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Use software decoder by default

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This commit is contained in:
Tuan Nghia Nguyen
2026-04-04 20:19:54 +11:00
parent 3a21026790
commit e134ebdf15
24 changed files with 693 additions and 215 deletions
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20
.claude/settings.local.json
View File

@@ -51,7 +51,25 @@
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\logdebug1.txt''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION20.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION21.log''\\).Count\")",
"Bash(powershell -Command \"Select-String ''NEW slot'' ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION22.log'' | ForEach-Object { if \\($_-match ''\\(\\\\d+x\\\\d+\\)''\\) { $matches[1] } } | Group-Object | Sort-Object Count -Descending | Format-Table Name, Count\")"
"Bash(powershell -Command \"Select-String ''NEW slot'' ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION22.log'' | ForEach-Object { if \\($_-match ''\\(\\\\d+x\\\\d+\\)''\\) { $matches[1] } } | Group-Object | Sort-Object Count -Descending | Format-Table Name, Count\")",
"Bash(ls -la /c/Projects/CLionProjects/ANSCORE/modules/ANSODEngine/*.cpp /c/Projects/CLionProjects/ANSCORE/modules/ANSODEngine/*.h)",
"Bash(grep -r \"cudaMalloc\\\\|cudaFree\\\\|cudaStreamCreate\\\\|cudaStreamDestroy\" /c/Projects/CLionProjects/ANSCORE/modules/ANSODEngine/*.cpp)",
"Bash(grep -n \"cudaStreamCreate\\\\|cudaEventCreate\\\\|cudaEventDestroy\\\\|cudaStreamDestroy\\\\|cudaStreamSynchronize\" /c/Projects/CLionProjects/ANSCORE/engines/TensorRTAPI/include/engine/*.inl)",
"Bash(dir \"C:\\\\Projects\\\\CLionProjects\\\\ANSCORE\\\\engines\\\\TensorRTAPI\\\\include\\\\engine\\\\*.h\" /b)",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION26.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION27.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION28.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION29.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION30.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\logging4.txt''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION31.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\loging5.txt''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION32.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION33.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION34.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION35.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION36.log''\\).Count\")",
"Bash(powershell -Command \"\\(Get-Content ''C:\\\\Users\\\\nghia\\\\Downloads\\\\ANSLEGION37.log''\\).Count\")"
]
}
}

10
MediaClient/media/rtsp_player.cpp
View File

@@ -258,7 +258,15 @@ void CRtspPlayer::stop()
// Set flags BEFORE stopping decoder so TCP rx thread stops calling decode()
m_bPlaying = FALSE;
m_bPaused = FALSE;
CVideoPlayer::StopVideoDecoder(); // Stop the video decoder
CVideoPlayer::StopVideoDecoder(); // Stop the video decoder + uninit (free VRAM)
// Close RTSP connection and shut down RX threads.
// Without this, stopped cameras keep TCP/UDP threads running,
// sockets open, and receiving network data — wasting CPU and
// network resources. With 100 cameras and only 5 running,
// 95 idle threads would consume CPU for no purpose.
// Start() → Setup() → open() will reconnect when needed.
m_rtsp.rtsp_close();
}
BOOL CRtspPlayer::pause()

108
MediaClient/media/video_player.cpp
View File

@@ -1275,6 +1275,90 @@ cv::Mat CVideoPlayer::avframeNV12ToCvMat(const AVFrame* frame)
return cv::Mat();
}
}
cv::Mat CVideoPlayer::avframeYUV420PToCvMat(const AVFrame* frame) {
try {
if (!frame || frame->width <= 0 || frame->height <= 0) {
return cv::Mat();
}
const int width = frame->width;
const int height = frame->height;
// YUV420P has 3 separate planes: Y (full res), U (half), V (half).
// OpenCV's cvtColor(COLOR_YUV2BGR_I420) expects a single contiguous buffer
// with Y on top (H rows) and U,V stacked below (H/2 rows total).
// Layout: [Y: W×H] [U: W/2 × H/2] [V: W/2 × H/2]
// Total height = H * 3/2, width = W, single channel.
// If all planes are contiguous with matching strides, wrap directly
const int yStride = frame->linesize[0];
const int uStride = frame->linesize[1];
const int vStride = frame->linesize[2];
// Fast path: planes are packed contiguously with stride == width
if (yStride == width && uStride == width / 2 && vStride == width / 2 &&
frame->data[1] == frame->data[0] + width * height &&
frame->data[2] == frame->data[1] + (width / 2) * (height / 2)) {
// Contiguous I420 — wrap directly, zero copy
cv::Mat yuv(height * 3 / 2, width, CV_8UC1, frame->data[0]);
cv::Mat bgrImage;
cv::cvtColor(yuv, bgrImage, cv::COLOR_YUV2BGR_I420);
if (m_nImageQuality == 1) {
bgrImage.convertTo(bgrImage, -1, 255.0 / 219.0, -16.0 * 255.0 / 219.0);
}
return bgrImage;
}
// Slow path: planes have padding (linesize > width) — copy to contiguous buffer
const int uvWidth = width / 2;
const int uvHeight = height / 2;
const int totalSize = width * height + uvWidth * uvHeight * 2;
cv::Mat yuv(height * 3 / 2, width, CV_8UC1);
uint8_t* dst = yuv.data;
// Copy Y plane (line by line if stride != width)
if (yStride == width) {
std::memcpy(dst, frame->data[0], width * height);
} else {
for (int row = 0; row < height; ++row) {
std::memcpy(dst + row * width, frame->data[0] + row * yStride, width);
}
}
dst += width * height;
// Copy U plane
if (uStride == uvWidth) {
std::memcpy(dst, frame->data[1], uvWidth * uvHeight);
} else {
for (int row = 0; row < uvHeight; ++row) {
std::memcpy(dst + row * uvWidth, frame->data[1] + row * uStride, uvWidth);
}
}
dst += uvWidth * uvHeight;
// Copy V plane
if (vStride == uvWidth) {
std::memcpy(dst, frame->data[2], uvWidth * uvHeight);
} else {
for (int row = 0; row < uvHeight; ++row) {
std::memcpy(dst + row * uvWidth, frame->data[2] + row * vStride, uvWidth);
}
}
cv::Mat bgrImage;
cv::cvtColor(yuv, bgrImage, cv::COLOR_YUV2BGR_I420);
if (m_nImageQuality == 1) {
bgrImage.convertTo(bgrImage, -1, 255.0 / 219.0, -16.0 * 255.0 / 219.0);
}
return bgrImage;
}
catch (const std::exception& e) {
std::cerr << "Exception in avframeYUV420PToCvMat: " << e.what() << std::endl;
return cv::Mat();
}
}
cv::Mat CVideoPlayer::avframeToCVMat(const AVFrame* pFrame) {
std::lock_guard<std::recursive_mutex> lock(_mutex);
try {
@@ -1287,8 +1371,9 @@ cv::Mat CVideoPlayer::avframeToCVMat(const AVFrame* pFrame) {
switch (pFrame->format) {
case AV_PIX_FMT_NV12:
return avframeNV12ToCvMat(pFrame);
case AV_PIX_FMT_YUV420P:
case AV_PIX_FMT_YUVJ420P:
return avframeAnyToCvmat(pFrame);
return avframeYUV420PToCvMat(pFrame);
default:
return avframeAnyToCvmat(pFrame);
@@ -1305,7 +1390,7 @@ CVideoPlayer::CVideoPlayer() :
, m_bAudioInited(FALSE)
, m_bPlaying(FALSE)
, m_bPaused(FALSE)
, m_nHWDecoding(HW_DECODING_AUTO)//(HW_DECODING_AUTO)// HW_DECODING_D3D11 //HW_DECODING_DISABLE
, m_nHWDecoding(HW_DECODING_DISABLE)// Software decode by default — saves VRAM (no NVDEC DPB surfaces)
, m_bUpdown(FALSE)
, m_bSnapshot(FALSE)
, m_nSnapVideoFmt(AV_PIX_FMT_YUVJ420P)
@@ -1740,6 +1825,13 @@ void CVideoPlayer::StopVideoDecoder() {
// Flush decoder to drain and discard any buffered frames,
// so stale reference frames don't corrupt the next session
decoder->flush();
// Free NVDEC decoder context and all GPU surfaces (DPB buffers).
// Stopped cameras should not hold VRAM — with 100 cameras created
// but only 5 running, the 95 idle decoders would consume ~5-10 GB.
// The decoder will be re-initialized automatically when the next
// video packet arrives after Start() is called.
decoder->uninit();
m_bVideoInited = FALSE;
}
// Clear queue but KEEP m_currentImage and m_lastJpegImage —
// getImage()/getJpegImage() will return the last good frame while decoder stabilizes
@@ -1842,6 +1934,13 @@ void CVideoPlayer::setTargetFPS(double intervalMs)
m_targetIntervalMs = intervalMs;
m_targetFPSInitialized = false; // reset timing on change
}
double CVideoPlayer::getLastFrameAgeMs()
{
std::lock_guard<std::recursive_mutex> lock(_mutex);
if (!m_lastDecoderFrameTimeSet) return 0.0;
auto now = std::chrono::steady_clock::now();
return std::chrono::duration<double, std::milli>(now - m_lastDecoderFrameTime).count();
}
void CVideoPlayer::playVideo(uint8* data, int len, uint32 ts, uint16 seq)
{
if (m_bRecording)
@@ -2061,6 +2160,11 @@ void CVideoPlayer::onVideoFrame(AVFrame* frame)
}
}
// Record wall-clock time of every decoded frame (even rate-limited ones).
// Used by getLastFrameAgeMs() to detect truly stale cameras.
m_lastDecoderFrameTime = std::chrono::steady_clock::now();
m_lastDecoderFrameTimeSet = true;
// --- Frame rate limiting ---
// Skip post-decode processing (clone, queue push, CUDA clone) if not enough
// time has elapsed since the last processed frame. The decode itself still

