examples/elderly_action_recognition.ipynb

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    "<!-- Autogenerated by `scripts/make_examples.py` -->\n",
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    "<div style=\"text-align: center;\">\n",
    "    <h1>Elderly Action Recognition Challenge</h1>\n",
    "    <h2>Dataset Preparation with FiftyOne</h2>\n",
    "</div>\n",
    "\n",
    "This notebook walks you through the process of preparing a dataset for the [Elderly Action Recognition Challenge](https://voxel51.com/computer-vision-events/elderly-action-recognition-challenge-wacv-2025). It covers essential steps such as importing data, parsing actions, assigning categories, splitting videos into clips, and exporting the dataset using [FiftyOne](https://docs.voxel51.com/).\n",
    "\n",
    "---\n",
    "\n",
    "**Useful Links:**\n",
    "- [Challenge Overview](https://voxel51.com/computer-vision-events/elderly-action-recognition-challenge-wacv-2025/)\n",
    "- [Submission Page](https://eval.ai/web/challenges/challenge-page/2427/overview)\n",
    "- [Join the Discussion](https://discord.com/channels/1266527359511564372/1319053378843836448)\n",
    "\n",
    "---\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "    <img src=\"https://github.com/user-attachments/assets/a97ed6ff-8aa8-4911-98b0-6f8dce36ab83\" alt=\"challenge-logo\" width=\"200\" style=\"margin-right: 20px;\">\n",
    "    <img src=\"https://github.com/user-attachments/assets/6b1d05e4-3da3-4591-b70f-764e5ad0e5da\" alt=\"fiftyone-logo\" width=\"200\">\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "**Goal**: Enable participants to work with the dataset efficiently and submit meaningful solutions to the challenge, ultimately advancing the field of action recognition for the elderly.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Requirements anf FiftyOne Installation\n",
    "\n",
    "First thing you need to do is create a Python environment in your system, if you are not familiar with that please take a look of this [ReadmeFile](https://github.com/voxel51/fiftyone-examples?tab=readme-ov-file#-prerequisites-for-beginners-), where we will explain how to create the environment. After that be sure you activate the created environment and install FiftyOne there."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install fiftyone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n",
    "In this section, we import all the necessary libraries and modules to work with the dataset, including FiftyOne, pandas, and re for regular expressions. These libraries provide the foundation for loading, processing, and interacting with the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import fiftyone as fo\n",
    "import fiftyone.types as fot\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Path for Dataset and Checking if Dataset Exists\n",
    "Here, we define the path to the dataset and ensure we are working with a clean dataset by checking if a dataset with the same name already exists. If it does, it will be deleted to prevent conflicts.\n",
    "\n",
    "For the educational purposes, we use the [GMDCSA24 Dataset](https://doi.org/10.5281/zenodo.12921216), a dataset specifically designed for elderly fall detection and Activities of Daily Living (ADLs). Additional information can be found on the dataset’s [GitHub Project Page](https://github.com/ekramalam/GMDCSA24-A-Dataset-for-Human-Fall-Detection-in-Videos) and the associated [Scientific Paper](https://www.sciencedirect.com/science/article/pii/S2352340924008552)."
