Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,10 @@ 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
+examples/cig.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/clip-cig.gif filter=lfs diff=lfs merge=lfs -text
+examples/clip-conflag.gif filter=lfs diff=lfs merge=lfs -text
+examples/clip-gu.gif filter=lfs diff=lfs merge=lfs -text
+examples/conf.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/gun.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/homealone.mp4 filter=lfs diff=lfs merge=lfs -text

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

.gitignore

@@ -0,0 +1,52 @@
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+*.dll
+
+# Virtual Environment
+venv/
+env/
+ENV/
+.venv/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Project specific
+inputs/*
+outputs/*
+!inputs/.gitkeep
+!outputs/.gitkeep
+inputs/
+outputs/
+
+# Model files
+*.pth
+*.onnx
+*.pt
+
+# Logs
+*.log
+
+certificate.pem

README.md

@@ -1,12 +1,165 @@
 ---
-title: Promptable Content Moderation
-emoji: 🏃
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 5.16.2
+title: promptable-content-moderation
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.16.1
 ---
+# Promptable Content Moderation with Moondream
+
+Welcome to the future of content moderation with Moondream 2B, a powerful and lightweight vision-language model that enables detection and moderation of video content using natural language prompts.
+
+[Try it now.](https://huggingface.co/spaces/moondream/content-moderation)
+
+## Features
+
+- Content moderation through natural language prompts
+- Multiple visualization styles
+- Intelligent scene detection and tracking:
+  - DeepSORT tracking with scene-aware reset
+  - Persistent moderation across frames
+  - Smart tracker reset at scene boundaries
+- Optional grid-based detection for improved accuracy on complex scenes
+- Frame-by-frame processing with IoU-based merging
+- Web-compatible output format
+- Test mode (process only first X seconds)
+- Advanced moderation analysis with multiple visualization plots
+
+## Examples
+
+| Prompt | Output |
+|------------------|----------------------|
+| "white cigarette"   | ![Demo](./examples/clip-cig.gif)     |
+| "gun"         | ![Demo](./examples/clip-gu.gif)      |
+| "confederate flag" | ![Demo](./examples/clip-conflag.gif) |
+
+## Requirements
+
+### Python Dependencies
+
+For Windows users, before installing other requirements, first install PyTorch with CUDA support:
+
+```bash
+pip install torch==2.5.1+cu121 torchvision==0.20.1+cu121 --index-url https://download.pytorch.org/whl/cu121
+```
+
+Then install the remaining dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+### System Requirements
+
+- FFmpeg (required for video processing)
+- libvips (required for image processing)
+
+Installation by platform:
+
+- Ubuntu/Debian: `sudo apt-get install ffmpeg libvips`
+- macOS: `brew install ffmpeg libvips`
+- Windows:
+  - Download FFmpeg from [ffmpeg.org](https://ffmpeg.org/download.html)
+  - Follow [libvips Windows installation guide](https://docs.moondream.ai/quick-start)
+
+## Installation
+
+1. Clone this repository and create a new virtual environment:
+
+```bash
+git clone https://github.com/vikhyat/moondream/blob/main/recipes/promptable-video-redaction
+python -m venv .venv
+source .venv/bin/activate  # On Windows: .venv\Scripts\activate
+```
+
+2. Install Python dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+3. Install ffmpeg and libvips:
+   - On Ubuntu/Debian: `sudo apt-get install ffmpeg libvips`
+   - On macOS: `brew install ffmpeg`
+   - On Windows: Download from [ffmpeg.org](https://ffmpeg.org/download.html)
+
+> Downloading libvips for Windows requires some additional steps, see [here](https://docs.moondream.ai/quick-start)
+
+## Usage
+
+The easiest way to use this tool is through its web interface, which provides a user-friendly experience for video content moderation.
+
+### Web Interface
+
+1. Start the web interface:
+
+```bash
+python app.py
+```
+
+2. Open the provided URL in your browser (typically <http://localhost:7860>)
+
+3. Use the interface to:
+   - Upload your video file
+   - Specify content to moderate (e.g., "face", "cigarette", "gun")
+   - Choose redaction style (default: obfuscated-pixel)
+   - OPTIONAL: Configure advanced settings
+     - Processing speed/quality
+     - Grid size for detection
+     - Test mode for quick validation (default: on, 3 seconds)
+   - Process the video and download results
+   - Analyze detection patterns with visualization tools
+
+## Output Files
+
+The tool generates two types of output files in the `outputs` directory:
+
+1. Processed Videos:
+   - Format: `[style]_[content_type]_[original_filename].mp4`
+   - Example: `censor_inappropriate_video.mp4`
+
+2. Detection Data:
+   - Format: `[style]_[content_type]_[original_filename]_detections.json`
+   - Contains frame-by-frame detection information
+   - Used for visualization and analysis
+
+## Technical Details
+
+### Scene Detection and Tracking
+
+The tool uses advanced scene detection and object tracking:
+
+1. Scene Detection:
+   - Powered by PySceneDetect's ContentDetector
+   - Automatically identifies scene changes in videos
+   - Configurable detection threshold (default: 30.0)
+   - Helps maintain tracking accuracy across scene boundaries
+
+2. Object Tracking:
+   - DeepSORT tracking for consistent object identification
+   - Automatic tracker reset at scene changes
+   - Maintains object identity within scenes
+   - Prevents tracking errors across scene boundaries
+
+3. Integration Benefits:
+   - More accurate object tracking
+   - Better handling of scene transitions
+   - Reduced false positives in tracking
+   - Improved tracking consistency
+
+## Best Practices
+
+- Use test mode for initial configuration
+- Enable grid-based detection for complex scenes
+- Choose appropriate redaction style based on content type:
+  - Censor: Complete content blocking
+  - Blur styles: Less intrusive moderation
+  - Bounding Box: Content review and analysis
+- Monitor system resources during processing
+- Use appropriate processing quality settings based on your needs
+
+## Notes
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+- Processing time depends on video length, resolution, GPU availability, and chosen settings
+- GPU is strongly recommended for faster processing
+- Grid-based detection increases accuracy but requires more processing time (each grid cell is processed independently)
+- Test mode processes only first X seconds (default: 3 seconds) for quick validation