8
MediaClient/media/video_player.h
View File

@@ -148,6 +148,7 @@ public:
// Image quality mode: 0=fast (OpenCV BT.601, ~2ms), 1=quality (sws BT.709+range, ~12ms)
virtual void setImageQuality(int mode) { m_nImageQuality = mode; }
void setTargetFPS(double intervalMs); // Set minimum interval between processed frames in ms (0 = no limit, 100 = ~10 FPS)
double getLastFrameAgeMs(); // Milliseconds since last frame arrived from decoder (0 if no frame yet)
virtual void setRtpMulticast(BOOL flag) {}
virtual void setRtpOverUdp(BOOL flag) {}
@@ -223,6 +224,7 @@ protected:
cv::Mat avframeAnyToCvmat(const AVFrame* frame);
cv::Mat avframeNV12ToCvMat(const AVFrame* frame);
cv::Mat avframeYUV420PToCvMat(const AVFrame* frame); // YUV420P/YUVJ420P → BGR (OpenCV, no sws_scale)
cv::Mat avframeYUVJ420PToCvmat(const AVFrame* frame);
cv::Mat avframeToCVMat(const AVFrame* frame);
@@ -273,6 +275,12 @@ protected:
std::chrono::steady_clock::time_point m_lastProcessedTime; // timestamp of last processed frame
bool m_targetFPSInitialized = false; // first-frame flag
// Wall-clock timestamp of last frame received from the decoder (NOT from getImage).
// Updated in onVideoFrame() for EVERY decoded frame, even rate-limited ones.
// Used by LabVIEW to detect truly stale cameras vs rate-limited ones.
std::chrono::steady_clock::time_point m_lastDecoderFrameTime;
bool m_lastDecoderFrameTimeSet = false;
BOOL m_bPlaying;
BOOL m_bPaused;

27
core/ANSLicensingSystem/ANSLicense.h
View File

@@ -1,6 +1,33 @@
#ifndef ANSLICENSE_H
#define ANSLICENSE_H
// ============================================================================
// Global debug toggle for DebugView (DbgView) logging.
// Define ANSCORE_DEBUGVIEW=1 to enable verbose OutputDebugStringA logging
// across all ANSCORE modules (ANSCV, ANSODEngine, TensorRT engine, etc.).
// Set to 0 for production builds to eliminate all debug output overhead.
// ============================================================================
#ifndef ANSCORE_DEBUGVIEW
#define ANSCORE_DEBUGVIEW 1 // 1 = enabled (debug), 0 = disabled (production)
#endif
// ANS_DBG: Debug logging macro for DebugView (OutputDebugStringA on Windows).
// Usage: ANS_DBG("MyModule", "value=%d ptr=%p", val, ptr);
// Output: [MyModule] value=42 ptr=0x1234
// When ANSCORE_DEBUGVIEW=0, compiles to nothing (zero overhead).
// NOTE: We avoid #include <windows.h> here to prevent winsock.h/winsock2.h
// conflicts. Instead, forward-declare OutputDebugStringA directly.
#if ANSCORE_DEBUGVIEW && defined(_WIN32)
extern "C" __declspec(dllimport) void __stdcall OutputDebugStringA(const char* lpOutputString);
#define ANS_DBG(tag, fmt, ...) do { \
char _ans_dbg_buf[1024]; \
snprintf(_ans_dbg_buf, sizeof(_ans_dbg_buf), "[" tag "] " fmt "\n", ##__VA_ARGS__); \
OutputDebugStringA(_ans_dbg_buf); \
} while(0)
#else
#define ANS_DBG(tag, fmt, ...) ((void)0)
#endif
#ifdef ANSLICENSE_EXPORTS
#define ANSLICENSE_API __declspec(dllexport)
#else

70
engines/TensorRTAPI/include/engine/EngineBuildLoadNetwork.inl
View File

@@ -623,6 +623,65 @@ bool Engine<T>::buildLoadNetwork(std::string onnxModelPath, const std::array<flo
template <typename T>
bool Engine<T>::loadNetwork(std::string trtModelPath, const std::array<float, 3>& subVals, const std::array<float, 3>& divVals, bool normalize)
{
// Install a custom OpenCV CUDA allocator that uses cudaMallocAsync/cudaFreeAsync
// instead of the default cudaMalloc/cudaFree. The stream-ordered allocator
// respects the cudaMemPool release threshold (set to 0), so freed memory is
// returned to the GPU immediately instead of being cached forever.
//
// The default cudaMalloc/cudaFree allocator caches all freed blocks permanently
// (no API to force release), causing VRAM to grow monotonically when GpuMat
// objects of varying sizes are allocated and freed repeatedly (different batch
// sizes, different image resolutions across cameras).
{
static std::once_flag s_allocatorFlag;
std::call_once(s_allocatorFlag, []() {
// Set release threshold to 0 on all GPUs
int deviceCount = 0;
cudaGetDeviceCount(&deviceCount);
for (int d = 0; d < deviceCount; ++d) {
cudaMemPool_t pool = nullptr;
if (cudaDeviceGetDefaultMemPool(&pool, d) == cudaSuccess && pool) {
uint64_t threshold = 0;
cudaMemPoolSetAttribute(pool, cudaMemPoolAttrReleaseThreshold, &threshold);
}
}
// Custom allocator: uses cudaMallocAsync on stream 0 (behaves like
// synchronous cudaMalloc but goes through the stream-ordered pool).
struct AsyncAllocator : cv::cuda::GpuMat::Allocator {
bool allocate(cv::cuda::GpuMat* mat, int rows, int cols, size_t elemSize) override {
// Same logic as OpenCV's default allocator, but using cudaMallocAsync
size_t step = elemSize * cols;
// Align step to 256 bytes (same as default allocator)
step = (step + 255) & ~size_t(255);
void* ptr = nullptr;
cudaError_t err = cudaMallocAsync(&ptr, step * rows, nullptr); // stream 0
if (err != cudaSuccess || !ptr) {
// Fallback to regular cudaMalloc if async not supported
err = cudaMalloc(&ptr, step * rows);
if (err != cudaSuccess) return false;
}
mat->data = static_cast<uchar*>(ptr);
mat->step = step;
mat->refcount = static_cast<int*>(cv::fastMalloc(sizeof(int)));
*mat->refcount = 1;
return true;
}
void free(cv::cuda::GpuMat* mat) override {
cudaFreeAsync(mat->data, nullptr); // stream 0 — goes through pool with threshold=0
cv::fastFree(mat->refcount);
mat->data = nullptr;
mat->datastart = nullptr;
mat->dataend = nullptr;
mat->refcount = nullptr;
}
};
static AsyncAllocator s_allocator;
cv::cuda::GpuMat::setDefaultAllocator(&s_allocator);
ANS_DBG("TRT_Load", "Custom CUDA async allocator installed — VRAM freed immediately on GpuMat release");
});
}
m_lastLoadFailedVRAM = false; // reset on each load attempt
m_subVals = subVals;
m_divVals = divVals;
@@ -958,11 +1017,13 @@ trt_cache_create_context:
m_context = std::unique_ptr<nvinfer1::IExecutionContext>(m_engine->createExecutionContext());
if (!m_context) {
ANS_DBG("TRT_Load", "ERROR: createExecutionContext returned null");
logEngineEvent("[Engine] loadNetwork FAIL: createExecutionContext returned null for "
+ trtModelPath, true);
return false;
}
ANS_DBG("TRT_Load", "Execution context created OK for %s", trtModelPath.c_str());
if (m_verbose) std::cout << "Info: Execution context created successfully" << std::endl;
// ============================================================================
@@ -1135,6 +1196,15 @@ trt_cache_create_context:
}
}
{
size_t vramFree = 0, vramTotal = 0;
cudaMemGetInfo(&vramFree, &vramTotal);
ANS_DBG("TRT_Load", "Buffers allocated: %zuMB, VRAM: %zuMB used / %zuMB free / %zuMB total",
totalAllocated / (1024*1024),
(vramTotal - vramFree) / (1024*1024),
vramFree / (1024*1024),
vramTotal / (1024*1024));
}
if (m_verbose) std::cout << "\nInfo: Total GPU memory allocated: " << totalAllocated / (1024 * 1024) << " MiB" << std::endl;
// -- Pinned output buffers (CUDA graph prerequisite) -----------------------

