Sync perimeter-breach-detection from metro-analytics-catalog

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.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

@@ -0,0 +1,21 @@
+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
+  - perimeter-breach-detection
+  - intrusion-detection
+  - zone-analytics
+  - tracking
+  - gstanalytics
+  - gvaanalytics
+  - edge-ai
+  - metro
+  - dlstreamer
+language:
+  - en
+---
+
+# Perimeter Breach Detection
+
+| Property | Value |
+|---|---|
+| **Category** | Object Detection + Tracking + Zone Analytics (GstAnalytics) |
+| **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** | `person` (COCO class 0) |
+
+---
+
+## Overview
+
+Perimeter Breach Detection is a Metro Analytics use case that flags people who breach a secured perimeter marked by yellow and black safety tape.
+It is built on [YOLO26](https://docs.ultralytics.com/models/yolo26/) for person detection, paired with a multi-object tracker that assigns persistent IDs across frames.
+The restricted region is a polygon that traces the safety tape: in the bundled sample video the yellow and black tape forms a diagonal boundary across the floor, and the default zone covers the keep-out side of that boundary.
+A person whose center falls inside the polygon is reported as a perimeter breach.
+The model is a quantized (INT8) state-of-the-art detector; smaller variants run at high FPS on edge hardware.
+
+Typical Metro deployments include:
+
+- **Fence-Line Protection** -- trigger when a person crosses a fence or barrier around depots, yards, or substations.
+- **Secured-Transportation Facilities** -- monitor taped-off loading docks, platforms, and maintenance bays that must stay clear.
+- **Work-Zone Safety** -- alert when a worker or bystander enters a taped hazard area near equipment or track work.
+- **Restricted-Area Enforcement** -- raise alerts when anyone enters a standoff boundary marked with safety tape.
+
+Available variants: `yolo26n`, `yolo26s`, `yolo26m`, `yolo26l`, `yolo26x`.
+Smaller variants (`yolo26n`, `yolo26s`) are recommended for high-FPS edge deployment.
+
+---
+
+## 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`, `opencv-python`; adds `nncf` for INT8).
+2. Downloads the sample worker-zone video (`worker-zone-detection.mp4`) into the current directory.
+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_perimeter_int8.xml` / `yolo26n_perimeter_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 frames from the bundled sample video.
+> For production accuracy, replace it with a representative set of frames from
+> the target deployment site.
+
+### OpenVINO Sample
+
+The sample below runs YOLO26 inference on the sample video and flags any person whose center falls inside the restricted zone as a breach.
+The zone is the `RESTRICTED_ZONE` polygon, which traces the yellow and black safety tape in the sample video's `1920x1080` pixel space: the tape runs as a diagonal boundary from `(1577, 0)` to `(434, 1079)`, and the polygon covers the keep-out side of that line.
+To adapt the perimeter to a different camera, edit the `RESTRICTED_ZONE` points.
+YOLO26 is end-to-end (NMS-free), so no manual non-maximum suppression is needed.
+Breaching people are drawn with a red box and a `BREACH` label; the annotated result is written to `output_openvino.mp4`.
+Change the `device` string to run on CPU, GPU, or NPU.
+
+```python
+import cv2
+import numpy as np
+import openvino as ov
+
+PERSON_CLASS_ID = 0
+CONF_THRESHOLD = 0.4
+INPUT_SIZE = 640
+INPUT_VIDEO = "worker-zone-detection.mp4"
+
+# Restricted zone traced from the yellow-and-black safety tape in the sample
+# video (1920x1080). The tape runs diagonally from (1577, 0) to (434, 1079);
+# this polygon covers the keep-out side of that boundary. Edit these points to
+# retrace the tape for a different camera.
+RESTRICTED_ZONE = np.array([[0, 0], [1577, 0], [434, 1079], [0, 1080]], dtype=np.int32)
+
+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")
+
+cap = cv2.VideoCapture(INPUT_VIDEO)
+fps = cap.get(cv2.CAP_PROP_FPS) or 25.0
+w0 = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+h0 = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+zone = RESTRICTED_ZONE
+
+writer = cv2.VideoWriter(
+    "output_openvino.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w0, h0)
+)
+
+sx, sy = w0 / INPUT_SIZE, h0 / INPUT_SIZE
+frame_idx = 0
+while True:
+    ok, frame = cap.read()
+    if not ok:
+        break
+    frame_idx += 1
+
+    blob = cv2.resize(frame, (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]
+    mask = (output[:, 4] >= CONF_THRESHOLD) & (output[:, 5].astype(int) == PERSON_CLASS_ID)
+
+    # Draw the translucent restricted zone first, then the person boxes on top.
