head_pos_estimation_ml (1).ipynb

{
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      "cell_type": "code",
      "execution_count": 5,
      "id": "e8281ae0",
      "metadata": {
        "id": "e8281ae0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import math\n",
        "from math import cos, sin\n",
        "from pathlib import Path\n",
        "import pandas as pd\n",
        "from PIL import Image, ImageFilter\n",
        "import glob\n",
        "from scipy.io import loadmat\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/Database/AFLW2000-3D.zip \\ -O file.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BM_LcN0rn0fd",
        "outputId": "9b98b4a5-61d6-45f2-d915-31ed8bf71381"
      },
      "id": "BM_LcN0rn0fd",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-06-17 14:45:51--  http://www.cbsr.ia.ac.cn/users/xiangyuzhu/projects/3DDFA/Database/AFLW2000-3D.zip\n",
            "Resolving www.cbsr.ia.ac.cn (www.cbsr.ia.ac.cn)... 159.226.21.60\n",
            "Connecting to www.cbsr.ia.ac.cn (www.cbsr.ia.ac.cn)|159.226.21.60|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 87413900 (83M) [application/x-zip-compressed]\n",
            "Saving to: ‘AFLW2000-3D.zip’\n",
            "\n",
            "AFLW2000-3D.zip     100%[===================>]  83.36M   278KB/s    in 5m 14s  \n",
            "\n",
            "2022-06-17 14:51:06 (272 KB/s) - ‘AFLW2000-3D.zip’ saved [87413900/87413900]\n",
            "\n",
            "--2022-06-17 14:51:06--  http://%20-o/\n",
            "Resolving  -o ( -o)... failed: Name or service not known.\n",
            "wget: unable to resolve host address ‘ -o’\n",
            "--2022-06-17 14:51:06--  http://file.zip/\n",
            "Resolving file.zip (file.zip)... failed: Name or service not known.\n",
            "wget: unable to resolve host address ‘file.zip’\n",
            "FINISHED --2022-06-17 14:51:06--\n",
            "Total wall clock time: 5m 15s\n",
            "Downloaded: 1 files, 83M in 5m 14s (272 KB/s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading AFLW2000 Data\n",
        "!unzip /content/AFLW2000-3D.zip"
      ],
      "metadata": {
        "id": "xmyzUkA1QIDs"
      },
      "id": "xmyzUkA1QIDs",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9bfce399",
      "metadata": {
        "id": "9bfce399"
      },
      "outputs": [],
      "source": [
        "mat = loadmat(r'/content/AFLW2000/image00004.mat')\n",
        "print(mat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "fc4d472f",
      "metadata": {
        "id": "fc4d472f",
        "outputId": "3a56897e-e22c-46e2-88a5-a5a654fc2696",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['__header__', '__version__', '__globals__', 'pt2d', 'Illum_Para', 'Color_Para', 'Tex_Para', 'Shape_Para', 'Exp_Para', 'Pose_Para', 'roi', 'pt3d_68'])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "mat.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "0e0984bf",
      "metadata": {
        "scrolled": true,
        "id": "0e0984bf",
        "outputId": "f1a6ddf8-7c2c-45fc-88df-3e84179e3360",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 68)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "mat['pt3d_68'].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "c08312e0",
      "metadata": {
        "id": "c08312e0",
        "outputId": "aa3b1386-70b8-4d5a-fc50-5a6664e921f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 4.7006503e-01,  1.1895334e+00,  3.0095881e-01,  3.3081970e+02,\n",
              "         1.7319424e+02, -4.4367504e+01,  1.1811422e-03]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "mat['Pose_Para']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a7c0388a",
      "metadata": {
        "id": "a7c0388a"
      },
      "outputs": [],
      "source": [
        "mat['pt3d_68']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "6a1321c5",
      "metadata": {
        "id": "6a1321c5"
      },
      "outputs": [],
      "source": [
        "def getx_y (file_path):\n",
        "    mat = loadmat(file_path)\n",
        "    points=mat['pt3d_68'][0:2]\n",
        "    \n",
        "    '''\n",
        "    x = mat['pt3d_68'][:1]\n",
        "    y =mat['pt3d_68'][1:2]\n",
        "    z =mat['pt3d_68'][2:3]\n",
        "    '''\n",
        "    return points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "93609f54",
      "metadata": {
        "id": "93609f54",
        "outputId": "7b892ed7-136d-4950-b886-76bf2362d90d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[281.23816, 277.33942, 274.87695, 269.52377, 257.78827, 240.96815,\n",
              "        221.80148, 205.8463 , 207.08545, 226.18564, 254.81445, 282.76337,\n",
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              "        244.74814, 257.117  , 271.07208, 213.09018, 202.01886, 190.40382,\n",
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              "        206.68646, 198.77388, 195.23763, 196.5263 , 199.83258, 211.0683 ,\n",
              "        226.80066, 210.58345, 201.24966, 196.73563, 195.09518, 198.44182,\n",
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              "        199.18753, 197.53447],\n",
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              "        196.58772, 197.89896, 205.15329, 227.52942, 239.4176 , 250.97882,\n",
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              "        303.56107, 303.8435 , 305.62286, 307.03455, 308.27655, 309.79312,\n",
              "        310.14154, 303.0938 , 300.72446, 300.06097, 303.2609 , 299.48517,\n",
              "        300.5986 , 302.2865 ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "getx_y(r'/content/AFLW2000/image00004.mat')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "fca1a0c8",
      "metadata": {
        "id": "fca1a0c8"
      },
      "outputs": [],
      "source": [
        "def getposlabel (file_path):\n",
        "    mat = loadmat(file_path)\n",
        "    pose = mat['Pose_Para'][0][:3]\n",
        "    \n",
        "    '''\n",
        "    x_label = pose[:1]\n",
        "    y_label =pose[1:2]\n",
        "    z_label =pose[2:3]\n",
        "    '''\n",
        "    return pose"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "96eba146",
      "metadata": {
        "id": "96eba146",
        "outputId": "a764cb21-ce5b-4ca5-98ad-e29bb6a453d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.47006503, 1.1895334 , 0.3009588 ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "getposlabel(r'/content/AFLW2000/image00004.mat')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "4c96cbec",
      "metadata": {
        "id": "4c96cbec"
      },
      "outputs": [],
      "source": [
        "from os import listdir\n",
        "\n",
        "def list_0f_pathes(file_path):\n",
