FMML-22-Module-05-Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FMML20210327_Module_5_Project.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lakshmilikithabonthu/Fmml/blob/main/FMML-22-Module-05-Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UyjeBK7XKs6J",
        "outputId": "15e063f2-6045-4b86-c08e-f109b1ad4a41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'counting-fingers-classical'...\n",
            "remote: Enumerating objects: 24, done.\u001b[K\n",
            "remote: Counting objects: 100% (24/24), done.\u001b[K\n",
            "remote: Compressing objects: 100% (19/19), done.\u001b[K\n",
            "remote: Total 24 (delta 5), reused 23 (delta 4), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (24/24), 2.21 MiB | 2.76 MiB/s, done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/Bhuvanesh09/counting-fingers-classical"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "from tqdm import tqdm"
      ],
      "metadata": {
        "id": "XbsbmYMNLtKi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"./counting-fingers-classical/data/fingerDataSet.pickle\", \"rb\") as infile:\n",
        "    dat = pickle.load(infile, encoding=\"bytes\")"
      ],
      "metadata": {
        "id": "XaHSzFloL686"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from PIL import Image, ImageDraw\n",
        "from IPython.display import display\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import imshow"
      ],
      "metadata": {
        "id": "ZmKv91vXMKQ6"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dat[0].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XK2QSITBMP9R",
        "outputId": "f7ca34bf-4269-42a9-a8ce-962a5c6eeecb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys([b'box', b'boxHeight', b'handPoints', b'trackingIndices', b'allFingerPoints', b'boxWidth', b'image', b'boxEdges', b'croppedImage', b'numFingers', b'numPointsInBox', b'handEdges', b'indexFingerPoints', b'picCount', b'image1bit'])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def highlightPoints(img, pts, color = \"yellow\"):\n",
        "    draw = ImageDraw.Draw(img)\n",
        "    for y, x in pts:\n",
        "        draw.point((x, y), fill = color)"
      ],
      "metadata": {
        "id": "LUq6LJ_BMftO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "imDat = dat[50]\n",
        "for key, item in imDat.items():\n",
        "    try:\n",
        "        print(key, type(item), item.shape)\n",
        "    except:\n",
        "        print(key, type(item), item)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y-AG5B14MjSu",
        "outputId": "bb662873-4355-4040-e9d1-18d7abddae31"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b'box' <class 'numpy.ndarray'> (202, 2)\n",
            "b'middleFingerPoints' <class 'numpy.ndarray'> (61, 2)\n",
            "b'handPoints' <class 'numpy.ndarray'> (621, 2)\n",
            "b'trackingIndices' <class 'dict'> {b'1': {b'tipIndices': (array([206, 207, 208]), array([183, 183, 183])), b'baseIndices': (array([213, 213, 214, 214, 215]), array([185, 186, 185, 186, 185]))}, b'3': {b'tipIndices': (array([204, 204, 205, 205, 206, 206]), array([200, 201, 200, 201, 200, 201])), b'baseIndices': (array([213, 214, 215]), array([199, 199, 199]))}, b'2': {b'tipIndices': (array([203, 204, 205]), array([192, 192, 192])), b'baseIndices': (array([211, 212, 213]), array([192, 192, 192]))}}\n",
            "b'allFingerPoints' <class 'numpy.ndarray'> (187, 2)\n",
            "b'boxWidth' <class 'numpy.int64'> ()\n",
            "b'image' <class 'numpy.ndarray'> (400, 400)\n",
            "b'boxEdges' <class 'list'> [173, 211, 192, 253]\n",
            "b'croppedImage' <class 'numpy.ndarray'> (61, 38)\n",
            "b'numFingers' <class 'int'> 3\n",
            "b'numPointsInBox' <class 'numpy.int64'> ()\n",
            "b'handEdges' <class 'list'> [181, 203, 200, 245]\n",
            "b'indexFingerPoints' <class 'numpy.ndarray'> (61, 2)\n",
            "b'picCount' <class 'int'> 91\n",
            "b'image1bit' <class 'numpy.ndarray'> (400, 400)\n",
            "b'ringFingerPoints' <class 'numpy.ndarray'> (65, 2)\n",
            "b'boxHeight' <class 'numpy.int64'> ()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imDat = dat[32]\n",
        "img = Image.fromarray(imDat[b'croppedImage'])\n",
        "img = img.convert(mode=\"RGB\")\n",
