Multimodal Video Analysis Application

Note: Due to performance limitations, Raspberry Pi 5 2GB, 4GB, RDK X5 4GB, and Jetson Orin Nano 4GB versions cannot run offline. Please refer to the corresponding chapter in <Online Large Model (Text Interaction)> for further information.

1. Concept Introduction

1.1 What is "Video Analysis"?

In the largemodel project, the multimodal video analysis function refers to enabling the robot to process a video and summarize its core content, describe key events, or answer specific questions about the video in natural language. This allows the robot to leap from understanding only static images to understanding a dynamic world with temporal relationships.

The core tool for this function is analyze_video. When a user provides a video file and asks a question (e.g., "Summarize what this video is about"), the system calls this tool to process and analyze the video and return the AI's text answer.

1.2 Implementation Principle Overview

The core challenge of offline video analysis lies in how to enable the model to efficiently process video data containing hundreds or thousands of frames. A mainstream implementation principle is as follows:

1. Keyframe Extraction: First, the system doesn't process every frame of the video. Instead, it extracts the most representative keyframes using algorithms (such as scene boundary detection or fixed-time interval sampling). This significantly reduces the amount of data that needs to be processed.
2. Image Encoding: Each extracted keyframe is fed into a visual encoder, similar to applications focused on visual understanding, and converted into a digital vector containing image information.
3. Temporal Information Fusion: This is the biggest difference from single-image understanding. The model needs to understand the temporal order between these keyframes. A recurrent neural network (RNN) or Transformer model is typically used to fuse the vectors of all keyframes, forming a "memory" vector that represents the dynamic content of the entire video.
4. Question Answering and Generation: The user's text question is encoded and then fused across modally with this "memory" vector. Finally, the language model generates a summary of the entire video or an answer to a specific question based on this fused information.

In simple terms, it's about condensing a video into a few key images and their order, then understanding the whole story like reading a comic strip and answering related questions.

2. Code Analysis

Key Code

1. Utility Layer Entry Point (largemodel/utils/tools_manager.py)

The analyze_video function in this file defines the execution flow of this tool.

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# From largemodel/utils/tools_manager.py
class ToolsManager:
    # ...
    def analyze_video(self, args):
        """
        Analyze video file and provide content description.
        
        :param args: Arguments containing video path.
        :return: Dictionary with video description and path.
        """
        self.node.get_logger().info(f"Executing analyze_video() tool with args: {args}")
        try:
            video_path = args.get("video_path")
            # ... (Intelligent path fallback mechanism)
            
            if video_path and os.path.exists(video_path):
                # ... (Building Prompt)
                
                # Use a fully isolated, one-time context for video analysis to ensure a plain text description. 
                simple_context = [{
                    "role": "system",
                    "content": "You are a video description assistant. ..."
                }]
                
                result = self.node.model_client.infer_with_video(video_path, prompt, message=simple_context)
                
                # ... (Processing results)
                return {
                    "description": description,
                    "video_path": video_path
                }
            # ... (Error Handling)

2. Model Interface Layer and Frame Extraction (largemodel/utils/large_model_interface.py)

The functions in this file are responsible for processing video files and passing them to the underlying model.

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# From largemodel/utils/large_model_interface.py
​
class model_interface:
    # ...
    def infer_with_video(self, video_path, text=None, message=None):
        """Unified video inference interface."""
        # ... (Prepare Message)
        try:
            # Determine which specific implementation to call based on self.llm_platform
            if self.llm_platform == 'ollama':
                response_content = self.ollama_infer(self.messages, video_path=video_path)
            # ... (The logic of other online platforms)
        # ...
        return {'response': response_content, 'messages': self.messages.copy()}
​
    def _extract_video_frames(self, video_path, max_frames=5):
        """Extract keyframes from a video for analysis. """
        try:
            import cv2
            # ... (Video reading and frame interval calculation)
            while extracted_count < max_frames:
                # ... (Looping through video frames)
                if frame_count % frame_interval == 0:
                    # ... (Save the frame as a temporary image)
                    frame_base64 = self.encode_file_to_base64(temp_path)
                    frame_images.append(frame_base64)
            # ...
            return frame_images
        # ... (Exception handling)

Code Analysis

The implementation of video analysis is more complex than image analysis. It requires a key preprocessing step at the model interface layer: frame extraction.

1. Tools Layer (tools_manager.py):

   • The analyze_video function is the entry point for video analysis. Its responsibilities are clear: accept a video file path and construct a prompt to request a description of the video content.
   • It initiates the analysis process by calling the self.node.model_client.infer_with_video method. Like the visual understanding tool, it is completely agnostic about the underlying model details and only handles passing the "video file" and "analysis instructions."