24
engines/TensorRTAPI/include/engine/EngineMultiGpu.inl
View File

@@ -607,6 +607,7 @@ bool Engine<T>::runInferenceFromPool(
// harmless — the second one finds a fresh slot immediately.
InferenceSlot* slot = nullptr;
bool kickedGrowth = false;
auto _poolAcquireStart = std::chrono::steady_clock::now();
{
std::unique_lock<std::mutex> lock(m_slotMutex);
@@ -630,6 +631,8 @@ bool Engine<T>::runInferenceFromPool(
}
if (!slot) {
ANS_DBG("TRT_Pool", "ALL SLOTS BUSY: %zu slots, active=%d — waiting for free slot",
n, m_activeCount.load());
// All slots busy. In elastic mode, proactively grow the
// pool in the background so the next request has a slot
// on a different GPU. We only kick once per wait cycle.
@@ -672,7 +675,17 @@ bool Engine<T>::runInferenceFromPool(
}
// -- 3. Still no slot => reject ---------------------------------------
{
double _acquireMs = std::chrono::duration<double, std::milli>(
std::chrono::steady_clock::now() - _poolAcquireStart).count();
if (_acquireMs > 100.0) {
ANS_DBG("TRT_Pool", "SLOW slot acquire: %.1fms slot=%p gpu=%d active=%d/%zu",
_acquireMs, (void*)slot, slot ? slot->deviceIndex : -1,
m_activeCount.load(), m_slots.size());
}
}
if (!slot) {
ANS_DBG("TRT_Pool", "ERROR: No slot available — all %zu slots busy, timeout", m_slots.size());
std::string errMsg = "[Engine] runInferenceFromPool FAIL: Capacity reached -- all "
+ std::to_string(m_activeCount.load()) + "/" + std::to_string(m_totalCapacity)
+ " slot(s) busy"
@@ -699,12 +712,23 @@ bool Engine<T>::runInferenceFromPool(
if (currentDev != slot->deviceIndex) {
cudaSetDevice(slot->deviceIndex);
}
ANS_DBG("TRT_Pool", "Slot dispatch: gpu=%d active=%d/%zu",
slot->deviceIndex, m_activeCount.load(), m_slots.size());
auto _slotStart = std::chrono::steady_clock::now();
result = slot->engine->runInference(inputs, featureVectors);
auto _slotEnd = std::chrono::steady_clock::now();
double _slotMs = std::chrono::duration<double, std::milli>(_slotEnd - _slotStart).count();
if (_slotMs > 500.0) {
ANS_DBG("TRT_Pool", "SLOW slot inference: %.1fms gpu=%d active=%d/%zu",
_slotMs, slot->deviceIndex, m_activeCount.load(), m_slots.size());
}
}
catch (const std::exception& ex) {
ANS_DBG("TRT_Pool", "ERROR: runInference threw: %s", ex.what());
std::cout << "Error [Pool]: runInference threw: " << ex.what() << std::endl;
}
catch (...) {
ANS_DBG("TRT_Pool", "ERROR: runInference threw unknown exception");
std::cout << "Error [Pool]: runInference threw unknown exception" << std::endl;
}

178
engines/TensorRTAPI/include/engine/EngineRunInference.inl
View File

@@ -1,8 +1,10 @@
#pragma once
#include <cstring>
#include <chrono>
#include <filesystem>
#include <semaphore>
#include "TRTCompat.h"
#include "ANSLicense.h" // ANS_DBG macro for DebugView logging
// Per-device mutex for CUDA graph capture.
// TRT's enqueueV3 uses shared internal resources (workspace, memory pools)
@@ -86,11 +88,9 @@ static inline cudaError_t cudaStreamSynchronize_Safe(cudaStream_t stream) {
cudaError_t err = cudaStreamQuery(stream);
if (err != cudaErrorNotReady) return err;
auto syncStart = std::chrono::steady_clock::now();
// Short Sleep(0) fast path (~10 iterations) catches sub-ms kernel completions.
// Then switch to Sleep(1) to give cleanup operations (cuArrayDestroy, cuMemFree)
// a window to acquire the exclusive nvcuda64 SRW lock.
// Previously used 1000 Sleep(0) iterations which hogged the SRW lock and
// caused ~20-second stalls when concurrent cleanup needed exclusive access.
for (int i = 0; i < 10; ++i) {
Sleep(0);
err = cudaStreamQuery(stream);
@@ -98,10 +98,21 @@ static inline cudaError_t cudaStreamSynchronize_Safe(cudaStream_t stream) {
}
// 1ms sleeps — adds negligible latency at 30 FPS but prevents SRW lock starvation.
int sleepCount = 0;
while (true) {
Sleep(1);
sleepCount++;
err = cudaStreamQuery(stream);
if (err != cudaErrorNotReady) return err;
if (err != cudaErrorNotReady) {
// Log if sync took too long (>500ms indicates GPU stall)
auto elapsed = std::chrono::duration<double, std::milli>(
std::chrono::steady_clock::now() - syncStart).count();
if (elapsed > 500.0) {
ANS_DBG("TRT_Engine", "SLOW SYNC: %.1fms (%d sleeps) stream=%p err=%d",
elapsed, sleepCount, (void*)stream, (int)err);
}
return err;
}
}
}
@@ -368,6 +379,71 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
return false;
}
// ============================================================================
// SM=100% DETECTOR — tracks inference timing trends to catch the exact
// moment GPU becomes saturated. Logs every 50 inferences with rolling
// average, and immediately when degradation is detected.
// ============================================================================
// Global (process-wide) counters shared across all engine instances/threads
static std::atomic<int64_t> s_globalInfCount{0};
static std::atomic<int> s_globalActiveInf{0}; // currently in-flight inferences
static std::atomic<double> s_globalLastAvgMs{0.0}; // last known avg inference time
const int64_t myInfNum = s_globalInfCount.fetch_add(1) + 1;
s_globalActiveInf.fetch_add(1);
// Per-thread tracking
{
static thread_local int64_t s_infCount = 0;
static thread_local std::chrono::steady_clock::time_point s_lastLog;
static thread_local double s_rollingAvgMs = 0.0;
static thread_local double s_baselineMs = 0.0; // avg during first 100 inferences
static thread_local double s_maxMs = 0.0;
static thread_local bool s_degradationLogged = false;
s_infCount++;
if (s_infCount == 1) {
s_lastLog = std::chrono::steady_clock::now();
ANS_DBG("TRT_SM100", "FIRST inference — engine alive, globalInf=%lld", myInfNum);
}
// Log every 50 inferences (more frequent than 500 to catch transitions)
if (s_infCount % 50 == 0) {
auto now = std::chrono::steady_clock::now();
double elapsed = std::chrono::duration<double>(now - s_lastLog).count();
double fps = (elapsed > 0) ? (50.0 / elapsed) : 0;
s_lastLog = now;
size_t vramFree = 0, vramTotal = 0;
cudaMemGetInfo(&vramFree, &vramTotal);
size_t vramUsedMB = (vramTotal - vramFree) / (1024 * 1024);
size_t vramFreeMB = vramFree / (1024 * 1024);
ANS_DBG("TRT_SM100", "#%lld [global=%lld active=%d] %.1f inf/sec avgMs=%.1f maxMs=%.1f batch=%d graphs=%zu VRAM=%zuMB/%zuMB",
s_infCount, myInfNum, s_globalActiveInf.load(),
fps, s_rollingAvgMs, s_maxMs,
(int)inputs[0].size(), m_graphExecs.size(),
vramUsedMB, vramFreeMB);
// Capture baseline from first 100 inferences
if (s_infCount == 100) {
s_baselineMs = s_rollingAvgMs;
ANS_DBG("TRT_SM100", "BASELINE established: %.1fms/inference", s_baselineMs);
}
// Detect degradation: avg >3x baseline AND baseline is set
if (s_baselineMs > 0 && s_rollingAvgMs > s_baselineMs * 3.0 && !s_degradationLogged) {
s_degradationLogged = true;
ANS_DBG("TRT_SM100", "*** DEGRADATION DETECTED *** avg=%.1fms baseline=%.1fms (%.1fx) VRAM=%zuMB/%zuMB active=%d",
s_rollingAvgMs, s_baselineMs, s_rollingAvgMs / s_baselineMs,
vramUsedMB, vramFreeMB, s_globalActiveInf.load());
}
// Reset max for next window
s_maxMs = 0.0;
}
}
const auto numInputs = m_inputDims.size();
if (inputs.size() != numInputs) {
std::cout << "Error: Wrong number of inputs. Expected: " << numInputs
@@ -457,6 +533,9 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
}
if (anyDimChanged) {
ANS_DBG("TRT_Engine", "Shape change detected: batch %d -> %d (graphsCached=%zu)",
m_lastBatchSize, batchSize, m_graphExecs.size());
// === First-time diagnostics (verbose, once) ===
const bool firstTime = !m_batchShapeChangeLogged;
@@ -536,7 +615,10 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
<< newDims.d[3] << "]" << std::endl;
}
ANS_DBG("TRT_Engine", "setInputShape('%s') [%d,%d,%d,%d]",
tensorName, newDims.d[0], newDims.d[1], newDims.d[2], newDims.d[3]);
if (!m_context->setInputShape(tensorName, newDims)) {
ANS_DBG("TRT_Engine", "ERROR: setInputShape FAILED for '%s'", tensorName);
std::cout << "Error: Failed to set input shape for '" << tensorName << "'" << std::endl;
return false;
}
@@ -576,6 +658,25 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
m_lastBatchSize = batchSize;
m_batchShapeChangeLogged = true;
// CRITICAL: Invalidate all cached CUDA graphs after shape change.
// Graphs were captured with the OLD context state (old tensor shapes).
// Launching them after setInputShape() produces undefined GPU behavior
// (invalid kernel sequences, SM lockup at 100%, inference hang).
if (!m_graphExecs.empty()) {
size_t destroyed = m_graphExecs.size();
for (auto& [bs, ge] : m_graphExecs) {
if (ge) cudaGraphExecDestroy(ge);
}
m_graphExecs.clear();
ANS_DBG("TRT_Engine", "INVALIDATED %zu cached CUDA graphs after shape change (batch=%d)",
destroyed, batchSize);
if (m_verbose || firstTime) {
std::cout << "Info: Invalidated " << destroyed
<< " cached CUDA graphs after shape change" << std::endl;
}
}
if (m_verbose && firstTime) {
std::cout << "\nInfo: Input shapes updated successfully for batch size "
<< batchSize << " ✓\n" << std::endl;
@@ -619,6 +720,7 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
//
// GpuMat-lifetime: preprocessedBuffers keeps GpuMats alive past the final
// cudaStreamSynchronize, so cudaFree() doesn't stall the pipeline.
auto _prepStart = std::chrono::steady_clock::now();
cv::cuda::Stream cvInferStream = cv::cuda::StreamAccessor::wrapStream(m_inferenceStream);
std::vector<cv::cuda::GpuMat> preprocessedBuffers;
preprocessedBuffers.reserve(numInputs);
@@ -647,6 +749,14 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
}
}
{
double _prepMs = std::chrono::duration<double, std::milli>(
std::chrono::steady_clock::now() - _prepStart).count();
if (_prepMs > 100.0) {
ANS_DBG("TRT_SM100", "SLOW PREPROCESS: %.1fms batch=%d (blobFromGpuMats+D2D)", _prepMs, batchSize);
}
}
// ============================================================================
// PRE-ALLOCATE OUTPUT STRUCTURE
// ============================================================================
@@ -690,6 +800,8 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
if (canGraph) {
auto& graphExec = m_graphExecs[batchSize]; // inserts nullptr on first access
if (!graphExec) {
ANS_DBG("TRT_Engine", "CUDA graph CAPTURE starting for batch=%d (cached=%zu)",
batchSize, m_graphExecs.size());
// First call for this batchSize -- capture a new graph.
// Serialise captures across all Engine instances on this device to
// prevent TRT's shared workspace from creating cross-stream
@@ -727,9 +839,13 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
if (cudaGraphInstantiate(&exec, graph, nullptr, nullptr, 0) == cudaSuccess)
graphExec = exec;
cudaGraphDestroy(graph);
ANS_DBG("TRT_Engine", "CUDA graph CAPTURED OK for batch=%d exec=%p",
batchSize, (void*)graphExec);
}
if (!graphExec) {
ANS_DBG("TRT_Engine", "CUDA graph capture FAILED for batch=%d — falling back to direct path",
batchSize);
std::cout << "Warning: CUDA graph capture failed for batchSize="
<< batchSize << " -- falling back to direct inference path." << std::endl;
// Disable graph acceleration for this Engine instance.
@@ -740,9 +856,17 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
}
if (graphExec) {
ANS_DBG("TRT_Engine", "CUDA graph LAUNCH batch=%d exec=%p", batchSize, (void*)graphExec);
// Launch the pre-captured graph (single API call replaces many).
auto _graphStart = std::chrono::steady_clock::now();
cudaGraphLaunch(graphExec, m_inferenceStream);
cudaStreamSynchronize_Safe(m_inferenceStream);
auto _graphEnd = std::chrono::steady_clock::now();
double _graphMs = std::chrono::duration<double, std::milli>(_graphEnd - _graphStart).count();
if (_graphMs > 500.0) {
ANS_DBG("TRT_SM100", "SLOW GRAPH: %.1fms batch=%d active=%d",
_graphMs, batchSize, s_globalActiveInf.load());
}
// CPU memcpy: pinned buffers -> featureVectors (interleaved by batch).
for (int batch = 0; batch < batchSize; ++batch) {
@@ -762,8 +886,16 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
// ----------------------
// Used when pinned buffers are unavailable or graph capture failed.
if (!graphUsed) {
ANS_DBG("TRT_Engine", "Direct path (no graph) batch=%d", batchSize);
auto enqueueStart = std::chrono::steady_clock::now();
bool success = TRT_ENQUEUE(m_context.get(), m_inferenceStream, m_buffers);
auto enqueueEnd = std::chrono::steady_clock::now();
double enqueueMs = std::chrono::duration<double, std::milli>(enqueueEnd - enqueueStart).count();
if (enqueueMs > 500.0) {
ANS_DBG("TRT_Engine", "SLOW ENQUEUE: %.1fms batch=%d (enqueueV3 blocked!)", enqueueMs, batchSize);
}
if (!success) {
ANS_DBG("TRT_Engine", "ERROR: enqueueV3 FAILED batch=%d", batchSize);
std::string debugInfo = "[Engine] runInference FAIL: enqueue returned false, batch="
+ std::to_string(batchSize)
+ ", dimsSpecified=" + (m_context->allInputDimensionsSpecified() ? "YES" : "NO");
@@ -805,8 +937,16 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
}
}
auto syncStart = std::chrono::steady_clock::now();
cudaError_t syncErr = cudaStreamSynchronize_Safe(m_inferenceStream);
auto syncEnd = std::chrono::steady_clock::now();
double syncMs = std::chrono::duration<double, std::milli>(syncEnd - syncStart).count();
if (syncMs > 500.0) {
ANS_DBG("TRT_Engine", "SLOW INFERENCE SYNC: %.1fms batch=%d (direct path)", syncMs, batchSize);
}
if (syncErr != cudaSuccess) {
ANS_DBG("TRT_Engine", "ERROR: cudaStreamSync FAILED err=%d (%s)",
(int)syncErr, cudaGetErrorString(syncErr));
std::string errMsg = "[Engine] runInference FAIL: cudaStreamSynchronize: "
+ std::string(cudaGetErrorString(syncErr));
std::cout << errMsg << std::endl;
@@ -815,5 +955,33 @@ bool Engine<T>::runInference(const std::vector<std::vector<cv::cuda::GpuMat>>& i
}
}
// ============================================================================
// SM=100% DETECTOR — end-of-inference timing
// ============================================================================
{
static thread_local double s_ema = 0;
static thread_local std::chrono::steady_clock::time_point s_prevEnd;
static thread_local bool s_firstDone = false;
auto _now = std::chrono::steady_clock::now();
if (s_firstDone) {
double sinceLastMs = std::chrono::duration<double, std::milli>(_now - s_prevEnd).count();
// If time between consecutive inferences jumps dramatically,
// something blocked the thread (SM=100% or mutex contention)
if (s_ema > 0 && sinceLastMs > s_ema * 3.0 && sinceLastMs > 500.0) {
size_t vf = 0, vt = 0;
cudaMemGetInfo(&vf, &vt);
ANS_DBG("TRT_SM100", "GAP DETECTED: %.1fms between inferences (avg=%.1fms, %.1fx) active=%d VRAM=%zuMB free",
sinceLastMs, s_ema, sinceLastMs / s_ema,
s_globalActiveInf.load(), vf / (1024*1024));
}
s_ema = (s_ema == 0) ? sinceLastMs : (0.9 * s_ema + 0.1 * sinceLastMs);
}
s_prevEnd = _now;
s_firstDone = true;
s_globalActiveInf.fetch_sub(1);
}
return true;
}

