FEN_Chess_Identifier_APS360_2021_Group_3_Project_Baseline_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FEN Chess Identifier APS360 2021 Group 3 Project - Baseline Model",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JoanneT8/FEN-Chess-Identifier-APS360-Summer-2021/blob/main/FEN_Chess_Identifier_APS360_2021_Group_3_Project_Baseline_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-oQfyXToPntL"
      },
      "source": [
        "## FEN Chess Identifier Project"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vAEqXKsPO8j"
      },
      "source": [
        "Welcome to Group 3's Google Colab Notebook! This is where we are going to build our FEN Chess Identifier machine learning model for APS360 Summer 2021.\n",
        "\n",
        "**Team members: John Lee, Kevin Karam, Morgan Tran, Joanne Tan**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxb2jIn37lUh"
      },
      "source": [
        "##  Project Overview\n",
        "[Why did we choose this? What kind of results do we want?]\n",
        "- Chess has been growing in popularity and this little tool would help online chess players quickly convert their current board (JEPG) into FEN to share with others to discuss and learn!\n",
        "- The whole group has a general interest in chess\n",
        "- We would like to successfully convert the inputed chess image into the correct FEN where it can later be inserted into your favourite online chess platform to re-create the exact same scenario for analysis or practice!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xFs6COgPx2E"
      },
      "source": [
        "## Google Colab Shared Link\n",
        "Baseline Model Link: https://colab.research.google.com/drive/1hBLG7VLoNU6S6vvo8Ff9rpU0ix5F1Wwj?usp=sharing\n",
        "\n",
        "Main Model Link: https://colab.research.google.com/drive/1gLG2Td7MzgWcH2JUzr1E3Rst3kRTovYm?usp=sharing "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XnHedjPPQDRf"
      },
      "source": [
        "## Git Repository\n",
        "Current Git repo is still private."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeeSSsUacGVh"
      },
      "source": [
        "# Clean the Data\n",
        "- Download the dataset\n",
        "- Resize all images to 200 x 200 pixels\n",
        "- Extract JPG name and replace all dashes with slashes, store into a dataframe?\n",
        "- Convert images into numpy arrays, store into csv file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fonNdl1Pcdhi",
        "outputId": "db7f5959-9858-492b-b5b5-c7dd1b353489"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHgiw7A4qCCh"
      },
      "source": [
        "### Resizing images to 200 x 200 px"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TxOKWFmgbef"
      },
      "source": [
        "Following steps to download Kaggle dataset from this article: https://towardsdatascience.com/downloading-datasets-into-google-drive-via-google-colab-bcb1b30b0166"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NY5OOGVSgZ2-"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()  #this will prompt you to upload the kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yjQeccgthlDV"
      },
      "source": [
        "# make sure you have pip installed\n",
        "!pip --version "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eS0kbYaoilzl"
      },
      "source": [
        "!pip install -q kaggle\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!ls ~/.kaggle\n",
        "!chmod 600 /root/.kaggle/kaggle.json  # set permission"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEP20ZqSeLey"
      },
      "source": [
        "# import the Kaggle dataset from: https://www.kaggle.com/koryakinp/chess-positions \n",
        "!kaggle datasets download -d koryakinp/chess-positions -p '/content/drive/MyDrive/APS360_Project_Group_3/Chess_Positions_Datset'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2UK83DHnXTw"
      },
      "source": [
        "import os\n",
        "os.chdir('/content/drive/MyDrive/APS360_Project_Group_3')  #change dir"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x7lD2N0z-OBz"
      },
      "source": [
        "**Note: Beware, unzipping this file takes approx. 2 hours 21 min!!!!!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpcPMCm2xfwh"
      },
      "source": [
        "!unzip -q archive.zip  #unzip dataset "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0FqJahW4tYt"
      },
      "source": [
        "# split a portion of the train data for a validation set\n",
        "# save 20k images for validation set, 60k for training\n",
        "\n",
        "#hmmm how do i split the train set in an easy way"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQmVHO4QGkgP"
      },
      "source": [
        "import numpy as np\n",
        "import time\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "import torchvision\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "import torchvision.transforms as transforms\n",
        "import os\n",
        "import shutil\n",
        "import glob\n",
        "import random\n",
        "from numpy import asarray\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jer102ojGyrc"
      },
      "source": [
        "train_dir = '/content/drive/MyDrive/APS360_Project_Group_3/dataset/train'\n",
        "val_dir = '/content/drive/MyDrive/APS360_Project_Group_3/dataset/validation'\n",
        "test_dir = '/content/drive/MyDrive/APS360_Project_Group_3/dataset/test'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1LpqakJQvI3"
      },
      "source": [
        "# why is this not working ughhhhhhh - it works on and off -- Reason: sometimes Google Colab finds the file to big to read \n",
        "# check to see if we have all the files\n",
        "counter_train = 0\n",
        "counter_val = 0\n",
        "counter_test = 0\n",
        "\n",
        "for file in os.listdir(train_dir):\n",
        "  # print(file)\n",
        "  counter_train += 1\n",
        "\n",
        "for file in os.listdir(val_dir):\n",
        "  counter_val += 1\n",
        "\n",
        "for file in os.listdir(test_dir):\n",
        "  counter_test += 1\n",
        "  \n",
        "print(counter_train)\n",
        "print(counter_val)\n",
        "print(counter_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5C5p2HauxtpI"
      },
      "source": [
        "# ------------- DON'T RUN AGAIN --------------\n",
        "# move 20,000 images into validation folder\n",
        "# counter = 0\n",
        "# for file in os.listdir(train_dir):\n",
        "#   curr_path = os.path.join(train_dir, file)\n",
        "#   if (counter < 20000):\n",
        "#     shutil.move(curr_path, val_dir)\n",
        "#     counter += 1\n",
        "#   else:\n",
        "#     break"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FipplZuRtZKH"
      },
      "source": [
        "Resizing images to 200 x 200 pixels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oz6rW5bDrFAf"
      },
      "source": [
        "#Import required Image library\n",
        "from PIL import Image\n",
        "counter = 0\n",
        "finished = 0\n",
        "other = 0\n",
        "new_size = (200,200)\n",
        "\n",
        "# resizing all images in train folder\n",
        "for file in os.listdir(train_dir):\n",
        "  curr_path = os.path.join(train_dir, file)\n",
        "\n",
        "  #Create an Image Object from an Image\n",
        "  im = Image.open(curr_path)\n",
        "\n",
        "  # #Display actual image\n",
        "  # im.show()\n",
        "\n",
        "  if (im.size[0] != 200):\n",
        "    #Make the new image half the width and half the height of the original image\n",
        "    resized_im = im.resize(new_size)\n",
        "\n",
        "    # #Display the resized imaged\n",
        "    # resized_im.show()\n",
        "    print(file)\n",
        "    print(resized_im.size)\n",
        "    print(counter)\n",
        "    print()\n",
        "\n",
        "    counter += 1\n",
        "    #Save the cropped image\n",
        "    resized_im.save(curr_path)\n",
        "  elif (im.size[0] == 200):\n",
        "    print(\"complete: \" + str(finished))\n",
        "    finished += 1\n",
        "    print(im.size)\n",
        "  else:\n",
        "    print(\"other: \" + str(other))\n",
        "    other += 1\n",
        "    print(im.size)\n",
        "\n",
        "print()\n",
        "print()\n",
        "print(\"Completed = \" + str(finished))\n",
        "print(\"resized = \" + str(counter))\n",
        "print(\"other = \" + str(other))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYym9CpXtL_i"
      },
      "source": [
        "counter = 0\n",
        "new_size = (200,200)\n",
        "\n",
        "# resizing all images in validation folder\n",
        "for file in os.listdir(val_dir):\n",
        "  curr_path = os.path.join(val_dir, file)\n",
        "\n",
        "  #Create an Image Object from an Image\n",
        "  im = Image.open(curr_path)\n",
        "\n",
        "  # #Display actual image\n",
        "  # im.show()\n",
        "\n",
        "  if (im.size[0] != 200):\n",
        "    #Make the new image half the width and half the height of the original image\n",
        "    resized_im = im.resize(new_size)\n",
        "\n",
        "    # #Display the resized imaged\n",
        "    # resized_im.show()\n",
        "    print(file)\n",
        "    print(resized_im.size)\n",
        "    print(counter)\n",
        "    print()\n",
        "\n",
        "    counter += 1\n",
        "    #Save the cropped image\n",
        "    resized_im.save(curr_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDM9yCLHtR7j"
      },
      "source": [
        "counter = 0\n",
        "new_size = (200,200)\n",
        "\n",
        "# resizing all images in test folder\n",
        "for file in os.listdir(test_dir):\n",
        "  curr_path = os.path.join(test_dir, file)\n",
        "\n",
        "  #Create an Image Object from an Image\n",
        "  im = Image.open(curr_path)\n",
        "\n",
        "  # #Display actual image\n",
        "  # im.show()\n",
        "\n",
        "  if (im.size[0] != 200):\n",
        "    #Make the new image half the width and half the height of the original image\n",
        "    resized_im = im.resize(new_size)\n",
        "\n",
        "    # #Display the resized imaged\n",
        "    # resized_im.show()\n",
        "    print(file)\n",
        "    print(resized_im.size)\n",
        "    print(counter)\n",
        "    print()\n",
        "\n",
        "    counter += 1\n",
        "    #Save the cropped image\n",
        "    resized_im.save(curr_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFndOlyC9DRL"
      },
      "source": [
        "Convert images into numpy array, and save to csv file along with the jpg modified name:\n",
        "https://www.geeksforgeeks.org/how-to-convert-an-image-to-numpy-array-and-saveit-to-csv-file-using-python/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTcqKV1-9LyL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjejrF1-9MRE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BueUUUFISaL7"
      },
      "source": [
