README.md

---
license: mit
license_link: LICENSE
library_name: openvino
pipeline_tag: object-detection
tags:
  - openvino
  - intel
  - yolo
  - yolo26
  - object-counting
  - coco
  - edge-ai
  - metro
  - dlstreamer
language:
  - en
---

# Object Counting

| Property | Value |
|---|---|
| **Category** | Object Detection + Counting (80-class COCO) |
| **Base Model** | [YOLO26](https://docs.ultralytics.com/models/yolo26/) (Ultralytics) |
| **Source Framework** | PyTorch (Ultralytics) |
| **Supported Precisions** | FP32, FP16, INT8 (mixed-precision) |
| **Inference Engine** | OpenVINO |
| **Hardware** | CPU, GPU, NPU |
| **Detected Class(es)** | All 80 COCO classes (counted per class) |

---

## Overview

Object Counting is a Metro Analytics use case that detects objects and reports
how many of each class are present in an image or per video frame.
It is built on [YOLO26](https://docs.ultralytics.com/models/yolo26/), a
state-of-the-art real-time object detector, quantized to INT8 for efficient
inference on Intel hardware.
Counting is implemented as a thin aggregation layer on top of the strongest
general-purpose detector, which keeps it accurate and reusable across classes.
The DLStreamer sample below demonstrates this on a traffic scene sample video,
counting **person**, **bicycle**, and **car** detections per frame.

Typical Metro deployments include:

- **Occupancy Counting** -- count people on a platform or in a waiting area.
- **Vehicle Counting** -- count cars, buses, and trucks at an intersection.
- **Inventory Counting** -- count bags, bottles, or other items in a zone.
- **Throughput Metrics** -- aggregate per-frame counts into time series.

Available variants: `yolo26n`, `yolo26s`, `yolo26m`, `yolo26l`, `yolo26x`.
Smaller variants (`yolo26n`, `yolo26s`) are recommended for high-FPS edge deployment; larger variants improve recall for small or distant objects.

For line-crossing counts (directional entry/exit), see the
[vehicle-entry-exit-logging](../vehicle-entry-exit-logging/) use case.

---

## Prerequisites

- Python 3.11+
- [Install OpenVINO](https://docs.openvino.ai/2026/get-started/install-openvino.html) (latest version)
- [Install Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/get_started/install/install_guide_ubuntu.html) (latest version)

Create and activate a Python virtual environment before running the scripts:

```bash
python3 -m venv .venv --system-site-packages
source .venv/bin/activate
```

> **Note:** The `--system-site-packages` flag is required so the virtual
> environment can access the system-installed OpenVINO and DLStreamer Python
> packages.

---

## Getting Started

### Download and Quantize Model

Run the provided script to download, export to OpenVINO IR, and optionally quantize:

```bash
chmod +x export_and_quantize.sh
./export_and_quantize.sh
```

This exports the default **yolo26n** model in **FP16** precision.

#### Optional: Select a Different Variant or Precision

```bash
./export_and_quantize.sh yolo26n FP32   # full-precision
./export_and_quantize.sh yolo26n INT8   # quantized
./export_and_quantize.sh yolo26s        # larger variant, default FP16
```

Replace `yolo26n` with any variant (`yolo26s`, `yolo26m`, `yolo26l`, `yolo26x`).
The second argument selects the precision (`FP32`, `FP16`, `INT8`); the default is **FP16**.

The script performs the following steps:

1. Installs dependencies (`openvino`, `ultralytics`; adds `nncf` for INT8).
2. Downloads a sample test image (`test.jpg`) and a sample test video
   (`test_video.mp4`, the
   [`person-bicycle-car-detection.mp4`](https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4)
   street scene from Intel IoT DevKit's sample-videos repository).
3. Downloads the PyTorch weights and exports to OpenVINO IR.
4. *(INT8 only)* Quantizes the model using NNCF post-training quantization.

Output files:

- `yolo26n_openvino_model/` -- FP32 or FP16 OpenVINO IR model directory.
- `yolo26n_objcount_int8.xml` / `yolo26n_objcount_int8.bin` -- INT8 quantized model *(only when `INT8` is selected)*.

