Update OpenVINO model generation (#23733)

docker/main/Dockerfile

@@ -81,10 +81,10 @@ RUN --mount=type=bind,source=docker/main/install_tempio.sh,target=/deps/install_
 FROM base_host AS ov-converter
 ARG DEBIAN_FRONTEND
 
-# Install OpenVino Runtime and Dev library
+# Install OpenVINO for model conversion
 COPY docker/main/requirements-ov.txt /requirements-ov.txt
 RUN apt-get -qq update \
-    && apt-get -qq install -y wget python3 python3-dev python3-distutils gcc pkg-config libhdf5-dev \
+    && apt-get -qq install -y wget python3 python3-distutils \
     && wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
     && python3 get-pip.py "pip" \

docker/main/build_ov_model.py

@@ -1,11 +1,42 @@
-import openvino as ov
-from openvino.tools import mo
+"""Convert the default SSDLite MobileNet v2 model to OpenVINO IR.
 
-ov_model = mo.convert_model(
+Replaces the legacy openvino-dev Model Optimizer conversion. The TensorFlow
+frontend converts the Object Detection API frozen graph natively; the four TF
+outputs are then repacked into the single [1, 1, 100, 7] DetectionOutput-style
+tensor that Frigate's OpenVINO detector expects, and the input is flipped to
+BGR to match the legacy reverse_input_channels behavior.
+"""
+
+import numpy as np
+import openvino as ov
+from openvino import opset8 as ops
+from openvino.preprocess import PrePostProcessor
+
+model = ov.convert_model(
     "/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
-    compress_to_fp16=True,
-    transformations_config="/usr/local/lib/python3.11/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
-    tensorflow_object_detection_api_pipeline_config="/models/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config",
-    reverse_input_channels=True,
+    input=[("image_tensor:0", [1, 300, 300, 3])],
 )
-ov.save_model(ov_model, "/models/ssdlite_mobilenet_v2.xml")
+
+# rows of (image_id, class_id, score, xmin, ymin, xmax, ymax)
+boxes = model.output("detection_boxes:0").get_node().input_value(0)
+classes = model.output("detection_classes:0").get_node().input_value(0)
+scores = model.output("detection_scores:0").get_node().input_value(0)
+
+# (ymin,xmin,ymax,xmax) -> (xmin,ymin,xmax,ymax)
+boxes = ops.gather(boxes, [1, 0, 3, 2], 2)
+classes = ops.unsqueeze(classes, 2)
+scores = ops.unsqueeze(scores, 2)
+image_id = ops.multiply(scores, np.float32(0.0))
+
+detections = ops.concat([image_id, classes, scores, boxes], 2)
+detections = ops.unsqueeze(detections, 1)
+detections.output(0).get_tensor().set_names({"detection_out"})
+
+model = ov.Model([detections], model.get_parameters(), "ssdlite_mobilenet_v2")
+
+ppp = PrePostProcessor(model)
+ppp.input().tensor().set_layout(ov.Layout("NHWC"))
+ppp.input().preprocess().reverse_channels()
+model = ppp.build()
+
+ov.save_model(model, "/models/ssdlite_mobilenet_v2.xml", compress_to_fp16=True)

docker/main/requirements-ov.txt

@@ -1,3 +1,2 @@
 numpy
-tensorflow
-openvino-dev>=2024.0.0
+openvino >= 2026.2.0