335 lines
12 KiB
C++
335 lines
12 KiB
C++
|
#include "ContainerStep1InferenceEngine.h"
|
|||
|
|
|
|||
|
|
//#include "myqueue.h"
|
|||
|
|
|
|||
|
|
using namespace ai_matrix;
|
|||
|
|
|
|||
|
|
namespace
|
|||
|
|
{
|
|||
|
|
//按照x坐标排列
|
|||
|
|
bool CompareX(const stDetection &a, const stDetection &b)
|
|||
|
|
{
|
|||
|
|
return a.bbox[0] < b.bbox[0];
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
ContainerStep1InferenceEngine::ContainerStep1InferenceEngine() {}
|
|||
|
|
|
|||
|
|
ContainerStep1InferenceEngine::~ContainerStep1InferenceEngine() {}
|
|||
|
|
|
|||
|
|
APP_ERROR ContainerStep1InferenceEngine::Init()
|
|||
|
|
{
|
|||
|
|
strPort0_ = engineName_ + "_" + std::to_string(engineId_) + "_0";
|
|||
|
|
std::vector<DataSourceConfig> vecDataSourceConfig = Config::getins()->getAllDataSourceConfig();
|
|||
|
|
if (vecDataSourceConfig.size() <= this->engineId_)
|
|||
|
|
{
|
|||
|
|
LogWarn << " -- " << engineName_ << "_" << engineId_ << " dataSource no set, Engine DeInit";
|
|||
|
|
return APP_ERR_OK;
|
|||
|
|
}
|
|||
|
|
this->dataSourceConfig_ = vecDataSourceConfig.at(engineId_);
|
|||
|
|
this->modelConfig_ = Config::getins()->getModelByContainerStep1Config();
|
|||
|
|
this->identifyConfig_ = Config::getins()->getIdentifyConfig();
|
|||
|
|
|
|||
|
|
int iFolderExist = access(modelConfig_.strModelPath.c_str(), R_OK);
|
|||
|
|
if (iFolderExist == -1)
|
|||
|
|
{
|
|||
|
|
LogError << "模型：" << modelConfig_.strModelPath << " 不存在!";
|
|||
|
|
return false;
|
|||
|
|
}
|
|||
|
|
class_num = this->modelConfig_.vecClass.size();
|
|||
|
|
score_threshold = this->modelConfig_.fScoreThreshold;
|
|||
|
|
input_size = GET_INPUT_SIZE(model_width, model_height);
|
|||
|
|
output_size = GET_OUTPUT_SIZE(model_width, model_height, clear_num, class_num);
|
|||
|
|
det_size = clear_num + class_num + 5;
|
|||
|
|
|
|||
|
|
int ret = initModel();
|
|||
|
|
if (ret != APP_ERR_OK)
|
|||
|
|
{
|
|||
|
|
LogError << "Failed to read model info, ret = " << ret;
|
|||
|
|
return ret;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
LogInfo << "Step1InferenceEngine Init ok";
|
|||
|
|
return APP_ERR_OK;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
APP_ERROR ContainerStep1InferenceEngine::initModel()
|
|||
|
|
{
|
|||
|
|
modelinfo.yolov5ClearityModelParam.uiClassNum = class_num;
|
|||
|
|
modelinfo.yolov5ClearityModelParam.uiClearNum = clear_num;
|
|||
|
|
modelinfo.yolov5ClearityModelParam.uiDetSize = det_size;
|
|||
|
|
modelinfo.yolov5ClearityModelParam.fScoreThreshold = score_threshold;
|
|||
|
|
modelinfo.yolov5ClearityModelParam.fNmsThreshold = nms_threshold;
|
|||
|
|
|
|||
|
|
modelinfo.modelCommonInfo.uiModelWidth = model_width;
|
|||
|
|
modelinfo.modelCommonInfo.uiModelHeight = model_height;
|
|||
|
|
modelinfo.modelCommonInfo.uiInputSize = input_size;
|
|||
|
|
modelinfo.modelCommonInfo.uiOutputSize = output_size;
|
|||
|
|
modelinfo.modelCommonInfo.uiChannel = INPUT_CHANNEL;
|
|||
|
|
modelinfo.modelCommonInfo.uiBatchSize = batch_size;
|
|||
|
|
modelinfo.modelCommonInfo.strInputBlobName = INPUT_BLOB_NAME;
|
|||
|
|
modelinfo.modelCommonInfo.strOutputBlobName = OUTPUT_BLOB_NAME;
|
|||
|
|
|
|||
|
|
string strModelName;
|
|||
|
|
|
|||