        "## How to differentiate between pieces?\n",
        "- 13 classes (6 classes for white, 6 for black, 1 for blanks)\n",
        "Augmenting pieces:\n",
        "- change resolutions\n",
        "- edge detection?\n",
        "- mirror (if piece not symmetrical)\n",
        "- add blank images\n",
        "- add noise\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "re3xf3HvrzPZ"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWAajcZTpNzC"
      },
      "source": [
        "# create folders (Joanne)\n",
        "#input 5 folders named: base, colour, noise, colournoise, mirror inside each of the 13 pieces\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "make_folders = ['base', 'colour', 'noise', 'colournoise', 'mirror']\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = os.path.join(pieces_path, folder)\n",
        "\n",
        "  for element in make_folders:\n",
        "    new_path_Name = os.path.join(curr_path, element)\n",
        "    os.mkdir(new_path_Name)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpbl26iJSDVS"
      },
      "source": [
        "# function for mirror (Kevin)\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = os.path.join(pieces_path, folder, \"base\")\n",
        "  target_path = os.path.join(pieces_path, folder, \"mirror\")\n",
        "\n",
        "  #https://stackoverflow.com/questions/11903037/copy-all-jpg-file-in-a-directory-to-another-directory-in-python\n",
        "  for file in glob.iglob(os.path.join(curr_path, \"*.png\")):\n",
        "    shutil.copy(file, target_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySC93NiNSm79"
      },
      "source": [
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  convert_PNG_to_JPEG(os.path.join(pieces_path, folder, \"mirror\"), (255, 255, 255))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQslPvsMoh2K"
      },
      "source": [
        "from PIL import Image, ImageOps\n",
        "\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = os.path.join(pieces_path, folder, \"mirror\")\n",
        "  number = 1\n",
        "  for file in glob.iglob(os.path.join(curr_path, \"*.jpeg\")):\n",
        "    im = Image.open(file)\n",
        "    im_mirror = ImageOps.mirror(im)\n",
        "    os.chdir(curr_path)\n",
        "    fileName = \"mirror\" + str(number)\n",
        "    im_mirror.save(fileName + \".jpeg\", 'JPEG')\n",
        "    number += 1\n",
        "number = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-js7sPwDZtyS"
      },
      "source": [
        "#renaming images to follow convention\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = os.path.join(pieces_path, folder, \"mirror\")\n",
        "  number = 1\n",
        "  formatted_piece = pieceAndColour(folder)\n",
        "  #print(folder)\n",
        "  #print(formatted_piece)\n",
        "  if formatted_piece == None:\n",
        "    break\n",
        "  for file in glob.iglob(os.path.join(curr_path, \"*.jpeg\")):\n",
        "    fileName = formatted_piece + \"_mirror\" + str(number) + \".jpeg\"\n",
        "    number += 1\n",
        "    print(fileName)\n",
        "    os.rename(file, fileName)\n",
        "number = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mkZpURIuoiks"
      },
      "source": [
        "# function for noise(moving the files)(John)\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = os.path.join(pieces_path, folder, \"base\")\n",
        "  target_path = os.path.join(pieces_path, folder, \"noise\")\n",
        "\n",
        "  #https://stackoverflow.com/questions/11903037/copy-all-jpg-file-in-a-directory-to-another-directory-in-python\n",
        "  for file in glob.iglob(os.path.join(curr_path, \"*.png\")):\n",
        "    shutil.copy(file, target_path)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e9usuvlfpDfw"
      },
      "source": [
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  convert_PNG_to_JPEG(os.path.join(pieces_path, folder, \"noise\"), (255, 255, 255))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C38njiNmqjcw"
      },
      "source": [
        "# removing all files with .png ending in noise folders\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "for folder in os.listdir(pieces_path):\n",
        "  noise_path = os.path.join(pieces_path, folder, \"noise\")\n",
        "  for file in os.listdir(noise_path):\n",
        "    if file.endswith(\".png\"):\n",
        "      os.remove(os.path.join(noise_path, file))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TPGZeIpoGzE"
      },
      "source": [
        "#Adding gaussian noise to the images\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = os.path.join(pieces_path, folder, \"noise\")\n",
        "  formatted_piece = pieceAndColour(folder)\n",
        "  number = 1\n",
        "  for file in glob.iglob(os.path.join(curr_path, \"*.jpeg\")):\n",
        "    nf = round(random.uniform(1.0,1.5), 2)\n",
        "    #https://www.geeksforgeeks.org/how-to-convert-images-to-numpy-array/\n",
        "    img= Image.open(file)\n",
        "    np_img = np.array(img)\n",
        "\n",
        "    tensor_img =torch.tensor(np_img)\n",
        "    #Taken from the lecture on autoencoders\n",
        "    noise = tensor_img + nf*torch.randn(*tensor_img.shape)\n",
        "\n",
        "    noisy_image = np.array(noise, dtype=np.uint8)\n",
        "    im = Image.fromarray(noisy_image)\n",
        "    os.chdir(curr_path)\n",
        "    fileName = formatted_piece+\"_noise\" + str(number)\n",
        "    im.save(fileName + \".jpeg\", 'JPEG')\n",
        "    number += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1r-YCei0x0q"
      },
      "source": [
        "# removing non-noisy images from the noise folder\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "for folder in os.listdir(pieces_path):\n",
        "  noise_path = os.path.join(pieces_path, folder, \"noise\")\n",
        "  for file in os.listdir(noise_path):\n",
        "    if file.endswith(\").jpeg\"):\n",
        "      os.remove(os.path.join(noise_path, file))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcycPxpFnLxX"
      },
      "source": [
        "# remove all images in every noise folder\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = os.path.join(pieces_path, folder, \"noise\")\n",
        "  for file in os.listdir(curr_path):\n",
        "    os.remove(curr_path+\"/\"+file)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pqS1ibXPSc8"
      },
      "source": [
        "#adding noise to just the blank squares\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = pieces_path\n",
        "  formatted_piece = \"blank\"\n",
        "  number = 1\n",
        "  for file in glob.iglob(os.path.join(curr_path, \"*.jpeg\")):\n",
        "    nf = round(random.uniform(3,5), 2)\n",
        "    #https://www.geeksforgeeks.org/how-to-convert-images-to-numpy-array/\n",
        "    img= Image.open(file)\n",
        "    np_img = np.array(img)\n",
        "\n",
        "    tensor_img =torch.tensor(np_img)\n",
        "    #Taken from the lecture on autoencoders\n",
        "    noise = tensor_img + nf*torch.randn(*tensor_img.shape)\n",
        "\n",
        "    noisy_image = np.array(noise, dtype=np.uint8)\n",
        "    im = Image.fromarray(noisy_image)\n",
        "    os.chdir('/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise')\n",
        "    fileName = formatted_piece+\"_noise\" + str(number)\n",
        "    im.save(fileName + \".jpeg\", 'JPEG')\n",
        "    number += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UviXqKCqy9i"
      },
      "source": [
        "#renaming images to follow convention\n",
        "# pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "# for folder in os.listdir(pieces_path):\n",
        "#   curr_path = os.path.join(pieces_path, folder, \"noise\")\n",
        "#   number = 1\n",
        "#   formatted_piece = pieceAndColour(folder)\n",
        "#   #print(folder)\n",
        "#   #print(formatted_piece)\n",
        "#   if formatted_piece == None:\n",
        "#     break\n",
        "#   for file in glob.iglob(os.path.join(curr_path, \"*.jpeg\")):\n",
        "#     fileName = formatted_piece + \"_noise\" + str(number) + \".jpeg\"\n",
        "#     number += 1\n",
        "#     print(fileName)\n",
        "#     os.rename(file, fileName)\n",
        "# number = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3KSYrI2pck4"
      },
      "source": [
        "#function that will output the proper format of piece and colour when given the folder it is in\n",
        "#ex. \n",
        "#in: pieceAndColour(blackKnight) \n",
        "#out: n_b\n",
        "def pieceAndColour(folder):\n",
        "  if(folder == \"whiteRook\"):\n",
        "    return \"r_w\"\n",
        "  elif(folder == \"whiteQueen\"):\n",
        "    return \"q_w\"\n",
        "  elif(folder == \"whitePawn\"):\n",
        "    return \"p_w\"\n",
        "  elif(folder == \"whiteKnight\"):\n",
        "    return \"n_w\"\n",
        "  elif(folder == \"whiteKing\"):\n",
        "    return \"k_w\"\n",
        "  elif(folder == \"whiteBishop\"):\n",
        "    return \"b_w\"\n",
        "  elif(folder == \"blackRook\"):\n",
        "    return \"r_b\"\n",
        "  elif(folder == \"blackQueen\"):\n",
        "    return \"q_b\"\n",
        "  elif(folder == \"blackPawn\"):\n",
        "    return \"p_b\"\n",
        "  elif(folder == \"blackKnight\"):\n",
        "    return \"n_b\"\n",
        "  elif(folder == \"blackKing\"):\n",
        "    return \"k_b\"\n",
        "  elif(folder == \"blackBishop\"):\n",
        "    return \"b_b\"\n",
        "  elif(folder == \"blank\"):\n",
        "    return \"blank\"\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XicsOjClokEs"
      },
      "source": [
        "# function for background colour (Morgan)\n",
        "# [@Morgan] use the convert convert_PNG_to_JPEG(dir_name, backgroundColour) to create the images\n",
        "# you just need to loop through every image in the folder, and randomize a R, G, B value each time\n",
        "import os\n",
        "from PIL import Image\n",
        "from random import randint \n",
        "import glob\n",
        "\n",
        "\n",
        "\n",
        "pieces_dataset_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "##os.listdir(pieces_dataset_path)\n",
        "\n",
        "dir_list = next(os.walk(pieces_dataset_path))[1]\n",
        "\n",
        "\n",
        "for i in dir_list:\n",
        "  if i == \"blank\":  \n",
        "    pass\n",
        "  else:\n",
        "\n",
        "#i = \"whiteKing\"\n",
        "    count = 1\n",
        "    prefix = pieceAndColour(i)\n",
        "    aug = \"colournoise\"\n",
        "\n",
        "    path = os.path.join(pieces_dataset_path, i)\n",
        "    base = os.path.join(path,\"base\")\n",
        "    end = os.path.join(path, aug)\n",
        "\n",
        "    name_b = prefix +\"_\" + aug\n",
        "\n",
        "\n",
        "    #remove any files in there\n",
        "    remove = glob.glob(end+\"/*\")\n",
        "    for r in remove:\n",
        "      print (r)\n",
        "      os.remove(r)\n",
        "\n",
        "\n",
        "    for f in os.listdir(base):\n",
