ANSCENTER/ANSCORE
2
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
3349b45ade87f5a5bb33333d4991da134cf74628
ANSCORE/tests/ANSLPR-UnitTest/ANSLPR-UnitTest.cpp

3834 lines
166 KiB
C++
Raw Blame History

#define NOMINMAX
#include <iostream>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include "boost/property_tree/ptree.hpp"
#include "boost/property_tree/json_parser.hpp"
#include "boost/foreach.hpp"
#include "boost/optional.hpp"
#include <fstream>
#include <sstream>
#include <iostream>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/objdetect.hpp>
#include "ANSLPR.h"
#include "ANSLPR_CPU.h"
#include "ANSOpenCV.h"
#include "ANSRTSP.h"
#include "ANSVideoPlayer.h"
#include "ANSFilePlayer.h"
#include <filesystem>
#include <thread>
#include <mutex>
#include <atomic>
#include <random>
#include <chrono>
#include <deque>
#include <set>
#include <map>
#include <cuda_runtime.h>
#include "EPLoader.h"
// Decode \\uXXXX (literal backslash-u-hex) sequences back to UTF-8.
// VectorDetectionToJsonString double-escapes Unicode for LabVIEW compatibility,
// so JSON strings contain literal "\u54c1" text instead of actual Unicode chars.
static std::string DecodeUnicodeEscapes(const std::string& input) {
std::string result;
result.reserve(input.size());
size_t i = 0;
while (i < input.size()) {
if (i + 5 < input.size() && input[i] == '\\' && input[i + 1] == 'u') {
// Parse 4 hex digits
std::string hex = input.substr(i + 2, 4);
char* end = nullptr;
uint32_t cp = static_cast<uint32_t>(strtoul(hex.c_str(), &end, 16));
if (end == hex.c_str() + 4) {
// Check for surrogate pair (\\uD800-DBFF followed by \\uDC00-DFFF)
if (cp >= 0xD800 && cp <= 0xDBFF && i + 11 < input.size()
&& input[i + 6] == '\\' && input[i + 7] == 'u') {
std::string hex2 = input.substr(i + 8, 4);
uint32_t cp2 = static_cast<uint32_t>(strtoul(hex2.c_str(), &end, 16));
if (end == hex2.c_str() + 4 && cp2 >= 0xDC00 && cp2 <= 0xDFFF) {
cp = 0x10000 + ((cp - 0xD800) << 10) + (cp2 - 0xDC00);
i += 12;
} else {
i += 6;
}
} else {
i += 6;
}
// Encode codepoint as UTF-8
if (cp < 0x80) {
result += static_cast<char>(cp);
} else if (cp < 0x800) {
result += static_cast<char>(0xC0 | (cp >> 6));
result += static_cast<char>(0x80 | (cp & 0x3F));
} else if (cp < 0x10000) {
result += static_cast<char>(0xE0 | (cp >> 12));
result += static_cast<char>(0x80 | ((cp >> 6) & 0x3F));
result += static_cast<char>(0x80 | (cp & 0x3F));
} else {
result += static_cast<char>(0xF0 | (cp >> 18));
result += static_cast<char>(0x80 | ((cp >> 12) & 0x3F));
result += static_cast<char>(0x80 | ((cp >> 6) & 0x3F));
result += static_cast<char>(0x80 | (cp & 0x3F));
}
} else {
result += input[i++];
}
} else {
result += input[i++];
}
}
return result;
}
template<typename T>
T GetOptionalValue(const boost::property_tree::ptree& pt, std::string attribute, T defaultValue) {
if (pt.count(attribute)) {
return pt.get<T>(attribute);
}
return defaultValue;
}
template <typename T>
T GetData(const boost::property_tree::ptree& pt, const std::string& key)
{
T ret;
if (boost::optional<T> data = pt.get_optional<T>(key))
{
ret = data.get();
}
return ret;
}
int ANSLPR_CPU_VideoTest() {
// Get the current working directory
std::filesystem::path currentPath = std::filesystem::current_path();
// Print the current working directory
std::cout << "Current working directory: " << currentPath << std::endl;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
ANSCENTER::ANSALPR* infHandle = new ANSCENTER::ANSALPR_CPU();
std::string licenseKey = "";
std::string modelZipFile = currentPath.string() + "\\ANS_GenericALPR_v1.0.zip";
modelZipFile = "C:\\ProgramData\\ANSCENTER\\Shared\\ANS_GenericALPR_v1.0.zip";
std::string videoFilePath = "C:\\Programs\\DemoAssets\\Videos\\ALRP\\ALPR1.mp4";
std::string lpnResult;
bool result = infHandle->Initialize(licenseKey, modelZipFile, "",0.5, 0.5);
std::cout << "Loading ANSLRP:" << result << std::endl;
cv::VideoCapture capture(videoFilePath);
if (!capture.isOpened()) {
printf("could not read this video file...\n");
return -1;
}
while (true)
{
cv::Mat frame;
if (!capture.read(frame)) // if not success, break loop
{
std::cout << "\n Cannot read the video file. please check your video.\n";
break;
}
auto start = std::chrono::system_clock::now();
infHandle->Inference(frame, lpnResult);
std::string detectionResult = lpnResult;
std::cout << "Result:" << detectionResult;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(frame, cv::Rect(x, y, width, height), 123, 2);
cv::putText(frame, cv::format("%s", class_name), cv::Point(x, y - 5),
0, 0.6, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
}
auto end = std::chrono::system_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
printf("Time = %lld ms\n", static_cast<long long int>(elapsed.count()));
cv::namedWindow("ANSLPR", cv::WINDOW_AUTOSIZE);
cv::imshow("ANSLPR", frame);
if (cv::waitKey(30) == 27) // Wait for 'esc' key press to exit
{
std::cout << "End of program faces.\n";
}
}
capture.release();
cv::destroyAllWindows();
}
int ANSLPR_BigSize_VideoTest() {
// Get the current working directory
std::filesystem::path currentPath = std::filesystem::current_path();
// Print the current working directory
std::cout << "Current working directory: " << currentPath << std::endl;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
ANSCENTER::ANSALPR* infHandle = new ANSCENTER::ANSALPR_CPU();
std::string licenseKey = "";
std::string modelZipFile = currentPath.string() + "\\ANS_GenericALPR_v1.0.zip";
modelZipFile = "C:\\ProgramData\\ANSCENTER\\Shared\\ANS_GenericALPR_v1.0.zip";
std::string videoFilePath = "C:\\Programs\\DemoAssets\\Videos\\ALRP\\3725.mp4";
std::string lpnResult;
bool result = infHandle->Initialize(licenseKey, modelZipFile, "",0.5,0.5);
std::cout << "Loading ANSLRP:" << result << std::endl;
infHandle->LoadEngine();
cv::VideoCapture capture(videoFilePath);
if (!capture.isOpened()) {
printf("could not read this video file...\n");
return -1;
}
while (true)
{
cv::Mat frame;
if (!capture.read(frame)) // if not success, break loop
{
std::cout << "\n Cannot read the video file. please check your video.\n";
break;
}
auto start = std::chrono::system_clock::now();
infHandle->Inference(frame, lpnResult,"MyCam");
std::string detectionResult = lpnResult;
std::cout << "Result:" << detectionResult;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(frame, cv::Rect(x, y, width, height), 123, 2);
cv::putText(frame, cv::format("%s", class_name), cv::Point(x, y - 5),
0, 1.2, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
}
auto end = std::chrono::system_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
printf("Time = %lld ms\n", static_cast<long long int>(elapsed.count()));
cv::resize(frame, frame, cv::Size(frame.cols / 2, frame.rows / 2)); // to half size or even smaller
cv::namedWindow("ANSLPR", cv::WINDOW_AUTOSIZE);
cv::imshow("ANSLPR", frame);
if (cv::waitKey(30) == 27) // Wait for 'esc' key press to exit
{
std::cout << "End of program faces.\n";
}
}
capture.release();
cv::destroyAllWindows();
}
std::string readJsonFile(const std::string& filePath) {
boost::property_tree::ptree pt;
boost::property_tree::read_json(filePath, pt);
std::ostringstream oss;
boost::property_tree::write_json(oss, pt, false);
return oss.str();
}
unsigned char* CVMatToBytes(cv::Mat image, unsigned int& bufferLengh)
{
int size = int(image.total() * image.elemSize());
std::cout << "size:" << size << std::endl;
unsigned char* bytes = new unsigned char[size]; // you will have to delete[] that later
std::memcpy(bytes, image.data, size * sizeof(unsigned char));
bufferLengh = size * sizeof(unsigned char);
return bytes;
}
int ANSLPR_CPU_Inferences_FileTest() {
// Get the current working directory
std::filesystem::path currentPath = std::filesystem::current_path();
// Print the current working directory
std::cout << "Current working directory: " << currentPath << std::endl;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
ANSCENTER::ANSALPR* infHandle;
std::string licenseKey = "";
std::string modelZipFile = "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ANS_GenericALPR_v1.0.zip";
std::string imageFilePath = "C:\\Projects\\ANSVIS\\Documentation\\TestImages\\ALPR\\LP1.jpg";
std::string StrBox = readJsonFile("C:\\Projects\\ANLS\\Documents\\bboxStr.json");
int result = CreateANSALPRHandle(& infHandle, "", modelZipFile.c_str(), "",0,0.5,0.5,0);
std::cout << "Init Result:" << result << std::endl;
unsigned int bufferLength = 0;
cv::Mat input = cv::imread(imageFilePath, cv::IMREAD_COLOR);
cv::Mat frame = input;
unsigned char* jpeg_string = CVMatToBytes(frame, bufferLength);
int height = frame.rows;
int width = frame.cols;
auto start = std::chrono::system_clock::now();
std::string detectionResult = ANSALPR_RunInferenceBinaryInCroppedImages(&infHandle, jpeg_string, width, height, StrBox.c_str());
std::cout << "Result:" << detectionResult;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(frame, cv::Rect(x, y, width, height), 123, 2);
cv::putText(frame, cv::format("%s:%d", class_name, class_id), cv::Point(x, y - 5),
0, 0.6, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
}
auto end = std::chrono::system_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
printf("Time = %lld ms\n", static_cast<long long int>(elapsed.count()));
//cv::namedWindow("ANSLPR", cv::WINDOW_AUTOSIZE);
//cv::imshow("ANSLPR", frame);
//cv::waitKey(0);
//cv::destroyAllWindows();
ReleaseANSALPRHandle(&infHandle);
}
int ANSLPR_CV_VideoTest() {
// Get the current working directory
std::filesystem::path currentPath = std::filesystem::current_path();
// Print the current working directory
std::cout << "Current working directory: " << currentPath << std::endl;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
ANSCENTER::ANSALPR* infHandle;
std::string licenseKey = "";
std::string modelZipFile = currentPath.string() + "\\ANS_GenericALPR_v1.0.zip";
modelZipFile = "C:\\Programs\\DemoAssets\\ModelsForANSVIS\\ANS_GenericALPR_v1.1.zip";
std::string videoFilePath = "C:\\Programs\\DemoAssets\\Videos\\ALRP\\3725.mp4";
std::string lpnResult;
int result = CreateANSALPRHandle(& infHandle, licenseKey.c_str(), modelZipFile.c_str(), "",0,0.5,0.5,0);
std::cout << "Loading ANSLRP:" << result << std::endl;
cv::VideoCapture capture(videoFilePath);
if (!capture.isOpened()) {
printf("could not read this video file...\n");
return -1;
}
while (true)
{
cv::Mat frame;
if (!capture.read(frame)) // if not success, break loop
{
std::cout << "\n Cannot read the video file. please check your video.\n";
break;
}
auto start = std::chrono::system_clock::now();
std::string jpegImage;
cv::Mat* image = nullptr; // ✅ Use a pointer to hold the allocated image
image = new cv::Mat(frame); // ✅ Allocate the image
ANSALPR_RunInferenceComplete_CPP(& infHandle, &image, "MyCam", 0, 0, lpnResult, jpegImage);
std::string detectionResult = lpnResult;
std::cout << "Result:" << detectionResult;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(frame, cv::Rect(x, y, width, height), 123, 2);
cv::putText(frame, cv::format("%s", class_name), cv::Point(x, y - 5),
0, 1.2, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
}
auto end = std::chrono::system_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
printf("Time = %lld ms\n", static_cast<long long int>(elapsed.count()));
cv::resize(frame, frame, cv::Size(1920,1080)); // to half size or even smaller
delete image;
cv::namedWindow("ANSLPR", cv::WINDOW_AUTOSIZE);
cv::imshow("ANSLPR", frame);
if (cv::waitKey(30) == 27) // Wait for 'esc' key press to exit
{
std::cout << "End of program faces.\n";
}
}
capture.release();
cv::destroyAllWindows();
ReleaseANSALPRHandle(&infHandle);
}
int ANSLPR_OD_VideoTest() {
// Get the current working directory
std::filesystem::path currentPath = std::filesystem::current_path();
// Print the current working directory
std::cout << "Current working directory: " << currentPath << std::endl;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
ANSCENTER::ANSALPR* infHandle;
std::string licenseKey = "";
std::string modelZipFile ="C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ANS_ALPR_v1.2.zip";// "C:\\Projects\\ANSVIS\\Models\\ANS_ALPR_v1.2.zip";// "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ServerOptimised\\ANS_ALPR_v1.1_NVIDIAGeForceRTX4070LaptopGPU.zip";
std::string videoFilePath = "C:\\ProgramData\\ANSCENTER\\Shared\\day.mp4";//"E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day.mp4";//
std::string lpnResult;
int engineType = 1;
double detectionThreshold = 0.5;
double ocrThreshold = 0.5;
double detectionColourThreshold = 0.5;
int result = CreateANSALPRHandle(&infHandle, licenseKey.c_str(), modelZipFile.c_str(), "", engineType, detectionThreshold, ocrThreshold, detectionColourThreshold);
std::cout << "Loading ANSLRP:" << result << std::endl;
int loadEngine = LoadANSALPREngineHandle(&infHandle);
std::cout << "Loading ANSLRP:" << loadEngine << std::endl;
cv::VideoCapture capture(videoFilePath);
if (!capture.isOpened()) {
printf("could not read this video file...\n");
return -1;
}
while (true)
{
cv::Mat frame;
if (!capture.read(frame)) // if not success, break loop
{
std::cout << "\n Cannot read the video file. please check your video.\n";
break;
}
auto start = std::chrono::system_clock::now();
std::string jpegImage;
cv::Mat* image = nullptr; // ✅ Use a pointer to hold the allocated image
image = new cv::Mat(frame); // ✅ Allocate the image
ANSALPR_RunInferenceComplete_CPP(&infHandle, &image, "MyCam", 0, 0, lpnResult, jpegImage);
auto end = std::chrono::system_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
printf("Time = %lld ms\n", static_cast<long long int>(elapsed.count()));
std::string detectionResult = lpnResult;
//std::cout << "Result:" << detectionResult;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(frame, cv::Rect(x, y, width, height), 123, 2);
cv::putText(frame, cv::format("%s", class_name), cv::Point(x, y - 5),
0, 1.2, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
}
cv::resize(frame, frame, cv::Size(1920, 1080)); // to half size or even smaller
delete image;
cv::namedWindow("ANSLPR", cv::WINDOW_AUTOSIZE);
cv::imshow("ANSLPR", frame);
if (cv::waitKey(30) == 27) // Wait for 'esc' key press to exit
{
std::cout << "End of program faces.\n";
}
}
capture.release();
cv::destroyAllWindows();
ReleaseANSALPRHandle(&infHandle);
}
int ANSLPR_OD_Inferences_FileTest() {
// Get the current working directory
std::filesystem::path currentPath = std::filesystem::current_path();
// Print the current working directory
std::cout << "Current working directory: " << currentPath << std::endl;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
ANSCENTER::ANSALPR* infHandle;
std::string licenseKey = "";
std::string modelZipFile = "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ServerOptimised\\ANS_ALPR_v1.2_NVIDIAGeForceRTX4070LaptopGPU.zip";
std::string imageFilePath = "E:\\Programs\\DemoAssets\\Images\\ALPRTest\\WrongOrder\\1109.jpg";//20250912_213850.717.jpg; 20250912_213850.511.jpg;//20250912_213850.411.jpg;//20250912_213850.261.jpg(65H115912:0.73) cororect (20250912_213850.071.jpg: 65H115833)
std::string lpnResult;
int engineType = 1;
double detectionThreshold = 0.3;
double ocrThreshold = 0.5;
double colourThreshold = 0.5;
int result = CreateANSALPRHandle(&infHandle, licenseKey.c_str(), modelZipFile.c_str(), "", engineType, detectionThreshold, ocrThreshold, colourThreshold);
std::cout << "Loading ANSLRP:" << result << std::endl;
auto start = std::chrono::system_clock::now();
int loadEngine = LoadANSALPREngineHandle(&infHandle);
std::cout << "Init Result:" << result << std::endl;
auto end = std::chrono::system_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
printf("Time to load engine = %lld ms\n", static_cast<long long int>(elapsed.count()));
unsigned int bufferLength = 0;
std::string jpegImage;
cv::Mat input = cv::imread(imageFilePath, cv::IMREAD_COLOR);
cv::Mat* image = nullptr; // ✅ Use a pointer to hold the allocated image
image = new cv::Mat(input); // ✅ Allocate the image
ANSALPR_RunInferenceComplete_CPP(&infHandle, &image, "MyCam", 0, 0, lpnResult, jpegImage);
std::string detectionResult = lpnResult;
std::cout << "Result:" << detectionResult;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(input, cv::Rect(x, y, width, height), 123, 2);
cv::putText(input, cv::format("%s", class_name), cv::Point(x, y - 5),
0, 1.2, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
}
delete image;
cv::resize(input, input, cv::Size(1920, 1080)); // to half size or even smaller
cv::namedWindow("ANSLPR", cv::WINDOW_AUTOSIZE);
cv::imshow("ANSLPR", input);
cv::waitKey(0);
cv::destroyAllWindows();
ReleaseANSALPRHandle(&infHandle);
return 0;
}
int ANSLPR_OD_INDOInferences_FileTest() {
// Get the current working directory
std::filesystem::path currentPath = std::filesystem::current_path();
// Print the current working directory
std::cout << "Current working directory: " << currentPath << std::endl;
boost::property_tree::ptree root;
boost::property_tree::ptree detectionObjects;
boost::property_tree::ptree pt;
ANSCENTER::ANSALPR* infHandle;
std::string licenseKey = "";
std::string modelZipFile = "E:\\Programs\\DemoAssets\\ModelsForANSVIS\\ANS_ALPR_IND_v1.1.zip";
std::string imageFilePath = "E:\\Programs\\TrainingWorkingStation\\IndoALPR\\Indonesian License Plate Dataset\\data\\train075.jpg";//20250912_213850.717.jpg; 20250912_213850.511.jpg;//20250912_213850.411.jpg;//20250912_213850.261.jpg(65H115912:0.73) cororect (20250912_213850.071.jpg: 65H115833)
std::string lpnResult;
int engineType = 1;
double detectionThreshold = 0.3;
double ocrThreshold = 0.5;
int result = CreateANSALPRHandle(&infHandle, licenseKey.c_str(), modelZipFile.c_str(), "", engineType, detectionThreshold, ocrThreshold, 0.5);
std::cout << "Loading ANSLRP:" << result << std::endl;
int loadEngine = LoadANSALPREngineHandle(&infHandle);
std::cout << "Init Result:" << result << std::endl;
unsigned int bufferLength = 0;
std::string jpegImage;
cv::Mat input = cv::imread(imageFilePath, cv::IMREAD_COLOR);
cv::Mat* image = nullptr; // ✅ Use a pointer to hold the allocated image
image = new cv::Mat(input); // ✅ Allocate the image
ANSALPR_RunInferenceComplete_CPP(&infHandle, &image, "MyCam", 0, 0, lpnResult, jpegImage);
std::string detectionResult = lpnResult;
std::cout << "Result:" << detectionResult;
if (!detectionResult.empty()) {
pt.clear();
std::stringstream ss;
ss.clear();
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type & child, pt.get_child("results"))
{
const boost::property_tree::ptree& result = child.second;
const auto class_id = GetData<int>(result, "class_id");
const auto class_name = GetData<std::string>(result, "class_name");
const auto x = GetData<float>(result, "x");
const auto y = GetData<float>(result, "y");
const auto width = GetData<float>(result, "width");
const auto height = GetData<float>(result, "height");
cv::rectangle(input, cv::Rect(x, y, width, height), 123, 2);
cv::putText(input, cv::format("%s", class_name), cv::Point(x, y - 5),
0, 1.2, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
}
auto end = std::chrono::system_clock::now();
delete image;
cv::resize(input, input, cv::Size(1920, 1080)); // to half size or even smaller
cv::namedWindow("ANSLPR", cv::WINDOW_AUTOSIZE);
cv::imshow("ANSLPR", input);
cv::waitKey(0);
cv::destroyAllWindows();
ReleaseANSALPRHandle(&infHandle);
return 0;
}
// ============================================================================
// Multi-GPU ALPR Stress Test — 4 parallel RTSP→ALPR tasks
//
// Purpose: Diagnose why dual RTX 5080 performs worse than single RTX 3050.