|
|
int nRet = yolov5model.YoloV5ClearityInferenceInit(&modelinfo,
|
|||
|
|
strModelName,
|
|||
|
|
this->modelConfig_.strModelPath);
|
|||
|
|
if (nRet != 0)
|
|||
|
|
{
|
|||
|
|
LogError << "YoloV5ClassifyInferenceInit nRet:" << nRet;
|
|||
|
|
return APP_ERR_COMM_READ_FAIL;
|
|||
|
|
}
|
|||
|
|
return APP_ERR_OK;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
APP_ERROR ContainerStep1InferenceEngine::DeInit()
|
|||
|
|
{
|
|||
|
|
yolov5model.YoloV5ClearityInferenceDeinit();
|
|||
|
|
LogInfo << "Step1InferenceEngine DeInit ok";
|
|||
|
|
return APP_ERR_OK;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* 获取第1步得分最高框
|
|||
|
|
* inParam : std::vector<stDetection> &vecResult 推理符合结果
|
|||
|
|
* outParam: std::vector<stDetection> &vecResult 每个类别得分最高结果
|
|||
|
|
* return : N/A
|
|||
|
|
*/
|
|||
|
|
void ContainerStep1InferenceEngine::getMaxScoreResult(std::vector<stDetection> &vecResult)
|
|||
|
|
{
|
|||
|
|
if (vecResult.size() < 2)
|
|||
|
|
{
|
|||
|
|
return;
|
|||
|
|
}
|
|||
|
|
std::map<ModelTarget, std::vector<stDetection>> mapResult;
|
|||
|
|
for (auto stDTemp : vecResult)
|
|||
|
|
{
|
|||
|
|
switch (stDTemp.class_id) {
|
|||
|
|
case T_CONTAINER:
|
|||
|
|
mapResult[T_CONTAINER].emplace_back(stDTemp);
|
|||
|
|
break;
|
|||
|
|
case MIRROR_CONTAINER:
|
|||
|
|
mapResult[MIRROR_CONTAINER].emplace_back(stDTemp);
|
|||
|
|
break;
|
|||
|
|
default:
|
|||
|
|
break;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//清空之前的结果
|
|||
|
|
vecResult.clear();
|
|||
|
|
// 每个类别中，获取得分最高的框
|
|||
|
|
for (auto & iter : mapResult)
|
|||
|
|
{
|
|||
|
|
int iMaxPos = -1;
|
|||
|
|
for (int i = 0; i < iter.second.size(); i++)
|
|||
|
|
{
|
|||
|
|
if (iMaxPos == -1)
|
|||
|
|
{
|
|||
|
|
iMaxPos = i;
|
|||
|
|
}
|
|||
|
|
else if (iter.second.at(i).class_conf > iter.second.at(iMaxPos).class_conf)
|
|||
|
|
{
|
|||
|
|
iMaxPos = i;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if (iMaxPos >= 0)
|
|||
|
|
{
|
|||
|
|
vecResult.emplace_back(iter.second.at(iMaxPos));
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* 设置大框类型
|
|||
|
|
* inParam : PostSubData &postSubData :推理结果
|
|||
|
|
* outParam: PostSubData &postSubData :推理结果
|
|||
|
|
* return : N/A
|
|||
|
|
*/
|
|||
|
|
void ContainerStep1InferenceEngine::getTargetType(SingleData &singleData)
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
/**
|
|||
|
|
* 过滤无效信息
|
|||
|
|
* inParam : std::vector<stDetection> &vecRet :识别结果数据
|
|||
|
|
: std::shared_ptr<VDetectInfo> &pVDetectInfo :帧信息数据
|
|||
|
|
* outParam: N/A
|
|||
|
|
* return : N/A
|
|||
|
|
*/
|
|||
|
|
void ContainerStep1InferenceEngine::filterInvalidInfo(std::vector<stDetection> &vecInferenceResult,
|
|||
|
|
std::shared_ptr<VDetectInfo> &pVDetectInfo)
|
|||
|
|
{
|
|||
|
|
std::vector<stDetection> vecSpaceInfo;
|
|||
|
|
for (auto it = vecInferenceResult.begin(); it != vecInferenceResult.end();)
|
|||
|
|
{
|
|||
|
|
// LogDebug << " 数据源:" << pVDetectInfo->iDataSource
|
|||
|
|
// << " frameId:" << pVDetectInfo->iFrameId
|
|||
|
|
// << " 类别:" << it->class_id
|
|||
|
|
// << " -- [" << it->bbox[0]
|
|||
|
|
// << "," << it->bbox[1]
|
|||
|
|
// << "," << it->bbox[2]
|
|||
|
|
// << "," << it->bbox[3]
|
|||
|
|
// << "]";
