metadata

title: Laban Movement Analysis
emoji: 🩰
colorFrom: purple
colorTo: green
app_file: app.py
sdk: gradio
sdk_version: 5.33.0
pinned: false
tags:
  - laban-movement-analysis
  - pose-estimation
  - movement-analysis
  - video-analysis
  - youtube
  - vimeo
  - mcp
  - agent-ready
  - computer-vision
  - mediapipe
  - yolo
  - gradio
  - agentic-analysis
  - overlay-video
  - temporal-patterns
short_description: Laban Movement Analysis (LMA) from pose estimation
license: apache-2.0

🩰 Laban Movement Analysis

Advanced video movement analysis platform combining Laban Movement Analysis (LMA) principles with modern AI pose estimation, intelligent analysis, and interactive visualization.

🌟 Key Features

📊 Multi-Model Pose Estimation

15 different pose estimation models from multiple sources:
- MediaPipe: mediapipe-lite, mediapipe-full, mediapipe-heavy
- MoveNet: movenet-lightning, movenet-thunder
- YOLO v8: yolo-v8-n/s/m/l/x (5 variants)
- YOLO v11: yolo-v11-n/s/m/l/x (5 variants)

🎥 Comprehensive Video Processing

JSON Analysis Output: Detailed movement metrics with temporal data
Annotated Video Generation: Pose overlay with Laban movement data
URL Support: Direct processing from YouTube, Vimeo, and video URLs
Custom Overlay Component: gradio_overlay_video for controlled layered visualization

🤖 Agentic Intelligence

SUMMARY Analysis: Narrative movement interpretation with temporal patterns
STRUCTURED Analysis: Quantitative breakdowns and statistical insights
MOVEMENT FILTERS: Pattern detection with intelligent filtering
Laban Interpretation: Professional movement quality assessment

🎨 Interactive Visualization

Standard Analysis Tab: Core pose estimation and LMA processing
Overlay Visualization Tab: Interactive layered video display
Agentic Analysis Tab: AI-powered movement insights and filtering

Installation

pip install gradio_labanmovementanalysis

Usage

# app.py  ─────────────────────────────────────────────────────────
"""
Laban Movement Analysis – modernised Gradio Space
Author: Csaba (BladeSzaSza)
"""

import gradio as gr
import os
# from backend.gradio_labanmovementanalysis import LabanMovementAnalysis
from gradio_labanmovementanalysis import LabanMovementAnalysis

# Import agent API if available
# Initialize agent API if available
agent_api = None
try:
    from gradio_labanmovementanalysis.agent_api import (
        LabanAgentAPI,
        PoseModel,
        MovementDirection,
        MovementIntensity
    )
    agent_api = LabanAgentAPI()
except Exception as e:
    print(f"Warning: Agent API not available: {e}")
    agent_api = None
# Initialize components
try:
    analyzer = LabanMovementAnalysis(
        enable_visualization=True
    )
    print("✅ Core features initialized successfully")
except Exception as e:
    print(f"Warning: Some features may not be available: {e}")
    analyzer = LabanMovementAnalysis()


def process_video_enhanced(video_input, model, enable_viz, include_keypoints):
    """Enhanced video processing with all new features."""
    if not video_input:
        return {"error": "No video provided"}, None
    
    try:
        # Handle both file upload and URL input
        video_path = video_input.name if hasattr(video_input, 'name') else video_input
        
        json_result, viz_result = analyzer.process_video(
            video_path,
            model=model,
            enable_visualization=enable_viz,
            include_keypoints=include_keypoints
        )
        return json_result, viz_result
    except Exception as e:
        error_result = {"error": str(e)}
        return error_result, None

def process_video_standard(video : str, model : str, include_keypoints : bool) -> dict:
    """
    Processes a video file using the specified pose estimation model and returns movement analysis results.

    Args:
        video (str): Path to the video file to be analyzed.
        model (str): The name of the pose estimation model to use (e.g., "mediapipe-full", "movenet-thunder", etc.).
        include_keypoints (bool): Whether to include raw keypoint data in the output.

    Returns:
        dict:
            - A dictionary containing the movement analysis results in JSON format, or an error message if processing fails.


    Notes:
        - Visualization is disabled in this standard processing function.
        - If the input video is None, both return values will be None.
        - If an error occurs during processing, the first return value will be a dictionary with an "error" key.
    """
    if video is None:
        return None
    try:
        json_output = analyzer.process(
            video,
            model=model,
            include_keypoints=include_keypoints
        )

        
        return json_output
    except (RuntimeError, ValueError, OSError) as e:
        return {"error": str(e)}

# ── 4.  Build UI ─────────────────────────────────────────────────
def create_demo() -> gr.Blocks:
    with gr.Blocks(
        title="Laban Movement Analysis",
        theme='gstaff/sketch',
        fill_width=True,
    ) as demo:
        # gr.api(process_video_standard, api_name="process_video")  # <-- Remove from here
        # ── Hero banner ──
        gr.Markdown(
            """
            # 🎭 Laban Movement Analysis 
            