        "newFingerPoints = [(y - min(imDat[b\"box\"][:,0]), x - min(imDat[b\"box\"][: , 1])) for y, x in imDat[b\"allFingerPoints\"]]\n",
        "# highlightPoints(img, [(173, 211), (192, 253)])\n",
        "highlightPoints(img, newFingerPoints)\n",
        "# highlightPoints(img, imDat[b\"box\"], \"blue\")\n",
        "\n",
        "\n",
        "imshow(img)\n",
        "plt.gcf().set_dpi(70)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "a43Hj2FdMnP0",
        "outputId": "fd439216-15e0-492c-b5c6-62b4990bf13b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 420x280 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FILTER = 11"
      ],
      "metadata": {
        "id": "YEmOC0LVMrZL"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class PointDat:\n",
        "    def __init__(self, imgAr, isFinger : bool, coor = None, idx = None):\n",
        "        self.imgAr = imgAr\n",
        "        self.isFinger = isFinger\n",
        "        self.idx = idx\n",
        "        self.coor = coor\n",
        "    \n",
        "    def __str__(self):\n",
        "        return \"Coors {} on idx {}\".format(self.coor, self.idx)\n",
        "    \n",
        "    def data(self):\n",
        "        return self.imgAr.reshape(-1)\n",
        "    \n",
        "    def getPointGrid(self):\n",
        "        return self.imgAr\n",
        "    \n",
        "    def getBoolean(self):\n",
        "        return self.isFinger\n",
        "    \n",
        "    def getCoors(self):\n",
        "        return self.coor"
      ],
      "metadata": {
        "id": "kVojj0qnMxiC"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def highlightPoints(img, pts, color = \"yellow\"):\n",
        "    draw = ImageDraw.Draw(img)\n",
        "    for y, x in pts:\n",
        "        draw.point((x, y), fill = color)\n",
        "        \n",
        "class ImgDat:\n",
        "    def __init__(self, dat, idx = None):\n",
        "        self.idx = idx\n",
        "        self.fullImage = dat[b\"image\"]\n",
        "        self.box = dat[b\"box\"]\n",
        "        self.topLeft = (int(min(dat[b\"box\"][:, 0])), int(min(dat[b\"box\"][:, 1])))\n",
        "        self.bottomRight = (int(max(dat[b\"box\"][:, 0])), int(max(dat[b\"box\"][:, 1])))\n",
        "\n",
        "        self.bitImage = dat[b\"image1bit\"][self.topLeft[0]:self.bottomRight[0], self.topLeft[1]:self.bottomRight[1]]\n",
        "        self.numFingers = dat[b\"numFingers\"]\n",
        "        self.boundImage = dat[b\"croppedImage\"]\n",
        "\n",
        "        self.fingerPoints = [(p1 - self.topLeft[0], p2 - self.topLeft[1]) for p1, p2 in dat[b\"allFingerPoints\"]]\n",
        "    \n",
        "    def showImage(self):\n",
        "        img = Image.fromarray(self.boundImage)\n",
        "        img = img.convert(mode = \"RGB\")\n",
        "        return img\n",
        "                              \n",
        "    def showFingers(self):\n",
        "        COLS = [\"orange\", \"pink\", \"lime\"]\n",
        "        img = Image.fromarray(self.boundImage)\n",
        "        img = img.convert(mode = \"RGB\")\n",
        "        highlightPoints(img, self.fingerPoints, COLS[self.numFingers - 1])\n",
        "        return img\n",
        "    \n",
        "    def showFullImage(self):\n",
        "        img = Image.fromarray(self.fullImage)\n",
        "        img = img.convert(mode = \"RGB\")\n",
        "        highlightPoints(img, self.box, \"yellow\")\n",
        "        return img\n",
        "    \n",
        "    def makePointsDataset(self):\n",
        "        F = FILTER//2\n",
        "        \n",
        "        collection = []\n",
        "        for r in range(F, self.bitImage.shape[0] - F):\n",
        "            for c in range(F, self.bitImage.shape[1] - F):\n",
        "#                 collection.append([self.bitImage[r-F:r+F, c-F:c+F], (r, c) in self.fingerPoints, (r, c)])\n",
        "                collection.append(PointDat(self.bitImage[r - F : r + F+1, c - F: c + F+1], (r, c) in self.fingerPoints, (r, c),\\\n",
        "                                            self.idx))\n",
        "        \n",
        "        return np.array(collection, dtype=object)\n",
        "                "
      ],
      "metadata": {
        "id": "JtCk9hFSM0E5"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sampleIndices = [0, 10, 52, 30, 40]\n",
