# Entity Relationship Diagram (ERD)

This document provides the complete Entity Relationship Diagram (ERD) and relational schema for the **reTraining** platform SQLite database (`app.db`), as implemented in [`backend/db.py`](file:///home/asus/feedmill_semarang_project/reTraining/backend/db.py).

---

## Architectural Storage Model

The platform uses a **hybrid storage architecture**:
* **SQLite Database (`data/app.db`)**: Stores relational metadata, foreign keys, job queues, triage rules, review states, model versions, and benchmark run telemetry.
* **Disk Filesystem (`data/projects/<slug>/`)**: Stores raw/extracted image pixels (`.jpg`), YOLO annotation labels (`.txt`), immutable dataset definitions (`data.yaml`), and trained neural network checkpoints (`.pt`).

---

## Mermaid Entity Relationship Diagram

```mermaid
erDiagram
    PROJECTS ||--o{ PROJECT_CLASSES : "defines"
    PROJECTS ||--o{ BATCHES : "contains"
    PROJECTS ||--o{ DATASETS : "compiles"
    PROJECTS ||--o{ DATASET_ITEMS : "aggregates"
    PROJECTS ||--o{ BASE_DATASETS : "mounts"
    PROJECTS ||--o{ TRIAGE_RULES : "configures"
    PROJECTS ||--o{ MODEL_VERSIONS : "produces"
    PROJECTS ||--o{ JOBS : "executes"
    PROJECTS ||--o{ VIDEO_CLOCK : "indexes"
    PROJECTS ||--o{ COUNT_RUNS : "benchmarks"

    BATCHES ||--o{ FRAMES : "extracts"
    BATCHES ||--o{ JOBS : "triggers"

    FRAMES ||--o{ ANNOTATIONS : "annotates"
    FRAMES ||--o{ DATASET_ITEMS : "includes"

    ANNOTATIONS ||--o| ANNOTATION_OVERRIDES : "overrides"

    DATASETS ||--o{ DATASET_ITEMS : "contains"

    PROJECTS {
        integer id PK "AUTOINCREMENT"
        text slug UK "Unique project identifier"
        text name "Display title"
        text label_type "bbox | polygon"
        text base_model_path "Path to initial .pt model"
        text base_model_kind "uploaded | pretrained | trained"
        text video_root "Root path for video archive"
        integer val_every "Validation split step (default 5)"
        text secondary_model_path "Optional companion model"
        text secondary_model_name "Companion model label"
        text secondary_model_classes "Companion model class JSON"
        text augment "Augmentation parameters JSON"
        real created_at "Unix epoch timestamp"
    }

    PROJECT_CLASSES {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        integer class_id "YOLO integer class index"
        text name "Class display name"
        text prompt "SAM3 natural language zero-shot prompt"
    }

    BATCHES {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        text video_path "Source video relative path"
        text date_label "Video date directory"
        text batch_label "Video file base name"
        real start_sec "Trim range start in seconds"
        real end_sec "Trim range end in seconds"
        real fps "Extraction sampling rate"
        text status "extracting | extracted | labeling | reviewing | approved | merged | failed"
        integer frame_count "Extracted frames count"
        real created_at "Unix epoch timestamp"
        real merged_at "Timestamp when batch merged to dataset"
    }

    FRAMES {
        integer id PK "AUTOINCREMENT"
        integer batch_id FK "References batches(id)"
        integer idx "Frame index within batch"
        text filename "Image file name (000001.jpg)"
        integer width "Frame pixel width"
        integer height "Frame pixel height"
        text review_status "pending | approved | rejected"
    }

    ANNOTATIONS {
        integer id PK "AUTOINCREMENT"
        integer frame_id FK "References frames(id)"
        integer class_id "Target YOLO class index"
        text geometry "Box or Polygon coordinates JSON"
        real score "SAM3 confidence score (0.0 - 1.0)"
        text source "auto | manual"
        real created_at "Unix epoch timestamp"
    }

    ANNOTATION_OVERRIDES {
        integer annotation_id PK, FK "References annotations(id)"
        text verdict "keep | ignore | reclass"
        integer target_class "New class index if reclassified"
        real decided_at "Unix epoch timestamp"
    }

    DATASETS {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        text name "Dataset version name (e.g. Master v1)"
        text note "Release description"
        text rule_version "Triage rule version identifier"
        text rules_json "Frozen triage rules JSON snapshot"
        real created_at "Unix epoch timestamp"
    }

    DATASET_ITEMS {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        integer dataset_id FK "References datasets(id)"
        integer frame_id FK "References frames(id)"
        text split "train | val"
        text image_rel "Relative image path in dataset"
        text label_rel "Relative label path in dataset"
        real added_at "Unix epoch timestamp"
    }

    BASE_DATASETS {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        text name "External baseline dataset name"
        text source "Import origin description"
        integer image_count "Number of baseline images"
        integer box_count "Number of baseline bounding boxes"
        text classes "JSON array of class names"
        real created_at "Unix epoch timestamp"
    }

    TRIAGE_RULES {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        text stage "Filter stage (default: dataprep)"
        integer position "Rule execution order position"
        text name "Rule human readable name"
        text predicate "Evaluation expression (area, aspect, conf)"
        text action "keep | ignore | reclass"
        integer target_class "Target class index if reclass"
        real created_at "Unix epoch timestamp"
    }

    MODEL_VERSIONS {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        integer version "Incrementing version integer"
        text name "Descriptive model name (arch-labelType-epochs-classes-date)"
        text weights_path "Path to trained best.pt weights"
        text parent_model_path "Path to base model used as starting point"
        text metrics "Trained model evaluation metrics JSON"
        text base_metrics "Base model evaluation metrics JSON"
        text rule_version "Triage rule version used for training"
        text augment "Augmentation settings JSON used"
        real created_at "Unix epoch timestamp"
    }

    JOBS {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        integer batch_id FK "References batches(id)"
        text type "extract | autolabel | merge | train | count | clock-scan | truck-scan"
        text status "queued | running | done | failed | cancelled"
        text params "Job parameters JSON"
        integer progress "Current completed units"
        integer total "Total units of work"
        text message "Human-readable status update"
        text error "Failure message or traceback"
        text log "Detailed execution log lines"
        real created_at "Unix epoch timestamp"
        real started_at "Job start timestamp"
        real finished_at "Job completion timestamp"
    }

    VIDEO_CLOCK {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        text video_rel "Relative path to video file"
        text folder_date "Date folder extracted from path"
        text started_at "Burned-in OCR clock string (YYYY-MM-DD HH:MM:SS)"
        text working_day "Operational shift day (06:00 - 05:59 cutoff)"
        real confidence "OCR reading confidence"
        integer agreeing "Number of sample frames agreeing"
        text source "ocr | manual"
        text error "OCR parsing error message"
        real read_at "Timestamp when OCR scan was executed"
        integer truck_hits "Frames with truck detected"
        integer truck_samples "Total sampled frames for truck check"
        text truck_model "Model used for truck scan"
        real truck_checked_at "Timestamp of truck scan"
    }

    COUNT_RUNS {
        integer id PK "AUTOINCREMENT"
        integer project_id FK "References projects(id)"
        text video_rel "Relative path to video evaluated"
        text date_label "Video date directory"
        text batch_label "Video file base name"
        integer loading "Counted loading direction crossings"
        integer unloading "Counted unloading direction crossings"
        integer net "Calculated net count (loading - unloading)"
        integer ground_truth "Physical verified hand-tally count"
        integer frames "Total video frames evaluated"
        real seconds "Inference elapsed time in seconds"
        text params "Counting line & ByteTrack parameters JSON"
        text model_path "YOLO model weights path used"
        text error "Error message if benchmark failed"
        real counted_at "Unix epoch timestamp"
    }
```

