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4a3b87d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | # Simple YOLO vehicle-count server
Two endpoints (upload image → get counts):
- `POST /count_total` returns **total vehicles** only.
- `POST /count_by_type` returns **car/motorcycle/bus/truck + total**.
- `POST /priority_decision` accepts **two images** and returns which lane has priority + gate open time.
## Setup
```powershell
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
```
## Run
```powershell
python app.py
```
## Example request (PowerShell)
```powershell
$img = "C:\path\to\frame.jpg"
Invoke-RestMethod -Method Post `
-Uri "http://127.0.0.1:8000/count_by_type" `
-Form @{ image = Get-Item $img }
```
## Example request (2 images, priority)
```powershell
$a = "C:\path\to\laneA.jpg"
$b = "C:\path\to\laneB.jpg"
Invoke-RestMethod -Method Post `
-Uri "http://127.0.0.1:8000/priority_decision" `
-Form @{ image_a = Get-Item $a; image_b = Get-Item $b }
```
## Notes
- Vehicle classes are: car, motorcycle, bus, truck (COCO ids: 2, 3, 5, 7).
- The first run will download a YOLO model (`yolov8n.pt`) automatically.
- Set detection threshold with `CONF` (default `0.25`) and model with `YOLO_MODEL`.
- Gate time policy is configurable with `CAR_SEC`, `MOTORCYCLE_SEC`, `BUS_SEC`, `TRUCK_SEC`, plus `MIN_GATE_SEC` and `MAX_GATE_SEC`.
- If you have custom models for winner/time, place them in `Model/models/`:
- `winner_classifier.pkl`
- `gate_seconds_regressor.pkl`
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