本文介绍使用Intel OpenVINO工具包实现图像超分辨率的过程，包括环境配置、模型加载、输入输出配置、模型加载到设备等步骤，并展示了如何进行推理及处理输出。
配置环境：
①C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\inference_engine\include
②D:\opencv\opencv344\opencv\build\install\include\opencv2
③D:\opencv\opencv344\opencv\build\install\include\opencv
④D:\opencv\opencv344\opencv\build\install\include
⑤C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\inference_engine\samples\cpp\common
①C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\ngraph\lib
②C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\inference_engine\lib\intel64\Debug
③D:\opencv\opencv344\opencv\build\install\x64\vc15\lib
④C:\Program Files (x86)\Intel\openvino_2021.2.185\deployment_tools\inference_engine\external\hddl\lib
hddlapi.lib
inference_engined.lib
opencv_aruco344d.lib
opencv_bgsegm344d.lib
opencv_bioinspired344d.lib
opencv_calib3d344d.lib
opencv_ccalib344d.lib
opencv_core344d.lib
opencv_datasets344d.lib
opencv_dnn344d.lib
opencv_dnn_objdetect344d.lib
opencv_dpm344d.lib
opencv_face344d.lib
opencv_features2d344d.lib
opencv_flann344d.lib
opencv_fuzzy344d.lib
opencv_hfs344d.lib
opencv_highgui344d.lib
opencv_imgcodecs344d.lib
opencv_imgproc344d.lib
opencv_img_hash344d.lib
opencv_line_descriptor344d.lib
opencv_ml344d.lib
opencv_objdetect344d.lib
opencv_optflow344d.lib
opencv_phase_unwrapping344d.lib
opencv_photo344d.lib
opencv_plot344d.lib
opencv_reg344d.lib
opencv_rgbd344d.lib
opencv_saliency344d.lib
opencv_shape344d.lib
opencv_stereo344d.lib
opencv_stitching344d.lib
opencv_structured_light344d.lib
opencv_superres344d.lib
opencv_surface_matching344d.lib
opencv_text344d.lib
opencv_tracking344d.lib
opencv_video344d.lib
opencv_videoio344d.lib
opencv_videostab344d.lib
opencv_xfeatures2d344d.lib
opencv_ximgproc344d.lib
opencv_xobjdetect344d.lib
opencv_xphoto344d.lib
ngraphd.lib
onnx_importerd.lib
代码：
// ForOpenvinoTest.cpp : 此文件包含 "main" 函数。程序执行将在此处开始并结束。
//
// Copyright (C) 2018-2020 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <vector>
#include <memory>
#include <string>
#include <samples/common.hpp>
#include <windows.h>
#include <wchar.h>
#include <inference_engine.hpp>
#include <samples/ocv_common.hpp>
#include <samples/classification_results.h>
using namespace InferenceEngine;
#if defined(ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
#define tcout std::wcout
#define file_name_t std::wstring
#define imread_t imreadW
#define ClassificationResult_t ClassificationResultW
#else
#define tcout std::cout
#define file_name_t std::string
#define imread_t cv::imread
#define ClassificationResult_t ClassificationResult
#endif
std::wstring StringToWString(const std::string& str)
{
int num = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), -1, NULL, 0);
wchar_t *wide = new wchar_t[num];
MultiByteToWideChar(CP_UTF8, 0, str.c_str(), -1, wide, num);
std::wstring w_str(wide);
delete[] wide;
return w_str;
}
//int wmain(int argc, char *argv[]) {
//#else
int main(int argc) {
// --------------------------- 1. Load inference engine instance负载推理引擎实例 -------------------------------------
Core ie;
// 2. 读模型Read a model in OpenVINO Intermediate Representation (.xml and .bin files) or ONNX (.onnx file) format
CNNNetwork network = ie.ReadNetwork("C:/Users/sjsys/Desktop/1/single-image-super-resolution-1032.xml");
// --------------------------- 3. Configure input & output 配置输入输出---------------------------------------------
// --------------------------- Prepare input blobs 准备输入blob-----------------------------------------------------
/** Collect images**/
std::cout<< "Preparing input blobs" << std::endl;
/** Taking information about all topology inputs **/
ICNNNetwork::InputShapes inputShapes(network.getInputShapes());
std::string lrInputBlobName = inputShapes.begin()->first;
SizeVector lrShape = inputShapes[lrInputBlobName];
// A model like single-image-super-resolution-???? may take bicubic interpolation of the input image as the
// second input
std::string bicInputBlobName;
if (inputShapes.size() == 2) {
bicInputBlobName = (++inputShapes.begin())->first;
SizeVector bicShape = inputShapes[bicInputBlobName];
if (bicShape.size() != 4) {