67
engines/TensorRTAPI/include/engine/EngineUtilities.inl
View File

@@ -27,25 +27,29 @@ template <typename T>
cv::cuda::GpuMat Engine<T>::resizeKeepAspectRatioPadRightBottom(const cv::cuda::GpuMat& input,
size_t height, size_t width,
const cv::Scalar& bgcolor) {
// Ensure input is valid
if (input.empty()) {
return cv::cuda::GpuMat();
}
// Create a CUDA stream
cv::cuda::Stream stream;
// Calculate aspect ratio and unpadded dimensions
// Use a thread_local stream to avoid creating a new CUDA stream per call.
// Creating cv::cuda::Stream() each call leaks stream handles under WDDM.
thread_local cv::cuda::Stream stream;
float r = std::min(static_cast<float>(width) / input.cols, static_cast<float>(height) / input.rows);
size_t unpad_w = static_cast<size_t>(r * input.cols);
size_t unpad_h = static_cast<size_t>(r * input.rows);
// Resize the input image
cv::cuda::GpuMat re;
re.create(unpad_h, unpad_w, input.type());
re.create(static_cast<int>(unpad_h), static_cast<int>(unpad_w), input.type());
cv::cuda::resize(input, re, re.size(), 0, 0, cv::INTER_LINEAR, stream);
// Create the output image and fill with the background color
cv::cuda::GpuMat out;
out.create(height, width, input.type());
out.create(static_cast<int>(height), static_cast<int>(width), input.type());
out.setTo(bgcolor, stream);
// Copy the resized content into the top-left corner of the output image
// Copy the resized content into the top-left corner
re.copyTo(out(cv::Rect(0, 0, re.cols, re.rows)), stream);
stream.waitForCompletion();
return out;
@@ -195,41 +199,51 @@ cv::cuda::GpuMat Engine<T>::blobFromGpuMats(const std::vector<cv::cuda::GpuMat>
const int W = batchInput[0].cols;
const int batch = static_cast<int>(batchInput.size());
const size_t planeSize = static_cast<size_t>(H) * W; // pixels per channel
const int totalElems = batch * 3 * static_cast<int>(planeSize);
// Output blob: planar NCHW layout stored as a single-channel GpuMat.
// Total elements = batch * 3 * H * W.
cv::cuda::GpuMat blob(1, batch * 3 * static_cast<int>(planeSize), CV_32FC1);
// thread_local cached buffers — reused across calls on the same thread.
// KEY: allocate for MAX seen size, never shrink. This prevents the VRAM leak
// caused by OpenCV's GpuMat pool growing unbounded when batch sizes alternate
// (e.g., batch=1,6,1,6 → each size triggers new alloc, old goes to pool, never freed).
thread_local cv::cuda::GpuMat tl_blob;
thread_local cv::cuda::GpuMat tl_floatImg;
thread_local int tl_blobMaxElems = 0;
if (totalElems > tl_blobMaxElems) {
tl_blob = cv::cuda::GpuMat(1, totalElems, CV_32FC1);
tl_blobMaxElems = totalElems;
size_t blobBytes = static_cast<size_t>(totalElems) * sizeof(float);
ANS_DBG("TRT_Preproc", "blobFromGpuMats: ALLOC blob batch=%d %dx%d %.1fMB (new max)",
batch, W, H, blobBytes / (1024.0 * 1024.0));
}
// Use a sub-region of the cached blob for the current batch
cv::cuda::GpuMat blob = tl_blob.colRange(0, totalElems);
for (int img = 0; img < batch; ++img) {
// 1. Convert to float and normalise while still in HWC (interleaved) format.
// Channel-wise subtract / divide operate correctly on interleaved data.
cv::cuda::GpuMat floatImg;
if (normalize) {
batchInput[img].convertTo(floatImg, CV_32FC3, 1.f / 255.f, stream);
batchInput[img].convertTo(tl_floatImg, CV_32FC3, 1.f / 255.f, stream);
} else {
batchInput[img].convertTo(floatImg, CV_32FC3, 1.0, stream);
batchInput[img].convertTo(tl_floatImg, CV_32FC3, 1.0, stream);
}
cv::cuda::subtract(floatImg, cv::Scalar(subVals[0], subVals[1], subVals[2]), floatImg, cv::noArray(), -1, stream);
cv::cuda::divide(floatImg, cv::Scalar(divVals[0], divVals[1], divVals[2]), floatImg, 1, -1, stream);
cv::cuda::subtract(tl_floatImg, cv::Scalar(subVals[0], subVals[1], subVals[2]), tl_floatImg, cv::noArray(), -1, stream);
cv::cuda::divide(tl_floatImg, cv::Scalar(divVals[0], divVals[1], divVals[2]), tl_floatImg, 1, -1, stream);
// 2. Split normalised HWC image into CHW planes directly into the blob.
size_t offset = static_cast<size_t>(img) * 3 * planeSize;
if (swapRB) {
// BGR input -> RGB planes: B goes to plane 2, G to plane 1, R to plane 0
std::vector<cv::cuda::GpuMat> channels{
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset + 2 * planeSize), // B -> plane 2
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset + planeSize), // G -> plane 1
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset)}; // R -> plane 0
cv::cuda::split(floatImg, channels, stream);
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset + 2 * planeSize),
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset + planeSize),
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset)};
cv::cuda::split(tl_floatImg, channels, stream);
} else {
// BGR input -> BGR planes: keep channel order
std::vector<cv::cuda::GpuMat> channels{
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset),
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset + planeSize),
cv::cuda::GpuMat(H, W, CV_32FC1, blob.ptr<float>() + offset + 2 * planeSize)};
cv::cuda::split(floatImg, channels, stream);
cv::cuda::split(tl_floatImg, channels, stream);
}
}
@@ -239,7 +253,6 @@ cv::cuda::GpuMat Engine<T>::blobFromGpuMats(const std::vector<cv::cuda::GpuMat>
template <typename T> void Engine<T>::clearGpuBuffers() {
if (!m_buffers.empty()) {
// Free ALL I/O GPU buffers (both inputs and outputs).
// Previously only outputs were freed, leaking input allocations from loadNetwork().
for (void* ptr : m_buffers) {
if (ptr) {
Util::checkCudaErrorCode(cudaFree(ptr));
@@ -247,4 +260,8 @@ template <typename T> void Engine<T>::clearGpuBuffers() {
}
m_buffers.clear();
}
// Note: blob/floatImg caches are thread_local inside blobFromGpuMats (static method).
// They are cleaned up automatically when threads exit.
ANS_DBG("TRT_Engine", "clearGpuBuffers: I/O buffers released");
}