app.py

@@ -0,0 +1,533 @@
+#!/usr/bin/env python3
+# import spaces first
+import spaces
+import gradio as gr
+import os
+from main import load_moondream, process_video, load_sam_model
+import shutil
+import torch
+from visualization import visualize_detections
+from persistence import load_detection_data
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+import pandas as pd
+from video_visualization import create_video_visualization
+
+# Get absolute path to workspace root
+WORKSPACE_ROOT = os.path.dirname(os.path.abspath(__file__))
+
+# Check CUDA availability
+print(f"Is CUDA available: {torch.cuda.is_available()}")
+# We want to get True
+print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
+# GPU Name
+
+# Initialize Moondream model globally for reuse (will be loaded on first use)
+model, tokenizer = None, None
+
+# Uncomment for Hugging Face Spaces
+@spaces.GPU(duration=120)
+def process_video_file(
+    video_file, target_object, box_style, ffmpeg_preset, grid_rows, grid_cols, test_mode, test_duration
+):
+    """Process a video file through the Gradio interface."""
+    try:
+        if not video_file:
+            raise gr.Error("Please upload a video file")
+
+        # Load models if not already loaded
+        global model, tokenizer
+        if model is None or tokenizer is None:
+            model, tokenizer = load_moondream()
+
+        # Ensure input/output directories exist using absolute paths
+        inputs_dir = os.path.join(WORKSPACE_ROOT, "inputs")
+        outputs_dir = os.path.join(WORKSPACE_ROOT, "outputs")
+        os.makedirs(inputs_dir, exist_ok=True)
+        os.makedirs(outputs_dir, exist_ok=True)
+
+        # Copy uploaded video to inputs directory
+        video_filename = f"input_{os.path.basename(video_file)}"
+        input_video_path = os.path.join(inputs_dir, video_filename)
+        shutil.copy2(video_file, input_video_path)
+
+        try:
+            # Process the video
+            output_path = process_video(
+                input_video_path,
+                target_object,
+                test_mode=test_mode,
+                test_duration=test_duration,
+                ffmpeg_preset=ffmpeg_preset,
+                grid_rows=grid_rows,
+                grid_cols=grid_cols,
+                box_style=box_style,
+            )
+
+            # Get the corresponding JSON path
+            base_name = os.path.splitext(os.path.basename(video_filename))[0]
+            json_path = os.path.join(outputs_dir, f"{box_style}_{target_object}_{base_name}_detections.json")
+
+            # Verify output exists and is readable
+            if not output_path or not os.path.exists(output_path):
+                print(f"Warning: Output path {output_path} does not exist")
+                # Try to find the output based on expected naming convention
+                expected_output = os.path.join(
+                    outputs_dir, f"{box_style}_{target_object}_{video_filename}"
+                )
+                if os.path.exists(expected_output):
+                    output_path = expected_output
+                else:
+                    # Try searching in outputs directory for any matching file
+                    matching_files = [
+                        f
+                        for f in os.listdir(outputs_dir)
+                        if f.startswith(f"{box_style}_{target_object}_")
+                    ]
+                    if matching_files:
+                        output_path = os.path.join(outputs_dir, matching_files[0])
+                    else:
+                        raise gr.Error("Failed to locate output video")
+
+            # Convert output path to absolute path if it isn't already
+            if not os.path.isabs(output_path):
+                output_path = os.path.join(WORKSPACE_ROOT, output_path)
+
+            print(f"Returning output path: {output_path}")
+            return output_path, json_path
+
+        finally:
+            # Clean up input file
+            try:
+                if os.path.exists(input_video_path):
+                    os.remove(input_video_path)
+            except:
+                pass
+
+    except Exception as e:
+        print(f"Error in process_video_file: {str(e)}")
+        raise gr.Error(f"Error processing video: {str(e)}")
+
+def create_visualization_plots(json_path):
+    """Create visualization plots and return them as images."""
+    try:
+        # Load the data
+        data = load_detection_data(json_path)
+        if not data:
+            return None, None, None, None, None, None, None, None, "No data found"
+
+        # Convert to DataFrame
+        rows = []
+        for frame_data in data["frame_detections"]:
+            frame = frame_data["frame"]
+            timestamp = frame_data["timestamp"]
+            for obj in frame_data["objects"]:
+                rows.append({
+                    "frame": frame,
+                    "timestamp": timestamp,
+                    "keyword": obj["keyword"],
+                    "x1": obj["bbox"][0],
+                    "y1": obj["bbox"][1],
+                    "x2": obj["bbox"][2],
+                    "y2": obj["bbox"][3],
+                    "area": (obj["bbox"][2] - obj["bbox"][0]) * (obj["bbox"][3] - obj["bbox"][1]),
+                    "center_x": (obj["bbox"][0] + obj["bbox"][2]) / 2,
+                    "center_y": (obj["bbox"][1] + obj["bbox"][3]) / 2
+                })
+
+        if not rows:
+            return None, None, None, None, None, None, None, None, "No detections found in the data"
+
+        df = pd.DataFrame(rows)
+        plots = []
+
+        # Create each plot and convert to image
+        for plot_num in range(8):  # Increased to 8 plots
+            plt.figure(figsize=(8, 6))
+            
+            if plot_num == 0:
+                # Plot 1: Number of detections per frame (Original)
+                detections_per_frame = df.groupby("frame").size()
+                plt.plot(detections_per_frame.index, detections_per_frame.values)
+                plt.xlabel("Frame")
+                plt.ylabel("Number of Detections")
+                plt.title("Detections Per Frame")
+            
+            elif plot_num == 1:
+                # Plot 2: Distribution of detection areas (Original)
+                df["area"].hist(bins=30)
+                plt.xlabel("Detection Area (normalized)")
+                plt.ylabel("Count")
+                plt.title("Distribution of Detection Areas")
+            
+            elif plot_num == 2:
+                # Plot 3: Average detection area over time (Original)
+                avg_area = df.groupby("frame")["area"].mean()
+                plt.plot(avg_area.index, avg_area.values)
+                plt.xlabel("Frame")
+                plt.ylabel("Average Detection Area")
+                plt.title("Average Detection Area Over Time")
+            
+            elif plot_num == 3:
+                # Plot 4: Heatmap of detection centers (Original)
+                plt.hist2d(df["center_x"], df["center_y"], bins=30)
+                plt.colorbar()
+                plt.xlabel("X Position")
+                plt.ylabel("Y Position")
+                plt.title("Detection Center Heatmap")
+
+            elif plot_num == 4:
+                # Plot 5: Time-based Detection Density
+                # Shows when in the video most detections occur
+                df["time_bucket"] = pd.qcut(df["timestamp"], q=20, labels=False)
+                time_density = df.groupby("time_bucket").size()
+                plt.bar(time_density.index, time_density.values)
+                plt.xlabel("Video Timeline (20 segments)")
+                plt.ylabel("Number of Detections")
+                plt.title("Detection Density Over Video Duration")
+
+            elif plot_num == 5:
+                # Plot 6: Screen Region Analysis
+                # Divide screen into 3x3 grid and show detection counts
+                try:
+                    df["grid_x"] = pd.qcut(df["center_x"], q=3, labels=["Left", "Center", "Right"], duplicates='drop')
+                    df["grid_y"] = pd.qcut(df["center_y"], q=3, labels=["Top", "Middle", "Bottom"], duplicates='drop')
+                    region_counts = df.groupby(["grid_y", "grid_x"]).size().unstack(fill_value=0)
+                    plt.imshow(region_counts, cmap="YlOrRd")
+                    plt.colorbar(label="Detection Count")
+                    for i in range(3):
+                        for j in range(3):
+                            plt.text(j, i, region_counts.iloc[i, j], ha="center", va="center")
+                    plt.xticks(range(3), ["Left", "Center", "Right"])
+                    plt.yticks(range(3), ["Top", "Middle", "Bottom"])
+                    plt.title("Screen Region Analysis")
+                except Exception as e:
+                    plt.text(0.5, 0.5, "Insufficient variation in detection positions", 
+                            ha='center', va='center')
+                    plt.title("Screen Region Analysis (Not Available)")
+
+            elif plot_num == 6:
+                # Plot 7: Detection Size Categories
+                # Categorize detections by size for content moderation
+                try:
+                    size_labels = [
+                        "Small (likely far/background)",
+                        "Medium-small",
+                        "Medium-large",
+                        "Large (likely foreground/close)"
+                    ]
+                    
+                    # Handle cases with limited unique values
+                    unique_areas = df["area"].nunique()
+                    if unique_areas >= 4:
+                        df["size_category"] = pd.qcut(df["area"], q=4, labels=size_labels, duplicates='drop')
+                    else:
+                        # Alternative binning for limited unique values
+                        df["size_category"] = pd.cut(df["area"], 
+                                                   bins=unique_areas, 
+                                                   labels=size_labels[:unique_areas])
+                    
+                    size_dist = df["size_category"].value_counts()
+                    plt.pie(size_dist.values, labels=size_dist.index, autopct="%1.1f%%")
+                    plt.title("Detection Size Distribution")
+                except Exception as e:
+                    plt.text(0.5, 0.5, "Insufficient variation in detection sizes", 
+                            ha='center', va='center')
+                    plt.title("Detection Size Distribution (Not Available)")
+
+            elif plot_num == 7:
+                # Plot 8: Temporal Pattern Analysis
+                # Show patterns of when detections occur in sequence
+                try:
+                    detection_gaps = df.sort_values("frame")["frame"].diff()
+                    if len(detection_gaps.dropna().unique()) > 1:
+                        plt.hist(detection_gaps.dropna(), bins=min(30, len(detection_gaps.dropna().unique())), 
+                               edgecolor="black")
+                        plt.xlabel("Frames Between Detections")
+                        plt.ylabel("Frequency")
+                        plt.title("Detection Temporal Pattern Analysis")
+                    else:
+                        plt.text(0.5, 0.5, "Uniform detection intervals", ha='center', va='center')
+                        plt.title("Temporal Pattern Analysis (Uniform)")
+                except Exception as e:
+                    plt.text(0.5, 0.5, "Insufficient temporal data", ha='center', va='center')
+                    plt.title("Temporal Pattern Analysis (Not Available)")
+
+            # Save plot to bytes
+            buf = io.BytesIO()
+            plt.savefig(buf, format='png', bbox_inches='tight')
+            buf.seek(0)
+            plots.append(Image.open(buf))
+            plt.close()
+
+        # Enhanced summary text
+        summary = f"""Summary Statistics:
+Total frames analyzed: {len(data['frame_detections'])}
+Total detections: {len(df)}
+Average detections per frame: {len(df) / len(data['frame_detections']):.2f}
+
+Detection Patterns:
+- Peak detection count: {df.groupby('frame').size().max()} (in a single frame)
+- Most common screen region: {df.groupby(['grid_y', 'grid_x']).size().idxmax()}
+- Average detection size: {df['area'].mean():.3f}
+- Median frames between detections: {detection_gaps.median():.1f}
+
+Video metadata:
+"""
+        for key, value in data["video_metadata"].items():
+            summary += f"{key}: {value}\n"
+
+        return plots[0], plots[1], plots[2], plots[3], plots[4], plots[5], plots[6], plots[7], summary
+
+    except Exception as e:
+        print(f"Error creating visualization: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return None, None, None, None, None, None, None, None, f"Error creating visualization: {str(e)}"
+
+# Create the Gradio interface
+with gr.Blocks(title="Promptable Content Moderation") as app:
+    with gr.Tabs():
+        with gr.Tab("Process Video"):
+            gr.Markdown("# Promptable Content Moderation with Moondream")
+            gr.Markdown(
+                """
+            Powered by [Moondream 2B](https://github.com/vikhyat/moondream).
+
+            Upload a video and specify what to moderate. The app will process each frame and moderate any visual content that matches the prompt. For help, join the [Moondream Discord](https://discord.com/invite/tRUdpjDQfH).
+            """
+            )
+
+            with gr.Row():
+                with gr.Column():
+                    # Input components
+                    video_input = gr.Video(label="Upload Video")
+
+                    detect_input = gr.Textbox(
+                        label="What to Moderate",
+                        placeholder="e.g. face, cigarette, gun, etc.",
+                        value="face",
+                        info="Moondream can moderate anything that you can describe in natural language",
+                    )
+
+                    gr.Examples(
+                        examples=[
+                            ["examples/cig.mp4", "cigarette"],
+                            ["examples/gun.mp4", "gun"],
+                            ["examples/homealone.mp4", "face"],
+                            ["examples/conf.mp4", "confederate flag"],
+                        ],
+                        inputs=[video_input, detect_input],
+                        label="Try these examples",
+                    )
+
+                    process_btn = gr.Button("Process Video", variant="primary")
+
+                    with gr.Accordion("Advanced Settings", open=False):
+                        box_style_input = gr.Radio(
+                            choices=["censor", "bounding-box", "hitmarker", "sam", "sam-fast", "fuzzy-blur", "pixelated-blur", "intense-pixelated-blur", "obfuscated-pixel"],
+                            value="obfuscated-pixel",
+                            label="Visualization Style",
+                            info="Choose how to display moderations: censor (black boxes), bounding-box (red boxes with labels), hitmarker (COD-style markers), sam (precise segmentation), sam-fast (faster but less precise segmentation), fuzzy-blur (Gaussian blur), pixelated-blur (pixelated with blur), obfuscated-pixel (advanced pixelation with neighborhood averaging)",
+                        )
+                        preset_input = gr.Dropdown(
+                            choices=[
+                                "ultrafast",
+                                "superfast",
+                                "veryfast",
+                                "faster",
+                                "fast",
+                                "medium",
+                                "slow",
+                                "slower",
+                                "veryslow",
+                            ],
+                            value="medium",
+                            label="Processing Speed (faster = lower quality)",
+                        )
+                        with gr.Row():
+                            rows_input = gr.Slider(
+                                minimum=1, maximum=4, value=1, step=1, label="Grid Rows"
+                            )
+                            cols_input = gr.Slider(
+                                minimum=1, maximum=4, value=1, step=1, label="Grid Columns"
+                            )
+  
+                        test_mode_input = gr.Checkbox(
+                            label="Test Mode (Process first 3 seconds only)",
+                            value=True,
+                            info="Enable to quickly test settings on a short clip before processing the full video (recommended). If using the data visualizations, disable.",
+                        )
+
+                        test_duration_input = gr.Slider(
+                            minimum=1,
+                            maximum=10,
+                            value=3,
+                            step=1,
+                            label="Test Mode Duration (seconds)",
+                            info="Number of seconds to process in test mode"
+                        )
+
+                        gr.Markdown(
+                            """
+                        Note: Processing in test mode will only process the first 3 seconds of the video and is recommended for testing settings.
+                        """
+                        )
+
+                        gr.Markdown(
+                            """
+                        We can get a rough estimate of how long the video will take to process by multiplying the videos framerate * seconds * the number of rows and columns and assuming 0.12 seconds processing time per detection.
+                        For example, a 3 second video at 30fps with 2x2 grid, the estimated time is 3 * 30 * 2 * 2 * 0.12 = 43.2 seconds (tested on a 4090 GPU).
+                        
+                        Note: Using the SAM visualization style will increase processing time significantly as it performs additional segmentation for each detection. The sam-fast option uses a smaller model for faster processing at the cost of some accuracy.
+                        """
+                        )
+
+                with gr.Column():
+                    # Output components
+                    video_output = gr.Video(label="Processed Video")
+                    json_output = gr.Text(label="Detection Data Path", visible=False)
+
+                    # About section under the video output
+                    gr.Markdown(
+                        """
+                    ### Links:
+                    - [GitHub Repository](https://github.com/vikhyat/moondream)
+                    - [Hugging Face](https://huggingface.co/vikhyatk/moondream2)
+                    - [Quick Start](https://docs.moondream.ai/quick-start)
+                    - [Moondream Recipes](https://docs.moondream.ai/recipes)
+                    """
+                    )
+
+        with gr.Tab("Analyze Results"):
+            gr.Markdown("# Detection Analysis")
+            gr.Markdown(
+                """
+            Analyze the detection results from processed videos. The analysis includes:
+            - Basic detection statistics and patterns
+            - Temporal and spatial distribution analysis
+            - Size-based categorization
+            - Screen region analysis
+            - Detection density patterns
+            """
+            )
+            
+            with gr.Row():
+                json_input = gr.File(
+                    label="Upload Detection Data (JSON)",
+                    file_types=[".json"],
+                )
+                analyze_btn = gr.Button("Analyze", variant="primary")
+
+            with gr.Row():
+                with gr.Column():
+                    plot1 = gr.Image(
+                        label="Detections Per Frame",
+                    )
+                    plot2 = gr.Image(
+                        label="Detection Areas Distribution",
+                    )
+                    plot5 = gr.Image(
+                        label="Detection Density Timeline",
+                    )
+                    plot6 = gr.Image(
+                        label="Screen Region Analysis",
+                    )
+                
+                with gr.Column():
+                    plot3 = gr.Image(
+                        label="Average Detection Area Over Time",
+                    )
+                    plot4 = gr.Image(
+                        label="Detection Center Heatmap",
+                    )
+                    plot7 = gr.Image(
+                        label="Detection Size Categories",
+                    )
+                    plot8 = gr.Image(
+                        label="Temporal Pattern Analysis",
+                    )
+            
+            stats_output = gr.Textbox(
+                label="Statistics",
+                info="Summary of key metrics and patterns found in the detection data.",
+                lines=12,
+                max_lines=15,
+                interactive=False
+            )
+
+        # with gr.Tab("Video Visualizations"):
+        #     gr.Markdown("# Real-time Detection Visualization")
+        #     gr.Markdown(
+        #         """
+        #     Watch the detection patterns unfold in real-time. Choose from:
+        #     - Timeline: Shows number of detections over time
+        #     - Gauge: Simple yes/no indicator for current frame detections
+        #     """
+        #     )
+            
+        #     with gr.Row():
+        #         json_input_realtime = gr.File(
+        #             label="Upload Detection Data (JSON)",
+        #             file_types=[".json"],
+        #         )
+        #         viz_style = gr.Radio(
+        #             choices=["timeline", "gauge"],
+        #             value="timeline",
+        #             label="Visualization Style",
+        #             info="Choose between timeline view or simple gauge indicator"
+        #         )
+        #         visualize_btn = gr.Button("Visualize", variant="primary")
+
+        #     with gr.Row():
+        #         video_visualization = gr.Video(
+        #             label="Detection Visualization",
+        #             interactive=False
+        #         )
+        #         stats_realtime = gr.Textbox(
+        #             label="Video Statistics",
+        #             lines=6,
+        #             max_lines=8,
+        #             interactive=False
+        #         )
+
+    # Event handlers
+    process_outputs = process_btn.click(
+        fn=process_video_file,
+        inputs=[
+            video_input,
+            detect_input,
+            box_style_input,
+            preset_input,
+            rows_input,
+            cols_input,
+            test_mode_input,
+            test_duration_input,
+        ],
+        outputs=[video_output, json_output],
+    )
+
+    # Auto-analyze after processing
+    process_outputs.then(
+        fn=create_visualization_plots,
+        inputs=[json_output],
+        outputs=[plot1, plot2, plot3, plot4, plot5, plot6, plot7, plot8, stats_output],
+    )
+
+    # Manual analysis button
+    analyze_btn.click(
+        fn=create_visualization_plots,
+        inputs=[json_input],
+        outputs=[plot1, plot2, plot3, plot4, plot5, plot6, plot7, plot8, stats_output],
+    )
+
+    # Video visualization button
+    # visualize_btn.click(
+    #     fn=lambda json_file, style: create_video_visualization(json_file.name if json_file else None, style),
+    #     inputs=[json_input_realtime, viz_style],
+    #     outputs=[video_visualization, stats_realtime],
+    # )
+
+if __name__ == "__main__":
+    app.launch(share=True)