+    overlay = frame.copy()
+    cv2.fillPoly(overlay, [zone], (0, 0, 255))
+    cv2.addWeighted(overlay, 0.25, frame, 0.75, 0, frame)
+    cv2.polylines(frame, [zone], True, (0, 255, 255), 2)
+
+    breaches = 0
+    for det in output[mask]:
+        x1, y1 = int(det[0] * sx), int(det[1] * sy)
+        x2, y2 = int(det[2] * sx), int(det[3] * sy)
+        center = (int((x1 + x2) / 2), int((y1 + y2) / 2))
+        inside = cv2.pointPolygonTest(zone, center, False) >= 0
+        color = (0, 0, 255) if inside else (0, 255, 0)
+        cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+        if inside:
+            breaches += 1
+            cv2.putText(
+                frame, "BREACH", (x1, max(y1 - 6, 12)),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2,
+            )
+
+    cv2.putText(
+        frame, f"Breaches: {breaches}", (10, 30),
+        cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2,
+    )
+    if breaches:
+        print(f"frame {frame_idx}: perimeter breach - {breaches} person(s) inside zone", flush=True)
+    writer.write(frame)
+
+cap.release()
+writer.release()
+print("Saved: output_openvino.mp4")
+```
+
+**Device targets:**
+
+- `"CPU"` -- default, works on all Intel platforms.
+- `"GPU"` -- Intel integrated or discrete GPU.
+- `"NPU"` -- Intel NPU (different throughput profile; validate with `benchmark_app -d NPU`).
+
+#### Expected Output
+
+![OpenVINO expected output](expected_output_openvino.gif)
+
+`output_openvino.mp4` shows the restricted perimeter shaded in red, a green box around each person outside the zone, and a red `BREACH` box around anyone inside it.
+
+### DLStreamer Sample
+
+The pipeline below runs the FP16 YOLO26 detector on the sample video via `gvadetect`, tracks each person with `gvatrack`, and uses DLStreamer's `gvaanalytics` element to test membership in the restricted zone.
+The zone is the same `RESTRICTED_ZONE` polygon used by the OpenVINO sample, passed to `gvaanalytics` as a JSON zone, so no polygon math is required in the code.
+`gvaanalytics` attaches `GstAnalyticsZoneMtd` to every tracked person whose center falls inside the polygon.
+The pipeline ends in an `appsink`; for each frame a callback reads the analytics metadata, shades the restricted zone, draws a green box around people outside it and a red `BREACH` box around anyone inside, and writes the annotated result to `output_dlstreamer.mp4`.
+A perimeter-breach event is printed the first time each tracked person enters the zone.
+
+> **Notes on running this sample:**
+>
+> - Use the FP16 IR (`yolo26n_openvino_model/yolo26n.xml`).
+>   On DLStreamer 2026.1, `gvadetect` cannot auto-derive a YOLO post-processor
+>   from the INT8 model produced by the bundled script.
+>   To use the INT8 model, supply a matching `model-proc` JSON.
+> - 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
+import json
+import sys
+import gi
+
+gi.require_version("Gst", "1.0")
+gi.require_version("GstApp", "1.0")
+gi.require_version("GstAnalytics", "1.0")
+gi.require_version("DLStreamerMeta", "1.0")
+from gi.repository import Gst, GLib, GstApp, GstAnalytics, DLStreamerMeta
+
+Gst.init([])
+
+# Register DLStreamerMeta types so GstAnalytics iteration can handle them.
+_ov = sys.modules["gi.overrides.GstAnalytics"]
+_ov.__mtd_types__[DLStreamerMeta.ZoneMtd.get_mtd_type()] = DLStreamerMeta.relation_meta_get_zone_mtd
+
+# Import OpenCV after Gst.init to avoid a GStreamer re-initialization conflict.
+import cv2
+import numpy as np
+
+MODEL = "yolo26n_openvino_model/yolo26n.xml"
+VIDEO = "worker-zone-detection.mp4"
+DEVICE = "GPU"  # change to "CPU" or "NPU" as needed
+
+# Restricted zone traced from the yellow-and-black safety tape in the sample
+# video (1920x1080), matching the OpenVINO sample. Edit these points to retrace
+# the tape for a different camera.