        "      name = name_b + str(count)\n",
        "\n",
        "      name = name + \".jpeg\"\n",
        "      fn = os.path.join(base,f)\n",
        "      file = os.path.join (end,name)\n",
        "      image = Image.open(fn).convert(\"RGBA\")\n",
        "      colour_R = randint(0,255)\n",
        "      colour_G = randint(0,255)\n",
        "      colour_B = randint(0,255)\n",
        "      finalColour = (colour_R, colour_G, colour_B)\n",
        "      new_image = Image.new(\"RGBA\", image.size, finalColour)\n",
        "      new_image.paste(image, mask=image)\n",
        "      new_image.convert(\"RGB\").save(file, \"JPEG\")\n",
        "      count +=1\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XLC7iRM02wX",
        "outputId": "545c2cbc-ca55-4ff8-8eb9-c373d20009b5"
      },
      "source": [
        "\"\"\"\n",
        "Code to mirror [Morgan]\n",
        "\"\"\"\n",
        "\n",
        "import os\n",
        "from PIL import Image, ImageOps\n",
        "from random import randint \n",
        "import glob\n",
        "\n",
        "\n",
        "\n",
        "pieces_dataset_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "dir_list = next(os.walk(pieces_dataset_path))[1]\n",
        "#dir_list = [\"whiteKing\"]\n",
        "\n",
        "for i in dir_list:\n",
        "  if i == \"blank\":  \n",
        "    pass\n",
        "  else:\n",
        "    end_path = pieces_dataset_path +\"/\"+i+\"/mirror\"\n",
        "    remove = glob.glob(end_path+\"/*\")\n",
        "    for r in remove:\n",
        "      print (r)\n",
        "      os.remove(r)\n",
        "\n",
        "    prefix = pieceAndColour(i)\n",
        "    \n",
        "    aug_path = os.path.join(pieces_dataset_path, i)\n",
        "    aug_list = next(os.walk(aug_path))[1]\n",
        "\n",
        "                    \n",
        "    for aug in aug_list:\n",
        "      if aug == \"mirror\":\n",
        "        pass\n",
        "      else:\n",
        "      \n",
        "        cur_path = os.path.join(aug_path, aug)\n",
        "        count = 1\n",
        "        name_b = prefix +\"_\" + aug+\"_mirror\"\n",
        "        \n",
        "        for f in os.listdir(cur_path):\n",
        "          fn = os.path.join(cur_path,f)\n",
        "          name = name_b+str(count) + \".jpeg\"\n",
        "          im = Image.open(fn)\n",
        "          im_mirror = ImageOps.mirror(im).convert(\"RGB\")\n",
        "          dest = os.path.join (end_path, name)\n",
        "\n",
        "          im_mirror.save(dest, 'JPEG')\n",
        "          count+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_base_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_noise_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colournoise_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/whiteKing/mirror/k_w_colour_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackPawn/mirror/p_b_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackBishop/mirror/b_b_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackRook/mirror/r_b_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKnight/mirror/n_b_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackQueen/mirror/q_b_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror33.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror34.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror35.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror36.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror37.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror38.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror39.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror40.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror41.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror42.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror43.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror44.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror45.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror46.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror47.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror48.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror49.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror50.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror51.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror52.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror53.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror54.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror55.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror56.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror57.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror58.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror59.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror60.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror61.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror62.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror63.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror64.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror65.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror66.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror67.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror68.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror69.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror70.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror71.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror72.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror73.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror74.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror75.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror76.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror77.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror78.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror79.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror80.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror81.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror82.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror83.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror84.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror85.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror86.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror87.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror88.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror89.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror90.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror91.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror92.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror93.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror94.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror95.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror96.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror97.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror98.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror99.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror100.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror101.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror102.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror103.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror104.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror105.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror106.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror107.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror108.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror109.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror110.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror111.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror112.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror113.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror114.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror115.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror116.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror117.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror118.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror119.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror120.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror121.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror122.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror123.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror124.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror125.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror126.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror127.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror128.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror129.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror130.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror131.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror132.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror133.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror134.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror135.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror136.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror137.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror138.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror139.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror140.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror141.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror142.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror143.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror144.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror145.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror146.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror147.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror148.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror149.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror150.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror151.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror152.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror153.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror154.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror155.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror156.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror157.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror158.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror159.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror160.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror161.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror162.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror163.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror164.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror165.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror166.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror167.