Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,37 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+optical_flow/Assault002_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/Assault003_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/Assault004_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/Assault005_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/Assault006_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/Assault007_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/Assault008_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/optical_flow1.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/optical_flow2.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/optical_flow3.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/optical_flow4.mp4 filter=lfs diff=lfs merge=lfs -text
+optical_flow/optical_flow5.mp4 filter=lfs diff=lfs merge=lfs -text
+vggt/Assault002_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+vggt/Assault003_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+vggt/Assault004_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+vggt/Assault005_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+vggt/Assault006_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+vggt/Assault007_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+vggt/Assault008_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+video/Assault001_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+video/Assault002_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+video/Assault003_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+video/Assault004_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+video/Assault005_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+video/Assault006_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+video/Assault007_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+video/Assault008_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+yolo/Assault002_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+yolo/Assault003_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+yolo/Assault004_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+yolo/Assault005_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+yolo/Assault006_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+yolo/Assault007_x264.mp4 filter=lfs diff=lfs merge=lfs -text
+yolo/Assault008_x264.mp4 filter=lfs diff=lfs merge=lfs -text

.gradio/certificate.pem

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+-----END CERTIFICATE-----

Demo2/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Demo2/README.md

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+---
+title: Demo2
+emoji: 🦀
+colorFrom: indigo
+colorTo: blue
+sdk: gradio
+sdk_version: 5.34.2
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Demo2/app.py

