Copy_of_Colab_DEMO_mouse_openfield.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.6.9"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/IveySong/DeepLabCutFork/blob/main/Copy_of_Colab_DEMO_mouse_openfield.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGChzLdc-lUJ"
      },
      "source": [
        "# DeepLabCut Toolbox - Colab Demo on Topview Mouse Data\n",
        "https://github.com/DeepLabCut/DeepLabCut\n",
        "\n",
        "![alt text](https://images.squarespace-cdn.com/content/v1/57f6d51c9f74566f55ecf271/1559935526258-KFYZC8BDHK01ZIDPNVIX/mouse_skel_trail.gif?format=450w)\n",
        "\n",
        "Demo supporting: Nath\\*, Mathis\\* et al. *Using DeepLabCut for markerless3D  pose estimation during behavior across species. Nature Protocols, 2019\n",
        "\n",
        "This notebook demonstrates the necessary steps to use DeepLabCut on our demo data. We provide a sub-set of the mouse data from Mathis et al, 2018 Nature Neuroscience.\n",
        "\n",
        "This demo notebook mostly shows the most simple code to train and evaluate your model, but many of the functions have additional features, so please check out the overview & the protocol paper!\n",
        "\n",
        "This notebook illustrates how to use the cloud to:\n",
        "\n",
        "- load demo data\n",
        "- create a training set\n",
        "- train a network\n",
        "- evaluate a network\n",
        "- analyze a novel video"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txoddlM8hLKm"
      },
      "source": [
        "## First, go to \"Runtime\" ->\"change runtime type\"->select \"Python3\", and then select \"GPU\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "metadata": {
        "id": "-krec9sOReFu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "01269f6a-0012-4b2f-c2b3-ee8a1bbe0fd6"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.12.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.test.gpu_device_name()"
      ],
      "metadata": {
        "id": "Kc1EsBMeRyWB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "1e7e1e1d-f46a-445e-efb9-b9b82b2719f1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import pprint\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLZ3Av1xTDoR",
        "outputId": "a3571fe9-b4f2-40aa-969f-c6d5c5c9d781"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ew6r4hotoQjt"
      },
      "source": [
        "# Clone the entire deeplabcut repo so we can use the demo data:\n",
        "!git clone -l -s https://github.com/DeepLabCut/DeepLabCut.git cloned-DLC-repo\n",
        "%cd cloned-DLC-repo\n",
        "!ls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q23BzhA6CXxu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b605729-4a59-4a18-9b92-28ec1d00a537"
      },
      "source": [
        "#(this will take a few minutes to install all the dependences!)\n",
        "\n",
        "!pip install deeplabcut"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: filterpy\n",
            "  Building wheel for filterpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for filterpy: filename=filterpy-1.4.5-py3-none-any.whl size=110459 sha256=caea0fd669c989331ac8637b42ef995c2330a32357623eaea7d8e680582f2b22\n",
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            "Successfully built filterpy\n",
            "Installing collected packages: torch, ruamel.yaml.clib, msgpack-numpy, tensorpack, ruamel.yaml, huggingface-hub, filterpy, dlclibrary, deeplabcut\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.0.1+cu118\n",
            "    Uninstalling torch-2.0.1+cu118:\n",
            "      Successfully uninstalled torch-2.0.1+cu118\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchaudio 2.0.2+cu118 requires torch==2.0.1, but you have torch 1.12.0 which is incompatible.\n",
            "torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.12.0 which is incompatible.\n",
            "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.12.0 which is incompatible.\n",
            "torchvision 0.15.2+cu118 requires torch==2.0.1, but you have torch 1.12.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed deeplabcut-2.3.5 dlclibrary-0.0.4 filterpy-1.4.5 huggingface-hub-0.16.4 msgpack-numpy-0.4.8 ruamel.yaml-0.17.32 ruamel.yaml.clib-0.2.7 tensorpack-0.11 torch-1.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XymV_Hnlp1OJ"
      },
      "source": [
        "## PLEASE, click \"restart runtime\" from the output above before proceeding!"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['DLClight']='True'\n",
        "os.environ['Colab']='True'"
      ],
      "metadata": {
        "id": "Hhxli6syVPA2"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDaY78dFoxyD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5727738-08f3-4988-ceae-4acbdf8a214c"
      },
      "source": [
        "%cd /content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30\n",
        "!ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30\n",
            "config.yaml  labeled-data  videos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXufoX6INe6w"
      },
      "source": [
        "import deeplabcut"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7ZlDr3wV4D1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "113221c8-fc2c-4a5a-e6d2-f1197a17bc7d"
      },
      "source": [
        "#create a path variable that links to the config file:\n",
        "path_config_file = '/content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/config.yaml'\n",
        "\n",
        "# Loading example data set:\n",
        "deeplabcut.load_demo_data(path_config_file)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded, now creating training data...\n",
            "Downloading a ImageNet-pretrained model from http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz....\n",
            "The training dataset is successfully created. Use the function 'train_network' to start training. Happy training!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c4FczXGDoEJU"
      },
      "source": [
        "## Start training:\n",
        "This function trains the network for a specific shuffle of the training dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pOvDq_2oEJW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c64cbe9-32e3-4c3d-9553-4b097a59b601"
      },
      "source": [
        "#let's also change the display and save_iters just in case Colab takes away the GPU...\n",
        "#if that happens, you can reload from a saved point. Typically, you want to train to 200,000 + iterations.\n",
        "#more info and there are more things you can set: https://github.com/DeepLabCut/DeepLabCut/wiki/DOCSTRINGS#train_network\n",
        "\n",
        "deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100,saveiters=500, maxiters=1000)\n",
        "\n",
        "#this will run until you stop it (CTRL+C), or hit \"STOP\" icon, or when it hits the end (default, 1.03M iterations).\n",
        "#Whichever you chose, you will see what looks like an error message, but it's not an error - don't worry...."
