Sync worker-safety-detection from metro-analytics-catalog

LICENSE

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+This directory contains two categories of content under different licenses.
+
+
+Scripts and Documentation
+-------------------------
+
+The scripts (export_and_quantize.sh) and documentation (README.md) in this
+directory are original works by Intel Corporation, licensed under the
+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.
+
+
+Worker Safety Gear Detection Model
+-----------------------------------
+
+The Worker Safety Gear Detection model is developed by Intel and distributed
+via the Intel Edge AI Resources repository under the Apache License 2.0.
+
+    Source:  https://github.com/open-edge-platform/edge-ai-resources
+    Model:   https://github.com/open-edge-platform/edge-ai-resources/blob/main/models/INT8/worker-safety-gear-detection.zip
+
+Users must comply with the applicable license terms when using or distributing
+the model weights.

README.md

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----
-license: other
-license_name: other
-license_link: LICENSE
----
+# Worker Safety Detection
+
+> **Validated with:** OpenVINO 2026.1.0, DLStreamer 2026.0, Python 3.11+
+
+| Property | Value |
+|---|---|
+| **Category** | Object Detection (PPE / Safety Compliance) |
+| **Base Model** | [Worker Safety Gear Detection](https://github.com/open-edge-platform/edge-ai-resources/blob/main/models/FP32/worker-safety-gear-detection.zip) (Intel Edge AI Resources, Geti-trained) |
+| **Source Framework** | Intel Geti (OpenVINO IR) |
+| **Supported Precisions** | FP32 |
+| **Inference Engine** | OpenVINO |
+| **Hardware** | CPU, GPU, NPU |
+| **Detected Class(es)** | `safety_jacket` (class 0), `safety_helmet` (class 1) |
+
+---
+
+## Overview
+
+Worker Safety Compliance Detection is a Metro Analytics use case that detects safety gear on workers to verify compliance with personal protective equipment (PPE) requirements.
+It uses a pre-trained FP32 detection model from [Intel Edge AI Resources](https://github.com/open-edge-platform/edge-ai-resources), trained with [Intel Geti](https://geti.intel.com/) on worker safety imagery.
+The model detects two classes: `safety_jacket` (high-visibility vest) and `safety_helmet` (hard hat).
+Frames where expected PPE is not detected indicate non-compliance.
+
+The FP32 model ships as an OpenVINO IR, ready for deployment on Intel CPUs and GPUs without additional conversion steps.
+
+Typical deployments include:
+
+- **Construction Site Safety** -- verify that all workers wear hard hats and high-visibility vests before entering active zones.
+- **Warehouse Compliance** -- enforce PPE policies at loading docks and forklift areas.
+- **Industrial Zone Monitoring** -- continuous compliance scanning at facility entry points.
+- **Automated Incident Reporting** -- generate alerts when expected `safety_helmet` or `safety_jacket` detections are absent for detected persons.
+
+---
+
+## 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
+```
+
+---
+
+## Getting Started
+
+### Download Model
+
+Run the provided script to download and extract the pre-trained FP32 model from Intel Edge AI Resources:
+
+```bash
+chmod +x export_and_quantize.sh
+./export_and_quantize.sh
+```
+
+The script performs the following steps:
+
+1. Installs dependencies (`openvino`).
+2. Downloads a sample test video (`test_video.avi`) from Intel Edge AI Resources.
+3. Downloads `worker-safety-gear-detection.zip` from the Intel Edge AI Resources repository.
+4. Extracts the FP32 OpenVINO IR model.
+
+Output files:
+
+- `./models/worker-safety-gear-detection/` -- extracted model directory containing the FP32 OpenVINO IR (`model.xml`, `model.bin`).
+
+> **Note:** The FP32 model is ready for production use on CPU and GPU.
+> An INT8 variant is also available from the [INT8 models directory](https://github.com/open-edge-platform/edge-ai-resources/tree/main/models/INT8) for higher throughput.
+
+### DLStreamer Sample