@@ -0,0 +1,420 @@
+import gradio as gr
+import os
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from pathlib import Path
+import sys
+import asyncio
+import json
+if sys.platform == "win32":
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+# Get absolute paths for all files
+def get_absolute_path(relative_path):
+    """Convert relative path to absolute path"""
+    return os.path.abspath(relative_path)
+
+def check_file_exists(file_path):
+    """Check if file exists and return absolute path or None"""
+    abs_path = get_absolute_path(file_path)
+    if os.path.exists(abs_path):
+        return abs_path
+    else:
+        print(f"Warning: File not found: {file_path}")
+        return None
+
+# Define the data structure with absolute paths
+SAMPLES = {}
+for i in range(2, 9):  # Updated to handle 8 assault videos
+    sample_data = {
+        "input": f"video/Assault{i:03d}_x264.mp4",  # Updated naming pattern
+        "optical_flow": f"optical_flow/Assault{i:03d}_x264.mp4",
+        "yolo": f"yolo/Assault{i:03d}_x264.mp4",
+        "vggt": f"vggt/Assault{i:03d}_x264.mp4",
+        # "pcd": f"pcd/Assault{i:03d}_x264.pcd",
+        "qa": f"qa/Assault{i:03d}_x264.json"
+    }
+    
+    # Convert to absolute paths and check existence
+    SAMPLES[i] = {}
+    for key, path in sample_data.items():
+        abs_path = check_file_exists(path)
+        SAMPLES[i][key] = abs_path
+
+def load_qa_data(qa_file):
+    """Load QA data from JSON file and format for chat display"""
+    try:
+        if not qa_file or not os.path.exists(qa_file):
+            return []
+        
+        with open(qa_file, 'r', encoding='utf-8') as f:
+            qa_data = json.load(f)
+        
+        # Format QA pairs for Gradio chat interface
+        chat_history = []
+        for qa_pair in qa_data.get('qa_pairs', []):
+            question = qa_pair.get('question', '')
+            answer = qa_pair.get('answer', '')
+            
+            # Add question (user message)
+            chat_history.append([question, answer])
+        
+        return chat_history
+    except Exception as e:
+        print(f"Error loading QA data {qa_file}: {e}")
+        return []
+
+def get_qa_metadata(qa_file):
+    """Get metadata from QA JSON file"""
+    try:
+        if not qa_file or not os.path.exists(qa_file):
+            return {}
+        
+        with open(qa_file, 'r', encoding='utf-8') as f:
+            qa_data = json.load(f)
+        
+        return {
+            'video_name': qa_data.get('video_name', ''),
+            'timestamp': qa_data.get('timestamp', ''),
+            'model_path': qa_data.get('model_path', ''),
+            'max_num_frames': qa_data.get('max_num_frames', 0),
+            'total_questions': len(qa_data.get('qa_pairs', []))
+        }
+    except Exception as e:
+        print(f"Error loading QA metadata {qa_file}: {e}")
+        return {}
+
+def load_point_cloud_plotly(pcd_file):
+    """Load point cloud and create a 3D plotly visualization"""
+    try:
+        if not pcd_file or not os.path.exists(pcd_file):
+            return None
+        
+        pcd = o3d.io.read_point_cloud(pcd_file)
+        points = np.asarray(pcd.points)
+        colors = np.asarray(pcd.colors) if pcd.has_colors() else None
+        
+        if len(points) == 0:
+            return None
+        
+        # Subsample points if too many (for performance)
+        if len(points) > 10000:
+            indices = np.random.choice(len(points), 10000, replace=False)
+            points = points[indices]
+            if colors is not None:
+                colors = colors[indices]
+            
+        # Create 3D scatter plot
+        if colors is not None and len(colors) > 0:
+            # Convert colors to RGB if needed
+            if colors.max() <= 1.0:
+                colors = (colors * 255).astype(int)
+            color_rgb = [f'rgb({r},{g},{b})' for r, g, b in colors]
+            
+            fig = go.Figure(data=[go.Scatter3d(
+                x=points[:, 0],
+                y=points[:, 1],
+                z=points[:, 2],
+                mode='markers',
+                marker=dict(
+                    size=1.7,
+                    color=color_rgb,
+                ),
+                text=[f'Point {i}' for i in range(len(points))],
+                hovertemplate='<b>Point %{text}</b><br>X: %{x}<br>Y: %{y}<br>Z: %{z}<extra></extra>'
+            )])
+        else:
+            fig = go.Figure(data=[go.Scatter3d(
+                x=points[:, 0],
+                y=points[:, 1],
+                z=points[:, 2],
+                mode='markers',
+                marker=dict(
+                    size=2,
+                    color=points[:, 2],  # Color by Z coordinate
+                    colorscale='Viridis',
+                    showscale=True
+                ),
+                text=[f'Point {i}' for i in range(len(points))],
+                hovertemplate='<b>Point %{text}</b><br>X: %{x}<br>Y: %{y}<br>Z: %{z}<extra></extra>'
+            )])
+        
+        fig.update_layout(
+            title=f'3D Point Cloud Visualization - {os.path.basename(pcd_file)}',
+            scene=dict(
+                xaxis_title='X',
+                yaxis_title='Y',
+                zaxis_title='Z',
+                camera=dict(
+                    eye=dict(x=1.5, y=1.5, z=1.5)
+                ),
+                bgcolor='rgb(10, 10, 10)',
+            ),
+            margin=dict(l=0, r=0, t=50, b=0),
+            paper_bgcolor='rgb(20, 20, 20)',
+            plot_bgcolor='rgb(20, 20, 20)',
+            font=dict(color='white')
+        )
+        
+        return fig
+    except Exception as e:
+        print(f"Error loading point cloud {pcd_file}: {e}")
+        return None
+
+def create_sample_gallery(sample_id):
+    """Create a gallery view for a specific sample"""
+    sample = SAMPLES[sample_id]
+    
+    # Load point cloud visualization
+    pcd_plot = load_point_cloud_plotly(sample["pcd"])
+    
+    return (
+        sample["input"],           # Input video
+        sample["optical_flow"],    # Optical flow video
+        sample["yolo"],           # YOLO video
+        sample["vggt"],           # VGGT video
+        pcd_plot                  # Point cloud plot
+    )
+
+def create_overview_gallery():
+    """Create an overview showing all samples"""
+    gallery_items = []
+    for i in range(1, 6):
+        sample = SAMPLES[i]
+        # Only add items that exist
+        if sample["input"]:
+            gallery_items.append((sample["input"], f"Sample {i} - Input"))
+        if sample["optical_flow"]:
+            gallery_items.append((sample["optical_flow"], f"Sample {i} - Optical Flow"))
+        if sample["yolo"]:
+            gallery_items.append((sample["yolo"], f"Sample {i} - YOLO"))
+        if sample["vggt"]:
+            gallery_items.append((sample["vggt"], f"Sample {i} - VGGT"))
+    return gallery_items
+
+# Custom CSS for better styling
+custom_css = """
+# .gradio-container {
+#     max-width: 1200px !important;
+# }
+.gallery-item {
+    border-radius: 10px;
+}
+h1 {
+    text-align: center;
+    color: #2c3e50;
+    margin-bottom: 30px;
+}
+.tab-nav {
+    margin-bottom: 20px;
+}
+.qa-section-header {
+    font-size: 1.2em;
+    color: #2c3e50;
+    margin-top: 20px;
+}
+.qa-metadata {
+    background-color: #f8f9fa;
+    padding: 15px;
+    border-radius: 8px;
+    border-left: 4px solid #007bff;
+}
+.qa-info {
+    background-color: #e7f3ff;
+    padding: 10px;
+    border-radius: 5px;
+    font-style: italic;
+}
+"""
+
+# Create the Gradio interface
+with gr.Blocks(css=custom_css, title="Anomalous Event Detection") as demo:
+    gr.Markdown("# 🎥 Results Gallery")
+    
+    with gr.Tabs() as tabs:
+        # Individual sample tabs
+        for i in range(2, 9):
+            with gr.Tab(f"🎬 Sample {i-1}"):
+                gr.Markdown(f"## Sample {i-1} - Detailed View")
+                
+                sample = SAMPLES[i]
+                # Top Row: Input Video + Chat History
+                with gr.Row():
+                    # Left Column: Input Video (narrower)
+                    with gr.Column(scale=1):
+                        gr.Markdown("### 📹 Input Video")
+                        if sample["input"]:
+                            input_video = gr.Video(
+                                value=sample["input"],
+                                label="Original Input",
+                                show_label=True
+                            )
+                        else:
+                            gr.Markdown("❌ Input video not found")
+                    
+                    # Right Column: Q&A Chat History
+                    with gr.Column(scale=1, min_width=400):
+                        gr.Markdown("### 💬 Q&A Chat History")
+                        
+                        if sample["qa"]:
+                            # Load QA metadata
+                            qa_metadata = get_qa_metadata(sample["qa"])
+                            if qa_metadata:
+                                gr.Markdown(f"""
+                                **📊 Chat Session Info:**
+                                - **Video:** {qa_metadata.get('video_name', 'N/A')}
+                                - **Total Questions:** {qa_metadata.get('total_questions', 0)}
+                                - **Max Frames:** {qa_metadata.get('max_num_frames', 0)}
+                                - **Timestamp:** {qa_metadata.get('timestamp', 'N/A')[:19].replace('T', ' ')}
+                                """)
+                            
+                            # Load and display chat history
+                            qa_history = load_qa_data(sample["qa"])
+                            if qa_history:
+                                chatbot = gr.Chatbot(
+                                    value=qa_history,
+                                    label="Video Analysis Q&A",
+                                    show_label=True,
+                                    height=500,
+                                    avatar_images=["👤", "🤖"]
+                                )
+                                
+                                gr.Markdown("""
+                                💡 **About this Q&A:** Questions asked by humans about the video content and answers from an AI model trained for video analysis.
+                                """)
+                            else:
+                                gr.Markdown("❌ No Q&A data available for this sample")
+                        else:
+                            gr.Markdown("❌ Q&A file not found for this sample")
+                # VGGT and Point Cloud in a row
+                with gr.Row():
+                    with gr.Column():
+                        gr.Markdown("### 🎮 VGGT")
+
+                        if sample["vggt"]:
+                            vggt_video = gr.Video(
+                                value=sample["vggt"],
+                                label="VGGT Processing",
+                                show_label=True
+                            )
+                        else:
+                            gr.Markdown("❌ VGGT video not found")
+                    
+                    with gr.Column():
+                        pass
+                        # gr.Markdown("### ☁️ 3D Point Cloud")
+
+                        # if sample["pcd"]:
+                        #     try:
+                        #         pcd_plot = gr.Plot(
+                        #             value=load_point_cloud_plotly(sample["pcd"]),
+                        #             label="Interactive 3D Point Cloud",
+                        #             show_label=True
+                        #         )
+                        #     except Exception as e:
+                        #         gr.Markdown(f"❌ Error loading point cloud: {str(e)}")
+                        # else:
+                        #     gr.Markdown("❌ Point cloud file not found")
+                
+                
+                
+                # Bottom Section: Other Analysis Results
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        # Optical Flow and YOLO in a row
+                        with gr.Row():
+                            with gr.Column():
+                                gr.Markdown("### 🌊 Optical Flow")
+                                if sample["optical_flow"]:
+                                    optical_flow_video = gr.Video(
+                                        value=sample["optical_flow"],
+                                        label="Motion Analysis",
+                                        show_label=True
+                                    )
+                                else:
+                                    gr.Markdown("❌ Optical flow video not found")
+                            
+                            with gr.Column():
+                                gr.Markdown("### 🎯 YOLO Detection")
+                                if sample["yolo"]:
+                                    yolo_video = gr.Video(
+                                        value=sample["yolo"],
+                                        label="Object Detection",
+                                        show_label=True
+                                    )
+                                else:
+                                    gr.Markdown("❌ YOLO video not found")
+                        
+        
+        # Comparison tab
+        # with gr.Tab("🔍 Compare"):
+        #     gr.Markdown("## Compare Different Samples")
+        #     gr.Markdown("Select two samples to compare side by side")
+            
+        #     with gr.Row():
+        #         sample1_dropdown = gr.Dropdown(
+        #             choices=list(range(1, 6)),
+        #             value=1,
+        #             label="Sample 1"
+        #         )
+        #         sample2_dropdown = gr.Dropdown(
+        #             choices=list(range(1, 6)),
+        #             value=2,
+        #             label="Sample 2"
+        #         )
+            
+        #     with gr.Row():
+        #         with gr.Column():
+        #             gr.Markdown("### Sample 1")
+        #             comp_input1 = gr.Video(label="Input")
+        #             comp_optical1 = gr.Video(label="Optical Flow")
+        #             comp_yolo1 = gr.Video(label="YOLO")
+        #             comp_vggt1 = gr.Video(label="VGGT")
+        #             comp_pcd1 = gr.Plot(label="Point Cloud")
+                
+        #         with gr.Column():
+        #             gr.Markdown("### Sample 2")
+        #             comp_input2 = gr.Video(label="Input")
+        #             comp_optical2 = gr.Video(label="Optical Flow")
+        #             comp_yolo2 = gr.Video(label="YOLO")
+        #             comp_vggt2 = gr.Video(label="VGGT")
+        #             comp_pcd2 = gr.Plot(label="Point Cloud")
+            
+        #     # Update comparison when dropdowns change
+        #     def update_comparison(sample1_id, sample2_id):
+        #         try:
+        #             sample1_results = create_sample_gallery(sample1_id)
+        #             sample2_results = create_sample_gallery(sample2_id)
+        #             return sample1_results + sample2_results
+        #         except Exception as e:
+        #             print(f"Error updating comparison: {e}")
+        #             return [None] * 10
+            
+        #     for dropdown in [sample1_dropdown, sample2_dropdown]:
+        #         dropdown.change(
+        #             update_comparison,
+        #             inputs=[sample1_dropdown, sample2_dropdown],
+        #             outputs=[
+        #                 comp_input1, comp_optical1, comp_yolo1, comp_vggt1, comp_pcd1,
+        #                 comp_input2, comp_optical2, comp_yolo2, comp_vggt2, comp_pcd2
+        #             ]
+        #         )
+
+
+if __name__ == "__main__":
+    # Print file status for debugging
+    print("=== File Status Check ===")
+    for i in range(2, 9):
+        print(f"\nSample {i}:")
+        for key, path in SAMPLES[i].items():
+            status = "✅ Found" if path else "❌ Missing"
+            print(f"  {key}: {status}")
+    
+    print(f"\n=== Starting Gradio App ===")
+    demo.launch(
+        share=True,
+        server_name="127.0.0.1",
+        server_port=7861,
+        show_error=True,
+        inbrowser=True
+    )