// Each task has its own RTSP reader + ALPR engine. Tasks 0-1 read stream A,
// tasks 2-3 read stream B. All 4 run in parallel threads.
//
// The display composites all 4 views into a single resizable window with a
// log panel at the bottom showing per-task stats and GPU diagnostics.
// ============================================================================
// Thread-safe logger: collects timestamped messages for on-screen log + file
static const char* LOG_FILE_PATH = "C:\\Temp\\ALPRdebug.txt";
class ThreadSafeLog {
public:
void init() {
std::lock_guard<std::mutex> lk(m_mtx);
m_file.open(LOG_FILE_PATH, std::ios::out | std::ios::trunc);
if (m_file.is_open()) {
auto now = std::chrono::system_clock::now();
auto t = std::chrono::system_clock::to_time_t(now);
char timeBuf[64];
struct tm lt;
localtime_s(&lt, &t);
strftime(timeBuf, sizeof(timeBuf), "%Y-%m-%d %H:%M:%S", &lt);
m_file << "================================================================\n";
m_file << " ANSLPR Multi-GPU Stress Test Debug Log\n";
m_file << " Started: " << timeBuf << "\n";
m_file << " Log file: " << LOG_FILE_PATH << "\n";
m_file << "================================================================\n\n";
m_file.flush();
}
}
void add(const std::string& msg) {
std::lock_guard<std::mutex> lk(m_mtx);
// Full timestamp for file: HH:MM:SS.mmm
auto now = std::chrono::system_clock::now();
auto t = std::chrono::system_clock::to_time_t(now);
auto ms = std::chrono::duration_cast<std::chrono::milliseconds>(
now.time_since_epoch()).count() % 1000;
struct tm lt;
localtime_s(&lt, &t);
char ts[32];
snprintf(ts, sizeof(ts), "[%02d:%02d:%02d.%03lld] ",
lt.tm_hour, lt.tm_min, lt.tm_sec, static_cast<long long>(ms));
std::string line = std::string(ts) + msg;
m_lines.push_back(line);
if (m_lines.size() > 200) m_lines.pop_front();
// Write to file immediately (flush so user can read mid-run)
if (m_file.is_open()) {
m_file << line << "\n";
m_file.flush();
}
}
std::deque<std::string> getRecent(size_t n) {
std::lock_guard<std::mutex> lk(m_mtx);
size_t start = (m_lines.size() > n) ? m_lines.size() - n : 0;
return std::deque<std::string>(m_lines.begin() + start, m_lines.end());
}
void close() {
std::lock_guard<std::mutex> lk(m_mtx);
if (m_file.is_open()) m_file.close();
}
private:
std::mutex m_mtx;
std::deque<std::string> m_lines;
std::ofstream m_file;
};
// Per-task shared state (written by worker thread, read by display thread)
struct TaskState {
std::mutex mtx;
cv::Mat displayFrame; // latest frame with detections drawn
double fps = 0.0;
double inferenceMs = 0.0;
int frameCount = 0;
int detectionCount= 0;
std::string lastPlate;
bool engineLoaded = false;
bool streamOk = false;
std::string statusMsg = "Initializing...";
// GPU resource tracking (set during engine load)
int gpuDeviceId = -1; // which GPU this task's engine landed on
size_t vramUsedBytes = 0; // VRAM consumed by this task's engine
// Grab/Inference timing (updated by worker thread)
double lastGrabMs = 0.0;
double lastInfMs = 0.0;
};
// Snapshot of GPU state for real-time monitoring
struct GpuSnapshot {
int deviceId = 0;
std::string name;
size_t totalMiB = 0;
size_t freeMiB = 0;
size_t usedMiB = 0;
};
// Safe check: is CUDA runtime available? (prevents crash on CPU-only PCs)
static bool IsCudaAvailable() {
static int cached = -1;
if (cached < 0) {
HMODULE h = LoadLibraryA("nvcuda.dll");
cached = (h != nullptr) ? 1 : 0;
if (h) FreeLibrary(h);
}
return cached == 1;
}
// Query current GPU VRAM usage for all devices
static std::vector<GpuSnapshot> QueryGpuVram() {
std::vector<GpuSnapshot> snapshots;
if (!IsCudaAvailable()) return snapshots;
int deviceCount = 0;
if (cudaGetDeviceCount(&deviceCount) != cudaSuccess) return snapshots;
for (int i = 0; i < deviceCount; i++) {
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, i);
int prevDevice;
cudaGetDevice(&prevDevice);
cudaSetDevice(i);
size_t freeMem = 0, totalMem = 0;
cudaMemGetInfo(&freeMem, &totalMem);
cudaSetDevice(prevDevice);
GpuSnapshot s;
s.deviceId = i;
s.name = prop.name;
s.totalMiB = totalMem / (1024 * 1024);
s.freeMiB = freeMem / (1024 * 1024);
s.usedMiB = s.totalMiB - s.freeMiB;
snapshots.push_back(s);
}
return snapshots;
}
// Measure per-GPU free VRAM (returns array indexed by device)
static std::vector<size_t> GetPerGpuFreeMiB() {
std::vector<size_t> result;
if (!IsCudaAvailable()) return result;
int deviceCount = 0;
if (cudaGetDeviceCount(&deviceCount) != cudaSuccess) return result;
int prevDevice;
cudaGetDevice(&prevDevice);
for (int i = 0; i < deviceCount; i++) {
cudaSetDevice(i);
size_t freeMem = 0, totalMem = 0;
cudaMemGetInfo(&freeMem, &totalMem);
result.push_back(freeMem / (1024 * 1024));
}
cudaSetDevice(prevDevice);
return result;
}
static std::atomic<bool> g_running{true};
static ThreadSafeLog g_log;
// Log GPU info using CUDA runtime
static void LogGpuInfo() {
if (!IsCudaAvailable()) {
g_log.add("No NVIDIA GPU detected — running in CPU mode");
printf("[GPU] No NVIDIA GPU detected — running in CPU mode\n");
return;
}
int deviceCount = 0;
cudaError_t err = cudaGetDeviceCount(&deviceCount);
if (err != cudaSuccess) {
g_log.add("CUDA ERROR: cudaGetDeviceCount failed: " + std::string(cudaGetErrorString(err)));
printf("[GPU] CUDA ERROR: %s\n", cudaGetErrorString(err));
return;
}
printf("============================================================\n");
printf(" GPU DEVICE REPORT — %d device(s) detected\n", deviceCount);
printf("============================================================\n");
g_log.add("GPU DEVICE REPORT: " + std::to_string(deviceCount) + " device(s)");
for (int i = 0; i < deviceCount; i++) {
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, i);
size_t freeMem = 0, totalMem = 0;
cudaSetDevice(i);
cudaMemGetInfo(&freeMem, &totalMem);
char buf[512];
snprintf(buf, sizeof(buf),
" GPU[%d] %s | SM %d.%d | VRAM: %.0f MiB total, %.0f MiB free",
i, prop.name, prop.major, prop.minor,
totalMem / 1048576.0, freeMem / 1048576.0);
printf("%s\n", buf);
g_log.add(buf);
snprintf(buf, sizeof(buf),
" GPU[%d] PCIe Bus %d, Device %d | Async Engines: %d | Concurrent Kernels: %d",
i, prop.pciBusID, prop.pciDeviceID,
prop.asyncEngineCount, prop.concurrentKernels);
printf("%s\n", buf);
g_log.add(buf);
}
printf("============================================================\n");
}
// Global inference mutex: serializes inference on non-NVIDIA GPUs (DirectML/OpenVINO).
// DirectML is not thread-safe when multiple ORT sessions run concurrently on the
// same integrated GPU — causes access violations on 4K frames.
// On NVIDIA, each task has its own CUDA context so no serialization needed.
static std::mutex g_inferenceMutex;
// Worker thread: reads RTSP frames and runs ALPR inference
// RTSP client and ALPR engine are pre-created on the main thread to avoid
// race conditions in CreateANSRTSPHandle / CreateANSALPRHandle.
// Takes rtspClientPtr (pointer to array slot) + streamGuard mutex so the
// CHAOS thread can safely destroy+recreate the stream without use-after-free.
static void ALPRWorkerThread(int taskId,
ANSCENTER::ANSRTSPClient** rtspClientPtr,
std::mutex* streamGuard,
ANSCENTER::ANSALPR* alprHandle,
TaskState& state) {
char tag[32];
snprintf(tag, sizeof(tag), "[Task%d]", taskId);
std::string prefix(tag);
g_log.add(prefix + " Worker thread started");
printf("%s Worker thread started\n", tag);
// --- Main inference loop ---
int width = 0, height = 0;
int64_t pts = 0;
int emptyFrames = 0;
std::string cameraId = "Cam" + std::to_string(taskId);
// FPS tracking with sliding window
std::deque<std::chrono::steady_clock::time_point> fpsTimestamps;
// Timing accumulators for periodic benchmarking
double totalGrabMs = 0, totalInfMs = 0;
int grabCount = 0, infCount = 0;
double maxGrabMs = 0, maxInfMs = 0;
auto benchStart = std::chrono::steady_clock::now();
bool hwDecodeLogged = false;
while (g_running.load()) {
// Lock the stream guard to prevent CHAOS from destroying the client
// while we're mid-frame-grab or mid-inference.
std::unique_lock<std::mutex> streamLock(*streamGuard);
// Re-read the client pointer each iteration — CHAOS may have
// destroyed+recreated it, so our old pointer could be dangling.
ANSCENTER::ANSRTSPClient* rtspClient = *rtspClientPtr;
if (rtspClient == nullptr) {
streamLock.unlock();
emptyFrames++;
if (emptyFrames % 100 == 1) {
g_log.add(prefix + " Stream destroyed by CHAOS, waiting... (count=" + std::to_string(emptyFrames) + ")");
}
std::this_thread::sleep_for(std::chrono::milliseconds(50));
continue;
}
// Read frame from RTSP via ANSCV
auto grabStart = std::chrono::steady_clock::now();
cv::Mat* framePtr = nullptr;
GetRTSPCVImage(&rtspClient, width, height, pts, &framePtr);
auto grabEnd = std::chrono::steady_clock::now();
double grabMs = std::chrono::duration<double, std::milli>(grabEnd - grabStart).count();
if (framePtr == nullptr || framePtr->empty()) {
emptyFrames++;
if (emptyFrames % 100 == 1) {
g_log.add(prefix + " Empty frame (count=" + std::to_string(emptyFrames) + ")");
}
if (emptyFrames > 300) {
g_log.add(prefix + " Too many empty frames (reconnect disabled for long test)");
// ReconnectRTSP(&rtspClient); // Disabled for VRAM stability testing
emptyFrames = 0;
}
streamLock.unlock();
if (framePtr) delete framePtr;
std::this_thread::sleep_for(std::chrono::milliseconds(10));
continue;
}
emptyFrames = 0;
// Log HW decode status once after first successful frame
if (!hwDecodeLogged) {
hwDecodeLogged = true;
int hwActive = rtspClient->IsHWDecodingActive() ? 1 : 0;
bool isCuda = rtspClient->IsCudaHWAccel();
int hwGpu = rtspClient->GetHWDecodingGpuIndex();
char hwBuf[256];
const char* hwType = !hwActive ? "INACTIVE (software/CPU)"
: isCuda ? "ACTIVE (CUDA/NVDEC zero-copy)"
: "ACTIVE (D3D11VA/NVDEC cpu-nv12)";
snprintf(hwBuf, sizeof(hwBuf), "%s HW Decode: %s (GPU index: %d)",
tag, hwType, hwGpu);
g_log.add(hwBuf);
printf("%s\n", hwBuf);
}
totalGrabMs += grabMs;
grabCount++;
if (grabMs > maxGrabMs) maxGrabMs = grabMs;
// Run ALPR inference
bool isNvidia = (ANSCENTER::EPLoader::Current().type == ANSCENTER::EngineType::NVIDIA_GPU);
fprintf(stderr, "[Worker T%d] frame %d: calling inference %dx%d...\n",
taskId, state.frameCount + 1, framePtr->cols, framePtr->rows);
auto infStart = std::chrono::steady_clock::now();
std::string lpnResult, jpegImage;
{
std::unique_lock<std::mutex> infLock(g_inferenceMutex, std::defer_lock);
if (!isNvidia) infLock.lock();
ANSALPR_RunInferenceComplete_CPP(&alprHandle, &framePtr, cameraId.c_str(), 0, 0, lpnResult, jpegImage);
}
fprintf(stderr, "[Worker T%d] frame %d: inference done, result len=%zu\n",
taskId, state.frameCount + 1, lpnResult.size());
// Release stream lock — inference is done, CHAOS can now safely destroy.
streamLock.unlock();
auto infEnd = std::chrono::steady_clock::now();
double infMs = std::chrono::duration<double, std::milli>(infEnd - infStart).count();
totalInfMs += infMs;
infCount++;
if (infMs > maxInfMs) maxInfMs = infMs;
// Parse detections and draw on frame
cv::Mat display = framePtr->clone();
int detCount = 0;
std::string lastPlateText;
if (!lpnResult.empty()) {
try {
boost::property_tree::ptree pt;
std::stringstream ss(lpnResult);
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type& child, pt.get_child("results")) {
const boost::property_tree::ptree& det = child.second;
const auto class_name = GetData<std::string>(det, "class_name");
const auto x = GetData<float>(det, "x");
const auto y = GetData<float>(det, "y");
const auto w = GetData<float>(det, "width");
const auto h = GetData<float>(det, "height");
cv::rectangle(display, cv::Rect((int)x, (int)y, (int)w, (int)h),
cv::Scalar(0, 255, 0), 2);
cv::putText(display, class_name, cv::Point((int)x, (int)y - 5),
cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 0), 2);
lastPlateText = class_name;
detCount++;
}
}
catch (...) {}
}
// Update FPS (sliding window over last 2 seconds)
auto now = std::chrono::steady_clock::now();
fpsTimestamps.push_back(now);
while (!fpsTimestamps.empty() &&
std::chrono::duration<double>(now - fpsTimestamps.front()).count() > 2.0) {
fpsTimestamps.pop_front();
}
double fps = fpsTimestamps.size() / 2.0;
// Draw OSD on frame
char osd[128];
snprintf(osd, sizeof(osd), "Task%d | %.1f FPS | Inf: %.0f ms | #%d",
taskId, fps, infMs, state.frameCount + 1);
cv::putText(display, osd, cv::Point(10, 30),
cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 255), 2);
// Update shared state
{
std::lock_guard<std::mutex> lk(state.mtx);
state.displayFrame = display;
state.fps = fps;
state.inferenceMs = infMs;
state.lastGrabMs = grabMs;
state.lastInfMs = infMs;
state.frameCount++;
state.detectionCount += detCount;
if (!lastPlateText.empty()) state.lastPlate = lastPlateText;
}
// Periodic logging (every 100 frames)
if ((state.frameCount % 100) == 0) {
double avgGrab = grabCount > 0 ? totalGrabMs / grabCount : 0;
double avgInf = infCount > 0 ? totalInfMs / infCount : 0;
double elapsed = std::chrono::duration<double>(
std::chrono::steady_clock::now() - benchStart).count();
char buf[512];
snprintf(buf, sizeof(buf),
"%s Frame %d | FPS=%.1f | Grab: avg=%.1fms max=%.0fms | Inf: avg=%.1fms max=%.0fms | "
"GrabPct=%.0f%% InfPct=%.0f%% | Det=%d",
tag, state.frameCount, fps,
avgGrab, maxGrabMs,
avgInf, maxInfMs,
(totalGrabMs / (elapsed * 1000.0)) * 100.0,
(totalInfMs / (elapsed * 1000.0)) * 100.0,
state.detectionCount);
g_log.add(buf);
printf("%s\n", buf);
// Reset accumulators
totalGrabMs = totalInfMs = 0;
maxGrabMs = maxInfMs = 0;
grabCount = infCount = 0;
benchStart = std::chrono::steady_clock::now();
}
delete framePtr;
}
g_log.add(prefix + " Worker loop exited");
}
// =============================================================================
// ANSLPR_SingleTask_Test — 1 stream, 1 AI task. For isolating DirectML/ORT
// issues on non-NVIDIA GPUs. If this works but 2-task crashes, it's concurrency.