   ]
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  {
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    "# Define the path to your dataset\n",
    "dataset_path = \"/path/to/the/GMDCSA24/folder\"  # Replace with the actual path\n",
    "dataset_name = \"ADL_Fall_Videos\"\n",
    "\n",
    "# Check if the dataset already exists\n",
    "if fo.dataset_exists(dataset_name):\n",
    "    # Delete the existing dataset\n",
    "    fo.delete_dataset(dataset_name)\n",
    "\n",
    "# Create a FiftyOne dataset\n",
    "fo_dataset = fo.Dataset(dataset_name)"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Helper Functions\n",
    "This section defines two critical helper functions that are essential for processing the dataset:\n",
    "\n",
    "1. Function to Parse the Classes:\n",
    "\n",
    "- Extracts action names and their corresponding time ranges from the dataset.\n",
    "- Splits each video into shorter clips based on these time ranges to prepare a new dataset focused on individual actions.\n",
    "\n",
    "2. Function to Get Category per Action:\n",
    "\n",
    "- Maps actions to predefined categories based on their type.\n",
    "- Categorization is essential to meet one of the challenge goals: grouping actions into higher-level classifications.\n",
    "\n",
    "\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "\n",
    "| **Category**                      | **Actions**                                                                                     |\n",
    "|------------------------------------|-----------------------------------------------------------------------------------------------|\n",
    "| **Locomotion and Posture Transitions** | Walking, Sitting down / Standing up, Getting up / Lying down, Exercising, Looking for something |\n",
    "| **Object Manipulation**            | Spreading bedding / Folding bedding, Wiping table, Cleaning dishes, Cooking, Vacuuming the floor |\n",
    "| **Hygiene and Personal Care**      | Washing hands, Brushing teeth, Taking medicine                                                |\n",
    "| **Eating and Drinking**            | Eating, Drinking                                                                              |\n",
    "| **Communication and Gestures**     | Talking, Phone call, Waving a hand, Shaking hands, Hugging                                    |\n",
    "| **Leisure and Stationary Actions** | Reading, Watching TV                                                                          |\n",
    "\n",
    "</div>"
   ]
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    "# Function to parse the Classes column\n",
    "def parse_classes(classes_str):\n",
    "    actions = []\n",
    "    if pd.isna(classes_str):\n",
    "        return actions\n",
    "\n",
    "    # Split by ';' to handle multiple actions\n",
    "    class_entries = classes_str.split(';')\n",
    "    for entry in class_entries:\n",
    "        match = re.match(r\"(.+?)\\[(.+?)\\]\", entry.strip())\n",
    "        if match:\n",
    "            action = match.group(1).strip() # Extract action name\n",
    "            time_ranges = match.group(2).strip() # Extract time ranges within brackets\n",
    "\n",
    "            #print(\"Action=\", action)\n",
    "            #print(\"Time_Group=\", time_ranges)\n",
    "\n",
    "            # Split time ranges by ';' and process each range\n",