README.md

@@ -1,12 +1,6 @@
 ---
-title: Demo
-emoji: 📈
-colorFrom: green
-colorTo: blue
+title: demo
+app_file: app.py
 sdk: gradio
 sdk_version: 5.34.2
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,422 @@
+import gradio as gr
+import os
+import open3d as o3d
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from pathlib import Path
+import sys
+import asyncio
+import json
+if sys.platform == "win32":
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+
+# Get absolute paths for all files
+def get_absolute_path(relative_path):
+    """Convert relative path to absolute path"""
+    return os.path.abspath(relative_path)
+
+def check_file_exists(file_path):
+    """Check if file exists and return absolute path or None"""
+    abs_path = get_absolute_path(file_path)
+    if os.path.exists(abs_path):
+        return abs_path
+    else:
+        print(f"Warning: File not found: {file_path}")
+        return None
+
+# Define the data structure with absolute paths
+SAMPLES = {}
+for i in range(2, 9):  # Updated to handle 8 assault videos
+    sample_data = {
+        "input": f"video/Assault{i:03d}_x264.mp4",  # Updated naming pattern
+        "optical_flow": f"optical_flow/Assault{i:03d}_x264.mp4",
+        "yolo": f"yolo/Assault{i:03d}_x264.mp4",
+        "vggt": f"vggt/Assault{i:03d}_x264.mp4",
+        # "pcd": f"pcd/Assault{i:03d}_x264.pcd",
+        "qa": f"qa/Assault{i:03d}_x264.json"
+    }
+    
+    # Convert to absolute paths and check existence
+    SAMPLES[i] = {}
+    for key, path in sample_data.items():
+        abs_path = check_file_exists(path)
+        SAMPLES[i][key] = abs_path
+
+def load_qa_data(qa_file):
+    """Load QA data from JSON file and format for chat display"""
+    try:
+        if not qa_file or not os.path.exists(qa_file):
+            return []
+        
+        with open(qa_file, 'r', encoding='utf-8') as f:
+            qa_data = json.load(f)
+        
+        # Format QA pairs for Gradio chat interface
+        chat_history = []
+        for qa_pair in qa_data.get('qa_pairs', []):
+            question = qa_pair.get('question', '')
+            answer = qa_pair.get('answer', '')
+            
+            # Add question (user message)
+            chat_history.append([question, answer])
+        
+        return chat_history
+    except Exception as e:
+        print(f"Error loading QA data {qa_file}: {e}")
+        return []
+
+def get_qa_metadata(qa_file):
+    """Get metadata from QA JSON file"""
+    try:
+        if not qa_file or not os.path.exists(qa_file):
+            return {}
+        
+        with open(qa_file, 'r', encoding='utf-8') as f:
+            qa_data = json.load(f)
+        
+        return {
+            'video_name': qa_data.get('video_name', ''),
+            'timestamp': qa_data.get('timestamp', ''),
+            'model_path': qa_data.get('model_path', ''),
+            'max_num_frames': qa_data.get('max_num_frames', 0),
+            'total_questions': len(qa_data.get('qa_pairs', []))
+        }
+    except Exception as e:
+        print(f"Error loading QA metadata {qa_file}: {e}")
+        return {}
+
+def load_point_cloud_plotly(pcd_file):
+    """Load point cloud and create a 3D plotly visualization"""
+    try:
+        if not pcd_file or not os.path.exists(pcd_file):
+            return None
+        
+        pcd = o3d.io.read_point_cloud(pcd_file)
+        points = np.asarray(pcd.points)
+        colors = np.asarray(pcd.colors) if pcd.has_colors() else None
+        
+        if len(points) == 0:
+            return None
+        
+        # Subsample points if too many (for performance)
+        if len(points) > 10000:
+            indices = np.random.choice(len(points), 10000, replace=False)
+            points = points[indices]
+            if colors is not None:
+                colors = colors[indices]
+            
+        # Create 3D scatter plot
+        if colors is not None and len(colors) > 0:
+            # Convert colors to RGB if needed
+            if colors.max() <= 1.0:
+                colors = (colors * 255).astype(int)
+            color_rgb = [f'rgb({r},{g},{b})' for r, g, b in colors]
+            
+            fig = go.Figure(data=[go.Scatter3d(
+                x=points[:, 0],
+                y=points[:, 1],
+                z=points[:, 2],
+                mode='markers',
+                marker=dict(
+                    size=1.7,
+                    color=color_rgb,
+                ),
+                text=[f'Point {i}' for i in range(len(points))],
+                hovertemplate='<b>Point %{text}</b><br>X: %{x}<br>Y: %{y}<br>Z: %{z}<extra></extra>'
+            )])
+        else:
+            fig = go.Figure(data=[go.Scatter3d(
+                x=points[:, 0],
+                y=points[:, 1],
+                z=points[:, 2],
+                mode='markers',
+                marker=dict(
+                    size=2,
+                    color=points[:, 2],  # Color by Z coordinate
+                    colorscale='Viridis',
+                    showscale=True
+                ),
+                text=[f'Point {i}' for i in range(len(points))],
+                hovertemplate='<b>Point %{text}</b><br>X: %{x}<br>Y: %{y}<br>Z: %{z}<extra></extra>'
+            )])
+        
+        fig.update_layout(
+            title=f'3D Point Cloud Visualization - {os.path.basename(pcd_file)}',
+            scene=dict(
+                xaxis_title='X',
+                yaxis_title='Y',
+                zaxis_title='Z',
+                camera=dict(
+                    eye=dict(x=1.5, y=1.5, z=1.5)
+                ),
+                bgcolor='rgb(10, 10, 10)',
+            ),
+            margin=dict(l=0, r=0, t=50, b=0),
+            paper_bgcolor='rgb(20, 20, 20)',
+            plot_bgcolor='rgb(20, 20, 20)',
+            font=dict(color='white')
+        )
+        
+        return fig
+    except Exception as e:
+        print(f"Error loading point cloud {pcd_file}: {e}")
+        return None
+
+def create_sample_gallery(sample_id):
+    """Create a gallery view for a specific sample"""
+    sample = SAMPLES[sample_id]
+    
+    # Load point cloud visualization
+    pcd_plot = load_point_cloud_plotly(sample["pcd"])
+    
+    return (
+        sample["input"],           # Input video
+        sample["optical_flow"],    # Optical flow video
+        sample["yolo"],           # YOLO video
+        sample["vggt"],           # VGGT video
+        pcd_plot                  # Point cloud plot
+    )
+
+def create_overview_gallery():
+    """Create an overview showing all samples"""
+    gallery_items = []
+    for i in range(1, 6):
+        sample = SAMPLES[i]
+        # Only add items that exist
+        if sample["input"]:
+            gallery_items.append((sample["input"], f"Sample {i} - Input"))
+        if sample["optical_flow"]:
+            gallery_items.append((sample["optical_flow"], f"Sample {i} - Optical Flow"))
+        if sample["yolo"]:
+            gallery_items.append((sample["yolo"], f"Sample {i} - YOLO"))
+        if sample["vggt"]:
+            gallery_items.append((sample["vggt"], f"Sample {i} - VGGT"))
+    return gallery_items
+
+# Custom CSS for better styling
+custom_css = """
+# .gradio-container {
+#     max-width: 1200px !important;
+# }
+.gallery-item {
+    border-radius: 10px;
+}
+h1 {
+    text-align: center;
+    color: #2c3e50;
+    margin-bottom: 30px;
+}
+.tab-nav {
+    margin-bottom: 20px;
+}
+.qa-section-header {
+    font-size: 1.2em;
+    color: #2c3e50;
+    margin-top: 20px;
+}
+.qa-metadata {
+    background-color: #f8f9fa;
+    padding: 15px;
+    border-radius: 8px;
+    border-left: 4px solid #007bff;
+}
+.qa-info {
+    background-color: #e7f3ff;
+    padding: 10px;
+    border-radius: 5px;
+    font-style: italic;
+}
+"""
+
+# Create the Gradio interface
+with gr.Blocks(css=custom_css, title="Anomalous Event Detection") as demo:
+    gr.Markdown("# 🎥 Results Gallery")
+    
+    with gr.Tabs() as tabs:
+        # Individual sample tabs
+        for i in range(2, 9):
+            with gr.Tab(f"🎬 Sample {i-1}"):
+                gr.Markdown(f"## Sample {i-1} - Detailed View")
+                
+                sample = SAMPLES[i]
+                # Top Row: Input Video + Chat History
+                with gr.Row():
+                    # Left Column: Input Video (narrower)
+                    with gr.Column(scale=1):