+
+The pipeline below runs the worker safety FP32 detector on the sample video via
+`gvadetect`, overlays bounding boxes with `gvawatermark`, and saves the
+annotated result to `output.mp4`.
+
+> **Notes on running this sample:**
+>
+> - The Geti-exported model embeds post-processing and labels internally.
+>   `gvadetect` auto-discovers the model type for Geti-exported IRs.
+> - 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("GstVideo", "1.0")
+from gi.repository import Gst
+from gstgva import VideoFrame
+
+Gst.init(None)
+
+MODEL_XML = "models/worker-safety-gear-detection/deployment/Detection/model/model.xml"
+INPUT_VIDEO = "test_video.avi"
+
+# For GPU: change device=CPU to device=GPU and add vapostproc after decodebin.
+# For NPU: change device=CPU to device=NPU (batch-size=1, nireq=4 recommended).
+pipeline_str = (
+    f"filesrc location={INPUT_VIDEO} ! decodebin3 ! "
+    f"gvadetect model={MODEL_XML} device=CPU threshold=0.4 ! queue ! "
+    f"gvawatermark ! videoconvert ! video/x-raw,format=I420 ! "
+    f"openh264enc ! h264parse ! "
+    f"mp4mux ! filesink name=sink location=output.mp4"
+)
+pipeline = Gst.parse_launch(pipeline_str)
+
+
+def on_buffer(pad, info):
+    buf = info.get_buffer()
+    caps = pad.get_current_caps()
+    frame = VideoFrame(buf, caps=caps)
+    for region in frame.regions():
+        label = region.label()
+        print(f"  [PPE] {label} conf={region.confidence():.2f}", flush=True)
+    return Gst.PadProbeReturn.OK
+
+
+sink = pipeline.get_by_name("sink")
+sink_pad = sink.get_static_pad("sink")
+sink_pad.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=CPU` -- default in the sample code.
+- `device=GPU` -- add `vapostproc` after `decodebin` for zero-copy color conversion.
+- `device=NPU` -- use `batch-size=1` and `nireq=4` for best NPU utilization.
+
+---
+
+## License
+
+Copyright (C) Intel Corporation. All rights reserved.
+Licensed under the MIT License. See [LICENSE](LICENSE) for details.
+
+## References
+
+- [Intel Edge AI Resources -- Worker Safety Gear Detection Model](https://github.com/open-edge-platform/edge-ai-resources/blob/main/models/FP32/worker-safety-gear-detection.zip)
+- [Worker Safety Gear Detection Application (Edge AI Suites)](https://github.com/open-edge-platform/edge-ai-suites/tree/main/manufacturing-ai-suite/industrial-edge-insights-vision)
+- [Intel Geti Platform](https://geti.intel.com/)
+- [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
+#
+# Download the pre-trained FP32 Worker Safety Gear Detection model
+# from Intel Edge AI Resources.
+# Usage: ./export_and_quantize.sh [OUTPUT_DIR]
+# Example: ./export_and_quantize.sh ./models
+
+set -euo pipefail
+
+OUTPUT_DIR="${1:-./models}"
+MODEL_DIR="${OUTPUT_DIR}/worker-safety-gear-detection"
+ZIP_URL="https://github.com/open-edge-platform/edge-ai-resources/raw/main/models/FP32/worker-safety-gear-detection.zip"
+ZIP_FILE="${OUTPUT_DIR}/worker-safety-gear-detection.zip"
+
+echo "--- Installing dependencies ---"
+pip install -qU "openvino>=2026.0.0"
+
+echo "--- Downloading sample test video ---"
+if [[ ! -f test_video.avi ]]; then
+    wget -q -O test_video.avi \
+        https://github.com/open-edge-platform/edge-ai-resources/raw/refs/heads/main/videos/Safety_Full_Hat_and_Vest.avi
+    echo "Downloaded: test_video.avi"
+else
+    echo "Already present: test_video.avi"
+fi
+
+echo "--- Downloading worker-safety-gear-detection FP32 model ---"
+mkdir -p "${OUTPUT_DIR}"
+if [[ ! -f "${ZIP_FILE}" ]]; then
+    wget -q -O "${ZIP_FILE}" "${ZIP_URL}"
+    echo "Downloaded: ${ZIP_FILE}"
+else
+    echo "Already downloaded: ${ZIP_FILE}"
+fi
+
+echo "--- Extracting model ---"
+unzip -qo "${ZIP_FILE}" -d "${MODEL_DIR}"
+echo "Extracted to: ${MODEL_DIR}"
+
+# Locate the model XML.
+MODEL_XML=$(find "${MODEL_DIR}" -name "model.xml" -type f | head -1)
+if [[ -z "${MODEL_XML}" ]]; then
+    echo "ERROR: model.xml not found in extracted archive." >&2
+    exit 1
+fi
+echo "Model IR found: ${MODEL_XML}"
+echo "--- Done ---"