            Pose estimation • AI action recognition • Movement Analysis 
            """
        )
        with gr.Tabs():
            # Tab 1: Standard Analysis
            with gr.Tab("🎬 Standard Analysis"):
                gr.Markdown("""
                ### Upload a video file to analyze movement using traditional LMA metrics with pose estimation.
                """)
                # ── Workspace ──
                with gr.Row(equal_height=True):
                    # Input column
                    with gr.Column(scale=1, min_width=260):
                        
                        analyze_btn_enh = gr.Button("🚀 Analyze Movement", variant="primary", size="lg")
                        video_in = gr.Video(label="Upload Video", sources=["upload"], format="mp4")
                        # URL input option
                        url_input_enh = gr.Textbox(
                            label="Or Enter Video URL",
                            placeholder="YouTube URL, Vimeo URL, or direct video URL",
                            info="Leave file upload empty to use URL"
                        )
                       
                        gr.Markdown("**Model Selection**")
                        
                        model_sel = gr.Dropdown(
                            choices=[
                                # MediaPipe variants
                                "mediapipe-lite", "mediapipe-full", "mediapipe-heavy",
                                # MoveNet variants
                                "movenet-lightning", "movenet-thunder",
                                # YOLO v8 variants
                                "yolo-v8-n", "yolo-v8-s", "yolo-v8-m", "yolo-v8-l", "yolo-v8-x",
                                # YOLO v11 variants
                                "yolo-v11-n", "yolo-v11-s", "yolo-v11-m", "yolo-v11-l", "yolo-v11-x"
                            ],
                            value="mediapipe-full",
                            label="Advanced Pose Models",
                            info="15 model variants available"
                        )
                        
                        with gr.Accordion("Analysis Options", open=False):
                            enable_viz = gr.Radio([("Yes", 1), ("No", 0)], value=1, label="Visualization")
                            include_kp = gr.Radio([("Yes", 1), ("No", 0)], value=0, label="Raw Keypoints")

                        gr.Examples(
                            examples=[
                                ["examples/balette.mp4"],
                                ["https://www.youtube.com/shorts/RX9kH2l3L8U"],
                                ["https://vimeo.com/815392738"],
                                ["https://vimeo.com/548964931"],
                                ["https://videos.pexels.com/video-files/5319339/5319339-uhd_1440_2560_25fps.mp4"],
                            ],
                            inputs=url_input_enh,
                            label="Examples"
                        )


                    # Output column
                    with gr.Column(scale=2, min_width=320):
                        viz_out = gr.Video(label="Annotated Video", scale=1, height=400)
                        with gr.Accordion("Raw JSON", open=True):
                            json_out = gr.JSON(label="Movement Analysis", elem_classes=["json-output"])

                # Wiring
                def process_enhanced_input(file_input, url_input, model, enable_viz, include_keypoints):
                    """Process either file upload or URL input."""
                    video_source = file_input if file_input else url_input
                    return process_video_enhanced(video_source, model, enable_viz, include_keypoints)
                
                analyze_btn_enh.click(
                    fn=process_enhanced_input,
                    inputs=[video_in, url_input_enh, model_sel, enable_viz, include_kp],
                    outputs=[json_out, viz_out],
                    api_name="analyze_enhanced"
                )

        # Footer
        with gr.Row():
            gr.Markdown(
                """
                **Built by Csaba Bolyós**  
                [GitHub](https://github.com/bladeszasza) • [HF](https://huggingface.co/BladeSzaSza)
                """
            )
    return demo

# Register API endpoint OUTSIDE the UI

gr.api(process_video_standard, api_name="process_video")

if __name__ == "__main__":
    demo = create_demo()
    demo.launch(server_name="0.0.0.0",
                share=True,
                server_port=int(os.getenv("PORT", 7860)),
                mcp_server=True)

`LabanMovementAnalysis`

Initialization

name	type	default	description
`default_model`	`str`	`"mediapipe"`	Default pose estimation model ("mediapipe", "movenet", "yolo")
`enable_visualization`	`bool`	`True`	Whether to generate visualization video by default
`include_keypoints`	`bool`	`False`	Whether to include raw keypoints in JSON output
`enable_webrtc`	`bool`	`False`	Whether to enable WebRTC real-time analysis
`label`	`typing.Optional[str][str, None]`	`None`	Component label
`every`	`typing.Optional[float][float, None]`	`None`	None
`show_label`	`typing.Optional[bool][bool, None]`	`None`	None
`container`	`bool`	`True`	None
`scale`	`typing.Optional[int][int, None]`	`None`	None
`min_width`	`int`	`160`	None
`interactive`	`typing.Optional[bool][bool, None]`	`None`	None
`visible`	`bool`	`True`	None
`elem_id`	`typing.Optional[str][str, None]`	`None`	None
`elem_classes`	`typing.Optional[typing.List[str]][ typing.List[str][str], None ]`	`None`	None
`render`	`bool`	`True`	None

User function

The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).

When used as an Input, the component only impacts the input signature of the user function.
When used as an output, the component only impacts the return signature of the user function.

The code snippet below is accurate in cases where the component is used as both an input and an output.

As output: Is passed, processed data for analysis.
As input: Should return, analysis results.

def predict(
    value: typing.Dict[str, typing.Any][str, typing.Any]
) -> typing.Any:
    return value