    "            ranges = time_ranges.split(';')\n",
    "            #print(ranges)\n",
    "            for time_range in ranges:\n",
    "                time_match = re.match(r\"(\\d+(\\.\\d+)?) to (\\d+(\\.\\d+)?)\", time_range.strip())\n",
    "                if time_match:\n",
    "                    start_time = float(time_match.group(1))\n",
    "                    #print(\"Starttime=\", start_time)\n",
    "                    end_time = float(time_match.group(3))\n",
    "                    #print(\"Endtime=\", end_time)\n",
    "\n",
    "                    # Ensure start_time is less than or equal to end_time\n",
    "                    if start_time > end_time:\n",
    "                        continue  # Skip invalid ranges\n",
    "\n",
    "                    actions.append({\"action\": action, \"start_time\": start_time, \"end_time\": end_time})\n",
    "\n",
    "    return actions\n",
    "\n",
    "# Function to assign categories based on actions\n",
    "def get_category(action):\n",
    "    locomotion = [\"Walking\", \"Sitting down / Standing up\", \"Getting up / Lying down\", \"Exercising\", \"Looking for something\"]\n",
    "    manipulation = [\"Spreading bedding / Folding bedding\", \"Wiping table\", \"Cleaning dishes\", \"Cooking\", \"Vacuuming the floor\"]\n",
    "    hygiene = [\"Washing hands\", \"Brushing teeth\", \"Taking medicine\"]\n",
    "    eating_drinking = [\"Eating\", \"Drinking\"]\n",
    "    communication = [\"Talking\", \"Phone call\", \"Waving a hand\", \"Shaking hands\", \"Hugging\"]\n",
    "    leisure = [\"Reading\", \"Watching TV\"]\n",
    "\n",
    "    if action in locomotion:\n",
    "        return \"Locomotion and Posture Transitions\"\n",
    "    elif action in manipulation:\n",
    "        return \"Object Manipulation\"\n",
    "    elif action in hygiene:\n",
    "        return \"Hygiene and Personal Care\"\n",
    "    elif action in eating_drinking:\n",
    "        return \"Eating and Drinking\"\n",
    "    elif action in communication:\n",
    "        return \"Communication and Gestures\"\n",
    "    elif action in leisure:\n",
    "        return \"Leisure and Stationary Actions\"\n",
    "    else:\n",
    "        return \"Unknown\""
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iteration in the Main Folders, Per Subject, and Splitting Video by Actions Using FiftyOne\n",
    "This section iterates through the dataset folder structure to:\n",
    "\n",
    "- Process each subject’s actions and assign relevant metadata, including categories.\n",
    "- Split videos into clips using FiftyOne’s advanced capabilities.\n",
    "\n",
    "<div style=\"border-left: 4px solid #3498db; padding: 6px;\">\n",
    "<strong>Note:</strong> The implementation of this section may vary depending on the dataset structure you are working with.\n",
    "</div>"
   ]
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     "text": [
      "/Users/paularamos/Downloads/EAR_Datasets_Temp/GMDCSA24/Subject 2/ADL.csv\n",
      "/Users/paularamos/Downloads/EAR_Datasets_Temp/GMDCSA24/Subject 2/Fall.csv\n",
      "/Users/paularamos/Downloads/EAR_Datasets_Temp/GMDCSA24/Subject 3/ADL.csv\n",
      "/Users/paularamos/Downloads/EAR_Datasets_Temp/GMDCSA24/Subject 3/Fall.csv\n",
      "/Users/paularamos/Downloads/EAR_Datasets_Temp/GMDCSA24/Subject 4/ADL.csv\n",
      "/Users/paularamos/Downloads/EAR_Datasets_Temp/GMDCSA24/Subject 4/Fall.csv\n",