+                        gr.Markdown("### 📹 Input Video")
+                        if sample["input"]:
+                            input_video = gr.Video(
+                                value=sample["input"],
+                                label="Original Input",
+                                show_label=True
+                            )
+                        else:
+                            gr.Markdown("❌ Input video not found")
+                    
+                    # Right Column: Q&A Chat History
+                    with gr.Column(scale=1, min_width=400):
+                        gr.Markdown("### 💬 Q&A Chat History")
+                        
+                        if sample["qa"]:
+                            # Load QA metadata
+                            qa_metadata = get_qa_metadata(sample["qa"])
+                            if qa_metadata:
+                                gr.Markdown(f"""
+                                **📊 Chat Session Info:**
+                                - **Video:** {qa_metadata.get('video_name', 'N/A')}
+                                - **Total Questions:** {qa_metadata.get('total_questions', 0)}
+                                - **Max Frames:** {qa_metadata.get('max_num_frames', 0)}
+                                - **Timestamp:** {qa_metadata.get('timestamp', 'N/A')[:19].replace('T', ' ')}
+                                """)
+                            
+                            # Load and display chat history
+                            qa_history = load_qa_data(sample["qa"])
+                            if qa_history:
+                                chatbot = gr.Chatbot(
+                                    value=qa_history,
+                                    label="Video Analysis Q&A",
+                                    show_label=True,
+                                    height=500,
+                                    avatar_images=["���", "🤖"]
+                                )
+                                
+                                gr.Markdown("""
+                                💡 **About this Q&A:** Questions asked by humans about the video content and answers from an AI model trained for video analysis.
+                                """)
+                            else:
+                                gr.Markdown("❌ No Q&A data available for this sample")
+                        else:
+                            gr.Markdown("❌ Q&A file not found for this sample")
+                # VGGT and Point Cloud in a row
+                with gr.Row():
+                    with gr.Column():
+                        gr.Markdown("### 🎮 VGGT")
+
+                        if sample["vggt"]:
+                            vggt_video = gr.Video(
+                                value=sample["vggt"],
+                                label="VGGT Processing",
+                                show_label=True
+                            )
+                        else:
+                            gr.Markdown("❌ VGGT video not found")
+                    
+                    with gr.Column():
+                        pass
+                        # gr.Markdown("### ☁️ 3D Point Cloud")
+
+                        # if sample["pcd"]:
+                        #     try:
+                        #         pcd_plot = gr.Plot(
+                        #             value=load_point_cloud_plotly(sample["pcd"]),
+                        #             label="Interactive 3D Point Cloud",
+                        #             show_label=True
+                        #         )
+                        #     except Exception as e:
+                        #         gr.Markdown(f"❌ Error loading point cloud: {str(e)}")
+                        # else:
+                        #     gr.Markdown("❌ Point cloud file not found")
+                
+                
+                
+                # Bottom Section: Other Analysis Results
+                with gr.Row():
+                    with gr.Column(scale=2):
+                        # Optical Flow and YOLO in a row
+                        with gr.Row():
+                            with gr.Column():
+                                gr.Markdown("### 🌊 Optical Flow")
+                                if sample["optical_flow"]:
+                                    optical_flow_video = gr.Video(
+                                        value=sample["optical_flow"],
+                                        label="Motion Analysis",
+                                        show_label=True
+                                    )
+                                else:
+                                    gr.Markdown("❌ Optical flow video not found")
+                            
+                            with gr.Column():
+                                gr.Markdown("### 🎯 YOLO Detection")
+                                if sample["yolo"]:
+                                    yolo_video = gr.Video(
+                                        value=sample["yolo"],
+                                        label="Object Detection",
+                                        show_label=True
+                                    )
+                                else:
+                                    gr.Markdown("❌ YOLO video not found")
+                        
+        
+        # Comparison tab
+        # with gr.Tab("🔍 Compare"):
+        #     gr.Markdown("## Compare Different Samples")
+        #     gr.Markdown("Select two samples to compare side by side")
+            
+        #     with gr.Row():
+        #         sample1_dropdown = gr.Dropdown(
+        #             choices=list(range(1, 6)),
+        #             value=1,
+        #             label="Sample 1"
+        #         )
+        #         sample2_dropdown = gr.Dropdown(
+        #             choices=list(range(1, 6)),
+        #             value=2,
+        #             label="Sample 2"
+        #         )
+            
+        #     with gr.Row():
+        #         with gr.Column():
+        #             gr.Markdown("### Sample 1")
+        #             comp_input1 = gr.Video(label="Input")
+        #             comp_optical1 = gr.Video(label="Optical Flow")
+        #             comp_yolo1 = gr.Video(label="YOLO")
+        #             comp_vggt1 = gr.Video(label="VGGT")
+        #             comp_pcd1 = gr.Plot(label="Point Cloud")
+                
+        #         with gr.Column():
+        #             gr.Markdown("### Sample 2")
+        #             comp_input2 = gr.Video(label="Input")
+        #             comp_optical2 = gr.Video(label="Optical Flow")
+        #             comp_yolo2 = gr.Video(label="YOLO")
+        #             comp_vggt2 = gr.Video(label="VGGT")
+        #             comp_pcd2 = gr.Plot(label="Point Cloud")
+            
+        #     # Update comparison when dropdowns change
+        #     def update_comparison(sample1_id, sample2_id):
+        #         try:
+        #             sample1_results = create_sample_gallery(sample1_id)
+        #             sample2_results = create_sample_gallery(sample2_id)
+        #             return sample1_results + sample2_results
+        #         except Exception as e:
+        #             print(f"Error updating comparison: {e}")
+        #             return [None] * 10
+            
+        #     for dropdown in [sample1_dropdown, sample2_dropdown]:
+        #         dropdown.change(
+        #             update_comparison,
+        #             inputs=[sample1_dropdown, sample2_dropdown],
+        #             outputs=[
+        #                 comp_input1, comp_optical1, comp_yolo1, comp_vggt1, comp_pcd1,
+        #                 comp_input2, comp_optical2, comp_yolo2, comp_vggt2, comp_pcd2
+        #             ]
+        #         )
+
+
+if __name__ == "__main__":
+    # Print file status for debugging
+    print("=== File Status Check ===")
+    for i in range(2, 9):
+        print(f"\nSample {i}:")
+        for key, path in SAMPLES[i].items():
+            status = "✅ Found" if path else "❌ Missing"
+            print(f"  {key}: {status}")
+    
+    print(f"\n=== Starting Gradio App ===")
+    demo.launch(
+        share=True,
+        server_name="127.0.0.1",
+        server_port=7861,
+        show_error=True,
+        inbrowser=True
+    )