39
modules/ANSCV/ANSFLV.cpp
View File

@@ -218,44 +218,25 @@ namespace ANSCENTER {
}
bool ANSFLVClient::areImagesIdentical(const cv::Mat& img1, const cv::Mat& img2) {
// Quick size and type checks
if (img1.size() != img2.size() || img1.type() != img2.type()) {
return false;
}
// Use decoder frame age — returns "stale" only if no decoder output for 5+ seconds.
double ageMs = _playerClient->getLastFrameAgeMs();
if (ageMs > 5000.0) return true; // Truly stale
if (ageMs > 0.0) return false; // Decoder alive
// Handle empty images
if (img1.empty()) {
return img2.empty();
}
// Fallback for startup (no frame decoded yet)
if (img1.empty() && img2.empty()) return true;
if (img1.empty() || img2.empty()) return false;
if (img1.size() != img2.size() || img1.type() != img2.type()) return false;
if (img1.data == img2.data) return true;
if (img1.isContinuous() && img2.isContinuous()) {
const size_t totalBytes = img1.total() * img1.elemSize();
// Fast rejection: sample 5 positions across contiguous memory
const size_t quarter = totalBytes / 4;
const size_t half = totalBytes / 2;
const size_t threeQuarter = 3 * totalBytes / 4;
if (img1.data[0] != img2.data[0] ||
img1.data[quarter] != img2.data[quarter] ||
img1.data[half] != img2.data[half] ||
img1.data[threeQuarter] != img2.data[threeQuarter] ||
img1.data[totalBytes - 1] != img2.data[totalBytes - 1]) {
return false;
}
// Full comparison
return std::memcmp(img1.data, img2.data, totalBytes) == 0;
}
// Row-by-row comparison for non-continuous images (e.g., ROI sub-matrices)
const size_t rowSize = img1.cols * img1.elemSize();
for (int i = 0; i < img1.rows; i++) {
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) {
return false;
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) return false;
}
}
return true;
}
cv::Mat ANSFLVClient::GetImage(int& width, int& height, int64_t& pts) {

37
modules/ANSCV/ANSMJPEG.cpp
View File

@@ -208,44 +208,23 @@ namespace ANSCENTER {
}
bool ANSMJPEGClient::areImagesIdentical(const cv::Mat& img1, const cv::Mat& img2) {
// Quick size and type checks
if (img1.size() != img2.size() || img1.type() != img2.type()) {
return false;
}
double ageMs = _playerClient->getLastFrameAgeMs();
if (ageMs > 5000.0) return true;
if (ageMs > 0.0) return false;
// Handle empty images
if (img1.empty()) {
return img2.empty();
}
if (img1.empty() && img2.empty()) return true;
if (img1.empty() || img2.empty()) return false;
if (img1.size() != img2.size() || img1.type() != img2.type()) return false;
if (img1.data == img2.data) return true;
if (img1.isContinuous() && img2.isContinuous()) {
const size_t totalBytes = img1.total() * img1.elemSize();
// Fast rejection: sample 5 positions across contiguous memory
const size_t quarter = totalBytes / 4;
const size_t half = totalBytes / 2;
const size_t threeQuarter = 3 * totalBytes / 4;
if (img1.data[0] != img2.data[0] ||
img1.data[quarter] != img2.data[quarter] ||
img1.data[half] != img2.data[half] ||
img1.data[threeQuarter] != img2.data[threeQuarter] ||
img1.data[totalBytes - 1] != img2.data[totalBytes - 1]) {
return false;
}
// Full comparison
return std::memcmp(img1.data, img2.data, totalBytes) == 0;
}
// Row-by-row comparison for non-continuous images (e.g., ROI sub-matrices)
const size_t rowSize = img1.cols * img1.elemSize();
for (int i = 0; i < img1.rows; i++) {
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) {
return false;
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) return false;
}
}
return true;
}
cv::Mat ANSMJPEGClient::GetImage(int& width, int& height, int64_t& pts) {

37
modules/ANSCV/ANSRTMP.cpp
View File

@@ -213,43 +213,22 @@ namespace ANSCENTER {
}
bool ANSRTMPClient::areImagesIdentical(const cv::Mat& img1, const cv::Mat& img2) {
// Quick size and type checks
if (img1.size() != img2.size() || img1.type() != img2.type()) {
return false;
}
double ageMs = _playerClient->getLastFrameAgeMs();
if (ageMs > 5000.0) return true;
if (ageMs > 0.0) return false;
// Handle empty images
if (img1.empty()) {
return img2.empty();
}
if (img1.empty() && img2.empty()) return true;
if (img1.empty() || img2.empty()) return false;
if (img1.size() != img2.size() || img1.type() != img2.type()) return false;
if (img1.data == img2.data) return true;
if (img1.isContinuous() && img2.isContinuous()) {
const size_t totalBytes = img1.total() * img1.elemSize();
// Fast rejection: sample 5 positions across contiguous memory
// Catches 99.99% of different frames immediately
const size_t quarter = totalBytes / 4;
const size_t half = totalBytes / 2;
const size_t threeQuarter = 3 * totalBytes / 4;
if (img1.data[0] != img2.data[0] ||
img1.data[quarter] != img2.data[quarter] ||
img1.data[half] != img2.data[half] ||
img1.data[threeQuarter] != img2.data[threeQuarter] ||
img1.data[totalBytes - 1] != img2.data[totalBytes - 1]) {
return false;
}
// Full comparison
return std::memcmp(img1.data, img2.data, totalBytes) == 0;
}
// Row-by-row comparison for non-continuous images (e.g., ROI sub-matrices)
const size_t rowSize = img1.cols * img1.elemSize();
for (int i = 0; i < img1.rows; i++) {
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) {
return false;
}
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) return false;
}
return true;

