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hehaoyang 2023-10-20 23:36:22 +08:00
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#include "HxDataBase.h"
QMutex DataBase::mutex;
QSqlDatabase DataBase::database;
QString DataBase::device_id;
QString DataBase::ftp_address;
QString DataBase::ftp_username;
QString DataBase::ftp_password;
int DataBase::algorithm_type;
QString DataBase::dsm_video_input_source;
QString DataBase::adas_video_input_source;
QStringList DataBase::bsd_video_input_source;
QStringList DataBase::bsd_warn_regions;
QSqlDatabase DataBase::open(QString filepath, QString connectionName)
{
QSqlDatabase db;
if (QSqlDatabase::contains(connectionName))
db = QSqlDatabase::database(connectionName);
else
db = QSqlDatabase::addDatabase("QSQLITE", connectionName);
db.setDatabaseName(filepath);
if (!db.open())
{
QString bk_filepath = filepath + QString(".[%1].bk").arg(QDateTime::currentDateTime().toString("yyyyMMddHHmmss"));
QFile::copy(filepath, bk_filepath);
QFile::remove(filepath);
db.open();
}
return db;
}
/**
* @brief
* @param connectionName
*/
void DataBase::close(QString connectionName) { QSqlDatabase::removeDatabase(connectionName); }
/**
* @brief Setting
* @param name name字段值
* @return
*/
QVariant DataBase::read_setting(QString name)
{
QVariant value;
QMutexLocker lock(&mutex);
QSqlQuery query(database);
query.prepare("SELECT [value] FROM [Setting] WHERE [name]=:name");
query.bindValue(":name", name);
if (query.exec())
{
if (query.next())
value = query.value("value");
}
query.clear();
return value;
}
/**
* @brief Setting (int)
* @param name name字段值
* @param defaultValue (int)
* @return (int)
*/
int DataBase::read_setting(QString name, int defaultValue)
{
auto value = read_setting(name);
return value.isNull() ? defaultValue : value.toInt();
}
/**
* @brief Setting (bool)
* @param name name字段值
* @param defaultValue (bool)
* @return (bool)
*/
bool DataBase::read_setting(QString name, bool defaultValue)
{
auto value = read_setting(name);
return value.isNull() ? defaultValue : value.toBool();
}
/**
* @brief Setting (string)
* @param name name字段值
* @param defaultValue (string)
* @return (string)
*/
QString DataBase::read_setting(QString name, QString defaultValue)
{
auto value = read_setting(name);
return value.isNull() ? defaultValue : value.toString();
}
/**
* @brief Setting
* @param name name字段值
* @param value
*/
void DataBase::write_setting(QString name, QString value)
{
QMutexLocker lock(&mutex);
QSqlQuery query(database);
query.prepare("REPLACE INTO [Setting] ([name], [value]) VALUES (:name, :value);");
query.bindValue(":name", name);
query.bindValue(":value", value);
if (!query.exec())
{
qDebug("write_setting failed, name=%s, value=%s", name.toUtf8().data(), value.toUtf8().data());
}
query.clear();
}
void DataBase::initialization()
{
database = open("DataBase.db", "DataBase");
if (!database.isOpen())
{
qDebug("配置信息创建失败, 程序无法继续启动!");
return;
}
QSqlQuery query(database);
/* 创建 程序配置信息 表 */
if (!query.exec("CREATE TABLE IF NOT EXISTS [Setting] ("
" [name] TEXT NOT NULL PRIMARY KEY,"
" [value] TEXT"
");"))
{
qDebug("配置表创建失败");
return;
}
/* */
device_id = read_setting("device_id", QString("TVIS"));
/* */
ftp_address = read_setting("ftp_address", QString("192.168.10.10:7616"));
/* */
ftp_username = read_setting("ftp_username", QString("nvruser"));
/* */
ftp_password = read_setting("ftp_password", QString("Sztvis_789."));
/* 算法类型, 默认 BSD */
algorithm_type = read_setting("algorithm_type", 0x02);
/* DSM 视频输入源 (string) */
dsm_video_input_source = read_setting("dsm_video_input_source", QString(""));
/* Adas 视频输入源 (string) */
adas_video_input_source = read_setting("adas_video_input_source", QString(""));
/* BSD 视频输入源 (string) */
bsd_video_input_source = read_setting("bsd_video_input_source", QString(";;;;;;")).split(";");
bsd_warn_regions = read_setting(
"bsd_warn_regions",
QString("0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0*"
"0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0*"
"0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0*"
"0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0*"
"0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0*"
"0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0"))
.split("*");
}
bool DataBase::save_setting()
{
database.transaction();
write_setting("device_id", device_id);
write_setting("ftp_address", ftp_address);
write_setting("ftp_username", ftp_username);
write_setting("ftp_password", ftp_password);
write_setting("algorithm_type", QString::number(algorithm_type));
write_setting("dsm_video_input_source", dsm_video_input_source);
write_setting("adas_video_input_source", adas_video_input_source);
write_setting("bsd_video_input_source", bsd_video_input_source.join(";"));
write_setting("bsd_warn_regions", bsd_warn_regions.join("*"));
return database.commit();
}
/**
* @brief BSD报警区域
* @param channel
* @return BsdWarnRegion
*/
BsdWarnRegion DataBase::get_bsd_warn_region(int channel)
{
BsdWarnRegion ret;
auto data = bsd_warn_regions[channel];
if (data == "")
data = "0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0$0,0 0,0 0,0 0,0";
auto array = data.split("$");
auto points = array[0].split(" ");
for (int a = 0; a < 4; a++)
{
auto p = points.at(a).split(",");
ret.tFirstRegionPoint[a].x = p[0].toInt();
ret.tFirstRegionPoint[a].y = p[1].toInt();
}
points = array[1].split(" ");
for (int a = 0; a < 4; a++)
{
auto p = points.at(a).split(",");
ret.tSecondRegionPoint[a].x = p[0].toInt();
ret.tSecondRegionPoint[a].y = p[1].toInt();
}
points = array[2].split(" ");
for (int a = 0; a < 4; a++)
{
auto p = points.at(a).split(",");
ret.tThirdRegionPoint[a].x = p[0].toInt();
ret.tThirdRegionPoint[a].y = p[1].toInt();
}
return ret;
}

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#ifndef HXDATABASE_H
#define HXDATABASE_H
#include <QDateTime>
#include <QMutex>
#include <QSqlQuery>
#include <QStorageInfo>
#include <QSqlDatabase>
#include <QVariant>
#include <QString>
#include "MvObjectEventDetect.h"
#define BSD_MAX_CHANNEL 6
class DataBase
{
private:
/**
* @brief
* @param filepath
* @param connectionName
* @return QSqlDatabase
*/
static QSqlDatabase open(QString filepath, QString connectionName);
/**
* @brief
* @param connectionName
*/
static void close(QString connectionName);
/**
* @brief Setting
* @param name name字段值
* @return
*/
static QVariant read_setting(QString name);
/**
* @brief Setting (int)
* @param name name字段值
* @param defaultValue (int)
* @return (int)
*/
static int read_setting(QString name, int defaultValue);
/**
* @brief Setting (bool)
* @param name name字段值
* @param defaultValue (bool)
* @return (bool)
*/
static bool read_setting(QString name, bool defaultValue);
/**
* @brief Setting (string)
* @param name name字段值
* @param defaultValue (string)
* @return (string)
*/
static QString read_setting(QString name, QString defaultValue);
/**
* @brief Setting
* @param name name字段值
* @param value
*/
static void write_setting(QString name, QString value);
public:
/**
* @brief
*/
static void initialization();
/**
* @brief
* @return true: ; false: ;
*/
static bool save_setting();
/**
* @brief BSD报警区域
* @param channel
* @return BsdWarnRegion
*/
static BsdWarnRegion get_bsd_warn_region(int channel);
private:
static QMutex mutex;
static QSqlDatabase database;
public:
/* 设备编号 */
static QString device_id;
/* FTP地址 */
static QString ftp_address;
/* FTP用户名 */
static QString ftp_username;
/* FTP密码 */
static QString ftp_password;
/**
*
*/
static int algorithm_type;
/**
* DSM
*/
static QString dsm_video_input_source;
/**
* ADAS
*/
static QString adas_video_input_source;
/**
* BSD
*/
static QStringList bsd_video_input_source;
/**
* BSD
*/
static QStringList bsd_warn_regions;
};
#endif // HXDATABASE_H