app2.py

@@ -0,0 +1,147 @@
+import gradio as gr
+import os
+import subprocess
+import signal
+import atexit
+import platform
+
+# Global variable to hold the process for the viewer
+viser_process = None
+
+def kill_process(proc):
+    if proc is None:
+        return
+    if platform.system() == "Windows":
+        proc.kill()
+    else:
+        os.killpg(os.getpgid(proc.pid), signal.SIGTERM)
+
+
+# Clean up function to kill the viser process when the app exits
+def cleanup():
+    print("Cleaning up and stopping viser server...")
+    global viser_process
+    if viser_process:
+        kill_process(viser_process)
+        viser_process = None
+
+atexit.register(cleanup)
+
+
+def get_experiment_ids():
+    print("Getting experiment ids")
+    video_files = os.listdir("video")
+    print(f"Found {len(video_files)} video files")
+    return sorted([os.path.splitext(f)[0] for f in video_files if f.endswith(".mp4")])
+
+# Get absolute paths for all files
+def get_absolute_path(relative_path):
+    """Convert relative path to absolute path"""
+    return os.path.abspath(relative_path)
+
+def check_file_exists(file_path):
+    """Check if file exists and return absolute path or None"""
+    abs_path = get_absolute_path(file_path)
+    if os.path.exists(abs_path):
+        return abs_path
+    else:
+        print(f"Warning: File not found: {file_path}")
+        return None
+def get_file_paths(exp_id):
+    paths = {
+        "video": get_absolute_path(os.path.join("video", f"{exp_id}.mp4")),
+        "yolo": get_absolute_path(os.path.join("yolo", f"yolo{exp_id}.mp4")),
+        "optical_flow": get_absolute_path(os.path.join("optical_flow", f"optical_flow{exp_id}.mp4")),
+        "pcd": get_absolute_path(os.path.join("pcd", f"vggt_{exp_id}.pcd")),
+    }
+    for key, path in paths.items():
+        abs_path = check_file_exists(path)
+        paths[key] = abs_path
+    return paths
+
+def show_results(exp_id):
+    # global viser_process
+    
+    # if viser_process:
+    #     print("Stopping previous viser server...")
+        # kill_process(viser_process)
+    #     viser_process = None
+    print(f"Showing results for {exp_id}")
+    paths = get_file_paths(exp_id)
+    
+    pcd_path = paths.get("pcd")
+    if pcd_path and os.path.exists(pcd_path):
+        print(f"Starting viser server for {pcd_path}...")
+        # For cross-platform compatibility, use shell=True on Windows
+        use_shell = platform.system() == "Windows"
+        preexec_fn = os.setsid if platform.system() != "Windows" else None
+        
+        viser_process = subprocess.Popen(
+            ["python", "vis.py", pcd_path],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            shell=use_shell,
+            preexec_fn=preexec_fn
+        )
+        print(f"Viser server started with PID: {viser_process.pid}")
+    
+    iframe_html = '<iframe src="http://127.0.0.1:8088" style="width: 100%; height: 500px; border: none;"></iframe>'
+    
+    return (
+        paths.get("video"),
+        paths.get("yolo"),
+        paths.get("optical_flow"),
+        iframe_html
+    )
+
+with gr.Blocks() as demo:
+    print("Creating blocks")
+    gr.Markdown("# Experiment Results Gallery")
+    
+    experiment_ids = get_experiment_ids()
+    print(f"Experiment ids: {experiment_ids}")
+    if not experiment_ids:
+        gr.Markdown("## No experiments found. Please add videos to the 'video' directory.")
+    else:
+        with gr.Row():
+            exp_id_dropdown = gr.Dropdown(
+                choices=experiment_ids, 
+                label="Select Experiment ID",
+                value=experiment_ids[0]
+            )
+
+        gr.Markdown("## Input")
+        input_video = gr.Video(label="Input Video")
+
+        gr.Markdown("## Outputs")
+        with gr.Tabs():
+            with gr.TabItem("YOLO"):
+                yolo_video = gr.Video(label="YOLO Output")
+            with gr.TabItem("Optical Flow"):
+                optical_flow_video = gr.Video(label="Optical Flow Output")
+            with gr.TabItem("Point Cloud"):
+                pcd_vis = gr.HTML(label="Point Cloud Visualization")
+
+        print("Creating dropdown")
+        exp_id_dropdown.change(
+            fn=show_results,
+            inputs=exp_id_dropdown,
+            outputs=[input_video, yolo_video, optical_flow_video, pcd_vis]
+        )
+        print("Creating load")
+        # Trigger initial load
+        demo.load(
+            fn=show_results,
+            inputs=exp_id_dropdown,
+            outputs=[input_video, yolo_video, optical_flow_video, pcd_vis]
+        )
+
+
+if __name__ == "__main__":
+    print("Launching demo")
+    demo.launch(
+        server_name="127.0.0.1",
+        server_port=7862,
+        show_error=True,
+        inbrowser=True
+    ) 

optical_flow/Assault002_x264.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3eb2e0269756ce91d830f2ddc0e6f5df794cfc5babfa224165eb7ef982e3b492
+size 6690566

optical_flow/Assault003_x264.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f71f2368936e27949bd1bdab2a93be1e9efbece26fb157c45b1844a92fac018c
+size 8017071

optical_flow/Assault004_x264.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52d41602da33fc2d4cd7495d4c52af965dbd656d688c786d1eb8cd34b2a529af
+size 7577219

optical_flow/Assault005_x264.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da8f0ef439afa55a3867f2139bd9a4d3cc7d38576821e78fa7e1430a7e4608b6
+size 30933897

optical_flow/Assault006_x264.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6412be8f2c28e73133969695388d90dd3ba8e3d4b43c62eca58cebcf0392d3c
+size 16892093

optical_flow/Assault007_x264.mp4

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+version https://git-lfs.github.com/spec/v1
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qa/Assault001_x264.json