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3]],\n",
            " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n",
            " 'alpha_r': 0.02,\n",
            " 'apply_prob': 0.5,\n",
            " 'batch_size': 1,\n",
            " 'contrast': {'clahe': True,\n",
            "              'claheratio': 0.1,\n",
            "              'histeq': True,\n",
            "              'histeqratio': 0.1},\n",
            " 'convolution': {'edge': False,\n",
            "                 'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]},\n",
            "                 'embossratio': 0.1,\n",
            "                 'sharpen': False,\n",
            "                 'sharpenratio': 0.3},\n",
            " 'crop_pad': 0,\n",
            " 'cropratio': 0.4,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'decay_steps': 30000,\n",
            " 'deterministic': False,\n",
            " 'display_iters': 1000,\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'init_weights': '/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 0.05,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'lr_init': 0.0005,\n",
            " 'max_input_size': 1500,\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/Documentation_data-openfield_95shuffle1.pickle',\n",
            " 'min_input_size': 64,\n",
            " 'mirror': False,\n",
            " 'multi_stage': False,\n",
            " 'multi_step': [[0.005, 10000],\n",
            "                [0.02, 430000],\n",
            "                [0.002, 730000],\n",
            "                [0.001, 1030000]],\n",
            " 'net_type': 'resnet_50',\n",
            " 'num_joints': 4,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': False,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'pos_dist_thresh': 17,\n",
            " 'project_path': '/content/drive/MyDrive/Colab '\n",
            "                 'Notebooks/openfield-Pranav-2018-10-30',\n",
            " 'regularize': False,\n",
            " 'rotation': 25,\n",
            " 'rotratio': 0.4,\n",
            " 'save_iters': 50000,\n",
            " 'scale_jitter_lo': 0.5,\n",
            " 'scale_jitter_up': 1.25,\n",
            " 'scoremap_dir': 'test',\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/Colab '\n",
            "                    'Notebooks/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/train/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selecting single-animal trainer\n",
            "Batch Size is 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n",
            "  warnings.warn('`layer.apply` is deprecated and '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading ImageNet-pretrained resnet_50\n",
            "Max_iters overwritten as 1000\n",
            "Display_iters overwritten as 100\n",
            "Save_iters overwritten as 500\n",
            "Training parameter:\n",
            "{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': '/content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/train/snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'sgd', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 1, 'dataset_type': 'imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': False, 'pairwise_predict': False, 'all_joints': [[0], [1], [2], [3]], 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'], 'alpha_r': 0.02, 'apply_prob': 0.5, 'contrast': {'clahe': True, 'claheratio': 0.1, 'histeq': True, 'histeqratio': 0.1, 'gamma': False, 'sigmoid': False, 'log': False, 'linear': False}, 'convolution': {'edge': False, 'emboss': {'alpha': [0.0, 1.0], 'strength': [0.5, 1.5]}, 'embossratio': 0.1, 'sharpen': False, 'sharpenratio': 0.3}, 'cropratio': 0.4, 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat', 'decay_steps': 30000, 'display_iters': 1000, 'init_weights': '/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt', 'lr_init': 0.0005, 'max_input_size': 1500, 'metadataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/Documentation_data-openfield_95shuffle1.pickle', 'min_input_size': 64, 'multi_stage': False, 'multi_step': [[0.005, 10000], [0.02, 430000], [0.002, 730000], [0.001, 1030000]], 'net_type': 'resnet_50', 'num_joints': 4, 'pos_dist_thresh': 17, 'project_path': '/content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 50000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25, 'covering': True, 'elastic_transform': True, 'motion_blur': True, 'motion_blur_params': {'k': 7, 'angle': (-90, 90)}}\n",
            "Starting training....\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "iteration: 100 loss: 0.0561 lr: 0.005\n",
            "iteration: 200 loss: 0.0222 lr: 0.005\n",
            "iteration: 300 loss: 0.0192 lr: 0.005\n",
            "iteration: 400 loss: 0.0190 lr: 0.005\n",
            "iteration: 500 loss: 0.0164 lr: 0.005\n",
            "iteration: 600 loss: 0.0148 lr: 0.005\n",
            "iteration: 700 loss: 0.0138 lr: 0.005\n",
            "iteration: 800 loss: 0.0132 lr: 0.005\n",
            "iteration: 900 loss: 0.0124 lr: 0.005\n",
            "iteration: 1000 loss: 0.0107 lr: 0.005\n",
            "Exception in thread Thread-10 (load_and_enqueue):\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py\", line 1378, in _do_call\n",
            "    return fn(*args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py\", line 1361, in _run_fn\n",
            "    return self._call_tf_sessionrun(options, feed_dict, fetch_list,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py\", line 1454, in _call_tf_sessionrun\n",
            "    return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,\n",
            "tensorflow.python.framework.errors_impl.CancelledError: Enqueue operation was cancelled\n",
            "\t [[{{node fifo_queue_enqueue}}]]\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
            "    self.run()\n",
            "  File \"/usr/lib/python3.10/threading.py\", line 953, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 85, in load_and_enqueue\n",
            "    sess.run(enqueue_op, feed_dict=food)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py\", line 968, in run\n",
            "    result = self._run(None, fetches, feed_dict, options_ptr,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py\", line 1191, in _run\n",
            "    results = self._do_run(handle, final_targets, final_fetches,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py\", line 1371, in _do_run\n",
            "    return self._do_call(_run_fn, feeds, fetches, targets, options,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/client/session.py\", line 1397, in _do_call\n",
            "    raise type(e)(node_def, op, message)  # pylint: disable=no-value-for-parameter\n",
            "tensorflow.python.framework.errors_impl.CancelledError: Graph execution error:\n",
            "\n",
            "Detected at node 'fifo_queue_enqueue' defined at (most recent call last):\n",
            "    File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n",
            "      return _run_code(code, main_globals, None,\n",
            "    File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n",
            "      exec(code, run_globals)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n",
            "      app.launch_new_instance()\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n",