        "fig, axs = plt.subplots(5, 3, figsize=(8, 16), gridspec_kw={'width_ratios': [3, 1, 1]})\n",
        "for r in range(5):\n",
        "    p = ImgDat(dat[sampleIndices[r]], sampleIndices[r])\n",
        "    axs[r][0].imshow(p.showFullImage())\n",
        "    axs[r][1].imshow(p.showImage())\n",
        "    axs[r][2].imshow(p.showFingers())\n",
        "fig.set_dpi(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7zEuSsJbM3a-",
        "outputId": "8dde2f86-dbf9-4ddb-efdc-77c3a1c399a2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x1600 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p = ImgDat(dat[52], 52)\n",
        "d = p.makePointsDataset()"
      ],
      "metadata": {
        "id": "ej3ZFtXCM8bm"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "posPoints = [point for point in d if point.getBoolean()]\n",
        "negPoints = [point for point in d if not point.getBoolean()]"
      ],
      "metadata": {
        "id": "E-XiDouRM_V2"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "fig, axs = plt.subplots(3, 5, figsize=(15, 9))\n",
        "for ax in axs:\n",
        "    for a in ax:\n",
        "        point = np.random.choice(posPoints)\n",
        "        a.imshow(point.getPointGrid())\n",
        "        a.title.set_text(str(point))\n",
        "        a.set_xticks([FILTER//2])\n",
        "        a.set_yticks([FILTER//2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "07pouN67NCZM",
        "outputId": "3ed9979c-aaa8-4797-a742-b0acfe3d7113"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x648 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(0)\n",
        "fig, axs = plt.subplots(3, 5, figsize=(15, 9))\n",
        "for ax in axs:\n",
        "    for a in ax:\n",
        "        point = np.random.choice(negPoints)\n",
        "        a.imshow(point.getPointGrid())\n",
        "        a.title.set_text(str(point))\n",
        "        a.set_xticks([FILTER//2])\n",
        "        a.set_yticks([FILTER//2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "aXg7FP86NFRJ",
        "outputId": "b7bcd0ee-b0dd-44cf-b922-f4e281f74367"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x648 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def singleton(cls, *args, **kw):\n",
        "    instances = {}\n",
        "    def _singleton(*args, **kw):\n",
        "        if cls not in instances:\n",
        "            instances[cls] = cls(*args, **kw)\n",
        "        return instances[cls]\n",
        "    return _singleton\n",
        "\n",
        "@singleton\n",
        "class fingersFromPoints:\n",
        "    \n",
        "    def findAdjacent(self, coordinates, coordinate, cluster):\n",
        "        distances = np.sqrt(np.sum((coordinates-coordinate)**2,1))\n",
        "        newCoordIndices = np.where(np.logical_and(distances<1.3, distances >0))[0]\n",
        "\n",
        "        for newCoordIndex in newCoordIndices:\n",
        "            if newCoordIndex not in cluster:\n",
        "                cluster.append(newCoordIndex)\n",
        "                self.findAdjacent(coordinates, coordinates[newCoordIndex, :], cluster)\n",
        "                \n",
        "    def findClusters(self, coordinates):\n",
        "        allIndices = np.arange(len(coordinates))\n",
        "        clusters = []\n",
        "\n",
        "        while len(allIndices) > 0:\n",
        "\n",
        "            cluster = [allIndices[0]]\n",
        "            self.findAdjacent(coordinates, coordinates[allIndices[0],:], cluster)\n",
        "            allIndices = np.setdiff1d(allIndices, np.array((cluster)))\n",
        "            clusters.append(cluster)\n",
        "\n",
        "        return clusters\n",
        "    \n",
        "    def findClusterCoors(self, coors):\n",
        "        \n",
        "        clusters = self.findClusters(coors)\n",
        "        newClus = []\n",
        "        for c in clusters:\n",
        "            temp = []\n",
        "            for idx in c:\n",
        "                temp.append(coors[idx])\n",
        "            newClus.append(temp)\n",
        "        return newClus    \n",
        "    \n",
        "    def countFingers(self, clusters, minFingerSize = 15, fingerCutoff = 0.5):\n",
        "        sizes = []\n",