        "    mylist= [f for f in os.listdir(file_path) if f.endswith('.mat')]\n",
        "    return mylist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "0f27d25f",
      "metadata": {
        "id": "0f27d25f"
      },
      "outputs": [],
      "source": [
        "def get_features_labels(file_path):\n",
        "    labels=[]\n",
        "    features=[]\n",
        "    mylist=list_0f_pathes(file_path)\n",
        "    \n",
        "    for f in mylist:\n",
        "        path = file_path+f\n",
        "\n",
        "        labels.append(getposlabel(path))\n",
        "        features.append(getx_y(path))\n",
        "    return labels , features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "965bb33b",
      "metadata": {
        "id": "965bb33b"
      },
      "outputs": [],
      "source": [
        "labels, featues =get_features_labels(r'/content/AFLW2000/')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "ea40b459",
      "metadata": {
        "id": "ea40b459"
      },
      "outputs": [],
      "source": [
        "def img_name(file_path):\n",
        "    img= [f for f in os.listdir(file_path) if f.endswith('.mat')]\n",
        "    img=[i.replace('.mat','') for i in img]\n",
        "\n",
        "    return img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "cbf03820",
      "metadata": {
        "id": "cbf03820"
      },
      "outputs": [],
      "source": [
        "index=img_name(r'/content/AFLW2000')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "83db0967",
      "metadata": {
        "id": "83db0967"
      },
      "outputs": [],
      "source": [
        "x_column = ['x' + str(i) for i in range(0,68)]\n",
        "y_column = ['y' + str(i) for i in range(0,68)]\n",
        "#z_column = ['z' + str(i) for i in range(0,68)]\n",
        "\n",
        "x =  [i[0] for i in featues]\n",
        "y =  [i[1] for i in featues]\n",
        "#z =  [i[2] for i in featues]\n",
        "\n",
        "dfx = pd.DataFrame(x, columns = x_column)\n",
        "dfy = pd.DataFrame(y, columns = y_column)\n",
        "#dfz = pd.DataFrame(z, columns = z_column)\n",
        "\n",
        "df_labels = pd.DataFrame (labels , columns = ['pitch', 'yaw', 'roll'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "47935c62",
      "metadata": {
        "id": "47935c62"
      },
      "outputs": [],
      "source": [
        "df_features = pd.concat([dfx,dfy], axis=1)\n",
        "df=pd.concat([dfx,dfy,df_labels], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "9874601f",
      "metadata": {
        "id": "9874601f"
      },
      "outputs": [],
      "source": [
        "df_features['index'] = index\n",
        "df_labels['index'] = index\n",
        "df_features = df_features.set_index('index')\n",
        "df_labels = df_labels.set_index('index')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "eafd238c",
      "metadata": {
        "id": "eafd238c"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(df_features, df_labels, test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "3d744ee8",
      "metadata": {
        "id": "3d744ee8",
        "outputId": "f33bb040-3bba-4abf-b029-74f0622787df",
        "colab": {
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          "height": 519
        }
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          "output_type": "execute_result",
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              "index                                                                    \n",
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              "...                ...         ...         ...         ...         ...   \n",
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              "\n",
              "                    x5          x6          x7          x8          x9  ...  \\\n",
              "index                                                                   ...   \n",
              "image00392  169.909409  198.594910  225.257965  230.216675  209.427856  ...   \n",
              "image00032  160.506271  180.784744  196.830383  200.384430  187.811630  ...   \n",
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              "\n",
              "                   y58         y59         y60         y61         y62  \\\n",
              "index                                                                    \n",
              "image00392  309.782501  309.857483  311.646240  308.176758  309.030548   \n",
              "image00032  339.452454  333.172241  325.615601  325.684540  324.110352   \n",
              "image00713  320.853027  313.842529  299.601807  275.869324  273.654541   \n",
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              "image02635  318.909607  310.197632  295.848633  297.845276  298.352173   \n",
              "...                ...         ...         ...         ...         ...   \n",
              "image03703  336.936371  338.311707  331.230164  312.514954  307.669739   \n",
              "image03762  320.121155  313.412537  305.168701  306.480408  308.022278   \n",
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              "image02069  335.557678  327.418243  313.159729  312.387177  312.589783   \n",
              "image03977  334.682007  329.585205  315.843842  308.270508  307.175293   \n",
              "\n",
              "                   y63         y64         y65         y66         y67  \n",
              "index                                                                   \n",
              "image00392  310.360229  313.022156  302.758728  301.742981  301.333221  \n",
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              "...                ...         ...         ...         ...         ...  \n",
              "image03703  302.908569  301.528900  314.465149  320.656250  324.954010  \n",
              "image03762  310.008423  314.491943  313.998199  312.622131  310.211853  \n",
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              "image02069  313.691467  317.289978  324.065399  324.946533  322.182892  \n",
              "image03977  307.392578  313.551270  322.056793  323.253448  322.695435  \n",
              "\n",
              "[2000 rows x 136 columns]"
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>image00392</th>\n",
              "      <td>118.079071</td>\n",
              "      <td>122.504181</td>\n",
              "      <td>125.383766</td>\n",
              "      <td>131.188416</td>\n",
              "      <td>146.092346</td>\n",
              "      <td>169.909409</td>\n",
              "      <td>198.594910</td>\n",
              "      <td>225.257965</td>\n",
              "      <td>230.216675</td>\n",
              "      <td>209.427856</td>\n",
              "      <td>...</td>\n",
