void FFN<
LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::Predict(arma::mat& predictors, arma::mat& responses)
{
deterministic = true;
arma::mat responsesTemp;
ResetParameter(network);
Forward(arma::mat(predictors.colptr(0), predictors.n_rows, 1, false, true),
network);
OutputPrediction(responsesTemp, network);
responses = arma::mat(responsesTemp.n_elem, predictors.n_cols);
responses.col(0) = responsesTemp.col(0);
for (size_t i = 1; i < predictors.n_cols; i++)
{
Forward(arma::mat(predictors.colptr(i), predictors.n_rows, 1, false, true),
network);
responsesTemp = arma::mat(responses.colptr(i), responses.n_rows, 1, false,
true);
OutputPrediction(responsesTemp, network);
responses.col(i) = responsesTemp.col(0);
}
}
double CNN<
LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::Evaluate(const arma::mat& /* unused */,
const size_t i,
const bool deterministic)
{
this->deterministic = deterministic;
ResetParameter(network);
Forward(predictors.slices(i, (i + 1) * sampleSize - 1), network);
return OutputError(arma::mat(responses.colptr(i), responses.n_rows, 1, false,
true), error, network);
}
double RNN<
LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::Evaluate(const arma::mat& /* unused */,
const size_t i,
const bool deterministic)
{
this->deterministic = deterministic;
arma::mat input = arma::mat(predictors.colptr(i), predictors.n_rows,
1, false, true);
arma::mat target = arma::mat(responses.colptr(i), responses.n_rows,
1, false, true);
// Initialize the activation storage only once.
if (activations.empty())
InitLayer(input, target, network);
double networkError = 0;
seqLen = input.n_rows / inputSize;
ResetParameter(network);
error = arma::mat(outputSize, outputSize < target.n_elem ? seqLen : 1);
// Iterate through the input sequence and perform the feed forward pass.
for (seqNum = 0; seqNum < seqLen; seqNum++)
{
// Perform the forward pass and save the activations.
Forward(input.rows(seqNum * inputSize, (seqNum + 1) * inputSize - 1),
network);
SaveActivations(network);
// Retrieve output error of the subsequence.
if (seqOutput)
{
arma::mat seqError = error.unsafe_col(seqNum);
arma::mat seqTarget = target.submat(seqNum * outputSize, 0,
(seqNum + 1) * outputSize - 1, 0);
networkError += OutputError(seqTarget, seqError, network);
}
}
// Retrieve output error of the complete sequence.
if (!seqOutput)
return OutputError(target, error, network);
return networkError;
}
//创建参数
bool CServiceParameter::CreateParameter(LPCTSTR pszModuleName)
{
//重置参数
ResetParameter();
//加载模块
if (LoadGameServiceModule(pszModuleName)==false)
{
AfxMessageBox(TEXT("“服务组件不存在”或“无法识别”，无法创建游戏房间！"),MB_ICONINFORMATION);
return false;
}
//配置参数
INT_PTR nResult=DoModal();
//释放模块
FreeGameServiceModule();
return (nResult==IDOK);
}
void CNN<
LayerTypes, OutputLayerType, InitializationRuleType, PerformanceFunction
>::Predict(arma::cube& predictors, arma::mat& responses)
{
deterministic = true;
arma::mat responsesTemp;
ResetParameter(network);
Forward(predictors.slices(0, sampleSize - 1), network);
OutputPrediction(responsesTemp, network);
responses = arma::mat(responsesTemp.n_elem, predictors.n_slices);
responses.col(0) = responsesTemp.col(0);
for (size_t i = 1; i < (predictors.n_slices / sampleSize); i++)
{
Forward(predictors.slices(i, (i + 1) * sampleSize - 1), network);
responsesTemp = arma::mat(responses.colptr(i), responses.n_rows, 1, false,
true);
OutputPrediction(responsesTemp, network);
responses.col(i) = responsesTemp.col(0);
}
}
//通用的初始化代码
//初始化PCI1761、数据库、根据参数将系统切换到真实/虚拟相机模式
//真实模式下调用TiggerStartWatch，开启监视线程
void ControllerModel::baseInit()
{
stringstream ss;
//初始化PCI1761
if (IsRealModel)
{
if (!pci1761.init())
{
pci1761Inited = false;
}
else
pci1761Inited = true;
}
// 数据库配置初始化
if (1 == 1)
{
int accEnable = 0;
SettingHelper::GetKeyInt("DATABASE", "ACCDB_ENABLE", accEnable);//读取是否启用数据库模块
ss.clear();
ss << "ACCDB_ENABLE = " << accEnable << endl;
MFCConsole::Output(ss.str());
if (accEnable != 0)
{
string db_path;
bool accConnFlag = false;
if (SettingHelper::GetKeyString("DATABASE", "ACCDB_PATH", db_path))//读取数据库所在路径
{
accConnFlag = Statistics::InitDate(db_path);
MFCConsole::Output("db_path:");
MFCConsole::Output(db_path);
MFCConsole::Output("\r\n");
}
else
{
accConnFlag = Statistics::InitDate("src//..//瓷砖缺陷检测数据库.mdb");
SettingHelper::AddKey("DATABASE", "ACCDB_PATH", "src//..//瓷砖缺陷检测数据库.mdb");
MFCConsole::Output("db_path:src//..//瓷砖缺陷检测数据库.mdb\r\n");
}
// 统计数据初始化
if (accConnFlag)
spotsMainView->UpdateStatistics();
else
AfxMessageBox(L"无法连接到数据库！");
}
}
// 初始化工作线程
if (e2vInited && pci1761Inited)
{
//若为真实相机模式，则实例化两个工人，并启动触发器监视进程
//初始化工人
worker1 = new Worker("worker1:");
worker2 = new Worker("worker2:");
worker1->P_Controller = this;
worker2->P_Controller = this;
TiggerStartWatch();
spotsMainView->SwitchModel2Virtual(false);
MFCConsole::Output("已切换到真实相机模式。\r\n");
}
else
{
PauseFlag = true;
if (IsRealModel)
{
IsRealModel = 0;
if (!e2vInited && !pci1761Inited)
AfxMessageBox(L"线阵相机&pci1761初始化失败！已切换到虚拟相机模式。");
else if (!pci1761Inited)
AfxMessageBox(L"pci1761初始化失败！已切换到虚拟相机模式。");
else if (!e2vInited)
AfxMessageBox(L"线阵相机初始化失败！已切换到虚拟相机模式。");
}
else
MFCConsole::Output("已切换到虚拟相机模式。\r\n");
//若为虚拟相机模式，则实例化虚拟相机类与1个工人
//开启虚拟相机
cv::Mat virtualImg;
worker1 = new Worker("Virtual Worker");
worker1->image = virtualImg;
worker1->P_Controller = this;
spotsMainView->SwitchModel2Virtual(true);
}
//读取参数配置
//设置工人算法参数
ResetParameter();
if (pci1761Inited)
{
//初始化面阵相机
if (MainHueScanner::Enabled)
{
P_mainHueScanner = new MainHueScanner(this);
if (!P_mainHueScanner->HasInited)
{
delete P_mainHueScanner;
P_mainHueScanner = NULL;
AfxMessageBox(L"未能初始化面阵相机，该模块被禁用！");
}
}
}
//初始化UI
cv::Mat white(2, 2, CV_8U, cv::Scalar(255));
logImg.InitDrawingBoard();
spotsMainView->ShowBigImg(white);
spotsMainView->ShowLogImg(logImg.DrawingBoard);
}