            "      app.start()\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\n",
            "      self.io_loop.start()\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n",
            "      self.asyncio_loop.run_forever()\n",
            "    File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n",
            "      self._run_once()\n",
            "    File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n",
            "      handle._run()\n",
            "    File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n",
            "      self._context.run(self._callback, *self._args)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\n",
            "      lambda f: self._run_callback(functools.partial(callback, future))\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\n",
            "      ret = callback()\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\n",
            "      self.ctx_run(self.run)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n",
            "      yielded = self.gen.send(value)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\n",
            "      yield gen.maybe_future(dispatch(*args))\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n",
            "      yielded = ctx_run(next, result)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n",
            "      yield gen.maybe_future(handler(stream, idents, msg))\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n",
            "      yielded = ctx_run(next, result)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\n",
            "      self.do_execute(\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n",
            "      yielded = ctx_run(next, result)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n",
            "      res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n",
            "      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n",
            "      result = self._run_cell(\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n",
            "      return runner(coro)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n",
            "      coro.send(None)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n",
            "      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n",
            "      if (await self.run_code(code, result,  async_=asy)):\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
            "      exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "    File \"<ipython-input-7-da9942723da2>\", line 5, in <cell line: 5>\n",
            "      deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100,saveiters=500, maxiters=1000)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 212, in train_network\n",
            "      train(\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 171, in train\n",
            "      batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n",
            "    File \"/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 71, in setup_preloading\n",
            "      enqueue_op = q.enqueue(placeholders_list)\n",
            "Node: 'fifo_queue_enqueue'\n",
            "Enqueue operation was cancelled\n",
            "\t [[{{node fifo_queue_enqueue}}]]\n",
            "\n",
            "Original stack trace for 'fifo_queue_enqueue':\n",
            "  File \"/usr/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n",
            "    return _run_code(code, main_globals, None,\n",
            "  File \"/usr/lib/python3.10/runpy.py\", line 86, in _run_code\n",
            "    exec(code, run_globals)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n",
            "    app.launch_new_instance()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/traitlets/config/application.py\", line 992, in launch_instance\n",
            "    app.start()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelapp.py\", line 619, in start\n",
            "    self.io_loop.start()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tornado/platform/asyncio.py\", line 195, in start\n",
            "    self.asyncio_loop.run_forever()\n",
            "  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n",
            "    self._run_once()\n",
            "  File \"/usr/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n",
            "    handle._run()\n",
            "  File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n",
            "    self._context.run(self._callback, *self._args)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 685, in <lambda>\n",
            "    lambda f: self._run_callback(functools.partial(callback, future))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tornado/ioloop.py\", line 738, in _run_callback\n",
            "    ret = callback()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 825, in inner\n",
            "    self.ctx_run(self.run)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 786, in run\n",
            "    yielded = self.gen.send(value)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 361, in process_one\n",
            "    yield gen.maybe_future(dispatch(*args))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n",
            "    yielded = ctx_run(next, result)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 261, in dispatch_shell\n",
            "    yield gen.maybe_future(handler(stream, idents, msg))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n",
            "    yielded = ctx_run(next, result)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/kernelbase.py\", line 539, in execute_request\n",
            "    self.do_execute(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tornado/gen.py\", line 234, in wrapper\n",
            "    yielded = ctx_run(next, result)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py\", line 302, in do_execute\n",
            "    res = shell.run_cell(code, store_history=store_history, silent=silent)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/ipykernel/zmqshell.py\", line 539, in run_cell\n",
            "    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2975, in run_cell\n",
            "    result = self._run_cell(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3030, in _run_cell\n",
            "    return runner(coro)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/async_helpers.py\", line 78, in _pseudo_sync_runner\n",
            "    coro.send(None)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3257, in run_cell_async\n",
            "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3473, in run_ast_nodes\n",
            "    if (await self.run_code(code, result,  async_=asy)):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-7-da9942723da2>\", line 5, in <cell line: 5>\n",
            "    deeplabcut.train_network(path_config_file, shuffle=1, displayiters=100,saveiters=500, maxiters=1000)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/training.py\", line 212, in train_network\n",
            "    train(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 171, in train\n",
            "    batch, enqueue_op, placeholders = setup_preloading(batch_spec)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/core/train.py\", line 71, in setup_preloading\n",