              "      <td>309.782501</td>\n",
              "      <td>309.857483</td>\n",
              "      <td>311.646240</td>\n",
              "      <td>308.176758</td>\n",
              "      <td>309.030548</td>\n",
              "      <td>310.360229</td>\n",
              "      <td>313.022156</td>\n",
              "      <td>302.758728</td>\n",
              "      <td>301.742981</td>\n",
              "      <td>301.333221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image00032</th>\n",
              "      <td>147.385315</td>\n",
              "      <td>143.063782</td>\n",
              "      <td>137.998077</td>\n",
              "      <td>137.558670</td>\n",
              "      <td>144.704132</td>\n",
              "      <td>160.506271</td>\n",
              "      <td>180.784744</td>\n",
              "      <td>196.830383</td>\n",
              "      <td>200.384430</td>\n",
              "      <td>187.811630</td>\n",
              "      <td>...</td>\n",
              "      <td>339.452454</td>\n",
              "      <td>333.172241</td>\n",
              "      <td>325.615601</td>\n",
              "      <td>325.684540</td>\n",
              "      <td>324.110352</td>\n",
              "      <td>324.208252</td>\n",
              "      <td>324.898712</td>\n",
              "      <td>327.016724</td>\n",
              "      <td>328.294220</td>\n",
              "      <td>327.821472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image00713</th>\n",
              "      <td>151.435928</td>\n",
              "      <td>152.768555</td>\n",
              "      <td>159.093079</td>\n",
              "      <td>162.986267</td>\n",
              "      <td>166.111145</td>\n",
              "      <td>172.283371</td>\n",
              "      <td>178.266968</td>\n",
              "      <td>192.071304</td>\n",
              "      <td>216.814880</td>\n",
              "      <td>244.857391</td>\n",
              "      <td>...</td>\n",
              "      <td>320.853027</td>\n",
              "      <td>313.842529</td>\n",
              "      <td>299.601807</td>\n",
              "      <td>275.869324</td>\n",
              "      <td>273.654541</td>\n",
              "      <td>276.197113</td>\n",
              "      <td>303.795471</td>\n",
              "      <td>313.638123</td>\n",
              "      <td>314.514465</td>\n",
              "      <td>312.809143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image04161</th>\n",
              "      <td>287.339905</td>\n",
              "      <td>293.237000</td>\n",
              "      <td>299.991058</td>\n",
              "      <td>303.875641</td>\n",
              "      <td>302.763855</td>\n",
              "      <td>293.917572</td>\n",
              "      <td>280.079224</td>\n",
              "      <td>265.784851</td>\n",
              "      <td>261.302094</td>\n",
              "      <td>269.675171</td>\n",
              "      <td>...</td>\n",
              "      <td>309.087646</td>\n",
              "      <td>298.956726</td>\n",
              "      <td>286.858032</td>\n",
              "      <td>292.606049</td>\n",
              "      <td>297.002411</td>\n",
              "      <td>300.855957</td>\n",
              "      <td>311.535126</td>\n",
              "      <td>304.652740</td>\n",
              "      <td>300.632568</td>\n",
              "      <td>294.974121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image02635</th>\n",
              "      <td>105.226700</td>\n",
              "      <td>107.043846</td>\n",
              "      <td>111.611664</td>\n",
              "      <td>116.958908</td>\n",
              "      <td>126.286842</td>\n",
              "      <td>143.974548</td>\n",
              "      <td>166.572372</td>\n",
              "      <td>192.635956</td>\n",
              "      <td>218.971237</td>\n",
              "      <td>234.956451</td>\n",
              "      <td>...</td>\n",
              "      <td>318.909607</td>\n",
              "      <td>310.197632</td>\n",
              "      <td>295.848633</td>\n",
              "      <td>297.845276</td>\n",
              "      <td>298.352173</td>\n",
              "      <td>296.357239</td>\n",
              "      <td>294.022675</td>\n",
              "      <td>307.780334</td>\n",
              "      <td>310.579132</td>\n",
              "      <td>308.797485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image03703</th>\n",
              "      <td>76.409111</td>\n",
              "      <td>93.178192</td>\n",
              "      <td>112.765045</td>\n",
              "      <td>131.377533</td>\n",
              "      <td>155.166046</td>\n",
              "      <td>184.105881</td>\n",
              "      <td>211.112854</td>\n",
              "      <td>242.685120</td>\n",
              "      <td>282.474915</td>\n",
              "      <td>313.088348</td>\n",
              "      <td>...</td>\n",
              "      <td>336.936371</td>\n",
              "      <td>338.311707</td>\n",
              "      <td>331.230164</td>\n",
              "      <td>312.514954</td>\n",
              "      <td>307.669739</td>\n",
              "      <td>302.908569</td>\n",
              "      <td>301.528900</td>\n",
              "      <td>314.465149</td>\n",
              "      <td>320.656250</td>\n",
              "      <td>324.954010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image03762</th>\n",
              "      <td>221.754700</td>\n",
              "      <td>220.525146</td>\n",
              "      <td>222.120316</td>\n",
              "      <td>222.511536</td>\n",
              "      <td>220.486450</td>\n",
              "      <td>217.571381</td>\n",
              "      <td>212.242050</td>\n",
              "      <td>211.050446</td>\n",
              "      <td>224.505585</td>\n",
              "      <td>249.205811</td>\n",
              "      <td>...</td>\n",
              "      <td>320.121155</td>\n",
              "      <td>313.412537</td>\n",
              "      <td>305.168701</td>\n",
              "      <td>306.480408</td>\n",
              "      <td>308.022278</td>\n",
              "      <td>310.008423</td>\n",
              "      <td>314.491943</td>\n",
              "      <td>313.998199</td>\n",
              "      <td>312.622131</td>\n",
              "      <td>310.211853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image04019</th>\n",
              "      <td>215.944183</td>\n",
              "      <td>217.485840</td>\n",
              "      <td>222.244141</td>\n",
              "      <td>224.804169</td>\n",
              "      <td>224.483307</td>\n",
              "      <td>221.582748</td>\n",
              "      <td>216.068008</td>\n",