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror168.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror169.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror170.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror171.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror172.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror173.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror174.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror175.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror176.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror177.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror178.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror179.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror180.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror181.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror182.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror183.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror184.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror185.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror186.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror187.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror188.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror189.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror190.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror191.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror192.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror193.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror194.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror195.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror196.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror197.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror198.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror199.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror200.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror201.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror202.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror203.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror204.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror205.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror206.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror207.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror208.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror209.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror210.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror211.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror212.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror213.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror214.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror215.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror216.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror217.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror218.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror219.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror220.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror221.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror222.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror223.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blackKing/mirror/k_b_mirror224.jpeg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZuicsnMMgf7"
      },
      "source": [
        "# Image generation of colour and noise\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  curr_path = os.path.join(pieces_path, folder, \"colournoise\")\n",
        "  formatted_piece = pieceAndColour(folder)\n",
        "  number = 1\n",
        "  for file in glob.iglob(os.path.join(curr_path, \"*.jpeg\")):\n",
        "    nf = round(random.uniform(1.0,1.5), 2)\n",
        "    #https://www.geeksforgeeks.org/how-to-convert-images-to-numpy-array/\n",
        "    img= Image.open(file)\n",
        "    np_img = np.array(img)\n",
        "\n",
        "    tensor_img =torch.tensor(np_img)\n",
        "\n",
        "    noise = tensor_img + nf*torch.randn(*tensor_img.shape)\n",
        "\n",
        "    noisy_image = np.array(noise, dtype=np.uint8)\n",
        "    im = Image.fromarray(noisy_image)\n",
        "    os.chdir(curr_path)\n",
        "    fileName = formatted_piece+\"_colournoise\" + str(number)\n",
        "    im.save(fileName + \".jpeg\", 'JPEG')\n",
        "    number += 1\n",
        "  number = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laR96CB0ojq6"
      },
      "source": [
        "## function for converting png to jpeg (Joanne)\n",
        "# https://stackoverflow.com/questions/43258461/convert-png-to-jpeg-using-pillow \n",
        "# https://stackoverflow.com/questions/50739732/how-to-covert-png-to-jpeg-using-pillow-while-image-color-is-black\n",
        "\n",
        "# Example of how to use this function:\n",
        "# convert_PNG_to_JPEG('/content/drive/MyDrive/APS360_Project_Group_3/pieces/testing_Joanne', (255,255,255))\n",
        "# or\n",
        "# backgroundColour = (255,255,255)\n",
        "# convert_PNG_to_JPEG('/content/drive/MyDrive/APS360_Project_Group_3/pieces/testing_Joanne', backgroundColour)\n",
        "\n",
        "from PIL import Image\n",
        "import os\n",
        "\n",
        "def convert_PNG_to_JPEG(dir_name, backgroundColour):\n",
        "  # fill_color_white = (255, 255, 255)\n",
        "  \n",
        "  # goes through each file in the directory\n",
        "  for file in os.listdir(dir_name):\n",
        "    curr_path = os.path.join(dir_name, file)\n",
        "\n",
        "    # Getting only the file name without the file extension\n",
        "    fileName = os.path.splitext(curr_path)[0]\n",
        "\n",
        "    # Convert the PNG to JPEG\n",
        "    im = Image.open(curr_path)\n",
        "\n",
        "    im = im.convert(\"RGBA\")   # it had mode P after DL it from OP\n",
        "    if im.mode in ('RGBA'):\n",
        "      # create background, overlay it on top of the image\n",
        "      background = Image.new(im.mode[:-1], im.size, backgroundColour)\n",
        "\n",
        "      # put the png image on top of the background, then get rid of transparency\n",
        "      background.paste(im, im.split()[-1]) # omit transparency\n",
        "      im = background\n",
        "\n",
        "    rgb_im = im.convert('RGB')\n",
        "    rgb_im.save(fileName + '.jpeg', 'JPEG')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psgCkWQuYL-t",
        "outputId": "c8bb4fdb-9f67-47ef-b22f-9d3bc8af87a3"
      },
      "source": [
        "# Creating Blanks (Joanne)\n",
        "# Populating blanks in the base and colour folder of blanks class\n",
        "from PIL import Image  \n",
        "from random import randint \n",
        "import os\n",
        "\n",
        "image_size = (200, 200)\n",
        "basePath_blankClass = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base'\n",
        "blankPath = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank'\n",
        "os.chdir(blankPath) # change directory\n",
        "\n",
        "\n",
        "variationFolders = ['base', 'colour']\n",
        "for folder in variationFolders:\n",
        "  curr_path = os.path.join(blankPath, folder)\n",
        "\n",
        "  # 16 whites, 16 black images in base folder\n",
        "  if (folder == 'base'):\n",
        "    # delete what's in the folder\n",
        "    for file in os.listdir(curr_path):\n",
        "      os.remove(os.path.join(curr_path, file))\n",
        "      print(\"removed: \" + os.path.join(curr_path, file))\n",
        "\n",
        "    for i in range(1,17):\n",
        "      blackBackground = (0,0,0)\n",
        "      img  = Image.new(\"RGB\", image_size, blackBackground)\n",
        "      saveDestination = os.path.join(curr_path,'blank'+str(i)+'.jpeg')\n",
        "      img.save(saveDestination, 'JPEG')\n",
        "      print(saveDestination)\n",
        "\n",
        "    for i in range(17,33):\n",
        "      whiteBackground = (255,255,255)\n",
        "      img  = Image.new(\"RGB\", image_size, whiteBackground)\n",
        "      saveDestination = os.path.join(curr_path,'blank'+str(i)+'.jpeg')\n",
        "      img.save(saveDestination, 'JPEG')\n",
        "      print(saveDestination)\n",
        "    \n",
        "\n",
        "  if (folder == 'colour'):\n",
        "    for file in os.listdir(curr_path):\n",
        "      os.remove(os.path.join(curr_path, file))\n",
        "      print(\"removed: \" + os.path.join(curr_path, file))\n",
        "\n",
        "    for i in range(1,33):\n",
        "      colour_R = randint(0,255)\n",
        "      colour_G = randint(0,255)\n",
        "      colour_B = randint(0,255)\n",
        "      finalColour = (colour_R, colour_G, colour_B)\n",
        "\n",
        "      img  = Image.new(\"RGB\", image_size, finalColour)\n",
        "      saveDestination = os.path.join(curr_path,'blank_colour'+str(i)+'.jpeg')\n",
        "      img.save(saveDestination, 'JPEG')\n",
        "      print(saveDestination)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank2.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank3.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank4.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank5.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank6.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank7.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank9.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank8.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank10.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank11.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank12.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank13.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank14.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank15.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank17.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank18.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank20.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank19.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank21.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank22.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank23.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank25.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank24.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank26.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank27.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank28.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank29.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank30.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank31.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank32.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base/blank32.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour1.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour2.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour3.