      "/Users/paularamos/Downloads/EAR_Datasets_Temp/GMDCSA24/Subject 1/ADL.csv\n",
      "/Users/paularamos/Downloads/EAR_Datasets_Temp/GMDCSA24/Subject 1/Fall.csv\n"
     ]
    }
   ],
   "source": [
    "# Iterate through the main folders (one per subject)\n",
    "for subject_folder in os.listdir(dataset_path):\n",
    "    subject_path = os.path.join(dataset_path, subject_folder)\n",
    "\n",
    "    if not os.path.isdir(subject_path):\n",
    "        continue\n",
    "\n",
    "    # Extract the subject number from the folder name\n",
    "    subject_number = subject_folder.split(\"_\")[-1]  # Adjust the split logic if needed\n",
    "\n",
    "    # Look for ADL and Fall folders and CSV files\n",
    "    adl_folder = os.path.join(subject_path, \"ADL\")\n",
    "    fall_folder = os.path.join(subject_path, \"Fall\")\n",
    "\n",
    "    label_files = [f for f in os.listdir(subject_path) if f.endswith(\".csv\")]\n",
    "\n",
    "    # Load metadata from CSV files\n",
    "    for label_file in label_files:\n",
    "        label_path = os.path.join(subject_path, label_file)\n",
    "        metadata = pd.read_csv(label_path)\n",
    "        print(label_path)\n",
    "\n",
    "        for _, row in metadata.iterrows():\n",
    "            file_name = row[\"File Name\"]\n",
    "            length = row[\"Length (seconds)\"]\n",
    "            time_of_recording = row[\"Time of Recording\"]\n",
    "            attire = row[\"Attire\"]\n",
    "            description = row[\"Description\"]\n",
    "            classes = row[\" Classes\"]\n",
    "\n",
    "            # Parse the Classes column\n",
    "            parsed_classes = parse_classes(classes)\n",
    "\n",
    "            # Determine the file's path\n",
    "            if \"ADL\" in label_path:\n",
    "                video_path = os.path.join(adl_folder, file_name)\n",
    "                subset = \"ADL\"\n",
    "            elif \"Fall\" in label_path:\n",
    "                video_path = os.path.join(fall_folder, file_name)\n",
    "                subset = \"Fall\"\n",
    "            else:\n",
    "                continue\n",
    "\n",
    "            if not os.path.exists(video_path):\n",
    "                print(f\"Video file not found: {video_path}\")\n",
    "                continue\n",
    "            \n",
    "            # Create a FiftyOne sample\n",
    "            metadata = fo.VideoMetadata.build_for(video_path)\n",
    "            sample = fo.Sample(filepath=video_path, metadata=metadata)\n",
    "            \n",
    "            #temporaldetection using actions detections on labeled dataset\n",
    "            temp_detections = []\n",
    "            \n",
    "            for action in parsed_classes:\n",
    "                start_time = float(action[\"start_time\"])\n",
    "                end_time = float(action[\"end_time\"])\n",
    "\n",
    "                # Check if end_time exceeds video duration\n",
    "                if end_time > metadata.duration:\n",
    "                    end_time = metadata.duration\n",
    "\n",
    "                event = fo.TemporalDetection.from_timestamps(\n",
    "                            [start_time, end_time],\n",
    "                            label=action[\"action\"],\n",
    "                            sample=sample,\n",
    "                            )\n",