              "      <td>215.282974</td>\n",
              "      <td>227.881607</td>\n",
              "      <td>251.183899</td>\n",
              "      <td>...</td>\n",
              "      <td>287.338196</td>\n",
              "      <td>290.638733</td>\n",
              "      <td>291.829376</td>\n",
              "      <td>275.993225</td>\n",
              "      <td>271.773468</td>\n",
              "      <td>268.936157</td>\n",
              "      <td>271.595642</td>\n",
              "      <td>274.138672</td>\n",
              "      <td>277.629211</td>\n",
              "      <td>281.308441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image02069</th>\n",
              "      <td>118.915154</td>\n",
              "      <td>123.823540</td>\n",
              "      <td>131.038361</td>\n",
              "      <td>138.485748</td>\n",
              "      <td>149.135880</td>\n",
              "      <td>161.405441</td>\n",
              "      <td>174.447479</td>\n",
              "      <td>193.240234</td>\n",
              "      <td>223.058640</td>\n",
              "      <td>254.241547</td>\n",
              "      <td>...</td>\n",
              "      <td>335.557678</td>\n",
              "      <td>327.418243</td>\n",
              "      <td>313.159729</td>\n",
              "      <td>312.387177</td>\n",
              "      <td>312.589783</td>\n",
              "      <td>313.691467</td>\n",
              "      <td>317.289978</td>\n",
              "      <td>324.065399</td>\n",
              "      <td>324.946533</td>\n",
              "      <td>322.182892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image03977</th>\n",
              "      <td>131.530029</td>\n",
              "      <td>135.373749</td>\n",
              "      <td>140.409943</td>\n",
              "      <td>145.074951</td>\n",
              "      <td>151.495544</td>\n",
              "      <td>163.441864</td>\n",
              "      <td>177.779785</td>\n",
              "      <td>194.508881</td>\n",
              "      <td>222.142090</td>\n",
              "      <td>250.304565</td>\n",
              "      <td>...</td>\n",
              "      <td>334.682007</td>\n",
              "      <td>329.585205</td>\n",
              "      <td>315.843842</td>\n",
              "      <td>308.270508</td>\n",
              "      <td>307.175293</td>\n",
              "      <td>307.392578</td>\n",
              "      <td>313.551270</td>\n",
              "      <td>322.056793</td>\n",
              "      <td>323.253448</td>\n",
              "      <td>322.695435</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2000 rows × 136 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edc59db8-778c-40e5-b555-ee3edcb45832')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-edc59db8-778c-40e5-b555-ee3edcb45832 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-edc59db8-778c-40e5-b555-ee3edcb45832');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "df_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "8c4a7b99",
      "metadata": {
        "id": "8c4a7b99"
      },
      "outputs": [],
      "source": [
        "y_train_pitch= y_train['pitch']\n",
        "y_train_yaw= y_train['yaw']\n",
        "y_train_roll= y_train['roll']\n",
        "\n",
        "y_test_pitch= y_test['pitch']\n",
        "y_test_yaw=y_test['yaw']\n",
        "y_test_roll= y_test['roll']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = { 'max_depth': [6,8],\n",
        "           'learning_rate': [0.01, 0.05],\n",
        "           'n_estimators': [100, 500]}\n",
        "xgbr = xgb.XGBRegressor(seed = 20)\n",
        "clf = GridSearchCV(estimator=xgbr, \n",
        "                   param_grid=params,\n",
        "                   scoring='neg_mean_squared_error', \n",
        "                   verbose=1)\n",
        "clf.fit(X_train.values,y_train_pitch)\n",
        "print(\"Best parameters:\", clf.best_params_)\n",
        "print(\"Lowest RMSE: \", (-clf.best_score_)**(1/2.0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "PNgeNlr8a-hu",
        "outputId": "9e4bf0a3-60a6-46c6-b63d-e396f6467f92"
      },
      "id": "PNgeNlr8a-hu",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n",
            "[16:03:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:03:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:03:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:03:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:03:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:03:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:04:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-69f1f8659808>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m                    \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'neg_mean_squared_error'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m                    verbose=1)\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train_pitch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Best parameters:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Lowest RMSE: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    889\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1390\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1392\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    849\u001b[0m                     )\n\u001b[1;32m    850\u001b[0m                     for (cand_idx, parameters), (split_idx, (train, test)) in product(\n\u001b[0;32m--> 851\u001b[0;31m                         \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    852\u001b[0m                     )\n\u001b[1;32m    853\u001b[0m                 )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1044\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1046\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1047\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    859\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 861\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    862\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    863\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    777\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    778\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    780\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    781\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 263\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 263\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    678\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, callbacks)\u001b[0m\n\u001b[1;32m    394\u001b[0m                               \u001b[0mevals_result\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m                               \u001b[0mverbose_eval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxgb_model\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxgb_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m                               callbacks=callbacks)\n\u001b[0m\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevals_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xgboost/training.