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour4.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour5.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour6.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour7.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour8.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour9.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour10.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour11.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour12.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour13.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour14.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour15.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour16.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour17.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour18.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour19.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour20.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour21.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour22.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour23.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour24.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour25.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour26.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour27.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour28.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour29.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour30.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour31.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour/blank_colour32.jpeg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ag-j44kYaAtL",
        "outputId": "e99516de-f51f-4c08-cee9-baa110a0f420"
      },
      "source": [
        "#Generating colournoise for blank class\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colour'\n",
        "\n",
        "for file in os.listdir('/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise'):\n",
        "  removeFile = os.path.join('/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise', file)\n",
        "  os.remove(removeFile)\n",
        "  print(\"removed: \" + removeFile)\n",
        "\n",
        "# for folder in os.listdir(pieces_path):\n",
        "curr_path = pieces_path\n",
        "formatted_piece = \"blank\"\n",
        "number = 1\n",
        "for file in glob.iglob(os.path.join(curr_path, \"*.jpeg\")):\n",
        "  nf = round(random.uniform(3,5), 2)\n",
        "  #https://www.geeksforgeeks.org/how-to-convert-images-to-numpy-array/\n",
        "  img= Image.open(file)\n",
        "  np_img = np.array(img)\n",
        "\n",
        "  tensor_img =torch.tensor(np_img)\n",
        "  #Taken from the lecture on autoencoders\n",
        "  noise = tensor_img + nf*torch.randn(*tensor_img.shape)\n",
        "\n",
        "  noisy_image = np.array(noise, dtype=np.uint8)\n",
        "  im = Image.fromarray(noisy_image)\n",
        "  fileName = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/' + formatted_piece+\"_colournoise\" + str(number) + \".jpeg\"\n",
        "  im.save(fileName, 'JPEG')\n",
        "  number += 1\n",
        "  print(fileName)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise1.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise2.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise3.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise4.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise5.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise6.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise7.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise8.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise9.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise10.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise11.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise12.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise13.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise14.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise15.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise16.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise17.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise18.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise19.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise20.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise21.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise22.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise23.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise24.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise25.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise26.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise27.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise28.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise29.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise30.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise31.jpeg\n",
            "removed: /content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise32.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/colournoise/blank_colournoise32.jpeg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZ_DLGScb47G",
        "outputId": "50ab08d4-bd30-4a8d-c852-7fe1d38f0390"
      },
      "source": [
        "# generating noise for blank class\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/base'\n",
        "\n",
        "for file in os.listdir('/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise'):\n",
        "  removeFile = os.path.join('/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise', file)\n",
        "  os.remove(removeFile)\n",
        "  print(\"removed: \" + removeFile)\n",
        "\n",
        "formatted_piece = \"blank\"\n",
        "number = 1\n",
        "for file in glob.iglob(os.path.join(pieces_path, \"*.jpeg\")):\n",
        "  nf = round(random.uniform(3,5), 2)\n",
        "  #https://www.geeksforgeeks.org/how-to-convert-images-to-numpy-array/\n",
        "  img= Image.open(file)\n",
        "  np_img = np.array(img)\n",
        "\n",
        "  tensor_img =torch.tensor(np_img)\n",
        "  #Taken from the lecture on autoencoders\n",
        "  noise = tensor_img + nf*torch.randn(*tensor_img.shape)\n",
        "\n",
        "  noisy_image = np.array(noise, dtype=np.uint8)\n",
        "  im = Image.fromarray(noisy_image)\n",
        "  fileName = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/' + formatted_piece+\"_noise\" + str(number) + \".jpeg\"\n",
        "  im.save(fileName, 'JPEG')\n",
        "  number += 1\n",
        "  print(fileName)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise1.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise2.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise3.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise4.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise5.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise6.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise7.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise8.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise9.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise10.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise11.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise12.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise13.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise14.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise15.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise16.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise17.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise18.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise19.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise20.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise21.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise22.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise23.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise24.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise25.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise26.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise27.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise28.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise29.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise30.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise31.jpeg\n",
            "/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/noise/blank_noise32.jpeg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48d38PFNegIR"
      },
      "source": [
        "'''\n",
        "Joanne\n",
        "Mirroring everything in the blank class and put it into the mirror folder\n",
        "'''\n",
        "\n",
        "import os\n",
        "from PIL import Image, ImageOps\n",
        "from random import randint \n",
        "import glob\n",
        "\n",
        "pieces_dataset_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank'\n",
        "target_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces/blank/mirror'\n",
        "dir_list = next(os.walk(pieces_dataset_path))[1]\n",
        "print(dir_list)\n",
        "\n",
        "# first delete whatever is in the mirror folder in the blank class\n",
        "for file in os.listdir(target_path):\n",
        "  removeFile = os.path.join(target_path, file)\n",
        "  os.remove(removeFile)\n",
        "  print(\"removed: \" + removeFile)\n",
        "\n",
        "for folder in dir_list:\n",
        "  if (folder == 'mirror'):\n",
        "    continue\n",
        "  curr_path = os.path.join(pieces_dataset_path, folder)\n",
        "\n",
        "  counter = 1\n",
        "  for file in os.listdir(curr_path):\n",
        "    im = Image.open(os.path.join(curr_path, file))\n",
        "\n",
        "    # mirroring all the images in blank class and put it into mirror\n",
        "    im_mirror = ImageOps.mirror(im).convert(\"RGB\")\n",
        "    dest = os.path.join (target_path, 'blank_' + folder + '_mirror' + str(counter) + '.jpeg')\n",
        "\n",
        "    im_mirror.save(dest, 'JPEG')\n",
        "    counter+=1\n",
        "    print(dest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7EJRroCA9fW"
      },
      "source": [
        "# copy all pngs to another folder for safe keeping\n",
        "png_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces_png'\n",
        "os.chdir(png_path)\n",
        "\n",
        "pieces = ['whiteRook', 'whiteQueen', 'whitePawn', 'whiteKnight', 'whiteKing', 'whiteBishop', 'blackRook', 'blackQueen', 'blackPawn', 'blackKnight', 'blackKing', 'blackBishop']\n",
        "\n",
        "for chessPiece in pieces:\n",
        "  os.mkdir(chessPiece)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rfN0yS-CBK5"
      },
      "source": [
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "png_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces_png'\n",
        "\n",
        "for pieceFolderName in pieces:\n",
        "  baseFolder = os.path.join(pieces_path, pieceFolderName, 'base')\n",