2. Model Interface Layer (large_model_interface.py):

   • The infer_with_video function is the scheduler that connects the upper-level tools with the underlying model. It dispatches tasks to the corresponding implementation functions based on the platform configuration (self.llm_platform).
   • Unlike processing single images, processing videos requires an additional step. The _extract_video_frames method demonstrates the general logic for implementing this step: it uses the cv2 library to read the video and extract several keyframes (by default, five).
   • Each extracted frame is treated as a separate image and typically encoded as a Base64 string.
   • Ultimately, a request containing multiple frame image data and analysis instructions is sent to the main model. The model performs a comprehensive analysis of these consecutive images to generate a description of the entire video content.
   • This "video-to-multiple-images" preprocessing is completely encapsulated in the model interface layer and is transparent to the tool layer.

In summary, the general process of video analysis is: ToolsManager initiates an analysis request -> model_interface intercepts the request and calls _extract_video_frames to decompose the video file into multiple keyframe images -> model_interface sends these images, along with analysis instructions, to the corresponding model platform according to the configuration -> the model returns a comprehensive description of the video -> the results are finally returned to ToolsManager. This design ensures the stability and versatility of upper-layer applications.

3. Practical Operations

3.1 Configuring the Offline Large Model

3.1.1 Configuring the LLM Platform (yahboom.yaml)

This file determines which large model platform the model_service node loads as its primary language model.

Raspberry Pi 5 requires entering a Docker container, while RDK X5 and Orin controllers do not:

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./ros2_docker.sh

1. If you need to enter the same Docker container later to run other commands, simply enter ./ros2_docker.sh again in the host terminal.

   1. Open the file in the terminal:

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   vim ~/yahboom_ws/src/largemodel/config/yahboom.yaml
   

2. Modify/Confirm llm_platform:

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   model_service:                          #Model server node parameters
     ros__parameters:
       language: 'zh'                      #Large Model Interface Language
       useolinetts: True                   #This item is invalid in text mode and can be ignored
   ​
       # Large model configuration
       llm_platform: 'ollama'              # Key: Make sure it's 'ollama'
       regional_setting : "China"
       camera_type: 'csi'                 # Camera type: 'csi', 'usb'
       mjpeg_stream_url: 'http://172.17.0.1:8080/camera.mjpg' # MJPEG stream URL for CSI camera in Docker 
   

3.1.2 Configuring the Model Interface (large_model_interface.yaml)

This file defines which visual model to use when the ollama platform is selected.

1. Open the file in a terminal.

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vim ~/yahboom_ws/src/largemodel/config/large_model_interface.yaml

2.Find the Ollama configuration

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#.....
## Offline Large Language Models
# Ollama Configuration
ollama_host: "http://localhost:11434" # Ollama server address
ollama_model: "llava" # Key: Replace this with the multimodal model you downloaded, such as "llava"
#.....

Note: Please ensure that the model specified in the configuration parameters (e.g., llava) can handle multimodal input.

3.2 Launching and Testing the Functionality (Text Input Mode)

**Note: The performance will be worse when using a model with a small number of parameters or when the memory usage is at its limit. For a better experience with this feature, please refer to the corresponding chapter in <Online Large Model (Text Interaction)>

1. Prepare the Video File:

Place the video file you want to test in the following path: ~/yahboom_ws/src/largemodel/resources_file/analyze_video

Then name the video test_video.mp4

2. Launch the largemodel main program (text mode):

Open a terminal and run the following command:

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ros2 launch largemodel largemodel_control.launch.py text_chat_mode:=true

3. Send Text Commands:

Open another terminal and run the following command:

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ros2 run text_chat text_chat

Then start typing the text: "Analyze this video."

4. Observation Results:

In the first terminal running the main program, you will see log output showing that the system received instructions, called the analyze_video tool, extracted keyframes, and finally printed the AI's summary of the video content.

4. Common Problems and Solutions

Problem 1: Error messages include "Video file not found" or "Unable to extract keyframes from video".

Solution:

1. Check Path: Ensure you have placed the video file in the specified path, named it test_video.mp4, and have permission to read the file.
2. Video Format/Encoding: Ensure the video file is not corrupted and is in .mp4 format.

Problem 2: Analyzing a long video is very time-consuming.

Solution:

1. Use a Lighter Model: With limited hardware resources, switch to a video understanding model with fewer parameters.