+RESTRICTED_ZONE = np.array([[0, 0], [1577, 0], [434, 1079], [0, 1080]], dtype=np.int32)
+ZONE_JSON = json.dumps([{
+    "id": "restricted_zone",
+    "type": "polygon",
+    "points": [{"x": int(x), "y": int(y)} for x, y in RESTRICTED_ZONE],
+}])
+
+pipeline = Gst.parse_launch(
+    f"filesrc location={VIDEO} ! decodebin3 ! videoconvert ! "
+    f"gvadetect model={MODEL} device={DEVICE} threshold=0.4 ! queue ! "
+    f"gvatrack tracking-type=short-term-imageless ! queue ! "
+    f"gvaanalytics name=analytics ! queue ! "
+    f"videoconvert ! video/x-raw,format=BGR ! "
+    f"appsink name=sink emit-signals=true max-buffers=4 drop=false sync=false"
+)
+pipeline.get_by_name("analytics").set_property("zones", ZONE_JSON)
+
+writer = {"w": None}
+# Track IDs that have already triggered a breach event, so each intruder is
+# reported only once.
+flagged = set()
+
+
+def on_sample(appsink):
+    sample = appsink.emit("pull-sample")
+    if sample is None:
+        return Gst.FlowReturn.OK
+    buf = sample.get_buffer()
+    caps = sample.get_caps().get_structure(0)
+    w = caps.get_value("width")
+    h = caps.get_value("height")
+    ok_fr, fr_n, fr_d = caps.get_fraction("framerate")
+    fps = (fr_n / fr_d) if (ok_fr and fr_d) else 30.0
+
+    ok, minfo = buf.map(Gst.MapFlags.READ)
+    if not ok:
+        return Gst.FlowReturn.OK
+    frame = np.ndarray((h, w, 3), buffer=minfo.data, dtype=np.uint8).copy()
+    buf.unmap(minfo)
+
+    now = buf.pts / Gst.SECOND if buf.pts != Gst.CLOCK_TIME_NONE else 0.0
+
+    # Shade the restricted zone and outline the tape boundary.
+    overlay = frame.copy()
+    cv2.fillPoly(overlay, [RESTRICTED_ZONE], (0, 0, 255))
+    cv2.addWeighted(overlay, 0.25, frame, 0.75, 0, frame)
+    cv2.polylines(frame, [RESTRICTED_ZONE], True, (0, 255, 255), 2)
+
+    breaches = 0
+    rmeta = GstAnalytics.buffer_get_analytics_relation_meta(buf)
+    if rmeta:
+        for od in rmeta.iter_on_type(GstAnalytics.ODMtd):
+            if GLib.quark_to_string(od.get_obj_type()) != "person":
+                continue
+            _, x, y, bw, bh, _ = od.get_location()
+
+            # Find the tracking ID via the direct relation.
+            track_id = None
+            for trk in od.iter_direct_related(GstAnalytics.RelTypes.RELATE_TO, GstAnalytics.TrackingMtd):
+                success, tid, *_ = trk.get_info()
+                if success:
+                    track_id = tid
+                break
+
+            # gvaanalytics attaches a ZoneMtd relation when the person is in the zone.
+            in_zone = any(
+                True for _ in od.iter_direct_related(GstAnalytics.RelTypes.RELATE_TO, DLStreamerMeta.ZoneMtd)
+            )
+            color = (0, 0, 255) if in_zone else (0, 255, 0)
+            cv2.rectangle(frame, (int(x), int(y)), (int(x + bw), int(y + bh)), color, 3)
+            label = f"id {track_id}" if track_id is not None else "person"
+            if in_zone:
+                breaches += 1
+                cv2.putText(frame, f"BREACH {label}", (int(x), max(int(y) - 8, 14)),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+                if track_id is not None and track_id not in flagged:
+                    flagged.add(track_id)
+                    print(f"PERIMETER BREACH id={track_id} t={now:.1f}s "
+                          f"entered restricted zone at ({int(x + bw / 2)},{int(y + bh)})", flush=True)
+            else:
+                cv2.putText(frame, label, (int(x), max(int(y) - 8, 14)),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+    cv2.putText(frame, f"Breaches: {breaches}", (10, 40),
+                cv2.FONT_HERSHEY_SIMPLEX, 1.1, (0, 0, 255), 3)
+    if writer["w"] is None:
+        writer["w"] = cv2.VideoWriter(
+            "output_dlstreamer.mp4", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)
+        )
+    writer["w"].write(frame)
+    return Gst.FlowReturn.OK
+
+
+pipeline.get_by_name("sink").connect("new-sample", on_sample)
+pipeline.set_state(Gst.State.PLAYING)
+pipeline.get_bus().timed_pop_filtered(Gst.CLOCK_TIME_NONE, Gst.MessageType.EOS | Gst.MessageType.ERROR)
+pipeline.set_state(Gst.State.NULL)
+if writer["w"] is not None:
+    writer["w"].release()
+```
+
+Expected output:
+
+```text
+PERIMETER BREACH id=2 t=3.8s entered restricted zone at (799,1067)
+PERIMETER BREACH id=8 t=13.7s entered restricted zone at (789,1069)
+...
