{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":59156,"databundleVersionId":13874099,"sourceType":"competition"}],"dockerImageVersionId":31153,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"id":"403ef7a3-3c3c-45e0-a06c-2265c3b96a2e","cell_type":"markdown","source":"# 🐁 Mouse Tracking Video Renderer 🐁\n\nWhile looking at the articles about the Social Action Recognition in Mice, I noticed something: the regions of the mice are marked from a bird's eye view, and unfortunately, in our datasets, they are found as parquets.\n\n* CalMS21 at NeurIPS (https://arxiv.org/pdf/2104.02710.pdf), You can see the green and blue dotted lines in the picture below.\n  \n![Screenshot 2025-11-06 at 15.01.31.png](attachment:884cdd2f-f604-4a59-9037-977b6e19cc8b.png)\n\nI decided to convert the Parquet dataset into video and wanted to share the implementation with you. I've set it up for a single instance, but you can adapt it to process all of them with a small adjustment in the code. Be aware that this might require a significant amount of memory.\n![image.png](attachment:a4923895-0fcc-43cc-84c0-ee36d92db472.png)\n\n### Contents\n1. Loading required libraries\n2. Defining video creation functions\n3. Rendering the video\n4. Playing the video inside the Kaggle notebook (If it's over 100 MB, it may be struggle to open it in Kaggle Notebook. Please pay attention to the codes in the cells, I've explained them as clearly as possible.)\n\n### Contact \n\nI'm always open to discussing new projects, collaboration opportunities, or interesting ideas. Let's connect!\n\n<div style=\"text-align: left;\">\n  <a href=\"https://www.linkedin.com/in/osman-bayram-785931250/\" target=\"_blank\">\n    <img src=\"https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff\" alt=\"LinkedIn\">\n  </a>\n  <br> <a href=\"https://github.com/OsmanByrm\" target=\"_blank\">\n    <img src=\"https://img.shields.io/badge/GitHub-%23121011.svg?logo=github&logoColor=white\" alt=\"GitHub\">\n  </a>\n</div>","metadata":{},"attachments":{"a4923895-0fcc-43cc-84c0-ee36d92db472.png":{"image/png":"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"},"884cdd2f-f604-4a59-9037-977b6e19cc8b.png":{"image/png":"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"}}},{"id":"6a8dfba4","cell_type":"markdown","source":"## 1. Loading Required Libraries\n\nFirst we load the libraries needed to create and display the video.","metadata":{}},{"id":"d269e70c","cell_type":"code","source":"from pathlib import Path\nfrom dataclasses import dataclass\nfrom typing import Dict, List, Optional, Tuple, Iterable\n\nimport cv2\nimport numpy as np\nimport pandas as pd\nfrom IPython.display import Video, display\n\nprint(\"Libraries loaded successfully!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:49.739838Z","iopub.execute_input":"2025-11-06T11:09:49.740170Z","iopub.status.idle":"2025-11-06T11:09:52.507274Z","shell.execute_reply.started":"2025-11-06T11:09:49.740142Z","shell.execute_reply":"2025-11-06T11:09:52.506397Z"}},"outputs":[],"execution_count":null},{"id":"f7b7b766","cell_type":"markdown","source":"## 2. Data Structures and Helper Functions\n\nWe define a dataclass to hold video metadata and the helper functions needed.","metadata":{}},{"id":"b9d5c1e1","cell_type":"code","source":"@dataclass\nclass Metadata:\n    \"\"\"Holds video metadata information\"\"\"\n    lab_id: str\n    video_id: str\n    fps: float\n    width: int\n    height: int","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.508637Z","iopub.execute_input":"2025-11-06T11:09:52.509016Z","iopub.status.idle":"2025-11-06T11:09:52.514509Z","shell.execute_reply.started":"2025-11-06T11:09:52.508994Z","shell.execute_reply":"2025-11-06T11:09:52.513518Z"}},"outputs":[],"execution_count":null},{"id":"90959322","cell_type":"code","source":"def load_metadata(metadata_csv: Path, video_id: str, lab_id_hint: Optional[str] = None) -> Metadata:\n    \"\"\"\n    Load metadata information for the specified video_id from a CSV file.