            "    enqueue_op = q.enqueue(placeholders_list)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/data_flow_ops.py\", line 346, in enqueue\n",
            "    return gen_data_flow_ops.queue_enqueue_v2(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py\", line 4068, in queue_enqueue_v2\n",
            "    _, _, _op, _outputs = _op_def_library._apply_op_helper(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/op_def_library.py\", line 795, in _apply_op_helper\n",
            "    op = g._create_op_internal(op_type_name, inputs, dtypes=None,\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\", line 3814, in _create_op_internal\n",
            "    ret = Operation(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The network is now trained and ready to evaluate. Use the function 'evaluate_network' to evaluate the network.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RiDwIVf5-3H_"
      },
      "source": [
        "We recommend you run this for ~1,000 iterations, just as a demo. This should take around 20 min. Note, that **when you hit \"STOP\" you will get a KeyInterrupt \"error\"! No worries! :)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZygsb2DoEJc"
      },
      "source": [
        "## Start evaluating:\n",
        "This function evaluates a trained model for a specific shuffle/shuffles at a particular state or all the states on the data set (images)\n",
        "and stores the results as .csv file in a subdirectory under **evaluation-results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nv4zlbrnoEJg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        },
        "outputId": "58b4aee2-8f19-4383-db85-bf940f6226da"
      },
      "source": [
        "%matplotlib notebook\n",
        "deeplabcut.evaluate_network(path_config_file,plotting=True)\n",
        "\n",
        "# Here you want to see a low pixel error! Of course, it can only be as good as the labeler, so be sure your labels are good!"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3]],\n",
            " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n",
            " 'batch_size': 1,\n",
            " 'crop_pad': 0,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'deterministic': False,\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'init_weights': '/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 1.0,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'mirror': False,\n",
            " 'net_type': 'resnet_50',\n",
            " 'num_joints': 4,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': True,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'regularize': False,\n",
            " 'scoremap_dir': 'test',\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/Colab '\n",
            "                    'Notebooks/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/test/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n",
            "  warnings.warn('`layer.apply` is deprecated and '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running  DLC_resnet50_openfieldOct30shuffle1_1000  with # of training iterations: 1000\n",
            "Running evaluation ...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "116it [00:05, 19.49it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analysis is done and the results are stored (see evaluation-results) for snapshot:  snapshot-1000\n",
            "Results for 1000  training iterations: 95 1 train error: 10.91 pixels. Test error: 13.96  pixels.\n",
            "With pcutoff of 0.4  train error: 10.66 pixels. Test error: 12.64 pixels\n",
            "Thereby, the errors are given by the average distances between the labels by DLC and the scorer.\n",
            "Plotting...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
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              "};\n",
              "\n",
              "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
              "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
              "    // which will in turn request a refresh of the image.\n",
              "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.send_message = function (type, properties) {\n",
              "    properties['type'] = type;\n",
              "    properties['figure_id'] = this.id;\n",
              "    this.ws.send(JSON.stringify(properties));\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.send_draw_message = function () {\n",
              "    if (!this.waiting) {\n",
              "        this.waiting = true;\n",
              "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
              "    var format_dropdown = fig.format_dropdown;\n",
              "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
              "    fig.ondownload(fig, format);\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
              "    var size = msg['size'];\n",
              "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
              "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
              "        fig.send_message('refresh', {});\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
              "    var x0 = msg['x0'] / fig.ratio;\n",
              "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
              "    var x1 = msg['x1'] / fig.ratio;\n",
              "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
              "    x0 = Math.floor(x0) + 0.5;\n",
              "    y0 = Math.floor(y0) + 0.5;\n",
              "    x1 = Math.floor(x1) + 0.5;\n",
              "    y1 = Math.floor(y1) + 0.5;\n",
              "    var min_x = Math.min(x0, x1);\n",
              "    var min_y = Math.min(y0, y1);\n",
              "    var width = Math.abs(x1 - x0);\n",
              "    var height = Math.abs(y1 - y0);\n",
              "\n",
              "    fig.rubberband_context.clearRect(\n",
              "        0,\n",
              "        0,\n",
              "        fig.canvas.width / fig.ratio,\n",
              "        fig.canvas.height / fig.ratio\n",
              "    );\n",
              "\n",
              "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
              "    // Updates the figure title.\n",
              "    fig.header.textContent = msg['label'];\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
              "    fig.canvas_div.style.cursor = msg['cursor'];\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
              "    fig.message.textContent = msg['message'];\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
              "    // Request the server to send over a new figure.\n",
              "    fig.send_draw_message();\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
              "    fig.image_mode = msg['mode'];\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
              "    for (var key in msg) {\n",
              "        if (!(key in fig.buttons)) {\n",
              "            continue;\n",
              "        }\n",
              "        fig.buttons[key].disabled = !msg[key];\n",
              "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
              "    if (msg['mode'] === 'PAN') {\n",
              "        fig.buttons['Pan'].classList.add('active');\n",
              "        fig.buttons['Zoom'].classList.remove('active');\n",
              "    } else if (msg['mode'] === 'ZOOM') {\n",
