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Overview

We will construct workflows designed to enforce safety equipment compliance in construction areas. Specifically, we will show a few possible approaches related to how you could construct a Workflow to detect if workers are wearing PPE.

We will be using this image to test our results:

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Approach 1: Using ClassificationAgent

from overeasy import *
from PIL import Image

workflow = Workflow([
    # Detect each head in the input image
    BoundingBoxSelectAgent(classes=["person's head"], model=OwlV2()),  
    # Applies Non-Maximum Suppression to the detected heads
    # It's reccomended to use NMS when using OwlV2
    NMSAgent(iou_threshold=0.5),  
    # Splits the input image into an image of each detected head
    SplitAgent(),
    # Classifies PPE using CLIP
    ClassificationAgent(classes=["hard hat", "no hard hat"]),  
    # Maps the returned class names 
    ClassMapAgent({"hard hat": "has ppe", "no hard hat": "no ppe"}),
    # Combines results back into a BoundingBox Detection
    JoinAgent()  
])

image = Image.open("./construction.jpg")
result, graph = workflow.execute(image)
workflow.visualize(graph)

This approach is very fast as it only uses local open-source models. However, it is somewhat more error-prone compared to the methods described below. This is because the ClassificationAgent relies on CLIP embedding models for zero-shot classification, which, while fast, does not offer the same level of robust image understanding as models like GPTVision.

Prerequisite: The following approaches leverage the Instructor library to generate structured data. If you’re not familiar with the library we recommend reading the Docs.

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Approach 2: Using InstructorImageAgent

from overeasy import *
from pydantic import BaseModel
from PIL import Image

#Structured output
class PPE(BaseModel):
    hardhat: bool

workflow = Workflow([
    # Detects each person in the input image
    BoundingBoxSelectAgent(classes=["person"]),  
    # Splits the input image into an image of each detected person
    SplitAgent(),  
    # Generates structured data in the form of the response_model
    InstructorImageAgent(response_model=PPE),  
    # Converts the structured data into a Classification Detection
    ToClassificationAgent(fn=lambda x: "has ppe" if x.hardhat else "no ppe"),  
    # Combines results back into a BoundingBox Detection
    JoinAgent()  
])

image = Image.open("./construction.jpg")
result, graph = workflow.execute(image)
workflow.visualize(graph)

The InstructorImageAgent generates structured outputs directly from images, streamlining the process with fewer steps while maintaining robustness. The BoundingBoxSelectAgent allows the InstructorImageAgent to focus its analysis on person detections.

In this example, we were able to split our image at the person level, instead of having to select for heads. This is possible because the InstructorImageAgent is able to leverage GPTVision to provide additional robustness.

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Approach 3: Using DenseCaptioningAgent

from overeasy import *
from pydantic import BaseModel
from PIL import Image

class PPE(BaseModel):
    hardhat: bool

workflow = Workflow([
    # Detects each person in the input image
    BoundingBoxSelectAgent(classes=["person"]),
    # Splits the input image into an image of each detected person
    SplitAgent(),  
    # Generates text captions for each image
    DenseCaptioningAgent(),  
    # Converts the text captions into structured data
    InstructorTextAgent(response_model=PPE),  
    # Converts the structured data into a Classification Detection
    ToClassificationAgent(fn=lambda x: "has ppe" if x.hardhat else "no ppe"),  
    # Combines results back into a BoundingBox Detection
    JoinAgent()  
])

image = Image.open("./construction.jpg")
result, graph = workflow.execute(image)
workflow.visualize(graph)

This approach is similiar to the last approach, but takes the roundabout step of converting the image to a text caption before returning structured data. The benefit of this approach is efficiency.

Many effective ImageCaptioning models can be run locally, which means they can avoid expensive calls to Multimodal Language Models. In this scenario InstructorTextAgent can leverage a much cheaper model like GPT-3.5, or even a local LLM. We also can leverage robust models like GPTVision to improve the robustness of our model in DenseCaptioningAgent if necessary.

This approach provides the flexibility in choosing between robustness and speed.