\n    \n    Args:\n        metadata_csv: Path to the train.csv or test.csv file\n        video_id: ID of the video to render\n        lab_id_hint: Optional lab_id to disambiguate when multiple matches exist\n    \n    Returns:\n        Metadata: Video metadata information\n    \"\"\"\n    df = pd.read_csv(metadata_csv)\n    df[\"video_id\"] = df[\"video_id\"].astype(str)\n\n    query = df[df[\"video_id\"] == str(video_id)]\n    if lab_id_hint:\n        query = query[query[\"lab_id\"] == lab_id_hint]\n\n    if query.empty:\n        raise ValueError(f\"Video {video_id} not found: {metadata_csv}\")\n    if len(query) > 1 and not lab_id_hint:\n        options = \", \".join(sorted(query[\"lab_id\"].unique()))\n        raise ValueError(\n            f\"Multiple lab_id found for video {video_id}. Please specify lab_id. Options: {options}\"\n        )\n\n    row = query.iloc[0]\n    return Metadata(\n        lab_id=str(row[\"lab_id\"]),\n        video_id=str(row[\"video_id\"]),\n        fps=float(row[\"frames_per_second\"]),\n        width=int(row[\"video_width_pix\"]),\n        height=int(row[\"video_height_pix\"]),\n    )\n\nprint(\"load_metadata function defined\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.515456Z","iopub.execute_input":"2025-11-06T11:09:52.515807Z","iopub.status.idle":"2025-11-06T11:09:52.559449Z","shell.execute_reply.started":"2025-11-06T11:09:52.515777Z","shell.execute_reply":"2025-11-06T11:09:52.558438Z"}},"outputs":[],"execution_count":null},{"id":"ddc0853b","cell_type":"code","source":"def build_color_map(num_entries: int) -> Dict[int, Tuple[int, int, int]]:\n    \"\"\"\n    Creates a different color palette for each mouse.\n    \n    Args:\n        num_entries: Number of colors\n    \n    Returns:\n        Dict: Mapping from Mouse ID to (B, G, R) color tuple\n    \"\"\"\n    palette: List[Tuple[int, int, int]] = [\n        (0, 153, 255),    # Orange\n        (0, 204, 102),    # Green\n        (255, 153, 0),    # Blue\n        (204, 0, 255),    # Pink\n        (255, 51, 51),    # Red\n        (102, 255, 204),  # Light green\n        (255, 102, 178),  # Light pink\n        (153, 255, 51),   # Lime green\n    ]\n    return {idx + 1: palette[idx % len(palette)] for idx in range(num_entries)}\n\nprint(\"build_color_map function defined\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.561589Z","iopub.execute_input":"2025-11-06T11:09:52.562276Z","iopub.status.idle":"2025-11-06T11:09:52.579270Z","shell.execute_reply.started":"2025-11-06T11:09:52.562251Z","shell.execute_reply":"2025-11-06T11:09:52.578276Z"}},"outputs":[],"execution_count":null},{"id":"f9cb6b79","cell_type":"code","source":"def compute_centroids(frame_df: pd.DataFrame) -> Dict[int, Tuple[int, int]]:\n    \"\"\"\n    Calculates the centroid by taking the average of body part coordinates for each mouse.\n    \n    Args:\n        frame_df: Tracking data for a single frame\n    \n    Returns:\n        Dict: Mapping from Mouse ID to (x, y) centroid coordinates\n    \"\"\"\n    centroids: Dict[int, Tuple[int, int]] = {}\n    for mouse_id, group in frame_df.groupby(\"mouse_id\"):\n        coords = group[[\"x\", \"y\"]].to_numpy(dtype=np.float32)\n        if np.isnan(coords).all():\n            continue\n        mean_x = float(np.nanmean(coords[:, 0]))\n        mean_y = float(np.nanmean(coords[:, 1]))\n        centroids[int(mouse_id)] = (int(round(mean_x)), int(round(mean_y)))\n    return centroids\n\nprint(\"compute_centroids function defined\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.580132Z","iopub.execute_input":"2025-11-06T11:09:52.580437Z","iopub.status.idle":"2025-11-06T11:09:52.604607Z","shell.execute_reply.started":"2025-11-06T11:09:52.580417Z","shell.execute_reply":"2025-11-06T11:09:52.603451Z"}},"outputs":[],"execution_count":null},{"id":"5f470874","cell_type":"code","source":"def gather_active_annotations(\n    annotation_df: pd.DataFrame,\n    frame_number: int,\n) -> List[Tuple[int, int, str]]:\n    \"\"\"\n    Finds active behaviors in a specific frame.