frankbryce/datalabeler.ipynb

## datalabeler.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DataLabeler20200502.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNKVp6P1u9rora2MA92KmLy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/frankbryce/0e6d244f225963499ad331431a182656/datalabeler.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3I8jdK_VwJz",
        "colab_type": "code",
        "outputId": "e722b129-1aa2-4439-a0ca-71f792484517",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "# Mount Google Drive in Colab\n",
        "from google.colab import drive\n",
        "mount_location = '/content/drive/'\n",
        "drive.mount(mount_location, force_remount=True)\n",
        "\n",
        "# DIRECTORY CONTAINING IMAGES TO BE LABELED\n",
        "directory = 'My Drive/Data/Minecraft/HUD'  # CHANGE IF NECESSARY\n",
        "path_prefix = mount_location + directory + '/'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ey0o0uhr-QME",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create helper methods for accessing and creating label and image files\n",
        "import os\n",
        "import re\n",
        "\n",
        "# Get list of image file names and label file names\n",
        "img_ext = \".png\"\n",
        "lbl_ext = \".txt\"\n",
        "img_regex = re.compile(r'.*image([0-9]+)\\.png')\n",
        "lbl_regex = re.compile(r'.*label([0-9]+)\\.txt')\n",
        "\n",
        "def getFileIdx(f):\n",
        "  # getFileIdx returns the index of the image (in string form)\n",
        "  #\n",
        "  # Example: getFileIdx(\"image001.png\") returns \"001\"\n",
        "  assert(isinstance(f, str))\n",
        "  m = img_regex.match(f)\n",
        "  if not m:\n",
        "    m = lbl_regex.match(f)\n",
        "  if not m:\n",
        "    raise Exception(\"{filename} didn't match {img_regex} or {lbl_regex}\".format(\n",
        "        img_regex=img_regex,\n",
        "        lbl_regex=lbl_regex,\n",
        "        filename=f))\n",
        "  idx = str(m.group(1))\n",
        "  if not idx:\n",
        "    raise Exception('No index found before file extension for {filename}',\n",
        "                    filename=f)\n",
        "  return idx\n",
        "\n",
        "def makeLabelFilename(idx):\n",
        "  # makeLabelFilename returns the label filename for a give index string.\n",
        "  # \n",
        "  # Example: makeLabelFilename(\"001\") returns \"label001.txt\"\n",
        "  assert(isinstance(idx, str))\n",
        "  return 'label{idx}'.format(idx=idx) + lbl_ext\n",
        "\n",
        "def makeImageFilename(idx):\n",
        "  # makeImageFilename returns the image filename for a give index string.\n",
        "  # \n",
        "  # Example: makeImageFilename(\"001\") returns \"image001.txt\"\n",
        "  assert(isinstance(idx, str))\n",
        "  return 'image{idx}'.format(idx=idx) + img_ext"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFKtqcPvA5P7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Label missing lbl files\n",
        "import IPython\n",
        "import ipywidgets as widgets\n",
        "from google.colab import output\n",
        "\n",
        "from matplotlib.pyplot import figure, imshow, axis\n",
        "from matplotlib.image import imread\n",
        "\n",
        "import random\n",
        "from shutil import copy\n",
        "\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "def SetMissingLabels():\n",
        "  # get list of image and label files\n",
        "  img_files = []\n",
        "  lbl_files = []\n",
        "  for f in os.listdir(path_prefix):\n",
        "    if img_regex.match(f):\n",
        "      img_files.append(f)\n",
        "    if lbl_regex.match(f):\n",
        "      lbl_files.append(f)\n",
        "\n",
        "  # get highest index of missing label file\n",
        "  maxMissingIdx = -1\n",
        "  for img_file in img_files:\n",
        "    sIdx = getFileIdx(img_file)\n",
        "    nIdx = int(sIdx)\n",
        "    lbl_file = makeLabelFilename(sIdx)\n",
        "    if not os.path.exists(path_prefix + lbl_file) and nIdx > maxMissingIdx:\n",
        "      maxMissingIdx = nIdx\n",
        "\n",
        "  # show an image and let user input a label for the image\n",
        "  def labelPrompt(idx, lbls):\n",
        "    if idx == -1:\n",
        "      print(\"Nothing to Label\")\n",