        "        for cluster in clusters:\n",
        "            sizes.append(len(cluster))\n",
        "\n",
        "        sizes = np.array(sizes)\n",
        "\n",
        "        if len(sizes) > 0:\n",
        "            if np.max(sizes) > minFingerSize:\n",
        "                fingerCluster = np.argmax(sizes)\n",
        "                numFingers = sum(sizes>fingerCutoff*sizes[fingerCluster])\n",
        "            else:\n",
        "                numFingers = 0\n",
        "        else:\n",
        "            numFingers = 0\n",
        "\n",
        "        return numFingers\n",
        "    \n",
        "    def predictNumFingers(self, coors):\n",
        "        newCoors = np.array(coors)\n",
        "        clusters = self.findClusters(newCoors)\n",
        "        #print(clusters)\n",
        "        return self.countFingers(clusters)\n",
        "    "
      ],
      "metadata": {
        "id": "mKae7pA7NJK7"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predObj = fingersFromPoints()\n",
        "\n",
        "imgObj = ImgDat(dat[52], 52)\n",
        "\n",
        "assert imgObj.numFingers == predObj.predictNumFingers(imgObj.fingerPoints), \"Clustering has failed for the dataimage \\\n",
        "with index {}. Something is wrong! {} is being predicted instead of {})\".format(imgObj.idx, predObj.predictNumFingers(imgObj.fingerPoints),\\\n",
        "                                                                               imgObj.numFingers)"
      ],
      "metadata": {
        "id": "S-6SpEzKNQnN"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cor, tot = 0, 0\n",
        "incor = []\n",
        "for idx, d in enumerate(dat):\n",
        "    imgObj = ImgDat(d, idx)\n",
        "    if imgObj.numFingers == predObj.predictNumFingers(imgObj.fingerPoints):\n",
        "        cor += 1\n",
        "    else:\n",
        "        incor.append(imgObj)\n",
        "    tot += 1\n",
        "\n",
        "print(\"The Clustering is correct for: {:.2f} % of the datapoints' ground truth\".format(cor * 100 / tot))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m90oPcH1NVPU",
        "outputId": "ce679206-f4c1-4d4a-bcc7-5d71074b5f44"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Clustering is correct for: 96.30 % of the datapoints' ground truth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(incor))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gsml5QHcNYi2",
        "outputId": "95f2a772-e316-4e3a-93ac-7728a8976d61"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(2, 3, figsize = (12, 10))\n",
        "incorIndices = []\n",
        "for idx, (ax, imObj) in enumerate(zip(axs, incor)):\n",
        "    print(\"Incorrect for {}\".format(imObj.idx))\n",
        "    incorIndices.append(imObj.idx)\n",
        "    ax[0].imshow(imObj.showImage())\n",
        "    ax[1].imshow(imObj.showFingers())\n",
        "    img = imObj.showImage()\n",
        "    clusters = predObj.findClusterCoors(np.array(imObj.fingerPoints))\n",
        "    COLS = [\"green\", \"red\", \"blue\", \"yello\", \"orange\", \"pink\"]\n",
        "    for idx2, c in enumerate(clusters):\n",
        "        highlightPoints(img, c, COLS[idx2])\n",
        "    ax[2].imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "9qmD1mCHNbwW",
        "outputId": "76543b89-ae1d-4144-e7fe-b50e3d549e12"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Incorrect for 42\n",
            "Incorrect for 45\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "allImg = [ImgDat(d, idx) for idx, d in enumerate(dat)]"
      ],
      "metadata": {
        "id": "7pKVEoWYNeS1"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "Z9CbVI7uNhmv"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainImg, testImg = train_test_split(allImg, test_size = 10, train_size = 44, random_state = 0)"
      ],
      "metadata": {
        "id": "okvogvh7Nkpa"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainPoints = np.array([], dtype=object)\n",
        "for imgObj in tqdm(trainImg):\n",
        "    trainPoints = np.append(trainPoints, imgObj.makePointsDataset())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uy4Q8joqNmrk",
        "outputId": "99a4425e-5eb5-4997-bac7-e01bfbb9a338"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 44/44 [00:05<00:00,  7.79it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = np.array([p.data() for p in trainPoints]), np.array([p.getBoolean() for p in trainPoints])"