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)\u001b[0m\n\u001b[1;32m    214\u001b[0m                            \u001b[0mevals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                            \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m                            xgb_model=xgb_model, callbacks=callbacks)\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xgboost/training.py\u001b[0m in \u001b[0;36m_train_internal\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# Skip the first update if it is a recovery step.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_rabit_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mversion\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m   1107\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration),\n\u001b[0;32m-> 1109\u001b[0;31m                                                     dtrain.handle))\n\u001b[0m\u001b[1;32m   1110\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1111\u001b[0m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = { 'max_depth': [6,8],\n",
        "           'learning_rate': [0.01, 0.05],\n",
        "           'n_estimators': [100, 500]}\n",
        "xgbr = xgb.XGBRegressor(seed = 20)\n",
        "clf = GridSearchCV(estimator=xgbr, \n",
        "                   param_grid=params,\n",
        "                   scoring='neg_mean_squared_error', \n",
        "                   verbose=1)\n",
        "clf.fit(X_train.values,y_train_yaw)\n",
        "print(\"Best parameters:\", clf.best_params_)\n",
        "print(\"Lowest RMSE: \", (-clf.best_score_)**(1/2.0))"
      ],
      "metadata": {
        "id": "NLLBcn6ObjsM"
      },
      "id": "NLLBcn6ObjsM",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "params = { 'max_depth': [6,8],\n",
        "           'learning_rate': [0.01, 0.05],\n",
        "           'n_estimators': [100, 500]}\n",
        "xgbr = xgb.XGBRegressor(seed = 20)\n",
        "clf = GridSearchCV(estimator=xgbr, \n",
        "                   param_grid=params,\n",
        "                   scoring='neg_mean_squared_error', \n",
        "                   verbose=1)\n",
        "clf.fit(X_train.values,y_train_roll)\n",
        "print(\"Best parameters:\", clf.best_params_)\n",
        "print(\"Lowest RMSE: \", (-clf.best_score_)**(1/2.0))"
      ],
      "metadata": {
        "id": "NFI4ugi5bkuR"
      },
      "id": "NFI4ugi5bkuR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "3f2d4ec7",
      "metadata": {
        "id": "3f2d4ec7"
      },
      "outputs": [],
      "source": [
        "from xgboost import XGBRegressor\n",
        "\n",
        "model1 = XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, subsample=0.8, colsample_bytree=0.8, nthread=6 )\n",
        "model2 = XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, subsample=0.8, colsample_bytree=0.8, nthread=6)\n",
        "model3 = XGBRegressor(n_estimators=500, learning_rate=0.05, max_depth=6, subsample=0.8, colsample_bytree=0.8, nthread=6 )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "87852204",
      "metadata": {
        "scrolled": true,
        "id": "87852204",
        "outputId": "943fbb07-1374-4c63-dd11-1e68bf1f7855",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[16:05:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:05:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:05:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(colsample_bytree=0.8, learning_rate=0.05, max_depth=6,\n",
              "             n_estimators=500, nthread=6, subsample=0.8)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "model1.fit(X_train.values,y_train_pitch)\n",
        "model2.fit(X_train.values,y_train_yaw)\n",
        "model3.fit(X_train.values,y_train_roll)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "a59f41c1",
      "metadata": {
        "id": "a59f41c1",
        "outputId": "17840af1-760e-4c05-d612-b8a9779351b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['finalized_model3.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "#Saving models\n",
        "#https://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/\n",
        "import joblib\n",
        "# save the model to disk\n",
        "filename1 = 'finalized_model1.sav'\n",
        "joblib.dump(model1, filename1)\n",
        "filename2 = 'finalized_model2.sav'\n",
        "joblib.dump(model2, filename2)\n",
        "filename3 = 'finalized_model3.sav'\n",
        "joblib.dump(model3, filename3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "0a87a3f1",
      "metadata": {
        "id": "0a87a3f1"
      },
      "outputs": [],
      "source": [
        "pitch_predict =model1.predict(X_test.values)\n",
        "yaw_predict =model2.predict(X_test.values)\n",
        "roll_predict =model3.predict(X_test.values)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "b739b030",
      "metadata": {
        "id": "b739b030"
      },
      "outputs": [],
      "source": [
        "pitch_predict= pd.DataFrame(data = pitch_predict, index=X_test.index)\n",
        "yaw_predict = pd.DataFrame(data = yaw_predict, index=X_test.index)\n",
        "roll_predict = pd.DataFrame(data = roll_predict, index=X_test.index)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "57a4d287",
      "metadata": {
        "scrolled": true,
        "id": "57a4d287",
        "outputId": "b9717c30-1888-43e1-82f4-d8aaa36bd7f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 10800x7200 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "import matplotlib.pyplot as plt \n",
        "%matplotlib inline\n",
        "\n",
        "plt.plot(y_test_pitch, color = 'red',alpha=0.5)\n",
        "plt.plot(pitch_predict, color = 'blue', alpha=0.5)\n",
        "plt.title('prediction')\n",
        "plt.figure(figsize=(150,100))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "260370a7",
      "metadata": {
        "id": "260370a7",
        "outputId": "a05dc19f-6aed-4e13-b7fc-87e5d4a4e841",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7131622667897228\n",
            "0.11157296284503382\n",
            "0.9961369986944341\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "print(r2_score(y_test_pitch, pitch_predict))\n",
        "print(r2_score(y_test_roll, roll_predict))\n",
        "print(r2_score(y_test_yaw, yaw_predict))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "7fa9d23a",
      "metadata": {
        "id": "7fa9d23a",
