1_2_ML_tutorial_DataPreprocessing.ipynb

{
  "cells": [
    {
     "cell_type": "markdown",
     "metadata": {
      "colab_type": "text",
      "id": "view-in-github"
      },
     "source": [
      "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mlexchange/als_ml_tutorial/blob/main/1_2_ML_tutorial_DataPreprocessing.ipynb)"
     ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ftMMXnRzZ-Rt"
      },
      "source": [
        "# 1.2 Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzpHqd6EIo7l"
      },
      "source": [
        "In this section we will touch some basics of data preprocessing techniques for machine learning applications. For simplicity we will focus on image type data for demonstration."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdIUnDN-x_E_"
      },
      "source": [
        "## Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "T1wv2ZEhaE-Z"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import glob\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from PIL import ImageOps\n",
        "import skimage\n",
        "from skimage import io\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "from matplotlib import pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYByGjv4y62v"
      },
      "source": [
        "## Load Your Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "iE-t4vPlHqhZ"
      },
      "outputs": [],
      "source": [
        "# Download the data (400 scattering images 1.3Mb)\n",
        "# If not on Google Colab or on a machine without wget, comment out the two cells below,\n",
        "# download the linked file, and unzip in the directory of this notebook\n",
        "!wget -q 'https://drive.google.com/uc?id=1RTObsZOHLS4RUnwCegoxXD5EktvrSeI5' -O 'data_64x64_400.zip'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "6L2hTyZe7xQi"
      },
      "outputs": [],
      "source": [
        "!unzip -q -n 'data_64x64_400.zip' && rm 'data_64x64_400.zip'\n",
        "\n",
        "data_directory = \"./data_64x64_400\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1hi4jL81AgB"
      },
      "source": [
        "One of the options to load all images in your directory is by `glob.glob` function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "MMThdTcHzXso"
      },
      "outputs": [],
      "source": [
        "filenames = glob.glob(data_directory + '/*.jpg') # '*' is the wildcard which refers to all jpg files within the directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJXFkDbR7xQj",
        "outputId": "1dd432a7-6485-4260-c1d5-da441b534ef5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images: 400\n",
            "first 5:\n",
            "['./data_64x64_400/000374.jpg', './data_64x64_400/000293.jpg', './data_64x64_400/000043.jpg', './data_64x64_400/000191.jpg', './data_64x64_400/000377.jpg']\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of images: {len(filenames)}')\n",
        "print('first 5:')\n",
        "print(filenames[:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8MP8WeP0ann"
      },
      "source": [
        "You see filenames are not appeared in the order we would expect. If you want to load them in the desired order, there is a sort function available."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FN8Mls_1z5Zc",
        "outputId": "ba0f00ad-075e-46da-edcf-d9a32d0522a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "first 5:\n",
            "['./data_64x64_400/000001.jpg', './data_64x64_400/000002.jpg', './data_64x64_400/000003.jpg', './data_64x64_400/000004.jpg', './data_64x64_400/000005.jpg']\n"
          ]
        }
      ],
      "source": [
        "filenames = sorted(filenames)\n",
        "print('first 5:')\n",
        "print(filenames[:5])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eMNv-giY6Cfv"
      },
      "source": [
        "Next we load all images according to their name. There are a bunch of options to do so.\n",
        "\n",
        "Both **PIL.Image** (Python Imaging Library) and **skimage** (scikit-image) are popular libraries for image processing in Python, but they serve slightly different purposes and have different strengths. The choice between them depends on your specific needs and the complexity of your tasks. In some cases, you might even find it beneficial to use both libraries together."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToVBxfsK9Rqn"
      },
      "source": [
        "### Using PIL.Image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rXKCMaIs_-JI"
      },
      "source": [
        "**PIL.Image** is a powerful library for basic image processing tasks, such as opening, saving, and manipulating images.\n",
        "\n",
        "\n",
        "**When** should I use? If you're primarily dealing with simple image manipulation, like resizing, cropping, rotating, and want an easy-to-use library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "xXJpfnHn0G0a"
      },
      "outputs": [],
      "source": [
        "images = [] # We first create an empty list as a placeholder for all image arrays\n",
        "for img_name in filenames:\n",
        "    img = Image.open(img_name) # Read the image\n",
        "    images.append(img) # Append to the placeholder we created"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FOA-VoJoAwIk"
      },
      "source": [
        "Let's take a look for one example image we just loaded:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "cyzHBf2v7ElC",
        "outputId": "f35b8073-e0bf-4429-e8a3-0d9361c2538a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=L size=64x64>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "images[0] # Remember, python starts from 0, so this is the first image in our collection."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofbDwLDrBF1E"
      },
      "source": [
        "### Using skimage"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QdeXmt_7BXGx"
      },
      "source": [
        "**scikit-image (skimage)** is an image processing library that provides a wide range of advanced image processing algorithms and functions. Also it is built on top of NumPy and SciPy, making it great for scientific image analysis and research.\n",
        "\n",
        "**When** should I use? If you need to perform more sophisticated image processing techniques or need access to a variety of algorithms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Isx-n25bBNSL"
      },
      "outputs": [],
      "source": [
        "images = [] # A placeholder\n",
        "\n",
        "for img_name in filenames:\n",
        "  img = io.imread(img_name) # imread will load the image as a numpy array, which we will see shortly.\n",
        "  images.append(img)\n",
        "\n",
        "images = np.array(images) # We convert the list into a numpy array stack for easier processing later"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmqDB78oITN0"
      },
      "source": [
        "## Investigate Your Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-BfK-q13IZ80"
      },
      "source": [
        "Before you start with processing, it's always good to do a quick check if you have loaded exactly what you need.\n",
        "\n",
        "Let's take a look for the numpy stack `images` we just loaded with skimage, you can use `shape` to check the dimension of the array:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N1ypqYduCvKc",
        "outputId": "9f54798f-70a4-45af-dcc9-cf480a43a08a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 64, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "images.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lT6ywZTYE-8B"
      },
      "source": [
        "This means we have 400 images in this stack, each of them is 64 by 64 size. Take the first image as an example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9pMCYsoCzFr",
        "outputId": "3db59303-cfe5-4f4b-a6fd-9db134acfd9f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 56,  56,  57, ...,  53,  52,  52],\n",
              "       [ 57,  57,  58, ...,  53,  53,  51],\n",
              "       [ 57,  59,  60, ...,  53,  53,  53],\n",
              "       ...,\n",
              "       [122, 135, 140, ...,  77,  77,  77],\n",