        "      return\n",
        "    img_file = makeImageFilename('{:04d}'.format(idx))\n",
        "    display(IPython.display.HTML('<div>' + img_file + '</div>'))\n",
        "    display(IPython.display.Image(path_prefix + img_file, height=300, width=400))\n",
        "    display(IPython.display.HTML('''\n",
        "        <button id='button'>Add New Label</button>\n",
        "        <input type=\"text\" id=\"input\" name=\"fname\">\n",
        "        <script>\n",
        "          document.querySelector(\"#button\").onclick = () => {\n",
        "            google.colab.kernel.invokeFunction(\"notebook.LabelImage\",\n",
        "                [''' + str(idx) + ''', document.querySelector(\"#input\").value],\n",
        "                {});\n",
        "          };\n",
        "        </script>\n",
        "        <br>\n",
        "        Existing Labels:''' +\n",
        "        '\\n'.join([\n",
        "            '''<br>\n",
        "            <button id=\"button-''' + l + '''\">''' + l + '''</button>\n",
        "            <script>\n",
        "              document.querySelector(\"#button-''' + l + '''\").onclick = () => {\n",
        "                google.colab.kernel.invokeFunction(\"notebook.LabelImage\",\n",
        "                    [''' + str(idx) + ''', \"''' + l + '''\"],\n",
        "                    {});\n",
        "              };\n",
        "            </script>''' for l in sorted(lbls)\n",
        "        ])))\n",
        "\n",
        "  def labelImage(idx, lbl):\n",
        "    lbls.add(lbl)\n",
        "    lbl_file = makeLabelFilename('{:04d}'.format(idx))\n",
        "    with open(path_prefix + lbl_file, 'w') as f:\n",
        "      f.write(lbl)\n",
        "    IPython.display.clear_output()\n",
        "    idx -= 1\n",
        "    lbl_file = makeLabelFilename('{:04d}'.format(idx))\n",
        "    img_file = makeImageFilename('{:04d}'.format(idx))\n",
        "    while (os.path.exists(path_prefix + lbl_file)\n",
        "        or not os.path.exists(path_prefix + img_file)):\n",
        "      idx -= 1\n",
        "      if idx < 0:\n",
        "        return\n",
        "      lbl_file = makeLabelFilename('{:04d}'.format(idx))\n",
        "      img_file = makeImageFilename('{:04d}'.format(idx))\n",
        "    labelPrompt(idx, lbls)\n",
        "\n",
        "  # currently existing labels\n",
        "  lbls = set()\n",
        "  for lbl_file in lbl_files:\n",
        "    with open(path_prefix + lbl_file) as f:\n",
        "      lbls.add(f.readline())\n",
        "\n",
        "  labelPrompt(maxMissingIdx, lbls)\n",
        "  output.register_callback('notebook.LabelImage', labelImage)\n",
        "\n",
        "def ViewLabelSummary():\n",
        "  lbl_files = []\n",
        "  lbls = dict()\n",
        "  for f in os.listdir(path_prefix):\n",
        "    if lbl_regex.match(f):\n",
        "      lbl_files.append(f)\n",
        "      with open(path_prefix + f) as lbl_file:\n",
        "        lbl = lbl_file.readline()\n",
        "        if lbl in lbls:\n",
        "          lbls[lbl] += 1\n",
        "        else:\n",
        "          lbls[lbl] = 1\n",
        "  print(lbls)\n",
        "\n",
        "def ViewImagesForLabel(lbl, max_num=25, num_cols=5):\n",
        "  c = 0\n",
        "  list_of_files = []\n",
        "  for f in os.listdir(path_prefix):\n",
        "    if lbl_regex.match(f):\n",
        "      with open(path_prefix + f) as lbl_file:\n",
        "        if lbl == lbl_file.readline():\n",
        "          c += 1\n",
        "          img_file = makeImageFilename(getFileIdx(f))\n",
        "          list_of_files.append(path_prefix + img_file)\n",
        "          if c == max_num:\n",
        "            break\n",
        "  fig = figure()\n",
        "  nFiles = len(list_of_files)\n",
        "  print(list_of_files)\n",
        "  for i in range(nFiles):\n",
        "    fig.add_subplot(nFiles/num_cols+1,num_cols,i+1)\n",
        "    imshow(imread(list_of_files[i]),cmap='Greys_r')\n",
        "    axis('off')\n",
        "  \n",
        "def SplitIntoTrainTest(train_ratio=0.5):\n",
        "  random.seed(529726242)\n",
        "  lbl_to_file = dict()\n",
        "  for f in os.listdir(path_prefix):\n",
        "    if lbl_regex.match(f):\n",