    "                temp_detections.append(event)\n",
    "                \n",
    "            sample[\"events\"] = fo.TemporalDetections(detections=temp_detections)\n",
    "            \n",
    "            # Add metadata to the sample\n",
    "            sample[\"subset\"] = subset\n",
    "            sample[\"subject_number\"] = subject_number\n",
    "            sample[\"length\"] = length\n",
    "            sample[\"time_of_recording\"] = time_of_recording\n",
    "            sample[\"attire\"] = attire\n",
    "            sample[\"description\"] = description\n",
    "            sample[\"classes\"] = classes\n",
    "            #sample[\"events\"] = events\n",
    "\n",
    "            # Assign category based on actions\n",
    "            categories = [get_category(action[\"action\"]) for action in parsed_classes]\n",
    "            sample[\"category\"] = list(set(categories))  # Deduplicate categories\n",
    "\n",
    "            # Add the sample to the dataset\n",
    "            fo_dataset.add_sample(sample)\n",
    "            fo_dataset.compute_metadata()"
   ]
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   "source": [
    "## Check the dataset in the FiftyOne APP\n",
    "Launching FiftyOne in the browser allows you to visually explore the dataset and its metadata. You can:\n",
    "\n",
    "- Modify fields in the metadata as needed.\n",
    "- Split videos into clips or adjust metadata using the FiftyOne API.\n",
    "- Use FiftyOne's metadata documentation [here](https://docs.voxel51.com/user_guide/basics.html#metadata) and its guide on creating [clips](https://docs.voxel51.com/user_guide/using_views.html#clip-views) for additional details."
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CGl0zPCF5XRpjK2lmb1pny1UrqOpWtUUbeRaD7iZkrO7KFV6ueS8jJP8RjpbiZeMy39LUUuKrm3LoYqX40fU6KQWlSjcU6tWDtGbS6GqSETp3d0ixkp1qkl3pNjsK3dlI4eUpdEaqcFBWSAuiUyCURQFwIYENlJFmLlKxFUkzPVZec7iZAKe4ImwIqLXKvULMmwE3TjsQWS0JylaqiLbhlcmMjGxGURgXjElIsgIsZ6jvJj5vuszM1GarsSVZZaoCXsVJZDA10daaL2FUJfd7Dcy6EUWJygproDqpcgiMpGUOKuhXiLoygcSCc66BdEAVe5ZtC3LUBsXdCsRHZ9RkXqVxPgXqFheH/E+BpsZ8N+J8DXoRUQWpevqkVSfIHe2pZOGdlpWJgwYQViK0UqnJmmmzBsPw9ZO66FRvhsXFU33S9yhc/GLxH4LLzffYuv+GwjnPRmvDyWS19TM1qVjdNu9iWbZx4dC4qrXjTeoh1ZpaXETbk+8jEx9t7NlWdadtkuQ2nG2zM9KFndGiM/I1rROWuOkV6CatRKV/IbJ2iYarua2mi6lTPNsxGh7szmY3QAAVAAAAAAAA/D/AJvgIGUZqDd9mZym43hdZcnz8LFkzqxasmUzx6kwmovUst4WArnj1DPHqbc1gK549Qzx6gWArnj1DPHqBeLcZJrdajcTiamKqKdVpyStorGfPHqGePUDRiMVVxOTitPIrKysFbFVa1GnSm1lp+GyM+ePUM8eoHQo9r4qjTUE4zSVlmWqEV8XWr1o1Zy78dmlaxmzx6hnj1A3V+08RXpRpzy6NPMlZ3L/AGzi+HlzQv72XU52ePUM8eoGmpi61TErEOVqi2aVjTLtrFuKScE+qjqc3PHqGePUDUsdXjiniIytUlvpozRLtnFyaacIroo7nNzx6hnj1A1YXG1sLOUqTXe3TWjLrtLELEyrxcYyluktGYs8eoZ49QOhU7XxVS6bgotWcVHQou0sUuHaa+7Vl3UYs8eoZ49QOjV7XxdSGS8Y3Vm4rUzxxdWOFlh01w5O701M2ePUM8eoHRj2xi4xSUoWSt4RGJx1fEuDqNXhqmlYy549Qzx6gdKPbOLjDLeDfvOOoun2niqc5yUotzd3eJhzx6hnj1A6FTtbFVKcoScMslZ90KXa2KpRjFODjFWScTn549Qzx6gasVjK2MknVkrLZLRI6EVLs7sqoqkkqtbwxTvY4uePUM8eoDKdSdKpGpTlllHVM0YrtCvioQjUyrI7pxVncx549Qzx6gdKHbOLjDLeEv3mtREO0MRDESrqSzyVn3VqZM8eoZ49QN+I7VxVem4NxjFqzyrcxFc8eoZ49QLAVzx6hnj1AsBXPHqGePUCwFc8eoZ49QLAVzx6hnj1AsBXPHqGePUCwFc8eoZ49QLAVzx6hnj1AsBXPHqGePUCwFc8eoZ49QLAVzx6hnj1AsBXPHqGePUCwFc8eoZ49QLAVzx6hnj1AsKn4mXzx6i5O7bAgAACSCSAAAAB1GN4TfRouWwavSr+i/uacDgva3NymoU4K8pHt/pspjhbXTHiMgHRnheznCXCxcs8VfvLR/QRQw9GphatSddQnDaPU9M6ks21tlAesJUeEeJ7vDTtvqwo4SpWoVK0cqhT3uzXdPa7IA04LD0a/E4tZUsqur8zM9yyy3QAACgAAAAOlWwWEoYSFSpUqqc4XilazdvQwxoVZUnVUJOEd5W0MY5zLlJSwAmMXOSjFXbdkjaoA6K7HrWSlVpRm9VBy1MNalOjUlTqK0lujGOeOXEqblUAvSpTrTyU4uUuiNWBw+GrVHSxE6kKrdoqP/6FyymM2u2OOskvMbWpuME2uZavRjRxzpRbcYyS13H4+CjQi0/zf2Z5+pd9TCxm+YwAb8LgKc8P7RiavDp3srbsrisPg40XUw2Jc2nbLJav+h2/JjvS7YgHywlSOEWJeXJJ2Wuog3LL4UAAFAAAAAba+Ep08BQrpyzVHrd6IXjcPRoOHBrKrmV3bkYmcvhNswABtQAAAAbOzcJTxdWcaspJRjfui61GlKuqeD4lRW5rW5jvnd2ptnAdVwlehHNVpSiuttCtHD1q/wCFTlO3NIvdNb2pYDa2GrUPxaco35taCiyy+AASk5NJJtvZItVo1KMstSDjJ62Y2KAaFgcU4ZlQnb0ExpVJzyRhJz91LUkyl8U2qA+pgsTThmnRmo9bbGrs/CYTFxUZ1KqratpbW+RMupjJtNsFPSaJxEk1ZMrUWSUkuTsKkeL+q/nP8OefltwNVN2k9UtDbmfU4ibT0NmHxj0jU+Z5tMN+Z9RifQzzqJQclqUhiv3QNDopvXUh0IdBftX7rD2ldAi/BiRwUUeKXRke1LoyC/AiQ6XRlfaV5kPErowqypyg7pqwqtK5aWKS5MyVa2aV0mBWoxNTwr1JlUu9ismnFE+2vpbJ9zGXmzZgpXpW6MrhqcauFUX1Y6jRVJNJ3uUhhGUksBCRNgRLAggsQQRchtBJipVOgFpzSM8pNslu+5VhVGVcWxijcdCjda6IIy8Fy2RLw0uqNrUYIU7t6FWTbOqTXItGjJ8jVClJsfGkrEW6jmSg4ysy0KblyOhKMFyTZRK/IqzmMypW5FlRl0ZqhTu7taDOH5hm6Y1Rl0I4TNU3bRalPgRZCFTb5BwJvaJshT0vsTlfUqWRglSlHxJoQ8NOTbtodGpq7N3KZHe3Um17WGFFuWVJtA8O073OrCgoKxSph0ottlTUc2NPXUaoW5Gnhrki9Oi2+RNrcdMmUHBpXsb+Ck9VcXUWtmVJjtksGU0OHVF4wutETZcdMmUixtdL90XKCTtYEx2z2Cw5RV9higmtgXHTLk0BXSsauGuhR00mCTbmJO+x0Yq6JVON9Ypj0lyQLNEWsS4x3vYc4XWiMWLhJWavY1GLDLJkONjJmkuZbiT6smhovYz1p99W5FZSk9blHqNDo4SqpQbvqaM76nGjOUJJxdmbaGKzd2e/Uo2qTb1ehZu8bIzVavDSe9wjitPCwhroRb11B0I9CntK6Mj2ldGBfgq+mhV0UV9pXRh7THowqyoxWwOk+pT2mPRh7SlyYReKlTvdpoRVldlpYlWejMk6t23ZhVZ7i6m69Ac7sibu16Gftr6XqJxUdN4pnQw8lOjF+RTgKrQp30eRa/AbSpqlTUVrYoloFEsiQiEiSUQAABFwobKhJiZzvsQWnNLQRJtkkPyCqNFcrY1QbHRo85CIyKi5bEvDta3RseWGguzbsVZNsypPoXjQk+Rrp0m9XsPVJEW6jlSi4OzReFJyS0N8lHkk2RluuQak3GVUbbIvwJe6bKVNWvJal3FDTF051SlKNNtoxyOnjJ/cySSsctmozlwq9yAYyhHPO3kEJe4LcHuXoRz1oRezaQVvpYeSpLQl0pLdNHRVNK1uRStC1rO5GpJWDIwyM0u2wJOS0RNrcdMjiVcWbeGKce9bmNkx2zZCeG3yNUYJMcoabKwhcdMPDDKbuErbCnFJ2CTHbNlJyGhRjfVDMia8ILjpiyaFal+E0bnTVtimRc0CY7c6krVI6G5RuXjTgpXyocop8gWM6jZg4q24+UNNEYMTCUal03Y1GLDlFXIasZc0r7k8SXVk0bPbtuzO6lquZEOTe5Uuh1aFRSppp7jMz6s48KkqbvFm+jiFUVnuKNUW29WWknJW2MtSvw5JWuWWK08LAZwI8yOBBbJFfaV0ZDxK6MC3BViOAuZV4lX2Ye0x6MCeClsDpPkyvtMejI9pjbZhF05U42k7mapK5apiYtWszLKo9dwqJO7FvWrbqyVK71CP40fVGWlpJ062vJnSjZxTF1sMqss17MallSXQtEOJKiSibEAkBPIgAAkhgQVb6kSlYVObewEzqckJbbJZD1IqjK5Gx0abY6FFLxFkTbIqEntoRwHHdo2ycVohai5BqRnVJvkXVCVr2NlOi+Y1U0lqC6crK9hsaLa2NjUb91L1DJ0DWtsyovZIsqEvdN0KcVHXclxitSscMLoyS2I4b6GqUry20IV27EamMYq8XGndqxkZ0e0FlpxV76nNkajnVRsKbdNztsLSuzr4fD3w6i9NAsm3KZRodUhknKL5MS1qVlqw8b0xuVoMJZUUalBtXtoY2328bY2n0KuLN3DdtikYXYWY7ZcjJVM1xik9hrhpsglmmDIRlNsqatsJyoLMds+UMpojFXGcNP8AKEuOmPI+RWtdwXqbnSVtiuRNbAmO2LDL7z4GpQG06cI/lQ3KugLCIpLciSVtx06fRHNqKUKrv1NRixqyohozKclzDiStuTSnykktTPTqOFRvkysm2VY0jsRl3VlZOZ9TlUa86b026G+jWjVWm/QDRF82E4593oZpYnLNqzdi6xSf5WVF+BEOBFaJFPaV0Ye0rowLOiupHARV4mPRke0x6MC/BXLQh0XyaK+0x8w9qj5/IC0pOMLS36mWpItWxCkrJMyyqO2zIo/MIGqXmKJGqAACoAAAAAAAGUaTqyaTsluxZrwP5/h/cmV1Fk3VKmFcFfNdegvh+Zur/gyMhMbuLlNVTh+YcPzLgaZU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmHD8y4AU4fmUas7DhU/EwKgAASQSQAAAAbMF+FX9P8mvs3F06HEpV03SqKza5GXs+OaFaPVJf1HYTBqvKcJ1Y0pJaKXM9fQ7bhlMm8daPqdmKpCVTB1o1Yr8vNFsDTg+zcU5Qi5R2bWq0H4PBP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' style=\"width: 100%; max-width: 2692px;\"/>\n",
       "</div>\n",
       "\n",
       "<script type=\"text/javascript\">\n",
       "   (function() {\n",
       "     var container = document.getElementById(\"focontainer-90652850-41ef-4d0c-90c4-fd2aab6015e6\");\n",