deep_sort_integration.py

@@ -0,0 +1,73 @@
+import numpy as np
+import torch
+from deep_sort_realtime.deepsort_tracker import DeepSort
+from datetime import datetime
+
+class DeepSORTTracker:
+    def __init__(self, max_age=5):
+        """Initialize DeepSORT tracker."""
+        self.max_age = max_age
+        self.tracker = self._create_tracker()
+        
+    def _create_tracker(self):
+        """Create a new instance of DeepSort tracker."""
+        return DeepSort(
+            max_age=self.max_age,
+            embedder='mobilenet',  # Using default MobileNetV2 embedder
+            today=datetime.now().date()  # For track naming and daily ID reset
+        )
+        
+    def reset(self):
+        """Reset the tracker state by creating a new instance."""
+        print("Resetting DeepSORT tracker...")
+        self.tracker = self._create_tracker()
+        
+    def update(self, frame, detections):
+        """Update tracking with new detections.
+        
+        Args:
+            frame: Current video frame (numpy array)
+            detections: List of (box, keyword) tuples where box is [x1, y1, x2, y2] normalized
+            
+        Returns:
+            List of (box, keyword, track_id) tuples
+        """
+        if not detections:
+            return []
+            
+        height, width = frame.shape[:2]
+        
+        # Convert normalized coordinates to absolute and format detections
+        detection_list = []
+        for box, keyword in detections:
+            x1 = int(box[0] * width)
+            y1 = int(box[1] * height)
+            x2 = int(box[2] * width)
+            y2 = int(box[3] * height)
+            w = x2 - x1
+            h = y2 - y1
+            
+            # Format: ([left,top,w,h], confidence, detection_class)
+            detection_list.append(([x1, y1, w, h], 1.0, keyword))
+            
+        # Update tracker
+        tracks = self.tracker.update_tracks(detection_list, frame=frame)
+        
+        # Convert back to normalized coordinates with track IDs
+        tracked_objects = []
+        for track in tracks:
+            if not track.is_confirmed():
+                continue
+                
+            ltrb = track.to_ltrb()  # Get [left,top,right,bottom] format
+            x1, y1, x2, y2 = ltrb
+            
+            # Normalize coordinates
+            x1 = max(0.0, min(1.0, x1 / width))
+            y1 = max(0.0, min(1.0, y1 / height))
+            x2 = max(0.0, min(1.0, x2 / width))
+            y2 = max(0.0, min(1.0, y2 / height))
+            
+            tracked_objects.append(([x1, y1, x2, y2], track.det_class, track.track_id))
+            
+        return tracked_objects 

examples/cig.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4a8bed3b243fca48b8d02ba86be87045d084d9ee8fd739a124b7c16a0e2200c
+size 9771113

examples/conf.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad4ffb858565c3ed6d6a955c3ba802c76f5d9bb95a0054c92c74747b694b253b
+size 20389258

examples/gun.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80168314bef9499a80c563980429e3f674a4450333f34d6315b188eb16f8f85b
+size 7369081

examples/homealone.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ec8d4410d2b3937f57b40d6084e4f4fd8538b766a69229feb1320891d3ee78e5
+size 11023032