// =============================================================================
int ANSLPR_SingleTask_Test() {
ANSCENTER::ANSOPENCV::InitCameraNetwork();
g_log.init();
printf("\n");
printf("============================================================\n");
printf(" ANSLPR Single-Task Test — 1 Stream, 1 AI Task\n");
printf(" Press ESC to stop\n");
printf(" Log file: %s\n", LOG_FILE_PATH);
printf("============================================================\n\n");
g_log.add("============================================================");
g_log.add(" ANSLPR Single-Task Test — 1 Stream, 1 AI Task");
g_log.add("============================================================");
const std::string streamUrl = "rtsp://admin:admin123@103.156.0.133:8010/cam/realmonitor?channel=1&subtype=0";
g_log.add("Stream: " + streamUrl);
// --- Create RTSP client ---
ANSCENTER::ANSRTSPClient* rtspClient = nullptr;
printf("[Stream0] Creating RTSP handle...\n");
int rtspResult = CreateANSRTSPHandle(&rtspClient, "", "", "", streamUrl.c_str());
if (rtspResult != 1 || rtspClient == nullptr) {
printf("[Stream0] FAILED to create RTSP handle\n");
ANSCENTER::ANSOPENCV::DeinitCameraNetwork();
return -1;
}
SetRTSPImageQuality(&rtspClient, 0);
SetRTSPHWDecoding(&rtspClient, -1); // Force software decoding
StartRTSP(&rtspClient);
g_log.add("[Stream0] RTSP started (software decode)");
// --- Create single ALPR engine ---
ANSCENTER::ANSALPR* alprHandle = nullptr;
std::string modelZipFile = "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ANS_ALPR_v1.2.zip";
printf("[Task0] Creating ALPR handle...\n");
auto engineStart = std::chrono::steady_clock::now();
int createResult = CreateANSALPRHandle(&alprHandle, "", modelZipFile.c_str(), "",
1, 0.5, 0.5, 0.5);
if (createResult != 1 || alprHandle == nullptr) {
printf("[Task0] FAILED to create ALPR handle (result=%d)\n", createResult);
StopRTSP(&rtspClient); ReleaseANSRTSPHandle(&rtspClient);
ANSCENTER::ANSOPENCV::DeinitCameraNetwork();
return -1;
}
printf("[Task0] Loading ALPR engine...\n");
int loadResult = LoadANSALPREngineHandle(&alprHandle);
auto engineEnd = std::chrono::steady_clock::now();
double loadMs = std::chrono::duration<double, std::milli>(engineEnd - engineStart).count();
if (loadResult != 1) {
printf("[Task0] FAILED to load ALPR engine (result=%d)\n", loadResult);
ReleaseANSALPRHandle(&alprHandle);
StopRTSP(&rtspClient); ReleaseANSRTSPHandle(&rtspClient);
ANSCENTER::ANSOPENCV::DeinitCameraNetwork();
return -1;
}
printf("[Task0] Engine loaded in %.0f ms\n", loadMs);
g_log.add("[Task0] Engine loaded in " + std::to_string((int)loadMs) + " ms");
// --- Single-task worker + display ---
TaskState state;
state.engineLoaded = true;
state.streamOk = true;
state.statusMsg = "Running";
std::mutex streamGuard;
std::thread worker(ALPRWorkerThread, 0, &rtspClient, &streamGuard, alprHandle, std::ref(state));
const int cellW = 800, cellH = 600;
const int logPanelH = 80;
std::string windowTitle = "ANSLPR Single-Task Test";
cv::namedWindow(windowTitle, cv::WINDOW_NORMAL);
cv::resizeWindow(windowTitle, cellW, cellH + logPanelH);
auto testStart = std::chrono::steady_clock::now();
while (g_running.load()) {
cv::Mat canvas(cellH + logPanelH, cellW, CV_8UC3, cv::Scalar(30, 30, 30));
cv::Mat cell;
double fps = 0, infMs = 0;
int fCount = 0, dCount = 0;
std::string lastPlate;
{
std::lock_guard<std::mutex> lk(state.mtx);
if (!state.displayFrame.empty())
cv::resize(state.displayFrame, cell, cv::Size(cellW, cellH));
fps = state.fps;
infMs = state.inferenceMs;
fCount = state.frameCount;
dCount = state.detectionCount;
lastPlate = state.lastPlate;
}
if (cell.empty())
cell = cv::Mat(cellH, cellW, CV_8UC3, cv::Scalar(40, 40, 40));
cv::rectangle(cell, cv::Rect(0, cellH - 40, cellW, 40), cv::Scalar(0, 0, 0), cv::FILLED);
char bar[256];
snprintf(bar, sizeof(bar), "T0 | %.1f FPS | %.0fms | F:%d | D:%d | %s",
fps, infMs, fCount, dCount, lastPlate.empty() ? "-" : lastPlate.c_str());
cv::putText(cell, bar, cv::Point(5, cellH - 12),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 255, 0), 1);
cell.copyTo(canvas(cv::Rect(0, 0, cellW, cellH)));
cv::Mat logPanel = canvas(cv::Rect(0, cellH, cellW, logPanelH));
logPanel.setTo(cv::Scalar(20, 20, 20));
auto elapsed = std::chrono::duration<double>(std::chrono::steady_clock::now() - testStart).count();
char header[256];
snprintf(header, sizeof(header), "Elapsed: %.0fs | 1 camera, 1 AI task | %.1f FPS | Press ESC to stop",
elapsed, fps);
cv::putText(logPanel, header, cv::Point(10, 20),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(200, 200, 0), 1);
cv::imshow(windowTitle, canvas);
if (cv::waitKey(30) == 27) {
g_log.add("ESC pressed — stopping...");
printf("\nESC pressed — stopping...\n");
g_running.store(false);
}
}
if (worker.joinable()) worker.join();
printf("\n============================================================\n");
printf(" FINAL SUMMARY\n");
printf(" Frames: %d | Detections: %d | FPS: %.1f | InfMs: %.0f\n",
state.frameCount, state.detectionCount, state.fps, state.inferenceMs);
printf("============================================================\n");
ReleaseANSALPRHandle(&alprHandle);
StopRTSP(&rtspClient);
ReleaseANSRTSPHandle(&rtspClient);
g_log.close();
cv::destroyAllWindows();
ANSCENTER::ANSOPENCV::DeinitCameraNetwork();
return 0;
}
// =============================================================================
// ANSLPR_CPU_StressTest — Lightweight 2-task stress test for CPU-only PCs
// Uses ANSALPR_OD (engineType=1) which auto-falls-back to ONNX Runtime on CPU.
// No VRAM tracking, no NVDEC alignment, no chaos thread.
// =============================================================================
int ANSLPR_CPU_StressTest() {
ANSCENTER::ANSOPENCV::InitCameraNetwork();
g_log.init();
const int NUM_STREAMS = 2;
const int NUM_TASKS = 2;
printf("\n");
printf("============================================================\n");
printf(" ANSLPR CPU Stress Test — %d Parallel ALPR Tasks\n", NUM_TASKS);
printf(" Press ESC to stop\n");
printf(" Log file: %s\n", LOG_FILE_PATH);
printf("============================================================\n\n");
g_log.add("============================================================");
g_log.add(" ANSLPR CPU Stress Test — " + std::to_string(NUM_TASKS) + " Tasks");
g_log.add("============================================================");
// --- RTSP URLs (2 camera streams) ---
const std::string streamUrls[NUM_STREAMS] = {
"rtsp://admin:admin123@103.156.0.133:8010/cam/realmonitor?channel=1&subtype=0",
"rtsp://nhathuocngoclinh.zapto.org:600/rtsp/streaming?channel=01&subtype=0"
};
const int taskStreamMap[NUM_TASKS] = { 0, 1 };
for (int i = 0; i < NUM_STREAMS; i++)
g_log.add("Stream " + std::to_string(i) + ": " + streamUrls[i]);
// --- Task states ---
TaskState taskStates[NUM_TASKS];
// --- Create RTSP clients (software decoding) ---
ANSCENTER::ANSRTSPClient* rtspClients[NUM_STREAMS] = {};
for (int s = 0; s < NUM_STREAMS; s++) {
printf("[Stream%d] Creating RTSP handle...\n", s);
int result = CreateANSRTSPHandle(&rtspClients[s], "", "", "", streamUrls[s].c_str());
if (result != 1 || rtspClients[s] == nullptr) {
printf("[Stream%d] FAILED to create RTSP handle\n", s);
g_log.add("[Stream" + std::to_string(s) + "] RTSP create FAILED");
rtspClients[s] = nullptr;
continue;
}
SetRTSPImageQuality(&rtspClients[s], 0);
SetRTSPHWDecoding(&rtspClients[s], -1); // HW_DECODING_DISABLE: force software decoding
StartRTSP(&rtspClients[s]);
g_log.add("[Stream" + std::to_string(s) + "] RTSP started (software decode)");
}
// --- Create ALPR engines (engineType=1 → ANSALPR_OD, auto CPU/GPU) ---
ANSCENTER::ANSALPR* alprHandles[NUM_TASKS] = {};
std::string modelZipFile = "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ANS_ALPR_v1.2.zip";
int engineType = 1; // ANSALPR_OD: auto CPU/GPU
double detThresh = 0.5, ocrThresh = 0.5, colThresh = 0.5;
for (int i = 0; i < NUM_TASKS; i++) {
char tag[32];
snprintf(tag, sizeof(tag), "[Task%d]", i);
int streamIdx = taskStreamMap[i];
if (rtspClients[streamIdx] == nullptr) {
printf("%s Skipped — Stream%d not available\n", tag, streamIdx);
continue;
}
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].streamOk = true;
taskStates[i].statusMsg = "Loading ALPR engine...";
}
printf("%s Creating ALPR handle...\n", tag);
auto engineStart = std::chrono::steady_clock::now();
int createResult = CreateANSALPRHandle(&alprHandles[i], "", modelZipFile.c_str(), "",
engineType, detThresh, ocrThresh, colThresh);
if (createResult != 1 || alprHandles[i] == nullptr) {
printf("%s FAILED to create ALPR handle (result=%d)\n", tag, createResult);
g_log.add(std::string(tag) + " ALPR create FAILED");
continue;
}
printf("%s Loading ALPR engine...\n", tag);
int loadResult = LoadANSALPREngineHandle(&alprHandles[i]);
auto engineEnd = std::chrono::steady_clock::now();
double loadMs = std::chrono::duration<double, std::milli>(engineEnd - engineStart).count();
if (loadResult != 1) {
printf("%s FAILED to load ALPR engine (result=%d)\n", tag, loadResult);
g_log.add(std::string(tag) + " Engine load FAILED");
ReleaseANSALPRHandle(&alprHandles[i]);
alprHandles[i] = nullptr;
continue;
}
char buf[256];
snprintf(buf, sizeof(buf), "%s Engine loaded in %.0f ms (Stream%d)", tag, loadMs, streamIdx);
printf("%s\n", buf);
g_log.add(buf);
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].engineLoaded = true;
taskStates[i].statusMsg = "Running";
}
}
// --- Launch worker threads ---
std::mutex streamGuards[NUM_STREAMS];
std::thread workers[NUM_TASKS];
for (int i = 0; i < NUM_TASKS; i++) {
int streamIdx = taskStreamMap[i];
if (rtspClients[streamIdx] && alprHandles[i]) {
workers[i] = std::thread(ALPRWorkerThread, i,
&rtspClients[streamIdx],
&streamGuards[streamIdx],
alprHandles[i],
std::ref(taskStates[i]));
}
}
// --- Display loop ---
const int cellW = 640, cellH = 480;
const int logPanelH = 120;
const int gridCols = 2, gridRows = 1;
std::string windowTitle = "ANSLPR CPU Stress Test";
cv::namedWindow(windowTitle, cv::WINDOW_NORMAL);
cv::resizeWindow(windowTitle, cellW * gridCols, cellH * gridRows + logPanelH);
auto testStart = std::chrono::steady_clock::now();
while (g_running.load()) {
cv::Mat canvas(cellH * gridRows + logPanelH, cellW * gridCols, CV_8UC3, cv::Scalar(30, 30, 30));
for (int i = 0; i < NUM_TASKS; i++) {
int col = i % gridCols, row = i / gridCols;
cv::Rect roi(col * cellW, row * cellH, cellW, cellH);
cv::Mat cell;
double fps = 0, infMs = 0;
int fCount = 0, dCount = 0;
std::string statusMsg, lastPlate;
bool engineLoaded = false;
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
if (!taskStates[i].displayFrame.empty())
cv::resize(taskStates[i].displayFrame, cell, cv::Size(cellW, cellH));
fps = taskStates[i].fps;
infMs = taskStates[i].inferenceMs;
fCount = taskStates[i].frameCount;
dCount = taskStates[i].detectionCount;
statusMsg = taskStates[i].statusMsg;
lastPlate = taskStates[i].lastPlate;
engineLoaded = taskStates[i].engineLoaded;
}
if (cell.empty()) {
cell = cv::Mat(cellH, cellW, CV_8UC3, cv::Scalar(40, 40, 40));
cv::putText(cell, "Task " + std::to_string(i) + ": " + statusMsg,
cv::Point(20, cellH / 2),
cv::FONT_HERSHEY_SIMPLEX, 0.8, cv::Scalar(100, 100, 255), 2);
}
// Status bar
cv::rectangle(cell, cv::Rect(0, cellH - 40, cellW, 40), cv::Scalar(0, 0, 0), cv::FILLED);
char bar[256];
snprintf(bar, sizeof(bar), "T%d(S%d) | %.1f FPS | %.0fms | F:%d | D:%d | %s",
i, taskStreamMap[i], fps, infMs, fCount, dCount,
lastPlate.empty() ? "-" : lastPlate.c_str());
cv::Scalar barColor = engineLoaded ? cv::Scalar(0, 255, 0) : cv::Scalar(0, 100, 255);
cv::putText(cell, bar, cv::Point(5, cellH - 12),
cv::FONT_HERSHEY_SIMPLEX, 0.45, barColor, 1);
cell.copyTo(canvas(roi));
}
// Grid line
if (gridCols > 1)
cv::line(canvas, cv::Point(cellW, 0), cv::Point(cellW, cellH * gridRows),
cv::Scalar(100, 100, 100), 1);
// Log panel
cv::Rect logRoi(0, cellH * gridRows, cellW * gridCols, logPanelH);
cv::Mat logPanel = canvas(logRoi);
logPanel.setTo(cv::Scalar(20, 20, 20));
auto elapsed = std::chrono::duration<double>(std::chrono::steady_clock::now() - testStart).count();
double totalFps = 0;
for (int i = 0; i < NUM_TASKS; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
totalFps += taskStates[i].fps;
}
char header[256];
snprintf(header, sizeof(header),
"Elapsed: %.0fs | %d cameras, %d AI tasks | Total: %.1f FPS | Press ESC to stop",
elapsed, NUM_STREAMS, NUM_TASKS, totalFps);
cv::putText(logPanel, header, cv::Point(10, 20),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(200, 200, 0), 1);
// Per-task summary
for (int i = 0; i < NUM_TASKS; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
char tLine[256];
snprintf(tLine, sizeof(tLine),
"T%d(S%d): FPS=%.1f Inf=%.0fms Frames=%d Det=%d",
i, taskStreamMap[i], taskStates[i].fps, taskStates[i].inferenceMs,
taskStates[i].frameCount, taskStates[i].detectionCount);
cv::putText(logPanel, tLine, cv::Point(10, 42 + i * 18),
cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(200, 200, 200), 1);
}
// Recent log
auto recentLogs = g_log.getRecent(3);
int logY = 42 + NUM_TASKS * 18 + 5;
for (const auto& line : recentLogs) {
if (logY > logPanelH - 5) break;
std::string display = (line.size() > 120) ? line.substr(0, 117) + "..." : line;
cv::putText(logPanel, display, cv::Point(10, logY),
cv::FONT_HERSHEY_PLAIN, 1.0, cv::Scalar(140, 140, 140), 1);
logY += 15;
}
cv::imshow(windowTitle, canvas);
int key = cv::waitKey(30);
if (key == 27) {
g_log.add("ESC pressed — stopping...");
printf("\nESC pressed — stopping...\n");
g_running.store(false);
}
}
// --- Wait for workers ---
for (int i = 0; i < NUM_TASKS; i++) {
if (workers[i].joinable()) workers[i].join();
}
// --- Final summary ---
double totalElapsed = std::chrono::duration<double>(
std::chrono::steady_clock::now() - testStart).count();
printf("\n============================================================\n");
printf(" FINAL SUMMARY (runtime: %.0fs)\n", totalElapsed);
printf("============================================================\n");
double totalFpsFinal = 0;
for (int i = 0; i < NUM_TASKS; i++) {
char buf[256];
snprintf(buf, sizeof(buf), " Task %d (Stream %d): %d frames, %d detections, FPS=%.1f, InfMs=%.0f",
i, taskStreamMap[i], taskStates[i].frameCount, taskStates[i].detectionCount,
taskStates[i].fps, taskStates[i].inferenceMs);
printf("%s\n", buf);
g_log.add(buf);
totalFpsFinal += taskStates[i].fps;
}
printf(" Total throughput: %.1f FPS\n", totalFpsFinal);
printf("============================================================\n");
// --- Cleanup ---
for (int i = 0; i < NUM_TASKS; i++) {
if (alprHandles[i]) ReleaseANSALPRHandle(&alprHandles[i]);
}
for (int s = 0; s < NUM_STREAMS; s++) {
if (rtspClients[s]) {
StopRTSP(&rtspClients[s]);
ReleaseANSRTSPHandle(&rtspClients[s]);
}
}
g_log.close();
cv::destroyAllWindows();
ANSCENTER::ANSOPENCV::DeinitCameraNetwork();
return 0;
}
int ANSLPR_MultiGPU_StressTest() {
ANSCENTER::ANSOPENCV::InitCameraNetwork();
// --- Initialize log file ---
g_log.init();
printf("\n");
// --- Auto-detect GPU availability (safe on CPU-only PCs without CUDA runtime) ---
int gpuCount = 0;
bool hasGpu = false;
if (IsCudaAvailable()) {
cudaGetDeviceCount(&gpuCount);
hasGpu = (gpuCount > 0);
}
const char* modeStr = hasGpu ? "GPU (NVIDIA CUDA)" : "CPU (Software Decoding)";
printf("\n");
printf("============================================================\n");
printf(" ANSLPR Multi-Engine Stress Test — 5 Parallel ALPR Tasks\n");
printf(" Mode: %s\n", modeStr);
printf(" (4 cameras, 5 AI tasks — Task 4 shares Stream 2)\n");
printf(" Press ESC to stop\n");
printf(" Log file: %s\n", LOG_FILE_PATH);
printf("============================================================\n\n");
g_log.add("============================================================");
g_log.add(" ANSLPR Multi-Engine Stress Test — 5 Parallel ALPR Tasks");
g_log.add(" Mode: " + std::string(modeStr));
g_log.add("============================================================");
// --- Log GPU info for diagnostics (safe on CPU — prints "no GPU found") ---
LogGpuInfo();
// --- RTSP URLs (4 independent camera streams) ---
const std::string rtspUrl0 = "rtsp://admin:admin123@103.156.0.133:8010/cam/realmonitor?channel=1&subtype=0";
const std::string rtspUrl1 = "rtsp://cafe2471.ddns.net:600/rtsp/streaming?channel=01&subtype=0";
const std::string rtspUrl2 = "rtsp://nhathuocngoclinh.zapto.org:600/rtsp/streaming?channel=01&subtype=0";
const std::string rtspUrl3 = "rtsp://bnunitttd.ddns.net:554/rtsp/streaming?channel=01&subtype=0";
g_log.add("Stream 0: " + rtspUrl0);
g_log.add("Stream 1: " + rtspUrl1);
g_log.add("Stream 2: " + rtspUrl2);
g_log.add("Stream 3: " + rtspUrl3);
// =========================================================================
// Architecture: Camera Process + AI Task Process (mimics LabVIEW)
// -----------------------------------------------------------------------
// Camera Process: 4 independent RTSP streams acquire frames from cameras.
// AI Task Process: 5 AI tasks subscribe to camera streams and run inference
// in parallel. Multiple tasks can share one camera stream.
// Task 4 subscribes to Stream 2 (nhathuocngoclinh) to demonstrate the
// shared-camera subscription model used in LabVIEW.
// =========================================================================
const int NUM_STREAMS = 4;
const int NUM_TASKS = 5;
// --- Task states ---
TaskState taskStates[NUM_TASKS];
// =========================================================================
// CAMERA PROCESS: Create 4 independent RTSP readers (one per camera).
// These form the camera acquisition layer that AI tasks subscribe to.
// =========================================================================
ANSCENTER::ANSRTSPClient* rtspClients[NUM_STREAMS] = {};
const std::string streamUrls[NUM_STREAMS] = { rtspUrl0, rtspUrl1, rtspUrl2, rtspUrl3 };
// Map: task index -> stream index
// Tasks 0-3 map 1:1 to streams 0-3.