|
|||
|
|
// 根据配置文件中 设置的识别范围，过滤掉无效数据
|
|||
|
|
if (!(it->bbox[0] >= this->dataSourceConfig_.vecIdentifyAreas[0] &&
|
|||
|
|
it->bbox[1] >= this->dataSourceConfig_.vecIdentifyAreas[1] &&
|
|||
|
|
it->bbox[2] <= this->dataSourceConfig_.vecIdentifyAreas[2] &&
|
|||
|
|
it->bbox[3] <= this->dataSourceConfig_.vecIdentifyAreas[3]))
|
|||
|
|
{
|
|||
|
|
LogDebug << " 数据源:" << pVDetectInfo->iDataSource
|
|||
|
|
<< " frameId:" << pVDetectInfo->iFrameId
|
|||
|
|
<< " 类别:" << it->class_id
|
|||
|
|
<< " 坐标:[" << it->bbox[0]
|
|||
|
|
<< "," << it->bbox[1]
|
|||
|
|
<< "],[" << it->bbox[2]
|
|||
|
|
<< "," << it->bbox[3]
|
|||
|
|
<< "]"
|
|||
|
|
<< " 超出识别区域-识别区域：("
|
|||
|
|
<< this->dataSourceConfig_.vecIdentifyAreas[0] << ","
|
|||
|
|
<< this->dataSourceConfig_.vecIdentifyAreas[1] << "),("
|
|||
|
|
<< this->dataSourceConfig_.vecIdentifyAreas[2] << ","
|
|||
|
|
<< this->dataSourceConfig_.vecIdentifyAreas[3] << ")";
|
|||
|
|
it = vecInferenceResult.erase(it);
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 剔出反向集装箱
|
|||
|
|
if (it->class_id == MIRROR_CONTAINER)
|
|||
|
|
{
|
|||
|
|
// LogDebug << " 数据源:" << pVDetectInfo->iDataSource
|
|||
|
|
// << " frameId:" << pVDetectInfo->iFrameId
|
|||
|
|
// << " bigclassid:" << it->class_id << " 过滤 剔出反向集装箱";
|
|||
|
|
it = vecInferenceResult.erase(it);
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 剔除高度小于最低限制的大框
|
|||
|
|
if ((it->bbox[3] - it->bbox[1]) < this->identifyConfig_.iTargetMinHeight && pVDetectInfo->iDataSource != 0)
|
|||
|
|
{
|
|||
|
|
// LogDebug << " 数据源:" << pVDetectInfo->iDataSource
|
|||
|
|
// << " frameId:" << pVDetectInfo->iFrameId
|
|||
|
|
// << " bigclassid:" << it->class_id << " 过滤 大框高度小于最小值，疑似非目标区域";
|
|||
|
|
it = vecInferenceResult.erase(it);
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
// 剔除宽度小于最低限制的大框
|
|||
|
|
if ((it->bbox[2] - it->bbox[0]) < this->identifyConfig_.iTargetMinWidth)
|
|||
|
|
{
|
|||
|
|
// LogDebug << " 数据源:" << pVDetectInfo->iDataSource
|
|||
|
|
// << " frameId:" << pVDetectInfo->iFrameId
|
|||
|
|
// << " bigclassid:" << it->class_id << " 过滤 大框宽度小于最小值，疑似非目标区域";
|
|||
|
|
it = vecInferenceResult.erase(it);
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// 剔出左上角坐标在XXX以下的
|
|||
|
|
if (pVDetectInfo->iDataSource != 0 && it->bbox[1] > this->identifyConfig_.iTargetMinY)
|
|||
|
|
{
|
|||
|
|
// LogDebug << " 数据源:" << pVDetectInfo->iDataSource
|
|||
|
|
// << " frameId:" << pVDetectInfo->iFrameId
|
|||
|
|
// << " bigclassid:" << it->class_id << " 过滤 大框低于指定Y轴坐标，疑似误识别非目标";
|
|||
|
|
it = vecInferenceResult.erase(it);
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
++it;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
APP_ERROR ContainerStep1InferenceEngine::Process()
|
|||
|
|
{
|
|||
|
|
int iRet = APP_ERR_OK;
|
|||
|
|
|
|||
|
|
while (!isStop_)
|
|||
|
|
{
|
|||
|
|
std::shared_ptr<void> pVoidData0 = nullptr;
|
|||
|
|
inputQueMap_[strPort0_]->pop(pVoidData0);
|
|||
|
|
if (nullptr == pVoidData0)
|
|||
|
|
{
|
|||
|
|
usleep(1000); //1ms
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
std::shared_ptr<VDetectInfo> pVDetectInfo = std::static_pointer_cast<VDetectInfo>(pVoidData0);
|
|||
|
|
|
|||
|
|