main.py

@@ -0,0 +1,1236 @@
+#!/usr/bin/env python3
+import cv2, os, subprocess, argparse
+from PIL import Image
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, SamModel, SamProcessor
+from tqdm import tqdm
+import numpy as np
+from datetime import datetime
+from deep_sort_integration import DeepSORTTracker
+from scenedetect import detect, ContentDetector
+from functools import lru_cache
+
+# Constants
+DEFAULT_TEST_MODE_DURATION = 3  # Process only first 3 seconds in test mode by default
+FFMPEG_PRESETS = [
+    "ultrafast",
+    "superfast",
+    "veryfast",
+    "faster",
+    "fast",
+    "medium",
+    "slow",
+    "slower",
+    "veryslow",
+]
+FONT = cv2.FONT_HERSHEY_SIMPLEX  # Font for bounding-box-style labels
+
+# Detection parameters
+IOU_THRESHOLD = 0.5  # IoU threshold for considering boxes related
+
+# Hitmarker parameters
+HITMARKER_SIZE = 20  # Size of the hitmarker in pixels
+HITMARKER_GAP = 3  # Size of the empty space in the middle (reduced from 8)
+HITMARKER_THICKNESS = 2  # Thickness of hitmarker lines
+HITMARKER_COLOR = (255, 255, 255)  # White color for hitmarker
+HITMARKER_SHADOW_COLOR = (80, 80, 80)  # Lighter gray for shadow effect
+HITMARKER_SHADOW_OFFSET = 1  # Smaller shadow offset
+
+# SAM parameters
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Initialize model variables as None
+sam_model = None
+sam_processor = None
+slimsam_model = None
+slimsam_processor = None
+
+@lru_cache(maxsize=2)  # Cache both regular and slim SAM models
+def get_sam_model(slim=False):
+    """Get cached SAM model and processor."""
+    global sam_model, sam_processor, slimsam_model, slimsam_processor
+    
+    if slim:
+        if slimsam_model is None:
+            print("Loading SlimSAM model for the first time...")
+            slimsam_model = SamModel.from_pretrained("nielsr/slimsam-50-uniform").to(device)
+            slimsam_processor = SamProcessor.from_pretrained("nielsr/slimsam-50-uniform")
+        return slimsam_model, slimsam_processor
+    else:
+        if sam_model is None:
+            print("Loading SAM model for the first time...")
+            sam_model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
+            sam_processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
+        return sam_model, sam_processor
+
+def load_sam_model(slim=False):
+    """Load SAM model and processor with caching."""
+    return get_sam_model(slim=slim)
+
+def generate_color_pair():
+    """Generate a generic light blue and dark blue color pair for SAM visualization."""
+    dark_rgb = [0, 0, 139]  # Dark blue
+    light_rgb = [173, 216, 230]  # Light blue
+    return dark_rgb, light_rgb
+
+def create_mask_overlay(image, masks, points=None, labels=None):
+    """Create a mask overlay with contours for multiple SAM visualizations.
+    
+    Args:
+        image: PIL Image to overlay masks on
+        masks: List of binary masks or single mask
+        points: Optional list of (x,y) points for labels
+        labels: Optional list of label strings for each point
+    """
+    # Convert single mask to list for uniform processing
+    if not isinstance(masks, list):
+        masks = [masks]
+    
+    # Create empty overlays
+    overlay = np.zeros((*image.size[::-1], 4), dtype=np.uint8)
+    outline = np.zeros((*image.size[::-1], 4), dtype=np.uint8)
+    
+    # Process each mask
+    for i, mask in enumerate(masks):
+        # Convert binary mask to uint8
+        mask_uint8 = (mask > 0).astype(np.uint8)
+        
+        # Dilation to fill gaps
+        kernel = np.ones((5, 5), np.uint8)
+        mask_dilated = cv2.dilate(mask_uint8, kernel, iterations=1)
+        
+        # Find contours of the dilated mask
+        contours, _ = cv2.findContours(mask_dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        
+        # Generate random color pair for this segmentation
+        dark_color, light_color = generate_color_pair()
+        
+        # Add to the overlays
+        overlay[mask_dilated > 0] = [*light_color, 90]  # Light color with 35% opacity
+        cv2.drawContours(outline, contours, -1, (*dark_color, 255), 2)  # Dark color outline
+    
+    # Convert to PIL images
+    mask_overlay = Image.fromarray(overlay, 'RGBA')
+    outline_overlay = Image.fromarray(outline, 'RGBA')
+    
+    # Composite the layers
+    result = image.convert('RGBA')
+    result.paste(mask_overlay, (0, 0), mask_overlay)
+    result.paste(outline_overlay, (0, 0), outline_overlay)
+    
+    # Add labels if provided
+    if points and labels:
+        result_array = np.array(result)
+        for (x, y), label in zip(points, labels):
+            label_size = cv2.getTextSize(label, FONT, 0.5, 1)[0]
+            cv2.putText(
+                result_array,
+                label,
+                (int(x - label_size[0] // 2), int(y - 20)),
+                FONT,
+                0.5,
+                (255, 255, 255),
+                1,
+                cv2.LINE_AA,
+            )
+        result = Image.fromarray(result_array)
+    
+    return result
+
+def process_sam_detection(image, center_x, center_y, slim=False):
+    """Process a single detection point with SAM.
+    
+    Returns:
+        tuple: (mask, result_pil) where mask is the binary mask and result_pil is the visualization
+    """
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    
+    # Get appropriate model from cache
+    model, processor = get_sam_model(slim)
+    
+    # Process the image with SAM
+    inputs = processor(
+        image,
+        input_points=[[[center_x, center_y]]],
+        return_tensors="pt"
+    ).to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    mask = processor.post_process_masks(
+        outputs.pred_masks.cpu(),
+        inputs["original_sizes"].cpu(),
+        inputs["reshaped_input_sizes"].cpu()
+    )[0][0][0].numpy()
+    
+    # Create the visualization
+    result = create_mask_overlay(image, mask)
+    return mask, result
+
+def load_moondream():
+    """Load Moondream model and tokenizer."""
+    model = AutoModelForCausalLM.from_pretrained(
+        "vikhyatk/moondream2", trust_remote_code=True, device_map={"": "cuda"}
+    )
+    tokenizer = AutoTokenizer.from_pretrained("vikhyatk/moondream2")
+    return model, tokenizer
+
+
+def get_video_properties(video_path):
+    """Get basic video properties."""
+    video = cv2.VideoCapture(video_path)
+    fps = video.get(cv2.CAP_PROP_FPS)
+    frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
+    width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    video.release()
+    return {"fps": fps, "frame_count": frame_count, "width": width, "height": height}
+
+
+def is_valid_bounding_box(bounding_box):
+    """Check if bounding box coordinates are reasonable."""
+    x1, y1, x2, y2 = bounding_box
+    width = x2 - x1
+    height = y2 - y1
+
+    # Reject boxes that are too large (over 90% of frame in both dimensions)
+    if width > 0.9 and height > 0.9:
+        return False
+
+    # Reject boxes that are too small (less than 1% of frame)
+    if width < 0.01 or height < 0.01:
+        return False
+
+    return True
+
+
+def split_frame_into_grid(frame, grid_rows, grid_cols):
+    """Split a frame into a grid of tiles."""
+    height, width = frame.shape[:2]
+    tile_height = height // grid_rows
+    tile_width = width // grid_cols
+    tiles = []
+    tile_positions = []
+
+    for i in range(grid_rows):
+        for j in range(grid_cols):
+            y1 = i * tile_height
+            y2 = (i + 1) * tile_height if i < grid_rows - 1 else height
+            x1 = j * tile_width
+            x2 = (j + 1) * tile_width if j < grid_cols - 1 else width
+
+            tile = frame[y1:y2, x1:x2]
+            tiles.append(tile)
+            tile_positions.append((x1, y1, x2, y2))
+
+    return tiles, tile_positions
+
+
+def convert_tile_coords_to_frame(box, tile_pos, frame_shape):
+    """Convert coordinates from tile space to frame space."""
+    frame_height, frame_width = frame_shape[:2]
+    tile_x1, tile_y1, tile_x2, tile_y2 = tile_pos
+    tile_width = tile_x2 - tile_x1
+    tile_height = tile_y2 - tile_y1
+
+    x1_tile_abs = box[0] * tile_width
+    y1_tile_abs = box[1] * tile_height
+    x2_tile_abs = box[2] * tile_width
+    y2_tile_abs = box[3] * tile_height
+
+    x1_frame_abs = tile_x1 + x1_tile_abs
+    y1_frame_abs = tile_y1 + y1_tile_abs
+    x2_frame_abs = tile_x1 + x2_tile_abs
+    y2_frame_abs = tile_y1 + y2_tile_abs
+
+    x1_norm = x1_frame_abs / frame_width
+    y1_norm = y1_frame_abs / frame_height
+    x2_norm = x2_frame_abs / frame_width
+    y2_norm = y2_frame_abs / frame_height
+
+    x1_norm = max(0.0, min(1.0, x1_norm))
+    y1_norm = max(0.0, min(1.0, y1_norm))
+    x2_norm = max(0.0, min(1.0, x2_norm))
+    y2_norm = max(0.0, min(1.0, y2_norm))
+
+    return [x1_norm, y1_norm, x2_norm, y2_norm]
+
+
+def merge_tile_detections(tile_detections, iou_threshold=0.5):
+    """Merge detections from different tiles using NMS-like approach."""
+    if not tile_detections:
+        return []
+
+    all_boxes = []
+    all_keywords = []
+
+    # Collect all boxes and their keywords
+    for detections in tile_detections:
+        for box, keyword in detections:
+            all_boxes.append(box)
+            all_keywords.append(keyword)
+
+    if not all_boxes:
+        return []
+
+    # Convert to numpy for easier processing
+    boxes = np.array(all_boxes)
+
+    # Calculate areas
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    areas = (x2 - x1) * (y2 - y1)
+
+    # Sort boxes by area
+    order = areas.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        if order.size == 1:
+            break
+
+        # Calculate IoU with rest of boxes
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1)
+        h = np.maximum(0.0, yy2 - yy1)
+        inter = w * h
+
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        # Get indices of boxes with IoU less than threshold
+        inds = np.where(ovr <= iou_threshold)[0]
+        order = order[inds + 1]
+
+    return [(all_boxes[i], all_keywords[i]) for i in keep]
+
+
+def detect_objects_in_frame(model, tokenizer, image, target_object, grid_rows=1, grid_cols=1):
+    """Detect specified objects in a frame using grid-based analysis."""
+    if grid_rows == 1 and grid_cols == 1:
+        return detect_objects_in_frame_single(model, tokenizer, image, target_object)
+
+    # Convert numpy array to PIL Image if needed
+    if not isinstance(image, Image.Image):
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    # Split frame into tiles
+    tiles, tile_positions = split_frame_into_grid(image, grid_rows, grid_cols)
+
+    # Process each tile
+    tile_detections = []
+    for tile, tile_pos in zip(tiles, tile_positions):
+        # Convert tile to PIL Image
+        tile_pil = Image.fromarray(tile)
+
+        # Detect objects in tile
+        response = model.detect(tile_pil, target_object)
+
+        if response and "objects" in response and response["objects"]:
+            objects = response["objects"]
+            tile_objects = []
+
+            for obj in objects:
+                if all(k in obj for k in ["x_min", "y_min", "x_max", "y_max"]):
+                    box = [obj["x_min"], obj["y_min"], obj["x_max"], obj["y_max"]]
+
+                    if is_valid_bounding_box(box):
+                        # Convert tile coordinates to frame coordinates
+                        frame_box = convert_tile_coords_to_frame(
+                            box, tile_pos, image.shape
+                        )
+                        tile_objects.append((frame_box, target_object))
+
+            if tile_objects:  # Only append if we found valid objects
+                tile_detections.append(tile_objects)
+
+    # Merge detections from all tiles
+    merged_detections = merge_tile_detections(tile_detections)
+    return merged_detections
+
+
+def detect_objects_in_frame_single(model, tokenizer, image, target_object):
+    """Single-frame detection function."""
+    detected_objects = []
+
+    # Convert numpy array to PIL Image if needed
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+
+    # Detect objects
+    response = model.detect(image, target_object)
+
+    # Check if we have valid objects
+    if response and "objects" in response and response["objects"]:
+        objects = response["objects"]
+
+        for obj in objects:
+            if all(k in obj for k in ["x_min", "y_min", "x_max", "y_max"]):
+                box = [obj["x_min"], obj["y_min"], obj["x_max"], obj["y_max"]]
+                # If box is valid (not full-frame), add it
+                if is_valid_bounding_box(box):
+                    detected_objects.append((box, target_object))
+
+    return detected_objects
+
+
+def draw_hitmarker(
+    frame, center_x, center_y, size=HITMARKER_SIZE, color=HITMARKER_COLOR, shadow=True
+):
+    """Draw a COD-style hitmarker cross with more space in the middle."""
+    half_size = size // 2
+
+    # Draw shadow first if enabled
+    if shadow:
+        # Top-left to center shadow
+        cv2.line(
+            frame,
+            (
+                center_x - half_size + HITMARKER_SHADOW_OFFSET,
+                center_y - half_size + HITMARKER_SHADOW_OFFSET,
+            ),
+            (
+                center_x - HITMARKER_GAP + HITMARKER_SHADOW_OFFSET,
+                center_y - HITMARKER_GAP + HITMARKER_SHADOW_OFFSET,
+            ),
+            HITMARKER_SHADOW_COLOR,
+            HITMARKER_THICKNESS,
+        )
+        # Top-right to center shadow
+        cv2.line(
+            frame,
+            (
+                center_x + half_size + HITMARKER_SHADOW_OFFSET,
+                center_y - half_size + HITMARKER_SHADOW_OFFSET,
+            ),
+            (
+                center_x + HITMARKER_GAP + HITMARKER_SHADOW_OFFSET,
+                center_y - HITMARKER_GAP + HITMARKER_SHADOW_OFFSET,
+            ),
+            HITMARKER_SHADOW_COLOR,
+            HITMARKER_THICKNESS,
+        )
+        # Bottom-left to center shadow
+        cv2.line(
+            frame,
+            (
+                center_x - half_size + HITMARKER_SHADOW_OFFSET,
+                center_y + half_size + HITMARKER_SHADOW_OFFSET,
+            ),
+            (
+                center_x - HITMARKER_GAP + HITMARKER_SHADOW_OFFSET,
+                center_y + HITMARKER_GAP + HITMARKER_SHADOW_OFFSET,
+            ),
+            HITMARKER_SHADOW_COLOR,
+            HITMARKER_THICKNESS,
+        )
+        # Bottom-right to center shadow
+        cv2.line(
+            frame,
+            (
+                center_x + half_size + HITMARKER_SHADOW_OFFSET,
+                center_y + half_size + HITMARKER_SHADOW_OFFSET,
+            ),
+            (
+                center_x + HITMARKER_GAP + HITMARKER_SHADOW_OFFSET,
+                center_y + HITMARKER_GAP + HITMARKER_SHADOW_OFFSET,
+            ),
+            HITMARKER_SHADOW_COLOR,
+            HITMARKER_THICKNESS,
+        )
+
+    # Draw main hitmarker
+    # Top-left to center
+    cv2.line(
+        frame,
+        (center_x - half_size, center_y - half_size),