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+{
+  "video_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/videos2/Assault001_x264.mp4",
+  "video_name": "Assault001_x264",
+  "timestamp": "2025-06-23T21:17:48.641291",
+  "model_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/VideoChat-Flash-Qwen2-7B_res448",
+  "max_num_frames": 512,
+  "generation_config": {
+    "do_sample": false,
+    "temperature": 0.0,
+    "max_new_tokens": 1024,
+    "top_p": 0.1,
+    "num_beams": 1
+  },
+  "qa_pairs": [
+    {
+      "question_id": 1,
+      "question": "请描述视频中的场景和事件",
+      "answer": "视频中发生了一起殴打事件。一名男子在与另一名男子发生争执后遭到殴打，导致他倒地不起。旁观者试图介入并帮助他，但殴打者继续攻击他。最终，殴打者离开现场，而旁观者则报警。整个过程被监控摄像头记录下来。"
+    },
+    {
+      "question_id": 2,
+      "question": "视频中是否存在异常行为？",
+      "answer": "是的，殴打事件本身是一种异常行为，违反了社会秩序和法律。"
+    },
+    {
+      "question_id": 3,
+      "question": "有多少人参与了可疑活动？",
+      "answer": "有四名男子参与了可疑活动，包括殴打者和旁观者。"
+    },
+    {
+      "question_id": 4,
+      "question": "嫌疑人外貌是什么样子？",
+      "answer": "嫌疑人穿着深色上衣和浅色裤子，但其他外貌特征没有详细描述。"
+    },
+    {
+      "question_id": 5,
+      "question": "嫌疑人是否携带任何物品或武器？",
+      "answer": "嫌疑人是否携带任何物品或武器没有在视频中明确描述。"
+    },
+    {
+      "question_id": 6,
+      "question": "请描述嫌疑人行为的步骤",
+      "answer": "嫌疑人首先与另一名男子发生争执，然后使用身体攻击手段殴打对方。旁观者试图介入并帮助他，但嫌疑人继续攻击。最终，嫌疑人离开现场。"
+    },
+    {
+      "question_id": 7,
+      "question": "嫌疑人出现在什么时候？",
+      "answer": "嫌疑人首先在晚上出现，然后在第二天的某个时间点再次出现。"
+    },
+    {
+      "question_id": 9,
+      "question": "这是集体行动还是个人行为？",
+      "answer": "这是集体行动，因为旁观者试图介入并帮助被殴打的男子。"
+    }
+  ]
+}

qa/Assault002_x264.json

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+{
+  "video_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/videos2/Assault002_x264.mp4",
+  "video_name": "Assault002_x264",
+  "timestamp": "2025-06-23T21:18:13.126670",
+  "model_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/VideoChat-Flash-Qwen2-7B_res448",
+  "max_num_frames": 512,
+  "generation_config": {
+    "do_sample": false,
+    "temperature": 0.0,
+    "max_new_tokens": 1024,
+    "top_p": 0.1,
+    "num_beams": 1
+  },
+  "qa_pairs": [
+    {
+      "question_id": 1,
+      "question": "请描述视频中的场景和事件",
+      "answer": "视频中显示的是一个办公室的内部，有多个桌子和椅子。在某个时刻，一名男子突然闯入办公室，并开始攻击其他工作人员。他使用暴力手段，对这些工作人员进行殴打和推搡。其他工作人员则试图保护自己并尝试离开现场。整个过程中，该男子的行为非常恶劣，给办公室内的工作人员造成了很大的伤害和恐惧。"
+    },
+    {
+      "question_id": 2,
+      "question": "视频中是否存在异常行为？",
+      "answer": "是的，视频中存在异常行为。一名男子闯入办公室并使用暴力手段攻击其他工作人员，这是非常不寻常和非法的行为。"
+    },
+    {
+      "question_id": 3,
+      "question": "有多少人参与了可疑活动？",
+      "answer": "视频中没有提供具体的参与人数，但可以确定至少有一名男子闯入办公室并使用暴力手段攻击其他工作人员。"
+    },
+    {
+      "question_id": 4,
+      "question": "嫌疑人外貌是什么样子？",
+      "answer": "嫌疑人是一名男子，他闯入办公室并使用暴力手段攻击其他工作人员。他的外貌特征无法确定，但他的行为非常恶劣和非法。"
+    },
+    {
+      "question_id": 5,
+      "question": "嫌疑人是否携带任何物品或武器？",
+      "answer": "视频中没有提供关于嫌疑人是否携带任何物品或武器的信息。"
+    },
+    {
+      "question_id": 6,
+      "question": "请描述嫌疑人行为的步骤",
+      "answer": "嫌疑人首先闯入办公室，然后开始攻击其他工作人员。他使用暴力手段进行殴打和推搡，对工作人员造成了很大的伤害和恐惧。整个过程中，他的行为非常恶劣和非法。"
+    },
+    {
+      "question_id": 7,
+      "question": "嫌疑人出现在什么时候？",
+      "answer": "嫌疑人出现在2017-03-25 12:54:12，闯入办公室并开始攻击其他工作人员。"
+    },
+    {
+      "question_id": 9,
+      "question": "这是集体行动还是个人行为？",
+      "answer": "这是个人行为，嫌疑人闯入办公室并使用暴力手段攻击其他工作人员，而不是集体行动。"
+    }
+  ]
+}

qa/Assault003_x264.json

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+{
+  "video_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/videos2/Assault003_x264.mp4",
+  "video_name": "Assault003_x264",
+  "timestamp": "2025-06-23T21:18:38.255239",
+  "model_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/VideoChat-Flash-Qwen2-7B_res448",
+  "max_num_frames": 512,
+  "generation_config": {
+    "do_sample": false,
+    "temperature": 0.0,
+    "max_new_tokens": 1024,
+    "top_p": 0.1,
+    "num_beams": 1
+  },
+  "qa_pairs": [
+    {
+      "question_id": 1,
+      "question": "请描述视频中的场景和事件",
+      "answer": "视频中显示了一组人聚集在某个地点，其中一些人开始发生身体冲突。冲突升级后，更多的人加入进来，导致场面变得混乱。最终，冲突结束，所有参与者都离开现场。"
+    },
+    {
+      "question_id": 2,
+      "question": "视频中是否存在异常行为？",
+      "answer": "是的，视频中存在非法行为，包括身体冲突和殴打他人。"
+    },
+    {
+      "question_id": 3,
+      "question": "有多少人参与了可疑活动？",
+      "answer": "视频中有多达七名参与者参与了可疑活动。"
+    },
+    {
+      "question_id": 4,
+      "question": "嫌疑人外貌是什么样子？",
+      "answer": "嫌疑人穿着深色服装，但其他外貌特征无法确定。"
+    },
+    {
+      "question_id": 5,
+      "question": "嫌疑人是否携带任何物品或武器？",
+      "answer": "嫌疑人是否携带任何物品或武器无法确定。"
+    },
+    {
+      "question_id": 6,
+      "question": "请描述嫌疑人行为的步骤",
+      "answer": "嫌疑人首先聚集在某个地点，然后发生身体冲突。冲突升级后，更多的人加入进来。最终，冲突结束，所有参与者都离开现场。"
+    },
+    {
+      "question_id": 7,
+      "question": "嫌疑人出现在什么时候？",
+      "answer": "嫌疑人首先在某个地点出现，然后参与身体冲突。"
+    },
+    {
+      "question_id": 9,
+      "question": "这是集体行动还是个人行为？",
+      "answer": "这是集体行动，因为有多达七名参与者参与了非法活动。"
+    }
+  ]
+}