// Task 4 subscribes to Stream 2 (nhathuocngoclinh) — shared camera.
const int taskStreamMap[NUM_TASKS] = { 0, 1, 2, 3, 2 };
// Log task-to-stream subscription mapping
g_log.add("--- AI Task -> Camera Stream subscription ---");
for (int i = 0; i < NUM_TASKS; i++) {
g_log.add(" Task " + std::to_string(i) + " -> Stream " + std::to_string(taskStreamMap[i])
+ " (" + streamUrls[taskStreamMap[i]] + ")");
}
for (int s = 0; s < NUM_STREAMS; s++) {
printf("[Stream%d] Creating RTSP handle for %s...\n", s, streamUrls[s].c_str());
g_log.add("[Stream" + std::to_string(s) + "] Creating RTSP handle for " + streamUrls[s]);
int rtspResult = CreateANSRTSPHandle(&rtspClients[s], "", "", "", streamUrls[s].c_str());
if (rtspResult != 1 || rtspClients[s] == nullptr) {
printf("[Stream%d] FAILED to create RTSP handle (result=%d)\n", s, rtspResult);
g_log.add("[Stream" + std::to_string(s) + "] RTSP create FAILED");
rtspClients[s] = nullptr;
continue;
}
SetRTSPImageQuality(&rtspClients[s], 0);
if (hasGpu) SetRTSPHWDecoding(&rtspClients[s], 7); // CUDA HW decode only with GPU
StartRTSP(&rtspClients[s]);
g_log.add("[Stream" + std::to_string(s) + "] RTSP started");
}
// =========================================================================
// AI TASK PROCESS: Create 5 ALPR engines sequentially.
// Each AI task gets its own engine and subscribes to a camera stream.
// Task 4 shares Stream 2 (nhathuocngoclinh) with Task 2 — demonstrating
// the LabVIEW pattern where multiple AI tasks subscribe to one camera.
// =========================================================================
ANSCENTER::ANSALPR* alprHandles[NUM_TASKS] = {};
std::string modelZipFile = "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ANS_ALPR_v1.2.zip";
int engineType = 1; // ANSALPR_OD: auto-detects GPU/CPU, uses ONNX Runtime on CPU
double detThresh = 0.5, ocrThresh = 0.5, colThresh = 0.5;
for (int i = 0; i < NUM_TASKS; i++) {
char tag[32];
snprintf(tag, sizeof(tag), "[Task%d]", i);
int streamIdx = taskStreamMap[i];
if (rtspClients[streamIdx] == nullptr) {
printf("%s Skipped — Stream%d not available\n", tag, streamIdx);
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "Stream not available";
continue;
}
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].streamOk = true;
taskStates[i].statusMsg = "Loading ALPR engine...";
}
printf("%s Creating ALPR handle (engineType=%d)...\n", tag, engineType);
g_log.add(std::string(tag) + " Creating ALPR handle...");
auto engineStart = std::chrono::steady_clock::now();
int createResult = CreateANSALPRHandle(&alprHandles[i], "", modelZipFile.c_str(), "",
engineType, detThresh, ocrThresh, colThresh);
if (createResult != 1 || alprHandles[i] == nullptr) {
printf("%s FAILED to create ALPR handle (result=%d)\n", tag, createResult);
g_log.add(std::string(tag) + " ALPR create FAILED");
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "ALPR create failed";
continue;
}
printf("%s Loading ALPR engine (%s)...\n", tag, hasGpu ? "TensorRT" : "CPU");
g_log.add(std::string(tag) + " Loading ALPR engine...");
// Snapshot VRAM before engine load to measure consumption (GPU only)
std::vector<size_t> vramBefore;
if (hasGpu) vramBefore = GetPerGpuFreeMiB();
int loadResult = LoadANSALPREngineHandle(&alprHandles[i]);
auto engineEnd = std::chrono::steady_clock::now();
double loadMs = std::chrono::duration<double, std::milli>(engineEnd - engineStart).count();
if (loadResult != 1) {
printf("%s FAILED to load ALPR engine (result=%d)\n", tag, loadResult);
g_log.add(std::string(tag) + " Engine load FAILED");
ReleaseANSALPRHandle(&alprHandles[i]);
alprHandles[i] = nullptr;
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "Engine load failed";
continue;
}
int bestGpu = -1;
size_t maxDelta = 0;
if (hasGpu) {
// Snapshot VRAM after engine load — find which GPU lost the most VRAM
auto vramAfter = GetPerGpuFreeMiB();
size_t gpuCnt = vramBefore.size() < vramAfter.size() ? vramBefore.size() : vramAfter.size();
bestGpu = 0;
for (size_t g = 0; g < gpuCnt; g++) {
size_t delta = (vramBefore[g] > vramAfter[g]) ? (vramBefore[g] - vramAfter[g]) : 0;
if (delta > maxDelta) {
maxDelta = delta;
bestGpu = (int)g;
}
}
char buf[512];
snprintf(buf, sizeof(buf),
"%s Engine loaded in %.0f ms | GPU[%d] | VRAM used: %zu MiB (Stream%d)",
tag, loadMs, bestGpu, maxDelta, streamIdx);
printf("%s\n", buf);
g_log.add(buf);
// Log per-GPU VRAM state after this engine load
for (size_t g = 0; g < vramAfter.size(); g++) {
size_t total = 0;
auto gpus = QueryGpuVram();
if (g < gpus.size()) total = gpus[g].totalMiB;
char vbuf[256];
snprintf(vbuf, sizeof(vbuf),
" GPU[%zu] VRAM: %zu MiB free (of %zu MiB)",
g, vramAfter[g], total);
printf("%s\n", vbuf);
g_log.add(vbuf);
}
} else {
char buf[256];
snprintf(buf, sizeof(buf), "%s Engine loaded in %.0f ms (CPU mode, Stream%d)",
tag, loadMs, streamIdx);
printf("%s\n", buf);
g_log.add(buf);
}
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].engineLoaded = true;
taskStates[i].statusMsg = "Running";
taskStates[i].gpuDeviceId = bestGpu;
taskStates[i].vramUsedBytes = maxDelta * 1024 * 1024;
}
}
// --- Align NVDEC decode GPU with inference GPU for NV12 zero-copy ---
// (GPU only — software decoding on CPU doesn't use NVDEC)
if (hasGpu)
// Each stream should decode on the same GPU as its inference engine to enable
// direct NVDEC→TensorRT zero-copy (0.5ms vs 17ms preprocess per frame).
//
// Strategy: For each stream, count how many tasks run on each GPU (vote).
// Pick the GPU with the most tasks → maximises the number of NV12 zero-copy hits.
// If tied, prefer to keep the current decode GPU to avoid a reconnect.
// Additional tie-breaker: distribute streams across GPUs for decode load balance.
{
int streamPreferredGpu[NUM_STREAMS];
for (int s = 0; s < NUM_STREAMS; s++) streamPreferredGpu[s] = -1;
// Track how many streams have already been assigned to each GPU (for tie-breaking)
std::map<int, int> gpuStreamCount;
for (int s = 0; s < NUM_STREAMS; s++) {
if (!rtspClients[s]) continue;
// Count votes: how many tasks on this stream use each GPU
std::map<int, int> gpuVotes;
for (int i = 0; i < NUM_TASKS; i++) {
if (taskStreamMap[i] == s && alprHandles[i]) {
gpuVotes[taskStates[i].gpuDeviceId]++;
}
}
if (gpuVotes.empty()) continue;
// Find the GPU with the most votes
int currentGpu = rtspClients[s]->GetHWDecodingGpuIndex();
int bestGpu = -1;
int bestVotes = 0;
for (auto& [gpu, votes] : gpuVotes) {
if (votes > bestVotes) {
bestVotes = votes;
bestGpu = gpu;
} else if (votes == bestVotes) {
// Tie-break 1: prefer current decode GPU (avoids reconnect)
if (gpu == currentGpu && bestGpu != currentGpu) {
bestGpu = gpu;
}
// Tie-break 2: prefer GPU with fewer streams assigned (load balance)
else if (bestGpu != currentGpu && gpu != currentGpu) {
if (gpuStreamCount[gpu] < gpuStreamCount[bestGpu]) {
bestGpu = gpu;
}
}
}
}
streamPreferredGpu[s] = bestGpu;
gpuStreamCount[bestGpu]++;
char buf[512];
std::string voteStr;
for (auto& [gpu, votes] : gpuVotes) {
if (!voteStr.empty()) voteStr += ", ";
voteStr += "GPU[" + std::to_string(gpu) + "]=" + std::to_string(votes);
}
snprintf(buf, sizeof(buf),
"[Stream%d] GPU vote: {%s} -> preferred GPU[%d] (current: GPU[%d])",
s, voteStr.c_str(), bestGpu, currentGpu);
g_log.add(buf);
printf("%s\n", buf);
}
// Apply alignment: reconnect streams whose NVDEC is on the wrong GPU.
// IMPORTANT: If currentGpu == -1, the decoder hasn't initialized yet.
// Do NOT reconnect — it disrupts the initial RTSP handshake and causes
// 80+ seconds of empty frames. Just set preferredGpu; the decoder will
// use it when it naturally initializes.
for (int s = 0; s < NUM_STREAMS; s++) {
if (rtspClients[s] && streamPreferredGpu[s] >= 0) {
int currentGpu = rtspClients[s]->GetHWDecodingGpuIndex();
if (currentGpu < 0) {
// Decoder not yet initialized — set preferred GPU without reconnect
SetRTSPHWDecoding(&rtspClients[s], 7, streamPreferredGpu[s]);
char buf[256];
snprintf(buf, sizeof(buf),
"[Stream%d] NVDEC not yet initialized (GPU[-1]) -- set preferred GPU[%d] (no reconnect)",
s, streamPreferredGpu[s]);
g_log.add(buf);
printf("%s\n", buf);
} else if (currentGpu != streamPreferredGpu[s]) {
// Decoder is active on wrong GPU — reconnect disabled for VRAM stability testing
SetRTSPHWDecoding(&rtspClients[s], 7, streamPreferredGpu[s]);
// ReconnectRTSP(&rtspClients[s]); // Disabled for long test
char buf[256];
snprintf(buf, sizeof(buf),
"[Stream%d] NVDEC GPU realigned: GPU[%d] -> GPU[%d] (reconnected for zero-copy)",
s, currentGpu, streamPreferredGpu[s]);
g_log.add(buf);
printf("%s\n", buf);
} else {
char buf[256];
snprintf(buf, sizeof(buf),
"[Stream%d] NVDEC GPU already on GPU[%d] (zero-copy OK)",
s, currentGpu);
g_log.add(buf);
printf("%s\n", buf);
}
}
}
}
// --- Enable deep pipeline benchmarking on all ALPR handles ---
for (int i = 0; i < NUM_TASKS; i++) {
if (alprHandles[i]) {
alprHandles[i]->ActivateDebugger(true);
}
}
g_log.add("Debug benchmarking ENABLED on all ALPR handles");
// --- Per-stream mutex: prevents CHAOS from destroying a stream while a
// worker is mid-frame-grab or mid-inference (use-after-free fix). ---
std::mutex streamGuards[NUM_STREAMS];
// --- Launch worker threads ---
// Each AI task subscribes to its camera stream via taskStreamMap.
// Tasks sharing a stream (e.g. Task 2 & Task 4 on Stream 2) both get
// the same RTSP client pointer and share the stream's mutex guard.
g_log.add("Launching " + std::to_string(NUM_TASKS) + " worker threads...");
std::thread workers[NUM_TASKS];
for (int i = 0; i < NUM_TASKS; i++) {
int streamIdx = taskStreamMap[i];
if (rtspClients[streamIdx] && alprHandles[i]) {
workers[i] = std::thread(ALPRWorkerThread, i,
&rtspClients[streamIdx],
&streamGuards[streamIdx],
alprHandles[i],
std::ref(taskStates[i]));
}
}
// =========================================================================
// Camera Chaos Thread — simulates camera errors / reconnects
// Mimics LabVIEW behavior: cameras randomly go into Error/Recovering
// state, triggering Stop/Reconnect/Destroy+Recreate cycles that cause
// CUDA cleanup (cuArrayDestroy, cuMemFree) while inference is running.
// This is the exact scenario that triggers the nvcuda64 SRW lock deadlock.
// =========================================================================
std::atomic<bool> chaosEnabled{false}; // Disabled for VRAM stability long test
std::thread chaosThread([&]() {
std::mt19937 rng(std::random_device{}());
// Wait 10 seconds for system to stabilize before starting chaos
for (int i = 0; i < 100 && g_running.load(); i++) {
std::this_thread::sleep_for(std::chrono::milliseconds(100));
}
g_log.add("[CHAOS] Camera chaos thread started — every 10s, stop/destroy/recreate one camera (round-robin)");
printf("[CHAOS] Camera chaos thread started — 10s interval, round-robin across %d streams\n", NUM_STREAMS);
int chaosCount = 0;
int nextStream = 0; // Round-robin: cycle through streams 0,1,2,3,0,1,...
while (g_running.load() && chaosEnabled.load()) {
// Fixed 10-second interval between chaos events
for (int s = 0; s < 100 && g_running.load(); s++) {
std::this_thread::sleep_for(std::chrono::milliseconds(100));
}
if (!g_running.load()) break;
int streamIdx = nextStream;
nextStream = (nextStream + 1) % NUM_STREAMS;
chaosCount++;
char buf[512];
auto chaosStart = std::chrono::steady_clock::now();
// Lock stream guard: wait for any in-flight inference to finish
// before touching the RTSP client. This prevents use-after-free
// when CHAOS destroys a stream while a worker is mid-inference.
std::unique_lock<std::mutex> chaosLock(streamGuards[streamIdx]);
// Always use full DESTROY + RECREATE cycle.
// Reconnect() reuses internal player state which can leave stale
// CUDA resources and cause freezes. A clean destroy + recreate
// guarantees a fresh decoder/player with no leftover state.
{
bool wasAlive = (rtspClients[streamIdx] != nullptr);
snprintf(buf, sizeof(buf), "[CHAOS #%d] Stream%d: DESTROY + RECREATE (%s)",
chaosCount, streamIdx,
wasAlive ? "camera was running" : "camera was already offline");
g_log.add(buf);
printf("%s\n", buf);
// Stop and release old handle if it exists
if (rtspClients[streamIdx]) {
StopRTSP(&rtspClients[streamIdx]);
ReleaseANSRTSPHandle(&rtspClients[streamIdx]);
rtspClients[streamIdx] = nullptr;
}
// Release lock during offline sleep — worker sees nullptr and skips
int offlineMs = 500 + (rng() % 2500); // 0.5 - 3 seconds offline
chaosLock.unlock();
std::this_thread::sleep_for(std::chrono::milliseconds(offlineMs));
chaosLock.lock();
// Recreate the RTSP handle (under lock again)
int result = CreateANSRTSPHandle(&rtspClients[streamIdx], "", "", "",
streamUrls[streamIdx].c_str());
if (result == 1 && rtspClients[streamIdx]) {
SetRTSPImageQuality(&rtspClients[streamIdx], 0);
if (hasGpu) SetRTSPHWDecoding(&rtspClients[streamIdx], 7);
StartRTSP(&rtspClients[streamIdx]);
auto chaosEnd = std::chrono::steady_clock::now();
double chaosMs = std::chrono::duration<double, std::milli>(chaosEnd - chaosStart).count();
snprintf(buf, sizeof(buf), "[CHAOS #%d] Stream%d: RECREATED in %.0f ms (offline %d ms)",
chaosCount, streamIdx, chaosMs, offlineMs);
} else {
snprintf(buf, sizeof(buf), "[CHAOS #%d] Stream%d: RECREATE FAILED (result=%d)",
chaosCount, streamIdx, result);
}
g_log.add(buf);
printf("%s\n", buf);
}
}
g_log.add("[CHAOS] Camera chaos thread stopped (total events: " + std::to_string(chaosCount) + ")");
printf("[CHAOS] Camera chaos thread stopped (total events: %d)\n", chaosCount);
});
// --- Display loop (main thread) ---
// 3x2 grid layout: 5 tasks displayed in 3 columns x 2 rows
const int cellW = 480, cellH = 360; // Smaller cells for 3-column layout
const int logPanelH = 220;
const int gridCols = 3, gridRows = 2;
std::string windowTitle = hasGpu ? "ANSLPR Multi-GPU Stress Test" : "ANSLPR CPU Stress Test";
cv::namedWindow(windowTitle, cv::WINDOW_NORMAL);
cv::resizeWindow(windowTitle, cellW * gridCols, cellH * gridRows + logPanelH);
auto testStart = std::chrono::steady_clock::now();
auto lastGpuSnapshot = std::chrono::steady_clock::now();
int snapshotCount = 0;
while (g_running.load()) {
// --- Periodic GPU/perf snapshot every 10 seconds (written to log file) ---
auto now2 = std::chrono::steady_clock::now();
if (std::chrono::duration<double>(now2 - lastGpuSnapshot).count() >= 10.0) {
lastGpuSnapshot = now2;
snapshotCount++;
double elapsedSec = std::chrono::duration<double>(now2 - testStart).count();
g_log.add("---- PERIODIC SNAPSHOT #" + std::to_string(snapshotCount)
+ " (elapsed " + std::to_string((int)elapsedSec) + "s) ----");
// GPU VRAM
auto gpuSnap = QueryGpuVram();
for (const auto& gs : gpuSnap) {
char buf[256];
snprintf(buf, sizeof(buf),
" GPU[%d] %s | Used: %zu/%zu MiB (%.1f%%)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
gs.totalMiB > 0 ? 100.0 * gs.usedMiB / gs.totalMiB : 0.0);
g_log.add(buf);
}
// Per-task stats
double totalFpsSnap = 0;
for (int t = 0; t < NUM_TASKS; t++) {
std::lock_guard<std::mutex> lk(taskStates[t].mtx);
char buf[256];
snprintf(buf, sizeof(buf),
" T%d(S%d): GPU[%d] VRAM=%zuMiB FPS=%.1f GrabMs=%.0f InfMs=%.0f Frames=%d Det=%d",
t, taskStreamMap[t], taskStates[t].gpuDeviceId,
taskStates[t].vramUsedBytes / (1024 * 1024),
taskStates[t].fps, taskStates[t].lastGrabMs, taskStates[t].inferenceMs,
taskStates[t].frameCount, taskStates[t].detectionCount);
g_log.add(buf);
totalFpsSnap += taskStates[t].fps;
}
char buf[128];
snprintf(buf, sizeof(buf), " Total throughput: %.1f FPS", totalFpsSnap);
g_log.add(buf);
// Multi-GPU check
std::set<int> gpusUsed;
for (int t = 0; t < NUM_TASKS; t++) {
if (taskStates[t].gpuDeviceId >= 0) gpusUsed.insert(taskStates[t].gpuDeviceId);
}
if (gpusUsed.size() > 1) {
g_log.add(" MULTI-GPU: YES — tasks distributed across " + std::to_string(gpusUsed.size()) + " GPUs");
} else if (!gpusUsed.empty()) {
g_log.add(" MULTI-GPU: NO — all tasks on GPU[" + std::to_string(*gpusUsed.begin()) + "]");
}
g_log.add("---- END SNAPSHOT ----");
}
// Build 3x2 grid + log panel (5 tasks: 3 cols x 2 rows, cell [1][2] empty)
cv::Mat canvas(cellH * gridRows + logPanelH, cellW * gridCols, CV_8UC3, cv::Scalar(30, 30, 30));
// Place each task's frame in its cell
for (int i = 0; i < NUM_TASKS; i++) {
int row = i / gridCols, col = i % gridCols;
cv::Rect roi(col * cellW, row * cellH, cellW, cellH);
cv::Mat cell;
double fps = 0, infMs = 0;
int fCount = 0, dCount = 0;
int gpuId = -1;
size_t vramMiB = 0;
std::string statusMsg, lastPlate;
bool engineLoaded = false, streamOk = false;
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
if (!taskStates[i].displayFrame.empty()) {
cv::resize(taskStates[i].displayFrame, cell, cv::Size(cellW, cellH));
}
fps = taskStates[i].fps;
infMs = taskStates[i].inferenceMs;
fCount = taskStates[i].frameCount;
dCount = taskStates[i].detectionCount;
statusMsg = taskStates[i].statusMsg;