+        (center_x - HITMARKER_GAP, center_y - HITMARKER_GAP),
+        color,
+        HITMARKER_THICKNESS,
+    )
+    # Top-right to center
+    cv2.line(
+        frame,
+        (center_x + half_size, center_y - half_size),
+        (center_x + HITMARKER_GAP, center_y - HITMARKER_GAP),
+        color,
+        HITMARKER_THICKNESS,
+    )
+    # Bottom-left to center
+    cv2.line(
+        frame,
+        (center_x - half_size, center_y + half_size),
+        (center_x - HITMARKER_GAP, center_y + HITMARKER_GAP),
+        color,
+        HITMARKER_THICKNESS,
+    )
+    # Bottom-right to center
+    cv2.line(
+        frame,
+        (center_x + half_size, center_y + half_size),
+        (center_x + HITMARKER_GAP, center_y + HITMARKER_GAP),
+        color,
+        HITMARKER_THICKNESS,
+    )
+
+
+def draw_ad_boxes(frame, detected_objects, detect_keyword, model, box_style="censor"):
+    height, width = frame.shape[:2]
+
+    points = []
+    # Only get points if we need them for hitmarker or SAM styles
+    if box_style in ["hitmarker", "sam", "sam-fast"]:
+        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        try:
+            point_response = model.point(frame_pil, detect_keyword)
+            
+            if isinstance(point_response, dict) and 'points' in point_response:
+                points = point_response['points']
+        except Exception as e:
+            print(f"Error during point detection: {str(e)}")
+            points = []
+
+    # Only load SAM models and process points if we're using SAM styles and have points
+    if box_style in ["sam", "sam-fast"] and points:
+        # Start with the original PIL image
+        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        
+        # Collect all masks and points
+        all_masks = []
+        point_coords = []
+        point_labels = []
+        
+        for point in points:
+            try:
+                center_x = int(float(point["x"]) * width)
+                center_y = int(float(point["y"]) * height)
+
+                # Get mask and visualization
+                mask, _ = process_sam_detection(frame_pil, center_x, center_y, slim=(box_style == "sam-fast"))
+                
+                # Collect mask and point data
+                all_masks.append(mask)
+                point_coords.append((center_x, center_y))
+                point_labels.append(detect_keyword)
+                
+            except Exception as e:
+                print(f"Error processing individual SAM point: {str(e)}")
+                print(f"Point data: {point}")
+        
+        if all_masks:
+            # Create final visualization with all masks
+            result_pil = create_mask_overlay(frame_pil, all_masks, point_coords, point_labels)
+            frame = cv2.cvtColor(np.array(result_pil), cv2.COLOR_RGB2BGR)
+
+    # Process other visualization styles
+    for detection in detected_objects:
+        try:
+            # Handle both tracked and untracked detections
+            if len(detection) == 3:  # Tracked detection with ID
+                box, keyword, track_id = detection
+            else:  # Regular detection without tracking
+                box, keyword = detection
+                track_id = None
+
+            x1 = int(box[0] * width)
+            y1 = int(box[1] * height)
+            x2 = int(box[2] * width)
+            y2 = int(box[3] * height)
+
+            x1 = max(0, min(x1, width - 1))
+            y1 = max(0, min(y1, height - 1))
+            x2 = max(0, min(x2, width - 1))
+            y2 = max(0, min(y2, height - 1))
+
+            if x2 > x1 and y2 > y1:
+                if box_style == "censor":
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 0), -1)
+                elif box_style == "bounding-box":
+                    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 3)
+
+                    label = f"{detect_keyword}" if track_id is not None else detect_keyword
+                    label_size = cv2.getTextSize(label, FONT, 0.7, 2)[0]
+                    cv2.rectangle(
+                        frame, (x1, y1 - 25), (x1 + label_size[0], y1), (0, 0, 255), -1
+                    )
+                    cv2.putText(
+                        frame,
+                        label,
+                        (x1, y1 - 6),
+                        FONT,
+                        0.7,
+                        (255, 255, 255),
+                        2,
+                        cv2.LINE_AA,
+                    )
+                elif box_style == "fuzzy-blur":
+                    # Extract ROI
+                    roi = frame[y1:y2, x1:x2]
+                    # Apply Gaussian blur with much larger kernel for intense blur
+                    blurred_roi = cv2.GaussianBlur(roi, (125, 125), 0)
+                    # Replace original ROI with blurred version
+                    frame[y1:y2, x1:x2] = blurred_roi
+                elif box_style == "pixelated-blur":
+                    # Extract ROI
+                    roi = frame[y1:y2, x1:x2]
+                    # Pixelate by resizing down and up
+                    h, w = roi.shape[:2]
+                    temp = cv2.resize(roi, (10, 10), interpolation=cv2.INTER_LINEAR)
+                    pixelated = cv2.resize(temp, (w, h), interpolation=cv2.INTER_NEAREST)
+                    # Mix up the pixelated frame slightly by adding random noise
+                    noise = np.random.randint(0, 50, (h, w, 3), dtype=np.uint8)
+                    pixelated = cv2.add(pixelated, noise)
+                    # Apply stronger Gaussian blur to smooth edges
+                    blurred_pixelated = cv2.GaussianBlur(pixelated, (15, 15), 0)
+                    # Replace original ROI
+                    frame[y1:y2, x1:x2] = blurred_pixelated
+                elif box_style == "obfuscated-pixel":
+                    # Calculate expansion amount based on 10% of object dimensions
+                    box_width = x2 - x1
+                    box_height = y2 - y1
+                    expand_x = int(box_width * 0.10)
+                    expand_y = int(box_height * 0.10)
+                    
+                    # Expand the bounding box by 10% in all directions
+                    x1_expanded = max(0, x1 - expand_x)
+                    y1_expanded = max(0, y1 - expand_y)
+                    x2_expanded = min(width - 1, x2 + expand_x)
+                    y2_expanded = min(height - 1, y2 + expand_y)
+                    
+                    # Extract ROI with much larger padding for true background sampling
+                    padding = 100  # Much larger padding to get true background
+                    y1_pad = max(0, y1_expanded - padding)
+                    y2_pad = min(height, y2_expanded + padding)
+                    x1_pad = max(0, x1_expanded - padding)
+                    x2_pad = min(width, x2_expanded + padding)
+                    
+                    # Get the padded region including background
+                    padded_roi = frame[y1_pad:y2_pad, x1_pad:x2_pad]
+                    
+                    # Create mask that excludes a larger region around the detection
+                    h, w = y2_expanded - y1_expanded, x2_expanded - x1_expanded
+                    bg_mask = np.ones(padded_roi.shape[:2], dtype=bool)
+                    
+                    # Exclude a larger region around the detection from background sampling
+                    exclusion_padding = 50  # Area to exclude around detection
+                    exclude_y1 = padding - exclusion_padding
+                    exclude_y2 = padding + h + exclusion_padding
+                    exclude_x1 = padding - exclusion_padding
+                    exclude_x2 = padding + w + exclusion_padding
+                    
+                    # Make sure exclusion coordinates are valid
+                    exclude_y1 = max(0, exclude_y1)
+                    exclude_y2 = min(padded_roi.shape[0], exclude_y2)
+                    exclude_x1 = max(0, exclude_x1)
+                    exclude_x2 = min(padded_roi.shape[1], exclude_x2)
+                    
+                    # Mark the exclusion zone in the mask
+                    bg_mask[exclude_y1:exclude_y2, exclude_x1:exclude_x2] = False
+                    
+                    # If we have enough background pixels, calculate average color
+                    if np.any(bg_mask):
+                        bg_color = np.mean(padded_roi[bg_mask], axis=0).astype(np.uint8)
+                    else:
+                        # Fallback to edges if we couldn't get enough background
+                        edge_samples = np.concatenate([
+                            padded_roi[0],  # Top edge
+                            padded_roi[-1],  # Bottom edge
+                            padded_roi[:, 0],  # Left edge
+                            padded_roi[:, -1]  # Right edge
+                        ])
+                        bg_color = np.mean(edge_samples, axis=0).astype(np.uint8)
+                    
+                    # Create base pixelated version (of the expanded region)
+                    temp = cv2.resize(frame[y1_expanded:y2_expanded, x1_expanded:x2_expanded], 
+                                   (6, 6), interpolation=cv2.INTER_LINEAR)
+                    pixelated = cv2.resize(temp, (w, h), interpolation=cv2.INTER_NEAREST)
+                    
+                    # Blend heavily towards background color
+                    blend_factor = 0.9  # Much stronger blend with background
+                    blended = cv2.addWeighted(
+                        pixelated, 1 - blend_factor,
+                        np.full((h, w, 3), bg_color, dtype=np.uint8), blend_factor,
+                        0
+                    )
+                    
+                    # Replace original ROI with blended version (using expanded coordinates)
+                    frame[y1_expanded:y2_expanded, x1_expanded:x2_expanded] = blended
+                elif box_style == "intense-pixelated-blur":
+                    # Expand the bounding box by pixels in all directions
+                    x1_expanded = max(0, x1 - 15)
+                    y1_expanded = max(0, y1 - 15)
+                    x2_expanded = min(width - 1, x2 + 25)
+                    y2_expanded = min(height - 1, y2 + 25)
+
+                    # Extract ROI
+                    roi = frame[y1_expanded:y2_expanded, x1_expanded:x2_expanded]
+                    # Pixelate by resizing down and up
+                    h, w = roi.shape[:2]
+                    temp = cv2.resize(roi, (10, 10), interpolation=cv2.INTER_LINEAR)
+                    pixelated = cv2.resize(temp, (w, h), interpolation=cv2.INTER_NEAREST)
+                    # Mix up the pixelated frame slightly by adding random noise
+                    noise = np.random.randint(0, 50, (h, w, 3), dtype=np.uint8)
+                    pixelated = cv2.add(pixelated, noise)
+                    # Apply stronger Gaussian blur to smooth edges
+                    blurred_pixelated = cv2.GaussianBlur(pixelated, (15, 15), 0)
+                    # Replace original ROI
+                    frame[y1_expanded:y2_expanded, x1_expanded:x2_expanded] = blurred_pixelated
+                elif box_style == "hitmarker":
+                    if points:
+                        for point in points:
+                            try:
+                                print(f"Processing point: {point}")
+                                center_x = int(float(point["x"]) * width)
+                                center_y = int(float(point["y"]) * height)
+                                print(f"Converted coordinates: ({center_x}, {center_y})")
+
+                                draw_hitmarker(frame, center_x, center_y)
+
+                                label = f"{detect_keyword}" if track_id is not None else detect_keyword
+                                label_size = cv2.getTextSize(label, FONT, 0.5, 1)[0]
+                                cv2.putText(
+                                    frame,
+                                    label,
+                                    (center_x - label_size[0] // 2, center_y - HITMARKER_SIZE - 5),
+                                    FONT,
+                                    0.5,
+                                    HITMARKER_COLOR,
+                                    1,
+                                    cv2.LINE_AA,
+                                )
+                            except Exception as e:
+                                print(f"Error processing individual point: {str(e)}")
+                                print(f"Point data: {point}")
+
+        except Exception as e:
+            print(f"Error drawing {box_style} style box: {str(e)}")
+            print(f"Box data: {box}")
+            print(f"Keyword: {keyword}")
+
+    return frame
+
+
+def filter_temporal_outliers(detections_dict):
+    """Filter out extremely large detections that take up most of the frame.
+    Only keeps detections that are reasonable in size.
+
+    Args:
+        detections_dict: Dictionary of {frame_number: [(box, keyword, track_id), ...]}
+    """
+    filtered_detections = {}
+
+    for t, detections in detections_dict.items():
+        # Only keep detections that aren't too large
+        valid_detections = []
+        for detection in detections:
+            # Handle both tracked and untracked detections
+            if len(detection) == 3:  # Tracked detection with ID
+                box, keyword, track_id = detection
+            else:  # Regular detection without tracking
+                box, keyword = detection
+                track_id = None
+
+            # Calculate box size as percentage of frame
+            width = box[2] - box[0]
+            height = box[3] - box[1]
+            area = width * height
+
+            # If box is less than 90% of frame, keep it
+            if area < 0.9:
+                if track_id is not None:
+                    valid_detections.append((box, keyword, track_id))
+                else:
+                    valid_detections.append((box, keyword))
+
+        if valid_detections:
+            filtered_detections[t] = valid_detections
+
+    return filtered_detections
+
+
+def describe_frames(video_path, model, tokenizer, detect_keyword, test_mode=False, test_duration=DEFAULT_TEST_MODE_DURATION, grid_rows=1, grid_cols=1):
+    """Extract and detect objects in frames."""
+    props = get_video_properties(video_path)
+    fps = props["fps"]
+
+    # Initialize DeepSORT tracker
+    tracker = DeepSORTTracker()
+
+    # If in test mode, only process first N seconds
+    if test_mode:
+        frame_count = min(int(fps * test_duration), props["frame_count"])
+    else:
+        frame_count = props["frame_count"]
+
+    ad_detections = {}  # Store detection results by frame number
+
+    print("Extracting frames and detecting objects...")
+    video = cv2.VideoCapture(video_path)
+
+    # Detect scenes first
+    scenes = detect(video_path, scene_detector)
+    scene_changes = set(end.get_frames() for _, end in scenes)
+    print(f"Detected {len(scenes)} scenes")
+
+    frame_count_processed = 0