      ],
      "metadata": {
        "id": "kZuus8IWNpT3"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfyaV6nlN1sh",
        "outputId": "396b45e8-5e61-4dab-8485-815b180d8ce8"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70206, 121), (70206,))"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "clf = DecisionTreeClassifier(random_state = 0, max_depth = 3)"
      ],
      "metadata": {
        "id": "kTTJ5O-tN3rN"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "KCCtPSlHN58N",
        "outputId": "5adf9a11-eb20-41be-89b2-68fca7bfa32e"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Training Error in terms of points:\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(\"{:.2f} % of the training points classified correctly\".format(100*accuracy_score(clf.predict(X_train), y_train)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uFnMt36N8Q9",
        "outputId": "494a61fb-77ef-4043-e44b-8b929f925f37"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95.86 % of the training points classified correctly\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(10, 3, figsize = (9, 50))\n",
        "for idx, imgObj in enumerate(testImg):\n",
        "    testPoints = imgObj.makePointsDataset()\n",
        "    axs[idx][0].imshow(imgObj.showImage())\n",
        "    axs[idx][1].imshow(imgObj.showFingers())\n",
        "    \n",
        "    coors = []\n",
        "    for point in testPoints:\n",
        "        if clf.predict([point.data()])[0]:\n",
        "            coors.append(point.getCoors())\n",
        "    clusters = predObj.findClusterCoors(np.array(coors))\n",
        "#     clusters = sorted(clusters, key = lambda c : len(c))\n",
        "#     clusters = clusters[::-1]\n",
        "#     clusters = clusters[:5]\n",
        "    img = imgObj.showImage()\n",
        "    COLS = [\"green\", \"red\", \"blue\", \"yellow\", \"orange\", \"pink\"]\n",
        "    for idx2, c in enumerate(clusters):\n",
        "#         pass\n",
        "        highlightPoints(img, c, COLS[idx2%6])\n",
        "    axs[idx][2].imshow(img)\n",
        "    if predObj.predictNumFingers(coors) == imgObj.numFingers:\n",
        "        print(\"Predicted correctly for idx : {}, the number: {}\".format(imgObj.idx, imgObj.numFingers))\n",
        "    else:\n",
        "        print(\"Predicted incorrectly for idx : {}, the number: {}\".format(imgObj.idx, imgObj.numFingers))\n",
        "#     print(\"Num clusters seen: {}\".format(len(clusters)))\n",
        "        "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqP-1vTLN_Vp",
        "outputId": "9f5e6994-bcab-4ce2-ec2b-30d6762ce588"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted correctly for idx : 53, the number: 3\n",
            "Predicted correctly for idx : 33, the number: 2\n",
            "Predicted correctly for idx : 48, the number: 3\n",
            "Predicted correctly for idx : 26, the number: 2\n",
            "Predicted correctly for idx : 11, the number: 1\n",
            "Predicted correctly for idx : 2, the number: 1\n",
            "Predicted correctly for idx : 32, the number: 2\n",
            "Predicted correctly for idx : 42, the number: 3\n",
            "Predicted correctly for idx : 45, the number: 3\n",
            "Predicted correctly for idx : 30, the number: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree"
      ],
      "metadata": {
        "id": "UiEWLXaXOIdI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 7))\n",
        "imgObj = tree.plot_tree(clf, filled=True, ax=ax, impurity=False, proportion=True, rounded=True, fontsize = 10)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "uKngjADTOQ6n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "blank = np.ones((11, 11))\n",
        "img = Image.fromarray(blank)\n",
        "img = img.convert(mode=\"RGB\")\n",
        "\n",
        "imDraw = ImageDraw.Draw(img)\n",
        "imDraw.point((12%11, 12//11), fill = \"red\")\n",
        "imDraw.point((54%11, 54//11), fill = \"red\")\n",
        "imDraw.point((60%11, 60//11), fill = \"green\")\n",
        "plt.imshow(img)"
      ],
      "metadata": {
        "id": "GtNaeE2nORcj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QpS1sOaVOUt3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}