# Architecture components

This file lists each component from the architecture diagram in detail. Use it as a reference when implementing or modifying a module.

## Per-camera pipeline

### IP Camera / Raspberry Pi

- Role: image acquisition.
- Expected count: 2–3 channels.
- Output: continuous video stream.
- Notes:
  - Cameras may be replaced; recognition quality impact must be measurable.
  - Positioning tolerance: rotation ±5°, displacement up to 10% of frame.

### Position determination

- Role: decide when to capture a frame.
- Options:
  - Physical sensor on conveyor (simpler, deterministic);
  - AI-based detector (more flexible, needs training).
- Output: trigger signal to frame extractor.

### Video stream → frame

- Role: extract still frames from the camera stream on demand.
- Input: trigger signal.
- Output: raw image frame.

### Image preparing

- Role: make the image suitable for the YOLO model.
- Operations:
  - normalize pixel values / brightness / contrast;
  - filter noise;
  - crop to region of interest;
  - resize to model input size;
  - rotate to compensate for small alignment errors.
- Must be configurable per camera channel via JSON config.

## Inference layer

### YOLO instance

- Role: run defect detection on a prepared image.
- One instance per camera / tab.
- Each instance has:
  - own model weights;
  - own confidence threshold;
  - own class mapping;
  - own preprocessing parameters.

### AI Model

- Role: manage model artifacts and retraining lifecycle.
- Responsibilities:
  - load weights for YOLO instances;
  - export updated weights after retraining;
  - keep versioned model history;
  - serve as an abstraction between training pipeline and inference.

## Central services

### Main Server

- Role: coordination and API.
- Responsibilities:
  - read JSON config at startup;
  - manage camera channels;
  - dispatch prepared images to correct YOLO instance;
  - collect detection results;
  - persist events to DB;
  - expose HTTP/WebSocket API for WEB UI;
  - handle retraining requests.

### Database (DB)

- Role: persistent storage.
- Stores:
  - inspection events (sole ID, timestamp, channel, result);
  - original images;
  - annotated images with bounding boxes;
  - expert verification labels;
  - model versions;
  - configuration snapshots.

## User interface

### WEB UI

- Role: human operator and expert interface.
- Views:
  - history list with filtering;
  - detail view: original image, annotated image, probability, status;
  - expert feedback buttons: correct / incorrect;
  - multi-channel tabs (1, 2, 3) with per-channel YOLO settings;
  - live camera preview for mechanical setup;
  - settings form;
  - retraining panel with date-range restriction.

## Development / testing components

### Fake input data (factory environment emulation)

- Role: offline input generator.
- Used when cameras are not connected.
- Produces synthetic frames or replays recorded frames.

### Artificial image generator

- Role: augment datasets to simulate factory disturbances.
- Applied to:
  - initial dataset;
  - learning dataset.
- Transformations:
  - lighting effects;
  - PNG pattern overlays (dust, dirt, lens contamination);
  - rotation (±5°);
  - other noise.

### Learning Dataset

- Role: curated data used to train / retrain the model.
- Sources:
  - initial dataset;
  - artificial generator output;
  - verified production data from expert feedback.

### Learning / Training module

- Role: run model training and retraining.
- Inputs:
  - Learning Dataset;
  - configuration (hyperparameters, date range).
- Outputs:
  - new model weights;
  - training metrics;
  - updated AI Model entry.