#### Precision / Device Compatibility

| Precision | CPU | GPU | NPU |
|---|---|---|---|
| FP32 | Yes | Yes | No |
| FP16 | Yes | Yes | Yes |
| INT8 | Yes | Yes | Yes |

> **Note:** The INT8 calibration uses the bundled sample image.
> For production accuracy, replace it with a representative set of frames from
> the target deployment site.

### OpenVINO Sample

The sample below runs YOLO26 inference, then aggregates detections into a
per-class count and a total count for a single image.
YOLO26 is end-to-end (NMS-free), so no manual non-maximum suppression is needed.
Change the `device` string to run on CPU, GPU, or NPU.

```python
from collections import Counter

import cv2
import numpy as np
import openvino as ov

CONF_THRESHOLD = 0.4
INPUT_SIZE = 640

core = ov.Core()
model = core.read_model("yolo26n_openvino_model/yolo26n.xml")

# YOLO26 embeds the 80 COCO class names in rt_info -- read them instead of
# hardcoding the list. Ultralytics separates multi-word names with
# underscores (e.g. "traffic_light"), so restore spaces for display.
COCO_NAMES = [
    name.replace("_", " ")
    for name in model.get_rt_info()["model_info"]["labels"].value.split()
]

# Change device to "GPU" or "NPU" to run on integrated GPU or NPU.
compiled = core.compile_model(model, "CPU")

image = cv2.imread("test.jpg")
h0, w0 = image.shape[:2]

blob = cv2.resize(image, (INPUT_SIZE, INPUT_SIZE))
blob = cv2.cvtColor(blob, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
blob = blob.transpose(2, 0, 1)[np.newaxis, ...]  # NCHW

# YOLO26 end-to-end output: [1, 300, 6] = [x1, y1, x2, y2, confidence, class_id]
output = compiled([blob])[compiled.output(0)][0]
dets = output[output[:, 4] >= CONF_THRESHOLD]

sx, sy = w0 / INPUT_SIZE, h0 / INPUT_SIZE
counts = Counter(COCO_NAMES[int(d[5])] for d in dets)

print(f"Total objects: {len(dets)}")
print("Object counts:")
for name, n in sorted(counts.items(), key=lambda kv: (-kv[1], kv[0])):
    print(f"  {name}: {n}")

colors = np.random.RandomState(42).randint(0, 255, (80, 3)).tolist()
for det in dets:
    x1, y1, x2, y2 = (int(det[0] * sx), int(det[1] * sy),
                      int(det[2] * sx), int(det[3] * sy))
    cid = int(det[5])
    cv2.rectangle(image, (x1, y1), (x2, y2), colors[cid], 2)
    cv2.putText(image, COCO_NAMES[cid], (x1, y1 - 5),
                cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[cid], 2)

summary = ", ".join(f"{n} {name}" for name, n in counts.items())
cv2.putText(image, summary[:60], (10, 30),
            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.imwrite("output_openvino.jpg", image)
```

**Device targets:**

- `"CPU"` -- default, works on all Intel platforms.
- `"GPU"` -- Intel integrated or discrete GPU.
- `"NPU"` -- Intel NPU (validate with `benchmark_app -d NPU`).

### Try It on a Sample Image

The `export_and_quantize.sh` script downloads `test.jpg` automatically.
Re-run the OpenVINO sample above.
The script reads `test.jpg`, prints the per-class counts to the console, and writes the annotated frame to `output_openvino.jpg`.

Expected console output (representative):

```text
Total objects: 5
Object counts:
  person: 4
  bus: 1
```

#### Expected Output

![OpenVINO expected output](expected_output_openvino.jpg)

### DLStreamer Sample

The pipeline below runs the FP16 YOLO26 detector on the sample video via
`gvadetect`, overlays bounding boxes with `gvawatermark`, saves the annotated
result to `output_dlstreamer.mp4`, and prints the per-frame **person**,
**bicycle**, and **car** counts by reading the `GstAnalytics` detection
metadata. The sample video
([`person-bicycle-car-detection.mp4`](https://github.com/intel-iot-devkit/sample-videos/blob/master/person-bicycle-car-detection.mp4))
is a street scene containing pedestrians, a cyclist, and cars, matching the
three classes counted below.