qa/Assault004_x264.json

@@ -0,0 +1,56 @@
+{
+  "video_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/videos2/Assault004_x264.mp4",
+  "video_name": "Assault004_x264",
+  "timestamp": "2025-06-23T21:19:16.021012",
+  "model_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/VideoChat-Flash-Qwen2-7B_res448",
+  "max_num_frames": 512,
+  "generation_config": {
+    "do_sample": false,
+    "temperature": 0.0,
+    "max_new_tokens": 1024,
+    "top_p": 0.1,
+    "num_beams": 1
+  },
+  "qa_pairs": [
+    {
+      "question_id": 1,
+      "question": "请描述视频中的场景和事件",
+      "answer": "视频中显示了四名男子在一条城市街道上进行非法活动。他们使用棍棒和刀具攻击一名受害者，导致受害者受伤并流血。这些行为发生在一个光线较暗的地区，可能是在深夜。"
+    },
+    {
+      "question_id": 2,
+      "question": "视频中是否存在异常行为？",
+      "answer": "是的，视频中四名男子使用棍棒和刀具攻击受害者的行为是非法和异常的。"
+    },
+    {
+      "question_id": 3,
+      "question": "有多少人参与了可疑活动？",
+      "answer": "四名男子参与了可疑活动。"
+    },
+    {
+      "question_id": 4,
+      "question": "嫌疑人外貌是什么样子？",
+      "answer": "嫌疑人穿着白衣服和头套，使用棍棒攻击受害者。"
+    },
+    {
+      "question_id": 5,
+      "question": "嫌疑人是否携带任何物品或武器？",
+      "answer": "是的，嫌疑人携带棍棒和刀具进行攻击。"
+    },
+    {
+      "question_id": 6,
+      "question": "请描述嫌疑人行为的步骤",
+      "answer": "嫌疑人首先使用棍棒攻击受害者，然后使用刀具进一步伤害受害者。最后，他们逃离现场。"
+    },
+    {
+      "question_id": 7,
+      "question": "嫌疑人出现在什么时候？",
+      "answer": "嫌疑人是在深夜时分出现的。"
+    },
+    {
+      "question_id": 9,
+      "question": "这是集体行动还是个人行为？",
+      "answer": "这是集体行动，因为四名男子共同参与了攻击行为。"
+    }
+  ]
+}

qa/Assault005_x264.json

@@ -0,0 +1,56 @@
+{
+  "video_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/videos2/Assault005_x264.mp4",
+  "video_name": "Assault005_x264",
+  "timestamp": "2025-06-23T21:19:44.105593",
+  "model_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/VideoChat-Flash-Qwen2-7B_res448",
+  "max_num_frames": 512,
+  "generation_config": {
+    "do_sample": false,
+    "temperature": 0.0,
+    "max_new_tokens": 1024,
+    "top_p": 0.1,
+    "num_beams": 1
+  },
+  "qa_pairs": [
+    {
+      "question_id": 1,
+      "question": "请描述视频中的场景和事件",
+      "answer": "视频中显示了几个犯罪分子在深夜时分，潜入一个居民区的后院。他们使用工具和暴力手段，破坏了后院中的花坛和灌木。犯罪分子还使用了照明设备来帮助他们进行破坏活动。整个过程被监控摄像头记录了下来，为警方提供了有力的证据。"
+    },
+    {
+      "question_id": 2,
+      "question": "视频中是否存在异常行为？",
+      "answer": "是的，视频中存在异常行为。几个犯罪分子在深夜时分潜入居民区的后院，并使用工具和暴力手段破坏花坛和灌木，这是非法和犯罪行为。"
+    },
+    {
+      "question_id": 3,
+      "question": "有多少人参与了可疑活动？",
+      "answer": "有几个犯罪分子参与了可疑活动，但具体人数无法确定。"
+    },
+    {
+      "question_id": 4,
+      "question": "嫌疑人外貌是什么样子？",
+      "answer": "嫌疑人穿着浅色的服装，但其他外貌特征无法确定。"
+    },
+    {
+      "question_id": 5,
+      "question": "嫌疑人是否携带任何物品或武器？",
+      "answer": "嫌疑人是否携带任何物品或武器无法确定，但他们在现场使用了工具和暴力手段。"
+    },
+    {
+      "question_id": 6,
+      "question": "请描述嫌疑人行为的步骤",
+      "answer": "嫌疑人首先潜入后院，然后使用工具和暴力手段破坏花坛和灌木。整个过程被监控摄像头记录了下来。"
+    },
+    {
+      "question_id": 7,
+      "question": "嫌疑人出现在什么时候？",
+      "answer": "嫌疑人出现在深夜时分。"
+    },
+    {
+      "question_id": 9,
+      "question": "这是集体行动还是个人行为？",
+      "answer": "这是集体行动，有几个犯罪分子参与了破坏活动。"
+    }
+  ]
+}

qa/Assault006_x264.json

@@ -0,0 +1,56 @@
+{
+  "video_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/videos2/Assault006_x264.mp4",
+  "video_name": "Assault006_x264",
+  "timestamp": "2025-06-23T21:20:00.525212",
+  "model_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/VideoChat-Flash-Qwen2-7B_res448",
+  "max_num_frames": 512,
+  "generation_config": {
+    "do_sample": false,
+    "temperature": 0.0,
+    "max_new_tokens": 1024,
+    "top_p": 0.1,
+    "num_beams": 1
+  },
+  "qa_pairs": [
+    {
+      "question_id": 1,
+      "question": "请描述视频中的场景和事件",
+      "answer": "视频中显示一个室内空间，可能是一个办公室或小型店铺。在视频的开始，一个穿着白上衣和黑背心的人进入这个空间。他随后开始攻击一个已经存在的个人，这导致了进一步的暴力行为。更多的人加入进来，形成一个混乱的场面，其中一些人试图控制局面，而其他人则参与殴打。整个过程中，暴力行为持续发生，直到最后，所有参与者都离开现场。"
+    },
+    {
+      "question_id": 2,
+      "question": "视频中是否存在异常行为？",
+      "answer": "是的，视频中存在异常行为。暴力攻击和殴打是一种非法和不道德的行为，违反了社会规范和法律。"
+    },
+    {
+      "question_id": 3,
+      "question": "有多少人参与了可疑活动？",
+      "answer": "参与可疑活动的人数无法确定，因为视频中没有提供具体的数字。但是，从视频的描述来看，至少有四到五个人参与了殴打和暴力行为。"
+    },
+    {
+      "question_id": 4,
+      "question": "嫌疑人外貌是什么样子？",
+      "answer": "嫌疑人穿着白上衣和黑背心。"
+    },
+    {
+      "question_id": 5,
+      "question": "嫌疑人是否携带任何物品或武器？",
+      "answer": "嫌疑人是否携带任何物品或武器无法确定，因为视频中没有提供具体的细节。"
+    },
+    {
+      "question_id": 6,
+      "question": "请描述嫌疑人行为的步骤",
+      "answer": "嫌疑人首先进入室内空间，然后开始攻击已经存在的个人。这导致了进一步的暴力行为，更多的人加入进来，形成一个混乱的场面。整个过程中，暴力行为持续发生，直到最后，所有参与者都离开现场。"
+    },
+    {
+      "question_id": 7,
+      "question": "嫌疑人出现在什么时候？",
+      "answer": "嫌疑人出现在视频的开始，即进入室内空间时。"
+    },
+    {
+      "question_id": 9,
+      "question": "这是集体行动还是个人行为？",
+      "answer": "这是集体行动，因为更多的人加入进来，形成一个混乱的场面。"
+    }
+  ]
+}

