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anomaly
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+104
-27
lines changed

4 files changed

+104
-27
lines changed

‎src/cpp/py_bindings/py_anomaly.cpp‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,80 @@
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/*
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* Copyright (C) 2025 Intel Corporation
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* SPDX-License-Identifier: Apache-2.0
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*/
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#include <nanobind/ndarray.h>
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#include <nanobind/operators.h>
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#include <nanobind/stl/map.h>
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#include <nanobind/stl/string.h>
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#include <nanobind/stl/unique_ptr.h>
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#include <nanobind/stl/vector.h>
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#include "models/anomaly_model.h"
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#include "models/results.h"
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#include "py_utils.hpp"
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namespace pyutils = vision::nanobind::utils;
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void init_anomaly_detection(nb::module_& m) {
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nb::class_<AnomalyModel, ImageModel>(m, "AnomalyDetection")
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.def_static(
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"create_model",
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[](const std::string& model_path,
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const std::map<std::string, nb::object>& configuration,
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bool preload,
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const std::string& device) {
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auto ov_any_config = ov::AnyMap();
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for (const auto& item : configuration) {
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ov_any_config[item.first] = pyutils::py_object_to_any(item.second, item.first);
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}
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return AnomalyModel::create_model(model_path, ov_any_config, preload, device);
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},
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nb::arg("model_path"),
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nb::arg("configuration") = ov::AnyMap({}),
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nb::arg("preload") = true,
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nb::arg("device") = "AUTO")
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.def("__call__",
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[](AnomalyModel& self, const nb::ndarray<>& input) {
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return self.infer(pyutils::wrap_np_mat(input));
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})
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.def("infer_batch",
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[](AnomalyModel& self, const std::vector<nb::ndarray<>> inputs) {
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std::vector<ImageInputData> input_mats;
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input_mats.reserve(inputs.size());
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for (const auto& input : inputs) {
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input_mats.push_back(pyutils::wrap_np_mat(input));
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}
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return self.inferBatch(input_mats);
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})
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.def_prop_ro_static("__model__", [](nb::object) {
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return AnomalyModel::ModelType;
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});
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nb::class_<AnomalyResult, ResultBase>(m, "AnomalyResult")
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.def(nb::init<int64_t, std::shared_ptr<MetaData>>(), nb::arg("frameId") = -1, nb::arg("metaData") = nullptr)
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.def_prop_ro(
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"anomaly_map",
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[](AnomalyResult& r) {
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return nb::ndarray<uint8_t, nb::numpy, nb::c_contig>(r.anomaly_map.data,
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{static_cast<size_t>(r.anomaly_map.rows),
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static_cast<size_t>(r.anomaly_map.cols),
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static_cast<size_t>(r.anomaly_map.channels())});
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},
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nb::rv_policy::reference_internal)
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.def_ro("pred_boxes", &AnomalyResult::pred_boxes)
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.def_ro("pred_label", &AnomalyResult::pred_label)
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.def_prop_ro(
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"pred_mask",
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[](AnomalyResult& r) {
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return nb::ndarray<uint8_t, nb::numpy, nb::c_contig>(r.pred_mask.data,
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{static_cast<size_t>(r.pred_mask.rows),
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static_cast<size_t>(r.pred_mask.cols),
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static_cast<size_t>(r.pred_mask.channels())});
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},
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nb::rv_policy::reference_internal)
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.def_ro("pred_score", &AnomalyResult::pred_score);
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}

‎src/cpp/py_bindings/py_keypoint_detection.cpp‎

Lines changed: 22 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -56,27 +56,26 @@ void init_keypoint_detection(nb::module_& m) {
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nb::class_<KeypointDetectionResult, ResultBase>(m, "KeypointDetectionResult")
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.def(nb::init<int64_t, std::shared_ptr<MetaData>>(), nb::arg("frameId") = -1, nb::arg("metaData") = nullptr)
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.def_prop_ro("keypoints", [](const KeypointDetectionResult& result) {
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if (!result.poses.empty()) {
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std::vector<size_t> shape = {result.poses[0].keypoints.size(), 2};
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return nb::ndarray<float, nb::numpy, nb::c_contig>(
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const_cast<void*>(static_cast<const void*>(result.poses[0].keypoints.data())),
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shape.size(),
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shape.data());
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}
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return nb::ndarray<float, nb::numpy, nb::c_contig>();
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},
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nb::rv_policy::reference_internal)
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.def_prop_ro("scores", [](const KeypointDetectionResult& result) {
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if (!result.poses.empty()) {
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std::vector<size_t> shape = {result.poses[0].scores.size()};
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return nb::ndarray<float, nb::numpy, nb::c_contig>(
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const_cast<void*>(static_cast<const void*>(result.poses[0].scores.data())),
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shape.size(),
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shape.data());
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}
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return nb::ndarray<float, nb::numpy, nb::c_contig>();
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},
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nb::rv_policy::reference_internal
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);
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.def_prop_ro(
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"keypoints",
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[](const KeypointDetectionResult& result) {
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if (!result.poses.empty()) {
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return nb::ndarray<float, nb::numpy, nb::c_contig>(
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const_cast<void*>(static_cast<const void*>(result.poses[0].keypoints.data())),
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{static_cast<size_t>(result.poses[0].keypoints.size()), 2});
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}
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return nb::ndarray<float, nb::numpy, nb::c_contig>();
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},
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nb::rv_policy::reference_internal)
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.def_prop_ro(
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"scores",
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[](const KeypointDetectionResult& result) {
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if (!result.poses.empty()) {
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return nb::ndarray<float, nb::numpy, nb::c_contig>(
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const_cast<void*>(static_cast<const void*>(result.poses[0].scores.data())),
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{static_cast<size_t>(result.poses[0].scores.size())});
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}
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return nb::ndarray<float, nb::numpy, nb::c_contig>();
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},
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nb::rv_policy::reference_internal);
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}

‎src/cpp/py_bindings/py_segmentation.cpp‎

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Original file line numberDiff line numberDiff line change
@@ -51,10 +51,6 @@ void init_segmentation(nb::module_& m) {
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return self.inferBatch(input_mats);
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})
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.def("postprocess",
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[](SegmentationModel& self, InferenceResult& infResult) {
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return self.postprocess(infResult);
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})
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.def_prop_ro_static("__model__", [](nb::object) {
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return SegmentationModel::ModelType;
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});

‎src/cpp/py_bindings/py_vision_api.cpp‎

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Original file line numberDiff line numberDiff line change
@@ -12,6 +12,7 @@ void init_classification(nb::module_& m);
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void init_segmentation(nb::module_& m);
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void init_instance_segmentation(nb::module_& m);
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void init_keypoint_detection(nb::module_& m);
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void init_anomaly_detection(nb::module_& m);
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NB_MODULE(py_model_api, m) {
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m.doc() = "Nanobind binding for OpenVINO Vision API library";
@@ -20,4 +21,5 @@ NB_MODULE(py_model_api, m) {
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init_keypoint_detection(m);
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init_segmentation(m);
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init_instance_segmentation(m);
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init_anomaly_detection(m);
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}

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