lastPlate = taskStates[i].lastPlate;
engineLoaded = taskStates[i].engineLoaded;
streamOk = taskStates[i].streamOk;
gpuId = taskStates[i].gpuDeviceId;
vramMiB = taskStates[i].vramUsedBytes / (1024 * 1024);
}
if (cell.empty()) {
cell = cv::Mat(cellH, cellW, CV_8UC3, cv::Scalar(40, 40, 40));
cv::putText(cell, "Task " + std::to_string(i) + ": " + statusMsg,
cv::Point(20, cellH / 2),
cv::FONT_HERSHEY_SIMPLEX, 0.8, cv::Scalar(100, 100, 255), 2);
}
// Draw status bar at bottom of each cell (2 lines)
cv::rectangle(cell, cv::Rect(0, cellH - 50, cellW, 50), cv::Scalar(0, 0, 0), cv::FILLED);
char bar1[256], bar2[256];
snprintf(bar1, sizeof(bar1), "T%d(S%d) | %.1f FPS | %.0fms | F:%d | D:%d | %s",
i, taskStreamMap[i], fps, infMs, fCount, dCount,
lastPlate.empty() ? "-" : lastPlate.c_str());
if (!hasGpu) {
snprintf(bar2, sizeof(bar2), "CPU mode (software decoding)");
} else if (gpuId >= 0) {
snprintf(bar2, sizeof(bar2), "GPU[%d] | VRAM: %zu MiB", gpuId, vramMiB);
} else {
snprintf(bar2, sizeof(bar2), "GPU: N/A");
}
cv::Scalar barColor = engineLoaded ? cv::Scalar(0, 255, 0) : cv::Scalar(0, 100, 255);
cv::putText(cell, bar1, cv::Point(5, cellH - 28),
cv::FONT_HERSHEY_SIMPLEX, 0.4, barColor, 1);
cv::putText(cell, bar2, cv::Point(5, cellH - 8),
cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 200, 255), 1);
cell.copyTo(canvas(roi));
}
// Draw grid lines
for (int c = 1; c < gridCols; c++)
cv::line(canvas, cv::Point(c * cellW, 0), cv::Point(c * cellW, cellH * gridRows),
cv::Scalar(100, 100, 100), 1);
for (int r = 1; r < gridRows; r++)
cv::line(canvas, cv::Point(0, r * cellH), cv::Point(cellW * gridCols, r * cellH),
cv::Scalar(100, 100, 100), 1);
// --- Log panel at bottom ---
cv::Rect logRoi(0, cellH * gridRows, cellW * gridCols, logPanelH);
cv::Mat logPanel = canvas(logRoi);
logPanel.setTo(cv::Scalar(20, 20, 20));
// Elapsed time header
auto elapsed = std::chrono::duration<double>(std::chrono::steady_clock::now() - testStart).count();
char header[256];
snprintf(header, sizeof(header),
"Elapsed: %.0fs | %d cameras, %d AI tasks | Press ESC to stop",
elapsed, NUM_STREAMS, NUM_TASKS);
cv::putText(logPanel, header, cv::Point(10, 18),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(200, 200, 0), 1);
// Aggregate stats + per-task GPU summary
double totalFps = 0;
for (int i = 0; i < NUM_TASKS; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
totalFps += taskStates[i].fps;
}
// Build dynamic task-GPU summary string
std::string taskGpuStr;
for (int i = 0; i < NUM_TASKS; i++) {
if (i > 0) taskGpuStr += " ";
taskGpuStr += "T" + std::to_string(i) + "(S" + std::to_string(taskStreamMap[i])
+ "):GPU" + std::to_string(taskStates[i].gpuDeviceId);
}
char aggLine[512];
snprintf(aggLine, sizeof(aggLine), "Total: %.1f FPS | %s",
totalFps, taskGpuStr.c_str());
cv::putText(logPanel, aggLine, cv::Point(10, 38),
cv::FONT_HERSHEY_SIMPLEX, 0.45, cv::Scalar(0, 255, 255), 1);
// Real-time GPU VRAM monitor (query every frame — cheap call)
auto gpuSnaps = QueryGpuVram();
int gpuLineY = 58;
for (const auto& gs : gpuSnaps) {
// Count tasks on this GPU and their total VRAM
int tasksOnGpu = 0;
size_t taskVramMiB = 0;
for (int i = 0; i < NUM_TASKS; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
if (taskStates[i].gpuDeviceId == gs.deviceId) {
tasksOnGpu++;
taskVramMiB += taskStates[i].vramUsedBytes / (1024 * 1024);
}
}
char gpuLine[256];
snprintf(gpuLine, sizeof(gpuLine),
"GPU[%d] %s | Used: %zu/%zu MiB | Tasks: %d (engine VRAM: %zu MiB)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
tasksOnGpu, taskVramMiB);
cv::putText(logPanel, gpuLine, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_SIMPLEX, 0.45, cv::Scalar(100, 255, 100), 1);
gpuLineY += 18;
}
// Per-task resource line (shows which stream each task subscribes to)
for (int i = 0; i < NUM_TASKS; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
char tLine[256];
snprintf(tLine, sizeof(tLine),
"T%d(S%d): GPU[%d] VRAM=%zuMiB FPS=%.1f Inf=%.0fms Frames=%d Det=%d",
i, taskStreamMap[i], taskStates[i].gpuDeviceId,
taskStates[i].vramUsedBytes / (1024 * 1024),
taskStates[i].fps, taskStates[i].inferenceMs,
taskStates[i].frameCount, taskStates[i].detectionCount);
cv::putText(logPanel, tLine, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(200, 200, 200), 1);
gpuLineY += 16;
}
// Recent log lines (remaining space)
auto recentLogs = g_log.getRecent(4);
for (const auto& line : recentLogs) {
if (gpuLineY > logPanelH - 5) break;
std::string display = (line.size() > 130) ? line.substr(0, 127) + "..." : line;
cv::putText(logPanel, display, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_PLAIN, 1.0, cv::Scalar(140, 140, 140), 1);
gpuLineY += 15;
}
cv::imshow(windowTitle, canvas);
int key = cv::waitKey(30);
if (key == 27) { // ESC
g_log.add("ESC pressed — stopping all tasks...");
printf("\nESC pressed — stopping...\n");
g_running.store(false);
}
}
// --- Stop chaos thread ---
chaosEnabled.store(false);
if (chaosThread.joinable()) chaosThread.join();
// --- Wait for all workers ---
printf("Waiting for %d worker threads to finish...\n", NUM_TASKS);
for (int i = 0; i < NUM_TASKS; i++) {
if (workers[i].joinable()) workers[i].join();
}
// --- Print final summary (console + log file) ---
double totalElapsed = std::chrono::duration<double>(
std::chrono::steady_clock::now() - testStart).count();
g_log.add("================================================================");
g_log.add(" FINAL PERFORMANCE SUMMARY");
g_log.add(" " + std::to_string(NUM_STREAMS) + " cameras, " + std::to_string(NUM_TASKS) + " AI tasks");
g_log.add(" Total runtime: " + std::to_string((int)totalElapsed) + " seconds");
g_log.add("================================================================");
printf("\n============================================================\n");
printf(" FINAL PERFORMANCE SUMMARY (runtime: %.0fs)\n", totalElapsed);
printf(" %d cameras, %d AI tasks\n", NUM_STREAMS, NUM_TASKS);
printf("============================================================\n");
double totalFpsFinal = 0;
for (int i = 0; i < NUM_TASKS; i++) {
char buf[512];
snprintf(buf, sizeof(buf),
" Task %d (Stream %d): GPU[%d] | VRAM=%zuMiB | %d frames, %d detections, FPS=%.1f, InfMs=%.0f",
i, taskStreamMap[i], taskStates[i].gpuDeviceId,
taskStates[i].vramUsedBytes / (1024 * 1024),
taskStates[i].frameCount, taskStates[i].detectionCount,
taskStates[i].fps, taskStates[i].inferenceMs);
printf("%s\n", buf);
g_log.add(buf);
totalFpsFinal += taskStates[i].fps;
}
auto finalGpu = QueryGpuVram();
for (const auto& gs : finalGpu) {
char buf[256];
snprintf(buf, sizeof(buf), " GPU[%d] %s: %zu/%zu MiB used (%.1f%%)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
gs.totalMiB > 0 ? 100.0 * gs.usedMiB / gs.totalMiB : 0.0);
printf("%s\n", buf);
g_log.add(buf);
}
// Multi-GPU verdict
std::set<int> finalGpusUsed;
for (int i = 0; i < NUM_TASKS; i++) {
if (taskStates[i].gpuDeviceId >= 0) finalGpusUsed.insert(taskStates[i].gpuDeviceId);
}
{
char buf[256];
snprintf(buf, sizeof(buf), " Total throughput: %.1f FPS across %d tasks (%d cameras)",
totalFpsFinal, NUM_TASKS, NUM_STREAMS);
printf("%s\n", buf);
g_log.add(buf);
}
if (finalGpusUsed.size() > 1) {
char buf[128];
snprintf(buf, sizeof(buf), " MULTI-GPU: YES — tasks on %zu different GPUs", finalGpusUsed.size());
printf("%s\n", buf);
g_log.add(buf);
} else if (!finalGpusUsed.empty()) {
char buf[128];
snprintf(buf, sizeof(buf), " MULTI-GPU: NO — all tasks on GPU[%d] only", *finalGpusUsed.begin());
printf("%s\n", buf);
g_log.add(buf);
g_log.add(" DIAGNOSIS: Engine pool sees only 1 GPU. On dual-GPU systems, check:");
g_log.add(" 1. Both GPUs visible to CUDA (nvidia-smi shows 2 devices)");
g_log.add(" 2. TRT engine files are compatible with both GPU architectures");
g_log.add(" 3. No CUDA_VISIBLE_DEVICES env var restricting GPU access");
}
// Log shared-camera subscription info
g_log.add(" Camera subscription: Task 2 and Task 4 both subscribe to Stream 2 (nhathuocngoclinh)");
printf("============================================================\n");
g_log.add("================================================================");
g_log.add(" Log saved to: " + std::string(LOG_FILE_PATH));
g_log.add("================================================================");
// --- Release all handles (sequentially on main thread) ---
for (int i = 0; i < NUM_TASKS; i++) {
if (alprHandles[i]) {
ReleaseANSALPRHandle(&alprHandles[i]);
}
}
for (int s = 0; s < NUM_STREAMS; s++) {
if (rtspClients[s]) {
StopRTSP(&rtspClients[s]);
ReleaseANSRTSPHandle(&rtspClients[s]);
}
}
g_log.close();
cv::destroyAllWindows();
ANSCENTER::ANSOPENCV::DeinitCameraNetwork();
return 0;
}
// =============================================================================
// VideoPlayer-based worker thread for SimulatedCam stress test
// Same structure as ALPRWorkerThread but uses ANSVideoPlayer instead of ANSRTSP
// =============================================================================
static void ALPRWorkerThread_VideoPlayer(int taskId,
ANSCENTER::ANSVIDEOPLAYER* vpClient,
ANSCENTER::ANSALPR* alprHandle,
TaskState& state) {
char tag[32];
snprintf(tag, sizeof(tag), "[Task%d]", taskId);
std::string prefix(tag);
g_log.add(prefix + " Worker thread started");
printf("%s Worker thread started\n", tag);
int width = 0, height = 0;
int64_t pts = 0;
int emptyFrames = 0;
std::string cameraId = "Cam" + std::to_string(taskId);
// FPS tracking with sliding window
std::deque<std::chrono::steady_clock::time_point> fpsTimestamps;
// Timing accumulators for periodic benchmarking
double totalGrabMs = 0, totalInfMs = 0;
int grabCount = 0, infCount = 0;
double maxGrabMs = 0, maxInfMs = 0;
auto benchStart = std::chrono::steady_clock::now();
while (g_running.load()) {
// Read frame from VideoPlayer
auto grabStart = std::chrono::steady_clock::now();
cv::Mat* framePtr = nullptr;
GetVideoPlayerCVImage(&vpClient, width, height, pts, &framePtr);
auto grabEnd = std::chrono::steady_clock::now();
double grabMs = std::chrono::duration<double, std::milli>(grabEnd - grabStart).count();
if (framePtr == nullptr || framePtr->empty()) {
emptyFrames++;
if (emptyFrames % 100 == 1) {
g_log.add(prefix + " Empty frame (count=" + std::to_string(emptyFrames) + ")");
}
if (emptyFrames > 300) {
g_log.add(prefix + " Too many empty frames, attempting reconnect...");
ReconnectVideoPlayer(&vpClient);
emptyFrames = 0;
}
if (framePtr) delete framePtr;
std::this_thread::sleep_for(std::chrono::milliseconds(10));
continue;
}
emptyFrames = 0;
totalGrabMs += grabMs;
grabCount++;
if (grabMs > maxGrabMs) maxGrabMs = grabMs;
// Run ALPR inference
auto infStart = std::chrono::steady_clock::now();
std::string lpnResult, jpegImage;
// Pass framePtr directly — NOT a copy. ANSGpuFrameRegistry::lookup()
// matches by cv::Mat* pointer, so `new cv::Mat(*framePtr)` would create
// a different pointer the registry doesn't know, breaking NV12 zero-copy.
ANSALPR_RunInferenceComplete_CPP(&alprHandle, &framePtr, cameraId.c_str(), 0, 0, lpnResult, jpegImage);
auto infEnd = std::chrono::steady_clock::now();
double infMs = std::chrono::duration<double, std::milli>(infEnd - infStart).count();
totalInfMs += infMs;
infCount++;
if (infMs > maxInfMs) maxInfMs = infMs;
// Parse detections and draw on frame
cv::Mat display = framePtr->clone();
int detCount = 0;
std::string lastPlateText;
if (!lpnResult.empty()) {
try {
boost::property_tree::ptree pt;
std::stringstream ss(lpnResult);
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type& child, pt.get_child("results")) {
const boost::property_tree::ptree& det = child.second;
const auto class_name = GetData<std::string>(det, "class_name");
const auto x = GetData<float>(det, "x");
const auto y = GetData<float>(det, "y");
const auto w = GetData<float>(det, "width");
const auto h = GetData<float>(det, "height");
cv::rectangle(display, cv::Rect((int)x, (int)y, (int)w, (int)h),
cv::Scalar(0, 255, 0), 2);
cv::putText(display, class_name, cv::Point((int)x, (int)y - 5),
cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 0), 2);
lastPlateText = class_name;
detCount++;
}
}
catch (...) {}
}
// Update FPS (sliding window over last 2 seconds)
auto now = std::chrono::steady_clock::now();
fpsTimestamps.push_back(now);
while (!fpsTimestamps.empty() &&
std::chrono::duration<double>(now - fpsTimestamps.front()).count() > 2.0) {
fpsTimestamps.pop_front();
}
double fps = fpsTimestamps.size() / 2.0;
// Draw OSD on frame
char osd[128];
snprintf(osd, sizeof(osd), "Task%d | %.1f FPS | Inf: %.0f ms | #%d",
taskId, fps, infMs, state.frameCount + 1);
cv::putText(display, osd, cv::Point(10, 30),
cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 255), 2);
// Update shared state
{
std::lock_guard<std::mutex> lk(state.mtx);
state.displayFrame = display;
state.fps = fps;
state.inferenceMs = infMs;
state.lastGrabMs = grabMs;
state.lastInfMs = infMs;
state.frameCount++;
state.detectionCount += detCount;
if (!lastPlateText.empty()) state.lastPlate = lastPlateText;
}
// Periodic logging (every 100 frames)
if ((state.frameCount % 100) == 0) {
double avgGrab = grabCount > 0 ? totalGrabMs / grabCount : 0;
double avgInf = infCount > 0 ? totalInfMs / infCount : 0;
double elapsed = std::chrono::duration<double>(
std::chrono::steady_clock::now() - benchStart).count();
char buf[512];
snprintf(buf, sizeof(buf),
"%s Frame %d | FPS=%.1f | Grab: avg=%.1fms max=%.0fms | Inf: avg=%.1fms max=%.0fms | "
"GrabPct=%.0f%% InfPct=%.0f%% | Det=%d",
tag, state.frameCount, fps,
avgGrab, maxGrabMs,
avgInf, maxInfMs,
(totalGrabMs / (elapsed * 1000.0)) * 100.0,
(totalInfMs / (elapsed * 1000.0)) * 100.0,
state.detectionCount);
g_log.add(buf);
printf("%s\n", buf);
// Reset accumulators
totalGrabMs = totalInfMs = 0;
maxGrabMs = maxInfMs = 0;
grabCount = infCount = 0;
benchStart = std::chrono::steady_clock::now();
}
delete framePtr;
}
g_log.add(prefix + " Worker loop exited");
}
// =============================================================================
// ANSLPR_MultiGPU_StressTest_SimulatedCam
// Same structure as ANSLPR_MultiGPU_StressTest but uses local video files
// via ANSVideoPlayer instead of live RTSP streams.
// =============================================================================
int ANSLPR_MultiGPU_StressTest_SimulatedCam() {
ANSCENTER::ANSOPENCV::InitCameraNetwork();
// --- Initialize log file ---
g_log.init();
printf("\n");
printf("============================================================\n");
printf(" ANSLPR Multi-GPU Stress Test (Simulated Cam)\n");
printf(" Using local video files via ANSVideoPlayer\n");
printf(" Press ESC to stop\n");
printf(" Log file: %s\n", LOG_FILE_PATH);
printf("============================================================\n\n");
g_log.add("============================================================");
g_log.add(" ANSLPR Multi-GPU Stress Test (Simulated Cam)");
g_log.add(" Using ANSVideoPlayer with local video files");
g_log.add("============================================================");
// --- Log GPU info for diagnostics ---
LogGpuInfo();
// --- Video file paths (4 files, one per task) ---
const std::string videoFile0 = "E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day.mp4";
const std::string videoFile1 = "E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day_1.mp4";
const std::string videoFile2 = "E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day_2.mp4";
const std::string videoFile3 = "E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day_3.mp4";
g_log.add("Video 0: " + videoFile0);
g_log.add("Video 1: " + videoFile1);
g_log.add("Video 2: " + videoFile2);
g_log.add("Video 3: " + videoFile3);
// --- Task states ---
TaskState taskStates[4];
// =========================================================================
// Create 4 VideoPlayer readers — one per task
// =========================================================================
const int NUM_STREAMS = 4;
ANSCENTER::ANSVIDEOPLAYER* vpClients[NUM_STREAMS] = {};
const std::string videoFiles[NUM_STREAMS] = { videoFile0, videoFile1, videoFile2, videoFile3 };
const int taskStreamMap[4] = { 0, 1, 2, 3 };
for (int s = 0; s < NUM_STREAMS; s++) {
printf("[Stream%d] Creating VideoPlayer for %s\n", s, videoFiles[s].c_str());
g_log.add("[Stream" + std::to_string(s) + "] Creating VideoPlayer for " + videoFiles[s]);
int result = CreateANSVideoPlayerHandle(&vpClients[s], "", videoFiles[s].c_str());
if (result != 1 || vpClients[s] == nullptr) {
printf("[Stream%d] FAILED to create VideoPlayer (result=%d)\n", s, result);
g_log.add("[Stream" + std::to_string(s) + "] VideoPlayer create FAILED");
vpClients[s] = nullptr;
continue;
}
// Don't call StartVideoPlayer here — play() will be called just before worker threads
// launch, so the video doesn't play to completion during the ~16s engine loading phase.