+    with tqdm(total=frame_count) as pbar:
+        while frame_count_processed < frame_count:
+            ret, frame = video.read()
+            if not ret:
+                break
+
+            # Check if current frame is a scene change
+            if frame_count_processed in scene_changes:
+                # Detect objects in the frame
+                detected_objects = detect_objects_in_frame(
+                    model, tokenizer, frame, detect_keyword, grid_rows=grid_rows, grid_cols=grid_cols
+                )
+
+            # Update tracker with current detections
+            tracked_objects = tracker.update(frame, detected_objects)
+
+            # Store results for every frame, even if empty
+            ad_detections[frame_count_processed] = tracked_objects
+
+            frame_count_processed += 1
+            pbar.update(1)
+
+    video.release()
+
+    if frame_count_processed == 0:
+        print("No frames could be read from video")
+        return {}
+
+    return ad_detections
+
+
+def create_detection_video(
+    video_path,
+    ad_detections,
+    detect_keyword,
+    model,
+    output_path=None,
+    ffmpeg_preset="medium",
+    test_mode=False,
+    test_duration=DEFAULT_TEST_MODE_DURATION,
+    box_style="censor",
+):
+    """Create video with detection boxes while preserving audio."""
+    if output_path is None:
+        # Create outputs directory if it doesn't exist
+        outputs_dir = os.path.join(
+            os.path.dirname(os.path.abspath(__file__)), "outputs"
+        )
+        os.makedirs(outputs_dir, exist_ok=True)
+
+        # Clean the detect_keyword for filename
+        safe_keyword = "".join(
+            x for x in detect_keyword if x.isalnum() or x in (" ", "_", "-")
+        )
+        safe_keyword = safe_keyword.replace(" ", "_")
+
+        # Create output filename
+        base_name = os.path.splitext(os.path.basename(video_path))[0]
+        output_path = os.path.join(
+            outputs_dir, f"{box_style}_{safe_keyword}_{base_name}.mp4"
+        )
+
+    print(f"Will save output to: {output_path}")
+
+    props = get_video_properties(video_path)
+    fps, width, height = props["fps"], props["width"], props["height"]
+
+    # If in test mode, only process first few seconds
+    if test_mode:
+        frame_count = min(int(fps * test_duration), props["frame_count"])
+        print(f"Test mode enabled: Processing first {test_duration} seconds ({frame_count} frames)")
+    else:
+        frame_count = props["frame_count"]
+        print("Full video mode: Processing entire video")
+
+    video = cv2.VideoCapture(video_path)
+
+    # Create temp output path by adding _temp before the extension
+    base, ext = os.path.splitext(output_path)
+    temp_output = f"{base}_temp{ext}"
+    temp_audio = f"{base}_audio.aac"  # Temporary audio file
+
+    out = cv2.VideoWriter(
+        temp_output, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)
+    )
+
+    print("Creating detection video...")
+    frame_count_processed = 0
+
+    with tqdm(total=frame_count) as pbar:
+        while frame_count_processed < frame_count:
+            ret, frame = video.read()
+            if not ret:
+                break
+
+            # Get detections for this exact frame
+            if frame_count_processed in ad_detections:
+                current_detections = ad_detections[frame_count_processed]
+                if current_detections:
+                    frame = draw_ad_boxes(
+                        frame, current_detections, detect_keyword, model, box_style=box_style
+                    )
+
+            out.write(frame)
+            frame_count_processed += 1
+            pbar.update(1)
+
+    video.release()
+    out.release()
+
+    # Extract audio from original video
+    try:
+        if test_mode:
+            # In test mode, extract only the required duration of audio
+            subprocess.run(
+                [
+                    "ffmpeg",
+                    "-y",
+                    "-i",
+                    video_path,
+                    "-t",
+                    str(test_duration),
+                    "-vn",  # No video
+                    "-acodec",
+                    "copy",
+                    temp_audio,
+                ],
+                check=True,
+            )
+        else:
+            subprocess.run(
+                [
+                    "ffmpeg",
+                    "-y",
+                    "-i",
+                    video_path,
+                    "-vn",  # No video
+                    "-acodec",
+                    "copy",
+                    temp_audio,
+                ],
+                check=True,
+            )
+    except subprocess.CalledProcessError as e:
+        print(f"Error extracting audio: {str(e)}")
+        if os.path.exists(temp_output):
+            os.remove(temp_output)
+        return None
+
+    # Merge processed video with original audio
+    try:
+        # Base FFmpeg command
+        ffmpeg_cmd = [
+            "ffmpeg",
+            "-y",
+            "-i",
+            temp_output,
+            "-i",
+            temp_audio,
+            "-c:v",
+            "libx264",
+            "-preset",
+            ffmpeg_preset,
+            "-crf",
+            "23",
+            "-c:a",
+            "aac",
+            "-b:a",
+            "192k",
+            "-movflags",
+            "+faststart",  # Better web playback
+        ]
+
+        if test_mode:
+            # In test mode, ensure output duration matches test_duration
+            ffmpeg_cmd.extend([
+                "-t",
+                str(test_duration),
+                "-shortest"  # Ensure output duration matches shortest input
+            ])
+
+        ffmpeg_cmd.extend([
+            "-loglevel",
+            "error",
+            output_path
+        ])
+
+        subprocess.run(ffmpeg_cmd, check=True)
+
+        # Clean up temporary files
+        os.remove(temp_output)
+        os.remove(temp_audio)
+
+        if not os.path.exists(output_path):
+            print(
+                f"Warning: FFmpeg completed but output file not found at {output_path}"
+            )
+            return None
+
+        return output_path
+
+    except subprocess.CalledProcessError as e:
+        print(f"Error merging audio with video: {str(e)}")
+        if os.path.exists(temp_output):
+            os.remove(temp_output)
+        if os.path.exists(temp_audio):
+            os.remove(temp_audio)
+        return None
+
+
+def process_video(
+    video_path,
+    target_object,
+    test_mode=False,
+    test_duration=DEFAULT_TEST_MODE_DURATION,
+    ffmpeg_preset="medium",
+    grid_rows=1,
+    grid_cols=1,
+    box_style="censor",
+):
+    """Process a video to detect and visualize specified objects."""
+    try:
+        print(f"\nProcessing: {video_path}")
+        print(f"Looking for: {target_object}")
+
+        # Load model
+        print("Loading Moondream model...")
+        model, tokenizer = load_moondream()
+
+        # Get video properties
+        props = get_video_properties(video_path)
+        
+        # Initialize scene detector with ContentDetector
+        scene_detector = ContentDetector(threshold=30.0)  # Adjust threshold as needed
+        
+        # Initialize DeepSORT tracker
+        tracker = DeepSORTTracker()
+
+        # If in test mode, only process first N seconds
+        if test_mode:
+            frame_count = min(int(props["fps"] * test_duration), props["frame_count"])
+        else:
+            frame_count = props["frame_count"]
+
+        ad_detections = {}  # Store detection results by frame number
+
+        print("Extracting frames and detecting objects...")
+        video = cv2.VideoCapture(video_path)
+
+        # Detect scenes first
+        scenes = detect(video_path, scene_detector)
+        scene_changes = set(end.get_frames() for _, end in scenes)
+        print(f"Detected {len(scenes)} scenes")
+
+        frame_count_processed = 0
+        with tqdm(total=frame_count) as pbar:
+            while frame_count_processed < frame_count:
+                ret, frame = video.read()
+                if not ret:
+                    break
+
+                # Check if current frame is a scene change
+                if frame_count_processed in scene_changes:
+                    print(f"Scene change detected at frame {frame_count_processed}. Resetting tracker.")
+                    tracker.reset()
+
+                # Detect objects in the frame
+                detected_objects = detect_objects_in_frame(
+                    model, tokenizer, frame, target_object, grid_rows=grid_rows, grid_cols=grid_cols
+                )
+
+                # Update tracker with current detections
+                tracked_objects = tracker.update(frame, detected_objects)
+
+                # Store results for every frame, even if empty
+                ad_detections[frame_count_processed] = tracked_objects
+
+                frame_count_processed += 1
+                pbar.update(1)
+
+        video.release()
+
+        if frame_count_processed == 0:
+            print("No frames could be read from video")
+            return {}
+
+        # Apply filtering
+        filtered_ad_detections = filter_temporal_outliers(ad_detections)
+        
+        # Build detection data structure
+        detection_data = {
+            "video_metadata": {
+                "file_name": os.path.basename(video_path),
+                "fps": props["fps"],
+                "width": props["width"],
+                "height": props["height"],
+                "total_frames": props["frame_count"],
+                "duration_sec": props["frame_count"] / props["fps"],
+                "detect_keyword": target_object,
+                "test_mode": test_mode,
+                "grid_size": f"{grid_rows}x{grid_cols}",
+                "box_style": box_style,
+                "timestamp": datetime.now().isoformat()
+            },
+            "frame_detections": [
+                {
+                    "frame": frame_num,
+                    "timestamp": frame_num / props["fps"],
+                    "objects": [
+                        {
+                            "keyword": kw,
+                            "bbox": list(box),  # Convert numpy array to list if needed
+                            "track_id": track_id if len(detection) == 3 else None
+                        }
+                        for detection in filtered_ad_detections.get(frame_num, [])
+                        for box, kw, *track_id in [detection]  # Unpack detection tuple, track_id will be empty list if not present
+                    ]
+                }
+                for frame_num in range(props["frame_count"] if not test_mode else min(int(props["fps"] * test_duration), props["frame_count"]))
+            ]
+        }
+        
+        # Save filtered data
+        outputs_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs")
+        os.makedirs(outputs_dir, exist_ok=True)
+        base_name = os.path.splitext(os.path.basename(video_path))[0]
+        json_path = os.path.join(outputs_dir, f"{box_style}_{target_object}_{base_name}_detections.json")
+        
+        from persistence import save_detection_data
+        if not save_detection_data(detection_data, json_path):
+            print("Warning: Failed to save detection data")
+
+        # Create video with filtered data
+        output_path = create_detection_video(
+            video_path,
+            filtered_ad_detections,
+            target_object,
+            model,
+            ffmpeg_preset=ffmpeg_preset,
+            test_mode=test_mode,
+            test_duration=test_duration,
+            box_style=box_style,
+        )
+
+        if output_path is None:
+            print("\nError: Failed to create output video")
+            return None
+
+        print(f"\nOutput saved to: {output_path}")
+        print(f"Detection data saved to: {json_path}")
+        return output_path
+        
+    except Exception as e:
+        print(f"Error processing video: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return None
+
+
+def main():
+    """Process all videos in the inputs directory."""
+    parser = argparse.ArgumentParser(
+        description="Detect objects in videos using Moondream2"
+    )
+    parser.add_argument(
+        "--test", action="store_true", help="Process only first 3 seconds of each video"
+    )
+    parser.add_argument(
+        "--test-duration",
+        type=int,
+        default=DEFAULT_TEST_MODE_DURATION,
+        help=f"Number of seconds to process in test mode (default: {DEFAULT_TEST_MODE_DURATION})"
+    )
+    parser.add_argument(
+        "--preset",
+        choices=FFMPEG_PRESETS,
+        default="medium",
+        help="FFmpeg encoding preset (default: medium). Faster presets = lower quality",
+    )
+    parser.add_argument(
+        "--detect",
+        type=str,
+        default="face",
+        help='Object to detect in the video (default: face, use --detect "thing to detect" to override)',
+    )
+    parser.add_argument(
+        "--rows",
+        type=int,
+        default=1,
+        help="Number of rows to split each frame into (default: 1)",
+    )
+    parser.add_argument(
+        "--cols",
+        type=int,
+        default=1,
+        help="Number of columns to split each frame into (default: 1)",
+    )
+    parser.add_argument(
+        "--box-style",
+        choices=["censor", "bounding-box", "hitmarker", "sam", "sam-fast", "fuzzy-blur", 
+                "pixelated-blur", "intense-pixelated-blur", "obfuscated-pixel"],
+        default="censor",
+        help="Style of detection visualization (default: censor)",
+    )
+    args = parser.parse_args()
+
+    input_dir = "inputs"
+    os.makedirs(input_dir, exist_ok=True)
+    os.makedirs("outputs", exist_ok=True)
+
+    video_files = [
+        f
+        for f in os.listdir(input_dir)
+        if f.lower().endswith((".mp4", ".avi", ".mov", ".mkv", ".webm"))
+    ]
+
+    if not video_files:
+        print("No video files found in 'inputs' directory")
+        return
+
+    print(f"Found {len(video_files)} videos to process")
+    print(f"Will detect: {args.detect}")
+    if args.test:
+        print("Running in test mode - processing only first 3 seconds of each video")
+    print(f"Using FFmpeg preset: {args.preset}")
+    print(f"Grid size: {args.rows}x{args.cols}")
+    print(f"Box style: {args.box_style}")
+
+    success_count = 0
+    for video_file in video_files:
+        video_path = os.path.join(input_dir, video_file)
+        output_path = process_video(
+            video_path,
+            args.detect,
+            test_mode=args.test,
+            test_duration=args.test_duration,
+            ffmpeg_preset=args.preset,
+            grid_rows=args.rows,
+            grid_cols=args.cols,
+            box_style=args.box_style,
+        )
+        if output_path:
+            success_count += 1
+
+    print(
+        f"\nProcessing complete. Successfully processed {success_count} out of {len(video_files)} videos."
+    )
+
+
+if __name__ == "__main__":
+    main()