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#include "HxTaskDispatch.h"
#include "HxVideoDevice.h"
#include <QJsonArray>
#include <QJsonObject>
#include <QJsonDocument>
/* 视频设备 */
VideoDevice adas_video_device, bsd_video_device[6], dsm_video_device;
TaskDispatch *dispatcher = new TaskDispatch();
/* 车辆状态: 1 直行; 2 左转弯; 3 右转弯; 4 倒车 */
static int vehicle_status = 0;
static int detect_channel = 0;
void TaskDispatch::initialization(void)
{
/* 算法模块初始化 */
#if USE_ALGORITHM
/* adas检测配置 */
strcpy(dispatcher->event_detect_config.szAdasDetectConfigPathName, "/opt/algmode/adas_detect.bin");
/* adas跟踪配置 */
strcpy(dispatcher->event_detect_config.szAdasTrackConfigPathName, "/opt/algmode/adas_track.bin");
/* bsd检测配置 */
strcpy(dispatcher->event_detect_config.szBsdDetectConfigPathName, "/opt/algmode/bsd_detect.bin");
// strcpy(dispatcher->event_detect_config.szRightBsdFrontDetectConfigPathName, "/opt/algmode/bsd_detect.bin");
/* dsm人脸检测配置 */
strcpy(dispatcher->event_detect_config.szDsmFaceDetectConfigPathName, "/opt/algmode/dsm_face_detect.bin");
/* dsm人脸特征点检测配置 */
strcpy(dispatcher->event_detect_config.szDsmFaceLandMarksDetectConfigPathName, "/opt/algmode/dsm_face_landmarks_detect.bin");
/* dsm人脸认证检测配置 */
strcpy(dispatcher->event_detect_config.szDsmFaceVerificationDetectConfigPathName, "/opt/algmode/dsm_face_verification_detect.bin");
/* dsm人眼认证检测配置 */
strcpy(dispatcher->event_detect_config.szDsmEyeLandMarksDetectConfigPathName, "/opt/algmode/dsm_eye_landmarks_detect.bin");
/* dsm人脸认证检测配置 */
strcpy(dispatcher->event_detect_config.szDsmSmokeConfPathName, "/opt/algmode/dsm_smoke_detect.bin");
strcpy(dispatcher->event_detect_config.szDsmCallConfPathName, "/opt/algmode/dsm_call_detect.bin");
strcpy(dispatcher->event_detect_config.szDsmFaceFeaturePathName, "/opt/algmode/dsm_face_feature.bin");
strcpy(dispatcher->event_detect_config.szDsmHeadPoseConfPathName, "/opt/algmode/data_68kp");
/* 输出调试信息 */
MvSetPrintf(false);
/* 目标跟踪事件检测初始化 */
auto result = MvObjectEventDetectInit(&dispatcher->event_detect_config, RIGHT_BSD_FRONT_DETECT_CHANNEL |
RIGHT_BSD_REAR_DETECT_CHANNEL |
LEFT_BSD_FRONT_DETECT_CHANNEL |
LEFT_BSD_REAR_DETECT_CHANNEL |
FRONT_BSD_DETECT_CHANNEL |
REAR_BSD_DETECT_CHANNEL |
DMS_DETECT_CHANNEL |
ADAS_DETECT_CHANNEL);
if (result != 0)
return;
#endif
/* ADAS 通道初始化 */
if ((DataBase::algorithm_type & 0x1) == 1)
{
adas_video_device.set(0, DataBase::adas_video_input_source);
}
/* BSD 通道初始化 */
if (((DataBase::algorithm_type >> 1) & 0x1) == 1)
{
/* BSD-前 */
bsd_video_device[0].set(6, DataBase::bsd_video_input_source[0], DataBase::get_bsd_warn_region(0));
/* BSD-后 */
bsd_video_device[1].set(7, DataBase::bsd_video_input_source[1], DataBase::get_bsd_warn_region(1));
/* BSD-左前 */
bsd_video_device[2].set(4, DataBase::bsd_video_input_source[2], DataBase::get_bsd_warn_region(2));
/* BSD-右前 */
bsd_video_device[3].set(1, DataBase::bsd_video_input_source[3], DataBase::get_bsd_warn_region(3));
/* BSD-左后 */
bsd_video_device[4].set(5, DataBase::bsd_video_input_source[4], DataBase::get_bsd_warn_region(4));
/* BSD-右后 */
bsd_video_device[5].set(3, DataBase::bsd_video_input_source[5], DataBase::get_bsd_warn_region(5));
}
/* DSM 通道初始化 */
if (((DataBase::algorithm_type >> 2) & 0x1) == 1)
{
dsm_video_device.set(2, DataBase::dsm_video_input_source);
}
dispatcher->start();
}
void TaskDispatch::listern(uint16_t port)
{
dispatcher->debug_tool = new HxSocketUtils(port);
connect(dispatcher->debug_tool, &HxSocketUtils::data_receive_event, dispatcher, &TaskDispatch::data_receive_event);
connect(dispatcher, &TaskDispatch::data_write_event, dispatcher->debug_tool, &HxSocketUtils::write);
}
void TaskDispatch::connect_to_host(QString address, int port)
{
dispatcher->platform = new HxSocketUtils(address, port);
connect(dispatcher->platform, &HxSocketUtils::data_receive_event, dispatcher, &TaskDispatch::data_receive_event);
connect(dispatcher, &TaskDispatch::data_write_event, dispatcher->platform, &HxSocketUtils::write);
}
void TaskDispatch::algorithm_alarm_event(int channel, int level, int event_type, int detect_type, int left, int top, int right, int bottom, int distance, int speed, QString base64_string, QString filepath)
{
QJsonObject root({{"type", 4}});
QJsonObject msgInfo({{"channel", channel},
{"level", level},
{"event_type", event_type},
{"detect_type", detect_type},
{"left", left},
{"top", top},
{"right", right},
{"bottom", bottom},
{"distance", distance},
{"speed", speed},
{"image", base64_string},
{"filepath", filepath}});
root.insert("msgInfo", msgInfo);
emit dispatcher->data_write_event(QJsonDocument(root).toJson(QJsonDocument::Compact));
// dispatcher->send_can_data(channel, pObjectTrackEventResult->nDangerLevel, pObjectTrackEventResult->objInfo[i].nDetectType);
}
CarInfoInput *TaskDispatch::get_car_info(void) { return &dispatcher->car_info; }
QString TaskDispatch::get_video_frame_data(int type, int channel, int id)
{
Q_UNUSED(channel);
switch(type)
{
case 0:
return adas_video_device.build_image(id);
case 1:
return bsd_video_device[channel].build_image(id);
case 2:
return dsm_video_device.build_image(id);
}
return "";
}
QString TaskDispatch::build_alarm_image(int type, int channel, int frame_id)
{
switch(type)
{
case 0:
return adas_video_device.build_image(frame_id);
case 1:
return bsd_video_device[channel].build_image(frame_id);
case 2:
return dsm_video_device.build_image(frame_id);
}
return "";
}
QString TaskDispatch::build_alarm_video(int type, int channel, int frame_id)
{
switch(type)
{
case 0:
return adas_video_device.build_video(frame_id);
case 1:
return bsd_video_device[channel].build_video(frame_id);
case 2:
return dsm_video_device.build_video(frame_id);
}
return "";
}
void TaskDispatch::update_heartbeat()
{
if (QDateTime::currentDateTime() > heartbeat_timestamp.addSecs(30))
{
heartbeat_timestamp = QDateTime::currentDateTime();
emit data_write_event(QJsonDocument(QJsonObject({{"type", 0}})).toJson(QJsonDocument::Compact));
}
}
void TaskDispatch::updata_vehiclue_status(QJsonObject object)
{
vehicle_status_update_time = QDateTime::currentDateTime();
car_info.fVelocity = object.value("speed").toDouble();
car_info.fAcceleration = object.value("acceleration").toDouble();
car_info.fDeceleration = object.value("deceleration").toDouble();
car_info.nBrake = object.value("brake").toInt();
car_info.nLLight = object.value("left_light").toInt();
car_info.nRLight = object.value("right_light").toInt();
car_info.fAlpha = object.value("steering_angle").toDouble();
car_info.fSteeingWheelAngle = object.value("seeing_wheel_angle").toDouble();
reversing_light = object.value("reversing_light").toInt();
}
void TaskDispatch::parsing_vehiclue_status(void)
{
if (dispatcher->vehicle_status_update_time.secsTo(QDateTime::currentDateTime()) >= 10)
car_info.fVelocity = 0;
if (car_info.fVelocity > 0)
{
auto _detect_channel = 0;
/* ADAS 通道初始化 */
if ((DataBase::algorithm_type & 0x1) == 1)
{
_detect_channel |= ADAS_DETECT_CHANNEL;
}
/* DSM 通道初始化 */
if (((DataBase::algorithm_type >> 2) & 0x1) == 1)
{
_detect_channel |= DMS_DETECT_CHANNEL;
}
/* BSD 类型 */
else if (((DataBase::algorithm_type >> 1) & 0x1) == 1)
{
auto status = 0;
/* 倒车 */
if (reversing_light == 1)
status = 4;
/* 左转弯 */
else if (car_info.nLLight == 1)
status = 2;
/* 右转弯 */
else if (car_info.nRLight == 1)
status = 3;
/* 无左右转弯,车辆直行 */
else
status = 1;
/* 车辆状态发生改变 */
if (status != 0 && vehicle_status != status)
{
qDebug() << "车辆状态发生改变 " << vehicle_status << " => " << status;
vehicle_status = status;
/* 禁止送帧 */
for (int i = 0; i < BSD_MAX_CHANNEL; i++)
bsd_video_device[i].set(false);
switch (status)
{
/* 直线行驶, 检测通道: A C D */
case 1:
/* 允许送帧 */
bsd_video_device[0].set(true);
bsd_video_device[2].set(true);
bsd_video_device[3].set(true);
/* 前侧, 左前, 右前 */
_detect_channel |= (FRONT_BSD_DETECT_CHANNEL | LEFT_BSD_FRONT_DETECT_CHANNEL | RIGHT_BSD_FRONT_DETECT_CHANNEL);
break;
/* 左转弯, 检测通道: C D E */
case 2:
/* 允许送帧 */
bsd_video_device[2].set(true);
bsd_video_device[3].set(true);
bsd_video_device[4].set(true);
/* 左前, 右前, 左后 */
_detect_channel |= (LEFT_BSD_FRONT_DETECT_CHANNEL | RIGHT_BSD_FRONT_DETECT_CHANNEL | LEFT_BSD_REAR_DETECT_CHANNEL);
break;
/* 右转弯, 检测通道: C D F */
case 3:
/* 允许送帧 */
bsd_video_device[2].set(true);
bsd_video_device[3].set(true);
bsd_video_device[5].set(true);
/* 左前, 右前, 右后 */
_detect_channel |= (LEFT_BSD_FRONT_DETECT_CHANNEL | RIGHT_BSD_FRONT_DETECT_CHANNEL | RIGHT_BSD_REAR_DETECT_CHANNEL);
break;
/* 倒车, 检测通道: B C D */
case 4:
/* 允许送帧 */
bsd_video_device[1].set(true);
bsd_video_device[2].set(true);
bsd_video_device[3].set(true);
/* 后侧, 左前, 左后 */
_detect_channel |=(REAR_BSD_DETECT_CHANNEL | LEFT_BSD_FRONT_DETECT_CHANNEL | RIGHT_BSD_FRONT_DETECT_CHANNEL);
break;
}
}
}
/* 确保状态改变后才调用该函数 */
if(detect_channel != _detect_channel)
{
detect_channel = _detect_channel;
#if USE_ALGORITHM
MvSetDetectChannel(detect_channel);
#endif
}
}
}
void TaskDispatch::run()
{
while (true)
{
QCoreApplication::processEvents();
/* 发送心跳数据 */
update_heartbeat();
/* 分析车辆行驶状态 */
parsing_vehiclue_status();
QThread::msleep(10);
}
}
void TaskDispatch::debug_tool_response_event(int type, std::initializer_list<QPair<QString, QJsonValue>> args)
{
QJsonObject root, msginfo;
for (std::initializer_list<QPair<QString, QJsonValue>>::const_iterator i = args.begin(); i != args.end(); ++i)
msginfo.insert(i->first, i->second);
root.insert("type", type);
root.insert("msgInfo", msginfo);
emit data_write_event(QJsonDocument(root).toJson(QJsonDocument::Compact));
}
void TaskDispatch::data_receive_event(QByteArray data)
{
int type = -1;
auto document = QJsonDocument::fromJson(data);
auto root = document.object();
if (root.contains("type"))
{
type = root.value("type").toInt();
}
auto msginfo = root.value("msgInfo").toObject();
switch (type)
{
/* heart */
case 0:
break;
/* 车辆状态 */
case 3:
updata_vehiclue_status(msginfo);
break;
/* 获取算法类型 */
case 0xF0:
debug_tool_response_event(type, {{"mode", DataBase::algorithm_type}});
break;
/* 设置算法类型 */
case 0xF1:
DataBase::algorithm_type = msginfo.value("mode").toInt();
debug_tool_response_event(type, {{"status", DataBase::save_setting()}});
break;
/* 获取ADAS视频输入源 */
case 0xF2:
debug_tool_response_event(type, {{"source", DataBase::adas_video_input_source}});
break;
/* 设置ADAS视频输入源 */
case 0xF3:
DataBase::adas_video_input_source = msginfo.value("source").toString();
debug_tool_response_event(type, {{"status", DataBase::save_setting()}});
break;
/* 抓拍 */
case 0xF4:
debug_tool_response_event(type, {{"data", adas_video_device.snap()}});
break;
/* 获取BSD视频输入源 */
case 0xF5:
debug_tool_response_event(type, {{"source", DataBase::bsd_video_input_source[msginfo.value("channel").toInt()]}});
break;
/* 设置BSD视频输入源 */
case 0xF6:
DataBase::bsd_video_input_source[msginfo.value("channel").toInt()] = msginfo.value("source").toString();
debug_tool_response_event(type, {{"status", DataBase::save_setting()}});
break;
/* 抓拍 */
case 0xF7:
debug_tool_response_event(type, {{"data", bsd_video_device[msginfo.value("channel").toInt()].snap()}});
break;
/* 获取Dsm视频输入源 */
case 0xF8:
debug_tool_response_event(type, {{"source", DataBase::dsm_video_input_source}});
break;
/* 设置Dsm视频输入源 */
case 0xF9:
DataBase::dsm_video_input_source = msginfo.value("source").toString();
debug_tool_response_event(type, {{"status", DataBase::save_setting()}});
break;
/* 抓拍 */
case 0xFA:
debug_tool_response_event(type, {{"data", dsm_video_device.snap()}});
break;
/* 获取BSD报警区域 */
case 0xFB:
debug_tool_response_event(type, {{"data", DataBase::bsd_warn_regions[msginfo.value("channel").toInt()]}});
break;
/* 设置BSD报警区域 */
case 0xFC:
DataBase::bsd_warn_regions[msginfo.value("channel").toInt()] = msginfo.value("data").toString();
debug_tool_response_event(type, {{"status", DataBase::save_setting()}});
break;
/* 获取设备编号及FTP信息 */
case 0xFD:
debug_tool_response_event(type, {{"device_id", DataBase::device_id},
{"ftp_address", DataBase::ftp_address},
{"ftp_username", DataBase::ftp_username},
{"ftp_password", DataBase::ftp_password}});
break;
/* 设置设备编号及FTP信息 */
case 0xFE:
DataBase::device_id = msginfo.value("device_id").toString();
DataBase::ftp_address = msginfo.value("ftp_address").toString();
DataBase::ftp_username = msginfo.value("ftp_username").toString();
DataBase::ftp_password = msginfo.value("ftp_password").toString();
debug_tool_response_event(type, {{"status", DataBase::save_setting()}});
break;
case 0xFFFF:
adas_video_device.test();
break;
}
}

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#ifndef HXTASKDISPATCH_H
#define HXTASKDISPATCH_H
#include <QThread>
#include "HxDataBase.h"
#include "HxUtils.h"
#include "MvObjectEventDetect.h"
#define ALGORITHM_TYPE_ADAS 0
#define ALGORITHM_TYPE_BSD 1
#define ALGORITHM_TYPE_DSM 2
class TaskDispatch : public QThread
{
Q_OBJECT
public:
/**
* @brief
*/
static void initialization(void);
/**
* @brief DebugTool监听程序
* @param port
*/
static void listern(uint16_t port);
/**
* @brief
* @param address
*/
static void connect_to_host(QString address, int port);
static void algorithm_alarm_event(int channel, int level, int event_type, int detect_type, int left, int top, int right, int bottom, int distance, int speed, QString base64_string, QString filepath);
static DataBase database(void);
static CarInfoInput *get_car_info(void);
static QString get_video_frame_data(int type, int channel, int id);
/**
* @brief
* @param type
* @param channel
* @param frame_id
* @return Base64
*/
static QString build_alarm_image(int type, int channel, int frame_id);
/**
* @brief
* @param type
* @param channel
* @param frame_id
*/
static QString build_alarm_video(int type, int channel, int frame_id);
protected:
void run() override;
private:
void update_heartbeat();
void updata_vehiclue_status(QJsonObject object);
void parsing_vehiclue_status(void);
void debug_tool_response_event(int type, std::initializer_list<QPair<QString, QJsonValue>> args);
signals:
void data_write_event(QByteArray data);
public slots:
void data_receive_event(QByteArray data);
private:
/* 事件检测配置信息*/
ObjectEventDetectConfig event_detect_config;
/* 心跳发送时间戳 */
QDateTime heartbeat_timestamp;
/* 车辆状态更新时间 */
QDateTime vehicle_status_update_time;
/* 边缘云 Socket (客户端) */
HxSocketUtils *platform;
/* DebugTool Socket (服务端) */
HxSocketUtils *debug_tool;
/* 车辆状态信息 */
CarInfoInput car_info;
/* 倒车灯10熄灭 */
int reversing_light = 0;
};
#endif // HXTASKDISPATCH_H

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#ifndef HXUTILS_H
#define HXUTILS_H
#include <QMutex>
#include <QHostInfo>
#include <QTcpSocket>
#include <QTcpServer>
#include <QDateTime>
#include <QCoreApplication>
#include <QProcess>
/**
* @brief SocketUtils socket
*/
class HxSocketUtils : public QObject
{
Q_OBJECT
public:
HxSocketUtils(quint16 port)
{
connect(&server, &QTcpServer::newConnection, this, &HxSocketUtils::new_connection);
server.listen(QHostAddress::Any, port);
}
HxSocketUtils(QString address, int port)
{
is_reconnect = true;
socket = new QTcpSocket();
connect(socket, &QTcpSocket::readyRead, this, &HxSocketUtils::ready_read);
connect(socket, SIGNAL(disconnected()), this, SLOT(disconnected()), Qt::QueuedConnection);
connect(this, &HxSocketUtils::reconnection_event, this, &HxSocketUtils::reconnection);
/* 域名解析 */
QHostInfo info = QHostInfo::fromName(address);
this->port = port;
this->address = info.addresses().at(0).toString();
reconnection();
}
signals:
void data_receive_event(QByteArray data);
void reconnection_event(void);
public slots:
void new_connection()
{
if (socket != nullptr)
socket->abort();
socket = server.nextPendingConnection();
connect(socket, &QTcpSocket::readyRead, this, &HxSocketUtils::ready_read);
connect(socket, SIGNAL(disconnected()), this, SLOT(disconnected()), Qt::QueuedConnection);
}
void write(QByteArray data)
{
if (socket == nullptr || socket->state() != QTcpSocket::ConnectedState)
return;
data.append('\n');
socket->write(data);
socket->flush();
}
void ready_read()
{
QByteArray msg = socket->readAll();
emit data_receive_event(msg.data());
}
void disconnected()
{
qDebug("socket disconnect");
if (is_reconnect)
{
qDebug("socket will be reconnected... ");
emit reconnection_event();
}
}
void reconnection()
{
/* 取消已有的连接 */
if (socket != nullptr)
{
socket->disconnectFromHost();
qDebug("socket release");
}
qDebug("socket connecting......");
/* 连接服务器 */
socket->connectToHost(address, port);
/* 等待连接 */
if (socket->waitForConnected(500))
{
qDebug("socket connect success");
}
else
{
qDebug("socket connect failed");
/* 使用 QThread::msleep 延时,会使 Socket 出现接收不到事件信息 (槽无法响应) */
auto time = QTime::currentTime().addMSecs(10000);
while (QTime::currentTime() < time)
QCoreApplication::processEvents(QEventLoop::AllEvents, 100);
emit reconnection_event();
}
}
private:
int port;
QString address;
bool is_reconnect = false;
QTcpServer server;
QTcpSocket *socket = nullptr;
};
class HxProcessUtils
{
public:
static int execute(QString command)
{
auto array = command.split(" ");
QString program = array.at(0);
QStringList arguments;
for (int i = 1; i < array.count(); i++)
arguments << array.at(i);
return QProcess::execute(program, arguments);
}
};
#endif // HXUTILS_H