              "       [117, 128, 136, ...,  77,  77,  77],\n",
              "       [114, 119, 132, ...,  77,  77,  77]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "images[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKsFZ4AHGCCz"
      },
      "source": [
        "You see the image is represented as a 2D numpy array, with each element value referring to the pixel intensity, in the format of uint8.\n",
        "\n",
        "What is **uint8**? It stands for \"unsigned 8-bit integer\": In short it's a memory efficient way to represent whole numbers that range from 0 to 255, which is perfect for the pixel value. The term \"unsigned\" indicates that the integer can only hold non-negative values.\n",
        "\n",
        "Now some visualizations:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7Zty1u9H8hm",
        "outputId": "921bc89d-bb9a-4041-8c66-42b3ca17428f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min pixel intensity: 28, Max pixel intensity: 255\n"
          ]
        }
      ],
      "source": [
        "print(f'Min pixel intensity: {np.min(images[0])}, Max pixel intensity: {np.max(images[0])}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "fwWie-zZIYpp",
        "outputId": "770c811c-92e4-4370-e115-b55a8b5eb559"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.imshow(images[0])\n",
        "plt.colorbar()\n",
        "plt.title('First Image')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xl4Gl75IKQ9-"
      },
      "source": [
        "Don't worry about the color, it's the `viridis` colormap in matplotlib default settings. We are still dealing with grayscale images in our case.\n",
        "\n",
        "Now let's take a look for the first 10 images in our dataset, and using grayscale colorbar:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "NX9BxKoUJyAC",
        "outputId": "538fabe7-15cf-4926-8fc4-2f6a48e1ffb5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "num_images_to_plot = 10\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "for i in range(num_images_to_plot):\n",
        "    plt.subplot(2, 5, i + 1)  # Create a 2x5 grid of subplots\n",
        "    plt.imshow(images[i], cmap = 'gray')\n",
        "    plt.colorbar(shrink=0.55)\n",
        "    plt.title(f\"Image {i + 1}\")\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGxb0y7zIW8E"
      },
      "source": [
        "## Image Data Processing in Numpy fashion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OfwuxS-wJNTh"
      },
      "source": [
        "### Grayscale Conversion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8qStlXpIlz7"
      },
      "source": [
        "The fist thing to consider here is that: do you have **grayscale** image (black and white), or **colored** image (RGB)?\n",
        "\n",
        "The second thing to consider here: if you deal with **RGB** images, does each channel have specific information for your analysis?\n",
        "\n",
        "If the answer is no, you should consider converting your RGB images to grayscale before feed into the neural networks.\n",
        "\n",
        "**Why**? It reduces the channel / pixel values needed to proceed, saves memory and computation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgcxJQjjKWEv"
      },
      "source": [
        "In our case, since the dataset is already in grayscale, we don't need to worry about it. But if you would like to convert RGB to grayscale, one example you could use is by **skimage**:\n",
        "\n",
        "`gray_image = skimage.color.rgb2gray(RGB_image)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaefASs7KwvZ"
      },
      "source": [
        "### Normalization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVTXdsDILEXq"
      },
      "source": [
        "This step is somehow critical in deep learning.\n",
        "\n",
        "**What** is normalization? It rescales the pixel intensity of all your images into a predefined range, it could be (0, 1), (-1, 1) in most cases.\n",
        "\n",
        "Why do we need that? Two major reasons behind:\n",
        "\n",
        "1. It will make it **fair to compare** across datasets. Because neural networks may asign dominating weights for those with bigger scales or units without normalization.\n",
        "\n",
        "2. It will make the pipeline more **robust** and help the learning converge faster due to the gradient descent nature used in the training process."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXbkt8L1VCMp"
      },
      "source": [
        "Various normalization methods can be used in this step. Below we introduce two common methods and compute them mathmatically in numpy array manner if you want to know what exactly happen to the data. Note that these steps can also be done in built-in functions in pytorch easily, and we will show that in later sessions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-eRhkoSWDtz"
      },
      "source": [
        "#### Min-Max Normalization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIU5E4N5WUC3"
      },
      "source": [
        "Oftenly called just by normalization, this method **scales to [0, 1]** by shifting the pixel value by the min pixel in image, and divide by the range."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "j34HEz8DLDPQ"
      },
      "outputs": [],
      "source": [
        "# Here we define a function for easier reusability.\n",
        "\n",
        "def min_max_normalize(image):\n",
        "\n",
        "    min_val = np.min(image)\n",
        "    max_val = np.max(image)\n",
        "\n",
        "    normalized_image = (image - min_val) / (max_val - min_val)\n",
        "\n",
        "    return normalized_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "4w1h8mDOXsTb"
      },
      "outputs": [],
      "source": [
        "image_example = images[0]\n",
        "normed = min_max_normalize(image_example)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rYLTn9x57xQx",
        "outputId": "da141695-9a73-44c0-814e-2b80e833799a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min pixel value in image: 28\n",
            "Max pixel value in image: 255\n",
            "------------------\n",
            "Min pixel value in normalized image: 0.0\n",
            "Max pixel value in normalized image: 1.0\n"
          ]
        }
      ],
      "source": [
        "print(f'Min pixel value in image: {np.min(image_example)}')\n",
        "print(f'Max pixel value in image: {np.max(image_example)}')\n",
        "print('------------------')\n",
        "print(f'Min pixel value in normalized image: {np.min(normed)}')\n",
        "print(f'Max pixel value in normalized image: {np.max(normed)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "KyO-yNBKYkgi",
        "outputId": "f4217d94-f4f2-4921-a282-cf2d6d0f149b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n",
        "\n",
        "raw_image = axes[0].imshow(image_example, cmap = 'gray')\n",
        "fig.colorbar(raw_image, shrink=0.7)\n",
        "axes[0].set_title('Raw Image')\n",
        "\n",
        "normed_image = axes[1].imshow(normed, cmap = 'gray')\n",
        "fig.colorbar(normed_image, shrink=0.7)\n",
        "axes[1].set_title('After Min-Max Normalization')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5nHgjpTZmc0"
      },
      "source": [
        " Although mapping the pixel value into a new confined range will change the brightness (mean of pixel values) and contrast (standard deviation of pixel values), the features are preserved."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uapnluuZy1G"
      },
      "source": [
        "#### Mean and Standard Deviation Normalization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMq8DKCUaJ_6"
      },
      "source": [
        "Also has names as **data standardization** or **z-score normalization**.\n",
        "\n",
        "This method normalizes the pixel values to have a **mean of 0** and a **standard deviation of 1**.\n",
        "\n",
        "**How**? shift by mean and divide by standard deviation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "MkPV8Mt5ZXOo"
      },
      "outputs": [],
      "source": [
        "def standardize(image):\n",
        "\n",
        "    mean = np.mean(image)\n",