SetVideoPlayerDisplayResolution(&vpClients[s], 1920, 1080);
g_log.add("[Stream" + std::to_string(s) + "] VideoPlayer created (display: 1920x1080)");
}
// =========================================================================
// Create 4 ALPR engines sequentially
// =========================================================================
ANSCENTER::ANSALPR* alprHandles[4] = {};
std::string modelZipFile = "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ANS_ALPR_v1.2.zip";
int engineType = 1; // NVIDIA_GPU
double detThresh = 0.5, ocrThresh = 0.5, colThresh = 0.5;
for (int i = 0; i < 4; i++) {
char tag[32];
snprintf(tag, sizeof(tag), "[Task%d]", i);
int streamIdx = taskStreamMap[i];
if (vpClients[streamIdx] == nullptr) {
printf("%s Skipped — Stream%d not available\n", tag, streamIdx);
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "Stream not available";
continue;
}
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].streamOk = true;
taskStates[i].statusMsg = "Loading ALPR engine...";
}
printf("%s Creating ALPR handle (engineType=%d)...\n", tag, engineType);
g_log.add(std::string(tag) + " Creating ALPR handle...");
auto engineStart = std::chrono::steady_clock::now();
int createResult = CreateANSALPRHandle(&alprHandles[i], "", modelZipFile.c_str(), "",
engineType, detThresh, ocrThresh, colThresh);
if (createResult != 1 || alprHandles[i] == nullptr) {
printf("%s FAILED to create ALPR handle (result=%d)\n", tag, createResult);
g_log.add(std::string(tag) + " ALPR create FAILED");
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "ALPR create failed";
continue;
}
printf("%s Loading ALPR engine (TensorRT)...\n", tag);
g_log.add(std::string(tag) + " Loading ALPR engine...");
// Snapshot VRAM before engine load to measure consumption
auto vramBefore = GetPerGpuFreeMiB();
int loadResult = LoadANSALPREngineHandle(&alprHandles[i]);
auto engineEnd = std::chrono::steady_clock::now();
double loadMs = std::chrono::duration<double, std::milli>(engineEnd - engineStart).count();
if (loadResult != 1) {
printf("%s FAILED to load ALPR engine (result=%d)\n", tag, loadResult);
g_log.add(std::string(tag) + " Engine load FAILED");
ReleaseANSALPRHandle(&alprHandles[i]);
alprHandles[i] = nullptr;
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "Engine load failed";
continue;
}
// Snapshot VRAM after engine load — find which GPU lost the most VRAM
auto vramAfter = GetPerGpuFreeMiB();
int bestGpu = 0;
size_t maxDelta = 0;
size_t gpuCount = vramBefore.size() < vramAfter.size() ? vramBefore.size() : vramAfter.size();
for (size_t g = 0; g < gpuCount; g++) {
size_t delta = (vramBefore[g] > vramAfter[g]) ? (vramBefore[g] - vramAfter[g]) : 0;
if (delta > maxDelta) {
maxDelta = delta;
bestGpu = (int)g;
}
}
char buf[512];
snprintf(buf, sizeof(buf),
"%s Engine loaded in %.0f ms | GPU[%d] | VRAM used: %zu MiB (Video%d)",
tag, loadMs, bestGpu, maxDelta, streamIdx);
printf("%s\n", buf);
g_log.add(buf);
// Log per-GPU VRAM state after this engine load
for (size_t g = 0; g < vramAfter.size(); g++) {
size_t total = 0;
if (g < vramBefore.size()) {
auto gpus = QueryGpuVram();
if (g < gpus.size()) total = gpus[g].totalMiB;
}
char vbuf[256];
snprintf(vbuf, sizeof(vbuf),
" GPU[%zu] VRAM: %zu MiB free (of %zu MiB)",
g, vramAfter[g], total);
printf("%s\n", vbuf);
g_log.add(vbuf);
}
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].engineLoaded = true;
taskStates[i].statusMsg = "Running";
taskStates[i].gpuDeviceId = bestGpu;
taskStates[i].vramUsedBytes = maxDelta * 1024 * 1024;
}
}
// --- No NVDEC realignment needed — ANSVideoPlayer uses cv::VideoCapture (CPU decode) ---
// --- Enable deep pipeline benchmarking on all ALPR handles ---
for (int i = 0; i < 4; i++) {
if (alprHandles[i]) {
alprHandles[i]->ActivateDebugger(true);
}
}
g_log.add("Debug benchmarking ENABLED on all ALPR handles");
// --- Start video playback NOW (just before workers need frames) ---
// This avoids the video playing to completion during the ~16s engine loading phase.
for (int s = 0; s < NUM_STREAMS; s++) {
if (vpClients[s]) {
StartVideoPlayer(&vpClients[s]);
g_log.add("[Stream" + std::to_string(s) + "] VideoPlayer play() started");
}
}
// --- Launch worker threads ---
g_log.add("Launching worker threads...");
std::thread workers[4];
for (int i = 0; i < 4; i++) {
int streamIdx = taskStreamMap[i];
if (vpClients[streamIdx] && alprHandles[i]) {
workers[i] = std::thread(ALPRWorkerThread_VideoPlayer, i,
vpClients[streamIdx], alprHandles[i],
std::ref(taskStates[i]));
}
}
// --- Display loop (main thread) ---
const int cellW = 640, cellH = 480;
const int logPanelH = 200;
const char* windowName = "ANSLPR Multi-GPU Stress Test (Simulated Cam)";
cv::namedWindow(windowName, cv::WINDOW_NORMAL);
cv::resizeWindow(windowName, cellW * 2, cellH * 2 + logPanelH);
auto testStart = std::chrono::steady_clock::now();
auto lastGpuSnapshot = std::chrono::steady_clock::now();
int snapshotCount = 0;
while (g_running.load()) {
// --- Periodic GPU/perf snapshot every 10 seconds ---
auto now2 = std::chrono::steady_clock::now();
if (std::chrono::duration<double>(now2 - lastGpuSnapshot).count() >= 10.0) {
lastGpuSnapshot = now2;
snapshotCount++;
double elapsedSec = std::chrono::duration<double>(now2 - testStart).count();
g_log.add("---- PERIODIC SNAPSHOT #" + std::to_string(snapshotCount)
+ " (elapsed " + std::to_string((int)elapsedSec) + "s) ----");
auto gpuSnap = QueryGpuVram();
for (const auto& gs : gpuSnap) {
char buf[256];
snprintf(buf, sizeof(buf),
" GPU[%d] %s | Used: %zu/%zu MiB (%.1f%%)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
gs.totalMiB > 0 ? 100.0 * gs.usedMiB / gs.totalMiB : 0.0);
g_log.add(buf);
}
double totalFpsSnap = 0;
for (int t = 0; t < 4; t++) {
std::lock_guard<std::mutex> lk(taskStates[t].mtx);
char buf[256];
snprintf(buf, sizeof(buf),
" T%d: GPU[%d] VRAM=%zuMiB FPS=%.1f GrabMs=%.0f InfMs=%.0f Frames=%d Det=%d",
t, taskStates[t].gpuDeviceId,
taskStates[t].vramUsedBytes / (1024 * 1024),
taskStates[t].fps, taskStates[t].lastGrabMs, taskStates[t].inferenceMs,
taskStates[t].frameCount, taskStates[t].detectionCount);
g_log.add(buf);
totalFpsSnap += taskStates[t].fps;
}
char buf[128];
snprintf(buf, sizeof(buf), " Total throughput: %.1f FPS", totalFpsSnap);
g_log.add(buf);
std::set<int> gpusUsed;
for (int t = 0; t < 4; t++) {
if (taskStates[t].gpuDeviceId >= 0) gpusUsed.insert(taskStates[t].gpuDeviceId);
}
if (gpusUsed.size() > 1) {
g_log.add(" MULTI-GPU: YES — tasks distributed across " + std::to_string(gpusUsed.size()) + " GPUs");
} else if (!gpusUsed.empty()) {
g_log.add(" MULTI-GPU: NO — all tasks on GPU[" + std::to_string(*gpusUsed.begin()) + "]");
}
g_log.add("---- END SNAPSHOT ----");
}
// Build 2x2 grid + log panel
cv::Mat canvas(cellH * 2 + logPanelH, cellW * 2, CV_8UC3, cv::Scalar(30, 30, 30));
for (int i = 0; i < 4; i++) {
int row = i / 2, col = i % 2;
cv::Rect roi(col * cellW, row * cellH, cellW, cellH);
cv::Mat cell;
double fps = 0, infMs = 0;
int fCount = 0, dCount = 0;
int gpuId = -1;
size_t vramMiB = 0;
std::string statusMsg, lastPlate;
bool engineLoaded = false, streamOk = false;
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
if (!taskStates[i].displayFrame.empty()) {
cv::resize(taskStates[i].displayFrame, cell, cv::Size(cellW, cellH));
}
fps = taskStates[i].fps;
infMs = taskStates[i].inferenceMs;
fCount = taskStates[i].frameCount;
dCount = taskStates[i].detectionCount;
statusMsg = taskStates[i].statusMsg;
lastPlate = taskStates[i].lastPlate;
engineLoaded = taskStates[i].engineLoaded;
streamOk = taskStates[i].streamOk;
gpuId = taskStates[i].gpuDeviceId;
vramMiB = taskStates[i].vramUsedBytes / (1024 * 1024);
}
if (cell.empty()) {
cell = cv::Mat(cellH, cellW, CV_8UC3, cv::Scalar(40, 40, 40));
cv::putText(cell, "Task " + std::to_string(i) + ": " + statusMsg,
cv::Point(20, cellH / 2),
cv::FONT_HERSHEY_SIMPLEX, 0.8, cv::Scalar(100, 100, 255), 2);
}
cv::rectangle(cell, cv::Rect(0, cellH - 50, cellW, 50), cv::Scalar(0, 0, 0), cv::FILLED);
char bar1[256], bar2[256];
snprintf(bar1, sizeof(bar1), "T%d | %.1f FPS | %.0fms | Frames:%d | Det:%d | %s",
i, fps, infMs, fCount, dCount,
lastPlate.empty() ? "-" : lastPlate.c_str());
if (gpuId >= 0) {
snprintf(bar2, sizeof(bar2), "GPU[%d] | VRAM: %zu MiB", gpuId, vramMiB);
} else {
snprintf(bar2, sizeof(bar2), "GPU: N/A");
}
cv::Scalar barColor = engineLoaded ? cv::Scalar(0, 255, 0) : cv::Scalar(0, 100, 255);
cv::putText(cell, bar1, cv::Point(5, cellH - 28),
cv::FONT_HERSHEY_SIMPLEX, 0.45, barColor, 1);
cv::putText(cell, bar2, cv::Point(5, cellH - 8),
cv::FONT_HERSHEY_SIMPLEX, 0.45, cv::Scalar(0, 200, 255), 1);
cell.copyTo(canvas(roi));
cv::line(canvas, cv::Point(cellW, 0), cv::Point(cellW, cellH * 2),
cv::Scalar(100, 100, 100), 1);
cv::line(canvas, cv::Point(0, cellH), cv::Point(cellW * 2, cellH),
cv::Scalar(100, 100, 100), 1);
}
// --- Log panel at bottom ---
cv::Rect logRoi(0, cellH * 2, cellW * 2, logPanelH);
cv::Mat logPanel = canvas(logRoi);
logPanel.setTo(cv::Scalar(20, 20, 20));
auto elapsed = std::chrono::duration<double>(std::chrono::steady_clock::now() - testStart).count();
char header[128];
snprintf(header, sizeof(header),
"Elapsed: %.0fs | Simulated Cam (VideoPlayer) | Press ESC to stop", elapsed);
cv::putText(logPanel, header, cv::Point(10, 18),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(200, 200, 0), 1);
double totalFps = 0;
for (int i = 0; i < 4; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
totalFps += taskStates[i].fps;
}
char aggLine[256];
snprintf(aggLine, sizeof(aggLine), "Total throughput: %.1f FPS | T0:GPU%d T1:GPU%d T2:GPU%d T3:GPU%d",
totalFps,
taskStates[0].gpuDeviceId, taskStates[1].gpuDeviceId,
taskStates[2].gpuDeviceId, taskStates[3].gpuDeviceId);
cv::putText(logPanel, aggLine, cv::Point(10, 38),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 255, 255), 1);
auto gpuSnaps = QueryGpuVram();
int gpuLineY = 58;
for (const auto& gs : gpuSnaps) {
int tasksOnGpu = 0;
size_t taskVramMiB = 0;
for (int i = 0; i < 4; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
if (taskStates[i].gpuDeviceId == gs.deviceId) {
tasksOnGpu++;
taskVramMiB += taskStates[i].vramUsedBytes / (1024 * 1024);
}
}
char gpuLine[256];
snprintf(gpuLine, sizeof(gpuLine),
"GPU[%d] %s | Used: %zu/%zu MiB | Tasks: %d (engine VRAM: %zu MiB)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
tasksOnGpu, taskVramMiB);
cv::putText(logPanel, gpuLine, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_SIMPLEX, 0.45, cv::Scalar(100, 255, 100), 1);
gpuLineY += 18;
}
for (int i = 0; i < 4; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
char tLine[256];
snprintf(tLine, sizeof(tLine),
"T%d: GPU[%d] VRAM=%zuMiB FPS=%.1f Inf=%.0fms Frames=%d Det=%d",
i, taskStates[i].gpuDeviceId,
taskStates[i].vramUsedBytes / (1024 * 1024),
taskStates[i].fps, taskStates[i].inferenceMs,
taskStates[i].frameCount, taskStates[i].detectionCount);
cv::putText(logPanel, tLine, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(200, 200, 200), 1);
gpuLineY += 16;
}
auto recentLogs = g_log.getRecent(4);
for (const auto& line : recentLogs) {
if (gpuLineY > logPanelH - 5) break;
std::string display = (line.size() > 130) ? line.substr(0, 127) + "..." : line;
cv::putText(logPanel, display, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_PLAIN, 1.0, cv::Scalar(140, 140, 140), 1);
gpuLineY += 15;
}
cv::imshow(windowName, canvas);
int key = cv::waitKey(30);
if (key == 27) { // ESC
g_log.add("ESC pressed — stopping all tasks...");
printf("\nESC pressed — stopping...\n");
g_running.store(false);
}
}
// --- Wait for all workers ---
printf("Waiting for worker threads to finish...\n");
for (int i = 0; i < 4; i++) {
if (workers[i].joinable()) workers[i].join();
}
// --- Print final summary ---
double totalElapsed = std::chrono::duration<double>(
std::chrono::steady_clock::now() - testStart).count();
g_log.add("================================================================");
g_log.add(" FINAL PERFORMANCE SUMMARY (Simulated Cam)");
g_log.add(" Total runtime: " + std::to_string((int)totalElapsed) + " seconds");
g_log.add("================================================================");
printf("\n============================================================\n");
printf(" FINAL PERFORMANCE SUMMARY — Simulated Cam (runtime: %.0fs)\n", totalElapsed);
printf("============================================================\n");
double totalFpsFinal = 0;
for (int i = 0; i < 4; i++) {
char buf[512];
snprintf(buf, sizeof(buf),
" Task %d: GPU[%d] | VRAM=%zuMiB | %d frames, %d detections, FPS=%.1f, InfMs=%.0f",
i, taskStates[i].gpuDeviceId,
taskStates[i].vramUsedBytes / (1024 * 1024),
taskStates[i].frameCount, taskStates[i].detectionCount,
taskStates[i].fps, taskStates[i].inferenceMs);
printf("%s\n", buf);
g_log.add(buf);
totalFpsFinal += taskStates[i].fps;
}
auto finalGpu = QueryGpuVram();
for (const auto& gs : finalGpu) {
char buf[256];
snprintf(buf, sizeof(buf), " GPU[%d] %s: %zu/%zu MiB used (%.1f%%)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
gs.totalMiB > 0 ? 100.0 * gs.usedMiB / gs.totalMiB : 0.0);
printf("%s\n", buf);
g_log.add(buf);
}
std::set<int> finalGpusUsed;
for (int i = 0; i < 4; i++) {
if (taskStates[i].gpuDeviceId >= 0) finalGpusUsed.insert(taskStates[i].gpuDeviceId);
}
{
char buf[256];
snprintf(buf, sizeof(buf), " Total throughput: %.1f FPS across 4 tasks", totalFpsFinal);
printf("%s\n", buf);
g_log.add(buf);
}
if (finalGpusUsed.size() > 1) {
char buf[128];
snprintf(buf, sizeof(buf), " MULTI-GPU: YES — tasks on %zu different GPUs", finalGpusUsed.size());
printf("%s\n", buf);
g_log.add(buf);
} else if (!finalGpusUsed.empty()) {
char buf[128];
snprintf(buf, sizeof(buf), " MULTI-GPU: NO — all tasks on GPU[%d] only", *finalGpusUsed.begin());
printf("%s\n", buf);
g_log.add(buf);
}
printf("============================================================\n");
g_log.add("================================================================");
g_log.add(" Log saved to: " + std::string(LOG_FILE_PATH));
g_log.add("================================================================");
// --- Release all handles ---
for (int i = 0; i < 4; i++) {
if (alprHandles[i]) {
ReleaseANSALPRHandle(&alprHandles[i]);
}
}
for (int s = 0; s < NUM_STREAMS; s++) {
if (vpClients[s]) {
StopVideoPlayer(&vpClients[s]);
ReleaseANSVideoPlayerHandle(&vpClients[s]);
}
}
g_log.close();
cv::destroyAllWindows();
ANSCENTER::ANSOPENCV::DeinitCameraNetwork();
return 0;
}
// =============================================================================
// Worker thread for FilePlayer-based stress test (uses ANSFILEPLAYER)
// Key difference from VideoPlayer worker: uses GetFilePlayerCVImage/ReconnectFilePlayer
// =============================================================================
static void ALPRWorkerThread_FilePlayer(int taskId,
ANSCENTER::ANSFILEPLAYER* fpClient,
ANSCENTER::ANSALPR* alprHandle,
TaskState& state) {
char tag[32];
snprintf(tag, sizeof(tag), "[Task%d]", taskId);
std::string prefix(tag);
g_log.add(prefix + " Worker thread started");
printf("%s Worker thread started\n", tag);
int width = 0, height = 0;
int64_t pts = 0;
int emptyFrames = 0;
std::string cameraId = "Cam" + std::to_string(taskId);
std::deque<std::chrono::steady_clock::time_point> fpsTimestamps;
double totalGrabMs = 0, totalInfMs = 0;
int grabCount = 0, infCount = 0;
double maxGrabMs = 0, maxInfMs = 0;
auto benchStart = std::chrono::steady_clock::now();
while (g_running.load()) {
auto grabStart = std::chrono::steady_clock::now();
cv::Mat* framePtr = nullptr;
GetFilePlayerCVImage(&fpClient, width, height, pts, &framePtr);
auto grabEnd = std::chrono::steady_clock::now();
double grabMs = std::chrono::duration<double, std::milli>(grabEnd - grabStart).count();
if (framePtr == nullptr || framePtr->empty()) {
emptyFrames++;
if (emptyFrames % 100 == 1) {
g_log.add(prefix + " Empty frame (count=" + std::to_string(emptyFrames) + ")");
}
if (emptyFrames > 300) {
g_log.add(prefix + " Too many empty frames, attempting reconnect...");
ReconnectFilePlayer(&fpClient);
emptyFrames = 0;
}
if (framePtr) delete framePtr;
std::this_thread::sleep_for(std::chrono::milliseconds(10));
continue;
}
emptyFrames = 0;
totalGrabMs += grabMs;
grabCount++;
if (grabMs > maxGrabMs) maxGrabMs = grabMs;
auto infStart = std::chrono::steady_clock::now();
std::string lpnResult, jpegImage;
// Pass framePtr directly — NOT a copy. ANSGpuFrameRegistry::lookup()
// matches by cv::Mat* pointer, so `new cv::Mat(*framePtr)` would create
// a different pointer the registry doesn't know, breaking NV12 zero-copy.