throw std::logic_error("Number of dimensions for both inputs must be 4");
}
if (lrShape[2] >= bicShape[2] && lrShape[3] >= bicShape[3]) {
lrInputBlobName.swap(bicInputBlobName);
lrShape.swap(bicShape);
}
else if (!(lrShape[2] <= bicShape[2] && lrShape[3] <= bicShape[3])) {
throw std::logic_error("Each spatial dimension of one input must surpass or be equal to a spatial"
"dimension of another input");
}
}
/** Collect images**/
std::vector<cv::Mat> inputImages;
std::string imageNames = "C:/Users/sjsys/Desktop/1/3.bmp";
cv::Mat img = cv::imread(imageNames, cv::IMREAD_COLOR);
inputImages.push_back(img);
if (inputImages.empty()) throw std::logic_error("Valid input images were not found!");
/** Setting batch size using image count **/
inputShapes[lrInputBlobName][0] = inputImages.size();
if (!bicInputBlobName.empty()) {
inputShapes[bicInputBlobName][0] = inputImages.size();
}
network.reshape(inputShapes);
std::cout << "Batch size is " << std::to_string(network.getBatchSize()) << std::endl;
// --------------------------- Prepare output blobs 准备输出blob----------------------------------------------------
std::cout << "Preparing output blobs" << std::endl;
OutputsDataMap outputInfo(network.getOutputsInfo());
// BlobMap outputBlobs;
std::string firstOutputName;
for (auto &item : outputInfo) {
if (firstOutputName.empty()) {
firstOutputName = item.first;
}
DataPtr outputData = item.second;
if (!outputData) {
throw std::logic_error("output data pointer is not valid");
}
item.second->setPrecision(Precision::FP32);
}
// --------------------------- 4. Loading model to the device 加载模型到设备------------------------------------------
ExecutableNetwork executable_network = ie.LoadNetwork(network, "CPU");
// --------------------------- 5. Create infer request创建请求 -------------------------------------------------
InferRequest infer_request = executable_network.CreateInferRequest();
// --------------------------- 6. Prepare input 准备输入--------------------------------------------------------
Blob::Ptr lrInputBlob = infer_request.GetBlob(lrInputBlobName);
for (size_t i = 0; i < inputImages.size(); ++i) {
cv::Mat img = inputImages[i];
matU8ToBlob<float_t>(img, lrInputBlob, i);
if (!bicInputBlobName.empty()) {
Blob::Ptr bicInputBlob = infer_request.GetBlob(bicInputBlobName);
int w = bicInputBlob->getTensorDesc().getDims()[3];
int h = bicInputBlob->getTensorDesc().getDims()[2];
cv::Mat resized;
cv::resize(img, resized, cv::Size(w, h), 0, 0, cv::INTER_CUBIC);
matU8ToBlob<float_t>(resized, bicInputBlob, i);
}
}
// --------------------------- 7. Do inference 做推理--------------------------------------------------------
infer_request.Infer();
// --------------------------- 8. Process output流程输出 ------------------------------------------------------
Blob::Ptr output = infer_request.GetBlob(firstOutputName);
LockedMemory<const void> outputBlobMapped = as<MemoryBlob>(output)->rmap();
const auto outputData = outputBlobMapped.as<float*>();
size_t numOfImages = output->getTensorDesc().getDims()[0];
size_t numOfChannels = output->getTensorDesc().getDims()[1];
size_t h = output->getTensorDesc().getDims()[2];
size_t w = output->getTensorDesc().getDims()[3];
size_t nunOfPixels = w * h;
std::cout << "Output size [N,C,H,W]: " << numOfImages << ", " << numOfChannels << ", " << h << ", " << w << std::endl;
for (size_t i = 0; i < numOfImages; ++i) {
std::vector<cv::Mat> imgPlanes;
if (numOfChannels == 3) {
imgPlanes = std::vector<cv::Mat>{
cv::Mat(h, w, CV_32FC1, &(outputData[i * nunOfPixels * numOfChannels])),
cv::Mat(h, w, CV_32FC1, &(outputData[i * nunOfPixels * numOfChannels + nunOfPixels])),
cv::Mat(h, w, CV_32FC1, &(outputData[i * nunOfPixels * numOfChannels + nunOfPixels * 2])) };
}
else {
imgPlanes = std::vector<cv::Mat>{ cv::Mat(h, w, CV_32FC1, &(outputData[i * nunOfPixels * numOfChannels])) };
cv::threshold(imgPlanes[0], imgPlanes[0], 0.5f, 1.0f, cv::THRESH_BINARY);
};
for (auto & img : imgPlanes)
img.convertTo(img, CV_8UC1, 255);
cv::Mat resultImg;
cv::merge(imgPlanes, resultImg);
cv::imshow("result", resultImg);
}
std::cout << "程序完成啦!" << std::endl;
cv::waitKey(0);
return EXIT_SUCCESS;
}
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