              "        fig.buttons['Pan'].classList.remove('active');\n",
              "        fig.buttons['Zoom'].classList.add('active');\n",
              "    } else {\n",
              "        fig.buttons['Pan'].classList.remove('active');\n",
              "        fig.buttons['Zoom'].classList.remove('active');\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function () {\n",
              "    // Called whenever the canvas gets updated.\n",
              "    this.send_message('ack', {});\n",
              "};\n",
              "\n",
              "// A function to construct a web socket function for onmessage handling.\n",
              "// Called in the figure constructor.\n",
              "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
              "    return function socket_on_message(evt) {\n",
              "        if (evt.data instanceof Blob) {\n",
              "            var img = evt.data;\n",
              "            if (img.type !== 'image/png') {\n",
              "                /* FIXME: We get \"Resource interpreted as Image but\n",
              "                 * transferred with MIME type text/plain:\" errors on\n",
              "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
              "                 * to be part of the websocket stream */\n",
              "                img.type = 'image/png';\n",
              "            }\n",
              "\n",
              "            /* Free the memory for the previous frames */\n",
              "            if (fig.imageObj.src) {\n",
              "                (window.URL || window.webkitURL).revokeObjectURL(\n",
              "                    fig.imageObj.src\n",
              "                );\n",
              "            }\n",
              "\n",
              "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
              "                img\n",
              "            );\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        } else if (\n",
              "            typeof evt.data === 'string' &&\n",
              "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
              "        ) {\n",
              "            fig.imageObj.src = evt.data;\n",
              "            fig.updated_canvas_event();\n",
              "            fig.waiting = false;\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        var msg = JSON.parse(evt.data);\n",
              "        var msg_type = msg['type'];\n",
              "\n",
              "        // Call the  \"handle_{type}\" callback, which takes\n",
              "        // the figure and JSON message as its only arguments.\n",
              "        try {\n",
              "            var callback = fig['handle_' + msg_type];\n",
              "        } catch (e) {\n",
              "            console.log(\n",
              "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
              "                msg\n",
              "            );\n",
              "            return;\n",
              "        }\n",
              "\n",
              "        if (callback) {\n",
              "            try {\n",
              "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
              "                callback(fig, msg);\n",
              "            } catch (e) {\n",
              "                console.log(\n",
              "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
              "                    e,\n",
              "                    e.stack,\n",
              "                    msg\n",
              "                );\n",
              "            }\n",
              "        }\n",
              "    };\n",
              "};\n",
              "\n",
              "function getModifiers(event) {\n",
              "    var mods = [];\n",
              "    if (event.ctrlKey) {\n",
              "        mods.push('ctrl');\n",
              "    }\n",
              "    if (event.altKey) {\n",
              "        mods.push('alt');\n",
              "    }\n",
              "    if (event.shiftKey) {\n",
              "        mods.push('shift');\n",
              "    }\n",
              "    if (event.metaKey) {\n",
              "        mods.push('meta');\n",
              "    }\n",
              "    return mods;\n",
              "}\n",
              "\n",
              "/*\n",
              " * return a copy of an object with only non-object keys\n",
              " * we need this to avoid circular references\n",
              " * https://stackoverflow.com/a/24161582/3208463\n",
              " */\n",
              "function simpleKeys(original) {\n",
              "    return Object.keys(original).reduce(function (obj, key) {\n",
              "        if (typeof original[key] !== 'object') {\n",
              "            obj[key] = original[key];\n",
              "        }\n",
              "        return obj;\n",
              "    }, {});\n",
              "}\n",
              "\n",
              "mpl.figure.prototype.mouse_event = function (event, name) {\n",
              "    if (name === 'button_press') {\n",
              "        this.canvas.focus();\n",
              "        this.canvas_div.focus();\n",
              "    }\n",
              "\n",
              "    // from https://stackoverflow.com/q/1114465\n",
              "    var boundingRect = this.canvas.getBoundingClientRect();\n",
              "    var x = (event.clientX - boundingRect.left) * this.ratio;\n",
              "    var y = (event.clientY - boundingRect.top) * this.ratio;\n",
              "\n",
              "    this.send_message(name, {\n",
              "        x: x,\n",
              "        y: y,\n",
              "        button: event.button,\n",
              "        step: event.step,\n",
              "        modifiers: getModifiers(event),\n",
              "        guiEvent: simpleKeys(event),\n",
              "    });\n",
              "\n",
              "    return false;\n",
              "};\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
              "    // Handle any extra behaviour associated with a key event\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.key_event = function (event, name) {\n",
              "    // Prevent repeat events\n",
              "    if (name === 'key_press') {\n",
              "        if (event.key === this._key) {\n",
              "            return;\n",
              "        } else {\n",
              "            this._key = event.key;\n",
              "        }\n",
              "    }\n",
              "    if (name === 'key_release') {\n",
              "        this._key = null;\n",
              "    }\n",
              "\n",
              "    var value = '';\n",
              "    if (event.ctrlKey && event.key !== 'Control') {\n",
              "        value += 'ctrl+';\n",
              "    }\n",
              "    else if (event.altKey && event.key !== 'Alt') {\n",
              "        value += 'alt+';\n",
              "    }\n",
              "    else if (event.shiftKey && event.key !== 'Shift') {\n",
              "        value += 'shift+';\n",
              "    }\n",
              "\n",
              "    value += 'k' + event.key;\n",
              "\n",