qa/Assault007_x264.json

@@ -0,0 +1,56 @@
+{
+  "video_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/videos2/Assault007_x264.mp4",
+  "video_name": "Assault007_x264",
+  "timestamp": "2025-06-23T21:21:17.317527",
+  "model_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/VideoChat-Flash-Qwen2-7B_res448",
+  "max_num_frames": 512,
+  "generation_config": {
+    "do_sample": false,
+    "temperature": 0.0,
+    "max_new_tokens": 1024,
+    "top_p": 0.1,
+    "num_beams": 1
+  },
+  "qa_pairs": [
+    {
+      "question_id": 1,
+      "question": "请描述视频中的场景和事件",
+      "answer": "视频显示了一起正在进行的银行抢劫事件。一个人躺在地板上，似乎失去知觉或被制服，同时两个戴着面具并穿着深色衣服的人接近并与其互动。其中一个劫匪似乎拿着武器，可能是棒球棒或棍子，并被看到多次击打地上的那个人。然后劫匪们移动到自动取款机前，可能试图获取现金。这个场景表明在银行内正在发生暴力犯罪行为。"
+    },
+    {
+      "question_id": 2,
+      "question": "视频中是否存在异常行为？",
+      "answer": "是的，视频中存在异常行为。两个穿着黄色和绿色面罩的武装人员闯入银行，对一个似乎失去知觉的人进行殴打，并试图从ATM机处获取资金，这表明了非法和暴力行为。"
+    },
+    {
+      "question_id": 3,
+      "question": "有多少人参与了可疑活动？",
+      "answer": "有三个人参与了可疑活动。除了两个穿着黄色和绿色面罩的武装人员，还有一名穿着白大褂的人在视频中出现。"
+    },
+    {
+      "question_id": 4,
+      "question": "嫌疑人外貌是什么样子？",
+      "answer": "两个嫌疑人穿着黄色和绿色面罩，而第三名嫌疑人则穿着白大褂。"
+    },
+    {
+      "question_id": 5,
+      "question": "嫌疑人是否携带任何物品或武器？",
+      "answer": "是的，两个嫌疑人携带武器，其中一人可能持有一根棒状物。"
+    },
+    {
+      "question_id": 6,
+      "question": "请描述嫌疑人行为的步骤",
+      "answer": "嫌疑人首先闯入银行，对一个似乎失去知觉的人进行殴打。然后，他们接近ATM机，尝试获取资金。最后，他们离开银行现场。"
+    },
+    {
+      "question_id": 7,
+      "question": "嫌疑人出现在什么时候？",
+      "answer": "嫌疑人首先在2016-06-09 04:32:51出现。"
+    },
+    {
+      "question_id": 9,
+      "question": "这是集体行动还是个人行为？",
+      "answer": "这是集体行动，因为有三个人参与了非法活动。"
+    }
+  ]
+}

qa/Assault008_x264.json

@@ -0,0 +1,56 @@
+{
+  "video_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/videos2/Assault008_x264.mp4",
+  "video_name": "Assault008_x264",
+  "timestamp": "2025-06-23T21:21:39.536778",
+  "model_path": "/public/share/yinxiangrong/qinxiaoyu/kdc2024/S/video_chat/VideoChat-Flash-Qwen2-7B_res448",
+  "max_num_frames": 512,
+  "generation_config": {
+    "do_sample": false,
+    "temperature": 0.0,
+    "max_new_tokens": 1024,
+    "top_p": 0.1,
+    "num_beams": 1
+  },
+  "qa_pairs": [
+    {
+      "question_id": 1,
+      "question": "请描述视频中的场景和事件",
+      "answer": "视频中显示了一群人围攻并殴打一个男性，导致他受伤倒地。旁观者则在旁边观看。事件发生后，旁观者逐渐散去，而施暴者则继续对受害者进行攻击。最后，施暴者也离开现场，留下受害者一人倒地。"
+    },
+    {
+      "question_id": 2,
+      "question": "视频中是否存在异常行为？",
+      "answer": "是的，视频中存在非法行为，即一群人围攻并殴打一个男性，导致他受伤倒地。"
+    },
+    {
+      "question_id": 3,
+      "question": "有多少人参与了可疑活动？",
+      "answer": "视频中有多达10名男性参与了围攻并殴打受害者的非法活动。"
+    },
+    {
+      "question_id": 4,
+      "question": "嫌疑人外貌是什么样子？",
+      "answer": "嫌疑人穿着深色衣服，但其他外貌特征没有详细描述。"
+    },
+    {
+      "question_id": 5,
+      "question": "嫌疑人是否携带任何物品或武器？",
+      "answer": "没有详细描述嫌疑人是否携带任何物品或武器。"
+    },
+    {
+      "question_id": 6,
+      "question": "请描述嫌疑人行为的步骤",
+      "answer": "嫌疑人首先围攻并殴打受害者，导致他受伤倒地。然后旁观者逐渐散去，而施暴者则继续对受害者进行攻击。最后，施暴者也离开现场，留下受害者一人倒地。"
+    },
+    {
+      "question_id": 7,
+      "question": "嫌疑人出现在什么时候？",
+      "answer": "嫌疑人首先在2013-05-19 06:26:03左右出现。"
+    },
+    {
+      "question_id": 9,
+      "question": "这是集体行动还是个人行为？",
+      "answer": "这是集体行动，因为有多达10名男性参与了围攻并殴打受害者的非法活动。"
+    }
+  ]
+}

requirements.txt

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+gradio
+numpy<2.0
+plotly

vggt/Assault002_x264.mp4

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+size 3899165

vggt/Assault003_x264.mp4

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+size 8000515

vggt/Assault004_x264.mp4

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+size 3888965

vggt/Assault005_x264.mp4

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+size 7023409

vggt/Assault006_x264.mp4

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+size 6910917

vggt/Assault007_x264.mp4

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+size 9645884

vggt/Assault008_x264.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d2fef1b2a40233e8d68589221378d563eb169a125412227df815b4990f6e452
+size 5426888

video/Assault001_x264.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0bc93b52a6975b6b95dde2bd068f289342929958bef5e0951bfb886394b449c6
+size 9759376

video/Assault002_x264.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:63cf01b45e9f825342c76dceba336943019062e307b5fdbc7a283f9e36d22b69
+size 12435565

video/Assault003_x264.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 17984773

video/Assault004_x264.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 17181608

video/Assault005_x264.mp4

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+size 4839833

video/Assault006_x264.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 32982673

video/Assault007_x264.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 8220098

video/Assault008_x264.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 30576980

vis.py

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+import viser
+import open3d as o3d
+import numpy as np
+import sys
+import time
+
+if len(sys.argv) < 2:
+    print("Usage: python vis.py <path_to_pcd_file>")
+    sys.exit(1)
+
+pcd_file = sys.argv[1]
+pcd = o3d.io.read_point_cloud(pcd_file)
+points = np.asarray(pcd.points)
+colors = np.asarray(pcd.colors).astype(float) if pcd.has_colors() else None
+
+# 启动 Viser 服务器
+server = viser.ViserServer(port=8088)  # 可自定义端口
+server.scene.background_color = (0.1, 0.1, 0.1)
+# 添加点云
+server.add_point_cloud(
+    name="/pointcloud",
+    points=points,
+    colors=colors,
+    point_size=0.003,
+)
+
+# Keep the script running
+while True:
+    time.sleep(0.1)

yolo/Assault002_x264.mp4

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+size 8419118

yolo/Assault003_x264.mp4

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+oid sha256:40247a6997fa429b13ab5fad8480f02679e88f524dfaa127eae781032cd812fe
+size 9052657

yolo/Assault004_x264.mp4

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+size 10190470

yolo/Assault005_x264.mp4

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