\n    \n    Args:\n        annotation_df: Annotation data\n        frame_number: Frame number\n    \n    Returns:\n        List: (agent_id, target_id, action) tuples\n    \"\"\"\n    if annotation_df is None or annotation_df.empty:\n        return []\n\n    active = annotation_df[\n        (annotation_df[\"start_frame\"] <= frame_number)\n        & (annotation_df[\"stop_frame\"] >= frame_number)\n    ]\n    return list(\n        active[[\"agent_id\", \"target_id\", \"action\"]].itertuples(index=False, name=None)\n    )\n\ndef format_actions(actions: Iterable[Tuple[int, int, str]]) -> List[str]:\n    \"\"\"\n    Converts annotation information to readable text format.\n    \"\"\"\n    lines: List[str] = []\n    for agent_id, target_id, action in actions:\n        agent = f\"mouse{agent_id}\"\n        target = f\"mouse{target_id}\" if agent_id != target_id else \"self\"\n        lines.append(f\"{agent}->{target}: {action}\")\n    return lines\n\ndef draw_annotation_overlay(\n    canvas: np.ndarray,\n    lines: List[str],\n    origin: Tuple[int, int] = (10, 30),\n) -> None:\n    \"\"\"\n    Writes annotation information on top of the video.\n    \"\"\"\n    if not lines:\n        return\n\n    font = cv2.FONT_HERSHEY_SIMPLEX\n    font_scale = 0.6\n    line_height = 22\n    for idx, text in enumerate(lines[:8]):\n        position = (origin[0], origin[1] + idx * line_height)\n        cv2.putText(canvas, text, position, font, font_scale, (255, 255, 255), 1, cv2.LINE_AA)\n\nprint(\"Annotation helper functions defined\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.605479Z","iopub.execute_input":"2025-11-06T11:09:52.605772Z","iopub.status.idle":"2025-11-06T11:09:52.624711Z","shell.execute_reply.started":"2025-11-06T11:09:52.605751Z","shell.execute_reply":"2025-11-06T11:09:52.623795Z"}},"outputs":[],"execution_count":null},{"id":"2f856c42","cell_type":"markdown","source":"## 3. Main Video Rendering Function\n\nMain function that reads tracking data from parquet files and converts it to MP4 video.","metadata":{}},{"id":"4e24ee6c","cell_type":"code","source":"def render_video(\n    metadata: Metadata,\n    tracking_path: Path,\n    annotation_path: Optional[Path],\n    output_path: Path,\n    frame_skip: int = 1,\n    radius: int = 4,\n    thickness: int = -1,\n    draw_annotations: bool = False,\n    output_width: int = 1228,  # Output video width, you can also change (piksel)\n    output_height: int = 1068,  # Output video hight, you can also change (piksel) originial size 1228x1068\n) -> None:\n    \"\"\"\n    Creates an MP4 video from a tracking parquet file.\n    \n    Args:\n        metadata: Video metadata information\n        tracking_path: Path to the tracking parquet file\n        annotation_path: Path to the annotation parquet file (optional)\n        output_path: Path to the output video file\n        frame_skip: Render every N frames (for speedup)\n        radius: Diameter of body part points (pixels)\n        thickness: Circle thickness (negative value creates filled circle)\n        draw_annotations: Add behavior information on top of video\n        output_width: Output video width in pixels (default: like 480)\n        output_height: Output video height in pixels (default: like 480)\n    \"\"\"\n    if not tracking_path.exists():\n        raise FileNotFoundError(f\"Tracking parquet not found: {tracking_path}\")\n\n    print(f\"Reading tracking data: {tracking_path}\")\n    tracking_df = pd.read_parquet(tracking_path)\n    \n    # Validate that this is a tracking file with required columns\n    required_columns = [\"video_frame\", \"mouse_id\", \"x\", \"y\"]\n    missing_columns = [col for col in required_columns if col not in tracking_df.columns]\n    if missing_columns:\n        available_columns = list(tracking_df.columns)\n        raise ValueError(\n            f\"Invalid tracking file. Missing required columns: {missing_columns}\\n\"\n            f\"Available columns: {available_columns}\\n\"\n            f\"Make sure you're using a tracking file (from train_tracking/ or test_tracking/), \"\n            f\"not an annotation file (from train_annotation/).