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VEST_output_loader.ipynb
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| { | |
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| "metadata": { | |
| "colab": { | |
| "name": "VEST_output_loader.ipynb", | |
| "provenance": [], | |
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| "authorship_tag": "ABX9TyMDzK8B3hDk6GZFP0URKN2W", | |
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| "name": "python3", | |
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| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/jcheong0428/785394bd3710e309790c1a99170d7da5/vest_output_loader.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Example code to parse and load the VEST json output into a Pandas dataframe\n", | |
| "_Written by Jin Hyun Cheong_\n", | |
| "\n", | |
| "[VEST](https://jinhyuncheong.com/vest/vest.html) is a video event segmentation tool to code start and stop times of events in a video clip. In the VEST json outputs, segments include start and stop times while points only include start times. This code shows how you can use the `parse_vest_json()` function to get dataframe for the segmentations and points. " | |
| ], | |
| "metadata": { | |
| "id": "cJDB8SmVOxbn" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": { | |
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| "base_uri": "https://localhost:8080/", | |
| "height": 928 | |
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| "id": "zyRBO8dxDVkj", | |
| "outputId": "9f5aaedc-af9a-4c06-cc16-3383b53820b9" | |
| }, | |
| "outputs": [ | |
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| " <td>14.178363</td>\n", | |
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| " <td>14.662548</td>\n", | |
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| " buttonEl.style.display =\n", | |
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| " const element = document.querySelector('#df-7219f9d9-df45-410a-b4a0-f89df9c60bde');\n", | |
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| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
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| " <td>12.889067</td>\n", | |
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| " <td>1</td>\n", | |
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| " <td>13.175818</td>\n", | |
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| " <td>1</td>\n", | |
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| " <td>13.538035</td>\n", | |
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| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-d1f82dbb-a520-4310-947b-004284f1521f');\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", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ], | |
| "text/plain": [ | |
| " startTime id labelText\n", | |
| "peaks.point.0 11.075451 peaks.point.0 222\n", | |
| "peaks.point.1 11.217249 peaks.point.1 222\n", | |
| "peaks.point.2 11.361147 peaks.point.2 222\n", | |
| "peaks.point.3 11.460108 peaks.point.3 222\n", | |
| "peaks.point.4 11.571997 peaks.point.4 222\n", | |
| "peaks.point.5 11.708106 peaks.point.5 222\n", | |
| "peaks.point.6 11.854410 peaks.point.6 222\n", | |
| "peaks.point.7 11.990123 peaks.point.7 222\n", | |
| "peaks.point.8 12.147687 peaks.point.8 222\n", | |
| "peaks.point.9 12.729908 peaks.point.9 1\n", | |
| "peaks.point.10 12.889067 peaks.point.10 1\n", | |
| "peaks.point.11 13.040318 peaks.point.11 1\n", | |
| "peaks.point.12 13.175818 peaks.point.12 1\n", | |
| "peaks.point.13 13.303282 peaks.point.13 1\n", | |
| "peaks.point.14 13.423370 peaks.point.14 1\n", | |
| "peaks.point.15 13.538035 peaks.point.15 1" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "\n", | |
| "def parse_vest_json(f):\n", | |
| " \"\"\"Parse json output from VEST\n", | |
| "\n", | |
| " Args:\n", | |
| " f: path to VEST json output\n", | |
| " \n", | |
| " Returns:\n", | |
| " segmentations dataframe\n", | |
| " points dataframe\n", | |
| " \"\"\"\n", | |
| " seg_df, pts_df = pd.DataFrame(), pd.DataFrame()\n", | |
| "\n", | |
| " df = pd.read_json(f, lines=True)\n", | |
| " seg_cols = [col for col in df.columns if 'segment' in col]\n", | |
| " _seg_df = df[seg_cols].dropna().T\n", | |
| " for rowix, row in _seg_df.iterrows():\n", | |
| " seg_df = pd.concat([seg_df, pd.DataFrame(row.values[0],index=[row.name])])\n", | |
| "\n", | |
| " pts_cols = [col for col in df.columns if 'point' in col]\n", | |
| " _pts_df = df[pts_cols].dropna().T\n", | |
| " for rowix, row in _pts_df.iterrows():\n", | |
| " pts_df = pd.concat([pts_df, pd.DataFrame(row.values[0], index=[row.name])])\n", | |
| " return seg_df, pts_df\n", | |
| "\n", | |
| "\n", | |
| "path_to_file = \"/content/segmentation_results.json\"\n", | |
| "seg_df, pts_df = parse_vest_json(f=path_to_file)\n", | |
| "display(seg_df, pts_df)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "" | |
| ], | |
| "metadata": { | |
| "id": "U22G7yVjD4Tc" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ] | |
| } |
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