       "     var overlay = document.getElementById(\"fooverlay-90652850-41ef-4d0c-90c4-fd2aab6015e6\");\n",
       "     fetch(`http://localhost:5151/fiftyone`)\n",
       "     .then(() => {\n",
       "        overlay.addEventListener(\"click\", () => {\n",
       "          fetch(`http://localhost:5151/event`, {\n",
       "            method: \"POST\",\n",
       "            body: JSON.stringify({\n",
       "              event: \"reactivate_notebook_cell\",\n",
       "              data: { subscription: \"90652850-41ef-4d0c-90c4-fd2aab6015e6\" },\n",
       "              subscription: \"90652850-41ef-4d0c-90c4-fd2aab6015e6\"\n",
       "            })\n",
       "          })\n",
       "        });\n",
       "        container.addEventListener(\"mouseenter\", () => overlay.style.display = \"block\");\n",
       "        container.addEventListener(\"mouseleave\", () => overlay.style.display = \"none\");\n",
       "     });\n",
       "   })();\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "session = fo.launch_app(fo_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Clips based-on individual actions\n",
    "\n",
    "Using the ```\"events\"``` field in each sample, you can split videos into clips based on their specific actions. The ```to_clips()``` unction in FiftyOne creates a view with one sample per clip, defined by the field or expression specified in the video collection.\n",
    "\n",
    "More documentation can be found [here](https://docs.voxel51.com/api/fiftyone.core.clips.html?highlight=to_clip#fiftyone.core.clips.ClipsView.to_clips)\n",
    "\n",
    "<div style=\"border-left: 4px solid #3498db; padding: 6px;\">\n",
    "<strong>Note:</strong> After running this section, check the \"events\" labels in the metadata menu. Clicking on each event will display the specific section of the video where the action occurs.\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"800\"\n",
       "            src=\"http://localhost:5151/?notebook=True&subscription=93c103e8-b2ae-411f-a9f0-47f7e1e0ad26\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1682f6a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:    ADL_Fall_Videos\n",
      "Media type: video\n",
      "Num clips:  335\n",
      "Clip fields:\n",
      "    id:               fiftyone.core.fields.ObjectIdField\n",
      "    sample_id:        fiftyone.core.fields.ObjectIdField\n",
      "    filepath:         fiftyone.core.fields.StringField\n",
      "    support:          fiftyone.core.fields.FrameSupportField\n",
      "    tags:             fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:         fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.VideoMetadata)\n",
      "    created_at:       fiftyone.core.fields.DateTimeField\n",
      "    last_modified_at: fiftyone.core.fields.DateTimeField\n",
      "    events:           fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "Frame fields:\n",
      "    id:               fiftyone.core.fields.ObjectIdField\n",
      "    frame_number:     fiftyone.core.fields.FrameNumberField\n",
      "    created_at:       fiftyone.core.fields.DateTimeField\n",
      "    last_modified_at: fiftyone.core.fields.DateTimeField\n",
      "View stages:\n",
      "    1. ToClips(field_or_expr='events', config=None)\n"
     ]
    }
   ],
   "source": [
    "view = fo_dataset.to_clips(\"events\")\n",
    "session.view = view\n",
