# Zone-based counting

*Estimated read time: 4 minutes*

Many applications (e.g., traffic, retail, safety) need counts within specific regions, not across the entire frame. Zone-based counting lets you define polygonal areas and track totals per zone, optionally per class. This guide uses a YOLOv8 detection model and an interactive zone tool so you can adjust zones live, without changing the code.

## How it works

* A detection model runs on each frame to identify objects.
* A `ZoneCounter` analyzer checks which detections fall inside your polygons and updates the counts.
* You can interactively adjust zone vertices in the display window to fine-tune boundaries—no code changes needed.

## Basic Zone Counter

<figure><img src="https://387437463-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fw4TFcrlOvSs7ZfsEpUnx%2Fuploads%2Fgit-blob-f900f8d5336cf08fa31dd1d3ea33a687a87fceac%2Faxelera-cookbook--traffic--cars-on-a-freeway-passing-through-two-zone-counters.gif?alt=media" alt="Cars on a freeway passing through two zone counters."><figcaption><p>Cars on a freeway passing through two zone counters.</p></figcaption></figure>

Use this static setup when you want fixed polygon zones with automatic overlays. Update the coordinates in `polygon_zones` to match your scene so `ZoneCounter` can draw per-zone totals and populate `inference_result.zone_counts`.

### Example

{% code overflow="wrap" %}

```python
from degirum_tools import (
    ModelSpec,
    Display,
    ZoneCounter,
    AnchorPoint,
    remote_assets,
)
import degirum_tools

class_list = ["car", "motorbike", "truck"]
model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_axelera_metis_1",
    zoo_url="degirum/axelera",
    inference_host_address="@local",
    model_properties={
        "device_type": ["AXELERA/METIS"],
        "overlay_color": [(255, 0, 0)],
        "output_class_set": set(class_list),
    },
)
model = model_spec.load_model()

video_source = remote_assets.traffic

polygon_zones = [
    [[265, 260], [730, 260], [870, 450], [120, 450]],
    [[400, 100], [610, 100], [690, 200], [320, 200]],
]

zone_counter = ZoneCounter(
    count_polygons=polygon_zones,
    class_list=class_list,
    per_class_display=True,
    triggering_position=AnchorPoint.CENTER,
)

degirum_tools.attach_analyzers(model, [zone_counter])

with Display("AI Camera") as output_display:
    for inference_result in degirum_tools.predict_stream(model, video_source):
        output_display.show(inference_result.image_overlay)
        print("Zone counts:", inference_result.zone_counts)
```

{% endcode %}

## Interactive Zone Counter

<figure><img src="https://387437463-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fw4TFcrlOvSs7ZfsEpUnx%2Fuploads%2Fgit-blob-ac5ddb672d7dfb985d23e819765acc39cd9d4267%2Faxelera-cookbook--traffic--cars-on-a-freeway-passing-through-two-interactive-zone-counters.gif?alt=media" alt="Cars on a freeway passing through two interactive zone counters."><figcaption><p>Cars on a freeway passing through two interactive zone counters.</p></figcaption></figure>

Pick the interactive workflow when you need to reposition zones during a live session. Drag an entire polygon with the left mouse button, then right-click a corner handle to refine that vertex while the stream keeps running.

{% hint style="info" %}

* **Interactive editing**: click and drag zone vertices in the display window to reshape; press `q` to close the window.
* **Classes**: use `output_class_set` to filter which classes are counted; omit it to include all detected labels.
* **Trigger point**: `AnchorPoint.CENTER` works well for people and vehicles; try `BOTTOM_CENTER` for more ground-contact accuracy.
* **Coordinates**: define polygons in image pixels for the resolution you'll display.
* **Performance**: fewer, larger zones are more efficient than many small ones; filtering to needed classes also helps reduce overhead.
* **Mouse controls**: use the left mouse button (LMB) to drag the entire zone; right-click (RMB) a corner handle to adjust that vertex precisely.
  {% endhint %}

### Example

{% code overflow="wrap" %}

```python
from degirum_tools import (
    ModelSpec,
    Display,
    ZoneCounter,
    AnchorPoint,
    remote_assets,
)
import degirum_tools

class_list = ["car", "motorbike", "truck"]
model_spec = ModelSpec(
    model_name="yolov8n_coco--640x640_quant_axelera_metis_1",
    zoo_url="degirum/axelera",
    inference_host_address="@local",
    model_properties={
        "device_type": ["AXELERA/METIS"],
        "overlay_color": [(255, 0, 0)],
        "output_class_set": set(class_list),
    },
)
model = model_spec.load_model()

video_source = remote_assets.traffic  # or your own video path

polygon_zones = [
    [[265, 260], [730, 260], [870, 450], [120, 450]],
    [[400, 100], [610, 100], [690, 200], [320, 200]],
]

zone_counter = ZoneCounter(
    count_polygons=polygon_zones,
    class_list=class_list,
    per_class_display=True,
    triggering_position=AnchorPoint.CENTER,
    window_name="AI Camera",
)

degirum_tools.attach_analyzers(model, [zone_counter])

with Display("AI Camera") as output_display:
    for inference_result in degirum_tools.predict_stream(model, video_source):
        output_display.show(inference_result.image_overlay)
```

{% endcode %}