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#include "HxVideoDevice.h"
/* bsd算法结果处理函数 */
void algorithm_alarm_callback(int nDataChannel, ObjectTrackEventResult *pObjectTrackEventResult, void *pPrivData)
{
Q_UNUSED(nDataChannel);
Q_UNUSED(pPrivData);
if (pObjectTrackEventResult->nEventType == 0)
return;
if (nDataChannel == 0) /* ADAS */
{
printf("ProcessAdasAlgResult nDataChannel=%d,nFrameId=%d,nObjectNumber=%d,nEventType=%x\n", nDataChannel, pObjectTrackEventResult->nFrameId, pObjectTrackEventResult->nObjectNumber, pObjectTrackEventResult->nEventType);
/* 生成报警图片 */
auto base64_string = TaskDispatch::build_alarm_image(ALGORITHM_TYPE_ADAS, 0, pObjectTrackEventResult->nFrameId);
/* 生成报警视频 */
auto filename = TaskDispatch::build_alarm_video(ALGORITHM_TYPE_ADAS, 0, pObjectTrackEventResult->nFrameId);
/* 上传 */
TaskDispatch::algorithm_alarm_event(0, 0, pObjectTrackEventResult->nEventType, 0, 0, 0, 0, 0, 0, 0, base64_string, filename);
}
else if (nDataChannel == 2) /* DSM */
{
printf("ProcessDsmAlgResult nDataChannel=%d,nFrameId=%d,nObjectNumber=%d,nEventType=%x\n", nDataChannel, pObjectTrackEventResult->nFrameId, pObjectTrackEventResult->nObjectNumber, pObjectTrackEventResult->nEventType);
/* 生成报警图片 */
auto base64_string = TaskDispatch::build_alarm_image(ALGORITHM_TYPE_DSM, 0, pObjectTrackEventResult->nFrameId);
/* 生成报警视频 */
auto filename = TaskDispatch::build_alarm_video(ALGORITHM_TYPE_DSM, 0, pObjectTrackEventResult->nFrameId);
/* 上传 */
TaskDispatch::algorithm_alarm_event(1, 0, pObjectTrackEventResult->nEventType, 0, 0, 0, 0, 0, 0, 0, base64_string, filename);
}
else if (nDataChannel == 1 || /* BSD-右前 */
nDataChannel == 3 || /* BSD-右后 */
nDataChannel == 4 || /* BSD-左前 */
nDataChannel == 5 || /* BSD-左后 */
nDataChannel == 6 || /* BSD-前 */
nDataChannel == 7) /* BSD-后 */
{
int channel = 0;
switch (nDataChannel)
{
case 6:
channel = 0;
break;
case 7:
channel = 1;
break;
case 4:
channel = 2;
break;
case 1:
channel = 3;
break;
case 5:
channel = 4;
break;
case 3:
channel = 5;
break;
}
qDebug("ProcessBsdAlgResult nDataChannel=%d,nFrameId=%d,nObjectNumber=%d,nEventType=%x,nDangerLevel=%d\n", nDataChannel, pObjectTrackEventResult->nFrameId, pObjectTrackEventResult->nObjectNumber, pObjectTrackEventResult->nEventType, pObjectTrackEventResult->nDangerLevel);
for (int i = 0; i < pObjectTrackEventResult->nObjectNumber; i++)
{
qDebug("pObjectTrackEventResult->objInfo[i].nDetectType=%d\n", pObjectTrackEventResult->objInfo[i].nDetectType);
qDebug("nLeft=%d,nTop=%d,nRight=%d,nBottom=%d\n", pObjectTrackEventResult->objInfo[i].nLeft, pObjectTrackEventResult->objInfo[i].nTop, pObjectTrackEventResult->objInfo[i].nRight, pObjectTrackEventResult->objInfo[i].nBottom);
if (pObjectTrackEventResult->objInfo[i].nDetectType == 0)
continue;
/* 生成报警图片 */
auto base64_string = TaskDispatch::build_alarm_image(ALGORITHM_TYPE_BSD, channel, pObjectTrackEventResult->nFrameId);
/* 生成报警视频 */
auto filename = TaskDispatch::build_alarm_video(ALGORITHM_TYPE_BSD, channel, pObjectTrackEventResult->nFrameId);
/* 上传 */
TaskDispatch::algorithm_alarm_event(channel,
pObjectTrackEventResult->nDangerLevel,
pObjectTrackEventResult->nEventType,
pObjectTrackEventResult->objInfo[i].nDetectType,
pObjectTrackEventResult->objInfo[i].nLeft,
pObjectTrackEventResult->objInfo[i].nTop,
pObjectTrackEventResult->objInfo[i].nRight,
pObjectTrackEventResult->objInfo[i].nBottom,
pObjectTrackEventResult->objInfo[i].fDist,
pObjectTrackEventResult->objInfo[i].fVelo,
base64_string,
filename);
}
}
}
VideoDevice::VideoDevice(void)
{
detect_frame_buffer.u32Width = 1280;
detect_frame_buffer.u32Height = 720;
MvGetFrameBlkInfo(&detect_frame_buffer);
bgr_frame_buffer.u32Width = 1280;
bgr_frame_buffer.u32Height = 720;
MvGetFrameBlkInfo(&bgr_frame_buffer);
}
void VideoDevice::set(int type, QString address)
{
this->type = type;
this->address = address;
#if USE_ALGORITHM
auto result = MvSetAlgResultFuncCallback(type, algorithm_alarm_callback, nullptr);
if (result != 0)
return;
#endif
start();
}
void VideoDevice::set(int type, QString address, BsdWarnRegion region)
{
Q_UNUSED(region);
this->type = type;
this->address = address;
this->detection_status = false;
#if USE_ALGORITHM
auto result = MvSetAlgResultFuncCallback(type, algorithm_alarm_callback, nullptr);
if (result != 0)
return;
MvSetBsdWarnRegion(type, &region);
#endif
start();
}
void VideoDevice::set(bool status) { detection_status = status; }
QString VideoDevice::snap() { return snap(video_frame); }
QString VideoDevice::build_image(int id)
{
if (video_frames_string.contains(id))
return snap(video_frames_string[id]);
return "";
}
QString VideoDevice::build_video(int id)
{
QString url = QString("ftp://%3/%4/%5/alarm/").arg(DataBase::ftp_address, QDateTime::currentDateTime().toString("yyyyMMdd"), DataBase::device_id);
QString filename = QString("%1.mp4").arg(QDateTime::currentDateTime().toString("yyyyMMddHHmmsszzz"));
QtConcurrent::run([=]()
{
if(video_frames_string.contains(id))
{
frame_mutex.lock();
auto first_key = video_frames_string.firstKey();
frame_mutex.unlock();
auto start_id = id - frame_fps * 10, end_id = id + frame_fps * 10;
start_id = start_id < first_key ? first_key : start_id;
qDebug() << "build video; start id = "<<start_id << " end id = " << end_id;
while(true)
{
frame_mutex.lock();
auto last_key = video_frames_string.lastKey();
frame_mutex.unlock();
// qDebug() << " end id = " << end_id << " last id = "<< last_key;
/* 判断最新的视频帧是否超过设定的时间 */
if(last_key >= end_id)
{
frame_mutex.lock();
auto _video_frames_string = video_frames_string;
_video_frames_string.detach();
frame_mutex.unlock();
VideoWriter writer(filename.toUtf8().data(), CV_FOURCC('m', 'p', '4', 'v'), frame_fps, Size(video_capture.get(CV_CAP_PROP_FRAME_WIDTH), video_capture.get(CV_CAP_PROP_FRAME_HEIGHT)), true);
for(int i = start_id; i <= end_id; i++)
{
auto __frame = imdecode(_video_frames_string[i], CV_LOAD_IMAGE_COLOR);
writer.write(__frame);
msleep(frame_fps);
qDebug() << " write "<< filename <<". frame id = " << i;
}
writer.release();
qDebug() << "video build finish";
HxProcessUtils::execute(QString("curl -u %1:%2 %3 --ftp-create-dirs -T %4")
.arg(DataBase::ftp_username,
DataBase::ftp_password,
url,
filename));
QFile::remove(filename);
return;
}
msleep(100);
}
} });
return url + filename;
}
void VideoDevice::test(void)
{
ObjectTrackEventResult pObjectTrackEventResult = {0};
pObjectTrackEventResult.nFrameId = video_frames_string.lastKey();
pObjectTrackEventResult.nEventType = EVENT_PCW;
pObjectTrackEventResult.nObjectNumber = 1;
pObjectTrackEventResult.objInfo[0].nDetectType = PEDESTRIAN_TYPE;
algorithm_alarm_callback(0, &pObjectTrackEventResult, nullptr);
}
vector<uint8_t> VideoDevice::compress(Mat frame, int quality)
{
vector<uint8_t> buffer;
vector<int> compress_params;
compress_params.push_back(IMWRITE_JPEG_QUALITY);
compress_params.push_back(quality);
imencode(".jpg", frame, buffer, compress_params);
return buffer;
}
QString VideoDevice::snap(Mat frame)
{
if (frame.data != nullptr)
return snap(compress(frame, 50));
return "";
}
QString VideoDevice::snap(vector<uint8_t> buffer)
{
QByteArray byteArray = QByteArray::fromRawData((const char *)buffer.data(), buffer.size());
return QString(byteArray.toBase64());
}
void VideoDevice::video_frame_queue_check()
{
if (video_frames_string.count() > frame_fps * 30)
{
frame_mutex.lock();
video_frames_string.remove(video_frames_string.firstKey());
frame_mutex.unlock();
}
}
void VideoDevice::read_frame()
{
if (address.isEmpty())
return;
if (detection_status)
{
if (!video_capture.isOpened())
{
video_capture = VideoCapture(address.toUtf8().data());
frame_fps = video_capture.get(CV_CAP_PROP_FPS);
qDebug() << address << " open";
}
auto car = TaskDispatch::get_car_info();
if (car->fVelocity >= 10)
{
if (!video_capture.read(video_frame))
{
qDebug() << address << " close";
video_capture.release();
return;
}
if (video_frame.data != nullptr)
{
frame_mutex.lock();
video_frames_string.insert(frame_id, compress(video_frame, 80));
frame_mutex.unlock();
#if USE_ALGORITHM
bgr_frame_buffer.nFrameId = frame_id; // 帧号
bgr_frame_buffer.u64PTS = QDateTime::currentMSecsSinceEpoch(); // 时间戳(毫秒)
memcpy(bgr_frame_buffer.pu8VirAddr, video_frame.data, 1280 * 720 * 3);
MvConvertImage(&bgr_frame_buffer, &detect_frame_buffer); // bgr->nv16
MvObjectEventDetect(this->type, &detect_frame_buffer, TaskDispatch::get_car_info());
#endif
frame_id++;
}
}
}
}
void VideoDevice::run()
{
frame_id = 0;
while (true)
{
video_frame_queue_check();
read_frame();
msleep(1);
}
}
int VideoDevice::MvGetFrameBlkInfo(VideoFrameDataInfo *pImageDataInfo)
{
Q_UNUSED(pImageDataInfo);
#if USE_ALGORITHM
if (pImageDataInfo == NULL)
return -1;
pImageDataInfo->pu8VirAddr = (unsigned char *)calloc(pImageDataInfo->u32Width * pImageDataInfo->u32Height * 3, sizeof(unsigned char));
if (pImageDataInfo->pu8VirAddr == NULL)
return -1;
#endif
return 0;
}
int VideoDevice::MvReleaseFrameBlkInfo(VideoFrameDataInfo *pImageDataInfo)
{
Q_UNUSED(pImageDataInfo);
#if USE_ALGORITHM
if (pImageDataInfo == NULL)
return -1;
if (pImageDataInfo->pu8VirAddr == NULL)
return -1;
free(pImageDataInfo->pu8VirAddr);
#endif
return 0;
}
int VideoDevice::MvConvertImage(VideoFrameDataInfo *pSrcImageDataInfo, VideoFrameDataInfo *pDstImageDataInfo)
{
Q_UNUSED(pSrcImageDataInfo);
Q_UNUSED(pDstImageDataInfo);
#if USE_ALGORITHM
rga_buffer_t src;
rga_buffer_t dst;
src = wrapbuffer_virtualaddr(pSrcImageDataInfo->pu8VirAddr, pSrcImageDataInfo->u32Width, pSrcImageDataInfo->u32Height, RK_FORMAT_BGR_888);
dst = wrapbuffer_virtualaddr(pDstImageDataInfo->pu8VirAddr, pDstImageDataInfo->u32Width, pDstImageDataInfo->u32Height, RK_FORMAT_YCbCr_420_SP);
if (src.width == 0 || dst.width == 0)
{
return -1;
}
src.format = RK_FORMAT_BGR_888;
dst.format = RK_FORMAT_YCbCr_422_SP;
IM_STATUS STATUS;
STATUS = imcvtcolor(src, dst, src.format, dst.format);
if (STATUS != IM_STATUS_SUCCESS)
{
qDebug("imcvtcolor error\n");
return -1;
}
pDstImageDataInfo->u64PTS = pSrcImageDataInfo->u64PTS;
pDstImageDataInfo->nFrameId = pSrcImageDataInfo->nFrameId;
#endif
return 0;
}

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#ifndef HXVIDEODEVICE_H
#define HXVIDEODEVICE_H
#include <QtConcurrent>
#include <QThread>
#include <QDateTime>
#include <QMap>
#include <QMutex>
#include "HxTaskDispatch.h"
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
#if ALGORITHM_ENABLE
#include "rkrga/rga.h"
#include "rkrga/im2d.hpp"
#endif
class VideoDevice : public QThread
{
Q_OBJECT
public:
VideoDevice(void);
void set(int type, QString address);
void set(int type, QString address, BsdWarnRegion region);
void set(bool status);
QString snap(void);
QString build_image(int id);
QString build_video(int id);
void test(void);
private:
vector<uint8_t> compress(Mat frame, int quality);
QString snap(Mat frame);
QString snap(vector<uint8_t> data);
void video_frame_queue_check();
void read_frame();
protected:
void run() override;
private:
int MvGetFrameBlkInfo(VideoFrameDataInfo *pImageDataInfo);
int MvReleaseFrameBlkInfo(VideoFrameDataInfo *pImageDataInfo);
int MvConvertImage(VideoFrameDataInfo *pSrcImageDataInfo, VideoFrameDataInfo *pDstImageDataInfo);
private:
int type;
int frame_id;
int frame_fps;
QString address;
bool detection_status = true;
QMap<int, vector<uint8_t>> video_frames_string;
QMutex frame_mutex;
Mat video_frame;
VideoCapture video_capture;
VideoFrameDataInfo detect_frame_buffer;
VideoFrameDataInfo bgr_frame_buffer;
};
#endif // HXVIDEODEVICE_H

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######################################################################
# Automatically generated by qmake (3.1) Fri Oct 20 22:09:25 2023
######################################################################
QT += sql network concurrent
TEMPLATE = app
# TARGET = app
CONFIG += debug_and_release
CONFIG(debug, debug|release){
TARGET = debug/app
} else {
TARGET = release/app
}
INCLUDEPATH += .
CONFIG += c++11
# You can make your code fail to compile if you use deprecated APIs.
# In order to do so, uncomment the following line.
# Please consult the documentation of the deprecated API in order to know
# how to port your code away from it.
# You can also select to disable deprecated APIs only up to a certain version of Qt.
#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000 # disables all the APIs deprecated before Qt 6.0.0
# Input
HEADERS += HxDataBase.h HxTaskDispatch.h HxUtils.h HxVideoDevice.h
SOURCES += HxDataBase.cpp HxTaskDispatch.cpp HxVideoDevice.cpp main.cpp
# DEFINES += USE_ALGORITHM
win32 {
LIBS += D:\Library\opencv\build\install\x64\mingw\lib\libopencv_*.a
INCLUDEPATH += D:\Library\opencv\build\install\include
}
unix {
LIBS += -L/usr/local/lib -lopencv_imgproc -lopencv_videoio -lopencv_imgcodecs -lopencv_highgui -lopencv_core
INCLUDEPATH += /usr/local/include
}
contains(DEFINES, USE_ALGORITHM) {
LIBS += -L$$PWD/external/ -lObjectEventDetect
#DEPENDPATH += $$PWD/external
PRE_TARGETDEPS += $$PWD/libraries/libObjectEventDetect.a
}
INCLUDEPATH += $$PWD/external
contains(DEFINES, USE_ALGORITHM) {
LIBS += -L$$PWD/external/npu/ -lrknn_api
INCLUDEPATH += $$PWD/external/npu
#DEPENDPATH += $$PWD/external/npu
}