        "    std = np.std(image)\n",
        "\n",
        "    standardized_image = (image - mean) / std\n",
        "\n",
        "    return standardized_image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "WBwZzKa4bz8P"
      },
      "outputs": [],
      "source": [
        "image_example = images[0]\n",
        "standardized = standardize(image_example)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHmsqHFk7xQy",
        "outputId": "e9226f91-5ab7-4e33-8c60-dcd9a0c2629e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Min pixel value in image: 28\n",
            "Max pixel value in image: 255\n",
            "Mean in image: 141.488525390625\n",
            "Standard deviation in image: 56.297595880526636\n",
            "------------------\n",
            "Min pixel value in standardized_image: -2.0158680600050407\n",
            "Max pixel value in standardized_image: 2.0162757011909753\n",
            "Mean in standardized_image: 0.0\n",
            "Standard deviation in standardized_image: 1.0\n"
          ]
        }
      ],
      "source": [
        "print(f'Min pixel value in image: {np.min(image_example)}')\n",
        "print(f'Max pixel value in image: {np.max(image_example)}')\n",
        "print(f'Mean in image: {np.mean(image_example)}')\n",
        "print(f'Standard deviation in image: {np.std(image_example)}')\n",
        "print('------------------')\n",
        "print(f'Min pixel value in standardized_image: {np.min(standardized)}')\n",
        "print(f'Max pixel value in standardized_image: {np.max(standardized)}')\n",
        "print(f'Mean in standardized_image: {np.mean(standardized)}')\n",
        "print(f'Standard deviation in standardized_image: {np.std(standardized)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "y8UcBOgRb6Dt",
        "outputId": "89da5285-f290-41aa-e019-28f05ca1e864"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n",
        "\n",
        "raw_image = axes[0].imshow(image_example, cmap = 'gray')\n",
        "fig.colorbar(raw_image, shrink=0.7)\n",
        "axes[0].set_title('Raw Image')\n",
        "\n",
        "stad_image = axes[1].imshow(standardized, cmap = 'gray')\n",
        "fig.colorbar(stad_image, shrink=0.7)\n",
        "axes[1].set_title('After Standardization')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPuykzlNAktz"
      },
      "source": [
        "Performing standardization keeps a good track of features in your image too, and resulting in a more ML-friendly dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzeWpdvDAzwB"
      },
      "source": [
        "### Image transformation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7x1TWFJgBFVw"
      },
      "source": [
        "There are lots of techniques we can apply to the image dataset in order to augment the performance of the training. These can be done in a numpy way using packages in **skimage** library to achieve resizing, cropping, flipping ... But you will see that **pytorch** also have handy tools to perform such tasks and it's easy to use."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XeZl-2V8B53n"
      },
      "source": [
        "## Convert Data into Tensors and Perform Augmentation in Pytorch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zcq_YCftCU8T"
      },
      "source": [
        "Since this workshop will be demonstrated mainly using Pytorch. We will need to know how to get your data into the friendly format - **Tensors**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PhAHE8UzCwz_"
      },
      "source": [
        "### Load data into Tensor Format"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEhsOj-BC6KA"
      },
      "source": [
        "**What** is tensor? If you come with some math background, please don't be confused. In PyTorch, we use tensors as a specialized data structure that are very similar to arrays and matrices to encode the inputs and outputs of a model, as well as the model’s parameters.\n",
        "\n",
        "Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sau4ijG2Eshi"
      },
      "source": [
        "You can directly pass in your array-like data into the `torch.tensor()` function, and the data format (in our case, uint8) will be automatically inferred."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udGPXGzUAfdH",
        "outputId": "d4ec7eeb-d46e-4ca5-e97c-6f7630f675eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[ 56,  56,  57,  ...,  53,  52,  52],\n",
              "         [ 57,  57,  58,  ...,  53,  53,  51],\n",
              "         [ 57,  59,  60,  ...,  53,  53,  53],\n",
              "         ...,\n",
              "         [122, 135, 140,  ...,  77,  77,  77],\n",
              "         [117, 128, 136,  ...,  77,  77,  77],\n",
              "         [114, 119, 132,  ...,  77,  77,  77]],\n",
              "\n",
              "        [[176, 120, 100,  ...,  97,  87,  81],\n",
              "         [131, 113, 107,  ...,  94,  87,  81],\n",
              "         [105, 110, 110,  ...,  93,  92,  90],\n",
              "         ...,\n",
              "         [ 20,  21,  23,  ...,  50,  52,  56],\n",
              "         [ 22,  23,  22,  ...,  26,  28,  33],\n",
              "         [ 22,  21,  22,  ...,  20,  20,  21]],\n",
              "\n",
              "        [[150, 146, 139,  ..., 109,  97,  96],\n",
              "         [133, 128, 127,  ..., 100,  99,  98],\n",
              "         [118, 118, 115,  ..., 100, 100, 100],\n",
              "         ...,\n",
              "         [209, 211, 217,  ..., 103, 100,  98],\n",
              "         [207, 209, 211,  ..., 100,  99,  98],\n",
              "         [205, 207, 208,  ..., 101, 100, 102]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[130, 132, 133,  ...,  83,  83,  83],\n",
              "         [135, 138, 139,  ...,  84,  83,  83],\n",
              "         [140, 143, 145,  ...,  84,  83,  83],\n",
              "         ...,\n",
              "         [ 49,  49,  50,  ...,  32,  32,  32],\n",
              "         [ 49,  48,  49,  ...,  32,  32,  32],\n",
              "         [ 48,  48,  48,  ...,  32,  31,  29]],\n",
              "\n",
              "        [[ 95,  96,  97,  ...,  86,  86,  85],\n",
              "         [ 98,  98,  99,  ...,  86,  86,  86],\n",
              "         [100, 102, 104,  ...,  87,  86,  86],\n",
              "         ...,\n",
              "         [ 41,  39,  40,  ...,  51,  53,  53],\n",
              "         [ 40,  39,  39,  ...,  50,  51,  51],\n",
              "         [ 40,  38,  38,  ...,  49,  50,  51]],\n",
              "\n",
              "        [[143, 139, 138,  ...,  77,  77,  76],\n",
              "         [142, 141, 140,  ...,  78,  77,  76],\n",
              "         [144, 144, 143,  ...,  79,  79,  77],\n",
              "         ...,\n",
              "         [ 49,  51,  52,  ...,  47,  46,  43],\n",
              "         [ 53,  54,  56,  ...,  46,  45,  44],\n",
              "         [ 56,  57,  57,  ...,  46,  44,  43]]], dtype=torch.uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "images_tensor= torch.tensor(images)\n",
        "images_tensor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zTMDx2qzEoe9",
        "outputId": "dfe29da8-6f81-4447-89d5-07125cd04f86"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tensor shape: torch.Size([400, 64, 64])\n"
          ]
        }
      ],
      "source": [
        "print(f'Tensor shape: {images_tensor.shape}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHvB-pA-FeJ2"
      },
      "source": [
        "You can see the information matches what we have seen in numpy fashion. 400 images with 64 by 64 pixels per image."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7bLpPoFFz1A"
      },
      "source": [
        "In our case, since we are dealing with grayscale images, the channel is 1. When you are dealing with colored images, or need to perform feature extraction methods, you will need to expand a seperate dimension to store the channel information. For clarity we add the channel here, usually as the 2nd element displayed in shape."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ens7bwzHICqb"
