Sync vehicle-detection from metro-analytics-catalog

README.md

@@ -1,6 +1,6 @@
 # Vehicle Detection
 
-> **Validated with:** OpenVINO 2026.1.0, NNCF 3.0.0, DLStreamer 2026.0, Ultralytics 8.3.0, Python 3.11+
+> **Validated with:** OpenVINO 2026.1.0, NNCF 3.0.0, DLStreamer 2026.0, Ultralytics 8.4.46, Python 3.11+
 
 | Property | Value |
 |---|---|
@@ -55,9 +55,17 @@ Run the provided script to download, export to OpenVINO IR, and optionally quant
 
 ```bash
 chmod +x export_and_quantize.sh
-./export_and_quantize.sh yolo26n        # default: FP16
+./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`).
@@ -83,9 +91,9 @@ Output files:
 | FP16 | Yes | Yes | Yes |
 | INT8 | Yes | Yes | Yes |
 
-> **Note:** For production accuracy, replace the random calibration tensors in
-> `export_and_quantize.sh` with a representative sample of frames from the
-> target deployment site.
+> **Note:** The INT8 calibration uses the bundled sample image.
+> For production accuracy, replace it with a representative set of frames from
+> the target deployment site.
 
 ### OpenVINO Sample
 
@@ -223,15 +231,9 @@ def on_buffer(pad, info):
     return Gst.PadProbeReturn.OK
 
 
-it = pipeline.iterate_elements()
-while True:
-    ok, elem = it.next()
-    if not ok:
-        break
-    if elem.get_factory() and elem.get_factory().get_name() == "gvawatermark":
-        pad = elem.get_static_pad("src")
-        pad.add_probe(Gst.PadProbeType.BUFFER, on_buffer)
-        break
+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()

export_and_quantize.sh

@@ -67,12 +67,19 @@ if [[ "${PRECISION}" == "INT8" ]]; then
 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')
 
+# Use the downloaded test image for calibration instead of random noise.
+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, ...]  # NCHW
+
 def transform_fn(data_item):
-    return np.random.rand(1, 3, 640, 640).astype(np.float32)
+    return img
 
 calibration_dataset = nncf.Dataset(list(range(300)), transform_fn)