Sync delivery-package-verification from metro-analytics-catalog

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+expected_output_dlstreamer.gif filter=lfs diff=lfs merge=lfs -text
+expected_output_openvino.gif filter=lfs diff=lfs merge=lfs -text

LICENSE

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+MIT License
+
+Copyright (c) Intel Corporation.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE

README.md

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+---
+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)

export_and_quantize.sh

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+#!/usr/bin/env bash
+# SPDX-License-Identifier: MIT
+# Copyright (C) Intel Corporation
+#
+# Export a YOLO26 detector to OpenVINO IR for the delivery-package-verification
+# use case (COCO backpack/handbag/suitcase relabeled to "package" at runtime).
+# Usage: ./export_and_quantize.sh [MODEL_VARIANT] [PRECISION]
+# Example: ./export_and_quantize.sh yolo26n FP16
+
+set -euo pipefail
+
+MODEL_NAME="${1:-yolo26n}"
+PRECISION="${2:-FP16}"
+PRECISION="$(echo "${PRECISION}" | tr '[:lower:]' '[:upper:]')"
+
+if [[ "${PRECISION}" != "FP32" && "${PRECISION}" != "FP16" && "${PRECISION}" != "INT8" ]]; then
+    echo "ERROR: unsupported precision '${PRECISION}'. Choose FP32, FP16, or INT8." >&2
+    exit 1
+fi
+
+echo "--- Installing dependencies ---"
+if [[ "${PRECISION}" == "INT8" ]]; then
+    pip install -qU openvino nncf ultralytics
+else
+    pip install -qU openvino ultralytics
+fi
+
+# Ask for approval before downloading models and sample files
+echo ""
+echo "This script will download:"
+echo "  - Model weights and/or sample files"
+echo ""
+read -p "Continue with downloads? (yes/no): " APPROVAL
+if [[ "${APPROVAL}" != "yes" ]]; then
+    echo "Download cancelled by user."
+    exit 0
+fi
+echo ""
+echo "--- Downloading sample test image ---"
+if [[ ! -f test.jpg ]]; then
+    wget -q -O test.jpg https://ultralytics.com/images/bus.jpg
+    echo "Downloaded: test.jpg"
+else
+    echo "Already present: test.jpg"
+fi
+echo ""
+echo "--- Downloading sample test video ---"
+if [[ ! -f test_video.mp4 ]]; then
+    wget -q -O test_video.mp4 \
+        "https://www.pexels.com/download/video/6170052/?fps=25&w=540&h=960"
+    echo "Downloaded: test_video.mp4"
+else
+    echo "Already present: test_video.mp4"
+fi
+
+if [[ "${PRECISION}" == "FP32" ]]; then
+    HALF_FLAG="False"
+    EXPORT_LABEL="FP32"
+else
+    HALF_FLAG="True"
+    EXPORT_LABEL="FP16"
+fi
+
+echo "--- Exporting ${MODEL_NAME} to OpenVINO IR (${EXPORT_LABEL}) ---"
+python3 -c "
+from ultralytics import YOLO
+
+model = YOLO('${MODEL_NAME}.pt')
+model.export(format='openvino', half=${HALF_FLAG}, dynamic=False, imgsz=640)
+print('Export complete: ${MODEL_NAME}_openvino_model/')
+"
+
+echo "--- Writing package label map (relabels backpack/handbag/suitcase -> package) ---"
+python3 - "${MODEL_NAME}" <<'PY'
+import sys
+import yaml
+
+name = sys.argv[1]
+with open(f"{name}_openvino_model/metadata.yaml") as f:
+    meta = yaml.safe_load(f)
+names = meta["names"]
+labels = [names[i] for i in range(len(names))]
+for i in (24, 26, 28):  # backpack, handbag, suitcase -> package
+    labels[i] = "package"
+with open("coco_package_labels.txt", "w") as f:
+    f.write("\n".join(labels) + "\n")
+print(f"Wrote coco_package_labels.txt ({len(labels)} labels)")
+PY
+
+if [[ "${PRECISION}" == "INT8" ]]; then
+    echo "--- Quantizing to INT8 with NNCF ---"
+    python3 -c "
+import nncf
+import openvino as ov
+import numpy as np
+import cv2
+
+core = ov.Core()
+model = core.read_model('${MODEL_NAME}_openvino_model/${MODEL_NAME}.xml')
+
+img = cv2.imread('test.jpg')
+img = cv2.resize(img, (640, 640))
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
+img = img.transpose(2, 0, 1)[np.newaxis, ...]
+
+def transform_fn(data_item):
+    return img
+
+calibration_dataset = nncf.Dataset(list(range(300)), transform_fn)
+
+quantized = nncf.quantize(
+    model,
+    calibration_dataset,
+    preset=nncf.QuantizationPreset.MIXED,
+    subset_size=300,
+)
+
+ov.save_model(quantized, '${MODEL_NAME}_package_int8.xml')
+print('Quantization complete: ${MODEL_NAME}_package_int8.xml')
+"
+fi
+echo "--- Done ---"