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/*
* 2015 ()
* Copyright (c) 2015,Motovis Intelligent Technologies (Shanghai) Co.,Ltd
*
* Motovis Intelligent Confidential Proprietary
* MvObjectEventDetect.h
*
* v1.0.0.0
* :
* 2019123
*/
#ifndef _OBJECT_EVENT_DETECT_H_
#define _OBJECT_EVENT_DETECT_H_
#ifdef __cplusplus
extern "C" {
#endif
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define EVENT_WARN_NUM 26 //报警类型种类
#define EVENT_ADAS_WARN_NUM 7 //adas报警类型种类
#define EVENT_DSM_WARN_NUM 15 //dsm报警类型种类
//数据通道类型
typedef enum _DETECT_CHANNEL_TYPE_
{
ADAS_DETECT_CHANNEL = 1, //adas数据通道
RIGHT_BSD_FRONT_DETECT_CHANNEL = 2, //右侧bsd(非鱼眼)--右前 从前往后
DMS_DETECT_CHANNEL = 4, //dms数据通道
RIGHT_BSD_REAR_DETECT_CHANNEL = 8, //右侧bsd(非鱼眼)--右后 从后往前
LEFT_BSD_FRONT_DETECT_CHANNEL = 0x10, //左侧bsd(非鱼眼)--左前
LEFT_BSD_REAR_DETECT_CHANNEL = 0x20, //左侧bsd(非鱼眼)--左后
FRONT_BSD_DETECT_CHANNEL = 0x40, //前侧bsd(非鱼眼)
REAR_BSD_DETECT_CHANNEL = 0x80 //后侧bsd(非鱼眼)
}DetectChannelType;
//数据功能类型
typedef enum _DETECT_FUNCTION_TYPE_
{
ADAS_FUNCTION_TYPE = 1, //adas类型
BSD_FUNCTION_TYPE = 2, //bsd功能类型
DSM_FUNCTION_TYPE = 4, //dsm功能类型
SECOND_BSD_FUNCTION_TYPE = 8, //第2bsd功能类型(bsd2)
BELT_FUNCTION_TYPE = 0x10, //安全带功能类型(dsm2)
OVERBOARD_FUNCTION_TYPE = 0x20, //超员功能类型
IMAGEFUSION_FUNCTION_TYPE = 0x40, //360图像融合功能类型
CARGO_FUNCTION_TYPE = 0x80, //货箱功能类型
ALIVE_FUNCTION_TYPE = 0x100, //活体功能类型
FACE_COUNT_FUNCTION_TYPE = 0x200, //人头计数功能类型
THIRD_BSD_FUNCTION_TYPE = 0x400, //第3bsd功能类型(bsd3)
FOURTH_BSD_FUNCTION_TYPE = 0x800 //第4bsd功能类型(bsd4)
}DetectFunctionType;
//bsd安装类型
typedef enum _BSD_INSTALL_TYPE_
{
BSD_RIGHT = 0, //右
BSD_FRONT = 1, //前
BSD_LEFT = 2, //左
BSD_BACK = 3 //后
}BsdInstallType;
//bsd车身位置
typedef enum _BSD_CARPOS_TYPE_
{
BSD_CARPOS_LEFT = 0, //左
BSD_CARPOS_RIGHT = 1, //右
BSD_CARPOS_UP = 2, //上
BSD_CARPOS_DOWN = 3 //下
}BsdCarPosType;
//bsd相机类型
typedef enum _BSD_CAMERA_TYPE_
{
BSD_CAMERA_NOFISHEYE = 0, //非鱼眼
BSD_CAMERA_FISHEYE = 1 //鱼眼
}BsdCameraType;
//视频图像帧信息
typedef struct _VideoFrameDataInfo
{
int nFrameId; //帧号
int u32Width; //视频图像宽
int u32Height; //视频图像高
long long u64PTS; //时间戳(毫秒)
unsigned char* pu8VirAddr; //视频图像帧数据虚拟地址
}VideoFrameDataInfo;
//标定点坐标
typedef struct _CalibrationPoint
{
int x; //X坐标
int y; //Y坐标
}CalibrationPoint;
//标定点坐标
typedef struct _CalibrationFloatPoint
{
float x; //X坐标
float y; //Y坐标
}CalibrationFloatPoint;
//自标定矩形区域
typedef struct _CalibrationRectRegion
{
int x; //X坐标
int y; //Y坐标
int nWidth; //宽
int nHeight; //高
}CalibrationRectRegion;
//360标定错误返回信息
typedef struct _CalibrationErrorInfo
{
float fDeviation[4]; //每路相机的标定误差,顺序分别是0-前, 1-右, 2-后, 3-左
int nRet[4][2]; //每路相机每块标定布自标定返回值
}CalibrationErrorInfo;
//虚拟线坐标点
typedef struct _VirtualLinePoint
{
CalibrationPoint ptBegin; //起点坐标
CalibrationPoint ptEnd; //终点坐标
}VirtualLinePoint;
//目标类型
typedef enum _ObjectDetectType
{
NONE_TYPE = 0, //未知类型
PEDESTRIAN_TYPE = 1, //行人
CAR_TYPE = 2, //小型汽车
BUS_TYPE = 3, //大巴
TRUCK_TYPE = 4, //卡车
MIDBUS_TYPE = 5, //中巴
MOTO_TYPE = 6, //机动车
NOMOTO_TYPE = 7 //非机动车
}ObjectDetectType;
//报警事件类型
typedef enum _ObjectEventType
{
EVENT_NONE = 0, //无报警
EVENT_LDW_LEFT = 1, //向左车道偏离报警
EVENT_LDW_RIGHT = 2, //向右车道偏离报警
EVENT_FCW = 4, //前车碰撞报警
EVENT_PCW = 8, //行人碰撞报警
EVENT_HMW = 0x10, //车距监测报警
EVENT_CROSS_WALK = 0x20, //人行横道检测报警
EVENT_BLIND = 0x40, //右侧盲区报警
EVENT_SMOKE = 0x80, //抽烟
EVENT_CALL = 0x100, //打电话
EVENT_YAWN = 0x200, //打哈欠
EVENT_NO_ALIGNMENT = 0x400, //未对准(偏离座位)
EVENT_CAMERA_SHIELD = 0x800, //摄像头遮挡
EVENT_ABNORMAL_IDENTITY = 0x1000, //身份异常(不是原来的驾驶员)
EVENT_ABSENT_DRIVER = 0x2000, //异常驾驶(检测不到脸)
EVENT_EYES_MASKED = 0x4000, //阻断型墨镜
EVENT_MOUTH_MASKED = 0x8000, //嘴部遮挡
EVENT_EYE_CLOSING = 0x10000, //闭眼
EVENT_SWINGING_LEFT = 0x20000, //分神驾驶(东张)
EVENT_SWINGING_RIGHT = 0x40000, //分神驾驶(西望)
EVENT_HEAD_UP = 0x80000, //分神驾驶(仰望)
EVENT_HEAD_DOWN = 0x100000, //分神驾驶(低头)
EVENT_NORMAL_IDENTITY = 0x200000, //身份正常
EVENT_ABNORMAL_BELT = 0x400000, //未系安全带
EVENT_ABNORMAL_HAND = 0x800000, //手脱离方向盘
EVENT_ABNORMAL_PHONE = 0x1000000, //玩手机
EVENT_ABNORMAL_OVERBOARD = 0x2000000 //超员
}ObjectEventType;
//车道线类型
typedef enum _LaneType
{
OTHER_LINE = 0, //其他车道线类型
WHITE_SOLID_LINE = 1, //白实线
YELLOW_SOLID_LINE = 2, //黄实线
DOUBLE_YELLOW_LINE = 3, //双黄线
DOUBLE_WHITE_LINE = 4 //双白线
}LaneType;
//目标信息
typedef struct _ObjectPara
{
int nObjectId; //目标编号
ObjectDetectType nDetectType; //目标类型
int nLeft; //目标位置左
int nTop; //目标位置上
int nRight; //目标位置右
int nBottom; //目标位置下
float fDist; //每个目标的距离(单位m最大值250最小值0)
float fVelo; //相对运动目标的相对速度(单位m/s)
float fTTC; //ttc时间(单位s)
int nTargetPosX; //落脚点横坐标bsd用
int nTargetPosY; //落脚点纵坐标bsd用
}ObjectPara;
//目标跟踪报警结果
typedef struct _ObjectTrackEventResult
{
int nFrameId; //图像帧号
long long lTimeStamp; //时间戳(毫秒)
int nObjectNumber; //目标数目
ObjectPara objInfo[255]; //目标信息
ObjectEventType nEventType; //报警事件类型
int nMainObjectId; //主目标编号
int nWarnFrameId[10]; //报警图像帧号
int nWarnFrameIdCount; //实际报警图像帧号数目
int nDangerLevel; //报警等级,1表示最高危险报警,2表示次高危险报警,3表示第三高危险报警
CalibrationPoint tFirstAlarmPoint[4]; //一级告警区域bsd用
CalibrationPoint tSecondAlarmPoint[4]; //二级预警区域bsd用
CalibrationPoint tThirdAlarmPoint[4]; //三级预警区域bsd用
int nFaceLandMarksNum; //人脸关键点
CalibrationPoint tFaceLandMarks[68]; //人脸关键点
LaneType nLeftLineType; //左车道线类型
LaneType nRightLineType; //右车道线类型
}ObjectTrackEventResult;
//车身信息
typedef struct _CarInfoInput
{
float fVelocity; //车速,单位: km/h
float fAcceleration; //车辆加速度 >0表示加速度,单位:m/s2
float fDeceleration; //车辆减速度 0表示速度为0或正在加速度 其他值表示减速度值,单位:m/s2
int nBrake; //刹车,1刹车 0未刹车
int nLLight; //左转向灯,1、点亮 0、不亮
int nRLight; //右转向灯,1、点亮 0、不亮
float fAlpha; //转向角,单位:度
float fSteeingWheelAngle; //方向盘转角,单位:度
}CarInfoInput;
//事件检测配置信息
typedef struct _ObjectEventDetectConfig
{
char szAdasDetectConfigPathName[256]; //adas检测配置
char szAdasTrackConfigPathName[256]; //adas跟踪配置
char szBsdDetectConfigPathName[256]; //右视非鱼眼bsd检测配置(从后往前)
char szRightBsdFrontDetectConfigPathName[256]; //右视非鱼眼bsd检测配置(从前往后)
char szFrontBsdDetectConfigPathName[256]; //前视bsd检测配置
char szRearBsdDetectConfigPathName[256]; //后视bsd检测配置
char szRightFisheyeBsdDetectConfigPathName[256]; //右视鱼眼bsd检测配置
char szBeltDetectConfigPathName[256]; //安全带检测配置
char szOverBoardDetectConfigPathName[256]; //超员检测配置
char szDsmFaceDetectConfigPathName[256]; //dsm人脸检测配置
char szDsmFaceLandMarksDetectConfigPathName[256]; //dsm人脸特征点检测配置
char szDsmFaceVerificationDetectConfigPathName[256]; //dsm人脸认证检测配置
char szDsmFaceFeaturePathName[256]; //dsm人脸特征存储路径配置
char szDsmHeadPoseConfPathName[256]; //dsm头部位姿配置
char szDsmEyeLandMarksDetectConfigPathName[256]; //dsm人眼特征点检测配置
char szDsmMaskDetectConfigPathName[256]; //口罩墨镜检测配置
char szDsmSmokeConfPathName[256]; //抽烟检测配置
char szDsmCallConfPathName[256]; //电话检测配置
char szDsmAliveDetectConfigPathName[256]; //活体检测配置
char szTsrDetectConfigPathName[256]; //tsr检测配置
char szLutTablePathName[256]; //360图像融合标定配置
char szCarPathName[256]; //车辆图像配置
char szCameraIntrinsicPathName[256]; //360相机内参配置
}ObjectEventDetectConfig;
//相机内外参
typedef struct _CameraCalibration
{
int fCarLen; //车长单位mm
int fCarWidth; //车宽(两个轮胎外侧之间的距离)单位mm
int fRefCenter; //相机与车辆中心之间的距离(从驾驶室往外看,左正右负)单位mm
int fRefTop; //相机到前保险杠距离单位mm
int fDisLen2Tyre; //镜头和前轮胎之间的距离单位mm,镜头前方为正向
int fCameraHeight; //相机距离地面高度单位mm
float fCameraFocus; //相机焦距单位mm
float fCameraDx; //sensor尺寸(像平面离散化系数CMOS unit cell size, 单位mm)
float fPitch; //俯仰角默认为0
float fYaw; //偏航角默认为0
}CameraCalibration;
//bsd报警区域
typedef struct _BsdWarnRegion
{
CalibrationPoint tFirstRegionPoint[4]; //一级报警区域4个点组成的任意四边形
CalibrationPoint tSecondRegionPoint[4]; //二级报警区域4个点组成的任意四边形
CalibrationPoint tThirdRegionPoint[4]; //三级报警区域4个点组成的任意四边形
}BsdWarnRegion;
//bsd报警距离
typedef struct _BsdWarnDistance
{
float fFirstWarnDistance; //一级报警距离(单位:mm)
float fSecondWarnDistance; //二级报警距离(单位:mm)
float fThirdWarnDistance; //三级报警距离(单位:mm)
float fFrontWarnDistance; //前方报警距离(单位:mm)
float fLeftWarnDistance; //左报警距离(单位:mm)
float fRightWarnDistance; //右报警距离(单位:mm)
}BsdWarnDistance;
//事件报警参数配置
typedef struct _EventWarnParamConfig
{
bool bStartEventWarnKind[EVENT_WARN_NUM]; //是否启动此类报警,一共21种报警类型0表示EVENT_LDW_LEFT20表示EVENT_HEAD_DOWN顺序与ObjectEventType宏定义一致
int nAbnormalWarnFrameCount[EVENT_WARN_NUM]; //累积多长时间开始报警,用帧数来表示(主要针对dsm)
int nNormalFrameCount[EVENT_WARN_NUM]; //累积多长时间恢复正常,用帧数来表示(主要针对dsm)
int nAbnormalWarnIntervalFrameCount[EVENT_WARN_NUM]; //间隔多长时间进行下一次报警,用帧数来表示(主要针对dsm)
float fAbnormalWarnScoreThreshold[EVENT_WARN_NUM]; //每种异常报警阈值,0-1之间0表示更容易触发报警1表示更难触发报警(主要针对dsm)
float fNormalWarnScoreThreshold[EVENT_WARN_NUM]; //每种报警恢复正常阈值,0-1之间0表示更容易恢复正常1表示更难恢复正常(主要针对dsm)
int nHmwTime; //HMW开启阈值,单位ms
int nPcwTime; //PCW开启阈值,单位ms
int nFcwTime; //FCW报警开启阈值,单位ms
float fHmwVel; //HMW报警车速阈值,单位km/h
float fFcwVel; //FCW报警车速阈值,单位km/h
float fLdwVel; //LDW报警车速阈值,单位km/h
float fPcwVel; //PCW报警车速阈值,单位km/h
float fBsdFirstVel; //BSD一级报警车速阈值,单位km/h
float fBsdSecondVel; //BSD二级报警车速阈值,单位km/h
float fBsdThirdVel; //BSD三级报警车速阈值,单位km/h