              "    this._key_event_extra(event, name);\n",
              "\n",
              "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
              "    return false;\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
              "    if (name === 'download') {\n",
              "        this.handle_save(this, null);\n",
              "    } else {\n",
              "        this.send_message('toolbar_button', { name: name });\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
              "    this.message.textContent = tooltip;\n",
              "};\n",
              "\n",
              "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
              "// prettier-ignore\n",
              "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
              "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n",
              "\n",
              "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n",
              "\n",
              "mpl.default_extension = \"png\";/* global mpl */\n",
              "\n",
              "var comm_websocket_adapter = function (comm) {\n",
              "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
              "    // object with the appropriate methods. Currently this is a non binary\n",
              "    // socket, so there is still some room for performance tuning.\n",
              "    var ws = {};\n",
              "\n",
              "    ws.binaryType = comm.kernel.ws.binaryType;\n",
              "    ws.readyState = comm.kernel.ws.readyState;\n",
              "    function updateReadyState(_event) {\n",
              "        if (comm.kernel.ws) {\n",
              "            ws.readyState = comm.kernel.ws.readyState;\n",
              "        } else {\n",
              "            ws.readyState = 3; // Closed state.\n",
              "        }\n",
              "    }\n",
              "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
              "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
              "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
              "\n",
              "    ws.close = function () {\n",
              "        comm.close();\n",
              "    };\n",
              "    ws.send = function (m) {\n",
              "        //console.log('sending', m);\n",
              "        comm.send(m);\n",
              "    };\n",
              "    // Register the callback with on_msg.\n",
              "    comm.on_msg(function (msg) {\n",
              "        //console.log('receiving', msg['content']['data'], msg);\n",
              "        var data = msg['content']['data'];\n",
              "        if (data['blob'] !== undefined) {\n",
              "            data = {\n",
              "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
              "            };\n",
              "        }\n",
              "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
              "        ws.onmessage(data);\n",
              "    });\n",
              "    return ws;\n",
              "};\n",
              "\n",
              "mpl.mpl_figure_comm = function (comm, msg) {\n",
              "    // This is the function which gets called when the mpl process\n",
              "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
              "\n",
              "    var id = msg.content.data.id;\n",
              "    // Get hold of the div created by the display call when the Comm\n",
              "    // socket was opened in Python.\n",
              "    var element = document.getElementById(id);\n",
              "    var ws_proxy = comm_websocket_adapter(comm);\n",
              "\n",
              "    function ondownload(figure, _format) {\n",
              "        window.open(figure.canvas.toDataURL());\n",
              "    }\n",
              "\n",
              "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
              "\n",
              "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
              "    // web socket which is closed, not our websocket->open comm proxy.\n",
              "    ws_proxy.onopen();\n",
              "\n",
              "    fig.parent_element = element;\n",
              "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
              "    if (!fig.cell_info) {\n",
              "        console.error('Failed to find cell for figure', id, fig);\n",
              "        return;\n",
              "    }\n",
              "    fig.cell_info[0].output_area.element.on(\n",
              "        'cleared',\n",
              "        { fig: fig },\n",
              "        fig._remove_fig_handler\n",
              "    );\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
              "    var width = fig.canvas.width / fig.ratio;\n",
              "    fig.cell_info[0].output_area.element.off(\n",
              "        'cleared',\n",
              "        fig._remove_fig_handler\n",
              "    );\n",
              "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
              "\n",
              "    // Update the output cell to use the data from the current canvas.\n",
              "    fig.push_to_output();\n",
              "    var dataURL = fig.canvas.toDataURL();\n",
              "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
              "    // the notebook keyboard shortcuts fail.\n",
              "    IPython.keyboard_manager.enable();\n",
              "    fig.parent_element.innerHTML =\n",
              "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "    fig.close_ws(fig, msg);\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
              "    fig.send_message('closing', msg);\n",
              "    // fig.ws.close()\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
              "    // Turn the data on the canvas into data in the output cell.\n",
              "    var width = this.canvas.width / this.ratio;\n",
              "    var dataURL = this.canvas.toDataURL();\n",
              "    this.cell_info[1]['text/html'] =\n",
              "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.updated_canvas_event = function () {\n",
              "    // Tell IPython that the notebook contents must change.\n",
              "    IPython.notebook.set_dirty(true);\n",
              "    this.send_message('ack', {});\n",
              "    var fig = this;\n",
              "    // Wait a second, then push the new image to the DOM so\n",
              "    // that it is saved nicely (might be nice to debounce this).\n",
              "    setTimeout(function () {\n",
              "        fig.push_to_output();\n",
              "    }, 1000);\n",
              "};\n",
              "\n",
              "mpl.figure.prototype._init_toolbar = function () {\n",
              "    var fig = this;\n",
              "\n",
              "    var toolbar = document.createElement('div');\n",
              "    toolbar.classList = 'btn-toolbar';\n",
              "    this.root.appendChild(toolbar);\n",
              "\n",
              "    function on_click_closure(name) {\n",
              "        return function (_event) {\n",
              "            return fig.toolbar_button_onclick(name);\n",
              "        };\n",
              "    }\n",
              "\n",
              "    function on_mouseover_closure(tooltip) {\n",
