df44f92a97
* Remove NV12 and I420 blobs and deprecate some legacy API * Fixed some errors * Remove NV12 blobs * Remote NV12 conversion * Fixed other warnings * Suppress version * Fix some warnings * Fixed version * Try to fix some warnings * Suppress warnings in C header * Suppress warnings in C * Fixed Windows exceptions * Try to fix warnings * Try to fix C bindings build * Suppress InferRequest * Fixed some build issues * Fixed some errors
126 lines
3.7 KiB
C++
126 lines
3.7 KiB
C++
// Copyright (C) 2018-2023 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <openvino/runtime/core.hpp>
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#include <openvino/opsets/opset8.hpp>
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#include <openvino/core/preprocess/pre_post_process.hpp>
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#ifndef IN_OV_COMPONENT
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# define IN_OV_COMPONENT
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# define WAS_OV_LIBRARY_DEFINED
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#endif
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#include "inference_engine.hpp"
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#ifdef WAS_OV_LIBRARY_DEFINED
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# undef IN_OV_COMPONENT
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# undef WAS_OV_LIBRARY_DEFINED
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#endif
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int main_new() {
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std::string model_path;
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std::string tensor_name;
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ov::Core core;
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std::shared_ptr<ov::Model> model = core.read_model(model_path);
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ov::preprocess::PrePostProcessor ppp(model);
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{
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//! [ov_mean_scale]
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ov::preprocess::PrePostProcessor ppp(model);
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ov::preprocess::InputInfo& input = ppp.input(tensor_name);
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// we only need to know where is C dimension
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input.model().set_layout("...C");
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// specify scale and mean values, order of operations is important
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input.preprocess().mean(116.78f).scale({ 57.21f, 57.45f, 57.73f });
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// insert preprocessing operations to the 'model'
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model = ppp.build();
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//! [ov_mean_scale]
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}
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{
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//! [ov_conversions]
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ov::preprocess::PrePostProcessor ppp(model);
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ov::preprocess::InputInfo& input = ppp.input(tensor_name);
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input.tensor().set_layout("NHWC").set_element_type(ov::element::u8);
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input.model().set_layout("NCHW");
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// layout and precision conversion is inserted automatically,
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// because tensor format != model input format
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model = ppp.build();
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//! [ov_conversions]
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}
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{
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//! [ov_color_space]
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ov::preprocess::PrePostProcessor ppp(model);
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ov::preprocess::InputInfo& input = ppp.input(tensor_name);
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input.tensor().set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES);
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// add NV12 to BGR conversion
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input.preprocess().convert_color(ov::preprocess::ColorFormat::BGR);
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// and insert operations to the model
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model = ppp.build();
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//! [ov_color_space]
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}
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{
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//! [ov_image_scale]
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ov::preprocess::PrePostProcessor ppp(model);
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ov::preprocess::InputInfo& input = ppp.input(tensor_name);
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// scale from the specified tensor size
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input.tensor().set_spatial_static_shape(448, 448);
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// need to specify H and W dimensions in model, others are not important
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input.model().set_layout("??HW");
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// scale to model shape
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input.preprocess().resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
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// and insert operations to the model
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model = ppp.build();
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//! [ov_image_scale]
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}
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return 0;
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}
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int main_old() {
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std::string model_path;
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std::string operation_name;
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InferenceEngine::Core core;
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InferenceEngine::CNNNetwork network = core.ReadNetwork(model_path);
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{
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//! [mean_scale]
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auto preProcess = network.getInputsInfo()[operation_name]->getPreProcess();
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preProcess.init(3);
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preProcess[0]->meanValue = 116.78f;
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preProcess[1]->meanValue = 116.78f;
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preProcess[2]->meanValue = 116.78f;
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preProcess[0]->stdScale = 57.21f;
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preProcess[1]->stdScale = 57.45f;
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preProcess[2]->stdScale = 57.73f;
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preProcess.setVariant(InferenceEngine::MEAN_VALUE);
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//! [mean_scale]
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}
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{
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//! [conversions]
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auto inputInfo = network.getInputsInfo()[operation_name];
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inputInfo->setPrecision(InferenceEngine::Precision::U8);
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inputInfo->setLayout(InferenceEngine::Layout::NHWC);
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// model input layout is always NCHW in Inference Engine
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// for shapes with 4 dimensions
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//! [conversions]
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}
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{
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//! [image_scale]
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auto preProcess = network.getInputsInfo()[operation_name]->getPreProcess();
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// Inference Engine supposes input for resize is always in NCHW layout
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// while for OpenVINO Runtime API 2.0 `H` and `W` dimensions must be specified
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// Also, current code snippet supposed resize from dynamic shapes
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preProcess.setResizeAlgorithm(InferenceEngine::ResizeAlgorithm::RESIZE_BILINEAR);
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//! [image_scale]
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}
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return 0;
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}
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