        "outputId": "30b53a8e-4eca-41bc-f495-f32785e29ebe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "index\n",
              "image00545   -0.643371\n",
              "image02085    0.059427\n",
              "image02028    0.162710\n",
              "image02612   -0.176080\n",
              "image02128   -0.151672\n",
              "                ...   \n",
              "image00788   -0.033565\n",
              "image03787    0.059445\n",
              "image03050   -0.201965\n",
              "image00893   -0.066223\n",
              "image00889    0.246211\n",
              "Name: pitch, Length: 400, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "y_test_pitch\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b61888c",
      "metadata": {
        "id": "0b61888c"
      },
      "source": [
        "## Drawing arrows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "783d0de0",
      "metadata": {
        "id": "783d0de0"
      },
      "outputs": [],
      "source": [
        "def draw_axis(img, yaw, pitch, roll, tdx=None, tdy=None, size = 100):\n",
        "\n",
        "    if tdx != None and tdy != None:\n",
        "        tdx = tdx\n",
        "        tdy = tdy\n",
        "    else:\n",
        "        height, width = img.shape[:2]\n",
        "        tdx = width / 2\n",
        "        tdy = height / 2\n",
        "\n",
        "    # X-Axis pointing to right. drawn in red\n",
        "    x1 = size * (cos(yaw) * cos(roll)) + tdx\n",
        "    y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy\n",
        "\n",
        "    # Y-Axis | drawn in green\n",
        "    #        v\n",
        "    x2 = size * (-cos(yaw) * sin(roll)) + tdx\n",
        "    y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy\n",
        "\n",
        "    # Z-Axis (out of the screen) drawn in blue\n",
        "    x3 = size * (sin(yaw)) + tdx\n",
        "    y3 = size * (-cos(yaw) * sin(pitch)) + tdy\n",
        "\n",
        "    cv2.line(img, (int(tdx), int(tdy)), (int(x1),int(y1)),(0,0,255),3)\n",
        "    cv2.line(img, (int(tdx), int(tdy)), (int(x2),int(y2)),(0,255,0),3)\n",
        "    cv2.line(img, (int(tdx), int(tdy)), (int(x3),int(y3)),(255,0,0),2)\n",
        "\n",
        "    return img"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def return_img_byname(img_name):\n",
        "  file_path = '/content/AFLW2000/'\n",
        "  image_path = file_path+ img_name+ '.jpg'\n",
        "    \n",
        "  capture = cv2.VideoCapture(image_path)\n",
        "  ret, frame = capture.read()\n",
        "  landmarks= df_features.loc[img_name]\n",
        "  nose_x, nose_y = landmarks['x32'], landmarks['y32']\n",
        "  pitch =pitch_predict.loc[img_name]\n",
        "  yaw =yaw_predict.loc[img_name]\n",
        "  roll= roll_predict.loc[img_name]\n",
        "  capture.release()\n",
        "  cv2.destroyAllWindows()\n",
        "\n",
        "  return frame,pitch,yaw,roll, nose_x, nose_y"
      ],
      "metadata": {
        "id": "T7qjAOi96kz9"
      },
      "id": "T7qjAOi96kz9",
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "3eb0e209",
      "metadata": {
        "id": "3eb0e209"
      },
      "outputs": [],
      "source": [
        "def show_image(img,pitch,yaw,roll,nose_x, nose_y):\n",
        "    #file_path = '/content/AFLW2000/'\n",
        "    #image_path = file_path+ img_name+ '.jpg'\n",
        "    \n",
        "    #capture = cv2.VideoCapture(image_path)\n",
        "    #ret, frame = capture.read()\n",
        "    frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # convert to RGB format for matplotlib\n",
        "    \"\"\"\n",
        "    landmarks= df_features.loc[img_name]\n",
        "    pitch =df_labels.loc[img_name]['pitch']\n",
        "    yaw= df_labels.loc[img_name]['yaw']\n",
        "    roll= df_labels.loc[img_name]['roll']\n",
        "    \"\"\"\n",
        "    \"\"\"\n",
        "    landmarks= df_features.loc[img_name]\n",
        "    pitch =pitch_predict.loc[img_name]\n",
        "    yaw =yaw_predict.loc[img_name]\n",
        "    roll= roll_predict.loc[img_name]\n",
        "    \"\"\"\n",
        "    plt.imshow(draw_axis(frame, yaw, pitch, roll, nose_x, nose_y))\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "d22978f3",
      "metadata": {
        "scrolled": false,
        "id": "d22978f3",
        "outputId": "0c1bef96-a5bb-4aa5-ab52-9f994e8aa59a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "frame, pitch, yaw ,roll, nose_x, nose_y = return_img_byname('image00788')\n",
        "show_image(frame,pitch,yaw,roll, nose_x, nose_y)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install opencv-python\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qw9dYYH4VCis",
        "outputId": "5cb7d9b5-8539-48b8-86be-7610f474c804"
      },
      "id": "qw9dYYH4VCis",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (4.1.2.30)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from opencv-python) (1.21.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "8bf588f8",
      "metadata": {
        "id": "8bf588f8",
        "outputId": "077bb781-37c4-4444-e959-815e5e237dea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   0\n",
              "index               \n",
              "image00545 -0.661230\n",
              "image02085  0.008286\n",
              "image02028  0.240474\n",
              "image02612 -0.271335\n",
              "image02128 -0.155821\n",
              "...              ...\n",
              "image00788 -0.016951\n",
              "image03787  0.082286\n",
              "image03050 -0.209555\n",
              "image00893 -0.019787\n",
              "image00889  0.310292\n",
              "\n",
              "[400 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c1de68ec-0e19-49ea-b134-fe91ae453394\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>index</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>image00545</th>\n",
              "      <td>-0.661230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image02085</th>\n",
              "      <td>0.008286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image02028</th>\n",
              "      <td>0.240474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image02612</th>\n",
              "      <td>-0.271335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image02128</th>\n",
              "      <td>-0.155821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image00788</th>\n",
              "      <td>-0.016951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image03787</th>\n",
              "      <td>0.082286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image03050</th>\n",
              "      <td>-0.209555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image00893</th>\n",
