vision-agent 0.2.34

Toolset for Vision Agent

🔍🤖 Vision Agent

Vision Agent is a library that helps you utilize agent frameworks to generate code to solve your vision task. Many current vision problems can easily take hours or days to solve, you need to find the right model, figure out how to use it and program it to accomplish the task you want. Vision Agent aims to provide an in-seconds experience by allowing users to describe their problem in text and have the agent framework generate code to solve the task for them. Check out our discord for updates and roadmaps!

Documentation

• Vision Agent Library Docs

Getting Started

Installation

To get started, you can install the library using pip:

pip install vision-agent

Ensure you have an OpenAI API key and set it as an environment variable (if you are using Azure OpenAI please see the Azure setup section):

export OPENAI_API_KEY="your-api-key"

Vision Agent

You can interact with the agent as you would with any LLM or LMM model:

>>> from vision_agent.agent import VisionAgent
>>> agent = VisionAgent()
>>> code = agent("What percentage of the area of the jar is filled with coffee beans?", media="jar.jpg")

Which produces the following code:

from vision_agent.tools import load_image, grounding_sam

def calculate_filled_percentage(image_path: str) -> float:
    # Step 1: Load the image
    image = load_image(image_path)
    
    # Step 2: Segment the jar
    jar_segments = grounding_sam(prompt="jar", image=image)
    
    # Step 3: Segment the coffee beans
    coffee_beans_segments = grounding_sam(prompt="coffee beans", image=image)
    
    # Step 4: Calculate the area of the segmented jar
    jar_area = 0
    for segment in jar_segments:
        jar_area += segment['mask'].sum()
    
    # Step 5: Calculate the area of the segmented coffee beans
    coffee_beans_area = 0
    for segment in coffee_beans_segments:
        coffee_beans_area += segment['mask'].sum()
    
    # Step 6: Compute the percentage of the jar area that is filled with coffee beans
    if jar_area == 0:
        return 0.0  # To avoid division by zero
    filled_percentage = (coffee_beans_area / jar_area) * 100
    
    # Step 7: Return the computed percentage
    return filled_percentage

To better understand how the model came up with it's answer, you can run it in debug mode by passing in the verbose argument:

>>> agent = VisionAgent(verbose=2)

You can also have it return more information by calling chat_with_workflow:

>>> results = agent.chat_with_workflow([{"role": "user", "content": "What percentage of the area of the jar is filled with coffee beans?"}], media="jar.jpg")
>>> print(results)
{
    "code": "from vision_agent.tools import ..."
    "test": "calculate_filled_percentage('jar.jpg')",
    "test_result": "...",
    "plan": [{"code": "...", "test": "...", "plan": "..."}, ...],
    "working_memory": ...,
}

With this you can examine more detailed information such as the etesting code, testing results, plan or working memory it used to complete the task.

Tools

There are a variety of tools for the model or the user to use. Some are executed locally while others are hosted for you. You can also ask an LLM directly to build a tool for you. For example:

>>> import vision_agent as va
>>> llm = va.llm.OpenAILLM()
>>> detector = llm.generate_detector("Can you build a jar detector for me?")
>>> detector("jar.jpg")
[{"labels": ["jar",],
  "scores": [0.99],
  "bboxes": [
    [0.58, 0.2, 0.72, 0.45],
  ]
}]

You can also add custom tools to the agent:

import vision_agent as va

@va.tools.register_tool(imports=["import numpy as np"])
def custom_tool(image_path: str) -> str:
    """My custom tool documentation.

    Parameters:
        image_path (str): The path to the image.

    Returns:
        str: The result of the tool.

    Example
    -------
    >>> custom_tool("image.jpg")
    """

    import numpy as np
    return np.zeros((10, 10))

You need to ensure you call @va.tools.register_tool with any imports it might use and ensure the documentation is in the same format above with description, Parameters:, Returns:, and Example\n-------. You can find an example use case here.

Azure Setup

If you want to use Azure OpenAI models, you can set the environment variable:

export AZURE_OPENAI_API_KEY="your-api-key"
export AZURE_OPENAI_ENDPOINT="your-endpoint"

You can then run Vision Agent using the Azure OpenAI models:

>>> import vision_agent as va
>>> agent = va.agent.VisionAgent(
>>>     planner=va.llm.AzureOpenAILLM(),
>>>     coder=va.lmm.AzureOpenAILMM(),
>>>     tester=va.lmm.AzureOpenAILMM(),
>>>     debugger=va.lmm.AzureOpenAILMM(),
>>> )