ANSALPR_RunInferenceComplete_CPP(&alprHandle, &framePtr, cameraId.c_str(), 0, 0, lpnResult, jpegImage);
auto infEnd = std::chrono::steady_clock::now();
double infMs = std::chrono::duration<double, std::milli>(infEnd - infStart).count();
totalInfMs += infMs;
infCount++;
if (infMs > maxInfMs) maxInfMs = infMs;
cv::Mat display = framePtr->clone();
int detCount = 0;
std::string lastPlateText;
if (!lpnResult.empty()) {
try {
boost::property_tree::ptree pt;
std::stringstream ss(lpnResult);
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type& child, pt.get_child("results")) {
const boost::property_tree::ptree& det = child.second;
const auto class_name = GetData<std::string>(det, "class_name");
const auto x = GetData<float>(det, "x");
const auto y = GetData<float>(det, "y");
const auto w = GetData<float>(det, "width");
const auto h = GetData<float>(det, "height");
cv::rectangle(display, cv::Rect((int)x, (int)y, (int)w, (int)h),
cv::Scalar(0, 255, 0), 2);
cv::putText(display, class_name, cv::Point((int)x, (int)y - 5),
cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 0), 2);
lastPlateText = class_name;
detCount++;
}
}
catch (...) {}
}
auto now = std::chrono::steady_clock::now();
fpsTimestamps.push_back(now);
while (!fpsTimestamps.empty() &&
std::chrono::duration<double>(now - fpsTimestamps.front()).count() > 2.0) {
fpsTimestamps.pop_front();
}
double fps = fpsTimestamps.size() / 2.0;
char osd[128];
snprintf(osd, sizeof(osd), "Task%d | %.1f FPS | Inf: %.0f ms | #%d",
taskId, fps, infMs, state.frameCount + 1);
cv::putText(display, osd, cv::Point(10, 30),
cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 255), 2);
{
std::lock_guard<std::mutex> lk(state.mtx);
state.displayFrame = display;
state.fps = fps;
state.inferenceMs = infMs;
state.lastGrabMs = grabMs;
state.lastInfMs = infMs;
state.frameCount++;
state.detectionCount += detCount;
if (!lastPlateText.empty()) state.lastPlate = lastPlateText;
}
if ((state.frameCount % 100) == 0) {
double avgGrab = grabCount > 0 ? totalGrabMs / grabCount : 0;
double avgInf = infCount > 0 ? totalInfMs / infCount : 0;
double elapsed = std::chrono::duration<double>(
std::chrono::steady_clock::now() - benchStart).count();
char buf[512];
snprintf(buf, sizeof(buf),
"%s Frame %d | FPS=%.1f | Grab: avg=%.1fms max=%.0fms | Inf: avg=%.1fms max=%.0fms | "
"GrabPct=%.0f%% InfPct=%.0f%% | Det=%d",
tag, state.frameCount, fps,
avgGrab, maxGrabMs,
avgInf, maxInfMs,
(totalGrabMs / (elapsed * 1000.0)) * 100.0,
(totalInfMs / (elapsed * 1000.0)) * 100.0,
state.detectionCount);
g_log.add(buf);
printf("%s\n", buf);
totalGrabMs = totalInfMs = 0;
maxGrabMs = maxInfMs = 0;
grabCount = infCount = 0;
benchStart = std::chrono::steady_clock::now();
}
delete framePtr;
}
g_log.add(prefix + " Worker loop exited");
}
// =============================================================================
// ANSLPR_MultiGPU_StressTest_FilePlayer
// Same as SimulatedCam but uses ANSFILEPLAYER (loops video continuously).
// =============================================================================
int ANSLPR_MultiGPU_StressTest_FilePlayer() {
ANSCENTER::ANSOPENCV::InitCameraNetwork();
g_log.init();
printf("\n");
printf("============================================================\n");
printf(" ANSLPR Multi-GPU Stress Test (FilePlayer — looping)\n");
printf(" Using local video files via ANSFilePlayer (HW decode)\n");
printf(" Press ESC to stop\n");
printf(" Log file: %s\n", LOG_FILE_PATH);
printf("============================================================\n\n");
g_log.add("============================================================");
g_log.add(" ANSLPR Multi-GPU Stress Test (FilePlayer — looping)");
g_log.add(" Using ANSFilePlayer with HW decode + NV12 zero-copy");
g_log.add("============================================================");
LogGpuInfo();
const std::string videoFile0 = "E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day.mp4";
const std::string videoFile1 = "E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day_1.mp4";
const std::string videoFile2 = "E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day_2.mp4";
const std::string videoFile3 = "E:\\Programs\\DemoAssets\\Videos\\ALRP\\PMH\\Day\\day_3.mp4";
g_log.add("Video 0: " + videoFile0);
g_log.add("Video 1: " + videoFile1);
g_log.add("Video 2: " + videoFile2);
g_log.add("Video 3: " + videoFile3);
TaskState taskStates[4];
// =========================================================================
// Create 4 FilePlayer readers
// =========================================================================
const int NUM_STREAMS = 4;
ANSCENTER::ANSFILEPLAYER* fpClients[NUM_STREAMS] = {};
const std::string videoFiles[NUM_STREAMS] = { videoFile0, videoFile1, videoFile2, videoFile3 };
const int taskStreamMap[4] = { 0, 1, 2, 3 };
for (int s = 0; s < NUM_STREAMS; s++) {
printf("[Stream%d] Creating FilePlayer for %s\n", s, videoFiles[s].c_str());
g_log.add("[Stream" + std::to_string(s) + "] Creating FilePlayer for " + videoFiles[s]);
int result = CreateANSFilePlayerHandle(&fpClients[s], "", videoFiles[s].c_str());
if (result != 1 || fpClients[s] == nullptr) {
printf("[Stream%d] FAILED to create FilePlayer (result=%d)\n", s, result);
g_log.add("[Stream" + std::to_string(s) + "] FilePlayer create FAILED");
fpClients[s] = nullptr;
continue;
}
// Don't start yet — start after engines are loaded
SetFilePlayerDisplayResolution(&fpClients[s], 1920, 1080);
g_log.add("[Stream" + std::to_string(s) + "] FilePlayer created (display: 1920x1080)");
}
// =========================================================================
// Create 4 ALPR engines sequentially
// =========================================================================
ANSCENTER::ANSALPR* alprHandles[4] = {};
std::string modelZipFile = "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ANS_ALPR_v1.2.zip";
int engineType = 1;
double detThresh = 0.5, ocrThresh = 0.5, colThresh = 0.5;
for (int i = 0; i < 4; i++) {
char tag[32];
snprintf(tag, sizeof(tag), "[Task%d]", i);
int streamIdx = taskStreamMap[i];
if (fpClients[streamIdx] == nullptr) {
printf("%s Skipped — Stream%d not available\n", tag, streamIdx);
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "Stream not available";
continue;
}
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].streamOk = true;
taskStates[i].statusMsg = "Loading ALPR engine...";
}
printf("%s Creating ALPR handle (engineType=%d)...\n", tag, engineType);
g_log.add(std::string(tag) + " Creating ALPR handle...");
auto engineStart = std::chrono::steady_clock::now();
int createResult = CreateANSALPRHandle(&alprHandles[i], "", modelZipFile.c_str(), "",
engineType, detThresh, ocrThresh, colThresh);
if (createResult != 1 || alprHandles[i] == nullptr) {
printf("%s FAILED to create ALPR handle (result=%d)\n", tag, createResult);
g_log.add(std::string(tag) + " ALPR create FAILED");
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "ALPR create failed";
continue;
}
printf("%s Loading ALPR engine (TensorRT)...\n", tag);
g_log.add(std::string(tag) + " Loading ALPR engine...");
auto vramBefore = GetPerGpuFreeMiB();
int loadResult = LoadANSALPREngineHandle(&alprHandles[i]);
auto engineEnd = std::chrono::steady_clock::now();
double loadMs = std::chrono::duration<double, std::milli>(engineEnd - engineStart).count();
if (loadResult != 1) {
printf("%s FAILED to load ALPR engine (result=%d)\n", tag, loadResult);
g_log.add(std::string(tag) + " Engine load FAILED");
ReleaseANSALPRHandle(&alprHandles[i]);
alprHandles[i] = nullptr;
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].statusMsg = "Engine load failed";
continue;
}
auto vramAfter = GetPerGpuFreeMiB();
int bestGpu = 0;
size_t maxDelta = 0;
for (size_t g = 0; g < vramBefore.size() && g < vramAfter.size(); g++) {
size_t delta = (vramBefore[g] > vramAfter[g]) ? vramBefore[g] - vramAfter[g] : 0;
if (delta > maxDelta) { maxDelta = delta; bestGpu = (int)g; }
}
char ebuf[256];
snprintf(ebuf, sizeof(ebuf), "%s Engine loaded in %d ms | GPU[%d] | VRAM used: %zu MiB (Video%d)",
tag, (int)loadMs, bestGpu, maxDelta, i);
printf("%s\n", ebuf);
g_log.add(ebuf);
for (size_t g = 0; g < vramAfter.size(); g++) {
size_t total = 0;
cudaDeviceProp prop;
if (cudaGetDeviceProperties(&prop, (int)g) == cudaSuccess) {
total = prop.totalGlobalMem / (1024 * 1024);
}
char vbuf[128];
snprintf(vbuf, sizeof(vbuf), " GPU[%zu] VRAM: %zu MiB free (of %zu MiB)", g, vramAfter[g], total);
printf("%s\n", vbuf);
g_log.add(vbuf);
}
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
taskStates[i].engineLoaded = true;
taskStates[i].statusMsg = "Running";
taskStates[i].gpuDeviceId = bestGpu;
taskStates[i].vramUsedBytes = maxDelta * 1024 * 1024;
}
}
// --- Enable debug benchmarking ---
for (int i = 0; i < 4; i++) {
if (alprHandles[i]) {
alprHandles[i]->ActivateDebugger(true);
}
}
g_log.add("Debug benchmarking ENABLED on all ALPR handles");
// --- Start video playback NOW (just before workers need frames) ---
for (int s = 0; s < NUM_STREAMS; s++) {
if (fpClients[s]) {
StartFilePlayer(&fpClients[s]);
g_log.add("[Stream" + std::to_string(s) + "] FilePlayer play() started");
}
}
// --- Launch worker threads ---
g_log.add("Launching worker threads...");
std::thread workers[4];
for (int i = 0; i < 4; i++) {
int streamIdx = taskStreamMap[i];
if (fpClients[streamIdx] && alprHandles[i]) {
workers[i] = std::thread(ALPRWorkerThread_FilePlayer, i,
fpClients[streamIdx], alprHandles[i],
std::ref(taskStates[i]));
}
}
// --- Display loop (main thread) ---
const int cellW = 640, cellH = 480;
const int logPanelH = 200;
const char* windowName = "ANSLPR Stress Test (FilePlayer — looping)";
cv::namedWindow(windowName, cv::WINDOW_NORMAL);
cv::resizeWindow(windowName, cellW * 2, cellH * 2 + logPanelH);
auto testStart = std::chrono::steady_clock::now();
auto lastGpuSnapshot = std::chrono::steady_clock::now();
int snapshotCount = 0;
while (g_running.load()) {
auto now2 = std::chrono::steady_clock::now();
if (std::chrono::duration<double>(now2 - lastGpuSnapshot).count() >= 10.0) {
lastGpuSnapshot = now2;
snapshotCount++;
double elapsedSec = std::chrono::duration<double>(now2 - testStart).count();
g_log.add("---- PERIODIC SNAPSHOT #" + std::to_string(snapshotCount)
+ " (elapsed " + std::to_string((int)elapsedSec) + "s) ----");
auto gpuSnap = QueryGpuVram();
for (const auto& gs : gpuSnap) {
char buf[256];
snprintf(buf, sizeof(buf),
" GPU[%d] %s | Used: %zu/%zu MiB (%.1f%%)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
gs.totalMiB > 0 ? 100.0 * gs.usedMiB / gs.totalMiB : 0.0);
g_log.add(buf);
}
double totalFpsSnap = 0;
for (int t = 0; t < 4; t++) {
std::lock_guard<std::mutex> lk(taskStates[t].mtx);
char buf[256];
snprintf(buf, sizeof(buf),
" T%d: GPU[%d] VRAM=%zuMiB FPS=%.1f GrabMs=%.0f InfMs=%.0f Frames=%d Det=%d",
t, taskStates[t].gpuDeviceId,
taskStates[t].vramUsedBytes / (1024 * 1024),
taskStates[t].fps, taskStates[t].lastGrabMs, taskStates[t].inferenceMs,
taskStates[t].frameCount, taskStates[t].detectionCount);
g_log.add(buf);
totalFpsSnap += taskStates[t].fps;
}
char buf[128];
snprintf(buf, sizeof(buf), " Total throughput: %.1f FPS", totalFpsSnap);
g_log.add(buf);
std::set<int> gpusUsed;
for (int t = 0; t < 4; t++) {
if (taskStates[t].gpuDeviceId >= 0) gpusUsed.insert(taskStates[t].gpuDeviceId);
}
if (gpusUsed.size() > 1) {
g_log.add(" MULTI-GPU: YES — tasks distributed across " + std::to_string(gpusUsed.size()) + " GPUs");
} else if (!gpusUsed.empty()) {
g_log.add(" MULTI-GPU: NO — all tasks on GPU[" + std::to_string(*gpusUsed.begin()) + "]");
}
g_log.add("---- END SNAPSHOT ----");
}
// Build 2x2 grid + log panel
cv::Mat canvas(cellH * 2 + logPanelH, cellW * 2, CV_8UC3, cv::Scalar(30, 30, 30));
for (int i = 0; i < 4; i++) {
int row = i / 2, col = i % 2;
cv::Rect roi(col * cellW, row * cellH, cellW, cellH);
cv::Mat cell;
double fps = 0, infMs = 0;
int fCount = 0, dCount = 0;
int gpuId = -1;
size_t vramMiB = 0;
std::string statusMsg, lastPlate;
bool engineLoaded = false, streamOk = false;
{
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
if (!taskStates[i].displayFrame.empty()) {
cv::resize(taskStates[i].displayFrame, cell, cv::Size(cellW, cellH));
}
fps = taskStates[i].fps;
infMs = taskStates[i].inferenceMs;
fCount = taskStates[i].frameCount;
dCount = taskStates[i].detectionCount;
statusMsg = taskStates[i].statusMsg;
lastPlate = taskStates[i].lastPlate;
engineLoaded = taskStates[i].engineLoaded;
streamOk = taskStates[i].streamOk;
gpuId = taskStates[i].gpuDeviceId;
vramMiB = taskStates[i].vramUsedBytes / (1024 * 1024);
}
if (cell.empty()) {
cell = cv::Mat(cellH, cellW, CV_8UC3, cv::Scalar(40, 40, 40));
cv::putText(cell, "Task " + std::to_string(i) + ": " + statusMsg,
cv::Point(20, cellH / 2),
cv::FONT_HERSHEY_SIMPLEX, 0.8, cv::Scalar(100, 100, 255), 2);
}
cv::rectangle(cell, cv::Rect(0, cellH - 50, cellW, 50), cv::Scalar(0, 0, 0), cv::FILLED);
char bar1[256], bar2[256];
snprintf(bar1, sizeof(bar1), "T%d | %.1f FPS | %.0fms | Frames:%d | Det:%d | %s",
i, fps, infMs, fCount, dCount,
lastPlate.empty() ? "-" : lastPlate.c_str());
if (gpuId >= 0) {
snprintf(bar2, sizeof(bar2), "GPU[%d] | VRAM: %zu MiB", gpuId, vramMiB);
} else {
snprintf(bar2, sizeof(bar2), "GPU: N/A");
}
cv::Scalar barColor = engineLoaded ? cv::Scalar(0, 255, 0) : cv::Scalar(0, 100, 255);
cv::putText(cell, bar1, cv::Point(5, cellH - 28),
cv::FONT_HERSHEY_SIMPLEX, 0.45, barColor, 1);
cv::putText(cell, bar2, cv::Point(5, cellH - 8),
cv::FONT_HERSHEY_SIMPLEX, 0.45, cv::Scalar(0, 200, 255), 1);
cell.copyTo(canvas(roi));
cv::line(canvas, cv::Point(cellW, 0), cv::Point(cellW, cellH * 2),
cv::Scalar(100, 100, 100), 1);
cv::line(canvas, cv::Point(0, cellH), cv::Point(cellW * 2, cellH),
cv::Scalar(100, 100, 100), 1);
}
// Log panel
cv::Rect logRoi(0, cellH * 2, cellW * 2, logPanelH);
cv::Mat logPanel = canvas(logRoi);
logPanel.setTo(cv::Scalar(20, 20, 20));
auto elapsed = std::chrono::duration<double>(std::chrono::steady_clock::now() - testStart).count();
char header[128];
snprintf(header, sizeof(header),
"Elapsed: %.0fs | FilePlayer (looping, HW decode) | Press ESC to stop", elapsed);
cv::putText(logPanel, header, cv::Point(10, 18),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(200, 200, 0), 1);
double totalFps = 0;
for (int i = 0; i < 4; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
totalFps += taskStates[i].fps;
}
char aggLine[256];
snprintf(aggLine, sizeof(aggLine), "Total throughput: %.1f FPS | T0:GPU%d T1:GPU%d T2:GPU%d T3:GPU%d",
totalFps,
taskStates[0].gpuDeviceId, taskStates[1].gpuDeviceId,
taskStates[2].gpuDeviceId, taskStates[3].gpuDeviceId);
cv::putText(logPanel, aggLine, cv::Point(10, 38),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 255, 255), 1);
auto gpuSnaps = QueryGpuVram();
int gpuLineY = 58;
for (const auto& gs : gpuSnaps) {
int tasksOnGpu = 0;
size_t taskVramMiB = 0;
for (int i = 0; i < 4; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
if (taskStates[i].gpuDeviceId == gs.deviceId) {
tasksOnGpu++;
taskVramMiB += taskStates[i].vramUsedBytes / (1024 * 1024);
}
}
char gpuLine[256];
snprintf(gpuLine, sizeof(gpuLine),
"GPU[%d] %s | Used: %zu/%zu MiB | Tasks: %d (engine VRAM: %zu MiB)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
tasksOnGpu, taskVramMiB);
cv::putText(logPanel, gpuLine, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_SIMPLEX, 0.45, cv::Scalar(100, 255, 100), 1);
gpuLineY += 18;
}
for (int i = 0; i < 4; i++) {
std::lock_guard<std::mutex> lk(taskStates[i].mtx);
char tLine[256];
snprintf(tLine, sizeof(tLine),
"T%d: GPU[%d] VRAM=%zuMiB FPS=%.1f Inf=%.0fms Frames=%d Det=%d",
i, taskStates[i].gpuDeviceId,
taskStates[i].vramUsedBytes / (1024 * 1024),
taskStates[i].fps, taskStates[i].inferenceMs,
taskStates[i].frameCount, taskStates[i].detectionCount);
cv::putText(logPanel, tLine, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(200, 200, 200), 1);
gpuLineY += 16;
}
auto recentLogs = g_log.getRecent(4);
for (const auto& line : recentLogs) {
if (gpuLineY > logPanelH - 5) break;
std::string display = (line.size() > 130) ? line.substr(0, 127) + "..." : line;
cv::putText(logPanel, display, cv::Point(10, gpuLineY),
cv::FONT_HERSHEY_PLAIN, 1.0, cv::Scalar(140, 140, 140), 1);
gpuLineY += 15;
}
cv::imshow(windowName, canvas);
int key = cv::waitKey(30);
if (key == 27) {
g_log.add("ESC pressed — stopping all tasks...");
printf("\nESC pressed — stopping...\n");
g_running.store(false);
}
}
// --- Wait for all workers ---
printf("Waiting for worker threads to finish...\n");
for (int i = 0; i < 4; i++) {
if (workers[i].joinable()) workers[i].join();
}
// --- Final summary ---
double totalElapsed = std::chrono::duration<double>(
std::chrono::steady_clock::now() - testStart).count();
g_log.add("================================================================");
g_log.add(" FINAL PERFORMANCE SUMMARY (FilePlayer — looping)");
g_log.add(" Total runtime: " + std::to_string((int)totalElapsed) + " seconds");
g_log.add("================================================================");
printf("\n============================================================\n");
printf(" FINAL PERFORMANCE SUMMARY — FilePlayer (runtime: %.0fs)\n", totalElapsed);
printf("============================================================\n");
double totalFpsFinal = 0;
for (int i = 0; i < 4; i++) {
char buf[512];
snprintf(buf, sizeof(buf),
" Task %d: GPU[%d] | VRAM=%zuMiB | %d frames, %d detections, FPS=%.1f, InfMs=%.0f",
i, taskStates[i].gpuDeviceId,
taskStates[i].vramUsedBytes / (1024 * 1024),
taskStates[i].frameCount, taskStates[i].detectionCount,
taskStates[i].fps, taskStates[i].inferenceMs);
printf("%s\n", buf);
g_log.add(buf);
totalFpsFinal += taskStates[i].fps;
}
auto finalGpu = QueryGpuVram();
for (const auto& gs : finalGpu) {
char buf[256];
snprintf(buf, sizeof(buf), " GPU[%d] %s: %zu/%zu MiB used (%.1f%%)",
gs.deviceId, gs.name.c_str(), gs.usedMiB, gs.totalMiB,
gs.totalMiB > 0 ? 100.0 * gs.usedMiB / gs.totalMiB : 0.0);
printf("%s\n", buf);
g_log.add(buf);
}
{
char buf[256];
snprintf(buf, sizeof(buf), " Total throughput: %.1f FPS across 4 tasks", totalFpsFinal);
printf("%s\n", buf);
g_log.add(buf);
}
printf("============================================================\n");
g_log.add("================================================================");
// --- Release all handles ---
for (int i = 0; i < 4; i++) {
if (alprHandles[i]) {
ReleaseANSALPRHandle(&alprHandles[i]);
}
}
for (int s = 0; s < NUM_STREAMS; s++) {
if (fpClients[s]) {
StopFilePlayer(&fpClients[s]);
ReleaseANSFilePlayerHandle(&fpClients[s]);
}
}
g_log.close();
cv::destroyAllWindows();
ANSCENTER::ANSOPENCV::DeinitCameraNetwork();
return 0;
}
// ANSLPR_OD_CPU_VideoTest — Uses ANSALPR_OD (engineType=1) on Intel CPU/iGPU.
