPd0B2/0mVyfE26HDFnKfB8GIQSgEpjqgzu7AE1hjrnvfXL39u3bClo2ylNLXIak5HdHsgAADNRJREFUyes00SbPBKKNsBIhxKOAYbbeOmV+Xv7a4nOu23zZhAmPZnoMj4ZamREwmtyc1ezpNKFgTMLeg41D9x6s73fOAHxjr5LWQoAMFi+/gh/7T7ONGDZ4q22vmXkg4J/G4QDnycplX20tkC5CCCTZCYDDlc0x78Pd0xWZxfr06dvcukanDTGzSFOntgbAtwKRHTB1HZQQKAqBadqwbA7Lso/1KW2WVMnOfgPPX3r/A/ffNmTw+Rlf2w4/t+jVbKcNpypDyuSdkVBQRcOBBhvPv7P5H5mOIxOGxZ2GacHKdEmCAza3YBhGxmO4ePiQzb++uqjN0GLjoZ5FLuT5NGiKlPFNYyIpkDUfVG9HBHkennxzx51//PPfZ27etKEo40GeQiIs0tTkDwOJI4DdmNzqqxC4PRSVlXsR0zlMm8POrIYbKCOobwq1+AeoqEPbrV4lMUeRGeQMf8IJobCJjOqaSNH+fVUt2RMNACgr25HWyophMcW0eMZhQQiBzTn8oZZtS+nbu2ftZcNKnuyam/hLrs8Ft1OGLLfg25B6WlXSfHBm5WPu8p23LPhgw6+3b91yxt3DEGGRpoO1BHX7/IAVBUBAJILcXAmbt2xD+X4/LDOzmQUnBJRS1DXFijZu2d6iwOjcpdhq4zb2qAqDkvGzDxSy6kQ4QbFp666xLRnP8pWfDHpvxabpqz765mK3gQRta5octp1BRSuk7hNwoL4p/M1/+Rv07du7duywrk9mu4A8nxMep9LC4kTJ4jmy5kNDyMbbyz6//d3lG3/Z4oF+y9h99913qsdwRpCc7Xb/z4MLJ/fvBDgUgDLA6aRwkFr8651tkDuMgG02fxcgAQEhFI3+UBGN15GLhg18vyXj7NjGvXztrsi9BBzBiJ7RMiBlMihT8NGa7dcf3FfZPs9DN7dpWxhszjHWrl1XMu0vy7ct33hw5Purq28b0T9/Zts2BV/7Sn7ghc2vJAwbdgu2rNuWgXA4hJHnZD/f3PF+Vds2bcLtPOGXQv56whTXkFg8eYmU8cyHMlBZhay5ENOBTzZWXKjxpmi+z7E5Oyc30+2j3yoRFmnq27fP9k3b9p2zZOnG0ssHK9CN5LtZSRcGmGGsreoAxdM2dY+t+e+OTY2NyHIR28ni5d26dv7Gjulfx+fL5os+Lr+PEAlN4TjMDPYYESR3d4VDIRysbRoYj4V9o4YPmp/u5y9cvGz0a+/terSilhSpmge6LeEvd0y64+v+/sF9lcoLi/b+T8uWljhgWzCMOHp3cmzu16dnizdCtStsG9BorLKutp4EDMf5lmUjnuGmrSSSfJ6EUlAQVO/bNybLiQO5Ptdmny/7tH+qh2R0U+4stmnTZx3+353j9v94LEO/rjKYV4I3i2HuJzZmLVKw+UAneHtNBnN4wS09zbdJDjMeRiRYjwHFKuY/97sWLQTOemPezeurtUl7j8QnNgTiiCbMjB6Uss04LD0KPepHoTeB3sW5K/qVtF1yydA+L3iz3A0dOxVbALC/ulLZsWvv4N3VjcPWl9VO+nRnfDBTPdBcWchxWeGbRuffPvWaMS8c72t8vHp17yffKv/3jgPoTWgze3x8CYdtGrASYajmftw4oc9Dd06f+scMD/Zf7n3inacPBt3Tdu8PIBzTYZp25pu3uA3LSiDWWAUj2oTSTt7AC3+9taCoc9dWqq94coiZRTMVFrYNrvi0/L7Fq6qQiCXQpxOFbgB9e0oY2MtG+6wGrPx4P6LBIBy5xSBM/VKz3uNL1kcAgCONIbR1+sv69CrdkekYz+lbunn9unVDE8R3vmnZME0bhpVZdWvKZFBZQaM/hn1HQl22l9eMPlzbeGdZxcHCLdt2Dli15rNxqzfvvXrOivIZq7fWja6qNTtIzhy4XC5kuSguH+j6y89+OP6x4x1+zZo1JbMWlT+1sTw6hDAVLZtZpGp5EIKQvxG1dU0jfnTtqPtbcMAvke3g3tr6BjmoawNN04ZlcdiZrtyQ1Awj9dvaer+W50bUo6Eyv6Bty2+6nCQiLDLwybaa+1Z+zrG1IoQOzhCynYBTJcj3UfQpJmjvaYAd2YcdO/2wLQuKOw/fuP5PCBhlsCwL8WigUzxYE+nbu+f2TMeosURF+RH6C0o4LBuIxo1mvxMSQkEoBaEyqCTDhoSoQVB9KICyqsaBn5XXjdxa3nDhjr2BgXUhhritgioeZPuyICsqxvRhT1w7rv+9OdnZx03LP81YMnfNztBIg0tgkowW76xKdR/jloFGfxAeKXTE55J2Zufktfgdu1PHDnWSEajefdi6DUgumWf6HAmQqrhFCCiVwQnBzt1Vo4OhYLtLLx48p6VjPVnEZUgaqirLsj5cs/PareUNY9fvrJt8wC9B1dxgsgNm+DBocDeKjBXomB1FlwKOnp0lOB1AXZBj024D723WIF/wVwAn3inFU9fdkUAdYIRx2eDcRYN6tpnbs2u71RcMvbDZM423Fyyd+OnOyBRbKZhSvq8e/lAckbjZoh9ygIPbZnLlh/Pk7INSaJoKhyJBUyW4ZGPPRb0ds379k4kPfPEzK3Ztz9l3qK74hQVlT5dVRwYFTTckRQOjLPVCbx22qcMy44g2ViFLs9C/e96GX95w8Y0d2heWt+/YpUVP1H6w/OOhyzbV3Xok6pm6e18TonEDpmnDzmTzVmoTmW0Z