\"\n        )\n    \n    tracking_df = tracking_df.sort_values(\"video_frame\")\n\n    annotation_df: Optional[pd.DataFrame] = None\n    if annotation_path and annotation_path.exists():\n        print(f\"Reading annotation data: {annotation_path}\")\n        annotation_df = pd.read_parquet(annotation_path)\n\n    frames = np.sort(tracking_df[\"video_frame\"].unique())\n    fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n    \n    # Ölçekleme faktörlerini hesapla\n    scale_x = output_width / metadata.width\n    scale_y = output_height / metadata.height\n    \n    print(f\"Original video size: {metadata.width}x{metadata.height}\")\n    print(f\"Output video size: {output_width}x{output_height}\")\n    print(f\"Scale factors: X={scale_x:.3f}, Y={scale_y:.3f}\")\n    \n    writer = cv2.VideoWriter(\n        str(output_path), \n        fourcc, \n        metadata.fps / frame_skip, \n        (output_width, output_height)\n    )\n    \n    if not writer.isOpened():\n        raise RuntimeError(f\"VideoWriter could not be opened: {output_path}\")\n\n    color_map = build_color_map(int(tracking_df[\"mouse_id\"].max()))\n    total_frames = len(frames)\n    \n    print(f\"Creating video: {total_frames} frames, rendering every {frame_skip} frames\")\n\n    try:\n        for idx, frame_number in enumerate(frames[::frame_skip]):\n            frame_df = tracking_df[tracking_df[\"video_frame\"] == frame_number]\n            canvas = np.zeros((output_height, output_width, 3), dtype=np.uint8)\n\n            # Draw body part points (koordinatları ölçeklendir)\n            for _, row in frame_df.iterrows():\n                x_orig = row[\"x\"]\n                y_orig = row[\"y\"]\n                if np.isnan(x_orig) or np.isnan(y_orig):\n                    continue\n                \n                # Koordinatları yeni boyuta ölçeklendir\n                x = int(round(x_orig * scale_x))\n                y = int(round(y_orig * scale_y))\n                \n                color = color_map.get(int(row[\"mouse_id\"]), (255, 255, 255))\n                cv2.circle(canvas, (x, y), radius, color, thickness)\n\n            # Draw mouse labels (centroid'leri ölçeklendir)\n            centroids = compute_centroids(frame_df)\n            for mouse_id, (cx_orig, cy_orig) in centroids.items():\n                # Centroid'leri ölçeklendir\n                cx = int(round(cx_orig * scale_x))\n                cy = int(round(cy_orig * scale_y))\n                \n                label = f\"mouse{mouse_id}\"\n                cv2.putText(\n                    canvas,\n                    label,\n                    (cx + 6, cy - 6),\n                    cv2.FONT_HERSHEY_SIMPLEX,\n                    0.4,  # Font boyutunu biraz küçülttük\n                    color_map.get(mouse_id, (200, 200, 200)),\n                    1,\n                    cv2.LINE_AA,\n                )\n\n            # Add annotation information\n            if draw_annotations and annotation_df is not None:\n                active_actions = gather_active_annotations(annotation_df, int(frame_number))\n                overlay_lines = format_actions(active_actions)\n                draw_annotation_overlay(canvas, overlay_lines)\n\n            # Footer text\n            footer_text = f\"video_id={metadata.video_id} frame={frame_number}/{frames[-1]}\"\n            cv2.putText(\n                canvas,\n                footer_text,\n                (10, output_height - 16),\n                cv2.FONT_HERSHEY_SIMPLEX,\n                0.5,  # Font boyutunu biraz küçülttük\n                (255, 255, 255),\n                1,\n                cv2.LINE_AA,\n            )\n            writer.write(canvas)\n\n            if (idx + 1) % 500 == 0:\n                print(f\"[{idx + 1}/{total_frames // frame_skip}] frame {frame_number} rendered\")\n    finally:\n        writer.release()\n\n    print(f\"Video successfully saved: {output_path}\")\n    print(f\"Final video size: {output_width}x{output_height}\")\n\nprint(\"render_video function defined\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.625746Z","iopub.execute_input":"2025-11-06T11:09:52.626164Z","iopub.status.idle":"2025-11-06T11:09:52.714007Z","shell.execute_reply.started":"2025-11-06T11:09:52.626136Z","shell.execute_reply":"2025-11-06T11:09:52.713067Z"}},"outputs":[],"execution_count":null},{"id":"4a59edc1","cell_type":"markdown","source":"## 4. Video Creation and Display\n\nNow we will create a video and play it inside the Kaggle notebook.