168
modules/ANSCV/ANSRTSP.cpp
View File

@@ -2,7 +2,9 @@
#include "ANSMatRegistry.h"
#include "ANSGpuFrameOps.h"
#include "GpuNV12SlotPool.h"
#include "ANSLicense.h" // ANS_DBG macro
#include <memory>
#include <chrono>
#include <format>
#include "media_codec.h"
#include <cstdint>
@@ -69,6 +71,7 @@ namespace ANSCENTER {
}
void ANSRTSPClient::Destroy() {
ANS_DBG("RTSP_Lifecycle", "DESTROY called: url=%s playing=%d", _url.c_str(), (int)_isPlaying);
// Move the player client pointer out of the lock scope, then
// close it OUTSIDE the mutex. close() calls cuArrayDestroy /
// cuMemFree which acquire an EXCLUSIVE SRW lock inside nvcuda64.
@@ -126,6 +129,24 @@ namespace ANSCENTER {
// belong to the global GpuNV12SlotPool, not the decoder.
if (clientToClose) {
clientToClose->close();
// Force CUDA runtime to release all cached memory from the destroyed
// NVDEC decoder. Without this, cuMemFree returns memory to the CUDA
// driver's internal cache, and the next camera creation allocates fresh
// memory → VRAM grows by ~200-300MB per destroy/create cycle.
// cudaDeviceSynchronize ensures all pending GPU ops are done, then
// cudaMemPool trim releases the freed blocks back to the OS.
cudaDeviceSynchronize();
cudaMemPool_t memPool = nullptr;
int currentDev = 0;
cudaGetDevice(&currentDev);
if (cudaDeviceGetDefaultMemPool(&memPool, currentDev) == cudaSuccess && memPool) {
cudaMemPoolTrimTo(memPool, 0); // Release all unused memory
}
size_t vramFree = 0, vramTotal = 0;
cudaMemGetInfo(&vramFree, &vramTotal);
ANS_DBG("RTSP_Destroy", "NVDEC closed + memPool trimmed GPU%d VRAM=%zuMB/%zuMB",
currentDev, (vramTotal - vramFree) / (1024*1024), vramFree / (1024*1024));
}
}
static void VerifyGlobalANSRTSPLicense(const std::string& licenseKey) {
@@ -211,6 +232,7 @@ namespace ANSCENTER {
_playerClient->setCrop(crop);
}
bool ANSRTSPClient::Reconnect() {
ANS_DBG("RTSP_Lifecycle", "RECONNECT called: url=%s playing=%d", _url.c_str(), (int)_isPlaying);
// 1. Mark as not-playing under the mutex FIRST. This makes GetImage()
// return the cached _pLastFrame instead of calling into the player,
// and blocks new TryIncrementInFlight calls (no new NV12 attachments).
@@ -253,8 +275,30 @@ namespace ANSCENTER {
// completed (or timed out), so close() is safe.
_logger.LogInfo("ANSRTSPClient::Reconnect",
"calling close() — NVDEC decoder will be destroyed", __FILE__, __LINE__);
auto _rc0 = std::chrono::steady_clock::now();
RTSP_DBG("[Reconnect] BEFORE close() this=%p", (void*)this);
_playerClient->close();
auto _rc1 = std::chrono::steady_clock::now();
// Force CUDA runtime to release cached memory from the destroyed NVDEC decoder.
cudaDeviceSynchronize();
auto _rc2 = std::chrono::steady_clock::now();
cudaMemPool_t memPool = nullptr;
int currentDev = 0;
cudaGetDevice(&currentDev);
if (cudaDeviceGetDefaultMemPool(&memPool, currentDev) == cudaSuccess && memPool) {
cudaMemPoolTrimTo(memPool, 0);
}
auto _rc3 = std::chrono::steady_clock::now();
{
size_t vf = 0, vt = 0;
cudaMemGetInfo(&vf, &vt);
double closeMs = std::chrono::duration<double, std::milli>(_rc1 - _rc0).count();
double syncMs = std::chrono::duration<double, std::milli>(_rc2 - _rc1).count();
double trimMs = std::chrono::duration<double, std::milli>(_rc3 - _rc2).count();
ANS_DBG("RTSP_Reconnect", "close=%.1fms sync=%.1fms trim=%.1fms VRAM=%zuMB/%zuMB",
closeMs, syncMs, trimMs, (vt - vf) / (1024*1024), vf / (1024*1024));
}
RTSP_DBG("[Reconnect] AFTER close() this=%p", (void*)this);
// 3. Re-setup and play under the mutex.
@@ -283,12 +327,9 @@ namespace ANSCENTER {
}
bool ANSRTSPClient::Stop() {
// Grab the player pointer and clear _isPlaying under the lock,
// then call stop() OUTSIDE the mutex. stop() internally calls
// StopVideoDecoder -> decoder->flush() which does CUDA calls
// that can block on the nvcuda64 SRW lock. Holding _mutex
// during that time blocks all other operations on this client
// and contributes to the convoy when many clients stop at once.
// Stop playback but keep the RTSP connection and NVDEC decoder alive.
// LabVIEW uses Stop/Start to pause cameras when no AI task is subscribed.
// The camera resumes instantly on Start() without re-connecting.
CRtspPlayer* player = nullptr;
{
std::lock_guard<std::recursive_mutex> lock(_mutex);
@@ -300,6 +341,7 @@ namespace ANSCENTER {
if (player) {
player->stop();
}
ANS_DBG("RTSP_Lifecycle", "STOP complete: handle=%p (connection kept alive)", (void*)this);
return true;
}
bool ANSRTSPClient::Pause() {
@@ -342,45 +384,44 @@ namespace ANSCENTER {
}
bool ANSRTSPClient::areImagesIdentical(const cv::Mat& img1, const cv::Mat& img2) {
// Quick size and type checks
if (img1.size() != img2.size() || img1.type() != img2.type()) {
return false;
double ageMs = _playerClient->getLastFrameAgeMs();
if (ageMs > 5000.0) {
ANS_DBG("RTSP_Stale", "FROZEN DETECTED: ageMs=%.1f url=%s playing=%d — camera truly stale",
ageMs, _url.c_str(), (int)_isPlaying);
return true; // Truly stale — no decoder output for 5+ seconds
}
if (ageMs > 0.0) {
return false; // Decoder is receiving frames — camera is alive
}
// Handle empty images
if (img1.empty()) {
return img2.empty();
}
// ageMs == 0 means no frame has been decoded yet (startup).
// Fall back to pixel comparison for backward compatibility.
if (img1.empty() && img2.empty()) return true;
if (img1.empty() || img2.empty()) return false;
if (img1.size() != img2.size() || img1.type() != img2.type()) return false;
// Same data pointer = same cv::Mat (shallow copy)
if (img1.data == img2.data) return true;
// Quick 5-point sampling
if (img1.isContinuous() && img2.isContinuous()) {
const size_t totalBytes = img1.total() * img1.elemSize();
// Fast rejection: sample 5 positions across contiguous memory
// Catches 99.99% of different frames immediately
const size_t quarter = totalBytes / 4;
const size_t half = totalBytes / 2;
const size_t threeQuarter = 3 * totalBytes / 4;
if (img1.data[0] != img2.data[0] ||
img1.data[quarter] != img2.data[quarter] ||
img1.data[half] != img2.data[half] ||
img1.data[threeQuarter] != img2.data[threeQuarter] ||
img1.data[totalBytes - 1] != img2.data[totalBytes - 1]) {
return false;
}
// Full comparison
return std::memcmp(img1.data, img2.data, totalBytes) == 0;
}
// Row-by-row comparison for non-continuous images (e.g., ROI sub-matrices)
const size_t rowSize = img1.cols * img1.elemSize();
for (int i = 0; i < img1.rows; i++) {
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) {
return false;
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) return false;
}
}
return true;
}
cv::Mat ANSRTSPClient::GetImage(int& width, int& height, int64_t& pts) {
@@ -414,6 +455,20 @@ namespace ANSCENTER {
if (currentPts == _pts && !_pLastFrame.empty()) {
width = _imageWidth;
height = _imageHeight;
// Return timestamp based on decoder frame age so LabVIEW can distinguish
// "rate-limited duplicate" from "camera truly stale".
// If decoder is still receiving frames (age < 5s), advance PTS so LabVIEW
// sees a changing timestamp and doesn't trigger false reconnect.
// If decoder is stale (age > 5s), return same PTS so LabVIEW detects it.
double ageMs = _playerClient->getLastFrameAgeMs();
if (ageMs > 0.0 && ageMs < 5000.0) {
// Camera alive but rate-limited — advance PTS to prevent false stale detection
_pts++;
} else if (ageMs >= 5000.0) {
// Camera stale — keep same PTS so LabVIEW triggers reconnect
ANS_DBG("RTSP_GetImage", "STALE PTS: ageMs=%.1f pts=%lld url=%s — not advancing PTS",
ageMs, (long long)_pts, _url.c_str());
}
pts = _pts;
return _pLastFrame;
}
@@ -891,6 +946,10 @@ namespace ANSCENTER {
std::lock_guard<std::recursive_mutex> lock(_mutex);
_useNV12FastPath = enable;
}
double ANSRTSPClient::GetLastFrameAgeMs() {
std::lock_guard<std::recursive_mutex> lock(_mutex);
return _playerClient->getLastFrameAgeMs();
}
AVFrame* ANSRTSPClient::GetNV12Frame() {
std::lock_guard<std::recursive_mutex> lock(_mutex);
if (!_isPlaying) return nullptr; // Player may be mid-reconnect (CUDA resources freed)
@@ -937,6 +996,7 @@ namespace ANSCENTER {
}
extern "C" __declspec(dllexport) int CreateANSRTSPHandle(ANSCENTER::ANSRTSPClient * *Handle, const char* licenseKey, const char* username, const char* password, const char* url) {
ANS_DBG("RTSP_Lifecycle", "CREATE: url=%s", url ? url : "null");
if (!Handle || !licenseKey || !url) return -1;
try {
auto ptr = std::make_unique<ANSCENTER::ANSRTSPClient>();
@@ -946,11 +1006,10 @@ extern "C" __declspec(dllexport) int CreateANSRTSPHandle(ANSCENTER::ANSRTSPClien
if (_username.empty() && _password.empty()) result = ptr->Init(licenseKey, url);
else result = ptr->Init(licenseKey, username, password, url);
if (result) {
// Default to CUDA/NVDEC HW decoding (mode 7) for NV12 zero-copy
// fast path. LabVIEW may not call SetRTSPHWDecoding after
// destroy+recreate cycles, so this ensures the new handle always
// uses the GPU decode path instead of falling back to D3D11VA/CPU.
ptr->SetHWDecoding(7); // HW_DECODING_CUDA
// Software decode by default — saves VRAM (no NVDEC DPB surfaces).
// With 100 cameras, HW decode would consume ~5-21 GB VRAM for idle decoders.
// User can enable HW decode per-camera via SetRTSPHWDecoding(handle, 7).
// ptr->SetHWDecoding(7); // Disabled — was HW_DECODING_CUDA
*Handle = ptr.release();
extern void anscv_unregister_handle(void*);
extern void anscv_register_handle(void*, void(*)(void*));
@@ -967,6 +1026,7 @@ extern "C" __declspec(dllexport) int CreateANSRTSPHandle(ANSCENTER::ANSRTSPClien
} catch (...) { return -1; }
}
extern "C" __declspec(dllexport) int ReleaseANSRTSPHandle(ANSCENTER::ANSRTSPClient * *Handle) {
ANS_DBG("RTSP_Lifecycle", "RELEASE: handle=%p", Handle ? (void*)*Handle : nullptr);
if (Handle == nullptr || *Handle == nullptr) return -1;
try {
extern void anscv_unregister_handle(void*);
@@ -982,25 +1042,27 @@ extern "C" __declspec(dllexport) int ReleaseANSRTSPHandle(ANSCENTER::ANSRTSPClie
// on any subsequent call, and prevents NEW NV12 GPU surface
// pointers from being handed out.
//
// Do NOT call Destroy()/close() here — close() frees the
// NVDEC GPU surfaces (cuArrayDestroy/cuMemFree) which may
// still be in use by a CUDA inference kernel that received
// the NV12 pointer from a GetRTSPCVImage call that already
// completed before this Release was called.
// Synchronous cleanup — ensures all GPU resources (NVDEC surfaces, VRAM)
// are fully released BEFORE LabVIEW creates a new camera.
// Previously deferred to a background thread, but that caused the old
// camera's resources to overlap with the new camera's allocations,
// leading to temporary VRAM doubling (~240MB per camera) and eventual
// VRAM exhaustion on cameras with frequent reconnects.
{
// Use the client's _mutex to safely set _isPlaying = false.
// This is the same lock GetImage/GetNV12Frame acquire.
raw->Stop(); // sets _isPlaying = false, stops playback
}
auto t0 = std::chrono::steady_clock::now();
raw->Stop();
auto t1 = std::chrono::steady_clock::now();
raw->Destroy();
auto t2 = std::chrono::steady_clock::now();
delete raw;
auto t3 = std::chrono::steady_clock::now();
// Defer the full cleanup (Destroy + delete) to a background thread
// so LabVIEW's UI thread is not blocked. Destroy() now waits
// precisely for in-flight inference to finish (via _inFlightFrames
// counter + condition variable) instead of the old 500ms sleep hack.
std::thread([raw]() {
try { raw->Destroy(); } catch (...) {}
try { delete raw; } catch (...) {}
}).detach();
double stopMs = std::chrono::duration<double, std::milli>(t1 - t0).count();
double destroyMs = std::chrono::duration<double, std::milli>(t2 - t1).count();
double deleteMs = std::chrono::duration<double, std::milli>(t3 - t2).count();
ANS_DBG("RTSP_Lifecycle", "RELEASE complete: stop=%.1fms destroy=%.1fms delete=%.1fms total=%.1fms",
stopMs, destroyMs, deleteMs, stopMs + destroyMs + deleteMs);
}
return 0;
} catch (...) {
@@ -1269,6 +1331,7 @@ extern "C" __declspec(dllexport) int GetRTSPImage(ANSCENTER::ANSRTSPClient** Han
}
}
extern "C" __declspec(dllexport) int StartRTSP(ANSCENTER::ANSRTSPClient **Handle) {
ANS_DBG("RTSP_Lifecycle", "START: handle=%p", Handle ? (void*)*Handle : nullptr);
if (Handle == nullptr || *Handle == nullptr) return -1;
try {
bool result = (*Handle)->Start();
@@ -1301,6 +1364,7 @@ extern "C" __declspec(dllexport) int ReconnectRTSP(ANSCENTER::ANSRTSPClient * *H
}
}
extern "C" __declspec(dllexport) int StopRTSP(ANSCENTER::ANSRTSPClient * *Handle) {
ANS_DBG("RTSP_Lifecycle", "STOP: handle=%p", Handle ? (void*)*Handle : nullptr);
if (Handle == nullptr || *Handle == nullptr) return -1;
try {
bool result = (*Handle)->Stop();
@@ -1462,9 +1526,15 @@ extern "C" __declspec(dllexport) void SetRTSPTargetFPS(ANSCENTER::ANSRTSPClient*
extern "C" __declspec(dllexport) void SetRTSPNV12FastPath(ANSCENTER::ANSRTSPClient** Handle, int enable) {
if (Handle == nullptr || *Handle == nullptr) return;
try {
(*Handle)->SetNV12FastPath(enable != 0); // 0=original CPU path (stable), 1=NV12 GPU fast path
(*Handle)->SetNV12FastPath(enable != 0);
} catch (...) { }
}
extern "C" __declspec(dllexport) double GetRTSPLastFrameAgeMs(ANSCENTER::ANSRTSPClient** Handle) {
if (Handle == nullptr || *Handle == nullptr) return -1.0;
try {
return (*Handle)->GetLastFrameAgeMs();
} catch (...) { return -1.0; }
}
extern "C" __declspec(dllexport) int SetCropFlagRTSP(ANSCENTER::ANSRTSPClient** Handle, int cropFlag) {
if (Handle == nullptr || *Handle == nullptr) return -1;
try {