> **Notes on running this sample:**
>
> - Use the FP16 IR (`yolo26n_openvino_model/yolo26n.xml`). Class names are
>   read automatically from the model's embedded `metadata.yaml` by
>   DLStreamer 2026.0+ -- no external `labels-file` is required.
> - Export `PYTHONPATH` so the DLStreamer Python module is importable:
>
>   ```bash
>   source /opt/intel/openvino_2026/setupvars.sh
>   source /opt/intel/dlstreamer/scripts/setup_dls_env.sh
>   export PYTHONPATH=/opt/intel/dlstreamer/python:\
>   /opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-}
>   ```

```python
from collections import Counter

import gi

gi.require_version("Gst", "1.0")
gi.require_version("GstAnalytics", "1.0")
from gi.repository import Gst, GLib, GstAnalytics

Gst.init([])

INPUT_VIDEO = "test_video.mp4"

# Only these classes are counted; the sample video contains pedestrians,
# a cyclist, and cars.
CLASSES_OF_INTEREST = {"person", "bicycle", "car"}

# For CPU: change device=GPU to device=CPU.
# For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended).
pipeline_str = (
    f"filesrc location={INPUT_VIDEO} ! decodebin3 ! "
    "videoconvert ! "
    "gvadetect model=yolo26n_openvino_model/yolo26n.xml "
    "device=GPU "
    "threshold=0.4 ! queue ! "
    "gvawatermark ! videoconvert ! video/x-raw,format=I420 ! "
    "openh264enc ! h264parse ! "
    "mp4mux ! filesink name=sink location=output_dlstreamer.mp4"
)
pipeline = Gst.parse_launch(pipeline_str)


def on_buffer(pad, info):
    buf = info.get_buffer()
    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
    if rmeta is None:
        return Gst.PadProbeReturn.OK
    counts = Counter()
    idx = 1
    while True:
        ok, od = rmeta.get_od_mtd(idx)
        if not ok:
            break
        label = GLib.quark_to_string(od.get_obj_type())
        if label in CLASSES_OF_INTEREST:
            counts[label] += 1
        idx += 1
    if counts:
        summary = ", ".join(f"{n} {name}" for name, n in counts.items())
        print(f"Object counts: {summary}", flush=True)
    return Gst.PadProbeReturn.OK


sink = pipeline.get_by_name("sink")
sink.get_static_pad("sink").add_probe(Gst.PadProbeType.BUFFER, on_buffer)

pipeline.set_state(Gst.State.PLAYING)
bus = pipeline.get_bus()
bus.timed_pop_filtered(
    Gst.CLOCK_TIME_NONE,
    Gst.MessageType.EOS | Gst.MessageType.ERROR,
)
pipeline.set_state(Gst.State.NULL)
```

**Device targets:**

- `device=GPU` -- default in the sample code.
- `device=CPU` -- change `device=GPU` to `device=CPU`.
- `device=NPU` -- change `device=GPU` to `device=NPU`; use `batch-size=1` and `nireq=4` for best NPU utilization.

#### Expected Output

![DLStreamer expected output](expected_output_dlstreamer.gif)

---

## License

Licensed under the MIT License. See [LICENSE](LICENSE) for details.

## References

- [YOLO26 Documentation](https://docs.ultralytics.com/models/yolo26/)
- [Ultralytics Object Counting Guide](https://docs.ultralytics.com/guides/object-counting/)
- [Intel DLStreamer gvadetect](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/elements/gvadetect.html)
- [OpenVINO Documentation](https://docs.openvino.ai/)
- [NNCF Post-Training Quantization](https://docs.openvino.ai/latest/nncf_ptq_introduction.html)
- [Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/index.html)