int nLdwDistance; //左右车道线距离报警阈值,单位cm(范围:-20cm~20cm越过车道线为负)
float fDsmVel; //DSM报警车速阈值,单位km/h
}EventWarnParamConfig;
//车道线的点阵列(最多四条车道线)
typedef struct _DARW_POINT_INFO
{
unsigned int pSrcPointX[4][600]; //每条车道线的点x坐标
unsigned int pSrcPointY[4][600]; //每条车道线的点y坐标
unsigned int nPointCounters[4]; //每条车道线的点数
int nLaneID[4]; //车道线编号(-1表示不存在此车道线,0,1,2,3表示从左往右的四条车道线)
}DrawPointInfo;
//人脸特征信息
typedef struct _FACE_FEATURE_INFO
{
float fFaceFeature[512]; //人脸特征
int nFaceNum; //人脸数目
int nLeft; //人脸位置左
int nTop; //人脸位置上
int nRight; //人脸位置右
int nBottom; //人脸位置下
int nErrorCode; //错误类型,0-成功1-提取特征失败2-无人脸3-人脸太偏
}FaceFeatureInfo;
//bsd路沿点阵列
typedef struct _CURB_POINT_INFO
{
unsigned int pSrcPointX[600];
unsigned int pSrcPointY[600];
unsigned int nPointCounters;
}CurbPointInfo;
//360相机外参
typedef struct _FisheyeCameraCalibration
{
///////////////////////////以下是相机外参,棋盘格必须保证相对车辆左右对称,上下平齐
float fCarLen; //车长单位mm
float fCarWidth; //车宽(两个轮胎外侧之间的距离)单位mm
float fFrontDistance; //车头到棋盘格的纵向距离单位mm, 默认1000mm
float fRearDistance; //车尾到棋盘格的纵向距离单位mm, 默认1000mm
float fLeftDistance; //车左侧到棋盘格的横向距离单位mm, 默认(2000-fCarWidth/2)mm
float fRightDistance; //车右侧到棋盘格的横向距离单位mm, 默认(2000-fCarWidth/2)mm
float fVisionField; //视野范围单位mm大车默认20000mm, 模型车8000mm
float fBigChessBoardWidth; //大棋盘格宽度单位mm, 默认800mm(如果是缩小版的棋盘格80mm)
float fSmallChessBoardWidth; //小棋盘格高度单位mm, 默认400mm(如果是缩小版的棋盘格40mm)
float fFusionAngle; //融合角度,默认60度
}FisheyeCameraCalibration;
/*
* :
* :pAlglibVersion-
* :pAlglibVersion-
* :0--1-
*/
int MvGetAlglibVersion(char* pAlglibVersion);
//算法结果回调函数
typedef void (*AlgResultProcessFunc)(int nDataChannel, ObjectTrackEventResult* pObjectTrackEventResult, void *pPrivData);
//车道线结果回调函数
typedef void (*AlgLaneLineProcessFunc)(DrawPointInfo* pPointInfo, void *pPrivData);
/*
* :
* :pObjectEventDetectConf-,nStartDetectChannel-nStartDetectChannel=ADAS_DETECT_CHANNEL|BSD_DETECT_CHANNEL|DMS_DETECT_CHANNEL
* :
* :0--1-
*/
int MvObjectEventDetectInit(ObjectEventDetectConfig *pObjectEventDetectConf, int nStartDetectChannel);
/*
* :
* :nDataChannel-0-adas,1-bsd(),2-dsm,3-bsd(),4-belt pFunc--, pPrivData-
* :
* :0--1-
*/
int MvSetAlgResultFuncCallback(int nDataChannel,AlgResultProcessFunc pFunc, void *pPrivData);
/*
* :线
* :pFunc--线, pPrivData-
* :
* :0--1-
*/
int MvSetAlgLaneLineFuncCallback(AlgLaneLineProcessFunc pFunc, void *pPrivData);
/* 函数功能:目标跟踪事件检测(每帧循环调用,结果在回调函数中返回)
* :nDataChannel-0-adas,1-bsd(),2-dsm,3-bsd(),4-belt pImageDataInfo--(adas,bsd:1280*720,dsm:1280*720), pCarInfo-
* :
* :0--1-
*/
int MvObjectEventDetect(int nDataChannel, VideoFrameDataInfo* pImageDataInfo, CarInfoInput* pCarInfo);
/*
* :
* :
* :
* :0--1-
*/
int MvObjectEventDetectUnInit();
////////////////////////////////////////////////////////////////////////////
/*
* :()
* :pCameraCalibration-
* :
* :0--1-
*/
int MvCameraCalibration(CameraCalibration* pCameraCalibration);
/*
* :
* :pCameraCalibration-
* :
* :0--1-
*/
int MvAutoCameraCalibration(CameraCalibration* pCameraCalibration);
/*
* :(4,adas通道会用到)
* :pCameraCalibration-,pCalibrationPoint-4
* :
* :0--1-
*/
int MvManualCameraCalibration(CameraCalibration* pCameraCalibration, CalibrationPoint* pCalibrationPoint);
/*
* :
* :pCameraCalibration-
* :
* :0--1-
*/
int MvGetCameraCalibrationInfo(CameraCalibration* pCameraCalibration);
/*
* :
* :
* :
* :0-,1-
*/
int MvGetSelfCalibrationStatus();
/*
* :
* :
* :pCalibrationPoint-(1280x720图像坐标)
* :
*/
void MvGetVanishPoint(CalibrationPoint* pCalibrationPoint);
/*
* :
* :pCalibrationPoint-(1280x720图像坐标)
* :
* :0--1-
*/
int MvSetVanishPoint(CalibrationPoint* pCalibrationPoint);
////////////////////////////////////////////////////////////////////////////
/*
* :bsd报警区域
* :nDataChannel-1-bsd(),3-bsd(),pBsdWarnRegion-bsd报警区域
* :
* :0--1-
*/
int MvSetBsdWarnRegion(int nDataChannel, BsdWarnRegion* pBsdWarnRegion);
/*
* :bsd相机标定
* :nDataChannel-1-bsd(),3-bsd(),pCameraCalibration-,pVanishPoint-,pBsdWarnDistance-bsd报警距离
* :pBsdWarnRegion-bsd报警区域
* :0--1-
*/
int MvBsdCalibration(int nDataChannel, CameraCalibration* pCameraCalibration, CalibrationPoint* pVanishPoint, BsdWarnDistance* pBsdWarnDistance, BsdWarnRegion* pBsdWarnRegion);
/*
* :bsd灭点位置
* :pBsdVanishPt-bsd灭点位置
* :
* :0--1-
*/
int MvGetBsdVanishPoint(int nDataChannel, CalibrationPoint* pBsdVanishPt) ;
/*
* :
* :tRegionPoint-
* :
* :0--1-
*/
int MvSetOverBoardWarnRegion(CalibrationPoint tRegionPoint[4]);
////////////////////////////////////////////////////////////////////////////
/*
* :dsm检测区域起始位置
* :pCropPoint-dsm检测区域起始位置
* :
* :0--1-
*/
int MvSetDsmCropPosition(CalibrationPoint* pCropPoint);
/*
* :dsm检测区域位置
* :
* :pPointLeftTop-dsm检测区域左上位置pPointRightBottom-dsm检测区域右下位置
* :0--1-
*/
int MvGetDsmCropRegion(CalibrationPoint* pPointLeftTop, CalibrationPoint* pPointRightBottom);
/*
* :jpg人脸特征
* :pJpgImageData-jpg图像地址,nJpgImageDataLen-jpg图像大小
* :pFaceFeatureInfo-jpg人脸特征信息
* :0--1-
*/
int MvGetJpgFaceFeature(unsigned char* pJpgImageData, int nJpgImageDataLen, FaceFeatureInfo* pFaceFeatureInfo);
/*
* :
* :pImageDataInfo-
* :pFaceFeatureInfo-
* :0--1-
*/
int MvGetFaceFeature(VideoFrameDataInfo* pImageDataInfo, FaceFeatureInfo* pFaceFeatureInfo);
/*
* :
* : pFaceFeatureInfo1-,nFeatureNum1-,pFaceFeatureInfo2-,nFeatureNum2-
* :
* : 0-(),-1- ()
*/
int MvGetFaceVerifyResult(FaceFeatureInfo* pFaceFeatureInfo1, int nFeatureNum1, FaceFeatureInfo* pFaceFeatureInfo2, int nFeatureNum2);
/*
* : DSM标定信息
* :
* : pYaw-()pPitch-()
* : 0-,-1-
*/
int MvGetDsmCalibration(float* pYaw, float* pPitch);
/*
* : DSM标定信息
* :
* : fYaw-()fPitch-()
* : 0-,-1-
*/
int MvSetDsmCalibration(float fYaw, float fPitch);
/*
* : DSM标定
* :
* : pPointLeftTop-dsm检测区域左上位置,fYaw-()fPitch-()
* : 0-,-1-
*/
int MvDsmCalibration(CalibrationPoint* pPointLeftTop, float* pYaw, float* pPitch);
/*
* :()
* :pEventWarnParamConfig-
* :
* :0--1-
*/
int MvSetEventWarnParamConfig(EventWarnParamConfig *pEventWarnParamConfig);
/*
* :()
* :
* :pEventWarnParamConfig-
* :0--1-
*/
int MvGetEventWarnParamConfig(EventWarnParamConfig *pEventWarnParamConfig);
/*
* :
* :bPrintf-
* :
* :0--1-
*/
int MvSetPrintf(bool bPrintf);
/*
* :()
* :szSignPath-
* :
* :0--1-
*/
int MvSetSignPath(char szSignPath[256]);
/*
* :()
* :szAuthHost-
* :
* :0--1-
*/
int MvSetAuthHost(char szAuthHost[256]);
/*
* :
* :
* :
* :0--1-
*/
int MvGetAuthStatus();
/*
* : ID
* : pChipId-ID,32
* :
* : 0-,-1-
*/
int MvGetAuthChipId(unsigned char* pChipId);
/*
* :线
* :fDistance-线(:mm,10),fLaneWidth-(:mm,3)
* :pLinePoint-线
* :0--1-
*/
int MvGetDistanceLine(float fDistance, float fLaneWidth, VirtualLinePoint* pLinePoint);
/*
* :线
* :fNearDistance-线(:mm,10),fFarDistance-线(:mm,30),fLaneWidth-(:mm,3)
* :pLeftLinePoint-线pRightLinePoint-线
* :0--1-
*/
int MvGetVirtualLaneLine(float fNearDistance, float fFarDistance, float fLaneWidth, VirtualLinePoint* pLeftLinePoint, VirtualLinePoint* pRightLinePoint);
/*
* :
* :
* :
* :fDistance-(:mm)
*/
float MvGetNearDistance();
/*
* :fcw报警灵敏
* :nFcwSensitivity-0-,1-,2-
* :
* :0--1-
*/
int MvSetFcwWarnSensitivity(int nFcwSensitivity);
/*
* :
* :
* :
* :
*/
int MvGetAuthFunctionType();
/*
* : imei ID
* : chImeiId-ID,16,
* :
* : 0-,-1-
*/
int MvSetImeiId(unsigned char chImeiId[16]);
/*
* :dsm标定模式
* :nMode-0-1-, fVelocityThreshold-,fTimeThreshold-,fAngleThreshold-
* :
* :0--1-
*/
int MvSetDsmCalibrationMode(int nMode, float fVelocityThreshold, float fTimeThreshold, float fAngleThreshold);
/* 函数功能:360图像融合
* :pInImageDataInfo--4(1280*720),0-, 1-, 2-, 3-, 4,pCarInfo-
* :pOutImageDataInfo-(640x640),
* :0--1-
*/
int MvImageFusion(VideoFrameDataInfo* pInImageDataInfo[4], VideoFrameDataInfo* pOutImageDataInfo, CarInfoInput* pCarInfo);
/* 函数功能:360图像融合标定
* :pCalibrationConfigPathName-,pInImageDataInfo--4(1280*720),0-, 1-, 2-, 3-,pCameraCalibration-,pImagePoints[4][8]-nMode-0,1,pCalibrationRectRegion-
* :pDeviation->10, pErrorInfo-360,pOutImageDataInfo-
* :0--1-
*/
int MvImageFusionCalibration(char* pCalibrationConfigPathName, VideoFrameDataInfo* pInImageDataInfo[4], FisheyeCameraCalibration* pCameraCalibration, CalibrationFloatPoint* pImagePoints, int nMode,float* pDeviation, CalibrationRectRegion* pCalibrationRectRegion, CalibrationErrorInfo* pErrorInfo, VideoFrameDataInfo* pOutImageDataInfo);
/*
* :
* :nStartDetectChannel-nStartDetectChannel=ADAS_FUNCTION_TYPE|BSD_FUNCTION_TYPE|DSM_FUNCTION_TYPE|SECOND_BSD_FUNCTION_TYPE,nMode-0-1-
* :
* :0--1-
*/
int MvReAuthorization(int nStartDetectChannel);
/*
* :
* :bConnectAuthServer-1-, 0-
* :
* :0--1-
*/
int MvConnectAuthServer(bool bConnectAuthServer);
/*
* :tsr检测
* :bSetTsrDetect-tsr检测
* :
* :0--1-
*/
int MvSetTsrDetect(bool bSetTsrDetect);
/*
* :(4)
* :nDetectChannel-nDetectChannel=RIGHT_BSD_FRONT_DETECT_CHANNEL|RIGHT_BSD_REAR_DETECT_CHANNEL|LEFT_BSD_FRONT_DETECT_CHANNEL|LEFT_BSD_REAR_DETECT_CHANNEL
* :
* :0--1-
*/
int MvSetDetectChannel(int nDetectChannel);
#ifdef __cplusplus
}
#endif
#endif // _OBJECT_EVENT_DETECT_H_