std::shared_ptr<InferenceResultData> pInferenceResultData = std::make_shared<InferenceResultData>();
|
|||
|
|
pInferenceResultData->iDataSource = pVDetectInfo->iDataSource;
|
|||
|
|
pInferenceResultData->bIsEnd = pVDetectInfo->bIsEnd;
|
|||
|
|
if (pVDetectInfo->bIsEnd)
|
|||
|
|
{
|
|||
|
|
outputQueMap_[strPort0_]->push(std::static_pointer_cast<void>(pInferenceResultData), true);
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if (pVDetectInfo->cvImage.empty())
|
|||
|
|
{
|
|||
|
|
usleep(1000); //1ms
|
|||
|
|
continue;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//进行推理
|
|||
|
|
std::vector<stDetection> vecInferenceResult;
|
|||
|
|
yolov5model.YoloV5ClearityInferenceModel(pVDetectInfo->cvImage, vecInferenceResult);
|
|||
|
|
|
|||
|
|
//过滤无效信息
|
|||
|
|
this->filterInvalidInfo(vecInferenceResult, pVDetectInfo);
|
|||
|
|
|
|||
|
|
// this->getMaxScoreResult(vecInferenceResult);
|
|||
|
|
|
|||
|
|
std::sort(vecInferenceResult.begin(),
|
|||
|
|
vecInferenceResult.end(),
|
|||
|
|
CompareX);
|
|||
|
|
|
|||
|
|
|
|||
|
|
pInferenceResultData->iFrameId = pVDetectInfo->iFrameId;
|
|||
|
|
|
|||
|
|
pInferenceResultData->strDetectDate = pVDetectInfo->strDetectDate;
|
|||
|
|
pInferenceResultData->strDetectTime = pVDetectInfo->strDetectTime;
|
|||
|
|
pInferenceResultData->cvImage = pVDetectInfo->cvImage;
|
|||
|
|
|
|||
|
|
// 筛选离中心点坐标最近的，如果一样近就选最右的
|
|||
|
|
float fCenterX = IMAGE_WIDTH/2;
|
|||
|
|
float fCenterY = IMAGE_HEIGHT/2;
|
|||
|
|
float fMin = 9999999.0f; // 最大值
|
|||
|
|
for (auto & inferenceResult : vecInferenceResult)
|
|||
|
|
{
|
|||
|
|
// LogDebug << " 帧:" << pInferenceResultData->iFrameId
|
|||
|
|
// << " 数据源:" << pInferenceResultData->iDataSource
|
|||
|
|
// << " --iClassId:" << inferenceResult.class_id
|
|||
|
|
// << " confidence=" << inferenceResult.class_conf
|
|||
|
|
// << " lx=" << inferenceResult.bbox[0]
|
|||
|
|
// << " ly=" << inferenceResult.bbox[1]
|
|||
|
|
// << " rx=" << inferenceResult.bbox[2]
|
|||
|
|
// << " ry=" << inferenceResult.bbox[3]
|
|||
|
|
// << " clear:" << inferenceResult.clear_conf;
|
|||
|
|
|
|||
|
|
float fCenterX_frame = (inferenceResult.bbox[0] + inferenceResult.bbox[2])/2;
|
|||
|
|
float fCenterY_frame = (inferenceResult.bbox[1] + inferenceResult.bbox[3])/2;
|
|||
|
|
|
|||
|
|
float x = std::abs(fCenterX_frame - fCenterX);
|
|||
|
|
float y = std::abs(fCenterY_frame - fCenterY);
|
|||
|
|
if ((pow(x,2) + pow(y,2)) < fMin)
|
|||
|
|
{
|
|||
|
|
SingleData singledata;
|
|||
|
|
singledata.iTargetType = CONTAINER;
|
|||
|
|
singledata.iClassId = inferenceResult.class_id;
|
|||
|
|
singledata.fScore = inferenceResult.class_conf;
|
|||
|
|
singledata.fLTX = inferenceResult.bbox[0];
|
|||
|
|
singledata.fLTY = inferenceResult.bbox[1];
|
|||
|
|
singledata.fRBX = inferenceResult.bbox[2];
|
|||
|
|
singledata.fRBY = inferenceResult.bbox[3];
|
|||
|
|
singledata.fClear = inferenceResult.clear_id;
|
|||
|
|
|
|||
|
|
pInferenceResultData->singleData = singledata;
|
|||
|
|
|
|||
|
|
fMin = pow(x,2) + pow(y,2);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
outputQueMap_[strPort0_]->push(std::static_pointer_cast<void>(pInferenceResultData), true);
|
|||
|
|
}
|
|||
|
|
return APP_ERR_OK;
|
|||
|
|
}
|