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发表于 2019-10-31 20:35:17
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本帖最后由 jsxyheu2014 于 2019-10-31 20:36 编辑
基于OpenVINO
#include <algorithm>
#include <fstream>
#include <iomanip>
#include <vector>
#include <string>
#include <chrono>
#include <memory>
#include <utility>
#include <format_reader_ptr.h>
#include <inference_engine.hpp>
#include <ext_list.hpp>
#include <samples/slog.hpp>
#include <samples/ocv_common.hpp>
#include "segmentation_demo.h"
using namespace InferenceEngine;
using namespace std;
using namespace cv;
//从图片中获得车和车牌(这里没有输出模型的定位结果,如果需要可以适当修改)
vector< pair<Mat, Mat> > GetCarAndPlate(Mat src)
{
vector<pair<Mat, Mat>> resultVector;
// 模型准备
InferencePlugin plugin(PluginDispatcher().getSuitablePlugin(TargetDevice::eCPU));
plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());//Extension,useful
//读取模型(xml和bin
CNNNetReader networkReader;
networkReader.ReadNetwork("E:/OpenVINO_modelZoo/vehicle-license-plate-detection-barrier-0106.xml");
networkReader.ReadWeights("E:/OpenVINO_modelZoo/vehicle-license-plate-detection-barrier-0106.bin");
CNNNetwork network = networkReader.getNetwork();
network.setBatchSize(1);
// 输入输出准备
InputsDataMap inputInfo(network.getInputsInfo());//获得输入信息
if (inputInfo.size() != 1) throw std::logic_error("错误,该模型应该为单输入");
string inputName = inputInfo.begin()->first;
OutputsDataMap outputInfo(network.getOutputsInfo());//获得输出信息
DataPtr& _output = outputInfo.begin()->second;
const SizeVector outputDims = _output->getTensorDesc().getDims();
string firstOutputName = outputInfo.begin()->first;
int maxProposalCount = outputDims[2];
int objectSize = outputDims[3];
if (objectSize != 7) {
throw std::logic_error("Output should have 7 as a last dimension");
}
if (outputDims.size() != 4) {
throw std::logic_error("Incorrect output dimensions for SSD");
}
_output->setPrecision(Precision::FP32);
_output->setLayout(Layout::NCHW);
// 模型读取和推断
ExecutableNetwork executableNetwork = plugin.LoadNetwork(network, {});
InferRequest infer_request = executableNetwork.CreateInferRequest();
Blob::Ptr lrInputBlob = infer_request.GetBlob(inputName); //data这个名字是我看出来的,实际上这里可以更统一一些
matU8ToBlob<float_t>(src, lrInputBlob, 0);//重要的转换函数,第3个参数是batchSize,应该是自己+1的
infer_request.Infer();
// --------------------------- 8. 处理结果-------------------------------------------------------
const float *detections = infer_request.GetBlob(firstOutputName)->buffer().as<float *>();
int i_car = 0;
int i_plate = 0;
for (int i = 0; i < 200; i++)
{
float confidence = detections[i * objectSize + 2];
float x_min = static_cast<int>(detections[i * objectSize + 3] * src.cols);
float y_min = static_cast<int>(detections[i * objectSize + 4] * src.rows);
float x_max = static_cast<int>(detections[i * objectSize + 5] * src.cols);
float y_max = static_cast<int>(detections[i * objectSize + 6] * src.rows);
Rect rect = cv::Rect(cv::Point(x_min, y_min), cv::Point(x_max, y_max));
if (confidence > 0.5)
{
if (rect.width > 150)//车辆
{
Mat roi = src(rect);
pair<Mat, Mat> aPair;
aPair.first = roi.clone();
resultVector.push_back(aPair);
i_car++;
}
else//车牌
{
Mat roi = src(rect);
resultVector[i_plate].second = roi.clone();
i_plate++;
}
}
}
return resultVector;
}
//从车的图片中识别车型
pair<string,string> GetCarAttributes(Mat src)
{