      },
      "source": [
        "If you want to pre-expand your numpy array and pass into the tensor, it can be done by using the `np.expand_dims()` function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "We2rWLUAHXDZ"
      },
      "outputs": [],
      "source": [
        "images_expanded = np.expand_dims(images, axis = 1) # Expand the dimension at the 2nd axis,\n",
        "                                                   # since our first axis referrs to the number of images\n",
        "images_expanded_tensor = torch.tensor(images_expanded)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1rS8oQe7xQ1",
        "outputId": "d318ab4d-91c5-45de-d799-827c133c39a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numpy array shape: (400, 64, 64)\n",
            "After expansion: (400, 1, 64, 64)\n",
            "Tensor shape: torch.Size([400, 1, 64, 64])\n"
          ]
        }
      ],
      "source": [
        "print(f'Numpy array shape: {images.shape}')\n",
        "print(f'After expansion: {images_expanded.shape}')\n",
        "print(f'Tensor shape: {images_expanded_tensor.shape}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7pR5BpfXSFJw"
      },
      "source": [
        "Alternatively, you can do that after passing you numpy array in tensor format:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "resLXJGcHbNX"
      },
      "outputs": [],
      "source": [
        "images_tensor = images_tensor.unsqueeze(1) # Remember python starts at 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qm5eUWoh7xQ1",
        "outputId": "5245b6e4-72bf-4172-a633-5ef94143a81b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "New tensor shape: torch.Size([400, 1, 64, 64])\n"
          ]
        }
      ],
      "source": [
        "print(f'New tensor shape: {images_tensor.shape}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZC02XaAWJv_M"
      },
      "source": [
        "If you want to know more about tensors, here is a good starting point: [PyTorch Tensor](https://pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Zp5xpRxL_P9"
      },
      "source": [
        "### Using `torchvision.transforms` to Manipulate Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0lss7RmlNIBW"
      },
      "source": [
        "`torchvision.transforms` is a handy module within the PyTorch ecosystem that provides a set of transformations that can be applied to image data. These transformations are commonly used to preprocess and augment images before feeding them into neural networks for tasks such as image classification, object detection, and more."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hD3jub5iOZsl"
      },
      "source": [
        "Another good thing about this package is that it allows you to concatenate multiple steps in one by using the `Compose()` function. Here is a simple example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "2unSYkYz0cM7"
      },
      "outputs": [],
      "source": [
        "# Define transformations\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),                       # Convert to tensor,\n",
        "                                                 # this step automatically adds the channel info as the first dimension,\n",
        "                                                 # and rescales the data to [0,1].\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "HYdXMK7Q0m_-"
      },
      "outputs": [],
      "source": [
        "image_example = images[0]\n",
        "tensor_image = transform(image_example)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rU6LGz6L7xQ3",
        "outputId": "967b4f20-845a-4a51-a43a-875ded915306"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Shape: (64, 64)\n",
            "Min: 28, Max: 255\n",
            "Tensor Image Shape: torch.Size([1, 64, 64])\n",
            "Min: 0.10980392247438431, Max: 1.0\n"
          ]
        }
      ],
      "source": [
        "print(f'Image Shape: {image_example.shape}')\n",
        "print(f'Min: {np.min(image_example)}, Max: {np.max(image_example)}')\n",
        "print(f'Tensor Image Shape: {tensor_image.shape}')\n",
        "print(f'Min: {torch.min(tensor_image)}, Max: {torch.max(tensor_image)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0X5q8if1h4m"
      },
      "source": [
        "What if you want to rescale your data to [-1, 1]? One way to \"cheat\" is to use `transforms.Normalize()` with mean and std of 0.5 and 0.5. The min value will become (0-0.5)/0.5 = -1 and max will become (1-0.5)/0.5 = 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "lD9H3WhoIjVU"
      },
      "outputs": [],
      "source": [
        "# Define transformations\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),                       # Convert to tensor, this step automatically adds the channel info as the first dimension,\n",
        "                                                 # and rescales the data to [0,1].\n",
        "    transforms.Normalize(mean=[0.5], std=[0.5]), # Standardize the data with given mean and std, pytorch uses Mean and Standard Deviation Normalization.\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "T2IXpNU1YA-j"
      },
      "outputs": [],
      "source": [
        "image_example = images[0]\n",
        "tensor_image = transform(image_example)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUTVhRTy7xQ3",
        "outputId": "bdf4bc04-825a-4037-8593-5525c8786fd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image Shape: (64, 64)\n",
            "Min: 28, Max: 255\n",
            "Tensor Image Shape: torch.Size([1, 64, 64])\n",
            "Min: -0.7803921699523926, Max: 1.0\n"
          ]
        }
      ],
      "source": [
        "print(f'Image Shape: {image_example.shape}')\n",
        "print(f'Min: {np.min(image_example)}, Max: {np.max(image_example)}')\n",
        "print(f'Tensor Image Shape: {tensor_image.shape}')\n",
        "print(f'Min: {torch.min(tensor_image)}, Max: {torch.max(tensor_image)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NOf9-_HJgFvG"
      },
      "source": [
        "### A Vanilla Pipeline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nwa_e6CTbhA9"
      },
      "source": [
        "Now, if we start from the very beginning, with our raw data in folder, here is a simple pipeline that loads all of our images into a tensor stack:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "N9Zk-atHgg3c"
      },
      "outputs": [],
      "source": [
        "# Define Data Directory\n",
        "data_directory = \"./data_64x64_400\"\n",
        "\n",
        "# Read filenames\n",
        "filenames = glob.glob(data_directory + '/*.jpg') # '*' is the wildcard which refers to all jpg files within the directory\n",
        "filenames = sorted(filenames) # This step is not necessary, but if you want the image in your stack to be appeared into a desired order, do so.\n",
        "\n",
        "\n",
        "dataset = [] # A placeholder\n",
        "\n",
        "for img_name in filenames:\n",
        "  img = io.imread(img_name) # Read image as numpy array, could use PIL too.\n",
        "\n",
        "  # Define transformations\n",
        "  transform = transforms.Compose([\n",
        "      transforms.ToTensor(), # Convert to tensor, channel will be taken care of automatically\n",
        "  ])\n",
        "\n",
        "  # Apply transformation\n",
        "  img_tensor = transform(img)\n",
        "\n",
        "  # Append to the dataset list\n",
        "  dataset.append(img_tensor)\n",
        "\n",
        "# Stack the dataset to form the 4D array we saw\n",
        "dataset = torch.stack(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cbIrWRK7xQ4",
        "outputId": "fe16bb40-ad94-448b-9864-2462287a1009"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images: 400\n",
            "Dataset Shape: torch.Size([400, 1, 64, 64])\n",
            "First Image Min: 0.05098039284348488\n",
            "First Image Max: 1.0\n"
          ]
        }
      ],
      "source": [
        "print(f'Number of images: {len(filenames)}')\n",
        "print(f'Dataset Shape: {dataset.shape}')\n",
        "\n",
        "first_img_tensor = dataset[0]\n",
        "print(f'First Image Min: {torch.min(img_tensor)}')\n",
