2. Multimodal Visual Understanding Applications

1. Concept Introduction

1.1 What is "Visual Understanding"?

In the largemodel project, the multimodal visual understanding feature enables robots to go beyond simply "seeing" a matrix of pixels and truly "understand" the content, objects, scenes, and relationships within an image. This is like giving robots a pair of thinking eyes.

The core tool for this feature is seewhat. When a user issues a command like "see what's here," the system invokes this tool, triggering a series of background operations that ultimately provide the user with AI-generated analysis of the live image in natural language.

1.2 Implementation Principle Overview

The basic principle is to input two different types of information—image (visual information) and text (linguistic information)—into a powerful multimodal large model (such as LLaVA).

1. Image Encoding: The model first uses a vision encoder to convert the input image into computer-interpretable digital vectors. These vectors capture image features such as color, shape, and texture.
2. Text Encoding: Simultaneously, the user's question (e.g., "What's on the table?") is converted into a text vector.
3. Cross-modal Fusion: In the most critical step, the model fuses the image vector and the text vector in a special "attention layer." Here, the model learns to "focus" on the parts of the image relevant to the question. For example, when asked about "table," the model will pay more attention to areas of the image that match the characteristics of a table.
4. Answer Generation: Finally, a large language model (LLM) generates a descriptive text answer based on this fused information.

Simply put, this involves highlighting the corresponding parts of the image with text, and then describing the highlighted parts with language.

2. Project Architecture

Key Code

2.1 Tool Layer Entry (largemodel/utils/tools_manager.py)

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

​x
# From largemodel/utils/tools_manager.py
​
class ToolsManager:
    # ...
​
    def seewhat(self):
        """
        Capture camera frame and analyze environment with AI model.
        
        :return: Dictionary with scene description and image path, or None if failed.
        """
        self.node.get_logger().info("Executing seewhat() tool")
        image_path = self.capture_frame()
        if image_path:
            # Use isolated context for image analysis. 
            analysis_text = self._get_actual_scene_description(image_path)
​
            # Return structured data for the tool chain. 
            return {
                "description": analysis_text,
                "image_path": image_path
            }
        else:
            # ... (Error handling)
            return None
​
    def _get_actual_scene_description(self, image_path, message_context=None):
        """
        Get AI-generated scene description for captured image.
        
        :param image_path: Path to captured image file.
        :return: Plain text description of scene.
        """
        try:
            # ... (Build Prompt)
            
            # Force use of a plain text system prompt with a clean, one-time context. 
            simple_context = [{
                "role": "system",
                "content": "You are an image description assistant. ..."
            }]
​
            result = self.node.model_client.infer_with_image(image_path, scene_prompt, message=simple_context)
            # ... (Processing Result)
            return description
        except Exception as e:
            # ...

2.2 Model interface layer (largemodel/utils/large_model_interface.py)

The infer_with_image function in this file is the unified entry point for all image understanding tasks. It is responsible for calling the specific model implementation according to the configuration.

xxxxxxxxxx
# From largemodel/utils/large_model_interface.py
​
class model_interface:
    # ...
    def infer_with_image(self, image_path, text=None, message=None):
        """Unified image inference interface. """
        # ... (Prepare message)
        try:
            # Determine which specific implementation to call based on the value of self.llm_platform
            if self.llm_platform == 'ollama':
                response_content = self.ollama_infer(self.messages, image_path=image_path)
            elif self.llm_platform == 'tongyi':
                # ... Logic for calling the Tongyi model
                pass
            # ... (Logic for other platforms)
        # ...
        return {'response': response_content, 'messages': self.messages.copy()}
​

Code Analysis

This feature's implementation involves two main layers: the tool layer defines the business logic, and the model interface layer is responsible for communicating with the large language model. This layered design is key to achieving platform versatility.

1. Tool Layer (tools_manager.py):

   • The seewhat function is the core business of the visual understanding function. It encapsulates the entire "seeing" action process: first, it calls the capture_frame method to obtain an image, then calls _get_actual_scene_description to prepare a prompt for the model to analyze the image.
   • The most critical step is calling the infer_with_image method of the model interface layer. It does not care about the underlying model; it only passes the two core data elements, "image" and "analysis instructions," to the model interface layer.
   • Finally, it packages the analysis results (plain text descriptions) received from the model interface layer into a structured dictionary and returns them. This allows upper-layer applications to easily use the analysis results.

2. Model Interface Layer (large_model_interface.py):

   • The infer_with_image function acts as a "dispatching center." Its primary responsibility is to check the current platform configuration (self.llm_platform) and, based on the configuration, dispatch tasks to specific handlers (such as ollama_infer or tongyi_infer).
   • This layer is key to adapting to different AI platforms. All platform-specific operations (such as data encoding and API call formats) are encapsulated within their respective handlers.
   • In this way, the business logic code in tools_manager.py remains unchanged to support a variety of different large-model backend services. It simply interacts with the unified, stable infer_with_image interface.

In summary, the seewhat tool's execution flow demonstrates a clear separation of responsibilities: ToolsManager defines the "what" (acquiring an image and requesting analysis), while model_interface defines the "how" (selecting the appropriate model platform based on the current configuration and interacting with it). This makes the tutorial's explanation universal, and the core code logic remains consistent regardless of whether the user is online or offline.

3.Practical Operations

3.1 Configuring Online LLM

First, enter the command in the terminal to enter the Docker container:

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./ros2_docker.sh

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

1. You will then need to update the key in the configuration file and open the model interface configuration file.large_model_interface.yaml:

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

2. Enter your API Key: Find the corresponding section and paste the API Key you just copied. This example uses the Tongyi Qianwen configuration.

   xxxxxxxxxx
   # large_model_interface.yaml
   ​
   ## Thousand Questions on Tongyi
   qianwen_api_key: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Paste your Key
   qianwen_model: "qwen-vl-max-latest" # You can choose the model as needed, such as qwen-turbo, qwen-plus
   

3. Open the main configuration file yahboom.yaml:

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

4. Select the online platform you want to use: Change the llm_platform parameter to the platform name you want to use.

   xxxxxxxxxx
   # yahboom.yaml
   ​
   model_service:
     ros__parameters:
       # ...
       llm_platform: 'tongyi'  #Optional platforms: 'ollama', 'tongyi', 'spark', 'qianfan', 'openrouter'
   

3.2 Start and test the function

1. After entering the Docker container, start the largemodel main program and enable text interaction mode:

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

2. Send text command: Open another terminal, enter the same Docker container, and run the following command:

   xxxxxxxxxx
   ros2 run text_chat text_chat
   

   Then, start typing your question.

3. Test:

   • Type your question in the terminal and press Enter. For example: What do you see?
   • Watch the terminal output. After a few moments, you should see a detailed answer from the large cloud-based model.