packages.txt

@@ -0,0 +1,2 @@
+libvips
+ffmpeg

persistence.py

@@ -0,0 +1,39 @@
+import json
+import os
+
+def save_detection_data(data, output_file):
+    """
+    Saves the detection data to a JSON file.
+
+    Args:
+        data (dict): The complete detection data structure.
+        output_file (str): Path to the output JSON file.
+    """
+    try:
+        # Create directory if it doesn't exist
+        os.makedirs(os.path.dirname(output_file), exist_ok=True)
+        
+        with open(output_file, "w") as f:
+            json.dump(data, f, indent=4)
+        print(f"Detection data saved to {output_file}")
+        return True
+    except Exception as e:
+        print(f"Error saving data: {str(e)}")
+        return False
+
+def load_detection_data(input_file):
+    """
+    Loads the detection data from a JSON file.
+
+    Args:
+        input_file (str): Path to the JSON file.
+        
+    Returns:
+        dict: The loaded detection data, or None if there was an error.
+    """
+    try:
+        with open(input_file, "r") as f:
+            return json.load(f)
+    except Exception as e:
+        print(f"Error loading data: {str(e)}")
+        return None 

requirements.txt

@@ -0,0 +1,26 @@
+gradio>=4.0.0
+torch>=2.0.0
+# if on windows: pip install torch==2.5.1+cu121 torchvision==0.20.1+cu121 --index-url https://download.pytorch.org/whl/cu121 
+transformers>=4.36.0
+opencv-python>=4.8.0
+pillow>=10.0.0
+numpy>=1.24.0
+tqdm>=4.66.0
+ffmpeg-python
+einops
+pyvips-binary
+pyvips
+accelerate
+# for spaces
+--extra-index-url https://download.pytorch.org/whl/cu113
+spaces
+# SAM dependencies
+torchvision>=0.20.1
+matplotlib>=3.7.0
+pandas>=2.0.0
+plotly
+# DeepSORT dependencies
+deep-sort-realtime>=1.3.2
+scikit-learn  # Required for deep-sort-realtime
+# Scene detection dependencies (for intelligent scene-aware tracking)
+scenedetect[opencv]>=0.6.2  # Provides scene change detection capabilities

