MultiCamAssembly: Distributed Robotic Vision System Software Framework

Project

Structure For a full folder tree with short descriptions for every file, see STRUCTURE.md Key Workflows Main App Workflow Power on remote robots and connect to the local network Verify SSH config (e.g. ssh/config_lightsoff) matches the network Run base/programs/App.py Guided steps: Device discovery and selection Remote code updates Component selection Model Training Workflow Create labeled data using Label Studio Update dataset paths in yolo/yolo_train.yaml Set hyperparameters and epochs in yolo/yolo_train.py Train model with yolo/yolo_train.py Copy trained weights to cambots/yolo/models/ Generate TensorRT engines on remote devices Validate inference on remote devices Pushing and Pulling Workflow Push code via deploy.bat (Windows) or deploy.sh (Linux) Command: ./deploy.sh lightsoff-j01 [config_file] Pushes cambots/ without deleting remote-only files Pull data via fetch.bat (Windows) or fetch.sh (Linux) Command: ./fetch.sh lightsoff-j01 [-d] Retrieves data from remote cambots/save/ to local save/ Camera Calibration Workflow Required only if cameras change Measure checkerboard cell size and update square_size Run calibrate_camera.py on remote devices Capture 20 checkerboard images Copy camera_calibration_data.npz to each remote device Logging Workflow Enable logging in base/programs/App.py UDP messages logged as JSONL files in logs/ Used for post-processing and algorithm prototyping