    "print(view)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting Dataset into a Video Classification Directory Tree\n",
    "\n",
    "To simplify the dataset structure, we export the GMDCSA24 Dataset as a classification dataset. The directory tree will reflect the individual labels, making it easier to train models.\n",
    "\n",
    "Using the view created from the ```\"events\"``` field, we export the dataset in the```types.VideoClassificationDirectoryTree``` format. This structure is ideal for machine learning workflows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 100% |█████████████████| 335/335 [2.5m elapsed, 0s remaining, 3.5 samples/s]      \n"
     ]
    }
   ],
   "source": [
    "view.export(\n",
    "    export_dir=\"/path/to/the/GMDCSA24/new_folder\",\n",
    "    dataset_type=fo.types.VideoClassificationDirectoryTree,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New dataset\n",
    "\n",
    "Creates a new dataset containing a copy of the contents of the view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:        2025.01.03.09.09.28\n",
      "Media type:  video\n",
      "Num samples: 335\n",
      "Persistent:  False\n",
      "Tags:        []\n",
      "Sample fields:\n",
      "    id:               fiftyone.core.fields.ObjectIdField\n",
      "    filepath:         fiftyone.core.fields.StringField\n",
      "    tags:             fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:         fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.VideoMetadata)\n",
      "    created_at:       fiftyone.core.fields.DateTimeField\n",
      "    last_modified_at: fiftyone.core.fields.DateTimeField\n",
      "    sample_id:        fiftyone.core.fields.ObjectIdField\n",
      "    support:          fiftyone.core.fields.FrameSupportField\n",
      "    events:           fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "Frame fields:\n",
      "    id:               fiftyone.core.fields.ObjectIdField\n",
      "    frame_number:     fiftyone.core.fields.FrameNumberField\n",
      "    created_at:       fiftyone.core.fields.DateTimeField\n",
      "    last_modified_at: fiftyone.core.fields.DateTimeField\n"
     ]
    }
   ],
   "source": [
    "new_dataset= view.clone()\n",
    "print(new_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting Dataset to FiftyOneDataset\n",
    "\n",
    "FiftyOne supports various dataset formats. In this notebook, we’ve worked with a custom dataset and added each sample manually. Now, we export it into a FiftyOne-compatible dataset to leverage additional capabilities.\n",
    "\n",
    "For more details on the dataset types supported by FiftyOne, refer to this [documentation]](https://docs.voxel51.com/api/fiftyone.types.dataset_types.html?highlight=dataset%20type#module-fiftyone.types.dataset_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exporting samples...\n",
      " 100% |████████████████████| 335/335 [830.3ms elapsed, 0s remaining, 403.5 docs/s]      \n",
      "Exporting frames...\n",
      " 100% |████████████████████████| 0/0 [185.3us elapsed, ? remaining, ? docs/s] \n"
     ]
    }
   ],
   "source": [
    "export_dir = \"/path/to/the/GMDCSA24/new_folder_FO_Dataset\"\n",
    "new_dataset.export(\n",
    "    export_dir=export_dir,\n",
    "    dataset_type=fo.types.FiftyOneDataset,\n",
    ")"
   ]
  }
 ],
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