pair<string, string> resultPair;
// --------------------------- 1.为IE准备插件-------------------------------------
InferencePlugin plugin(PluginDispatcher().getSuitablePlugin(TargetDevice::eCPU));
printPluginVersion(plugin, std::cout);//正确回显表示成功
plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());//Extension,useful
// --------------------------- 2.读取IR模型(xml和bin)---------------------------------
CNNNetReader networkReader;
networkReader.ReadNetwork("E:/OpenVINO_modelZoo/vehicle-attributes-recognition-barrier-0039.xml");
networkReader.ReadWeights("E:/OpenVINO_modelZoo/vehicle-attributes-recognition-barrier-0039.bin");
CNNNetwork network = networkReader.getNetwork();
// --------------------------- 3. 准备输入输出的------------------------------------------
InputsDataMap inputInfo(network.getInputsInfo());//获得输入信息
BlobMap inputBlobs; //保持所有输入的blob数据
if (inputInfo.size() != 1) throw std::logic_error("错误,该模型应该为单输入");
auto lrInputInfoItem = *inputInfo.begin();//开始读入
int w = static_cast<int>(lrInputInfoItem.second->getTensorDesc().getDims()[3]); //这种写法也是可以的,它的first就是data
int h = static_cast<int>(lrInputInfoItem.second->getTensorDesc().getDims()[2]);
network.setBatchSize(1);//只有1副图片,故BatchSize = 1
// --------------------------- 4. 读取模型 ------------------------------------------(后面这些操作应该可以合并了)
ExecutableNetwork executableNetwork = plugin.LoadNetwork(network, {});
// --------------------------- 5. 创建推断 -------------------------------------------------
InferRequest infer_request = executableNetwork.CreateInferRequest();
// --------------------------- 6. 将数据塞入模型 -------------------------------------------------
Blob::Ptr lrInputBlob = infer_request.GetBlob("input"); //data这个名字是我看出来的,实际上这里可以更统一一些
matU8ToBlob<float_t>(src, lrInputBlob, 0);//重要的转换函数,第3个参数是batchSize,应该是自己+1的
// --------------------------- 7. 推断结果 -------------------------------------------------
infer_request.Infer();//多张图片多次推断
// --------------------------- 8. 处理结果-------------------------------------------------------
// 7 possible colors for each vehicle and we should select the one with the maximum probability
auto colorsValues = infer_request.GetBlob("color")->buffer().as<float*>();
// 4 possible types for each vehicle and we should select the one with the maximum probability
auto typesValues = infer_request.GetBlob("type")->buffer().as<float*>();
const auto color_id = std::max_element(colorsValues, colorsValues + 7) - colorsValues;
const auto type_id = std::max_element(typesValues, typesValues + 4) - typesValues;
static const std::string colors[] = {
"white", "gray", "yellow", "red", "green", "blue", "black"
};
static const std::string types[] = {
"car", "bus", "truck", "van"
};
resultPair.first = colors[color_id];
resultPair.second = types[type_id];
return resultPair;
}
//识别车牌
string GetPlateNumber(Mat src)
{
// --------------------------- 1.为IE准备插件-------------------------------------
InferencePlugin plugin(PluginDispatcher().getSuitablePlugin(TargetDevice::eCPU));
plugin.AddExtension(std::make_shared<Extensions::Cpu::CpuExtensions>());//Extension,useful
// --------------------------- 2.读取IR模型(xml和bin)---------------------------------
CNNNetReader networkReader;
networkReader.ReadNetwork("E:/OpenVINO_modelZoo/license-plate-recognition-barrier-0001.xml");