2
modules/ANSCV/ANSRTSP.h
View File

@@ -106,6 +106,7 @@ namespace ANSCENTER
void SetTargetFPS(double intervalMs); // Set min interval between processed frames in ms (0 = no limit, 100 = ~10 FPS, 200 = ~5 FPS)
void SetNV12FastPath(bool enable); // true = NV12 GPU fast path (zero-copy inference), false = original CPU path (stable)
bool IsNV12FastPath() const { return _useNV12FastPath; }
double GetLastFrameAgeMs(); // Milliseconds since last frame from decoder (detects truly stale cameras, unaffected by SetTargetFPS)
AVFrame* GetNV12Frame(); // Returns cloned NV12 frame for GPU fast-path (caller must av_frame_free)
AVFrame* GetCudaHWFrame(); // Returns CUDA HW frame (device ptrs) for zero-copy inference
bool IsCudaHWAccel(); // true when decoder uses CUDA (NV12 stays in GPU VRAM)
@@ -145,4 +146,5 @@ extern "C" __declspec(dllexport) void SetRTSPImageQuality(ANSCENTER::ANSRTSPClie
extern "C" __declspec(dllexport) void SetRTSPDisplayResolution(ANSCENTER::ANSRTSPClient** Handle, int width, int height);
extern "C" __declspec(dllexport) void SetRTSPTargetFPS(ANSCENTER::ANSRTSPClient** Handle, double intervalMs);
extern "C" __declspec(dllexport) void SetRTSPNV12FastPath(ANSCENTER::ANSRTSPClient** Handle, int enable);
extern "C" __declspec(dllexport) double GetRTSPLastFrameAgeMs(ANSCENTER::ANSRTSPClient** Handle);
#endif

37
modules/ANSCV/ANSSRT.cpp
View File

@@ -221,43 +221,22 @@ namespace ANSCENTER {
}
bool ANSSRTClient::areImagesIdentical(const cv::Mat& img1, const cv::Mat& img2) {
// Quick size and type checks
if (img1.size() != img2.size() || img1.type() != img2.type()) {
return false;
}
double ageMs = _playerClient->getLastFrameAgeMs();
if (ageMs > 5000.0) return true;
if (ageMs > 0.0) return false;
// Handle empty images
if (img1.empty()) {
return img2.empty();
}
if (img1.empty() && img2.empty()) return true;
if (img1.empty() || img2.empty()) return false;
if (img1.size() != img2.size() || img1.type() != img2.type()) return false;
if (img1.data == img2.data) return true;
if (img1.isContinuous() && img2.isContinuous()) {
const size_t totalBytes = img1.total() * img1.elemSize();
// Fast rejection: sample 5 positions across contiguous memory
// Catches 99.99% of different frames immediately
const size_t quarter = totalBytes / 4;
const size_t half = totalBytes / 2;
const size_t threeQuarter = 3 * totalBytes / 4;
if (img1.data[0] != img2.data[0] ||
img1.data[quarter] != img2.data[quarter] ||
img1.data[half] != img2.data[half] ||
img1.data[threeQuarter] != img2.data[threeQuarter] ||
img1.data[totalBytes - 1] != img2.data[totalBytes - 1]) {
return false;
}
// Full comparison
return std::memcmp(img1.data, img2.data, totalBytes) == 0;
}
// Row-by-row comparison for non-continuous images (e.g., ROI sub-matrices)
const size_t rowSize = img1.cols * img1.elemSize();
for (int i = 0; i < img1.rows; i++) {
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) {
return false;
}
if (std::memcmp(img1.ptr(i), img2.ptr(i), rowSize) != 0) return false;
}
return true;

2
modules/ANSCV/ANSVideoPlayer.cpp
View File

@@ -136,7 +136,7 @@ namespace ANSCENTER {
if (!_hwDecodeActive && !_hwPlayer) {
try {
auto hwp = std::make_unique<CFilePlayer>();
hwp->setHWDecoding(HW_DECODING_AUTO); // CUDA → D3D11VA → DXVA2 → software
hwp->setHWDecoding(HW_DECODING_DISABLE); // Software decode by default — saves VRAM
if (hwp->open(_url)) {
_hwPlayer = std::move(hwp);
_hwDecodeActive = true;

2
modules/ANSCV/VideoPlayer.cpp
View File

@@ -93,7 +93,7 @@ CVideoPlayer::CVideoPlayer():
, m_bPaused(FALSE)
, m_bSizeChanged(FALSE)
//, m_nRenderMode(RENDER_MODE_KEEP)
, m_nHWDecoding(HW_DECODING_AUTO)
, m_nHWDecoding(HW_DECODING_DISABLE) // Software decode by default — saves VRAM
, m_nDstVideoFmt(AV_PIX_FMT_YUV420P)
, m_bUpdown(FALSE)
, m_bSnapshot(FALSE)

