Sync vehicle-detection from metro-analytics-catalog

README.md

@@ -1,6 +1,25 @@
-# Vehicle Detection
+---
+license: other
+license_name: intel-custom
+license_link: LICENSE
+library_name: openvino
+pipeline_tag: object-detection
+tags:
+  - openvino
+  - intel
+  - yolo
+  - yolo26
+  - vehicle-detection
+  - edge-ai
+  - metro
+  - dlstreamer
+datasets:
+  - detection-datasets/coco
+language:
+  - en
+---
 
-> **Validated with:** OpenVINO 2026.1.0, NNCF 3.0.0, DLStreamer 2026.0, Ultralytics 8.4.46, Python 3.11+
+# Vehicle Detection
 
 | Property | Value |
 |---|---|
@@ -78,7 +97,7 @@ The second argument selects the precision (`FP32`, `FP16`, `INT8`); the default
 The script performs the following steps:
 
 1. Installs dependencies (`openvino`, `ultralytics`; adds `nncf` for INT8).
-2. Downloads a sample test image (`test.jpg`).
+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.
 
@@ -154,7 +173,7 @@ for det in dets:
                 cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
     print(f"  {label} at ({x1},{y1})-({x2},{y2})")
 
-cv2.imwrite("output.jpg", image)
+cv2.imwrite("output_openvino.jpg", image)
 ```
 
 **Device targets:**
@@ -167,7 +186,7 @@ cv2.imwrite("output.jpg", image)
 
 The `export_and_quantize.sh` script downloads `test.jpg` automatically.
 Re-run the OpenVINO sample above.
-The script reads `test.jpg`, prints each detected vehicle to the console, and writes the annotated frame to `output.jpg`.
+The script reads `test.jpg`, prints each detected vehicle to the console, and writes the annotated frame to `output_openvino.jpg`.
 
 Expected console output:
 
@@ -176,12 +195,17 @@ Detected vehicles: 1
   bus 0.92 at (0,229)-(804,744)
 ```
 
+#### Expected Output
+
+![OpenVINO expected output](expected_output_openvino.jpg)
+
 ### DLStreamer Sample
 
-The pipeline below runs the FP16 YOLO26 detector on a single image via
+The pipeline below runs the FP16 YOLO26 detector on the sample video via
 `gvadetect`, filters detections to vehicle classes in a buffer probe using
 the DLStreamer Python bindings (`gstgva.VideoFrame`), overlays bounding boxes,
-and prints the vehicle count.
+saves the annotated result to `output_dlstreamer.mp4`, and prints the vehicle count per
+frame.
 
 > **Notes on running this sample:**
 >
@@ -197,8 +221,6 @@ and prints the vehicle count.
 >   /opt/intel/dlstreamer/gstreamer/lib/python3/dist-packages:${PYTHONPATH:-}
 >   ```
 
-**Image-based quick test** (uses `filesrc` with a single JPEG):
-
 ```python
 import gi
 
@@ -209,18 +231,20 @@ from gstgva import VideoFrame
 
 Gst.init(None)
 
+INPUT_VIDEO = "test_video.mp4"
 VEHICLE_LABELS = {"car", "motorcycle", "bus", "truck"}
 
-# For GPU: change device=CPU to device=GPU, add vapostproc !
-# video/x-raw(memory:VASurface) after jpegdec, and set
-# pre-process-backend=vaapi-surface-sharing on gvadetect.
-# For NPU: change device=CPU to device=NPU (batch-size=1 recommended).
+# For CPU: change device=GPU to device=CPU.
+# For NPU: change device=GPU to device=NPU (batch-size=1, nireq=4 recommended).
 pipeline_str = (
-    "filesrc location=test.jpg ! jpegdec ! videoconvert ! "
-    "video/x-raw,format=BGR ! "
+    f"filesrc location={INPUT_VIDEO} ! decodebin3 ! "
+    "videoconvert ! "
     "gvadetect model=yolo26n_openvino_model/yolo26n.xml "
-    "device=CPU threshold=0.4 ! queue ! "
-    "gvawatermark ! videoconvert ! jpegenc ! filesink name=sink location=output.jpg"
+    "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)
 
@@ -250,11 +274,15 @@ bus.timed_pop_filtered(
 pipeline.set_state(Gst.State.NULL)
 ```
 
+#### Expected Output
+
+![DLStreamer expected output](expected_output_dlstreamer.gif)
+
 **Device targets:**
 
-- `device=CPU` -- default in the sample code.
-- `device=GPU` -- add `vapostproc ! video/x-raw(memory:VASurface)` after `jpegdec` and set `pre-process-backend=vaapi-surface-sharing` on `gvadetect`.
-- `device=NPU` -- use `batch-size=1` and `nireq=4` for best NPU utilization.
+- `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.
 
 ---
 

export_and_quantize.sh

@@ -44,6 +44,15 @@ else
     echo "Already present: test.jpg"
 fi
 
+echo "--- Downloading sample test video ---"
+if [[ ! -f test_video.mp4 ]]; then
+    wget -q -O test_video.mp4 \
+        https://github.com/intel-iot-devkit/sample-videos/raw/master/car-detection.mp4
+    echo "Downloaded: test_video.mp4"
+else
+    echo "Already present: test_video.mp4"
+fi
+
 if [[ "${PRECISION}" == "FP32" ]]; then
     HALF_FLAG="False"
     EXPORT_LABEL="FP32"