---

## Entity Descriptions

### 1. Workspace & Multi-Project Isolation
* **`projects`**: Top-level entity isolating datasets, classes, models, and CCTV archives. Enforces geometry type (`bbox` vs `polygon`) and stores the active base model checkpoint.
* **`project_classes`**: YOLO class definitions for the project. Pairs each integer `class_id` with natural language text prompts utilized by SAM3 zero-shot auto-annotation.

### 2. Video Ingest & Annotation Lifecycle
* **`batches`**: A trimmed segment of raw industrial CCTV footage. Captures start/end timestamps, sampling FPS, frame counts, and progression from extraction to dataset merge.
* **`frames`**: Discrete JPEG stills extracted from a batch. Tracks individual review status (`pending`, `approved`, `rejected`).
* **`annotations`**: Object bounding boxes or polygon masks per frame with SAM3 / manual confidence scores and class assignments.
* **`annotation_overrides`**: Outlier inspection decisions (`keep`, `ignore`, `reclass`) resulting from interactive 2D scatter plot triage.

### 3. Master Datasets & Data Preparation
* **`datasets`**: Immutable, versioned master datasets (`v1`, `v2`, `v3`). Captures a permanent JSON snapshot of triage rules (`rules_json`) applied at compilation time.
* **`dataset_items`**: Junction entity binding frames into dataset versions with deterministic SHA-1 validation split partitioning (`train` vs `val`).
* **`base_datasets`**: External pre-labeled baseline datasets mounted as train-only supplements without polluting validation benchmarks.
* **`triage_rules`**: Ordered filtering predicates (e.g., box area, aspect ratio, confidence thresholds) applied during data preparation.

### 4. Continuous YOLO Retraining & Background Workers
* **`model_versions`**: Fine-tuned YOLO weights checkpoints. Stores side-by-side performance deltas ($\Delta\text{mAP50}$, $\Delta\text{Precision}$, $\Delta\text{Recall}$) evaluated against the identical frozen validation split.
* **`jobs`**: Centralized SQLite job queue for asynchronous background tasks (`extract`, `autolabel`, `train`, `count`, etc.) with atomic status management and progress streaming.

### 5. Shift OCR Indexing & Production Line Counter
* **`video_clock`**: OCR timestamps extracted from burned-in CCTV camera overlays, mapping recordings to 24-hour manufacturing shifts (06:00 AM to 05:59 AM next day) and detecting truck arrival presence.
* **`count_runs`**: Real-time production inference benchmarks running ByteTrack line-crossing counters against verified ground truth tallies.

---

## Performance & Database Indexes

The SQLite database enforces the following indexes to maintain sub-millisecond query performance:

| Index Name | Table | Indexed Columns | Purpose |
|---|---|---|---|
| `idx_frames_batch` | `frames` | `(batch_id, idx)` | Rapid frame lookup and sequential canvas scrubbing |
| `idx_annotations_frame` | `annotations` | `(frame_id)` | Sub-millisecond bounding box loading per canvas frame |
| `idx_batches_project` | `batches` | `(project_id)` | Fast batch library filtering by project |
| `idx_dataset_items_project`| `dataset_items` | `(project_id)` | Dataset compilation and split integrity checks |
| `idx_triage_rules_project` | `triage_rules` | `(project_id, stage, position)` | Fast ordered triage predicate evaluation |
| `idx_jobs_project` | `jobs` | `(project_id, created_at)` | UI job queue monitoring and polling |
| `idx_video_clock_project` | `video_clock` | `(project_id, working_day)` | Shift-based video library filtering |
| `idx_count_runs_project` | `count_runs` | `(project_id, date_label)` | Production counting benchmark reporting |