        "  target_path = os.path.join(png_path, pieceFolderName)\n",
        "  for file in os.listdir(baseFolder):\n",
        "    # print(file)\n",
        "    shutil.copy(os.path.join(baseFolder, file), target_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8oxcB_YGst_"
      },
      "source": [
        "# make all base images have a white background\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "\n",
        "for folder in os.listdir(pieces_path):\n",
        "  base_path = os.path.join(pieces_path, folder, 'base')\n",
        "  whiteBackground = (255,255,255)\n",
        "  convert_PNG_to_JPEG(base_path, whiteBackground)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWHpIUV0Dp64"
      },
      "source": [
        "'''\n",
        "Re-populating all white pieces base images (Joanne)\n",
        "'''\n",
        "whitePieces = ['whiteRook', 'whiteQueen', 'whitePawn', 'whiteKnight', 'whiteKing', 'whiteBishop']\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "png_pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces_png'\n",
        "whiteBackground = (255,255,255)\n",
        "\n",
        "for whitePiece in whitePieces:\n",
        "  base_path = os.path.join(pieces_path, whitePiece, 'base')\n",
        "  formatted_piece = pieceAndColour(whitePiece)\n",
        "  # first delete whatever is inside the base folder\n",
        "  for file in os.listdir(base_path):\n",
        "    os.remove(os.path.join(base_path, file))\n",
        "\n",
        "  png_path = os.path.join(png_pieces_path, whitePiece)\n",
        "  counter = 1\n",
        "  for file in os.listdir(png_path):\n",
        "    \n",
        "    curr_path = os.path.join(png_path, file)\n",
        "\n",
        "    # Getting only the file name without the file extension\n",
        "    fileName = os.path.splitext(curr_path)[0]\n",
        "\n",
        "    # Convert the PNG to JPEG\n",
        "    im = Image.open(curr_path)\n",
        "\n",
        "    im = im.convert(\"RGBA\")   # it had mode P after DL it from OP\n",
        "    if im.mode in ('RGBA'):\n",
        "      # create background, overlay it on top of the image\n",
        "      background = Image.new(im.mode[:-1], im.size, whiteBackground)\n",
        "\n",
        "      # put the png image on top of the background, then get rid of transparency\n",
        "      background.paste(im, im.split()[-1]) # omit transparency\n",
        "      im = background\n",
        "\n",
        "      rgb_im = im.convert('RGB')\n",
        "      os.chdir(base_path)\n",
        "      rgb_im.save(formatted_piece + '_base' + str(counter) + '.jpeg', 'JPEG')\n",
        "      counter += 1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXrkSmtOJk-C"
      },
      "source": [
        "# removing all files with .png ending in base folders\n",
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces'\n",
        "for folder in os.listdir(pieces_path):\n",
        "  base_path = os.path.join(pieces_path, folder, \"base\")\n",
        "  for file in os.listdir(base_path):\n",
        "    if file.endswith(\".png\"):\n",
        "      os.remove(os.path.join(base_path, file))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ysxs4VWKhLI",
        "outputId": "959204c1-69d9-47a9-b46c-1c09a52a3d08"
      },
      "source": [
        "# rename all jpeg images in base folders to match naming conventions\n",
        "for folder in os.listdir(pieces_path):\n",
        "  if folder == 'blank':\n",
        "    continue\n",
        "  base_path = os.path.join(pieces_path, folder, \"base\")\n",
        "  formatted_piece = pieceAndColour(folder)\n",
        "  print(formatted_piece)\n",
        "  counter = 1\n",
        "  for file in os.listdir(base_path):\n",
        "    newFileName = formatted_piece + \"_base\" + str(counter) + \".jpeg\"\n",
        "    os.rename(os.path.join(base_path, file), os.path.join(base_path, newFileName))\n",
        "    counter += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p_w\n",
            "r_w\n",
            "b_w\n",
            "n_w\n",
            "q_w\n",
            "k_w\n",
            "p_b\n",
            "b_b\n",
            "r_b\n",
            "n_b\n",
            "q_b\n",
            "k_b\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAYrG_mSQstw",
        "outputId": "e6dd5919-2d8b-43fc-8dae-fe1a9cc9dc1f"
      },
      "source": [
        "# rename all jpeg images in mirror folders to match naming conventions\n",
        "for folder in os.listdir(pieces_path):\n",
        "  if folder == 'blank':\n",
        "    continue\n",
        "  mirror_path = os.path.join(pieces_path, folder, \"mirror\")\n",
        "  formatted_piece = pieceAndColour(folder)\n",
        "  print(formatted_piece)\n",
        "  counter = 1\n",
        "  for file in os.listdir(mirror_path):\n",
        "    newFileName = formatted_piece + \"_mirror\" + str(counter) + \".jpeg\"\n",
        "    os.rename(os.path.join(mirror_path, file), os.path.join(mirror_path, newFileName))\n",
        "    counter += 1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p_w\n",
            "r_w\n",
            "b_w\n",
            "n_w\n",
            "q_w\n",
            "k_w\n",
            "p_b\n",
            "b_b\n",
            "r_b\n",
            "n_b\n",
            "q_b\n",
            "k_b\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3zQaaSnTFrI"
      },
      "source": [
        "# creating folders for pieces_dataset\n",
        "pieces_shortForm = ['r_w', 'q_w', 'p_w', 'n_w', 'k_w', 'b_w', 'r_b', 'q_b', 'p_b', 'n_b', 'k_b', 'b_b', 'blank']\n",
        "pieces_dataset_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset'\n",
        "\n",
        "os.chdir(pieces_dataset_path)\n",
        "\n",
        "os.mkdir('pieces_train')\n",
        "os.mkdir('pieces_val')\n",
        "os.mkdir('pieces_test')\n",
        "\n",
        "os.chdir(pieces_dataset_path)\n",
        "for folder in os.listdir(pieces_dataset_path):\n",
        "  for chessPiece in pieces_shortForm:\n",
        "    os.mkdir(os.path.join(pieces_dataset_path, folder, chessPiece))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsrSJeTCnv6J"
      },
      "source": [
        "#Populating the pieces_dataset 60/20/20\n",
        "\n",
        "import random\n",
        "\n",
        "mov_pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces'\n",
        "form = [\"base\",\"colour\",\"colournoise\",\"mirror\",\"noise\"]\n",
        "for folder in os.listdir(mov_pieces_path):\n",
        "  formatted_piece = pieceAndColour(folder)\n",
        "  for fold in form: \n",
        "    mov_path = os.path.join(mov_pieces_path, folder, fold)\n",
        "    count = 1\n",
        "    for file in glob.iglob(os.path.join(mov_path, \"*.jpeg\")):\n",
        "      moving_image = random.choice([\n",
        "    x for x in os.listdir(mov_path)\n",
        "    if os.path.isfile(os.path.join(mov_path, x))])\n",
        "      #https://www.codegrepper.com/code-examples/python/select+random+image+from+folder+python\n",
        "      if fold == \"mirror\":\n",
        "          if count<77:\n",
        "            shutil.move('/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces/{}/{}/{}'.format(folder,fold,moving_image),'/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces_train/{}'.format(formatted_piece))\n",
        "          elif count<103:\n",
        "            shutil.move('/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces/{}/{}/{}'.format(folder,fold,moving_image),'/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces_val/{}'.format(formatted_piece))\n",
        "          else:\n",
        "            shutil.move('/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces/{}/{}/{}'.format(folder,fold,moving_image),'/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces_test/{}'.format(formatted_piece))\n",
        "      else:\n",
        "          if count<21:\n",
        "            shutil.move('/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces/{}/{}/{}'.format(folder,fold,moving_image),'/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces_train/{}'.format(formatted_piece))\n",
        "          elif count<27:\n",
        "            shutil.move('/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces/{}/{}/{}'.format(folder,fold,moving_image),'/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces_val/{}'.format(formatted_piece))\n",
        "          else:\n",
        "            shutil.move('/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces/{}/{}/{}'.format(folder,fold,moving_image),'/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces_test/{}'.format(formatted_piece))\n",
        "      count+=1\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9b2UY_sxjJU",
        "outputId": "3e50dc06-831d-4e33-ab77-f070f9f343ab"
      },
      "source": [
        "# count number of files in pieces folder\n",
        "for piece in os.listdir('/content/drive/MyDrive/APS360_Project_Group_3/pieces'):\n",
        "  curr_path = os.path.join('/content/drive/MyDrive/APS360_Project_Group_3/pieces', piece)\n",
        "  \n",
        "  total = 0\n",
        "  for folderVariation in os.listdir(curr_path):\n",
        "    counter = 0\n",
        "    next_path = os.path.join(curr_path, folderVariation)\n",
        "    for file in os.listdir(next_path):\n",
        "      counter += 1\n",
        "    total = total + counter\n",
        "    # print (piece + folderVariation + \" total = \" + str(counter))\n",
        "  print(piece + \"total = \" + str(total))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "whitePawntotal = 256\n",
            "whiteRooktotal = 256\n",
            "whiteBishoptotal = 256\n",
            "whiteKnighttotal = 256\n",
            "whiteQueentotal = 256\n",
            "whiteKingtotal = 256\n",
            "blanktotal = 256\n",
            "blackPawntotal = 256\n",
            "blackBishoptotal = 256\n",
            "blackRooktotal = 256\n",
            "blackKnighttotal = 256\n",
            "blackQueentotal = 256\n",
            "blackKingtotal = 256\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PjL0bY2TTYe3"
      },
      "source": [
        "## How to detect board positions:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfROIrXDUxop"
      },
      "source": [
        "\n",
        "\n",
        "*   Total of 64 positions on the board, place one piece on each positions individually, generate data\n",
        "*   List item\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ITxg6q2RImL"
      },
      "source": [
        "# How to detect both Chess piece and board position\n",
        "- use bounding boxes to identify pieces on the chess board (can do this in PyTorch or Tensorflow) \n",
        "  - downside: you might need to do this manually\n",
        "- classify each box that was found by the neural network\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8OQxCOU68wa"
      },
      "source": [
        "# Creating the Baseline Model for Identifying a Chess Piece\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHYvg-bl7cvJ"
      },
      "source": [
        "Outline of Baseline Model:\n",
