README.md

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

# Delivery/Package Verification

| Property | Value |
|---|---|
| **Category** | Object Detection (Package and Parcel Detection) |
| **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)** | `package` (COCO `backpack`/`handbag`/`suitcase`, relabeled) |

---

## Overview

Delivery/Package Verification is a Metro Analytics use case that detects and
counts delivery parcels, bags, and luggage items in camera feeds.
It is built on [YOLO26](https://docs.ultralytics.com/models/yolo26/), a
state-of-the-art real-time object detector trained on the COCO dataset,
quantized to INT8 and filtered at runtime to the COCO classes that best match
delivery parcels -- `backpack`, `handbag`, and `suitcase` -- which are all
relabeled to a single `package` class in the output.

These COCO classes provide reliable coverage for typical delivery and package
verification scenarios (for example a courier carrying a cardboard box) without
requiring a custom-trained model.
For label or text reading on packages, pair this with the
[ocr-text-recognition](../ocr-text-recognition/) use case.

Typical Metro deployments include:

- **Delivery Dock Monitoring** -- verify parcels placed or removed at a loading area.
- **Abandoned Luggage Detection** -- flag unattended bags on platforms.
- **Package Counting** -- count parcels on a conveyor or at a drop-off zone.
- **Theft Prevention** -- alert when a package disappears from a monitored area.

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

---

## 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
```

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`).
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_package_int8.xml` / `.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 |

### OpenVINO Sample

The sample below runs YOLO26 inference on the sample video, filters detections
to the delivery-package classes (COCO `backpack`, `handbag`, `suitcase`, all
shown as `package`), annotates each frame, and writes the result to
`output_openvino.mp4` while printing the package count per frame.
Frames are letterboxed (aspect-ratio-preserving resize with padding) before
inference so the input matches how DLStreamer's `gvadetect` preprocesses.
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
import cv2
import numpy as np
import openvino as ov

# COCO classes used as delivery-package proxies; all shown as "package".
PACKAGE_CLASS_IDS = {24, 26, 28}  # backpack, handbag, suitcase
PACKAGE_LABEL = "package"
BOX_COLOR = (0, 200, 0)
CONF_THRESHOLD = 0.25
INPUT_SIZE = 640
INPUT_VIDEO = "test_video.mp4"

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

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


def letterbox(image, size=INPUT_SIZE):
    """Resize keeping aspect ratio and pad to a square (matches gvadetect)."""
    h, w = image.shape[:2]
    ratio = min(size / h, size / w)
    nw, nh = int(round(w * ratio)), int(round(h * ratio))
    resized = cv2.resize(image, (nw, nh))
    canvas = np.full((size, size, 3), 114, dtype=np.uint8)
    pad_x, pad_y = (size - nw) // 2, (size - nh) // 2
    canvas[pad_y:pad_y + nh, pad_x:pad_x + nw] = resized
    return canvas, ratio, pad_x, pad_y


cap = cv2.VideoCapture(INPUT_VIDEO)
fps = cap.get(cv2.CAP_PROP_FPS) or 30.0
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
writer = cv2.VideoWriter(
    "output_openvino.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))

frame_idx = 0
while True:
    ok, frame = cap.read()
    if not ok:
        break
    frame_idx += 1

    padded, ratio, pad_x, pad_y = letterbox(frame, INPUT_SIZE)
    blob = cv2.cvtColor(padded, 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]
    mask = (output[:, 4] >= CONF_THRESHOLD) & np.isin(
        output[:, 5].astype(int), list(PACKAGE_CLASS_IDS))
    dets = output[mask]

    for det in dets:
        # Undo the letterbox padding and scaling to map boxes back to the frame.
        x1 = int((det[0] - pad_x) / ratio)
        y1 = int((det[1] - pad_y) / ratio)
        x2 = int((det[2] - pad_x) / ratio)
        y2 = int((det[3] - pad_y) / ratio)
        conf = float(det[4])
        label = f"{PACKAGE_LABEL} {conf:.2f}"
        cv2.rectangle(frame, (x1, y1), (x2, y2), BOX_COLOR, 2)
        cv2.putText(frame, label, (x1, y1 - 5),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.6, BOX_COLOR, 2)

    writer.write(frame)
    print(f"Frame {frame_idx}: Packages detected: {len(dets)}", flush=True)

cap.release()
writer.release()
```

**Device targets:**

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

#### Expected Output

![OpenVINO expected output](expected_output_openvino.gif)

### DLStreamer Sample

The pipeline below runs the FP16 YOLO26 detector on the sample video via
`gvadetect`, renders only package bounding boxes using `gvawatermark` with
`displ-cfg=show-roi=package`, saves the annotated result to
`output_dlstreamer.mp4`, and prints the package count per frame.

> **Notes on running this sample:**
>
> - Use the FP16 IR (`yolo26n_openvino_model/yolo26n.xml`) together with the
>   `coco_package_labels.txt` label map produced by `export_and_quantize.sh`.
>   It relabels the COCO `backpack`/`handbag`/`suitcase` classes to `package`,
>   so `gvadetect` emits a single `package` class and `gvawatermark` renders a
>   `package` label.
> - 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
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"
PACKAGE_LABELS = {"package"}

# 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 "
    "labels-file=coco_package_labels.txt "
    "device=GPU "
    "threshold=0.25 ! queue ! "
    "gvawatermark displ-cfg=show-roi=package ! "
    "videoconvert ! video/x-raw,format=I420 ! "
    "openh264enc bitrate=4000000 ! 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
    packages = []
    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 PACKAGE_LABELS:
            packages.append(label)
        idx += 1
    if packages:
        print(f"Packages detected: {len(packages)} ({', '.join(packages)})",
              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/)
- [OpenVINO Documentation](https://docs.openvino.ai/)
- [Intel DLStreamer](https://docs.openedgeplatform.intel.com/2026.0/edge-ai-libraries/dlstreamer/index.html)