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app/external/algmode/adas_detect.bin vendored Normal file

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app/external/algmode/adas_track.bin vendored Normal file

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app/external/algmode/bsd_detect.bin vendored Normal file

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app/external/algmode/dsm_call_detect.bin vendored Normal file

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app/external/algmode/g_Settings.bin vendored Normal file

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app/external/npu/librknnrt.so vendored Normal file

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/****************************************************************************
*
* Copyright (c) 2017 - 2022 by Rockchip Corp. All rights reserved.
*
* The material in this file is confidential and contains trade secrets
* of Rockchip Corporation. This is proprietary information owned by
* Rockchip Corporation. No part of this work may be disclosed,
* reproduced, copied, transmitted, or used in any way for any purpose,
* without the express written permission of Rockchip Corporation.
*
*****************************************************************************/
#ifndef _RKNN_API_H
#define _RKNN_API_H
#ifdef __cplusplus
extern "C" {
#endif
#include <stdint.h>
/*
Definition of extended flag for rknn_init.
*/
/* set high priority context. */
#define RKNN_FLAG_PRIOR_HIGH 0x00000000
/* set medium priority context */
#define RKNN_FLAG_PRIOR_MEDIUM 0x00000001
/* set low priority context. */
#define RKNN_FLAG_PRIOR_LOW 0x00000002
/* asynchronous mode.
when enable, rknn_outputs_get will not block for too long because it directly retrieves the result of
the previous frame which can increase the frame rate on single-threaded mode, but at the cost of
rknn_outputs_get not retrieves the result of the current frame.
in multi-threaded mode you do not need to turn this mode on. */
#define RKNN_FLAG_ASYNC_MASK 0x00000004
/* collect performance mode.
when enable, you can get detailed performance reports via rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, ...),
but it will reduce the frame rate. */
#define RKNN_FLAG_COLLECT_PERF_MASK 0x00000008
/* allocate all memory in outside, includes weight/internal/inputs/outputs */
#define RKNN_FLAG_MEM_ALLOC_OUTSIDE 0x00000010
/* weight sharing with the same network structure */
#define RKNN_FLAG_SHARE_WEIGHT_MEM 0x00000020
/* send fence fd from outside */
#define RKNN_FLAG_FENCE_IN_OUTSIDE 0x00000040
/* get fence fd from inside */
#define RKNN_FLAG_FENCE_OUT_OUTSIDE 0x00000080
/* dummy init flag: could only get total_weight_size and total_internal_size by rknn_query*/
#define RKNN_FLAG_COLLECT_MODEL_INFO_ONLY 0x00000100
/*
Error code returned by the RKNN API.
*/
#define RKNN_SUCC 0 /* execute succeed. */
#define RKNN_ERR_FAIL -1 /* execute failed. */
#define RKNN_ERR_TIMEOUT -2 /* execute timeout. */
#define RKNN_ERR_DEVICE_UNAVAILABLE -3 /* device is unavailable. */
#define RKNN_ERR_MALLOC_FAIL -4 /* memory malloc fail. */
#define RKNN_ERR_PARAM_INVALID -5 /* parameter is invalid. */
#define RKNN_ERR_MODEL_INVALID -6 /* model is invalid. */
#define RKNN_ERR_CTX_INVALID -7 /* context is invalid. */
#define RKNN_ERR_INPUT_INVALID -8 /* input is invalid. */
#define RKNN_ERR_OUTPUT_INVALID -9 /* output is invalid. */
#define RKNN_ERR_DEVICE_UNMATCH -10 /* the device is unmatch, please update rknn sdk
and npu driver/firmware. */
#define RKNN_ERR_INCOMPATILE_PRE_COMPILE_MODEL -11 /* This RKNN model use pre_compile mode, but not compatible with current driver. */
#define RKNN_ERR_INCOMPATILE_OPTIMIZATION_LEVEL_VERSION -12 /* This RKNN model set optimization level, but not compatible with current driver. */
#define RKNN_ERR_TARGET_PLATFORM_UNMATCH -13 /* This RKNN model set target platform, but not compatible with current platform. */
/*
Definition for tensor
*/
#define RKNN_MAX_DIMS 16 /* maximum dimension of tensor. */
#define RKNN_MAX_NUM_CHANNEL 15 /* maximum channel number of input tensor. */
#define RKNN_MAX_NAME_LEN 256 /* maximum name lenth of tensor. */
#define RKNN_MAX_DYNAMIC_SHAPE_NUM 512 /* maximum number of dynamic shape for each input. */
#ifdef __arm__
typedef uint32_t rknn_context;
#else
typedef uint64_t rknn_context;
#endif
/*
The query command for rknn_query
*/
typedef enum _rknn_query_cmd {
RKNN_QUERY_IN_OUT_NUM = 0, /* query the number of input & output tensor. */
RKNN_QUERY_INPUT_ATTR = 1, /* query the attribute of input tensor. */
RKNN_QUERY_OUTPUT_ATTR = 2, /* query the attribute of output tensor. */
RKNN_QUERY_PERF_DETAIL = 3, /* query the detail performance, need set
RKNN_FLAG_COLLECT_PERF_MASK when call rknn_init,
this query needs to be valid after rknn_outputs_get. */
RKNN_QUERY_PERF_RUN = 4, /* query the time of run,
this query needs to be valid after rknn_outputs_get. */
RKNN_QUERY_SDK_VERSION = 5, /* query the sdk & driver version */
RKNN_QUERY_MEM_SIZE = 6, /* query the weight & internal memory size */
RKNN_QUERY_CUSTOM_STRING = 7, /* query the custom string */
RKNN_QUERY_NATIVE_INPUT_ATTR = 8, /* query the attribute of native input tensor. */
RKNN_QUERY_NATIVE_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */
RKNN_QUERY_NATIVE_NC1HWC2_INPUT_ATTR = 8, /* query the attribute of native input tensor. */
RKNN_QUERY_NATIVE_NC1HWC2_OUTPUT_ATTR = 9, /* query the attribute of native output tensor. */
RKNN_QUERY_NATIVE_NHWC_INPUT_ATTR = 10, /* query the attribute of native input tensor. */
RKNN_QUERY_NATIVE_NHWC_OUTPUT_ATTR = 11, /* query the attribute of native output tensor. */
RKNN_QUERY_DEVICE_MEM_INFO = 12, /* query the attribute of rknn memory information. */
RKNN_QUERY_INPUT_DYNAMIC_RANGE = 13, /* query the dynamic shape range of rknn input tensor. */
RKNN_QUERY_CURRENT_INPUT_ATTR = 14, /* query the current shape of rknn input tensor, only valid for dynamic rknn model*/
RKNN_QUERY_CURRENT_OUTPUT_ATTR = 15, /* query the current shape of rknn output tensor, only valid for dynamic rknn model*/
RKNN_QUERY_CMD_MAX
} rknn_query_cmd;
/*
the tensor data type.
*/
typedef enum _rknn_tensor_type {
RKNN_TENSOR_FLOAT32 = 0, /* data type is float32. */
RKNN_TENSOR_FLOAT16, /* data type is float16. */
RKNN_TENSOR_INT8, /* data type is int8. */
RKNN_TENSOR_UINT8, /* data type is uint8. */
RKNN_TENSOR_INT16, /* data type is int16. */
RKNN_TENSOR_UINT16, /* data type is uint16. */
RKNN_TENSOR_INT32, /* data type is int32. */
RKNN_TENSOR_UINT32, /* data type is uint32. */
RKNN_TENSOR_INT64, /* data type is int64. */
RKNN_TENSOR_BOOL,
RKNN_TENSOR_TYPE_MAX
} rknn_tensor_type;
inline static const char* get_type_string(rknn_tensor_type type)
{
switch(type) {
case RKNN_TENSOR_FLOAT32: return "FP32";
case RKNN_TENSOR_FLOAT16: return "FP16";
case RKNN_TENSOR_INT8: return "INT8";
case RKNN_TENSOR_UINT8: return "UINT8";
case RKNN_TENSOR_INT16: return "INT16";
case RKNN_TENSOR_UINT16: return "UINT16";
case RKNN_TENSOR_INT32: return "INT32";
case RKNN_TENSOR_UINT32: return "UINT32";
case RKNN_TENSOR_INT64: return "INT64";
case RKNN_TENSOR_BOOL: return "BOOL";
default: return "UNKNOW";
}
}
/*
the quantitative type.
*/
typedef enum _rknn_tensor_qnt_type {
RKNN_TENSOR_QNT_NONE = 0, /* none. */
RKNN_TENSOR_QNT_DFP, /* dynamic fixed point. */
RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC, /* asymmetric affine. */
RKNN_TENSOR_QNT_MAX
} rknn_tensor_qnt_type;
inline static const char* get_qnt_type_string(rknn_tensor_qnt_type type)
{
switch(type) {
case RKNN_TENSOR_QNT_NONE: return "NONE";
case RKNN_TENSOR_QNT_DFP: return "DFP";
case RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC: return "AFFINE";
default: return "UNKNOW";
}
}
/*
the tensor data format.
*/
typedef enum _rknn_tensor_format {
RKNN_TENSOR_NCHW = 0, /* data format is NCHW. */
RKNN_TENSOR_NHWC, /* data format is NHWC. */
RKNN_TENSOR_NC1HWC2, /* data format is NC1HWC2. */
RKNN_TENSOR_UNDEFINED,
RKNN_TENSOR_FORMAT_MAX
} rknn_tensor_format;
/*
the mode of running on target NPU core.
*/
typedef enum _rknn_core_mask {
RKNN_NPU_CORE_AUTO = 0, /* default, run on NPU core randomly. */
RKNN_NPU_CORE_0 = 1, /* run on NPU core 0. */
RKNN_NPU_CORE_1 = 2, /* run on NPU core 1. */
RKNN_NPU_CORE_2 = 4, /* run on NPU core 2. */
RKNN_NPU_CORE_0_1 = RKNN_NPU_CORE_0 | RKNN_NPU_CORE_1, /* run on NPU core 1 and core 2. */
RKNN_NPU_CORE_0_1_2 = RKNN_NPU_CORE_0_1 | RKNN_NPU_CORE_2, /* run on NPU core 1 and core 2 and core 3. */
RKNN_NPU_CORE_UNDEFINED,
} rknn_core_mask;
inline static const char* get_format_string(rknn_tensor_format fmt)
{
switch(fmt) {
case RKNN_TENSOR_NCHW: return "NCHW";
case RKNN_TENSOR_NHWC: return "NHWC";
case RKNN_TENSOR_NC1HWC2: return "NC1HWC2";
case RKNN_TENSOR_UNDEFINED: return "UNDEFINED";
default: return "UNKNOW";
}
}
/*
the information for RKNN_QUERY_IN_OUT_NUM.
*/
typedef struct _rknn_input_output_num {
uint32_t n_input; /* the number of input. */
uint32_t n_output; /* the number of output. */
} rknn_input_output_num;
/*
the information for RKNN_QUERY_INPUT_ATTR / RKNN_QUERY_OUTPUT_ATTR.
*/
typedef struct _rknn_tensor_attr {
uint32_t index; /* input parameter, the index of input/output tensor,
need set before call rknn_query. */
uint32_t n_dims; /* the number of dimensions. */
uint32_t dims[RKNN_MAX_DIMS]; /* the dimensions array. */
char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */
uint32_t n_elems; /* the number of elements. */
uint32_t size; /* the bytes size of tensor. */
rknn_tensor_format fmt; /* the data format of tensor. */
rknn_tensor_type type; /* the data type of tensor. */
rknn_tensor_qnt_type qnt_type; /* the quantitative type of tensor. */
int8_t fl; /* fractional length for RKNN_TENSOR_QNT_DFP. */
int32_t zp; /* zero point for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */
float scale; /* scale for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */
uint32_t w_stride; /* the stride of tensor along the width dimention of input,
Note: it is read-only, 0 means equal to width. */
uint32_t size_with_stride; /* the bytes size of tensor with stride. */
uint8_t pass_through; /* pass through mode, for rknn_set_io_mem interface.
if TRUE, the buf data is passed directly to the input node of the rknn model
without any conversion. the following variables do not need to be set.
if FALSE, the buf data is converted into an input consistent with the model
according to the following type and fmt. so the following variables
need to be set.*/
uint32_t h_stride; /* the stride along the height dimention of input,
Note: it is write-only, if it was set to 0, h_stride = height. */
} rknn_tensor_attr;
typedef struct _rknn_input_range {
uint32_t index; /* input parameter, the index of input/output tensor,
need set before call rknn_query. */
uint32_t shape_number; /* the number of shape. */
rknn_tensor_format fmt; /* the data format of tensor. */
char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */
uint32_t dyn_range[RKNN_MAX_DYNAMIC_SHAPE_NUM][RKNN_MAX_DIMS]; /* the dynamic input dimensions range. */
uint32_t n_dims; /* the number of dimensions. */
} rknn_input_range;
/*
the information for RKNN_QUERY_PERF_DETAIL.
*/
typedef struct _rknn_perf_detail {
char* perf_data; /* the string pointer of perf detail. don't need free it by user. */
uint64_t data_len; /* the string length. */
} rknn_perf_detail;
/*
the information for RKNN_QUERY_PERF_RUN.
*/
typedef struct _rknn_perf_run {
