I generated an interactive diagram for ROMP's codebase

[ivanmilevtues]

Hey, all I've always wanted to contribute to open-source projects. But at the very begining I found it a bit too difficult to navigate them and wrap my head around on the main flow and the main "logical" components. That is why I started working on a project to help people like me, who are visual learners. The project generates an interactive diagram starting from the highest level of abstraction and allows you to dive deeper into interesting components.

This is the highest level of abstraction for the ROMP project:

graph LR
    System_Configuration["System Configuration"]
    Data_Input_Preprocessing["Data Input & Preprocessing"]
    Core_Deep_Learning_Models["Core Deep Learning Models"]
    3D_Body_Model_SMPL_["3D Body Model (SMPL)"]
    Inference_3D_Reconstruction_Pipeline["Inference & 3D Reconstruction Pipeline"]
    Model_Training_Evaluation["Model Training & Evaluation"]
    Multi_person_Tracking["Multi-person Tracking"]
    Results_Visualization_Export["Results Visualization & Export"]
    System_Configuration -- "provides configuration parameters to" --> Data_Input_Preprocessing
    System_Configuration -- "supplies model configuration and loaded weights to" --> Core_Deep_Learning_Models
    Data_Input_Preprocessing -- "feeds preprocessed data to" --> Core_Deep_Learning_Models
    Data_Input_Preprocessing -- "provides input data to" --> Inference_3D_Reconstruction_Pipeline
    Data_Input_Preprocessing -- "supplies training data and ground truth to" --> Model_Training_Evaluation
    Core_Deep_Learning_Models -- "outputs raw predictions to" --> Inference_3D_Reconstruction_Pipeline
    Inference_3D_Reconstruction_Pipeline -- "requests 3D mesh generation from" --> 3D_Body_Model_SMPL_
    3D_Body_Model_SMPL_ -- "returns 3D meshes and poses to" --> Inference_3D_Reconstruction_Pipeline
    Inference_3D_Reconstruction_Pipeline -- "sends detection results for tracking to" --> Multi_person_Tracking
    Multi_person_Tracking -- "returns smoothed 3D pose results to" --> Inference_3D_Reconstruction_Pipeline
    Inference_3D_Reconstruction_Pipeline -- "forwards final 3D results to" --> Results_Visualization_Export
    Model_Training_Evaluation -- "updates model weights of" --> Core_Deep_Learning_Models
    Model_Training_Evaluation -- "sends evaluation metrics to" --> Results_Visualization_Export
    Results_Visualization_Export -- "utilizes for 3D rendering" --> 3D_Body_Model_SMPL_
    click System_Configuration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ROMP/System_Configuration.md" "Details"
    click Data_Input_Preprocessing href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ROMP/Data_Input_Preprocessing.md" "Details"
    click 3D_Body_Model_SMPL_ href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ROMP/3D_Body_Model_SMPL_.md" "Details"
    click Inference_3D_Reconstruction_Pipeline href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ROMP/Inference_3D_Reconstruction_Pipeline.md" "Details"
    click Model_Training_Evaluation href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ROMP/Model_Training_Evaluation.md" "Details"
    click Multi_person_Tracking href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ROMP/Multi_person_Tracking.md" "Details"
    click Results_Visualization_Export href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ROMP/Results_Visualization_Export.md" "Details"

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The generation tool is based on Static Analysis and LLMs and is fully open-source: https://github.com/CodeBoarding/CodeBoarding

You can see the full diagram here: https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/ROMP/on_boarding.md

Any feedback is appreciated! Would love to hear your opinion on diagrams as an exploration tooling and diagram first documentation!