              "      <td>-0.019787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>image00889</th>\n",
              "      <td>0.310292</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1de68ec-0e19-49ea-b134-fe91ae453394')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "pitch_predict"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# detecting  face landmarks "
      ],
      "metadata": {
        "id": "stnjqJp8rYi4"
      },
      "id": "stnjqJp8rYi4"
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "0c8b5668",
      "metadata": {
        "id": "0c8b5668",
        "outputId": "162d8938-13df-40d9-b194-e352465342fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imutils in /usr/local/lib/python3.7/dist-packages (0.5.4)\n"
          ]
        }
      ],
      "source": [
        "pip install imutils"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install cmake\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sCErWgwpCgp",
        "outputId": "b578b5d3-d459-44bf-97dc-202d5f006cfd"
      },
      "id": "2sCErWgwpCgp",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.7/dist-packages (3.22.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install dlib"
      ],
      "metadata": {
        "id": "FF--xqRFpC9S"
      },
      "id": "FF--xqRFpC9S",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2\n",
        "!bunzip2 /content/shape_predictor_68_face_landmarks.dat.bz2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bScyz43Xod3Y",
        "outputId": "9d7ace5a-81e4-49e8-9bc0-84b20a759979"
      },
      "id": "bScyz43Xod3Y",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-06-17 16:07:08--  http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2\n",
            "Resolving dlib.net (dlib.net)... 107.180.26.78\n",
            "Connecting to dlib.net (dlib.net)|107.180.26.78|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 64040097 (61M)\n",
            "Saving to: ‘shape_predictor_68_face_landmarks.dat.bz2’\n",
            "\n",
            "shape_predictor_68_ 100%[===================>]  61.07M  63.4MB/s    in 1.0s    \n",
            "\n",
            "2022-06-17 16:07:09 (63.4 MB/s) - ‘shape_predictor_68_face_landmarks.dat.bz2’ saved [64040097/64040097]\n",
            "\n",
            "bunzip2: Output file /content/shape_predictor_68_face_landmarks.dat already exists.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "outputId": "69198aa0-9380-495b-ae13-8ac3a471ebc6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7SV79BuxsN2R"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['roll', 'sin', 'clf', 'cos', 'mat']\n",
            "`%matplotlib` prevents importing * from pylab and numpy\n",
            "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
          ]
        }
      ],
      "source": [
        "%pylab inline\n",
        "from IPython.display import clear_output\n",
        "from imutils import face_utils\n",
        "import dlib\n",
        "import cv2"
      ],
      "id": "7SV79BuxsN2R"
    },
    {
      "cell_type": "code",
      "source": [
        "def detect_landmarks(img):\n",
        "  # get landmarks from dlib\n",
        "  p = \"/content/shape_predictor_68_face_landmarks.dat\"\n",
        "  detector = dlib.get_frontal_face_detector()\n",
        "  predictor = dlib.shape_predictor(p)\n",
        "  frame_count = 0\n",
        "  #path = '/content/AFLW2000/image00014.jpg'\n",
        "  #img = dlib.load_rgb_image(path)\n",
        "  dets = detector(img, 1)\n",
        "  landmarks = np.zeros((2, 68), dtype=int)\n",
        "\n",
        "  print(\"Number of faces detected: {}\".format(len(dets)))\n",
        "  for k, d in enumerate(dets):\n",
        "      # Get the landmarks/parts for the face in box d.\n",
        "      shape = predictor(img, d)\n",
        "      \n",
        "      shape = face_utils.shape_to_np(shape)\n",
        "      #print(shape.shape)\n",
        "      shape=shape.T\n",
        "      x=shape[0].reshape(1, -1)\n",
        "      y=shape[1].reshape(1, -1)\n",
        "      xd = pd.DataFrame(x, columns = x_column)\n",
        "      yd = pd.DataFrame(y, columns = y_column)\n",
        "      landmarks = pd.concat([xd,yd], axis=1)\n",
        "\n",
        "      #print(x.shape)\n",
        "\n",
        "      return landmarks"
      ],
      "metadata": {
        "id": "POmFSaGpqVP_"
      },
      "id": "POmFSaGpqVP_",
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = '/content/AFLW2000/image00014.jpg'\n",
        "img = dlib.load_rgb_image(path)\n",
        "\n",
        "\n",
        "marks = detect_landmarks(img)\n",
        "\n",
        "marks\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "Qxr0vGD6qrOq",
        "outputId": "06ad455c-06d1-44cb-b558-3b5d4ddf07c3"
      },
      "id": "Qxr0vGD6qrOq",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of faces detected: 1\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "\n",
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              "          const element = document.querySelector('#df-eba7efde-4d95-4bfc-a047-76186c380f4b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(marks.shape)"
      ],
      "metadata": {
        "id": "0BFFPCnnPeEN"
      },
      "id": "0BFFPCnnPeEN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def video_axis(path):\n",
        "  from google.colab.patches import cv2_imshow\n",
        "  cap = cv2.VideoCapture(path)\n",
        "  i=0\n",
        "  image_files=[]\n",
        "  while(cap.isOpened()):\n",
        "      ret, frame = cap.read()\n",
        "      #cv2_imshow( frame)\n",
        "      landmark = detect_landmarks(frame)\n",
        "      #print(landmark)\n",
        "      #cv2.imwrite('kang'+str(i)+'.jpg',frame)\n",
        "      i+=1\n",
        "      \n",
        "      if landmark is not None:\n",
        "        nose_x= landmark['x32'].iloc[0]\n",
        "        nose_y= landmark['y32'].iloc[0]\n",
        "        pitch = model1.predict(landmark.values)\n",
        "        yaw = model2.predict(landmark.values)\n",
        "        roll = model3.predict(landmark.values)\n",
        "        frame1 = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # convert to RGB format for matplotlib\n",
        "        pic=draw_axis(frame1, yaw, pitch, roll, nose_x,nose_y)\n",
        "        plt.imshow(pic)\n",
        "        plt.show()\n",
        "        plt.imsave('/content/images/'+str(i)+'.jpg', pic)\n",
        "        image_files.append('/content/images/'+str(i)+'.jpg')\n",
        "        print(image_files)\n",
        "      if cv2.waitKey(1) & 0xFF == ord('q'):\n",
        "          break\n",
        "\n",
        "  cap.release()\n",
        "  cv2.destroyAllWindows()\n",
        "  return image_files"
      ],
      "metadata": {