        "*   Start off by first identifying white and black pieces using the colours HSP value\n",
        "*   Identify the piece by comparing it with an existing group of images of each piece\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P362CjOUoxYK",
        "outputId": "75f6b8ff-227c-432a-a82e-607551ce1b4b"
      },
      "source": [
        "pieces_path = '/content/drive/MyDrive/APS360_Project_Group_3/baseline/default' #Location of the default piece images\n",
        "test_path = '/content/drive/MyDrive/APS360_Project_Group_3/baseline/test' #Location of the test images\n",
        "\n",
        "colour = None\n",
        "piece = None\n",
        "output = None\n",
        "answer = None\n",
        "total = 0\n",
        "correct = 0\n",
        "piece_correct = 0\n",
        "colour_correct = 0\n",
        "\n",
        "rook = None\n",
        "queen = None\n",
        "pawn = None\n",
        "knight = None\n",
        "king = None\n",
        "bishop = None\n",
        "blank = None\n",
        "\n",
        "#loads in default pieces as numpy arrays\n",
        "for file in glob.iglob(os.path.join(pieces_path, \"*.jpeg\")):\n",
        "\n",
        "  #https://www.geeksforgeeks.org/how-to-convert-images-to-numpy-array/\n",
        "  if file == \"/content/drive/MyDrive/APS360_Project_Group_3/baseline/default/r.jpeg\":\n",
        "    img= Image.open(file)\n",
        "    rook = np.array(img)\n",
        "  elif file == \"/content/drive/MyDrive/APS360_Project_Group_3/baseline/default/q.jpeg\":\n",
        "    img= Image.open(file)\n",
        "    queen = np.array(img)\n",
        "  elif file == \"/content/drive/MyDrive/APS360_Project_Group_3/baseline/default/p.jpeg\":\n",
        "    img= Image.open(file)\n",
        "    pawn = np.array(img)\n",
        "  elif file == \"/content/drive/MyDrive/APS360_Project_Group_3/baseline/default/n.jpeg\":\n",
        "    img= Image.open(file)\n",
        "    knight = np.array(img)\n",
        "  elif file == \"/content/drive/MyDrive/APS360_Project_Group_3/baseline/default/k.jpeg\":\n",
        "    img= Image.open(file)\n",
        "    king = np.array(img)\n",
        "  elif file == \"/content/drive/MyDrive/APS360_Project_Group_3/baseline/default/b.jpeg\":\n",
        "    img= Image.open(file)\n",
        "    bishop = np.array(img) \n",
        "  elif file == \"/content/drive/MyDrive/APS360_Project_Group_3/baseline/default/blank.jpeg\":\n",
        "    img= Image.open(file)\n",
        "    blank = np.array(img)\n",
        "\n",
        "#goes through wherever the images are being stored and checks them for colour and similarity to the piece, it will also keep track of accuracy\n",
        "for image in glob.iglob(os.path.join(test_path, \"*.jpeg\")):\n",
        "  img= Image.open(image)\n",
        "  input = np.array(img)\n",
        "\n",
        "  curr_path = os.path.join(test_path, image)\n",
        "  fileName = os.path.splitext(curr_path)[0]\n",
        "  answer = fileName[60:63]\n",
        "\n",
        "  #Setting the RGB value of the middle of the input image to its own variable\n",
        "  r = input[25][25][0]\n",
        "  g = input[25][25][1]\n",
        "  b = input[25][25][2]\n",
        "\n",
        "  #colour check using HSP value\n",
        "  #https://stackoverflow.com/questions/22603510/is-this-possible-to-detect-a-colour-is-a-light-or-dark-colour\n",
        "  hsp = math.sqrt(0.299 * (r * r) + 0.587 * (g * g) + 0.114 * (b * b))\n",
        "  #print(hsp)\n",
        "  if hsp > 3:\n",
        "    colour = \"w\"\n",
        "  else:\n",
        "    colour = \"b\"\n",
        "\n",
        "\n",
        "  #Checks the input image against all other piece types\n",
        "  totalDifference = None\n",
        "  lowestDifference = None\n",
        "  pieceName = None\n",
        "\n",
        "  totalDifference, pieceName = differenceCheck(input, rook, \"r\")\n",
        "  lowestDifference = totalDifference\n",
        "  piece = pieceName\n",
        "\n",
        "  totalDifference, pieceName = differenceCheck(input, queen, \"q\")\n",
        "  if totalDifference < lowestDifference:\n",
        "    lowestDifference = totalDifference\n",
        "    piece = pieceName\n",
        "\n",
        "  totalDifference, pieceName = differenceCheck(input, pawn, \"p\")\n",
        "  if totalDifference < lowestDifference:\n",
        "    lowestDifference = totalDifference\n",
        "    piece = pieceName\n",
        "\n",
        "  totalDifference, pieceName = differenceCheck(input, knight, \"n\")\n",
        "  if totalDifference < lowestDifference:\n",
        "    lowestDifference = totalDifference\n",
        "    piece = pieceName\n",
        "\n",
        "  totalDifference, pieceName = differenceCheck(input, king, \"k\")\n",
        "  if totalDifference < lowestDifference:\n",
        "    lowestDifference = totalDifference\n",
        "    piece = pieceName\n",
        "\n",
        "  totalDifference, pieceName = differenceCheck(input, bishop, \"b\")\n",
        "  if totalDifference < lowestDifference:\n",
        "    lowestDifference = totalDifference\n",
        "    piece = pieceName\n",
        "\n",
        "  totalDifference, pieceName = differenceCheck(input, blank, \"blank\")\n",
        "  if totalDifference < lowestDifference:\n",
        "    lowestDifference = totalDifference\n",
        "    piece = pieceName\n",
        "\n",
        "  if piece == \"blank\":\n",
        "    output = \"bla\"\n",
        "  else:\n",
        "    output = piece + \"_\" + colour\n",
        "  \n",
        "  #validate the guess\n",
        "  if output == answer:\n",
        "    correct +=1\n",
        "  if output[0] == answer[0] and output != \"bla\":\n",
        "    piece_correct += 1\n",
        "  if output[2] == answer[2] and output != \"bla\":\n",
        "    colour_correct += 1\n",
        "  \n",
        "  total += 1\n",
        "\n",
        "print(\"Total accuracy: \", correct/total)\n",
        "print(\"Colour accuracy: \", colour_correct /total)\n",
        "print(\"Piece accuracy: \", piece_correct/total)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:63: RuntimeWarning: overflow encountered in ubyte_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: RuntimeWarning: overflow encountered in ubyte_scalars\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Total accuracy:  0.14\n",
            "Colour accuracy:  0.34\n",
            "Piece accuracy:  0.16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufssz6-2pKB2"
      },
      "source": [
        "def differenceCheck(input, pieceName, name):\n",
        "  totalDifference = 0\n",
        "  for i in range(0,49):\n",
        "    for j in range(0,49):\n",
        "      totalDifference += abs(input[i][j][0] - pieceName[i][j][0])\n",
        "  return totalDifference, name"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_k3MzcaD7Cg5"
      },
      "source": [
        "# Our Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lapWqFms7iU1"
      },
      "source": [
        "- try overfitting using a small subsample of the dataset first (to see if we even have the capacity to do so)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GdQfzbv8zmy_"
      },
      "source": [
        "## Accuracy and Plotting Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_YPo13KzrUy"
      },
      "source": [
        "def plot_training_curve(path):\n",
        "    import matplotlib.pyplot as plt\n",
        "    train_acc = np.loadtxt(\"{}_train_accuracy.csv\".format(path))\n",
        "    val_acc = np.loadtxt(\"{}_val_accuracy.csv\".format(path))\n",
        "    \n",
        "    plt.title(\"Train vs Validation Accuracy\")\n",
        "    n = len(train_acc) # number of epochs\n",
        "    plt.plot(range(1,n+1), train_acc, label=\"Train\")\n",
        "    plt.plot(range(1,n+1), val_acc, label=\"Validation\")\n",
        "    plt.xlabel(\"Epoch\")\n",
        "    plt.ylabel(\"Accuracy\")\n",
        "    plt.legend(loc='best')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixJRH3Jzzv-a"
      },
      "source": [
        "def get_model_name(name, batch_size, learning_rate, epoch):\n",
        "    path = \"model_{0}_bs{1}_lr{2}_epoch{3}\".format(name,\n",
        "                                                   batch_size,\n",
        "                                                   learning_rate,\n",
        "                                                   epoch)\n",
        "    return path"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcFKuVxNz8m6"
      },
      "source": [
        "use_cuda = True"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YsVmtJk0DSM"
      },
      "source": [
        "# function from tut3b\n",
        "def get_accuracy(model,  data_loader):\n",
        "\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for imgs, labels in data_loader:\n",
        "        \n",
        "        #To Enable GPU Usage\n",
        "        if use_cuda and torch.cuda.is_available():\n",
        "          imgs = imgs.cuda()\n",
        "          labels = labels.cuda()\n",
        "        \n",
        "        output = model(imgs)\n",
        "        \n",
        "        #select index with maximum prediction score\n",
        "        pred = output.max(1, keepdim=True)[1]\n",
        "        correct += pred.eq(labels.view_as(pred)).sum().item()\n",
        "        total += imgs.shape[0]\n",
        "    return correct / total"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAxdolWKcqNP"
      },
      "source": [
        "## Model for Identifying Chess Piece"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoQQqY7q7IbO"
      },
      "source": [
        "# from Lab3 - have yet to modify\n",
        "class Gesture(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Gesture, self).__init__()\n",
        "        self.name = \"Gesture\"\n",
        "        self.conv1 = nn.Conv2d(3, 8, 8)\n",
        "        self.pool = nn.MaxPool2d(3, 3)\n",
        "        self.conv2 = nn.Conv2d(8, 12, 8)\n",
        "\n",
        "        self.fc1 = nn.Linear(12 * 19 * 19, 35)\n",
        "        self.fc2 = nn.Linear(35, 13)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(F.relu(self.conv1(x)))\n",
        "        x = self.pool(F.relu(self.conv2(x)))\n",
        "        # print(x.shape)\n",
        "        x = x.view(-1, 12 * 19 * 19)\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        #x = x.squeeze(1) # Flatten to [batch_size]\n",
        "        return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tABhiDfJyqJN"
      },
      "source": [
        "## Training (Identify Chess Piece)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TARsHxxuufH-",
        "outputId": "9d449f66-aa81-41be-b4fa-5de8152954d9"
      },
      "source": [
        "pieces_train = '/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces_train'\n",
        "pieces_val = '/content/drive/MyDrive/APS360_Project_Group_3/pieces_dataset/pieces_val'\n",
        "#pieces_test =  '/content/drive/MyDrive/APS360_Project_Group_3/test_pieces/test'\n",
        "\n",
        "# make sure all images are the same size and convert to tensors\n",