        "print(f'First Image Max: {torch.max(img_tensor)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QhZjbDN38SO"
      },
      "source": [
        "This gives you a normalized set ready to be passed into the neural network, as you will see in section 2.2."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKo8EjWl3rYc"
      },
      "source": [
        "### Data Augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qlRG_cq35UL"
      },
      "source": [
        "Suppose after some testing, you are not satisfied with the performance. One thing to consider is to add some augmentation to your dataset.\n",
        "\n",
        "**What** is data augmentation? The basic idea behind image data augmentation is to generate new training examples by applying various transformations to the original images without changing their semantic content.\n",
        "\n",
        "For **example**? Flipping, rotation, cropping + resizing, shifting, brightness and contrast adjustments ...\n",
        "\n",
        "**How** can I do that with one line of code? `torchvision.transforms`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-msxdpu5Z5I"
      },
      "source": [
        "Below we take a look for a few methods."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "lTK1O37gBsWM"
      },
      "outputs": [],
      "source": [
        "def plot(image1, tensor_image2):\n",
        "  fig, axes = plt.subplots(1, 2, figsize=(6, 3))\n",
        "\n",
        "  axes[0].imshow(image1, cmap = 'gray')\n",
        "  axes[0].set_title('Original Image')\n",
        "\n",
        "  image2 = tensor_image2.numpy()[0]\n",
        "  axes[1].imshow(image2, cmap = 'gray')\n",
        "  axes[1].set_title('Augmented')\n",
        "\n",
        "  plt.tight_layout()\n",
        "\n",
        "  plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44nslxsN5Xwm"
      },
      "source": [
        "#### RandomResizedCrop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "7vu5qT0P8ofZ",
        "outputId": "f01e5147-89f7-4793-b193-99bc0b768257"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "transform = transforms.Compose([\n",
        "      transforms.ToTensor(),                       # Convert to tensor, channel will be taken care of automatically\n",
        "      transforms.RandomResizedCrop(64),\n",
        "])\n",
        "\n",
        "# Apply transformation\n",
        "img = images[0]\n",
        "img_augmented = transform(img)\n",
        "plot(img, img_augmented)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECQLFMRuEB6y"
      },
      "source": [
        "#### RandomRotation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "P9dTxlUuEBSg",
        "outputId": "c9c8339e-6aeb-4225-e0f6-af30ef01b3e9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "transform = transforms.Compose([\n",
        "      transforms.ToTensor(),                       # Convert to tensor, channel will be taken care of automatically\n",
        "      transforms.RandomRotation(degrees=(0, 180)),\n",
        "])\n",
        "\n",
        "# Apply transformation\n",
        "img = images[0]\n",
        "img_augmented = transform(img)\n",
        "plot(img, img_augmented)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFRHhEb4Fyaq"
      },
      "source": [
        "#### Flipping"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_jqMH0GHjJG"
      },
      "source": [
        "Run below cells a few time to take a look."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "bzPEJJw3H3bA",
        "outputId": "ecfb47f6-c752-4af5-d995-ff60fdcea75d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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VjV5NW/rd736H8vJyLF++HG+99RZ69eqF1atX44knnvDL8f1h3LhxKCwsxMsvv4wPPvgA3bp1wxtvvIGVK1c6FqaLiIjA6tWr8cgjj2Dp0qUIDw/HHXfcgenTp2PAgAGONuvWrRu2bduGZ555BkuWLMHJkyeRnJyMnj174qmnngrCpyS6eJYtW4aYmBjcfPPNdW4PDw/H0KFDsWzZMpw8eRJz585FdXU1Fi5ciOjoaNx999144YUXvGJc7TpjTz/9NJ544gl06dIFS5YswdKlS/3+vMmf/exnSEtLw6xZs/DCCy+gsrIS7du3x/XXX48HHnigUcf861//ikmTJuHRRx9FVVUVpk+fju7duzc4XrRr1w7r16/HpEmTMGvWLCQlJeGhhx5CWlqaY1Yu+VeYJfMf1Ozs2LEDPXv2xBtvvMHpoQ307rvv4o477sDHH3+MAQMGBLs6RM3ONddcg7Zt22LdunXBrgpdgjjmqJmp60G0c+bMQXh4OG644YYg1Cj0yTa7cOEC5s6di4SEBPTq1StItSJqHqqrq3H+/HnHaxs2bMBnn32GQYMGBadSdMljWq2Zef7557F9+3YMHjwYkZGRWLNmDdasWYMJEyZ4rQNC35s0aRLOnTuHrKwsVFZW4p133sG//vUvzJw586ItoUDUXB0+fBjZ2dm49957kZaWhq+//hoLFy5Eampqgxa/JWoMptWamXXr1uGZZ57Brl27cObMGWRkZOC+++7D//k//8drHQ/63vLlyzF79mzs27cPFRUVuPzyy/Hwww9j4sSJwa4a0SWvpKQEEyZMwCeffILjx4+jVatWuOmmmzBr1qxGT04hMuHNEREREZGCY46IiIiIFAG7OZo3bx46duyImJgY9OvXz7GAFRHRxcR4RERuBCSt9tZbb+H+++/HwoUL0a9fP8yZMwcrV67E7t27kZycrH1vTU0Njhw5gvj4eL8tSkhEwWFZFsrKypCWlobw8OB0VPsSjwDGJKJLhat4ZAVA3759rdzcXLt84cIFKy0tzcrLyzO+99ChQxYA/vCHP5fQz6FDhwIRahrEl3hkWYxJ/OHPpfbTkHjk9+lJVVVV2L59O6ZOnWq/Fh4ejuzsbK/nVgFAZWUlKisr7bL1/zqy2rdvX++dnS//vbmZkSXPI+sTFRXlKLdo0cJRdvPUZrmv7gnQcpslOv9KSkoc5ZSUFO3+oULWq/bJ1XVtk+ueyLJ8NpKbp32fPn3aUY6Ojq63XvLYcpskt5v2d0PXfr4eq7H71tTUoKioCPHx8Y2uiy/cxiOAMam+fRmTGJPcCrWY5CYe+f3m6MSJE7hw4YLXxZ+SkoKvv/7aa/+8vDw888wzXq+Hh4cHJBC56dqX+8qyDB6mskp+Bn8GInksGSCbYiBy+0clj6W2t24bYP6eQzW14s/v1V83R7WC1WZu4xHAmFTfvoxJjEluhWpMakh7BX1hm6lTp2LKlCl2ubS0FOnp6QgLC2vQBzAFCzf7yz9w+V+Y3C7rJ8vq/jIYyDt/08Wu+0OS/4GYAqKuXWX7+PJHZ/qvwbRdrbfcV343pjZRt8v/4OR/ePJ7Nn1X6vvledUeCMDdf2myXia6gCq3mb4Lub8u2Mh93fzXGooYk77HmMSYJDX1mOQmHvn95uiyyy5DREQEioqKHK8XFRUhNTXVa//o6GivL5qIyB/cxiOAMYmIAjCVPyoqCr1790Z+fr79Wk1NDfLz85GVleXv0xER1YvxiIgaIyBptSlTpmDs2LHo06cP+vbtizlz5qC8vBwPPPBAIE5HRFQvxiMicisgN0ejR4/G8ePH8dRTT6GwsBDXXHMN1q5d6zUosqHc5PBN+8bExDjKai7XlAeXeV95LN3MEFOe3M1AStnlX1VV5SjL/HVSUpL23KpADuwz5fN1OWeZN5e5b7ldt7/8nuRnLi8vd5TlzAb5OdT2l+eV55L1lt+dut00i8nUfurn0m2ri/yM8u9Il7t3c91fDP6ORwBjkiwzJjEmAaEfk9xcSwEbkD1x4kQ+mJOIQgLjERG5wWerERERESmCPpW/PpGRkQ1a/0NOnzSVdWt5tGzZ0rFNdhWb6qM7tuzK9GWxNlM9TN3hwZpebVpHxc0aLKZ1LHRd2rKLX5ZN02bld6W2p5wmW1FRoT2XvA7ULmz5XtM0WnldqPvLbaZpsqb21nVPh+qaNb5iTGJM0r2XMclbU45J7DkiIiIiUvDmiIiIiEjBmyMiIiIiRciOOQoLC7NzkrqprzI/KveVudpWrVo5yvL9OvJYMs+rm0brz+mopucWmZ7DI+ute28gl+p3M21WHsuUb9aNHTCN+SguLnaU5TUj66nm7GNjYx3b5DUh8/vnzp1zlNVptPJ6k1NsZf5f1kt9v9uHS/qSz1f3DYWp/P7CmFQ/xiTGJCD0Y5Kba4c9R0REREQK3hwRERERKXhzRERERKQI2TFHLVu2tHOyunysXO9B5ldN+Xv1WKZ8vSy7WXfBlC81PWJArZvM+8q1JsrKyhxlj8fjKKu53WCOCTHl7HVL9btZmh9wfma3YwNMY0LUPLvM15vWEJFldf+zZ886tslrQr5Xl/+Xn9nUXv4c13GpYExiTGJMaj4xiT1HRERERAreHBEREREpeHNEREREpAjZMUfR0dF2HlvmMdXnDZly7pI8ljoewLQ+idu8pW7sgMzRm86tkseSeWDTc3hkbjdUuH1WkUp+JvleNX8tc+5yfQ3TWAvZvnFxcfbvciyAzLnL70puV48tr235jKTy8nJtPdXvXbd2CeA9pkG2p24NEt22S2mdI8YkxiTGpKYdk7jOEREREVEj8eaIiIiISMGbIyIiIiJFyI45OnDggJ2vlLlvNRdpWh9C5mJlWZdHN5Hn0q0TYsoZ+zI2w7SmyDfffOMoX6rr0Oio7Wt6ps/p06cd5YKCAkdZd82YxgrI7fK7U8umY8kcvBy3oG6X37lu37q4uWZ0z59qyhiTGo4xyYwx6eLHJDfvYc8RERERkYI3R0RERESKkE2rXXHFFV7dzbXUrjw5tVCdUgvop/uZyC5DObVQTl+V23Vdnaaudx25r5wCWVRU5ChnZmY6yr60Sahy011qekzCgQMHHOWkpCRHWV6XavvLLmlfyG5mOU1Wfo/yc6nvP3PmjGObrKdsPzm9V55L3V+XEqipqfGasttUMSbVjzHJG2NS6MUkN/GIPUdERERECt4cERERESl4c0RERESkCNkxRwkJCXb+XE6bVZdGl8vQ+zIl1LQkuyzL/L5uGq1piq0vZA5V5oWTk5Md5VDJ75vaIFCPnpDXiPxeS0tLHeXU1FRHWV5zan5ftr0pj+6GKd9fUVFR73sTExMdZZnvl48BUP/GAO98v256r25bU8aY1HCMSe4wJl2cmOTmOmPPEREREZGCN0dERERECtc3Rx999BFuu+02pKWlISwsDO+++65ju2VZeOqpp9CuXTvExsYiOzsbe/fu9Vd9iYhsjEdEFAiuxxyVl5fjRz/6EX7+859j5MiRXtuff/55vPTSS1i6dCk6deqEadOmIScnB7t27fJa/0MnNjbWzp/LdULUtTtMS4y7IfP7Mhcrz2XK1arHk/v6UpbbZG5W5n3l0v0yTxwopvy8XINF937T+AjTOiFqWe4r29O0lL9uLQ9TvXRrc5jI9oqPj9duV3P4sj3ke+V2mc9v1aqVo6yOKZHjCnx5/IVbFyseAYxJjEmMSdKlHJNc3xzdeuutuPXWW+vcZlkW5syZgyeffBLDhw8HALz22mtISUnBu+++i5/+9KeuK0hEVB/GIyIKBL/+i1dQUIDCwkJkZ2fbr3k8HvTr1w+bNm2q8z2VlZUoLS11/BAR+aox8QhgTCIiP98cFRYWAgBSUlIcr6ekpNjbpLy8PHg8HvsnPT3dn1UiomaqMfEIYEwiohBY52jq1KmYMmWKXS4tLUV6ejri4uK81hJpCNPzgXQ5Z/leuR6EKV/tJidveq+OrIfM78t6y+cchcqaIm7HS7hhyvfrtsl8tWxfHdNncHsNuTmWzMGr5Lozsu3lGiKyTeQaJLGxsfWeS20/f36nFwtjUsO26+rBmOSNMckpGDHJzWfxa89R7cJU8iGDRUVFXotW1YqOjkZCQoLjh4jIV42JRwBjEhH5+eaoU6dOSE1NRX5+vv1aaWkptmzZgqysLH+eiohIi/GIiBrLdVrtzJkz2Ldvn10uKCjAjh070KZNG2RkZGDy5Ml49tln0aVLF3vqbFpaGkaMGOHPehMRMR4RUUC4vjnatm0bBg8ebJdrc/Njx47FkiVL8Jvf/Abl5eWYMGECiouLMXDgQKxdu9b1miI6at7Q7fOBdDlHf65P4pZpnYtLUSA/o249GFNby/ERcn0Nf9ZLt93X5zip+X75rCaZr5efWa7jI+uivl/m+tWxAYH+mwqFeAQwJl0qGJP025t6THLz9+T65mjQoEHaxgwLC8OMGTMwY8YMt4cmInKF8YiIAoHPViMiIiJS8OaIiIiISBH0dY4aw9e8Z0OZ1icJ5LkocNx+