networkReader.ReadWeights("E:/OpenVINO_modelZoo/license-plate-recognition-barrier-0001.bin");
CNNNetwork network = networkReader.getNetwork();
network.setBatchSize(1);//只有1副图片,故BatchSize = 1
// --------------------------- 3. 准备输入输出的------------------------------------------
InputsDataMap inputInfo(network.getInputsInfo());//获得输入信息
BlobMap inputBlobs; //保持所有输入的blob数据
string inputSeqName;
if (inputInfo.size() == 2) {
auto sequenceInput = (++inputInfo.begin());
inputSeqName = sequenceInput->first;
}
else if (inputInfo.size() == 1) {
inputSeqName = "";
}
else {
throw std::logic_error("LPR should have 1 or 2 inputs");
}
InputInfo::Ptr& inputInfoFirst = inputInfo.begin()->second;
inputInfoFirst->setInputPrecision(Precision::U8);
string inputName = inputInfo.begin()->first;
//准备输出数据
OutputsDataMap outputInfo(network.getOutputsInfo());//获得输出信息
if (outputInfo.size() != 1) {
throw std::logic_error("LPR should have 1 output");
}
string firstOutputName = outputInfo.begin()->first;
DataPtr& _output = outputInfo.begin()->second;
const SizeVector outputDims = _output->getTensorDesc().getDims();
// --------------------------- 4. 读取模型 ------------------------------------------(后面这些操作应该可以合并了)
ExecutableNetwork executableNetwork = plugin.LoadNetwork(network, {});
// --------------------------- 5. 创建推断 -------------------------------------------------
InferRequest infer_request = executableNetwork.CreateInferRequest();
// --------------------------- 6. 将数据塞入模型 -------------------------------------------------
Blob::Ptr lrInputBlob = infer_request.GetBlob(inputName); //data这个名字是我看出来的,实际上这里可以更统一一些
matU8ToBlob<uint8_t>(src, lrInputBlob, 0);//重要的转换函数,第3个参数是batchSize,应该是自己+1的
// --------------------------- 7. 推断结果 -------------------------------------------------
infer_request.Infer();//多张图片多次推断
// --------------------------- 8. 处理结果-------------------------------------------------------
static std::vector<std::string> items = {
"0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
"<Anhui>", "<Beijing>", "<Chongqing>", "<Fujian>",
"<Gansu>", "<Guangdong>", "<Guangxi>", "<Guizhou>",
"<Hainan>", "<Hebei>", "<Heilongjiang>", "<Henan>",
"<HongKong>", "<Hubei>", "<Hunan>", "<InnerMongolia>",
"<Jiangsu>", "<Jiangxi>", "<Jilin>", "<Liaoning>",
"<Macau>", "<Ningxia>", "<Qinghai>", "<Shaanxi>",
"<Shandong>", "<Shanghai>", "<Shanxi>", "<Sichuan>",
"<Tianjin>", "<Tibet>", "<Xinjiang>", "<Yunnan>",
"<Zhejiang>", "<police>",
"A", "B", "C", "D", "E", "F", "G", "H", "I", "J",
"K", "L", "M", "N", "O", "P", "Q", "R", "S", "T",
"U", "V", "W", "X", "Y", "Z"
};
const auto data = infer_request.GetBlob(firstOutputName)->buffer().as<float*>();
std::string result;
for (size_t i = 0; i < 88; i++) {
if (data == -1)
break;
result += items[static_cast<size_t>(data)];
}
return result;
}
void main()
{
string imageNames = "E:/OpenVINO_modelZoo/沪A51V39.jpg";
Mat src = imread(imageNames);
if (src.empty())
return;
vector<pair<Mat, Mat>> CarAndPlateVector = GetCarAndPlate(src);
for (int i=0;i<CarAndPlateVector.size();i++)
{
pair<Mat, Mat> aPair = CarAndPlateVector;
pair<string, string> ColorAndType = GetCarAttributes(aPair.first);
string PlateNumber = GetPlateNumber(aPair.second);
cout << ColorAndType.first <<" "<<ColorAndType.second <<" "<< PlateNumber << endl;
}
cv::waitKey();
}
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