              "        return function (event) {\n",
              "            if (!event.currentTarget.disabled) {\n",
              "                return fig.toolbar_button_onmouseover(tooltip);\n",
              "            }\n",
              "        };\n",
              "    }\n",
              "\n",
              "    fig.buttons = {};\n",
              "    var buttonGroup = document.createElement('div');\n",
              "    buttonGroup.classList = 'btn-group';\n",
              "    var button;\n",
              "    for (var toolbar_ind in mpl.toolbar_items) {\n",
              "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
              "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
              "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
              "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
              "\n",
              "        if (!name) {\n",
              "            /* Instead of a spacer, we start a new button group. */\n",
              "            if (buttonGroup.hasChildNodes()) {\n",
              "                toolbar.appendChild(buttonGroup);\n",
              "            }\n",
              "            buttonGroup = document.createElement('div');\n",
              "            buttonGroup.classList = 'btn-group';\n",
              "            continue;\n",
              "        }\n",
              "\n",
              "        button = fig.buttons[name] = document.createElement('button');\n",
              "        button.classList = 'btn btn-default';\n",
              "        button.href = '#';\n",
              "        button.title = name;\n",
              "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
              "        button.addEventListener('click', on_click_closure(method_name));\n",
              "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
              "        buttonGroup.appendChild(button);\n",
              "    }\n",
              "\n",
              "    if (buttonGroup.hasChildNodes()) {\n",
              "        toolbar.appendChild(buttonGroup);\n",
              "    }\n",
              "\n",
              "    // Add the status bar.\n",
              "    var status_bar = document.createElement('span');\n",
              "    status_bar.classList = 'mpl-message pull-right';\n",
              "    toolbar.appendChild(status_bar);\n",
              "    this.message = status_bar;\n",
              "\n",
              "    // Add the close button to the window.\n",
              "    var buttongrp = document.createElement('div');\n",
              "    buttongrp.classList = 'btn-group inline pull-right';\n",
              "    button = document.createElement('button');\n",
              "    button.classList = 'btn btn-mini btn-primary';\n",
              "    button.href = '#';\n",
              "    button.title = 'Stop Interaction';\n",
              "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
              "    button.addEventListener('click', function (_evt) {\n",
              "        fig.handle_close(fig, {});\n",
              "    });\n",
              "    button.addEventListener(\n",
              "        'mouseover',\n",
              "        on_mouseover_closure('Stop Interaction')\n",
              "    );\n",
              "    buttongrp.appendChild(button);\n",
              "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
              "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
              "};\n",
              "\n",
              "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
              "    var fig = event.data.fig;\n",
              "    if (event.target !== this) {\n",
              "        // Ignore bubbled events from children.\n",
              "        return;\n",
              "    }\n",
              "    fig.close_ws(fig, {});\n",
              "};\n",
              "\n",
              "mpl.figure.prototype._root_extra_style = function (el) {\n",
              "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
              "};\n",
              "\n",
              "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
              "    // this is important to make the div 'focusable\n",
              "    el.setAttribute('tabindex', 0);\n",
              "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
              "    // off when our div gets focus\n",
              "\n",
              "    // location in version 3\n",
              "    if (IPython.notebook.keyboard_manager) {\n",
              "        IPython.notebook.keyboard_manager.register_events(el);\n",
              "    } else {\n",
              "        // location in version 2\n",
              "        IPython.keyboard_manager.register_events(el);\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
              "    // Check for shift+enter\n",
              "    if (event.shiftKey && event.which === 13) {\n",
              "        this.canvas_div.blur();\n",
              "        // select the cell after this one\n",
              "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
              "        IPython.notebook.select(index + 1);\n",
              "    }\n",
              "};\n",
              "\n",
              "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
              "    fig.ondownload(fig, null);\n",
              "};\n",
              "\n",
              "mpl.find_output_cell = function (html_output) {\n",
              "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
              "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
              "    // IPython event is triggered only after the cells have been serialised, which for\n",
              "    // our purposes (turning an active figure into a static one), is too late.\n",
              "    var cells = IPython.notebook.get_cells();\n",
              "    var ncells = cells.length;\n",
              "    for (var i = 0; i < ncells; i++) {\n",
              "        var cell = cells[i];\n",
              "        if (cell.cell_type === 'code') {\n",
              "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
              "                var data = cell.output_area.outputs[j];\n",
              "                if (data.data) {\n",
              "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
              "                    data = data.data;\n",
              "                }\n",
              "                if (data['text/html'] === html_output) {\n",
              "                    return [cell, data, j];\n",
              "                }\n",
              "            }\n",
              "        }\n",
              "    }\n",
              "};\n",
              "\n",
              "// Register the function which deals with the matplotlib target/channel.\n",
              "// The kernel may be null if the page has been refreshed.\n",
              "if (IPython.notebook.kernel !== null) {\n",
              "    IPython.notebook.kernel.comm_manager.register_target(\n",
              "        'matplotlib',\n",
              "        mpl.mpl_figure_comm\n",
              "    );\n",