r6bxAKa0TkP3bUhddMfSvTc6Olq7r3zWlcz3yxy++n75XvVc8jiXKsYk8gVj0sWJSW7iEXuOiIiIiBS8OSIiIiJShGxaLTIy0muqX6gxdWWq3Xymfd10YQdySftgCmTKQD2WXIbetKx/RESEoyyvy6aYfpCfQX5G2S2tW9pftpf63qbYNvVhTGr4eX3BmMSYBAQmJgXt8SFERERETR1vjoiIiIgUvDkiIiIiUoRsAn3r1q12zjFUctCmnLycJqjmPX3J50uyPWTutby83FH+7rvvtO/XkblbX9TU1DjKbqaQum0v3VgBUz6/pKTEUT548KCj3KJFi0adtyHbQ0VVVZW2rH6Xsv0qKyvr3K+pY0yqH2OSGWOSb/wRk9zEI/YcERERESl4c0RERESk4M0RERERkSJkxxylpaVp86i1TDlEuV3mwtWy3BbIRx/IvK4p56yW5foPst7FxcWOcvv27R3lpjIORK2n7nuT+wLeeXS5v0q2vcxlq+tnAN7L3qvfjWmtDtN4Cd12t8v+q2T7uB23oY4jAoCzZ8/We2zV+fPncfjwYVfnClWMSYxJjEn6eoZ6THITj9hzRERERKTgzRERERGRgjdHRERERIqQHnMUFRUFQL92h8zBy3J1dbWjrMvzXszn0chcrRzLIMtq3ljmkOVnjImJcZTT09Md5aaS31fbSNZZlmVOXncd6K6But7bunVrR1m2r47Mo8vvTve9y31NY0Jk2Z/fs2wz9TlHFRUVjm3qZ5bXZlPGmMSYxJjUtGOSm3jEniMiIiIiBW+OiIiIiBS8OSIiIiJShOyYozNnzti5Tl0O39d1P3x5Vo6bY8s8bu3YhYZur++4gHk9DVNuXHdsE906F/589pDpvLK9dGt5mMYCmJ45JfPWbtpMnlu+V623/Ayma8Q0HsCf1HPLz6Rbp6cpY0xiTGJMatoxyU08Ys8RERERkYI3R0RERESKkE2rlZeX2112gZzOqh7bl6XQAe8uRHV6pVzeXe7rhqynaSpmy5Yt6z2W7CY1dXuatuu6sE1dxbpHJZi66U1topZl16+sl6lN/Nk1rOsel/WSXcVyuqr8zLGxsfbvpkcGuL321ffL6+1SSqWpGJPqx5jEmARcWjGJPUdERERECt4cERERESlc3Rzl5eXh2muvRXx8PJKTkzFixAjs3r3bsU9FRQVyc3ORlJSEuLg4jBo1CkVFRX6tNBERwJhERIHhKsm8ceNG5Obm4tprr8X58+fxu9/9Drfccgt27dqFVq1aAQAeffRRrF69GitXroTH48HEiRMxcuRIfPLJJ64qZlnWRV06v/acOjIHquZP6yq7yeGb8tNqTlrmU2Xe98yZM9p66cY0BJJpWqduuqrM78upq26mwpqmo8rxELIs6+Jm6rZpXILu+zCNl5B5dbWNTGNATNO45bnUesqxAmr7BvqxEIxJjEm+YExqXjHJTTxydXO0du1aR3nJkiVITk7G9u3bccMNN6CkpASLFi3C8uXLMWTIEADA4sWLcdVVV2Hz5s3o37+/m9MREWkxJhFRIPg05qikpAQA0KZNGwDA9u3bUV1djezsbHufrl27IiMjA5s2barzGJWVlSgtLXX8EBE1BmMSEflDo2+OampqMHnyZAwYMADdu3cHABQWFiIqKgqJiYmOfVNSUlBYWFjncfLy8uDxeOwf+bRmIqKGYEwiIn9p9MIWubm5+OKLL/Dxxx/7VIGpU6diypQpdrm0tNSnYORmuXcTmRONi4tzlOU6IToypyzfa1pqXv0cps8kc7PyXMHK75uWwNeVZe5at35LXftXVlbav8uxAPK8su1lfl/ms9XxFPLYUiDbW7dGi2wP0/oksj1lm+jWjnFzrfoTYxJjkluMSc0rJrn5rI26OZo4cSL+8Y9/4KOPPkKHDh3s11NTU1FVVYXi4mLHf2pFRUVITU2t81jR0dGu/qCJiCTGJCLyJ1dpNcuyMHHiRKxatQoffvghOnXq5Njeu3dvtGjRAvn5+fZru3fvxsGDB5GVleWfGhMR/T+MSUQUCK56jnJzc7F8+XL8/e9/R3x8vJ2z93g8iI2Nhcfjwfjx4zFlyhS0adMGCQkJmDRpErKysjgrhIj8jjGJiALB1c3RggULAACDBg1yvL548WKMGzcOAPDiiy8iPDwco0aNQmVlJXJycjB//nz3FYuM9OlZP40hc5oyny9znJIu5yxz7nJfmTPWleV7TblZ+Tl0a6cE8hk9pu26ZxWZnnkky3J/dV0VuT6GPO+RI0ccZdNYAjUFU15e7th29uxZNEXympLXvu5v4WKuc8SYxJjkBmNS845JAVvnqCELoMXExGDevHmYN2+em0MTEbnGmEREgcBnqxEREREpeHNEREREpLi4CXQXdu/ebcyn+4Oau5VrR5jIHLyc/iu3q3RrhtT1Xt2aIjJfferUKUf5+PHj9dZDMj1Xx82zpUw5eV37APr8sGk9Et1YAVM95OKAR48edZTdXJcyv+/mmUdNhWw/te1lWzdljEmMSYxJTUN9MclNPGLPEREREZGCN0dEREREipBNq0VFRdldhbKLzE0XouyCld3U6hL5shtUTnVVp17WRTe10M20WEDflSyncarL0NclLS3NUdYt1e9Ll7WJ26X61brI7lBdF3VdZXV/03ROOWW0devWjrK8Ls6dO2f/Xl1d7diWkJDgKMsl8mUXty/t7abL2Nf0UEMf93AppdUYkxiTGJPcCbWYxLQaERERUSPx5oiIiIhIwZsjIiIiIkXIjjlKTU2189hqDh5wl5ts1aqVtqzmJuW0V7mvzKubymo9ZZ19WapfHkvNLwPAt99+6yhfc801jrIuv22aymriZnl2Nzl6ua9p+qncX801m5b1l/l7OT5Cbldz+jJfL8deyHOVlZU5yvK7dEOOLdDl12VOXn4mX68DtU47duzwy7GCjTGJMYkxyZ1Qi0lu4hF7joiIiIgUvDkiIiIiUvDmiIiIiEgRsmOOVKYl31Vy3Y+YmBjtsdQ1RmR+XzKt+6HLyZvW7tCtFSOP5XY9Et1jAdzWw0SXF5ZtL/c15d1VcnyEzPfrvhuZ+3f73ejOLdeskZ9B5u/l9anm6E1rxUjyc6nvl7l/2fYyv28aU6O7hnSPRbhUMCYxJkmMSd5CLSZxnSMiIiKiRuLNEREREZGCN0dEREREipAdcxQWFmbnIHU5Z5kfjY+P9zqOSj5XRs1ryhyn2zVE5PtVpjVFdM9AAvRrisi8rszNynELury5r/l+9dimZ/Lo1v2Q7zfta2oTdX+Z2zatKSLbU25X3y/HGch8vzy3zPer16+sl8z3m8ZxqExrssjnK8lzyb8z9Zry9ZlITQVjEmMSY1LziUnsOSIiIiJS8OaIiIiISMGbIyIiIiJFyI45ioyM9MqBq9tqxcXFObbJ/Kncrsvdyjyl23y/Licvj2U6l/wc6v7yvDIPLPPRuhyzadyBKd+ve/aQzCHLnLzcrtvflN83ldVjy/Po2hrwbs/6rkvA/fokslxeXm7/Lq9dU45eHkvNwcv8vWnshVyDRNf+Mveva5+mjDGJMYkxqfnEJPYcERERESl4c0RERESk4M0RERERkaJJDA6QeUt13QWZi23VqpWjLNfT0OVqfV1TROaJ1XO5HRugy7vLfKosnz592lFOT093lNX2NH0G3XOJ6qLmgWVOWOaMZX5armNRUVFh/3727FnHNpmvNo0lUD+n3FfW03QdyGvIn8+F0uXd5Xo5xcXF2veq9ZZ1dvPMqLr2V9dCke2nPktMtvWlgjGJMYkxqenFJDfxiD1HRERERApXN0cLFizA1VdfjYSEBCQkJCArKwtr1qyxt1dUVCA3NxdJSUmIi4vDqFGjUFRU5PdKExEBjElEFBiu0modOnTArFmz0KVLF1iWhaVLl2L48OH4z3/+gx/+8Id49NFHsXr1aqxcuRIejwcTJ07EyJEj8cknn7ivmDJtVnbdqdNAZZe1nCJqmvLoZql+ud3N/m6PLT+XOoVSTqdUu3oB59RLAOjUqRPqI7uoTdN3ZVen7KJVuy1lt6epS1uW1S5tuaS97NIuKyvTblePLc8jP5OaFgK8rymZMlDbX35m2Z7y3LIrXt3fNJ1X1vvMmTOoj7yeTOkEeW753emOpTuuvzEmMSYxJjEmSfXFJDfxyNXN0W233eYo/+EPf8CCBQuwefNmdOjQAYsWLcLy5csxZMgQAMDixYtx1VVXYfPmzejfv7+bUxERGTEmEVEgNHrM0YULF7BixQqUl5cjKysL27dvR3V1NbKzs+19unbtioyMDGzatKne41RWVqK0tNTxQ0TkFmMSEfmL65ujnTt3Ii4uDtHR0XjooYewatUqdOvWDYWFhYiKikJiYqJj/5SUFBQWFtZ7vLy8PHg8HvtHzmIgItJhTCIif3M9lf/KK6/Ejh07UFJSgrfffhtjx47Fxo0bG12BqVOnYsqUKXa5tLQU6enpOHTokJ3PlPlWNTeuW4YeMC+Rr+7vZun9urbrpqDqptQC3p9R5pDV6b9yKrDMER84cMBRlrluNScv6+V22qduuqXcZlpqXn4ONVcu99WNBQC8xwOoZd0YGQDYv3+/o3zkyBFHWX7Pat1Mn9E0vVct66aq1nUsOa5Dl5PXjX8A3E331Y350NXBXxiT6t/OmKTfxpjUvGKSm3jk+uYoKioKl19+OQCgd+/e+PTTT/HnP/8Zo0ePRlVVFYqLix3/qRUVFSE1NbXe40VHR3v9YRERNRRjEhH5m8/rHNXU1KCyshK9e/dGixYtkJ+fb2/bvXs3Dh48iKysLF9PQ0TUIIxJROQrVz1HU6dOxa233oqMjAyUlZVh+fLl2LBhAz744AN4PB6MHz8eU6ZMQZs2bZCQkIBJkyYhKyuLs0KIKCAYk4goEFzdHB07dgz3338/jh49Co/Hg6uvvhoffPABbr75ZgDAiy++iPDwcIwaNQqVlZXIycnB/PnzG1WxmJgYO48qc/RqLtyUYzctRa++35Tfd5v/V3P0ch0QXf6+rrI6HkC+V+Zq5XbZfrqcvMzrmpZw15HH0rUP4P2ZdWQ+X5blWAG1LNtL5s1N673I7ernlDl5ydQmalnuK8eyyM8h1w1R8/0y1y7b2pf1iHTXjC/XT0MwJjEmucGY5K05xSQ3146rm6NFixZpt8fExGDevHmYN2+em8MSETUKYxIRBQKfrUZERESk4M0RERERkcL1VP6LpVu3bnZOW/ccI5mnlLlYuXaH7llEpn1N55I5/DZt2ti/ezwe7b6m5zHpyNy2fG5U7aMTmhP5TB81Fy63ybU4Vq9e7Sh36NDBUZbrv6jtb3pOkfyudOuq6MYoAN75ffm51JWd5RgGqbi42FGWz8ZqrPPnz+PQoUN+OVawMSYxJvmCMSn4MclNPGLPEREREZGCN0dERERECt4cERERESlCdsxReHi4vb6CXLtDJddKkOsYmJ6l44Y8lsz3ywdcqjl8ma83PbdId265hoPM3crcrMwhq9t1z9GR520ItW7ye5NlOZ5Ct1aHm/MC+vER8hqQ75VjLeR35aZegSTbU9ZLvT7lNSK/ZzlWRV5DjV1bJtDrHF1MjEn1n5sxSX9egDEJCH5McvMe9hwRERERKXhzRERERKQI2bRay5Yt7S44X5bXN3Wbql13pim3cqpr27ZtHWVdF7acNiu7v01d8SrZJW2aBlpSUuIoq92Z8rxu6lEXtRtVtww9YF4CX20j2V5yX1lP2Z2rfhfympD1kNO01enPgPf0VbU7WLafPLapfXXXtux2lp9DdrWrU3hl+8kpt/Jal8eW04FDpRv/YmJMYkxiTGo+MYk9R0REREQK3hwRERERKXhzRERERKQI2TFHERERdo5Rlwc25WplWe6vHkvmU+XUy6SkJEdZ5vNlXjghIcH+XeZXZV5X5r5lPlXNycvl3svKyhxlmbuV29VjyZyxZJpmrMvzym26tge8vyu1nrL9ZC5bluW51PaW+8rcttwux2bINlO/K9N4CNO0ZLW93Y4VkGX1c8lp2XIarWnshbwemyPGJMYkxqTmE5PYc0RERESk4M0RERERkYI3R0RERESKkB1zpFuqX7fugm79EUCf/5c50NatWzvKMs8rl3SXZTUnbVrzQubkZVldOl3mceW+Mncr1xxR1yTx56MMAO/2r++8gPfaHLr1X+R7TceS34V6LN06HnW9V5blZ1THW8h6mZbEl+3ty5oisl7q9Szz83JMgxwzIutJjEmMSYxJzSkmseeIiIiISMGbIyIiIiIFb46IiIiIFCE75ig2NtbOT8pco5o3l3lKufaELMscvrpdrhkin1NkyvfLfKxK5vNlvlWWdetFyFy2zCnLPK+sl5rL1T2Tx1emZ0jJsqy3WhfZPqbnVcl8tW4dGknm8+V1oRvDIJ06dUpbL92znuQ2eQ25eZaQafyDvIbk34kcQ9LQ6+RSegYbYxJjEmNS045JburHniMiIiIiBW+OiIiIiBS8OSIiIiJShOyYo6+++srO6+ueY2R6Fo7MIevWFGnTpo1jm8zfy5yn3C6p+U25zofMt8q8r8xnq/vLNTHksffs2eMoyxyz7pk0prVP/EkeW7av+t3J8Q6m9+quA/l8Krnvtm3bHOXDhw971V2l5r5LSkoc206fPu0om8Z1qN+zzLnLfU3b1WOpa9LUVZbPupLXQXl5ufbc9fF1jZpQwpjEmMSY1LRjkpt4xJ4jIiIiIoVPN0ezZs1CWFgYJk+ebL9WUVGB3NxcJCUlIS4uDqNGjUJRUZGv9SQi0mI8IiJ/aXRa7dNPP8XLL7+Mq6++2vH6o48+itWrV2PlypXweDyYOHEiRo4ciU8++cTV8UtLS+3uadlNrZZl96Ocaqjr/gaA+Pj4eusgu/liY2MdZdk1rOtadtPdCOinJcpjye7HY8eOOcqyy9ZN1+LF7NLWnUtOf5bfo6yX7hENMq0hu78LCwsdZfm9y7qoj0KQ6QT53ciy/J7V60Buk2V5Dem2y2tG1lNe66ZHIahl2fbq93ix0mqBjkcAYxJjEmNSU49JAU+rnTlzBmPGjMGrr77qWGejpKQEixYtwp/+9CcMGTIEvXv3xuLFi/Gvf/0LmzdvbsypiIi0GI+IyN8adXOUm5uLoUOHIjs72/H69u3bUV1d7Xi9a9euyMjIwKZNm+o8VmVlJUpLSx0/REQN5c94BDAmEVEj0morVqzAv//9b3z66ade2woLCxEVFYXExETH6ykpKV7dgrXy8vLwzDPPuK0GEZHf4xHAmERELm+ODh06hEceeQTr1q3zyok21tSpUzFlyhS7XFpaivT0dNxwww32OXTL7+umWta1PS4uzlFOTU21f09OTnZsS0hIcJRlntdEzfvK/KnMr8ql0CU1nyqnMMqpmrt27XKUr7vuOkdZzbvKnLqvY0TU8RVuj6VbAl/mkOVS8/Iakfur350c0yHL69evd5RvuOEGR1leY+qU5+LiYsc2Ofj36NGjjrL8LtVjyanUsiyvKd3+8nqTvSHyGpL1ku+Xn7M+58+fx5EjRxq0r1uBiEcAY1ItxiTGJHmsph6T3MQjV2m17du349ixY+jVqxciIyMRGRmJjRs34qWXXkJkZCRSUlJQVVVV55eh/sGroqOjkZCQ4PghIjIJRDwCGJOIyGXP0U033YSdO3c6XnvggQfQtWtX/Pa3v0V6ejpatGiB/Px8jBo1CgCwe/duHDx4EFlZWf6rNRE1e4xHRBQorm6O4uPj0b17d8drrVq1QlJSkv36+PHjMWXKFLRp0wYJCQmYNGkSsrKy0L9/f//VmoiaPcYjIgoUvz8+5MUXX0R4eDhGjRqFyspK5OTkYP78+a6Pc+HCBXuNBN1aCXLtCFmWayPIvK9aNq3RIMn8tcyZqvlXNdcPeOfz5blMx1bJvK48lsxH6/Lusr3k2h0yBy+PpbahLl9f17lkvlodeyC/N7mvTH3I60BtI9OS9zINI3Pbsj3V9pbHNq3BIr8rtS5ul+rXrUEivyfTtd3Qx4MA3mNEGrrtYvBXPAIYkxiTGJOaekxyE498vjnasGGDoxwTE4N58+Zh3rx5vh6aiMgVxiMi8gc+W42IiIhIwZsjIiIiIoXfxxz5S1hYmJ0blTlSNW8sc8imslxjRF2LQrd2SV1M+VZ1HRC5r2m9Dfm8Gx35rCb5GeV2tSz3lbls+fwaSebR1bU+TMeWOXpJd26ZO5ZrYMh1QnQ5eF9z3bpnJJnWuJHjPtRrXXfdA95tL7er75fb5LgNeSzdOJjmijGJMYkxqfnEJPYcERERESl4c0RERESk4M0RERERkaJJjjlSc5G6nCbgnduW+VZ1u2l9Esm0loeaF5Y5ZbfHUj+XrFfLli21x5bPblJzuzJ3LZ8PJI8t21uX+5Y5eFkP05ojKtM4A93YCsA5VkNuM+X7Td+d+n3IvLm83uSYEV2O3pTfN+Xgde1rGhvgpizbK9hrGwUKYxJjkoox6dKOSew5IiIiIlLw5oiIiIhIwZsjIiIiIkWTHHPkJgcqyzLfr26XuVl5bLkOiJvnzJjWEJE5Z90aDnJdCjk2QG6XOXp1LY+kpCTHNpmDP3z4sKN87NgxRzk5OdlRTktLs3+Xz146ffq0oyzHEsj2i42NrXebKYcs20TNq5ue6SPPZcrvq9+Vaa0Oef3p1vIwrfPhZnyE22OZ1hDRrX1yqWJMYkxiTGo+MYk9R0REREQK3hwRERERKUI2rRYZGWl3B+q6BWWXoNxXdufqynKbaVqibgl8WRc5fVJ2f5uWUle3yy5p2SUrl6mX3ZcpKSn273Ia58mTJx3l1atXO8p/+ctfHOXJkyc7yiNGjLB/l93jsv3k0v0ej8dRLikpsX+Xn1l2UUu69jR118r2kt+FpL5f7XYHvB9HILfLa05NZciudXl9udlu2td0/cn3q22km+J9KaXcGJMYkxiTmnZMchOP2HNEREREpODNEREREZGCN0dEREREipAdc7Rv3z47Jy5z42pZ5illvlpOzZTTQNVcuMyLy1ysnD5ZXl7uKMtcrlqW02JlTlQeS1Lzr3Jq69mzZx3loqIiR1m2iZpHl3ndb775xlH+8MMPHeWvvvrKUV6zZo2jrE5nvfzyyx3bZPsVFxc7yjIfrE67lW1rWrpfHksdHyDz9/J73rlzp6Ms91enBktyzEJZWZmjLD+zHE+hXgemRwrI7br95TbZnvIaOnPmjKMsx1Oon0teyyrdtqaGMcmJMYkxqanFJDfxiD1HRERERAreHBEREREpeHNEREREpAjZMUf79++vd00RtWxaGl3mbmUOX837yn1NaziYcs5qflWuISLXATGtkaHmq01L9atrcdRVT/Uzyzy4HBuwZ88eR1m2gcz3q+uIfPfdd9r3yty3bs0R2X5uHm0AONtM7ivHPxw8eNBRlrnvxMRER1kdI2G6JuQ4Dvld6dYU0T0KwlQ2HUs3NqWu/dVrTh5bHeNhekRFU8KY5MSYxJjU1GKSm3jEniMiIiIiBW+OiIiIiBS8OSIiIiJShOyYox//+Md2TtbNs4dkrjYhIcFRTk9Pd5TbtGlj/966dWvHNrnehiTXXZDrSai5XFO+VOZ5deeW9ZR58m+//dZRzsnJcZTVdVbk+Ai5xsXcuXMd5f379zvKo0ePdpQffvhh+/e2bds6tsn2knlzmSdW20TuK8umZxPp1lGRz0/65z//6SjfeOONjrL6HChJfobTp087yrJ95RgI9TPLcRuyLK8p3Xa5zTTO4NSpU9pj6+qpOn/+PI4fP17v9qaEMYkxiTGpacckN/GIPUdEREREClc3R08//TTCwsIcP127drW3V1RUIDc3F0lJSYiLi8OoUaO8ZhoQEfkLYxIRBYLrnqMf/vCHOHr0qP3z8ccf29seffRRvP/++1i5ciU2btyII0eOYOTIkX6tMBGRijGJiPzN9ZijyMhIpKamer1eUlKCRYsWYfny5RgyZAgAYPHixbjqqquwefNm9O/f39V5qqqq7DyszNWa1o9QmZ7Poo4PkGuIyLEBMncr14uQeU51u8zFqmvBNOTYKvX5PoD3WAGZ79flZtXxDXWV+/Xr5yir65EAwKBBg+p9v3xmj8x1y/aW+6ttID+z6Rk58tjqseT1I9vatH6JpH6XMm8uy6YxDup35eb6Mm03PQPJtD6JbG91u1w7RG1feZ0HAmOSs44qxiTGJMak/9++buKR656jvXv3Ii0tDZ07d8aYMWPsxam2b9+O6upqZGdn2/t27doVGRkZ2LRpU73Hq6ysRGlpqeOHiKihGJOIyN9c3Rz169cPS5Yswdq1a7FgwQIUFBTg+uuvR1lZGQoLCxEVFeW1UmdKSgoKCwvrPWZeXh48Ho/9I2duEBHVhzGJiALBVVrt1ltvtX+/+uqr0a9fP2RmZuJ//ud/vJa5b6ipU6diypQpdrm0tBTp6emwLMvuApNdYWpZbpPdabIsu/LUrjnTcvryMQByyqnsNlW3y6ma8thyuq/s+lS7XU3L+svpu7Ksfg45rdHU/W3q/j18+LD9u+y2l1OcZfetrutYl7oBvK8DeS6V/C7k9yrL8nuV1HObun511588lu66r6usq5ck29OUEtAd201Kyd8YkxiTGJO8MSb5HpN8msqfmJiIK664Avv27UNqaiqqqqq8crRFRUV1jgeoFR0djYSEBMcPEVFjMCYRkT/4dHN05swZfPPNN2jXrh169+6NFi1aID8/396+e/duHDx4EFlZWT5XlIjIhDGJiPzBVVrtsccew2233YbMzEwcOXIE06dPR0REBO655x54PB6MHz8eU6ZMQZs2bZCQkIBJkyYhKyvL9awQIqKGYEwiokBwdXP03Xff4Z577sHJkyfRtm1bDBw4EJs3b7aXZH/xxRcRHh6OUaNGobKyEjk5OZg/f36jKqbL76u5SJmnlPuapkCq+VaZ45THlnlhmZOXx1bz+zJHLPPm8lhyu1o3N1NsAe8pp1FRUfbvMg8uxxXs2rXLUZY5+dqZQbUyMjLs32XeVx5b5v9lWY5LUMnvWbav+hkluU2+15/5fVnWfa+A85ozXY9u8v+mY5ny/ab9g4UxiTGJMUlfF8akxnF1c7RixQrt9piYGMybNw/z5s3zqVJERA3BmEREgcBnqxEREREpeHNEREREpHD9+JCLRZffV3OLpryjLMscspp/lblYuf6DmzVEAGceWTeuoK56xsXFOcq6VXplztiUQ1aXi5dLx8vc9p49e7THku2pHs+0HLw8lpt1LeRYi1atWmn3Vz+XKb8vjy2/V8nN8vq+rCniNgevvt/0Xl/XL2kOGJMYk3QYky6tmMSeIyIiIiIFb46IiIiIFLw5IiIiIlKE7Jgjlcy3qmXTM1Tkdt0aI27zzbJe8jEDurrJPLpcb0PmlNWHZ8p9Tc9IMj0vSGV6HpCsl8xfq89BkscyfVfyc6j7y/aS+XxTjr5ly5Z1/l7XsWRZntuUd1fJfL7M/8s2UcvyPLq/A9OxJNPaO6bnRDX0PMF87logMSYxJjEmNb2Y5CYeseeIiIiISMGbIyIiIiIFb46IiIiIFCE75ujIkSP22g+6Z9SY1oeQzweS+xcVFdm/JyUlObbJdT1k/l6eS+ZA1by66Rk9prUnVDKffPz4cUf50KFDjvJXX31Vbz1lDtb07CZJbT/g+6ee1zLlxSVdPlvm5E3rfMjvJjY21v5djlnweDyOckFBgaMcHx9f77EAoLi42P5drtFy8uRJbVmX/zeNDTCtU6Nu1639AjjHZdR1LHm9yjEm9XGzTkyoY0xiTGJMatoxydW6VQ3ek4iIiKgZ4M0RERERkSJk02r/+c9/7G5K3dLpsptUlmVXp+yyVbu45XRJ2VUpu8NlV6humqCpu1F29+m2y65e2f0tu5VPnDihPbZKHvs///mPo6x21wLAmjVrHOUrrrii3mNL8nuV3c5qe5qmhMrvXaYq1HPJ71Ged9++fY7yF198oT232j0su3Zl2fSIBnX6qml5fTnVVXcsU/e37OKW5HZd97TaPropxU0NYxJjEmNS045JbuIRe46IiIiIFLw5IiIiIlLw5oiIiIhIEbJjjm655RY7fy7zsWrZNC1WlmVOXi3LabMpKSmOsrpcfl1l0xRTlWlaoiyr+ViZz5dTMT/77DNHedCgQY6yOkXSlPedPXu2o3z48GFH+brrrnOU77//ftTH9F3JHL1KjtOQ36M8thyboY7dMH1P69evd5SvvvpqbT3V8RPHjh1zbJPfjWxf3fRp01RquV2W1WPLabGyXFpa6ijLsQOnT5/Wbq/P+fPnvcaXNFWMSYxJKsakpheT3MQj9hwRERERKXhzRERERKTgzRERERGRImTHHFVUVNjrW8h1LtSybh2FurbLY+nW6pBrR5jWdJBLvqs5Z3lsma+WZZlDVusi18CQ+Wr5mWVOWT22zLHLHLJpifzWrVujPrp1PQDzUu7q++VnNrWfPLfafqbvWa4DInP08nOoj0qQ+X1TDl5XNi3Vb8r/q+uAmB4VIa9leSy53fTYBbf7NQWMSYxJjElNOya5iUfsOSIiIiJS8OaIiIiISMGbIyIiIiJFyI45sizLzg/KPLBalnlHmReX22VuVs3tynypXEfBlGOWx1bL8r0y9ynz6DI/ffbsWft3mec1PX8pLi7OUVZzu6Yc+w9/+ENHeePGjY7yVVdd5Sirn0P3/Km6yrr21a0FA3h/ZnkdqO0t8/lyPITMfct8v/zu1OvE9Lwq0/Or1LrIa1f3d1DXsdW6yH1lPSX5t3ApjR1qLMYkxiTGpOYTk9hzRERERKRwfXN0+PBh3HvvvUhKSkJsbCx69OiBbdu22dsty8JTTz2Fdu3aITY2FtnZ2di7d69fK01EBDAeEVFguLo5On36NAYMGIAWLVpgzZo12LVrF2bPnu2YOvn888/jpZdewsKFC7Flyxa0atUKOTk5XtP2iIh8wXhERIHiaszRc889h/T0dCxevNh+rVOnTvbvlmVhzpw5ePLJJzF8+HAAwGuvvYaUlBS8++67+OlPf9rgc50/f97O0cp8rJq7lTlkmePU5dxl2ZTzlM9kkfWS1JyoXG9E5u9l/lTmp+Pj4+3fZa5WBnpTfl/Nm8scsmmcgTy3HFuQkJBg/y7bx00+H3CuySLz+bpnHgH6dUN0z/sBnGMpAKCkpMRRltfYmTNn7N9N687IsQS6/L9pvRzTuh/q55TXiKynbC957FB0MeMRwJjEmMSY1Jxikqueo/feew99+vTBXXfdheTkZPTs2ROvvvqqvb2goACFhYXIzs62X/N4POjXrx82bdpU5zErKytRWlrq+CEiMglEPAIYk4jI5c3R/v37sWDBAnTp0gUffPABHn74YfzqV7/C0qVLAQCFhYUAvJ8cnZKSYm+T8vLy4PF47J/09PTGfA4iamYCEY8AxiQicnlzVFNTg169emHmzJno2bMnJkyYgAcffBALFy5sdAWmTp2KkpIS++fQoUONPhYRNR+BiEcAYxIRuRxz1K5dO3Tr1s3x2lVXXYW//e1vAIDU1FQAQFFREdq1a2fvU1RUhGuuuabOY0ZHRzvyuLXOnj1r5ytlblzNTZry+aa1FNS8rylfX1ZW5ii7yTHLXK2aBwe889eyLuqxZB5c5u9lWbafei7ZHrL9JJn7lu2re66RbC/5vevWaJHtJ+naC3DWW36PMvctt8vnGElqXt20hogcW6BbJ8TN+iOA9+dQ28A0ANl07FAUiHgEMCbVYkxiTJLl5hSTXPUcDRgwALt373a8tmfPHmRmZgL4fjBkamoq8vPz7e2lpaXYsmULsrKy/FBdIqLvMR4RUaC46jl69NFHcd1112HmzJm4++67sXXrVrzyyit45ZVXAHx/pzx58mQ8++yz6NKlCzp16oRp06YhLS0NI0aMCET9iaiZYjwiokBxdXN07bXXYtWqVZg6dSpmzJiBTp06Yc6cORgzZoy9z29+8xuUl5djwoQJKC4uxsCBA7F27VqvLlqT6upqu1tSdr+p3ZVuu6zlNFC1m9DUhS0VFxc7ynLaopuuTdntLKejyu5dHdkmcnqqWjZ1VSYmJjrKcn91Oi+gnzbrtn1V8jPJLmr5PcuudnVqq/o74L0Uv/xe5Xcju97V79K0VL9peX11u2lf2R0uz63+3chrU34Xpmm1phRBMFzMeAQwJjEmOTEmXdoxyfWz1YYNG4Zhw4bVuz0sLAwzZszAjBkzfKoYEZEJ4xERBQKfrUZERESk4M0RERERkcJ1Wu1iOX/+vJ3TlblaNT8tp2KacsoyR+pLzlmS0y3VnKrMvZrywDJ3qy6/L5fTl0y5V3W7qb3kuAK5XU7JVfPwsh6+5IRlftq0vL4uvy9XPJbfm3yvaWq2ut20fL6bfL9pmX/ZBnJ/+Tl09TAtzR8KY4yCjTGJMUnFmHRpxyT2HBEREREpeHNEREREpAi5tFptV5muS01276pMq2rKLll1u+wONz3NWNZRTk9Vu/3kNnku2UXopmtTTgOVXZeyy1bXHSm7qGU3qXyvnHKqnsuf3Z6mLmxTl7ZaT1lnua/pCdnymlLTETI1YZrGLT+Xut10vblJi+imdDek3Fi1n78pp+UYk+o+N2MSY1JTi0lu4lGYFWJR67vvvuODHokuMYcOHUKHDh2CXY1GYUwiurQ0JB6F3M1RTU0Njhw5AsuykJGRgUOHDnk984e8lZaWIj09ne3VQGwv9xrTZpZloaysDGlpacZnZIUqxqTG4d+YO2wvdwIdj0IurRYeHo4OHTrYXaEJCQm8UFxge7nD9nLPbZvJ2UNNDWOSb9he7rC93AlUPGqa/8oRERERBQhvjoiIiIgUIXtzFB0djenTp3s9UI/qxvZyh+3lXnNvs+b++d1ie7nD9nIn0O0VcgOyiYiIiIIpZHuOiIiIiIKBN0dERERECt4cERERESl4c0RERESk4M0RERERkSJkb47mzZuHjh07IiYmBv369cPWrVuDXaWQkJeXh2uvvRbx8fFITk7GiBEjsHv3bsc+FRUVyM3NRVJSEuLi4jBq1CgUFRUFqcahY9asWQgLC8PkyZPt19hW3g4fPox7770XSUlJiI2NRY8ePbBt2zZ7u2VZeOqpp9CuXTvExsYiOzsbe/fuDWKNA4/xqG6MR75hTDILWjyyQtCKFSusqKgo67//+7+tL7/80nrwwQetxMREq6ioKNhVC7qcnBxr8eLF1hdffGHt2LHD+slPfmJlZGRYZ86csfd56KGHrPT0dCs/P9/atm2b1b9/f+u6664LYq2Db+vWrVbHjh2tq6++2nrkkUfs19lWTqdOnbIyMzOtcePGWVu2bLH2799vffDBB9a+ffvsfWbNmmV5PB7r3XfftT777DPr9ttvtzp16mSdO3cuiDUPHMaj+jEeNR5jklkw41FI3hz17dvXys3NtcsXLlyw0tLSrLy8vCDWKjQdO3bMAmBt3LjRsizLKi4utlq0aGGtXLnS3uerr76yAFibNm0KVjWDqqyszOrSpYu1bt0668Ybb7QDEdvK229/+1tr4MCB9W6vqamxUlNTrRdeeMF+rbi42IqOjrbefPPNi1HFi47xqOEYjxqGMalhghmPQi6tVlVVhe3btyM7O9t+LTw8HNnZ2di0aVMQaxaaSkpKAABt2rQBAGzfvh3V1dWO9uvatSsyMjKabfvl5uZi6NChjjYB2FZ1ee+999CnTx/cddddSE5ORs+ePfHqq6/a2wsKClBYWOhoM4/Hg379+l2SbcZ45A7jUcMwJjVMMONRyN0cnThxAhcuXEBKSorj9ZSUFBQWFgapVqGppqYGkydPxoABA9C9e3cAQGFhIaKiopCYmOjYt7m234oVK/Dvf/8beXl5XtvYVt7279+PBQsWoEuXLvjggw/w8MMP41e/+hWWLl0KAHa7NJe/T8ajhmM8ahjGpIYLZjyK9OndFFS5ubn44osv8PHHHwe7KiHp0KFDeOSRR7Bu3TrExMQEuzpNQk1NDfr06YOZM2cCAHr27IkvvvgCCxcuxNixY4NcOwpljEdmjEnuBDMehVzP0WWXXYaIiAiv0flFRUVITU0NUq1Cz8SJE/GPf/wD69evR4cOHezXU1NTUVVVheLiYsf+zbH9tm/fjmPHjqFXr16IjIxEZGQkNm7ciJdeegmRkZFISUlhWwnt2rVDt27dHK9dddVVOHjwIADY7dJc/j4ZjxqG8ahhGJPcCWY8Crmbo6ioKPTu3Rv5+fn2azU1NcjPz0dWVlYQaxYaLMvCxIkTsWrVKnz44Yfo1KmTY3vv3r3RokULR/vt3r0bBw8ebHbtd9NNN2Hnzp3YsWOH/dOnTx+MGTPG/p1t5TRgwACvqdh79uxBZmYmAKBTp05ITU11tFlpaSm2bNlySbYZ45Ee45E7jEnuBDUe+TScO0BWrFhhRUdHW0uWLLF27dplTZgwwUpMTLQKCwuDXbWge/jhhy2Px2Nt2LDBOnr0qP1z9uxZe5+HHnrIysjIsD788ENr27ZtVlZWlpWVlRXEWocOdWaIZbGtpK1bt1qRkZHWH/7wB2vv3r3WsmXLrJYtW1pvvPGGvc+sWbOsxMRE6+9//7v1+eefW8OHD7/kp/IzHtWN8ch3jEn1C2Y8CsmbI8uyrLlz51oZGRlWVFSU1bdvX2vz5s3BrlJIAFDnz+LFi+19zp07Z/3yl7+0WrdubbVs2dK64447rKNHjwav0iFEBiK2lbf333/f6t69uxUdHW117drVeuWVVxzba2pqrGnTplkpKSlWdHS0ddNNN1m7d+8OUm0vDsajujEe+Y4xSS9Y8SjMsizLt74nIiIioktHyI05IiIiIgom3hwRERERKXhzRERERKTgzRERERGRgjdHRERERAreHBEREREpeHNEREREpODNEREREZGCN0dERERECt4cERERESl4c0RERESk+L/3Qe1ksU8eKAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "transform = transforms.Compose([\n",
