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import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
def show():
st.markdown('<div class="main-header">📁 Dataset: MM-OR</div>', unsafe_allow_html=True)
st.markdown("---")
st.markdown("## 🗂️ MM-OR: A Large-scale Multimodal Operating Room Dataset")
st.markdown("""
This project utilizes the **MM-OR** dataset, a comprehensive collection of data recorded in a realistic operating room environment.
It is designed to support research in surgical workflow analysis, human activity recognition, and context-aware systems in healthcare.
""")
# Dataset overview
st.markdown("### 📊 Dataset High-Level Statistics")
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric(
label="📹 Surgical Procedures",
value="10",
)
with col2:
st.metric(
label="⏱️ Total Duration",
value=">100 hours",
)
with col3:
st.metric(
label="🏷️ Modalities",
value="3 (Video, Audio, Depth)",
)
with col4:
st.metric(
label="📂 Total Size",
value="~12 TB",
)
st.markdown("---")
# Dataset categories
st.markdown("### 🏥 Dataset Details")
st.info("The MM-OR dataset is the primary source of data for training and evaluating the models in this system.")
col1, col2 = st.columns(2)
with col1:
st.markdown("#### Key Features")
st.markdown("""
- **Multimodal Data**: Includes synchronized video, multi-channel audio, and depth information.
- **Multiple Views**: Video captured from multiple camera perspectives to provide a comprehensive view of the operating room.
- **Rich Annotations**: Detailed annotations of:
- Surgical roles (e.g., primary surgeon, assistant, nurse).
- Atomic actions and complex activities.
- Interactions between team members.
- **Realistic Environment**: Data was collected in a high-fidelity simulated operating room.
""")
with col2:
st.markdown("#### Data Modalities")
st.image("https://www.researchgate.net/publication/359174963/figure/fig1/AS:1143128108556288@1649553881835/An-overview-of-our-data-acquisition-system-in-the-operating-room-OR-We-record.jpg",
caption="Overview of the data acquisition system in the operating room.")
st.markdown("---")
st.markdown("### 📈 Data Distribution")
# Create sample data for visualization
procedure_data = {
'Surgical Procedure': [f'Procedure {i+1}' for i in range(10)],
'Duration (hours)': np.random.uniform(8, 12, 10).round(1),
'Number of Annotations': np.random.randint(1500, 3000, 10)
}
df_procedures = pd.DataFrame(procedure_data)
fig = px.bar(df_procedures, x='Surgical Procedure', y='Duration (hours)',
title='Duration per Surgical Procedure',
labels={'Duration (hours)': 'Duration (hours)'},
color='Surgical Procedure')
st.plotly_chart(fig, use_container_width=True)
st.markdown("For more information, please refer to the original publication: *MM-OR: A Large-scale Multimodal Operating Room Dataset for Human Activity Recognition*.")
st.markdown("The dataset is available on GitHub: [MM-OR Dataset](https://github.com/egeozsoy/MM-OR)")
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