        "id": "DKuogIHPxSYb"
      },
      "id": "DKuogIHPxSYb",
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_files= video_axis('/content/stock-footage-nervous-young-handsome-man-waiting-for-somebody-sighing-over-white-background.mp4')"
      ],
      "metadata": {
        "id": "aO1hPHsqZOWR"
      },
      "id": "aO1hPHsqZOWR",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_files"
      ],
      "metadata": {
        "id": "tAdb-qFnfxoA",
        "outputId": "e966e547-2eb8-4793-a605-c083bebb3639",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "tAdb-qFnfxoA",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/images/5.jpg',\n",
              " '/content/images/7.jpg',\n",
              " '/content/images/8.jpg',\n",
              " '/content/images/9.jpg',\n",
              " '/content/images/10.jpg',\n",
              " '/content/images/11.jpg',\n",
              " '/content/images/12.jpg',\n",
              " '/content/images/13.jpg',\n",
              " '/content/images/14.jpg',\n",
              " '/content/images/15.jpg',\n",
              " '/content/images/16.jpg',\n",
              " '/content/images/17.jpg',\n",
              " '/content/images/18.jpg',\n",
              " '/content/images/19.jpg',\n",
              " '/content/images/20.jpg',\n",
              " '/content/images/21.jpg',\n",
              " '/content/images/22.jpg',\n",
              " '/content/images/23.jpg',\n",
              " '/content/images/24.jpg',\n",
              " '/content/images/25.jpg',\n",
              " '/content/images/26.jpg',\n",
              " '/content/images/27.jpg',\n",
              " '/content/images/28.jpg',\n",
              " '/content/images/29.jpg',\n",
              " '/content/images/30.jpg',\n",
              " '/content/images/31.jpg',\n",
              " '/content/images/32.jpg',\n",
              " '/content/images/33.jpg',\n",
              " '/content/images/34.jpg',\n",
              " '/content/images/35.jpg',\n",
              " '/content/images/36.jpg',\n",
              " '/content/images/37.jpg',\n",
              " '/content/images/38.jpg',\n",
              " '/content/images/39.jpg',\n",
              " '/content/images/40.jpg',\n",
              " '/content/images/41.jpg',\n",
              " '/content/images/42.jpg',\n",
              " '/content/images/43.jpg',\n",
              " '/content/images/44.jpg',\n",
              " '/content/images/45.jpg',\n",
              " '/content/images/46.jpg',\n",
              " '/content/images/47.jpg',\n",
              " '/content/images/48.jpg',\n",
              " '/content/images/49.jpg',\n",
              " '/content/images/50.jpg',\n",
              " '/content/images/51.jpg',\n",
              " '/content/images/52.jpg',\n",
              " '/content/images/53.jpg',\n",
              " '/content/images/54.jpg',\n",
              " '/content/images/55.jpg',\n",
              " '/content/images/56.jpg',\n",
              " '/content/images/57.jpg',\n",
              " '/content/images/58.jpg',\n",
              " '/content/images/59.jpg',\n",
              " '/content/images/60.jpg',\n",
              " '/content/images/61.jpg',\n",
              " '/content/images/62.jpg',\n",
              " '/content/images/63.jpg',\n",
              " '/content/images/64.jpg',\n",
              " '/content/images/65.jpg',\n",
              " '/content/images/66.jpg',\n",
              " '/content/images/67.jpg',\n",
              " '/content/images/68.jpg',\n",
              " '/content/images/69.jpg',\n",
              " '/content/images/70.jpg',\n",
              " '/content/images/71.jpg',\n",
              " '/content/images/72.jpg',\n",
              " '/content/images/73.jpg',\n",
              " '/content/images/74.jpg',\n",
              " '/content/images/75.jpg',\n",
              " '/content/images/76.jpg',\n",
              " '/content/images/77.jpg',\n",
              " '/content/images/78.jpg',\n",
              " '/content/images/79.jpg',\n",
              " '/content/images/80.jpg',\n",
              " '/content/images/81.jpg',\n",
              " '/content/images/82.jpg',\n",
              " '/content/images/83.jpg',\n",
              " '/content/images/84.jpg',\n",
              " '/content/images/85.jpg',\n",
              " '/content/images/86.jpg',\n",
              " '/content/images/87.jpg',\n",
              " '/content/images/88.jpg',\n",
              " '/content/images/89.jpg',\n",
              " '/content/images/90.jpg',\n",
              " '/content/images/91.jpg',\n",
              " '/content/images/92.jpg',\n",
              " '/content/images/93.jpg',\n",
              " '/content/images/94.jpg',\n",
              " '/content/images/95.jpg',\n",
              " '/content/images/96.jpg',\n",
              " '/content/images/97.jpg',\n",
              " '/content/images/98.jpg',\n",
              " '/content/images/99.jpg',\n",
              " '/content/images/100.jpg',\n",
              " '/content/images/101.jpg',\n",
              " '/content/images/102.jpg',\n",
              " '/content/images/103.jpg',\n",
              " '/content/images/104.jpg',\n",
              " '/content/images/105.jpg',\n",
              " '/content/images/106.jpg',\n",
              " '/content/images/107.jpg',\n",
              " '/content/images/108.jpg',\n",
              " '/content/images/109.jpg',\n",
              " '/content/images/110.jpg',\n",
              " '/content/images/111.jpg',\n",
              " '/content/images/112.jpg']"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils.validation import indexable\n",
        "##https://stackoverflow.com/questions/44947505/how-to-make-a-movie-out-of-images-in-python\n",
        "#https://stackoverflow.com/questions/70115576/trying-to-convert-frames-into-videos-from-multiple-folder-with-subfolders\n",
        "\n",
        "import os\n",
        "import moviepy.video.io.ImageSequenceClip\n",
        "fps=25\n",
        "\n",
        "clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(image_files, fps=fps)\n",
        "clip.write_videofile('my_video.mp4')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSfDR7gz8g5_",
        "outputId": "4b10eb39-b748-402d-cca3-7edf78103ca2"
      },
      "id": "uSfDR7gz8g5_",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[MoviePy] >>>> Building video my_video.mp4\n",
            "[MoviePy] Writing video my_video.mp4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 108/108 [00:02<00:00, 44.29it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: my_video.mp4 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "6gX39wnkgWLm"
      },
      "id": "6gX39wnkgWLm",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "name": "head_pos_estimation ml.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}