        "      with open(path_prefix + f) as lbl_file:\n",
        "        lbl = lbl_file.readline()\n",
        "        if lbl in lbl_to_file:\n",
        "          lbl_to_file[lbl].append(f)\n",
        "        else:\n",
        "          lbl_to_file[lbl] = [f]\n",
        "  \n",
        "  if os.path.isdir(path_prefix + \"train\"):\n",
        "    for f in os.listdir(path_prefix + \"train\"):\n",
        "      os.remove(path_prefix + \"train/\" + f)\n",
        "  else:\n",
        "    os.mkdir(path_prefix + \"train\")\n",
        "\n",
        "  if os.path.isdir(path_prefix + \"test\"):\n",
        "    for f in os.listdir(path_prefix + \"test\"):\n",
        "      os.remove(path_prefix + \"test/\" + f)\n",
        "  else:\n",
        "    os.mkdir(path_prefix + \"test\")\n",
        "  \n",
        "  for lbl in lbl_to_file.keys():\n",
        "    train_files = random.sample(\n",
        "        lbl_to_file[lbl],\n",
        "        int(len(lbl_to_file[lbl])*train_ratio))\n",
        "    test_files = list(set(lbl_to_file[lbl]) - set(train_files))\n",
        "    for train_file in train_files:\n",
        "      copy(path_prefix + train_file, path_prefix + \"train/\")\n",
        "      img_file = makeImageFilename(getFileIdx(train_file))\n",
        "      copy(path_prefix + img_file, path_prefix + \"train/\")\n",
        "    for test_file in test_files:\n",
        "      copy(path_prefix + test_file, path_prefix + \"test/\")\n",
        "      img_file = makeImageFilename(getFileIdx(test_file))\n",
        "      copy(path_prefix + img_file, path_prefix + \"test/\")\n",
        "      \n",
        "\n",
        "# TODO: things I think that could make this better\n",
        "# def ResizeImages(width=32, height=32):\n",
        "#   for f in os.listdir(path_prefix):\n",
        "#     if img_regex.match(f):\n",
        "#       img = cv2.imread(path_prefix + f, cv2.IMREAD_UNCHANGED)\n",
        "#       res_img = cv2.resize(img, (width, height), interpolation=cv2.INTER_LINEAR)\n",
        "#       with open(path_prefix + f, \"w\") as res_file:\n",
        "#         # this doesn't work... need a way of getting raw bytes\n",
        "#         # res_file.write(res_img)\n",
        "#\n",
        "# def ReformatFilenames(): # all .png files are renamed to imageXXXX.png\n",
        "#                          # Don't overwrite existing imageXXXX.png files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zC3TI_K6vy5N",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "outputId": "74ab8f14-7b77-4b02-e758-7a09947dd340"
      },
      "source": [
        "# Driver code: uncomment the method you need to run\n",
        "\n",
        "# SetMissingLabels()\n",
        "# ViewLabelSummary()\n",
        "ViewImagesForLabel('hunger-full')\n",
        "# SplitIntoTrainTest()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['/content/drive/My Drive/Data/Minecraft/HUD/image0251.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0250.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0249.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0248.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0236.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0235.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0234.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0233.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0232.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0231.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0230.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0229.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0228.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0223.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0222.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0221.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0220.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0219.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0218.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0217.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0216.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0215.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0214.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0109.png', '/content/drive/My Drive/Data/Minecraft/HUD/image0104.png']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}