              "}\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div id='a9d6e390-f4dd-4c68-adbb-cc56cd5a2aef'></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 116/116 [00:24<00:00,  4.73it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The network is evaluated and the results are stored in the subdirectory 'evaluation_results'.\n",
            "Please check the results, then choose the best model (snapshot) for prediction. You can update the config.yaml file with the appropriate index for the 'snapshotindex'.\n",
            "Use the function 'analyze_video' to make predictions on new videos.\n",
            "Otherwise, consider adding more labeled-data and retraining the network (see DeepLabCut workflow Fig 2, Nath 2019)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxy5JG-kYKF4"
      },
      "source": [
        "**Check the images**:\n",
        "You can go look in the newly created \"evalutaion-results\" folder at the images. At around 3500 iterations, the error is ~3 pixels (but this can vary on how your demo data was split for training)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OVFLSKKfoEJk"
      },
      "source": [
        "## Start Analyzing videos:\n",
        "This function analyzes the new video. The user can choose the best model from the evaluation results and specify the correct snapshot index for the variable **snapshotindex** in the **config.yaml** file. Otherwise, by default the most recent snapshot is used to analyse the video.\n",
        "\n",
        "The results are stored in hd5 file in the same directory where the video resides.\n",
        "\n",
        "**On the demo data, this should take around ~ 3 min! (The demo frames are 640x480, which should run around 35 FPS on the google-provided GPU)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_LZiS_0oEJl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 942
        },
        "outputId": "c2040fbd-f767-4309-a54b-9b008de22994"
      },
      "source": [
        "videofile_path = ['/content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/videos/m3v1.mp4.mp4'] #Enter the list of videos to analyze.\n",
        "deeplabcut.analyze_videos(path_config_file,videofile_path, videotype='.mp4')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Config:\n",
            "{'all_joints': [[0], [1], [2], [3]],\n",
            " 'all_joints_names': ['snout', 'leftear', 'rightear', 'tailbase'],\n",
            " 'batch_size': 1,\n",
            " 'crop_pad': 0,\n",
            " 'dataset': 'training-datasets/iteration-0/UnaugmentedDataSet_openfieldOct30/openfield_Pranav95shuffle1.mat',\n",
            " 'dataset_type': 'imgaug',\n",
            " 'deterministic': False,\n",
            " 'fg_fraction': 0.25,\n",
            " 'global_scale': 0.8,\n",
            " 'init_weights': '/usr/local/lib/python3.10/dist-packages/deeplabcut/pose_estimation_tensorflow/models/pretrained/resnet_v1_50.ckpt',\n",
            " 'intermediate_supervision': False,\n",
            " 'intermediate_supervision_layer': 12,\n",
            " 'location_refinement': True,\n",
            " 'locref_huber_loss': True,\n",
            " 'locref_loss_weight': 1.0,\n",
            " 'locref_stdev': 7.2801,\n",
            " 'log_dir': 'log',\n",
            " 'mean_pixel': [123.68, 116.779, 103.939],\n",
            " 'mirror': False,\n",
            " 'net_type': 'resnet_50',\n",
            " 'num_joints': 4,\n",
            " 'optimizer': 'sgd',\n",
            " 'pairwise_huber_loss': True,\n",
            " 'pairwise_predict': False,\n",
            " 'partaffinityfield_predict': False,\n",
            " 'regularize': False,\n",
            " 'scoremap_dir': 'test',\n",
            " 'shuffle': True,\n",
            " 'snapshot_prefix': '/content/drive/MyDrive/Colab '\n",
            "                    'Notebooks/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1/test/snapshot',\n",
            " 'stride': 8.0,\n",
            " 'weigh_negatives': False,\n",
            " 'weigh_only_present_joints': False,\n",
            " 'weigh_part_predictions': False,\n",
            " 'weight_decay': 0.0001}\n",
            "/usr/local/lib/python3.10/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py:1694: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n",
            "  warnings.warn('`layer.apply` is deprecated and '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using snapshot-1000 for model /content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/dlc-models/iteration-0/openfieldOct30-trainset95shuffle1\n",
            "Starting to analyze %  /content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/videos/m3v1.mp4.mp4\n",
            "Loading  /content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/videos/m3v1.mp4.mp4\n",
            "Duration of video [s]:  77.67 , recorded with  30.0 fps!\n",
            "Overall # of frames:  2330  found with (before cropping) frame dimensions:  640 480\n",
            "Starting to extract posture\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2330/2330 [01:02<00:00, 37.05it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving results in /content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/videos...\n",
            "The videos are analyzed. Now your research can truly start! \n",
            " You can create labeled videos with 'create_labeled_video'\n",
            "If the tracking is not satisfactory for some videos, consider expanding the training set. You can use the function 'extract_outlier_frames' to extract a few representative outlier frames.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'DLC_resnet50_openfieldOct30shuffle1_1000'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCrUvQIvoEKD"
      },
      "source": [
        "## Create labeled video:\n",
        "This function is for visualiztion purpose and can be used to create a video in .mp4 format with labels predicted by the network. This video is saved in the same directory where the original video resides. This should run around 215 FPS on the demo video!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6aDF7Q7KoEKE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "outputId": "7383e095-1625-4009-f520-db26e5e56098"
      },
      "source": [
        "deeplabcut.create_labeled_video(path_config_file,videofile_path)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting to process video: /content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/videos/m3v1.mp4.mp4\n",
            "Loading /content/drive/MyDrive/Colab Notebooks/openfield-Pranav-2018-10-30/videos/m3v1.mp4.mp4 and data.\n",
            "Duration of video [s]: 77.67, recorded with 30.0 fps!\n",
            "Overall # of frames: 2330 with cropped frame dimensions: 640 480\n",
            "Generating frames and creating video.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 2330/2330 [00:14<00:00, 156.40it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[True]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GTiuJESoEKH"
      },
      "source": [
        "## Plot the trajectories of the analyzed videos:\n",
        "This function plots the trajectories of all the body parts across the entire video. Each body part is identified by a unique color."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gX21zZbXoEKJ"
      },
      "source": [
        "deeplabcut.plot_trajectories(path_config_file,videofile_path)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}