int64_t run_duration; /* real inference time (us) */
} rknn_perf_run;
/*
the information for RKNN_QUERY_SDK_VERSION.
*/
typedef struct _rknn_sdk_version {
char api_version[256]; /* the version of rknn api. */
char drv_version[256]; /* the version of rknn driver. */
} rknn_sdk_version;
/*
the information for RKNN_QUERY_MEM_SIZE.
*/
typedef struct _rknn_mem_size {
uint32_t total_weight_size; /* the weight memory size */
uint32_t total_internal_size; /* the internal memory size, exclude inputs/outputs */
uint64_t total_dma_allocated_size; /* total dma memory allocated size */
uint32_t total_sram_size; /* total system sram size reserved for rknn */
uint32_t free_sram_size; /* free system sram size reserved for rknn */
uint32_t reserved[10]; /* reserved */
} rknn_mem_size;
/*
the information for RKNN_QUERY_CUSTOM_STRING.
*/
typedef struct _rknn_custom_string {
char string[1024]; /* the string of custom, lengths max to 1024 bytes */
} rknn_custom_string;
/*
The flags of rknn_tensor_mem.
*/
typedef enum _rknn_tensor_mem_flags {
RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE = 1, /*Used to mark in rknn_destroy_mem() whether it is necessary to release the "mem" pointer itself.
If the flag RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE is set, rknn_destroy_mem() will call free(mem).*/
RKNN_TENSOR_MEMORY_FLAGS_FROM_FD = 2, /*Used to mark in rknn_create_mem_from_fd() whether it is necessary to release the "mem" pointer itself.
If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_FD is set, rknn_destroy_mem() will call free(mem).*/
RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS = 3, /*Used to mark in rknn_create_mem_from_phys() whether it is necessary to release the "mem" pointer itself.
If the flag RKNN_TENSOR_MEMORY_FLAGS_FROM_PHYS is set, rknn_destroy_mem() will call free(mem).*/
RKNN_TENSOR_MEMORY_FLAGS_UNKNOWN
} rknn_tensor_mem_flags;
/*
the memory information of tensor.
*/
typedef struct _rknn_tensor_memory {
void* virt_addr; /* the virtual address of tensor buffer. */
uint64_t phys_addr; /* the physical address of tensor buffer. */
int32_t fd; /* the fd of tensor buffer. */
int32_t offset; /* indicates the offset of the memory. */
uint32_t size; /* the size of tensor buffer. */
uint32_t flags; /* the flags of tensor buffer, reserved */
void * priv_data; /* the private data of tensor buffer. */
} rknn_tensor_mem;
/*
the input information for rknn_input_set.
*/
typedef struct _rknn_input {
uint32_t index; /* the input index. */
void* buf; /* the input buf for index. */
uint32_t size; /* the size of input buf. */
uint8_t pass_through; /* pass through mode.
if TRUE, the buf data is passed directly to the input node of the rknn model
without any conversion. the following variables do not need to be set.
if FALSE, the buf data is converted into an input consistent with the model
according to the following type and fmt. so the following variables
need to be set.*/
rknn_tensor_type type; /* the data type of input buf. */
rknn_tensor_format fmt; /* the data format of input buf.
currently the internal input format of NPU is NCHW by default.
so entering NCHW data can avoid the format conversion in the driver. */
} rknn_input;
/*
the output information for rknn_outputs_get.
*/
typedef struct _rknn_output {
uint8_t want_float; /* want transfer output data to float */
uint8_t is_prealloc; /* whether buf is pre-allocated.
if TRUE, the following variables need to be set.
if FALSE, the following variables do not need to be set. */
uint32_t index; /* the output index. */
void* buf; /* the output buf for index.
when is_prealloc = FALSE and rknn_outputs_release called,
this buf pointer will be free and don't use it anymore. */
uint32_t size; /* the size of output buf. */
} rknn_output;
/*
the extend information for rknn_init.
*/
typedef struct _rknn_init_extend {
rknn_context ctx; /* rknn context */
int32_t real_model_offset; /* real rknn model file offset, only valid when init context with rknn file path */
uint32_t real_model_size; /* real rknn model file size, only valid when init context with rknn file path */
uint8_t reserved[120]; /* reserved */
} rknn_init_extend;
/*
the extend information for rknn_run.
*/
typedef struct _rknn_run_extend {
uint64_t frame_id; /* output parameter, indicate current frame id of run. */
int32_t non_block; /* block flag of run, 0 is block else 1 is non block */
int32_t timeout_ms; /* timeout for block mode, in milliseconds */
int32_t fence_fd; /* fence fd from other unit */
} rknn_run_extend;
/*
the extend information for rknn_outputs_get.
*/
typedef struct _rknn_output_extend {
uint64_t frame_id; /* output parameter, indicate the frame id of outputs, corresponds to
struct rknn_run_extend.frame_id.*/
} rknn_output_extend;
/* rknn_init
initial the context and load the rknn model.
input:
rknn_context* context the pointer of context handle.
void* model if size > 0, pointer to the rknn model, if size = 0, filepath to the rknn model.
uint32_t size the size of rknn model.
uint32_t flag extend flag, see the define of RKNN_FLAG_XXX_XXX.
rknn_init_extend* extend the extend information of init.
return:
int error code.
*/
int rknn_init(rknn_context* context, void* model, uint32_t size, uint32_t flag, rknn_init_extend* extend);
/* rknn_dup_context
initial the context and load the rknn model.
input:
rknn_context* context_in the pointer of context in handle.
rknn_context* context_out the pointer of context out handle.
return:
int error code.
*/
int rknn_dup_context(rknn_context* context_in, rknn_context* context_out);
/* rknn_destroy
unload the rknn model and destroy the context.
input:
rknn_context context the handle of context.
return:
int error code.
*/
int rknn_destroy(rknn_context context);
/* rknn_query
query the information about model or others. see rknn_query_cmd.
input:
rknn_context context the handle of context.
rknn_query_cmd cmd the command of query.
void* info the buffer point of information.
uint32_t size the size of information.
return:
int error code.
*/
int rknn_query(rknn_context context, rknn_query_cmd cmd, void* info, uint32_t size);
/* rknn_inputs_set
set inputs information by input index of rknn model.
inputs information see rknn_input.
input:
rknn_context context the handle of context.
uint32_t n_inputs the number of inputs.
rknn_input inputs[] the arrays of inputs information, see rknn_input.
return:
int error code
*/
int rknn_inputs_set(rknn_context context, uint32_t n_inputs, rknn_input inputs[]);
/*
rknn_set_batch_core_num
set rknn batch core_num.
input:
rknn_context context the handle of context.
int core_num the core number.
return:
int error code.
*/
int rknn_set_batch_core_num(rknn_context context, int core_num);
/* rknn_set_core_mask
set rknn core mask.(only supported on RK3588 now)
RKNN_NPU_CORE_AUTO: auto mode, default value
RKNN_NPU_CORE_0: core 0 mode
RKNN_NPU_CORE_1: core 1 mode
RKNN_NPU_CORE_2: core 2 mode
RKNN_NPU_CORE_0_1: combine core 0/1 mode
RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode
input:
rknn_context context the handle of context.
rknn_core_mask core_mask the core mask.
return:
int error code.
*/
int rknn_set_core_mask(rknn_context context, rknn_core_mask core_mask);
/* rknn_run
run the model to execute inference.
input:
rknn_context context the handle of context.
rknn_run_extend* extend the extend information of run.
return:
int error code.
*/
int rknn_run(rknn_context context, rknn_run_extend* extend);
/* rknn_wait
wait the model after execute inference.
input:
rknn_context context the handle of context.
rknn_run_extend* extend the extend information of run.
return:
int error code.
*/
int rknn_wait(rknn_context context, rknn_run_extend* extend);
/* rknn_outputs_get
wait the inference to finish and get the outputs.
this function will block until inference finish.
the results will set to outputs[].
input:
rknn_context context the handle of context.
uint32_t n_outputs the number of outputs.
rknn_output outputs[] the arrays of output, see rknn_output.
rknn_output_extend* the extend information of output.
return:
int error code.
*/
int rknn_outputs_get(rknn_context context, uint32_t n_outputs, rknn_output outputs[], rknn_output_extend* extend);
/* rknn_outputs_release
release the outputs that get by rknn_outputs_get.
after called, the rknn_output[x].buf get from rknn_outputs_get will
also be free when rknn_output[x].is_prealloc = FALSE.
input:
rknn_context context the handle of context.
uint32_t n_ouputs the number of outputs.
rknn_output outputs[] the arrays of output.
return:
int error code
*/
int rknn_outputs_release(rknn_context context, uint32_t n_ouputs, rknn_output outputs[]);
/* new api for zero copy */
/* rknn_create_mem_from_phys (memory allocated outside)
initialize tensor memory from physical address.
input:
rknn_context ctx the handle of context.
uint64_t phys_addr physical address.
void *virt_addr virtual address.
uint32_t size the size of tensor buffer.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_phys(rknn_context ctx, uint64_t phys_addr, void *virt_addr, uint32_t size);
/* rknn_create_mem_from_fd (memory allocated outside)
initialize tensor memory from file description.
input:
rknn_context ctx the handle of context.
int32_t fd file description.
void *virt_addr virtual address.
uint32_t size the size of tensor buffer.
int32_t offset indicates the offset of the memory (virt_addr without offset).
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_fd(rknn_context ctx, int32_t fd, void *virt_addr, uint32_t size, int32_t offset);
/* rknn_create_mem_from_mb_blk (memory allocated outside)
create tensor memory from mb_blk.
input:
rknn_context ctx the handle of context.
void *mb_blk mb_blk allocate from system api.
int32_t offset indicates the offset of the memory.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_mb_blk(rknn_context ctx, void *mb_blk, int32_t offset);
/* rknn_create_mem (memory allocated inside)
create tensor memory.
input:
rknn_context ctx the handle of context.
uint32_t size the size of tensor buffer.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem(rknn_context ctx, uint32_t size);
/* rknn_destroy_mem (support allocate inside and outside)
destroy tensor memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the pointer of tensor memory information.
return:
int error code
*/
int rknn_destroy_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_weight_mem
set the weight memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information
return:
int error code.
*/
int rknn_set_weight_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_internal_mem
set the internal memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information
return:
int error code.
*/
int rknn_set_internal_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_io_mem
set the input and output tensors buffer.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information.
rknn_tensor_attr *attr the attribute of input or output tensor buffer.
return:
int error code.
*/
int rknn_set_io_mem(rknn_context ctx, rknn_tensor_mem *mem, rknn_tensor_attr *attr);
/* rknn_set_input_shape
set the input tensor shape (only valid for dynamic shape rknn model).
input:
rknn_context ctx the handle of context.
rknn_tensor_attr *attr the attribute of input or output tensor buffer.
return:
int error code.
*/
int rknn_set_input_shape(rknn_context ctx, rknn_tensor_attr* attr);
#ifdef __cplusplus
} //extern "C"
#endif
#endif //_RKNN_API_H