+```
+
+The annotated video is saved to `output_dlstreamer.mp4`.
+It shows the restricted zone shaded in red with the tape boundary outlined, a green box around each person outside the zone, and a red `BREACH` box around anyone inside it -- matching the OpenVINO output.
+
+#### Expected Output
+
+![DLStreamer expected output](expected_output_dlstreamer.gif)
+
+**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"` for the Intel NPU.
+
+---
+
+## License
+
+Licensed under the MIT License. See [LICENSE](LICENSE) for details.
+
+## References
+
+- [YOLO26 Documentation](https://docs.ultralytics.com/models/yolo26/)
+- [OpenVINO YOLO26 Notebook](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/yolov26-optimization/yolov26-object-detection.ipynb)
+- [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)

export_and_quantize.sh

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+#!/usr/bin/env bash
+# SPDX-License-Identifier: MIT
+# Copyright (C) Intel Corporation
+#
+# Export a YOLO26 person detector for perimeter breach detection to OpenVINO IR.
+# Usage: ./export_and_quantize.sh [MODEL_VARIANT] [PRECISION]
+# Example: ./export_and_quantize.sh yolo26n FP16
+#
+# Supported precisions:
+#   FP32  -- Full-precision floating-point weights
+#   FP16  -- Half-precision floating-point weights (default)
+#   INT8  -- Quantized 8-bit integer weights (requires NNCF)
+#
+# Precision / device compatibility:
+#   | Precision | CPU | GPU | NPU |
+#   |-----------|-----|-----|-----|
+#   | FP32      | Yes | Yes | No  |
+#   | FP16      | Yes | Yes | Yes |
+#   | INT8      | Yes | Yes | Yes |
+
+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 [[ "${PRECISION}" == "INT8" ]]; then
+    pip install -qU openvino nncf ultralytics opencv-python
+else
+    pip install -qU openvino ultralytics opencv-python
+fi
+
+# Ask for approval before downloading models and sample files
+echo ""
+echo "This script will download:"
+echo "  - Model weights and/or sample files"
+echo ""
+read -p "Continue with downloads? (yes/no): " APPROVAL
+if [[ "${APPROVAL}" != "yes" ]]; then
+    echo "Download cancelled by user."
+    exit 0
+fi
+
+echo ""
+echo "--- Downloading sample test video ---"
+if [[ ! -f worker-zone-detection.mp4 ]]; then
+    wget -O worker-zone-detection.mp4 \
+        https://github.com/intel-iot-devkit/sample-videos/raw/master/worker-zone-detection.mp4
+    echo "Downloaded: worker-zone-detection.mp4"
+else
+    echo "Already present: worker-zone-detection.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: ${MODEL_NAME}_openvino_model/')
+"
+
+if [[ "${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')
+
+# Extract frames from the sample video for calibration.
+cap = cv2.VideoCapture('worker-zone-detection.mp4')
+frames = []
+while len(frames) < 300:
+    ret, frame = cap.read()
+    if not ret:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
+        continue
+    img = cv2.resize(frame, (640, 640))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
+    img = img.transpose(2, 0, 1)[np.newaxis, ...]
+    frames.append(img)
+cap.release()
+
+def transform_fn(data_item):
+    return frames[data_item % len(frames)]
+
+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}_perimeter_int8.xml')
+print('Quantization complete: ${MODEL_NAME}_perimeter_int8.xml')
+"
+fi
+echo "--- Done ---"