\n\n### Set Parameters\n\nEdit the following parameters according to your data:","metadata":{}},{"id":"2a18cf33","cell_type":"code","source":"# Video parameters - Change these according to your data\nVIDEO_ID = \"44566106\"  # Video ID you want to render\nLAB_ID = None  # If there are multiple labs, write lab_id here, otherwise leave None\n\n# File paths \nMETADATA_CSV = Path(\"/kaggle/input/MABe-mouse-behavior-detection/train.csv\")\nTRACKING_ROOT = Path(\"/kaggle/input/MABe-mouse-behavior-detection/train_tracking\")\nANNOTATION_ROOT = Path(\"/kaggle/input/MABe-mouse-behavior-detection/train_annotation\")\n\n# Output file\nOUTPUT_VIDEO = Path(f\"video_{VIDEO_ID}.mp4\")\n\n# Rendering settings\nFRAME_SKIP = 1  # Render every N frames (1 = every frame)\nRADIUS = 4  # Diameter of body part points\nTHICKNESS = -1  # Circle thickness (-1 = filled)\nDRAW_ANNOTATIONS = True  # Show behavior information\n\nprint(\"Parameters set\")\nprint(f\"Video ID: {VIDEO_ID}\")\nprint(f\"Output file: {OUTPUT_VIDEO}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.714971Z","iopub.execute_input":"2025-11-06T11:09:52.715623Z","iopub.status.idle":"2025-11-06T11:09:52.735629Z","shell.execute_reply.started":"2025-11-06T11:09:52.715594Z","shell.execute_reply":"2025-11-06T11:09:52.734738Z"}},"outputs":[],"execution_count":null},{"id":"452ed10d","cell_type":"markdown","source":"### Load Metadata\n\nWe read video information from the CSV file.","metadata":{}},{"id":"9fff3da2","cell_type":"code","source":"metadata = load_metadata(METADATA_CSV, VIDEO_ID, LAB_ID)\n\nprint(f\"Metadata loaded successfully:\")\nprint(f\"  Lab ID: {metadata.lab_id}\")\nprint(f\"  Video ID: {metadata.video_id}\")\nprint(f\"  FPS: {metadata.fps}\")\nprint(f\"  Dimensions: {metadata.width}x{metadata.height} pixels\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.736551Z","iopub.execute_input":"2025-11-06T11:09:52.736857Z","iopub.status.idle":"2025-11-06T11:09:52.904125Z","shell.execute_reply.started":"2025-11-06T11:09:52.736831Z","shell.execute_reply":"2025-11-06T11:09:52.903428Z"}},"outputs":[],"execution_count":null},{"id":"5695c73d","cell_type":"markdown","source":"### Determine File Paths\n\nWe create paths for tracking and annotation parquet files.","metadata":{}},{"id":"3ce9ae02","cell_type":"code","source":"tracking_path = TRACKING_ROOT / metadata.lab_id / f\"{metadata.video_id}.parquet\"\nannotation_path = ANNOTATION_ROOT / metadata.lab_id / f\"{metadata.video_id}.parquet\"\n\nprint(f\"Tracking file: {tracking_path}\")\nprint(f\"Tracking file exists: {tracking_path.exists()}\")\nif tracking_path.exists():\n    print(f\"  Full path: {tracking_path.absolute()}\")\nelse:\n    print(f\"  WARNING: Tracking file not found! Check if path is correct.\")\n    print(f\"  TRACKING_ROOT: {TRACKING_ROOT}\")\n    print(f\"  lab_id: {metadata.lab_id}\")\n    print(f\"  video_id: {metadata.video_id}\")\n\nprint(f\"\\nAnnotation file: {annotation_path}\")\nprint(f\"Annotation file exists: {annotation_path.exists()}\")\nif annotation_path.exists():\n    print(f\"  Full path: {annotation_path.absolute()}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.906739Z","iopub.execute_input":"2025-11-06T11:09:52.906993Z","iopub.status.idle":"2025-11-06T11:09:52.918088Z","shell.execute_reply.started":"2025-11-06T11:09:52.906974Z","shell.execute_reply":"2025-11-06T11:09:52.917179Z"}},"outputs":[],"execution_count":null},{"id":"102e827b","cell_type":"markdown","source":"### Create the Video\n\nThis cell creates the video. It may take some time for large videos.","metadata":{}},{"id":"d3530787","cell_type":"code","source":"render_video(\n    metadata=metadata,\n    