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/****************************************************************************
*
* Copyright (c) 2017 - 2022 by Rockchip Corp. All rights reserved.
*
* The material in this file is confidential and contains trade secrets
* of Rockchip Corporation. This is proprietary information owned by
* Rockchip Corporation. No part of this work may be disclosed,
* reproduced, copied, transmitted, or used in any way for any purpose,
* without the express written permission of Rockchip Corporation.
*
*****************************************************************************/
#ifndef _RKNN_API_H
#define _RKNN_API_H
#ifdef __cplusplus
extern "C" {
#endif
#include <stdint.h>
/*
Definition of extended flag for rknn_init.
*/
/* set high priority context. */
#define RKNN_FLAG_PRIOR_HIGH 0x00000000
/* set medium priority context */
#define RKNN_FLAG_PRIOR_MEDIUM 0x00000001
/* set low priority context. */
#define RKNN_FLAG_PRIOR_LOW 0x00000002
/* asynchronous mode.
when enable, rknn_outputs_get will not block for too long because it directly retrieves the result of
the previous frame which can increase the frame rate on single-threaded mode, but at the cost of
rknn_outputs_get not retrieves the result of the current frame.
in multi-threaded mode you do not need to turn this mode on. */
#define RKNN_FLAG_ASYNC_MASK 0x00000004
/* collect performance mode.
when enable, you can get detailed performance reports via rknn_query(ctx, RKNN_QUERY_PERF_DETAIL, ...),
but it will reduce the frame rate. */
#define RKNN_FLAG_COLLECT_PERF_MASK 0x00000008
/* allocate all memory in outside, includes weight/internal/inputs/outputs */
#define RKNN_FLAG_MEM_ALLOC_OUTSIDE 0x00000010
/* weight sharing with the same network structure */
#define RKNN_FLAG_SHARE_WEIGHT_MEM 0x00000020
/* send fence fd from outside */
#define RKNN_FLAG_FENCE_IN_OUTSIDE 0x00000040
/* get fence fd from inside */
#define RKNN_FLAG_FENCE_OUT_OUTSIDE 0x00000080
/*
Error code returned by the RKNN API.
*/
#define RKNN_SUCC 0 /* execute succeed. */
#define RKNN_ERR_FAIL -1 /* execute failed. */
#define RKNN_ERR_TIMEOUT -2 /* execute timeout. */
#define RKNN_ERR_DEVICE_UNAVAILABLE -3 /* device is unavailable. */
#define RKNN_ERR_MALLOC_FAIL -4 /* memory malloc fail. */
#define RKNN_ERR_PARAM_INVALID -5 /* parameter is invalid. */
#define RKNN_ERR_MODEL_INVALID -6 /* model is invalid. */
#define RKNN_ERR_CTX_INVALID -7 /* context is invalid. */
#define RKNN_ERR_INPUT_INVALID -8 /* input is invalid. */
#define RKNN_ERR_OUTPUT_INVALID -9 /* output is invalid. */
#define RKNN_ERR_DEVICE_UNMATCH -10 /* the device is unmatch, please update rknn sdk
and npu driver/firmware. */
#define RKNN_ERR_INCOMPATILE_PRE_COMPILE_MODEL -11 /* This RKNN model use pre_compile mode, but not compatible with current driver. */
#define RKNN_ERR_INCOMPATILE_OPTIMIZATION_LEVEL_VERSION -12 /* This RKNN model set optimization level, but not compatible with current driver. */
#define RKNN_ERR_TARGET_PLATFORM_UNMATCH -13 /* This RKNN model set target platform, but not compatible with current platform. */
/*
Definition for tensor
*/
#define RKNN_MAX_DIMS 16 /* maximum dimension of tensor. */
#define RKNN_MAX_NUM_CHANNEL 15 /* maximum channel number of input tensor. */
#define RKNN_MAX_NAME_LEN 256 /* maximum name lenth of tensor. */
#ifdef __arm__
typedef uint32_t rknn_context;
#else
typedef uint64_t rknn_context;
#endif
/*
The query command for rknn_query
*/
typedef enum _rknn_query_cmd {
RKNN_QUERY_IN_OUT_NUM = 0, /* query the number of input & output tensor. */
RKNN_QUERY_INPUT_ATTR, /* query the attribute of input tensor. */
RKNN_QUERY_OUTPUT_ATTR, /* query the attribute of output tensor. */
RKNN_QUERY_PERF_DETAIL, /* query the detail performance, need set
RKNN_FLAG_COLLECT_PERF_MASK when call rknn_init,
this query needs to be valid after rknn_outputs_get. */
RKNN_QUERY_PERF_RUN, /* query the time of run,
this query needs to be valid after rknn_outputs_get. */
RKNN_QUERY_SDK_VERSION, /* query the sdk & driver version */
RKNN_QUERY_MEM_SIZE, /* query the weight & internal memory size */
RKNN_QUERY_CUSTOM_STRING, /* query the custom string */
RKNN_QUERY_NATIVE_INPUT_ATTR, /* query the attribute of native input tensor. */
RKNN_QUERY_NATIVE_OUTPUT_ATTR, /* query the attribute of native output tensor. */
RKNN_QUERY_CMD_MAX
} rknn_query_cmd;
/*
the tensor data type.
*/
typedef enum _rknn_tensor_type {
RKNN_TENSOR_FLOAT32 = 0, /* data type is float32. */
RKNN_TENSOR_FLOAT16, /* data type is float16. */
RKNN_TENSOR_INT8, /* data type is int8. */
RKNN_TENSOR_UINT8, /* data type is uint8. */
RKNN_TENSOR_INT16, /* data type is int16. */
RKNN_TENSOR_UINT16, /* data type is uint16. */
RKNN_TENSOR_INT32, /* data type is int32. */
RKNN_TENSOR_UINT32, /* data type is uint32. */
RKNN_TENSOR_INT64, /* data type is int64. */
RKNN_TENSOR_BOOL,
RKNN_TENSOR_TYPE_MAX
} rknn_tensor_type;
inline const char* get_type_string(rknn_tensor_type type)
{
switch(type) {
case RKNN_TENSOR_FLOAT32: return "FP32";
case RKNN_TENSOR_FLOAT16: return "FP16";
case RKNN_TENSOR_INT8: return "INT8";
case RKNN_TENSOR_UINT8: return "UINT8";
case RKNN_TENSOR_INT16: return "INT16";
case RKNN_TENSOR_UINT16: return "UINT16";
case RKNN_TENSOR_INT32: return "INT32";
case RKNN_TENSOR_UINT32: return "UINT32";
case RKNN_TENSOR_INT64: return "UINT64";
case RKNN_TENSOR_BOOL: return "BOOL";
default: return "UNKNOW";
}
}
/*
the quantitative type.
*/
typedef enum _rknn_tensor_qnt_type {
RKNN_TENSOR_QNT_NONE = 0, /* none. */
RKNN_TENSOR_QNT_DFP, /* dynamic fixed point. */
RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC, /* asymmetric affine. */
RKNN_TENSOR_QNT_MAX
} rknn_tensor_qnt_type;
inline const char* get_qnt_type_string(rknn_tensor_qnt_type type)
{
switch(type) {
case RKNN_TENSOR_QNT_NONE: return "NONE";
case RKNN_TENSOR_QNT_DFP: return "DFP";
case RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC: return "AFFINE";
default: return "UNKNOW";
}
}
/*
the tensor data format.
*/
typedef enum _rknn_tensor_format {
RKNN_TENSOR_NCHW = 0, /* data format is NCHW. */
RKNN_TENSOR_NHWC, /* data format is NHWC. */
RKNN_TENSOR_NC1HWC2, /* data format is NC1HWC2. */
RKNN_TENSOR_UNDEFINED,
RKNN_TENSOR_FORMAT_MAX
} rknn_tensor_format;
/*
the mode of running on target NPU core.
*/
typedef enum _rknn_core_mask {
RKNN_NPU_CORE_AUTO = 0, /* default, run on NPU core randomly. */
RKNN_NPU_CORE_0 = 1, /* run on NPU core 0. */
RKNN_NPU_CORE_1 = 2, /* run on NPU core 1. */
RKNN_NPU_CORE_2 = 4, /* run on NPU core 2. */
RKNN_NPU_CORE_0_1 = 3, /* run on NPU core 1 and core 2. */
RKNN_NPU_CORE_0_1_2 = 7, /* run on NPU core 1 and core 2 and core 3. */
RKNN_NPU_CORE_UNDEFINED,
} rknn_core_mask;
inline const char* get_format_string(rknn_tensor_format fmt)
{
switch(fmt) {
case RKNN_TENSOR_NCHW: return "NCHW";
case RKNN_TENSOR_NHWC: return "NHWC";
case RKNN_TENSOR_NC1HWC2: return "NC1HWC2";
case RKNN_TENSOR_UNDEFINED: return "UNDEFINED";
default: return "UNKNOW";
}
}
/*
the information for RKNN_QUERY_IN_OUT_NUM.
*/
typedef struct _rknn_input_output_num {
uint32_t n_input; /* the number of input. */
uint32_t n_output; /* the number of output. */
} rknn_input_output_num;
/*
the information for RKNN_QUERY_INPUT_ATTR / RKNN_QUERY_OUTPUT_ATTR.
*/
typedef struct _rknn_tensor_attr {
uint32_t index; /* input parameter, the index of input/output tensor,
need set before call rknn_query. */
uint32_t n_dims; /* the number of dimensions. */
uint32_t dims[RKNN_MAX_DIMS]; /* the dimensions array. */
char name[RKNN_MAX_NAME_LEN]; /* the name of tensor. */
uint32_t n_elems; /* the number of elements. */
uint32_t size; /* the bytes size of tensor. */
rknn_tensor_format fmt; /* the data format of tensor. */
rknn_tensor_type type; /* the data type of tensor. */
rknn_tensor_qnt_type qnt_type; /* the quantitative type of tensor. */
int8_t fl; /* fractional length for RKNN_TENSOR_QNT_DFP. */
int32_t zp; /* zero point for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */
float scale; /* scale for RKNN_TENSOR_QNT_AFFINE_ASYMMETRIC. */
uint32_t stride; /* the stride of tensor, 0 means equal to width. */
uint32_t size_with_stride; /* the bytes size of tensor with stride. */
uint8_t pass_through; /* pass through mode, for rknn_set_io_mem interface.
if TRUE, the buf data is passed directly to the input node of the rknn model
without any conversion. the following variables do not need to be set.
if FALSE, the buf data is converted into an input consistent with the model
according to the following type and fmt. so the following variables
need to be set.*/
} rknn_tensor_attr;
/*
the information for RKNN_QUERY_PERF_DETAIL.
*/
typedef struct _rknn_perf_detail {
char* perf_data; /* the string pointer of perf detail. don't need free it by user. */
uint64_t data_len; /* the string length. */
} rknn_perf_detail;
/*
the information for RKNN_QUERY_PERF_RUN.
*/
typedef struct _rknn_perf_run {
int64_t run_duration; /* real inference time (us) */
} rknn_perf_run;
/*
the information for RKNN_QUERY_SDK_VERSION.
*/
typedef struct _rknn_sdk_version {
char api_version[256]; /* the version of rknn api. */
char drv_version[256]; /* the version of rknn driver. */
} rknn_sdk_version;
/*
the information for RKNN_QUERY_MEM_SIZE.
*/
typedef struct _rknn_mem_size {
uint32_t total_weight_size; /* the weight memory size */
uint32_t total_internal_size; /* the internal memory size, exclude inputs/outputs */
} rknn_mem_size;
/*
the information for RKNN_QUERY_CUSTOM_STRING.
*/
typedef struct _rknn_custom_string {
char string[1024]; /* the string of custom, lengths max to 1024 bytes */
} rknn_custom_string;
/*
The flags of rknn_tensor_mem.
*/
typedef enum _rknn_tensor_mem_flags {
RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE = 1, /*Used to mark in rknn_destory_mem() whether it is necessary to release the "mem" pointer itself.
If the flag RKNN_TENSOR_MEMORY_FLAGS_ALLOC_INSIDE is set, rknn_destory_mem() will call free(mem).*/
RKNN_TENSOR_MEMORY_FLAGS_UNKNOWN
} rknn_tensor_mem_flags;
/*
the memory information of tensor.
*/
typedef struct _rknn_tensor_memory {
void* virt_addr; /* the virtual address of tensor buffer. */
uint64_t phys_addr; /* the physical address of tensor buffer. */
int32_t fd; /* the fd of tensor buffer. */
int32_t offset; /* indicates the offset of the memory. */
uint32_t size; /* the size of tensor buffer. */
uint32_t flags; /* the flags of tensor buffer, reserved */
void * priv_data; /* the private data of tensor buffer. */
} rknn_tensor_mem;
/*
the input information for rknn_input_set.
*/
typedef struct _rknn_input {
uint32_t index; /* the input index. */
void* buf; /* the input buf for index. */
uint32_t size; /* the size of input buf. */
uint8_t pass_through; /* pass through mode.
if TRUE, the buf data is passed directly to the input node of the rknn model
without any conversion. the following variables do not need to be set.
if FALSE, the buf data is converted into an input consistent with the model
according to the following type and fmt. so the following variables
need to be set.*/
rknn_tensor_type type; /* the data type of input buf. */
rknn_tensor_format fmt; /* the data format of input buf.
currently the internal input format of NPU is NCHW by default.
so entering NCHW data can avoid the format conversion in the driver. */
} rknn_input;
/*
the output information for rknn_outputs_get.
*/
typedef struct _rknn_output {
uint8_t want_float; /* want transfer output data to float */
uint8_t is_prealloc; /* whether buf is pre-allocated.
if TRUE, the following variables need to be set.
if FALSE, the following variables do not need to be set. */
uint32_t index; /* the output index. */
void* buf; /* the output buf for index.
when is_prealloc = FALSE and rknn_outputs_release called,
this buf pointer will be free and don't use it anymore. */
uint32_t size; /* the size of output buf. */
} rknn_output;
/*
the extend information for rknn_init.
*/
typedef struct _rknn_init_extend {
rknn_context ctx; /* rknn context */
uint8_t reserved[128]; /* reserved */
} rknn_init_extend;
/*
the extend information for rknn_run.
*/
typedef struct _rknn_run_extend {
uint64_t frame_id; /* output parameter, indicate current frame id of run. */
int32_t non_block; /* block flag of run, 0 is block else 1 is non block */
int32_t timeout_ms; /* timeout for block mode, in milliseconds */
int32_t fence_fd; /* fence fd from other unit */
} rknn_run_extend;
/*
the extend information for rknn_outputs_get.
*/
typedef struct _rknn_output_extend {
uint64_t frame_id; /* output parameter, indicate the frame id of outputs, corresponds to
struct rknn_run_extend.frame_id.*/
} rknn_output_extend;
/* rknn_init
initial the context and load the rknn model.
input:
rknn_context* context the pointer of context handle.
void* model if size > 0, pointer to the rknn model, if size = 0, filepath to the rknn model.
uint32_t size the size of rknn model.
uint32_t flag extend flag, see the define of RKNN_FLAG_XXX_XXX.
rknn_init_extend* extend the extend information of init.
return:
int error code.
*/
int rknn_init(rknn_context* context, void* model, uint32_t size, uint32_t flag, rknn_init_extend* extend);
/* rknn_dup_context
initial the context and load the rknn model.
input:
rknn_context* context_in the pointer of context in handle.
rknn_context* context_out the pointer of context out handle.
return:
int error code.
*/
int rknn_dup_context(rknn_context* context_in, rknn_context* context_out);
/* rknn_destroy
unload the rknn model and destroy the context.
input:
rknn_context context the handle of context.
return:
int error code.
*/
int rknn_destroy(rknn_context context);
/* rknn_query
query the information about model or others. see rknn_query_cmd.
input:
rknn_context context the handle of context.
rknn_query_cmd cmd the command of query.
void* info the buffer point of information.
uint32_t size the size of information.
return:
int error code.
*/
int rknn_query(rknn_context context, rknn_query_cmd cmd, void* info, uint32_t size);
/* rknn_inputs_set
set inputs information by input index of rknn model.
inputs information see rknn_input.
input:
rknn_context context the handle of context.
uint32_t n_inputs the number of inputs.
rknn_input inputs[] the arrays of inputs information, see rknn_input.
return:
int error code
*/
int rknn_inputs_set(rknn_context context, uint32_t n_inputs, rknn_input inputs[]);
/* rknn_set_core_mask
set rknn core mask.(only supported on RK3588 now)
RKNN_NPU_CORE_AUTO: auto mode, default value
RKNN_NPU_CORE_0: core 0 mode
RKNN_NPU_CORE_1: core 1 mode
RKNN_NPU_CORE_2: core 2 mode
RKNN_NPU_CORE_0_1: combine core 0/1 mode
RKNN_NPU_CORE_0_1_2: combine core 0/1/2 mode
input:
rknn_context context the handle of context.
rknn_core_mask core_mask the core mask.
return:
int error code.
*/
int rknn_set_core_mask(rknn_context context, rknn_core_mask core_mask);
/* rknn_run
run the model to execute inference.
input:
rknn_context context the handle of context.
rknn_run_extend* extend the extend information of run.
return:
int error code.
*/
int rknn_run(rknn_context context, rknn_run_extend* extend);
/* rknn_wait
wait the model after execute inference.
input:
rknn_context context the handle of context.
rknn_run_extend* extend the extend information of run.
return:
int error code.
*/
int rknn_wait(rknn_context context, rknn_run_extend* extend);
/* rknn_outputs_get
wait the inference to finish and get the outputs.
this function will block until inference finish.
the results will set to outputs[].
input:
rknn_context context the handle of context.
uint32_t n_outputs the number of outputs.
rknn_output outputs[] the arrays of output, see rknn_output.
rknn_output_extend* the extend information of output.
return:
int error code.
*/
int rknn_outputs_get(rknn_context context, uint32_t n_outputs, rknn_output outputs[], rknn_output_extend* extend);
/* rknn_outputs_release
release the outputs that get by rknn_outputs_get.
after called, the rknn_output[x].buf get from rknn_outputs_get will
also be free when rknn_output[x].is_prealloc = FALSE.
input:
rknn_context context the handle of context.
uint32_t n_ouputs the number of outputs.
rknn_output outputs[] the arrays of output.
return:
int error code
*/
int rknn_outputs_release(rknn_context context, uint32_t n_ouputs, rknn_output outputs[]);
/* new api for zero copy */
/* rknn_create_mem_from_phys (memory allocated outside)
initialize tensor memory from physical address.
input:
rknn_context ctx the handle of context.
uint64_t phys_addr physical address.
void *virt_addr virtual address.
uint32_t size the size of tensor buffer.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_phys(rknn_context ctx, uint64_t phys_addr, void *virt_addr, uint32_t size);
/* rknn_create_mem_from_fd (memory allocated outside)
initialize tensor memory from file description.
input:
rknn_context ctx the handle of context.
int32_t fd file description.
void *virt_addr virtual address.
uint32_t size the size of tensor buffer.
int32_t offset indicates the offset of the memory (virt_addr without offset).
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_fd(rknn_context ctx, int32_t fd, void *virt_addr, uint32_t size, int32_t offset);
/* rknn_create_mem_from_mb_blk (memory allocated outside)
create tensor memory from mb_blk.
input:
rknn_context ctx the handle of context.
void *mb_blk mb_blk allocate from system api.
int32_t offset indicates the offset of the memory.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem_from_mb_blk(rknn_context ctx, void *mb_blk, int32_t offset);
/* rknn_create_mem (memory allocated inside)
create tensor memory.
input:
rknn_context ctx the handle of context.
uint32_t size the size of tensor buffer.
return:
rknn_tensor_mem the pointer of tensor memory information.
*/
rknn_tensor_mem* rknn_create_mem(rknn_context ctx, uint32_t size);
/* rknn_destory_mem (support allocate inside and outside)
destory tensor memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the pointer of tensor memory information.
return:
int error code
*/
int rknn_destory_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_weight_mem
set the weight memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information
return:
int error code.
*/
int rknn_set_weight_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_internal_mem
set the internal memory.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information
return:
int error code.
*/
int rknn_set_internal_mem(rknn_context ctx, rknn_tensor_mem *mem);
/* rknn_set_io_mem
set the input and output tensors buffer.
input:
rknn_context ctx the handle of context.
rknn_tensor_mem *mem the array of tensor memory information.
rknn_tensor_attr *attr the attribute of input or output tensor buffer.
return:
int error code.
*/
int rknn_set_io_mem(rknn_context ctx, rknn_tensor_mem *mem, rknn_tensor_attr *attr);
#ifdef __cplusplus
} //extern "C"
#endif
#endif //_RKNN_API_H

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app/main.cpp Normal file
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#include <QCoreApplication>
#include "HxTaskDispatch.h"
int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
/* 需要设置的运行时默认工作路径 */
QDir::setCurrent(QCoreApplication::applicationDirPath());
/* 数据库模块初始化 */
DataBase::initialization();
TaskDispatch::initialization();
TaskDispatch::listern(9031);
TaskDispatch::connect_to_host("192.168.10.10", 9031);
// TaskDispatch::connect_to_host("10.211.55.2", 9031);
return a.exec();
}