        "      transforms.ToTensor(),                       # Convert to tensor, channel will be taken care of automatically\n",
        "      transforms.RandomHorizontalFlip()              # if no arguments are given, it means flip with probability of 0.5\n",
        "])\n",
        "\n",
        "# Apply transformation\n",
        "img = images[0]\n",
        "img_augmented = transform(img)\n",
        "plot(img, img_augmented)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "omh4F5naFddO",
        "outputId": "a80985e2-dd4d-409b-a3ec-ee81221e7d9e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "transform = transforms.Compose([\n",
        "      transforms.ToTensor(),                       # Convert to tensor, channel will be taken care of automatically\n",
        "      transforms.RandomVerticalFlip()              # if no arguments are given, it means flip with probability of 0.5\n",
        "])\n",
        "\n",
        "# Apply transformation\n",
        "img = images[0]\n",
        "img_augmented = transform(img)\n",
        "plot(img, img_augmented)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRM_x_rRIPOn"
      },
      "source": [
        "#### RandomApply"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "neQo-qtuIYVW"
      },
      "source": [
        "The `RandomApply` transform randomly applies a list of transforms, with a given probability."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "mX7FA16aIemn",
        "outputId": "a53f3f08-dcab-4bc9-acad-7915bfaee97f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "transform = transforms.Compose([\n",
        "      transforms.ToTensor(),                       # Convert to tensor, channel will be taken care of automatically\n",
        "      transforms.RandomApply(transforms=[transforms.RandomResizedCrop(64),\n",
        "                                         transforms.RandomVerticalFlip(),\n",
        "                                         transforms.RandomRotation(degrees=(0, 180)),\n",
        "                                         ],\n",
        "                             p=0.5)\n",
        "])\n",
        "\n",
        "# Apply transformation\n",
        "img = images[0]\n",
        "img_augmented = transform(img)\n",
        "plot(img, img_augmented)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWhRMPHmJrli"
      },
      "source": [
        "Of course there are more options to play with, if you are interested here is a good starting point: [ILLUSTRATION OF TRANSFORMS](https://pytorch.org/vision/stable/auto_examples/plot_transforms.html#sphx-glr-auto-examples-plot-transforms-py)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnFFY2u0MmBY"
      },
      "source": [
        "### Bonus Example Pipelines"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BDApyvfniMI5"
      },
      "source": [
        "#### Example 1: Standardized Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTOJ1HSoYbPW",
        "outputId": "516df234-95cf-48d6-f576-08505dcecb59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images: 400\n",
            "Dataset Shape: torch.Size([400, 1, 64, 64])\n",
            "First Image Min: -2.007962703704834\n",
            "First Image Max: 1.9421063661575317\n",
            "First Image Mean: 3.3527612686157227e-08\n",
            "First Image Std: 1.0001220703125\n"
          ]
        }
      ],
      "source": [
        "# Define Data Directory\n",
        "data_directory = \"./data_64x64_400\"\n",
        "\n",
        "# Read filenames\n",
        "filenames = glob.glob(data_directory + '/*.jpg') # '*' is the wildcard which refers to all jpg files within the directory\n",
        "filenames = sorted(filenames) # This step is not necessary, but if you want the image in your stack to be appeared into a desired order, do so.\n",
        "print(f'Number of images: {len(filenames)}')\n",
        "\n",
        "dataset = [] # A placeholder\n",
        "\n",
        "for img_name in filenames:\n",
        "  img = io.imread(img_name) # Read image as numpy array, could use PIL too.\n",
        "  # We need the mean and std for standardization, recall ToTensor() will scale [0, 255] to [0, 1], we divide by 255 to accompany that.\n",
        "  mean = np.mean(img) / 255\n",
        "  std = np.std(img) / 255\n",
        "  # Define transformations\n",
        "  transform = transforms.Compose([\n",
        "      transforms.ToTensor(),                       # Convert to tensor, channel will be taken care of automatically\n",
        "      transforms.Normalize(mean = [mean], std = [std]), # Normalize the data, pytorch uses Mean and Standard Deviation Normalization by default.\n",
        "  ])\n",
        "\n",
        "  # Apply transformation\n",
        "  img_tensor = transform(img)\n",
        "\n",
        "  # Append to the dataset list\n",
        "  dataset.append(img_tensor)\n",
        "\n",
        "# Stack the dataset to form the 4D array we saw\n",
        "dataset = torch.stack(dataset)\n",
        "print(f'Dataset Shape: {dataset.shape}')\n",
        "\n",
        "first_img_tensor = dataset[0]\n",
        "print(f'First Image Min: {torch.min(img_tensor)}')\n",
        "print(f'First Image Max: {torch.max(img_tensor)}')\n",
        "print(f'First Image Mean: {torch.mean(img_tensor)}')\n",
        "print(f'First Image Std: {torch.std(img_tensor)}')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R79lhOLyMuVl"
      },
      "source": [
        "#### Example 2: Dataset Expanded with Augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4c1tPhAminXM"
      },
      "source": [
        "\n",
        "In this example we add the original image and an augmented version into our datset, resulting in doubled size. For simplicity the standardization is omitted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yYbt84Eo866i",
        "outputId": "a60a60ce-da2c-49a1-81c9-8d7284feaa5a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of images: 400\n",
            "Dataset Shape: torch.Size([800, 1, 64, 64])\n"
          ]
        }
      ],
      "source": [
        "# Define Data Directory\n",
        "data_directory = \"./data_64x64_400\"\n",
        "\n",
        "# Read filenames\n",
        "filenames = glob.glob(data_directory + '/*.jpg') # '*' is the wildcard which refers to all jpg files within the directory\n",
        "filenames = sorted(filenames) # This step is not necessary, but if you want the image in your stack to be appeared into a desired order, do so.\n",
        "print(f'Number of images: {len(filenames)}')\n",
        "\n",
        "dataset = [] # A placeholder\n",
        "\n",
        "for img_name in filenames:\n",
        "  img = io.imread(img_name) # Read image as numpy array, could use PIL too.\n",
        "\n",
        "  # Define transformations\n",
        "  transform1 = transforms.Compose([\n",
        "      transforms.ToTensor(),                       # Convert to tensor, channel will be taken care of automatically\n",
        "  ])\n",
        "\n",
        "  transform2 = transforms.Compose([\n",
        "      transforms.ToTensor(),\n",
        "      transforms.RandomResizedCrop(64),\n",
        "      transforms.RandomRotation(degrees=(0, 180)),\n",
        "  ])\n",
        "\n",
        "  # Apply transformation, then append to the dataset list\n",
        "  img_tensor1 = transform1(img)\n",
        "  dataset.append(img_tensor1)\n",
        "\n",
        "  img_tensor2 = transform2(img)\n",
        "  dataset.append(img_tensor2)\n",
        "\n",
        "# Stack the dataset to form the 4D array we saw\n",
        "dataset = torch.stack(dataset)\n",
        "print(f'Dataset Shape: {dataset.shape}')\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}