        "transformations = transforms.Compose([transforms.Resize([200,200]),\n",
        "                                transforms.ToTensor()])\n",
        "\n",
        "# setting the paths for ImageFolder to split our data into train, validation, and test set\n",
        "train_set = torchvision.datasets.ImageFolder(pieces_train, transform = transformations)\n",
        "val_set = torchvision.datasets.ImageFolder(pieces_val, transform = transformations)\n",
        "#test_set = torchvision.datasets.ImageFolder(pieces_test, transform = transformations)\n",
        "\n",
        "print('Num training images: ', len(train_set))\n",
        "print('Num validation images: ', len(val_set))\n",
        "#print('Num test images: ', len(test_set))\n",
        "\n",
        "batch_size= 64\n",
        "\n",
        "# instead of using shuffle, we can set a sampler instead ()\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size, shuffle=True, num_workers = 1)\n",
        "val_loader = torch.utils.data.DataLoader(val_set, batch_size, shuffle=True, num_workers = 1)\n",
        "#test_loader = torch.utils.data.DataLoader(test_set, batch_size, shuffle=True, num_workers = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Num training images:  2028\n",
            "Num validation images:  650\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Tf2vAoYy39fm",
        "outputId": "d5e12c7b-d809-4942-cc04-52ef3e2d7fe6"
      },
      "source": [
        "# obtain one batch of training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "images = images.numpy() # convert images to numpy for display\n",
        "\n",
        "# plot the images in the batch, along with the corresponding labels\n",
        "# (code modified; originally taken from tut3b)\n",
        "fig = plt.figure(figsize=(5, 12))\n",
        "for idx in np.arange(13):\n",
        "    ax = fig.add_subplot(6, 3, idx+1, xticks=[], yticks=[])\n",
        "    plt.imshow(np.transpose(images[idx], (1, 2, 0)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x864 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQxSHVEJyrEx"
      },
      "source": [
        "# from Lab3\n",
        "def train_net_v2(net, batch_size=64, learning_rate=0.01, num_epochs=10):\n",
        " \n",
        "    # Fixed PyTorch random seed for reproducible result\n",
        "    torch.manual_seed(5)\n",
        "\n",
        "    # Define the Loss function and optimizer\n",
        "    # The loss function will be CrossEntropyLoss because we are trying to\n",
        "    # classify 9 different images\n",
        "    # Using Adam optimizer for the reasons described in text cell above\n",
        "    criterion = nn.CrossEntropyLoss()\n",
        "    optimizer = optim.Adam(net.parameters(), lr=learning_rate)\n",
        "    \n",
        "    train_acc, val_acc = [], []\n",
        "\n",
        "    # training\n",
        "    n = 0 # the number of iterations\n",
        "    for epoch in range(num_epochs):\n",
        "      print(\"Epoch num \"+str(epoch))\n",
        "\n",
        "      for imgs, labels in iter(train_loader):\n",
        "        #To Enable GPU Usage\n",
        "        if use_cuda and torch.cuda.is_available():\n",
        "          imgs = imgs.cuda()\n",
        "          labels = labels.cuda()\n",
        "\n",
        "        outputs = net(imgs)           # forward pass\n",
        "        loss = criterion(outputs, labels) # compute the total loss\n",
        "        loss.backward()               # backward pass (compute parameter updates)\n",
        "        optimizer.step()              # make the updates for each parameter\n",
        "        optimizer.zero_grad()         # a clean up step for PyTorch\n",
        "\n",
        "        n += 1\n",
        "\n",
        "      train_acc.append( get_accuracy(net, train_loader))\n",
        "      print(\"training accuracy = \" + str(train_acc[-1]))\n",
        "\n",
        "      val_acc.append( get_accuracy(net, val_loader))\n",
        "      print(\"validation accuracy = \" + str(val_acc[-1]))\n",
        "\n",
        "      model_path = get_model_name(net.name, batch_size, learning_rate, epoch)\n",
        "      torch.save(net.state_dict(), model_path)\n",
        "\n",
        "    # Write the train/test loss/err into CSV file for plotting later\n",
        "    epochs = np.arange(1, num_epochs + 1)\n",
        "    np.savetxt(\"{}_train_accuracy.csv\".format(model_path), train_acc)\n",
        "    np.savetxt(\"{}_val_accuracy.csv\".format(model_path), val_acc)\n",
        "\n",
        "\n",
        "    return train_acc, val_acc\n",
        "      "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0R5--Qc27Mzt"
      },
      "source": [
        "# Summary of Results V1 (for Progress Report July 9th, 2021)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XOBSoWNVywpY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab37728f-9b91-4b0d-b7ef-e467dc2aeab1"
      },
      "source": [
        "model = Gesture()\n",
        "if torch.cuda.is_available():\n",
        "    model.cuda()\n",
        "train_net_v2(model,  batch_size=128, learning_rate=0.01, num_epochs=30)\n",
        "print(\"DONE!!!!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch num 0\n",
            "training accuracy = 0.09171597633136094\n",
            "validation accuracy = 0.09538461538461539\n",
            "Epoch num 1\n",
            "training accuracy = 0.10059171597633136\n",
            "validation accuracy = 0.10307692307692308\n",
            "Epoch num 2\n",
            "training accuracy = 0.10502958579881656\n",
            "validation accuracy = 0.12\n",
            "Epoch num 3\n",
            "training accuracy = 0.1242603550295858\n",
            "validation accuracy = 0.13692307692307693\n",
            "Epoch num 4\n",
            "training accuracy = 0.13313609467455623\n",
            "validation accuracy = 0.15538461538461537\n",
            "Epoch num 5\n",
            "training accuracy = 0.14053254437869822\n",
            "validation accuracy = 0.16\n",
            "Epoch num 6\n",
            "training accuracy = 0.133629191321499\n",
            "validation accuracy = 0.15076923076923077\n",
            "Epoch num 7\n",
            "training accuracy = 0.1858974358974359\n",
            "validation accuracy = 0.2276923076923077\n",
            "Epoch num 8\n",
            "training accuracy = 0.16321499013806706\n",
            "validation accuracy = 0.18615384615384614\n",
            "Epoch num 9\n",
            "training accuracy = 0.133629191321499\n",
            "validation accuracy = 0.1476923076923077\n",
            "Epoch num 10\n",
            "training accuracy = 0.22534516765285997\n",
            "validation accuracy = 0.24615384615384617\n",
            "Epoch num 11\n",
            "training accuracy = 0.2943786982248521\n",
            "validation accuracy = 0.2969230769230769\n",
            "Epoch num 12\n",
            "training accuracy = 0.5877712031558185\n",
            "validation accuracy = 0.6046153846153847\n",
            "Epoch num 13\n",
            "training accuracy = 0.7075936883629191\n",
            "validation accuracy = 0.6784615384615384\n",
            "Epoch num 14\n",
            "training accuracy = 0.8076923076923077\n",
            "validation accuracy = 0.7569230769230769\n",
            "Epoch num 15\n",
            "training accuracy = 0.8574950690335306\n",
            "validation accuracy = 0.8092307692307692\n",
            "Epoch num 16\n",
            "training accuracy = 0.8856015779092702\n",
            "validation accuracy = 0.8615384615384616\n",
            "Epoch num 17\n",
            "training accuracy = 0.8939842209072978\n",
            "validation accuracy = 0.8692307692307693\n",
            "Epoch num 18\n",
            "training accuracy = 0.9442800788954635\n",
            "validation accuracy = 0.9107692307692308\n",
            "Epoch num 19\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9299802761341223\n",
            "validation accuracy = 0.8923076923076924\n",
            "Epoch num 20\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9580867850098619\n",
            "validation accuracy = 0.9246153846153846\n",
            "Epoch num 21\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9575936883629191\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "validation accuracy = 0.9107692307692308\n",
            "Epoch num 22\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9748520710059172\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "validation accuracy = 0.9384615384615385\n",
            "Epoch num 23\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9664694280078896\n",
            "validation accuracy = 0.916923076923077\n",
            "Epoch num 24\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9467455621301775\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "validation accuracy = 0.9\n",
            "Epoch num 25\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9644970414201184\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "validation accuracy = 0.9123076923076923\n",
            "Epoch num 26\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.967948717948718\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "validation accuracy = 0.9230769230769231\n",
            "Epoch num 27\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9891518737672583\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "validation accuracy = 0.9307692307692308\n",
            "Epoch num 28\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9866863905325444\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "validation accuracy = 0.9338461538461539\n",
            "Epoch num 29\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n",
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "training accuracy = 0.9861932938856016\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <function _MultiProcessingDataLoaderIter.__del__ at 0x7f10a8fea320>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1328, in __del__\n",
            "    self._shutdown_workers()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py\", line 1320, in _shutdown_workers\n",
            "    if w.is_alive():\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 151, in is_alive\n",
            "    assert self._parent_pid == os.getpid(), 'can only test a child process'\n",
            "AssertionError: can only test a child process\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "validation accuracy = 0.9292307692307692\n",
            "DONE!!!!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqSJttlvNxVS",
        "outputId": "a101917b-2dc8-4c44-8b63-2c43367f2cda"
      },
      "source": [
        "!python train.py --img 200 --batch 50 --epochs 200 \\\n",
        "  --adam --data /content/yolov5/FEN.yaml --weights yolov5x.pt \\\n",
        "  --name yolov5x_b50_ep200_run_train282_val61_adam --cache --project /content/drive/MyDrive/APS360_Project_Group_3/YOLOv5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "python3: can't open file 'train.py': [Errno 2] No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}