tracking_path=tracking_path,\n    annotation_path=annotation_path if annotation_path.exists() else None,\n    output_path=OUTPUT_VIDEO,\n    frame_skip=FRAME_SKIP,\n    radius=RADIUS,\n    thickness=THICKNESS,\n    draw_annotations=DRAW_ANNOTATIONS,\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:09:52.919036Z","iopub.execute_input":"2025-11-06T11:09:52.919333Z","iopub.status.idle":"2025-11-06T11:15:07.254265Z","shell.execute_reply.started":"2025-11-06T11:09:52.919309Z","shell.execute_reply":"2025-11-06T11:15:07.253010Z"}},"outputs":[],"execution_count":null},{"id":"56a24320","cell_type":"markdown","source":"### Playing the Video in Kaggle Notebook\n\nWe play the created video directly inside the notebook.","metadata":{}},{"id":"a6f84c30","cell_type":"code","source":"\"\"\"\n-> Issue: If the video is over 100 MB than Kaggle Notebook, it can't be opened.\nWe can open it by reducing the video size. **ATTENTION: SOME VIDEOS MAY NOT OPEN !**\n\n\"\"\"\n\nimport os\nfrom IPython.display import Video, display\n\n# Path of Video \nvideo_path = \"/kaggle/working/video_44566106.mp4\" # !! ATTENTION !!\npreview_path = \"/kaggle/working/video_preview.mp4\"\n\nos.system(f'ffmpeg -i \"{video_path}\" -vf scale=320:-1 -b:v 1M -an -y \"{preview_path}\"')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:15:07.255614Z","iopub.execute_input":"2025-11-06T11:15:07.255923Z","iopub.status.idle":"2025-11-06T11:16:00.825230Z","shell.execute_reply.started":"2025-11-06T11:15:07.255902Z","shell.execute_reply":"2025-11-06T11:16:00.824128Z"}},"outputs":[],"execution_count":null},{"id":"8e636cdf-b415-4d24-8ac9-5373bc2c70eb","cell_type":"code","source":"# Now we can safely open the small preview file in the cell\n## If you want to open video in cell pls remove comment \n# display(Video(preview_path, embed=True, width=320))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:16:00.826243Z","iopub.execute_input":"2025-11-06T11:16:00.826675Z","iopub.status.idle":"2025-11-06T11:16:00.830729Z","shell.execute_reply.started":"2025-11-06T11:16:00.826647Z","shell.execute_reply":"2025-11-06T11:16:00.829785Z"}},"outputs":[],"execution_count":null},{"id":"b5bf2cbd","cell_type":"markdown","source":"## 5. Additional Information\n\n### Video Properties\n\nLet's check the properties of the created video.","metadata":{}},{"id":"6b9d67a9","cell_type":"code","source":"import os\n\nif OUTPUT_VIDEO.exists():\n    file_size_mb = os.path.getsize(OUTPUT_VIDEO) / (1024 * 1024)\n    print(f\"Video file size: {file_size_mb:.2f} MB\")\n    print(f\"Video file path: {OUTPUT_VIDEO.absolute()}\")\n    \n    # Check video information with OpenCV\n    cap = cv2.VideoCapture(str(OUTPUT_VIDEO))\n    if cap.isOpened():\n        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n        fps = cap.get(cv2.CAP_PROP_FPS)\n        duration = frame_count / fps if fps > 0 else 0\n        \n        print(f\"Total frame count: {frame_count}\")\n        print(f\"FPS: {fps:.2f}\")\n        print(f\"Video duration: {duration:.2f} seconds\")\n        cap.release()\nelse:\n    print(\"Video file not found!\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-11-06T11:16:00.831983Z","iopub.execute_input":"2025-11-06T11:16:00.832291Z","iopub.status.idle":"2025-11-06T11:16:00.885537Z","shell.execute_reply.started":"2025-11-06T11:16:00.832271Z","shell.execute_reply":"2025-11-06T11:16:00.884650Z"}},"outputs":[],"execution_count":null},{"id":"25795f03","cell_type":"markdown","source":"## Notes\n\n- Video creation process may take a long time depending on the number of frames\n- You can create faster (but less smooth) videos by increasing the FRAME_SKIP parameter\n- You can show or hide behavior information on the video with the DRAW_ANNOTATIONS parameter\n- You can change the appearance of body part points with the RADIUS and THICKNESS parameters\n- The created video file is saved in the directory where the notebook is running\n- To download the video in the Kaggle environment, you can download it from the output files section on the right","metadata":{}}]}