// ANSALPR_OD auto-detects hardware (OpenVINO on Intel, DirectML on AMD, etc.)
// No CUDA calls — safe on non-NVIDIA systems.
int ANSLPR_OD_CPU_VideoTest() {
std::cout << "\n============================================================" << std::endl;
std::cout << " ANSLPR CPU/iGPU Test (ANSALPR_OD with auto-detect)" << std::endl;
std::cout << "============================================================\n" << std::endl;
std::string modelZipFile = "C:\\ProgramData\\ANSCENTER\\ANSVIS Server\\ANSALPR\\ANS_ALPR_v1.2.zip";
std::string videoFilePath = "C:\\ProgramData\\ANSCENTER\\Shared\\classroom.mp4";
std::cout << "Model: " << modelZipFile << std::endl;
std::cout << "Video: " << videoFilePath << std::endl;
ANSCENTER::ANSALPR* infHandle = nullptr;
int engineType = 1; // ANSALPR_OD (auto-detects HW internally)
double detThresh = 0.5, ocrThresh = 0.5, colThresh = 0.5;
// Step 1: Create handle
std::cout << "[LPR-CPU] Step 1: Creating handle..." << std::endl;
int createResult = CreateANSALPRHandle(&infHandle, "", modelZipFile.c_str(), "",
engineType, detThresh, ocrThresh, colThresh);
std::cout << "[LPR-CPU] CreateANSALPRHandle result: " << createResult << std::endl;
if (createResult != 1 || infHandle == nullptr) {
std::cerr << "[LPR-CPU] FAILED: CreateANSALPRHandle returned " << createResult << std::endl;
return -1;
}
// Step 2: Load engine
std::cout << "[LPR-CPU] Step 2: Loading engine..." << std::endl;
int loadResult = LoadANSALPREngineHandle(&infHandle);
std::cout << "[LPR-CPU] LoadANSALPREngineHandle result: " << loadResult << std::endl;
if (loadResult != 1) {
std::cerr << "[LPR-CPU] FAILED: LoadANSALPREngineHandle returned " << loadResult << std::endl;
ReleaseANSALPRHandle(&infHandle);
return -2;
}
// Step 3: Open video
std::cout << "[LPR-CPU] Step 3: Opening video..." << std::endl;
cv::VideoCapture capture(videoFilePath);
if (!capture.isOpened()) {
std::cerr << "[LPR-CPU] FAILED: Could not open video: " << videoFilePath << std::endl;
ReleaseANSALPRHandle(&infHandle);
return -3;
}
int totalFrames = static_cast<int>(capture.get(cv::CAP_PROP_FRAME_COUNT));
std::cout << "[LPR-CPU] Video opened: " << totalFrames << " frames" << std::endl;
// Step 4: Run inference
std::cout << "[LPR-CPU] Step 4: Running inference..." << std::endl;
boost::property_tree::ptree pt;
int frameIndex = 0;
int totalDetections = 0;
double totalInferenceMs = 0.0;
int maxFrames = 200;
while (frameIndex < maxFrames) {
cv::Mat frame;
if (!capture.read(frame)) {
std::cout << "[LPR-CPU] End of video at frame " << frameIndex << std::endl;
break;
}
frameIndex++;
unsigned int bufferLength = 0;
unsigned char* jpeg_bytes = CVMatToBytes(frame, bufferLength);
int height = frame.rows;
int width = frame.cols;
auto start = std::chrono::system_clock::now();
std::string detectionResult = ANSALPR_RunInferenceBinary(&infHandle, jpeg_bytes, width, height);
auto end = std::chrono::system_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end - start);
totalInferenceMs += static_cast<double>(elapsed.count());
delete[] jpeg_bytes;
if (!detectionResult.empty()) {
try {
pt.clear();
std::stringstream ss;
ss << detectionResult;
boost::property_tree::read_json(ss, pt);
int detCount = 0;
BOOST_FOREACH(const boost::property_tree::ptree::value_type& child, pt.get_child("results")) {
const boost::property_tree::ptree& r = child.second;
const auto class_name = GetData<std::string>(r, "class_name");
const auto x = GetData<float>(r, "x");
const auto y = GetData<float>(r, "y");
const auto w = GetData<float>(r, "width");
const auto h = GetData<float>(r, "height");
detCount++;
cv::rectangle(frame, cv::Rect(x, y, w, h), cv::Scalar(0, 255, 0), 2);
cv::putText(frame, class_name, cv::Point(x, y - 5),
0, 0.6, cv::Scalar(0, 0, 255), 1, cv::LINE_AA);
}
totalDetections += detCount;
}
catch (...) {}
}
if (frameIndex % 10 == 0) {
double avgSoFar = totalInferenceMs / frameIndex;
std::cout << "[LPR-CPU] Frame " << frameIndex << "/" << maxFrames
<< " | Time: " << elapsed.count() << "ms"
<< " | Avg: " << static_cast<int>(avgSoFar) << "ms"
<< " | Detections: " << totalDetections << std::endl;
}
cv::imshow("ANSLPR CPU Test", frame);
if (cv::waitKey(1) == 27) break;
}
// Summary
double avgMs = (frameIndex > 0) ? (totalInferenceMs / frameIndex) : 0.0;
std::cout << "\n=== LPR CPU Test Summary ===" << std::endl;
std::cout << "Frames processed: " << frameIndex << std::endl;
std::cout << "Total detections: " << totalDetections << std::endl;
std::cout << "Avg inference: " << avgMs << " ms/frame" << std::endl;
std::cout << "Total time: " << totalInferenceMs << " ms" << std::endl;
std::cout << (frameIndex > 0 ? "[LPR-CPU] PASSED" : "[LPR-CPU] FAILED") << std::endl;
capture.release();
cv::destroyAllWindows();
ReleaseANSALPRHandle(&infHandle);
return (frameIndex > 0) ? 0 : -4;
}
// ── ANSALPR_OCR test: Japanese license plate detection using ANSONNXOCR ──
// Render UTF-8 text onto a cv::Mat using Windows GDI (supports CJK/Unicode).
// cv::putText only handles ASCII — Japanese characters render as '?'.
#ifdef WIN32
static void putTextUnicode(cv::Mat& img, const std::string& text, cv::Point org,
double fontScale, cv::Scalar color, int thickness) {
int wlen = MultiByteToWideChar(CP_UTF8, 0, text.c_str(), -1, nullptr, 0);
std::wstring wtext(wlen - 1, 0);
MultiByteToWideChar(CP_UTF8, 0, text.c_str(), -1, &wtext[0], wlen);
HDC hdc = CreateCompatibleDC(nullptr);
int fontHeight = (int)(fontScale * 30);
HFONT hFont = CreateFontW(fontHeight, 0, 0, 0,
(thickness > 2) ? FW_BOLD : FW_NORMAL,
FALSE, FALSE, FALSE,
DEFAULT_CHARSET, OUT_DEFAULT_PRECIS, CLIP_DEFAULT_PRECIS,
ANTIALIASED_QUALITY, DEFAULT_PITCH | FF_SWISS, L"Yu Gothic UI");
HFONT hOldFont = (HFONT)SelectObject(hdc, hFont);
SIZE sz;
GetTextExtentPoint32W(hdc, wtext.c_str(), (int)wtext.size(), &sz);
BITMAPINFO bmi = {};
bmi.bmiHeader.biSize = sizeof(BITMAPINFOHEADER);
bmi.bmiHeader.biWidth = sz.cx;
bmi.bmiHeader.biHeight = -sz.cy;
bmi.bmiHeader.biPlanes = 1;
bmi.bmiHeader.biBitCount = 32;
bmi.bmiHeader.biCompression = BI_RGB;
void* bits = nullptr;
HBITMAP hBmp = CreateDIBSection(hdc, &bmi, DIB_RGB_COLORS, &bits, nullptr, 0);
HBITMAP hOldBmp = (HBITMAP)SelectObject(hdc, hBmp);
SetBkMode(hdc, TRANSPARENT);
SetTextColor(hdc, RGB((int)color[2], (int)color[1], (int)color[0]));
TextOutW(hdc, 0, 0, wtext.c_str(), (int)wtext.size());
cv::Mat textImg(sz.cy, sz.cx, CV_8UC4, bits);
for (int row = 0; row < sz.cy; ++row) {
for (int col = 0; col < sz.cx; ++col) {
cv::Vec4b px = textImg.at<cv::Vec4b>(row, col);
if (px[0] != 0 || px[1] != 0 || px[2] != 0) {
int dy = org.y + row;
int dx = org.x + col;
if (dy >= 0 && dy < img.rows && dx >= 0 && dx < img.cols) {
img.at<cv::Vec3b>(dy, dx) = cv::Vec3b(px[0], px[1], px[2]);
}
}
}
}
SelectObject(hdc, hOldBmp);
SelectObject(hdc, hOldFont);
DeleteObject(hBmp);
DeleteObject(hFont);
DeleteDC(hdc);
}
#endif
int ALPR_OCR_Test() {
std::cout << "=== ALPR_OCR_Test: Japanese License Plate (ANSALPR_OCR) ===" << std::endl;
std::filesystem::path currentPath = std::filesystem::current_path();
std::cout << "Current working directory: " << currentPath << std::endl;
ANSCENTER::ANSALPR* infHandle = nullptr;
std::string licenseKey = "";
std::string modelFilePath = "C:\\Projects\\ANSVIS\\Models\\ANS_GenericALPR_v2.0.zip";
std::string imagePath = "C:\\Programs\\ModelTraining\\JLPD\\data\\test3.jpg";
int engineType = 2; // ANSALPR_OCR
double detectionThreshold = 0.3;
double ocrThreshold = 0.5;
double colourThreshold = 0.0; // No colour detection for this test
// Step 1: Create handle
int createResult = CreateANSALPRHandle(&infHandle, licenseKey.c_str(),
modelFilePath.c_str(), "", engineType, detectionThreshold, ocrThreshold, colourThreshold);
std::cout << "CreateANSALPRHandle result: " << createResult << std::endl;
if (!createResult || !infHandle) {
std::cerr << "Failed to create ANSALPR_OCR handle" << std::endl;
return -1;
}
// Step 2: Set country to Japan
ANSALPR_SetCountry(&infHandle, 5); // JAPAN = 5
std::cout << "Country set to JAPAN" << std::endl;
// Step 3: Load engine
auto engineStart = std::chrono::high_resolution_clock::now();
int loadResult = LoadANSALPREngineHandle(&infHandle);
auto engineEnd = std::chrono::high_resolution_clock::now();
double engineMs = std::chrono::duration<double, std::milli>(engineEnd - engineStart).count();
std::cout << "LoadANSALPREngineHandle result: " << loadResult << " (" << engineMs << " ms)" << std::endl;
if (!loadResult) {
std::cerr << "Failed to load ANSALPR_OCR engine" << std::endl;
ReleaseANSALPRHandle(&infHandle);
return -2;
}
// Step 4: Load image
cv::Mat input = cv::imread(imagePath, cv::IMREAD_COLOR);
if (input.empty()) {
std::cerr << "Failed to load image: " << imagePath << std::endl;
ReleaseANSALPRHandle(&infHandle);
return -3;
}
std::cout << "Image loaded: " << input.cols << "x" << input.rows << std::endl;
cv::Mat frame = input.clone();
int width = frame.cols;
int height = frame.rows;
// Convert to raw BGR bytes for RunInferenceBinary
unsigned int bufferLength = static_cast<unsigned int>(frame.total() * frame.elemSize());
unsigned char* imageBytes = new unsigned char[bufferLength];
std::memcpy(imageBytes, frame.data, bufferLength);
// Step 5: Warmup run
auto warmupStart = std::chrono::high_resolution_clock::now();
std::string detectionResult = ANSALPR_RunInferenceBinary(&infHandle, imageBytes, width, height);
auto warmupEnd = std::chrono::high_resolution_clock::now();
double warmupMs = std::chrono::duration<double, std::milli>(warmupEnd - warmupStart).count();
std::cout << "Warmup inference: " << warmupMs << " ms" << std::endl;
std::cout << "ALPR Result: " << detectionResult << std::endl;
// Step 6: Benchmark
const int benchmarkIterations = 10;
std::vector<double> times;
times.reserve(benchmarkIterations);
for (int i = 0; i < benchmarkIterations; ++i) {
auto t0 = std::chrono::high_resolution_clock::now();
std::string result = ANSALPR_RunInferenceBinary(&infHandle, imageBytes, width, height);
auto t1 = std::chrono::high_resolution_clock::now();
double ms = std::chrono::duration<double, std::milli>(t1 - t0).count();
times.push_back(ms);
std::cout << " Run " << (i + 1) << "/" << benchmarkIterations << ": " << ms << " ms" << std::endl;
}
std::sort(times.begin(), times.end());
double sum = std::accumulate(times.begin(), times.end(), 0.0);
double avg = sum / benchmarkIterations;
double median = (benchmarkIterations % 2 == 0)
? (times[benchmarkIterations / 2 - 1] + times[benchmarkIterations / 2]) / 2.0
: times[benchmarkIterations / 2];
std::cout << "\n=== Benchmark (" << benchmarkIterations << " runs) ===" << std::endl;
std::cout << " Avg: " << avg << " ms" << std::endl;
std::cout << " Median: " << median << " ms" << std::endl;
std::cout << " Min: " << times.front() << " ms" << std::endl;
std::cout << " Max: " << times.back() << " ms" << std::endl;
std::cout << " FPS: " << (1000.0 / avg) << std::endl;
delete[] imageBytes;
// Step 7: Draw results on image
if (!detectionResult.empty()) {
try {
boost::property_tree::ptree pt;
std::stringstream ss(detectionResult);
boost::property_tree::read_json(ss, pt);
BOOST_FOREACH(const boost::property_tree::ptree::value_type& child, pt.get_child("results")) {
const boost::property_tree::ptree& res = child.second;
const auto class_name_raw = GetData<std::string>(res, "class_name");
const std::string class_name = DecodeUnicodeEscapes(class_name_raw);
const auto x = GetData<int>(res, "x");
const auto y = GetData<int>(res, "y");
const auto w = GetData<int>(res, "width");
const auto h = GetData<int>(res, "height");
cv::rectangle(frame, cv::Rect(x, y, w, h), cv::Scalar(0, 255, 0), 2);
std::string extraInfo = GetOptionalValue<std::string>(res, "extra_info", "");
std::cout << " Plate: " << class_name << std::endl;
if (!extraInfo.empty()) {
std::cout << " extra_info: " << extraInfo << std::endl;
}
#ifdef WIN32
{
int textH = (int)(1.5 * 30);
int ty = y - 5 - textH;
if (ty < 0) ty = y + 3;
putTextUnicode(frame, class_name, cv::Point(x, ty),
1.5, cv::Scalar(0, 0, 255), 3);
}
#else
cv::putText(frame, class_name, cv::Point(x, y - 5),
cv::FONT_HERSHEY_SIMPLEX, 1.0, cv::Scalar(0, 0, 255), 2, cv::LINE_AA);
#endif
}
}
catch (const std::exception& e) {
std::cerr << "JSON parse error: " << e.what() << std::endl;
}
}
// Step 8: Display result
cv::Mat display;
double scale = std::min(1920.0 / frame.cols, 1080.0 / frame.rows);
if (scale < 1.0) {
cv::resize(frame, display, cv::Size(), scale, scale);
} else {
display = frame;
}
cv::namedWindow("ALPR_OCR_Test", cv::WINDOW_AUTOSIZE);
cv::imshow("ALPR_OCR_Test", display);
cv::waitKey(0);
// Cleanup
ReleaseANSALPRHandle(&infHandle);
cv::destroyAllWindows();
frame.release();
input.release();
std::cout << "=== ALPR_OCR_Test complete ===" << std::endl;
return 0;
}
int main()
{
#ifdef WIN32
SetConsoleOutputCP(CP_UTF8);
SetConsoleCP(CP_UTF8);
#endif
// ANSLPR_OD_INDOInferences_FileTest();
//ANSLPR_OD_Inferences_FileTest();
//ANSLPR_OD_VideoTest();
//ANSLPR_BigSize_VideoTest();
//ANSLPR_CPU_VideoTest();
//for (int i = 0; i < 100; i++) {
// ANSLPR_CPU_Inferences_FileTest();
//}
//ANSLPR_SingleTask_Test();
//ANSLPR_CPU_StressTest();
//ANSLPR_MultiGPU_StressTest();
//ANSLPR_MultiGPU_StressTest_SimulatedCam();
// ANSLPR_MultiGPU_StressTest_FilePlayer();
//ANSLPR_OD_CPU_VideoTest();
ALPR_OCR_Test();
return 0;
}
Reference in New Issue View Git Blame Copy Permalink