video_visualization.py

@@ -0,0 +1,330 @@
+import os
+import tempfile
+import subprocess
+import matplotlib.pyplot as plt
+import pandas as pd
+import cv2
+import numpy as np
+from tqdm import tqdm
+from persistence import load_detection_data
+
+def create_frame_data(json_path):
+    """Create frame-by-frame detection data for visualization."""
+    try:
+        data = load_detection_data(json_path)
+        if not data:
+            print("No data loaded from JSON file")
+            return None
+        
+        if "video_metadata" not in data or "frame_detections" not in data:
+            print("Invalid JSON structure: missing required fields")
+            return None
+        
+        # Extract video metadata
+        metadata = data["video_metadata"]
+        if "fps" not in metadata or "total_frames" not in metadata:
+            print("Invalid metadata: missing fps or total_frames")
+            return None
+            
+        fps = metadata["fps"]
+        total_frames = metadata["total_frames"]
+        
+        # Create frame data
+        frame_counts = {}
+        for frame_data in data["frame_detections"]:
+            if "frame" not in frame_data or "objects" not in frame_data:
+                continue  # Skip invalid frame data
+            frame_num = frame_data["frame"]
+            frame_counts[frame_num] = len(frame_data["objects"])
+        
+        # Fill in missing frames with 0 detections
+        for frame in range(total_frames):
+            if frame not in frame_counts:
+                frame_counts[frame] = 0
+        
+        if not frame_counts:
+            print("No valid frame data found")
+            return None
+        
+        # Convert to DataFrame
+        df = pd.DataFrame(list(frame_counts.items()), columns=["frame", "detections"])
+        df["timestamp"] = df["frame"] / fps
+        
+        return df, metadata
+        
+    except Exception as e:
+        print(f"Error creating frame data: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return None
+
+def generate_frame_image(df, frame_num, temp_dir, max_y):
+    """Generate and save a single frame of the visualization."""
+    # Set the style to dark background
+    plt.style.use('dark_background')
+    
+    # Set global font to monospace
+    plt.rcParams['font.family'] = 'monospace'
+    plt.rcParams['font.monospace'] = ['DejaVu Sans Mono']
+    
+    plt.figure(figsize=(10, 6))
+    
+    # Plot data up to current frame
+    current_data = df[df['frame'] <= frame_num]
+    plt.plot(df['frame'], df['detections'], color='#1a1a1a', alpha=0.5)  # Darker background line
+    plt.plot(current_data['frame'], current_data['detections'], color='#00ff41')  # Matrix green
+    
+    # Add vertical line for current position
+    plt.axvline(x=frame_num, color='#ff0000', linestyle='-', alpha=0.7)  # Keep red for position
+    
+    # Set consistent axes
+    plt.xlim(0, len(df) - 1)
+    plt.ylim(0, max_y * 1.1)  # Add 10% padding
+    
+    # Add labels with Matrix green color
+    plt.title(f'FRAME {frame_num:04d} - DETECTIONS OVER TIME', color='#00ff41', pad=20)
+    plt.xlabel('FRAME NUMBER', color='#00ff41')
+    plt.ylabel('NUMBER OF DETECTIONS', color='#00ff41')
+    
+    # Add current stats in Matrix green with monospace formatting
+    current_detections = df[df['frame'] == frame_num]['detections'].iloc[0]
+    plt.text(0.02, 0.98, f'CURRENT DETECTIONS: {current_detections:02d}', 
+             transform=plt.gca().transAxes, verticalalignment='top',
+             color='#00ff41', family='monospace')
+    
+    # Style the grid and ticks
+    plt.grid(True, color='#1a1a1a', linestyle='-', alpha=0.3)
+    plt.tick_params(colors='#00ff41')
+    
+    # Save frame
+    frame_path = os.path.join(temp_dir, f'frame_{frame_num:05d}.png')
+    plt.savefig(frame_path, bbox_inches='tight', dpi=100, facecolor='black', edgecolor='none')
+    plt.close()
+    
+    return frame_path
+
+def generate_gauge_frame(df, frame_num, temp_dir, detect_keyword="OBJECT"):
+    """Generate a modern square-style binary gauge visualization frame."""
+    # Set the style to dark background
+    plt.style.use('dark_background')
+    
+    # Set global font to monospace
+    plt.rcParams['font.family'] = 'monospace'
+    plt.rcParams['font.monospace'] = ['DejaVu Sans Mono']
+    
+    # Create figure with 16:9 aspect ratio
+    plt.figure(figsize=(16, 9))
+    
+    # Get current detection state
+    current_detections = df[df['frame'] == frame_num]['detections'].iloc[0]
+    has_detection = current_detections > 0
+    
+    # Create a simple gauge visualization
+    plt.axis('off')
+    
+    # Set colors
+    if has_detection:
+        color = '#00ff41'  # Matrix green for YES
+        status = 'YES'
+        indicator_pos = 0.8  # Right position
+    else:
+        color = '#ff0000'  # Red for NO
+        status = 'NO'
+        indicator_pos = 0.2  # Left position
+    
+    # Draw background rectangle
+    background = plt.Rectangle((0.1, 0.3), 0.8, 0.2, 
+                             facecolor='#1a1a1a', 
+                             edgecolor='#333333',
+                             linewidth=2)
+    plt.gca().add_patch(background)
+    
+    # Draw indicator
+    indicator_width = 0.05
+    indicator = plt.Rectangle((indicator_pos - indicator_width/2, 0.25), 
+                            indicator_width, 0.3,
+                            facecolor=color,
+                            edgecolor=None)
+    plt.gca().add_patch(indicator)
+    
+    # Add tick marks
+    tick_positions = [0.2, 0.5, 0.8]  # NO, CENTER, YES
+    for x in tick_positions:
+        plt.plot([x, x], [0.3, 0.5], color='#444444', linewidth=2)
+    
+    # Add YES/NO labels
+    plt.text(0.8, 0.2, 'YES', color='#00ff41', fontsize=14,
+             ha='center', va='center', family='monospace')
+    plt.text(0.2, 0.2, 'NO', color='#ff0000', fontsize=14,
+             ha='center', va='center', family='monospace')
+    
+    # Add status box at top with detection keyword
+    plt.text(0.5, 0.8, f'{detect_keyword.upper()} DETECTED?', color=color,
+             fontsize=16, ha='center', va='center', family='monospace',
+             bbox=dict(facecolor='#1a1a1a', 
+                      edgecolor=color,
+                      linewidth=2,
+                      pad=10))
+    
+    # Add frame counter at bottom
+    plt.text(0.5, 0.1, f'FRAME: {frame_num:04d}', color='#00ff41',
+             fontsize=14, ha='center', va='center', family='monospace')
+    
+    # Add subtle grid lines for depth
+    for x in np.linspace(0.2, 0.8, 7):
+        plt.plot([x, x], [0.3, 0.5], color='#222222', linewidth=1, zorder=0)
+    
+    # Add glow effect to indicator
+    for i in range(3):
+        glow = plt.Rectangle((indicator_pos - (indicator_width + i*0.01)/2, 
+                            0.25 - i*0.01),
+                            indicator_width + i*0.01, 
+                            0.3 + i*0.02,
+                            facecolor=color,
+                            alpha=0.1/(i+1))
+        plt.gca().add_patch(glow)
+    
+    # Set consistent plot limits
+    plt.xlim(0, 1)
+    plt.ylim(0, 1)
+    
+    # Save frame with 16:9 aspect ratio
+    frame_path = os.path.join(temp_dir, f'gauge_{frame_num:05d}.png')
+    plt.savefig(frame_path, 
+                bbox_inches='tight', 
+                dpi=100, 
+                facecolor='black', 
+                edgecolor='none',
+                pad_inches=0)
+    plt.close()
+    
+    return frame_path
+
+def create_video_visualization(json_path, style="timeline"):
+    """Create a video visualization of the detection data."""
+    try:
+        if not json_path:
+            return None, "No JSON file provided"
+            
+        if not os.path.exists(json_path):
+            return None, f"File not found: {json_path}"
+            
+        # Load and process data
+        result = create_frame_data(json_path)
+        if result is None:
+            return None, "Failed to load detection data from JSON file"
+            
+        frame_data, metadata = result
+        if len(frame_data) == 0:
+            return None, "No frame data found in JSON file"
+        
+        total_frames = metadata["total_frames"]
+        detect_keyword = metadata.get("detect_keyword", "OBJECT")  # Get the detection keyword
+        
+        # Create temporary directory for frames
+        with tempfile.TemporaryDirectory() as temp_dir:
+            max_y = frame_data['detections'].max()
+            
+            # Generate each frame
+            print("Generating frames...")
+            frame_paths = []
+            with tqdm(total=total_frames, desc="Generating frames") as pbar:
+                for frame in range(total_frames):
+                    try:
+                        if style == "gauge":
+                            frame_path = generate_gauge_frame(frame_data, frame, temp_dir, detect_keyword)
+                        else:  # default to timeline
+                            frame_path = generate_frame_image(frame_data, frame, temp_dir, max_y)
+                        if frame_path and os.path.exists(frame_path):
+                            frame_paths.append(frame_path)
+                        else:
+                            print(f"Warning: Failed to generate frame {frame}")
+                        pbar.update(1)
+                    except Exception as e:
+                        print(f"Error generating frame {frame}: {str(e)}")
+                        continue
+            
+            if not frame_paths:
+                return None, "Failed to generate any frames"
+                
+            # Create output video path
+            output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "outputs")
+            os.makedirs(output_dir, exist_ok=True)
+            output_video = os.path.join(output_dir, f"detection_visualization_{style}.mp4")
+            
+            # Create temp output path
+            base, ext = os.path.splitext(output_video)
+            temp_output = f"{base}_temp{ext}"
+            
+            # First pass: Create video with OpenCV VideoWriter
+            print("Creating initial video...")
+            # Get frame size from first image
+            first_frame = cv2.imread(frame_paths[0])
+            height, width = first_frame.shape[:2]
+            
+            out = cv2.VideoWriter(
+                temp_output,
+                cv2.VideoWriter_fourcc(*"mp4v"),
+                metadata["fps"],
+                (width, height)
+            )
+            
+            with tqdm(total=total_frames, desc="Creating video") as pbar:  # Use total_frames here too
+                for frame_path in frame_paths:
+                    frame = cv2.imread(frame_path)
+                    out.write(frame)
+                    pbar.update(1)
+            
+            out.release()
+            
+            # Second pass: Convert to web-compatible format
+            print("Converting to web format...")
+            try:
+                subprocess.run(
+                    [
+                        "ffmpeg",
+                        "-y",
+                        "-i",
+                        temp_output,
+                        "-c:v",
+                        "libx264",
+                        "-preset",
+                        "medium",
+                        "-crf",
+                        "23",
+                        "-movflags",
+                        "+faststart",  # Better web playback
+                        "-loglevel",
+                        "error",
+                        output_video,
+                    ],
+                    check=True,
+                )
+
+                os.remove(temp_output)  # Remove the temporary file
+
+                if not os.path.exists(output_video):
+                    print(f"Warning: FFmpeg completed but output file not found at {output_video}")
+                    return None, "Failed to create video"
+
+                # Return video path and stats
+                stats = f"""Video Stats:
+FPS: {metadata['fps']}
+Total Frames: {metadata['total_frames']}
+Duration: {metadata['duration_sec']:.2f} seconds
+Max Detections in a Frame: {frame_data['detections'].max()}
+Average Detections per Frame: {frame_data['detections'].mean():.2f}"""
+                
+                return output_video, stats
+
+            except subprocess.CalledProcessError as e:
+                print(f"Error running FFmpeg: {str(e)}")
+                if os.path.exists(temp_output):
+                    os.remove(temp_output)
+                return None, f"Error creating visualization: {str(e)}"
+        
+    except Exception as e:
+        print(f"Error creating video visualization: {str(e)}")
+        import traceback
+        traceback.print_exc()
+        return None, f"Error creating visualization: {str(e)}" 

visualization.py

@@ -0,0 +1,98 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from persistence import load_detection_data
+import argparse
+
+def visualize_detections(json_path):
+    """
+    Visualize detection data from a JSON file.
+    
+    Args:
+        json_path (str): Path to the JSON file containing detection data.
+    """
+    # Load the persisted JSON data
+    data = load_detection_data(json_path)
+    if not data:
+        return
+
+    # Convert the frame detections to a DataFrame
+    rows = []
+    for frame_data in data["frame_detections"]:
+        frame = frame_data["frame"]
+        timestamp = frame_data["timestamp"]
+        for obj in frame_data["objects"]:
+            rows.append({
+                "frame": frame,
+                "timestamp": timestamp,
+                "keyword": obj["keyword"],
+                "x1": obj["bbox"][0],
+                "y1": obj["bbox"][1],
+                "x2": obj["bbox"][2],
+                "y2": obj["bbox"][3],
+                "area": (obj["bbox"][2] - obj["bbox"][0]) * (obj["bbox"][3] - obj["bbox"][1])
+            })
+
+    if not rows:
+        print("No detections found in the data")
+        return
+
+    df = pd.DataFrame(rows)
+
+    # Create a figure with multiple subplots
+    fig = plt.figure(figsize=(15, 10))
+    
+    # Plot 1: Number of detections per frame
+    plt.subplot(2, 2, 1)
+    detections_per_frame = df.groupby("frame").size()
+    plt.plot(detections_per_frame.index, detections_per_frame.values)
+    plt.xlabel("Frame")
+    plt.ylabel("Number of Detections")
+    plt.title("Detections Per Frame")
+
+    # Plot 2: Distribution of detection areas
+    plt.subplot(2, 2, 2)
+    df["area"].hist(bins=30)
+    plt.xlabel("Detection Area (normalized)")
+    plt.ylabel("Count")
+    plt.title("Distribution of Detection Areas")
+
+    # Plot 3: Average detection area over time
+    plt.subplot(2, 2, 3)
+    avg_area = df.groupby("frame")["area"].mean()
+    plt.plot(avg_area.index, avg_area.values)
+    plt.xlabel("Frame")
+    plt.ylabel("Average Detection Area")
+    plt.title("Average Detection Area Over Time")
+
+    # Plot 4: Heatmap of detection centers
+    plt.subplot(2, 2, 4)
+    df["center_x"] = (df["x1"] + df["x2"]) / 2
+    df["center_y"] = (df["y1"] + df["y2"]) / 2
+    plt.hist2d(df["center_x"], df["center_y"], bins=30)
+    plt.colorbar()
+    plt.xlabel("X Position")
+    plt.ylabel("Y Position")
+    plt.title("Detection Center Heatmap")
+
+    # Adjust layout and display
+    plt.tight_layout()
+    plt.show()
+
+    # Print summary statistics
+    print("\nSummary Statistics:")
+    print(f"Total frames analyzed: {len(data['frame_detections'])}")
+    print(f"Total detections: {len(df)}")
+    print(f"Average detections per frame: {len(df) / len(data['frame_detections']):.2f}")
+    print(f"\nVideo metadata:")
+    for key, value in data["video_metadata"].items():
+        print(f"{key}: {value}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Visualize object detection data")
+    parser.add_argument("json_file", help="Path to the JSON file containing detection data")
+    args = parser.parse_args()
+    
+    visualize_detections(args.json_file)
+
+if __name__ == "__main__":
+    main()