0CP1SET8yJLDKCnKwfl9Oj0ydkSff+bmZB/oXNz9tChTIMLiBLZs2VL44cZ9Nx/x6yXL1u2bGoxRWJDh9XqhyAokiYJJEixTh95QDjtyACRWA0U/DEpsUEmBSd2wnN2gFI1K4/5F8v/HEzqikRiMeAN8GlDa2YdLz2/7g1yf88DYMaM/as45zH9/xdiPtzbcEEb+DQ3+CJpCOuoDUZgGh8V5C+4QAIwSMEagSAxetwKZySDguG6YetMPr7l01tG/u+rjT/tt3nVk4v7aeO9d+4NTKg6bAJPhdXsgSTTj7elfx+YcpmUhGPAjHgmA2HEM6ZWDvl1zHylu593Qt3fXxb169gpkevzFS1cOX7HF/+PyOnlqIJxAQjdhteR1ZHPE4wnoRgLBxhpQ6HBINgb2KkTHtu5ZOW7lwLk92y9o365gZ2lpz4zH3VIiLE5g5UerB9z76sFNumHAsgBFkeF2yHA7VTCarHaVbBSUnDHYlgHT0GHq0dS7LgWTFCiaA4Smv7PesjkM00ZdUxjxWAKWZUJGFEN7++bcOKHvbwcO6FfdnPNY9cm6fgvXRz4ziYZgOIb9RwKIxkyEYjosK/niAsexq4DjvXS/uJP6aEg4VQmKwqDKElRFhkpj6FZgvvDnO6778dHP27Z9e8HLi6sf/3RH4xTDJOCEIcutQlNlaKqU/FqtXTc0dTrRmIFYwkA4EkMkHAKDDrcG/Ora0quvnTS2RdP9RUtWjHx3C1kejpmIJ4zMd56mxmvZNgyTIxiJIhrTEY/HYZkGqK0DdgL/98vh6N4xK7d7ae/Gloy7JcSzISfgcTnrrh/u+gWhHAQwCSGpwjBfLDn/xRuHBICa+viq5s9+OdyMcxc4hxSKmDk5XuWA1+1u9gNmI4adv3XEMJA167eUrNtyeLLPlfuwBQWBUBTxhIGEYSEaN2BZHMldlMk7/UfzgxAkZ1GUQKIUTk0GkxgoARTEkK1F5vz06vN+3LO05L/e9VRVDfUoxKpCj2snQEAJrOQVh5n6OJkIwCk4XDAsp8MwLCUaN30uh9bid+fxl45cwdjHI3bsaRyp65bD5mjxbtfkiCk4VNhcZZbFFd2wnJG4mWXEI7NAj3/v59siZhZnoRffePeWqkPBcyO0cJoFCZZNEI/rMC079WGBc566akquMjBGQQkDIRQUOhhJoEt27NH+Pdov6VnSYUW34s6n9bKf0HIiLATsqapmVfsODwqEYwWhcDzfH4oWGoatEEKgKCymKVK4TZ63PNvrPpzt8xzo06v7KZsKC6eOCAtBENIiHiQTBCEtIiwEQUiLCAtBENIiwkIQhLSIsBAEIS0iLARBSIsIC0EQ0iLCQhCEtIiwEAQhLSIsBEFIiwgLQRDSIsJCEIS0iLAQBCEtIiwEQUiLCAtBENIiwkIQhLSIsBAEIS0iLARBSIsIC0EQ0iLCQhCEtIiwEAQhLSIsBEFIiwgLQRDSIsJCEIS0iLAQBCEtIiwEQUiLCAtBENIiwkIQhLSIsBAEIS0iLARBSIsIC0EQ0iLCQhCEtIiwEAQhLSIsBEFIiwgLQRDSIsJCEIS0iLAQBCEtIiwEQUiLCAtBENIiwkIQhLSIsBAEIS0iLARBSIsIC0EQ0iLCQhCEtIiwEAQhLSIsBEFIiwgLQRDSIsJCEIS0iLAQBCEtIiwEQUiLCAtBENIiwkIQhLSIsBAEIS3/H45lOmTylwdHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow_2d(image_png, 'simpleitk.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing a 2D Images\n",
    "\n",
    "After applying any image processing algorithm to a file is needed to store the resulting file. SimpleITK also support image writing. \n",
    "\n",
    "It offers support different image formats such as: jpg, png, tiff, among others.\n",
    "\n",
    "In the following example. The function sitk.WriteImage is used to convert a tif image to png. The output image can be seen in the local folder [images/](./images/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = './images/'\n",
    "# prefix = \"/content/registration_tutorial/images/\"\n",
    "\n",
    "image_tif = sitk.ReadImage(prefix + 'brain.tif')\n",
    "sitk.WriteImage(image_tif, prefix + 'brain.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: The functions image_info( ), imshow_2d( ) are stored in the local file utils.py"
   ]
  }
 ],
 "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}