16
modules/ANSODEngine/ANSODEngine.cpp
View File

@@ -3,6 +3,7 @@
#include <cmath>
#include <json.hpp>
#include "ANSODEngine.h"
#include "ANSLicense.h" // ANS_DBG macro
#include "ANSYOLOOD.h"
#include "ANSTENSORRTOD.h"
#include "ANSTENSORRTCL.h"
@@ -879,6 +880,9 @@ namespace ANSCENTER
std::vector<Object> allResults;
allResults.clear();
try {
ANS_DBG("ODEngine", "SAHI START: %dx%d tile=%dx%d overlap=%.1f cam=%s",
input.cols, input.rows, tiledWidth, tiledHeight, overLap, camera_id.c_str());
auto _sahiStart = std::chrono::steady_clock::now();
cv::Mat image = input.clone();
if (image.empty() || !image.data || !image.u) {
return allResults;
@@ -920,6 +924,16 @@ namespace ANSCENTER
//4. Apply Non-Maximum Suppression (NMS) to merge overlapping results
float iouThreshold = 0.1;
std::vector<Object> finalResults = ANSUtilityHelper::ApplyNMS(allResults, iouThreshold);
{
double _sahiMs = std::chrono::duration<double, std::milli>(
std::chrono::steady_clock::now() - _sahiStart).count();
ANS_DBG("ODEngine", "SAHI DONE: %.1fms patches=%zu results=%zu cam=%s",
_sahiMs, patches.size() + 1, finalResults.size(), camera_id.c_str());
if (_sahiMs > 2000.0) {
ANS_DBG("ODEngine", "SAHI SLOW: %.1fms — %zu patches held _mutex entire time!",
_sahiMs, patches.size() + 1);
}
}
image.release();
return finalResults;
}
@@ -2103,6 +2117,8 @@ namespace ANSCENTER
// No coarse _mutex — sub-components (engines, trackers) have their own locks.
// LabVIEW semaphore controls concurrency at the caller level.
try {
ANS_DBG("ODEngine", "RunInferenceWithOption: cam=%s %dx%d mode=%s",
camera_id.c_str(), input.cols, input.rows, activeROIMode.c_str());
int mode = 0;
double confidenceThreshold = 0.35;
std::vector<int> trackingObjectIds;

19
modules/ANSODEngine/ANSRTYOLO.cpp
View File

@@ -1,5 +1,6 @@
#include "ANSRTYOLO.h"
#include "Utility.h"
#include "ANSLicense.h" // ANS_DBG macro for DebugView
#include <future>
#include <numeric>
#include <cmath>
@@ -903,7 +904,6 @@ namespace ANSCENTER {
return {};
}
// Check if model is classification (output ndims <= 2)
const auto& outputDims = m_trtEngine->getOutputDims();
const bool isClassification = !outputDims.empty() && outputDims[0].nbDims <= 2;
@@ -914,11 +914,8 @@ namespace ANSCENTER {
cv::cuda::GpuMat resized;
if (imgRGB.rows != inputH || imgRGB.cols != inputW) {
if (isClassification) {
// Classification: direct resize (no letterbox padding)
// Must use explicit stream to avoid conflict with CUDA Graph capture on null stream
cv::cuda::resize(imgRGB, resized, cv::Size(inputW, inputH), 0, 0, cv::INTER_LINEAR, stream);
} else {
// Detection/Seg/Pose/OBB: letterbox resize + right-bottom pad
resized = Engine<float>::resizeKeepAspectRatioPadRightBottom(imgRGB, inputH, inputW);
}
}
@@ -1831,8 +1828,7 @@ namespace ANSCENTER {
}
// --- 2. Preprocess under lock ---
// Try NV12 fast path first (12MB upload vs 24MB BGR for 4K)
// Falls back to standard GPU preprocessing if no NV12 data available.
ANS_DBG("YOLO", "Preprocess START %dx%d", inputImage.cols, inputImage.rows);
ImageMetadata meta;
std::vector<std::vector<cv::cuda::GpuMat>> input;
bool usedNV12 = false;
@@ -1874,11 +1870,22 @@ namespace ANSCENTER {
}
// --- 3. TRT Inference (mutex released for concurrent GPU slots) ---
ANS_DBG("YOLO", "TRT inference START nv12=%d inputSize=%dx%d",
(int)usedNV12,
input.empty() ? 0 : (input[0].empty() ? 0 : input[0][0].cols),
input.empty() ? 0 : (input[0].empty() ? 0 : input[0][0].rows));
auto _trtStart = std::chrono::steady_clock::now();
std::vector<std::vector<std::vector<float>>> featureVectors;
if (!m_trtEngine->runInference(input, featureVectors)) {
ANS_DBG("YOLO", "ERROR: TRT runInference FAILED");
_logger.LogError("ANSRTYOLO::DetectObjects", "Error running inference", __FILE__, __LINE__);
return {};
}
auto _trtEnd = std::chrono::steady_clock::now();
double _trtMs = std::chrono::duration<double, std::milli>(_trtEnd - _trtStart).count();
if (_trtMs > 500.0) {
ANS_DBG("YOLO", "SLOW TRT inference: %.1fms", _trtMs);
}
double msInference = dbg ? elapsed() : 0;
// --- 4. Transform output ---

1
modules/ANSODEngine/ANSRTYOLO.h
View File

@@ -81,6 +81,7 @@ namespace ANSCENTER {
std::vector<std::vector<cv::cuda::GpuMat>> PreprocessBatch(
const std::vector<cv::Mat>& inputImages, BatchMetadata& outMetadata);
// ── Detection pipeline ───────────────────────────────────────────
std::vector<Object> DetectObjects(const cv::Mat& inputImage,
const std::string& camera_id);

1
modules/ANSODEngine/NV12PreprocessHelper.cpp
View File

@@ -1,6 +1,7 @@
#include "NV12PreprocessHelper.h"
#include "ANSGpuFrameRegistry.h"
#include "ANSEngineCommon.h"
#include "ANSLicense.h" // ANS_DBG macro
#include <opencv2/cudaimgproc.hpp>
#include <opencv2/cudawarping.hpp>
#include <opencv2/core/cuda_stream_accessor.hpp>

16
modules/ANSODEngine/dllmain.cpp
View File

@@ -6,6 +6,7 @@
#include "engine/TRTEngineCache.h" // clearAll() on DLL_PROCESS_DETACH
#include "engine/EnginePoolManager.h" // clearAll() on DLL_PROCESS_DETACH
#include <climits> // INT_MIN
#include "ANSLicense.h" // ANS_DBG macro for DebugView
// Process-wide flag: when true, all engines force single-GPU path (no pool, no idle timers).
// Defined here, declared extern in EngineBuildLoadNetwork.inl.
@@ -1696,6 +1697,8 @@ static int RunInferenceComplete_LV_Impl(
auto* engine = guard.get();
try {
auto _t0 = std::chrono::steady_clock::now();
// Save/restore thread-local to support nested calls (custom model DLLs
// calling back into ANSODEngine via ANSLIB.dll).
GpuFrameData* savedFrame = tl_currentGpuFrame();
@@ -1708,6 +1711,10 @@ static int RunInferenceComplete_LV_Impl(
int originalWidth = localImage.cols;
int originalHeight = localImage.rows;
ANS_DBG("LV_Inference", "START cam=%s %dx%d gpuFrame=%p nv12=%s",
cameraId ? cameraId : "?", originalWidth, originalHeight,
(void*)gpuFrame, gpuFrame ? "YES" : "NO");
if (originalWidth == 0 || originalHeight == 0) {
tl_currentGpuFrame() = savedFrame;
return -2;
@@ -1717,8 +1724,17 @@ static int RunInferenceComplete_LV_Impl(
// Safe: *cvImage holds a refcount, keeping gpuFrame alive during inference.
// Only use OWN gpuFrame — never inherit outer caller's frame (dimension mismatch on crops).
tl_currentGpuFrame() = gpuFrame;
auto _t1 = std::chrono::steady_clock::now();
std::vector<ANSCENTER::Object> outputs = engine->RunInferenceWithOption(localImage, cameraId, activeROIMode);
auto _t2 = std::chrono::steady_clock::now();
tl_currentGpuFrame() = savedFrame;
double prepMs = std::chrono::duration<double, std::milli>(_t1 - _t0).count();
double infMs = std::chrono::duration<double, std::milli>(_t2 - _t1).count();
if (infMs > 500.0) {
ANS_DBG("LV_Inference", "SLOW cam=%s prep=%.1fms inf=%.1fms results=%zu",
cameraId ? cameraId : "?", prepMs, infMs, outputs.size());
}
bool getJpeg = (getJpegString == 1);
std::string stImage;
// NOTE: odMutex was removed here. All variables in this scope are local

5
modules/ANSODEngine/engine.h
View File

@@ -402,6 +402,9 @@ private:
cudaStream_t m_memoryStream; // ADD THIS - separate stream for memory operations
std::vector<cv::cuda::GpuMat> m_preprocessedInputs; // Keep inputs alive
// Note: blobFromGpuMats and resizeKeepAspectRatioPadRightBottom are static,
// so cached buffers use thread_local inside the functions themselves.
// Thermal management (ADD THESE)
//int m_consecutiveInferences;
@@ -431,7 +434,7 @@ private:
Logger m_logger;
bool m_verbose{ true }; // false for non-probe pool slots
bool m_disableGraphs{ false }; // true for pool slots — concurrent graph captures corrupt CUDA context
bool m_disableGraphs{ true }; // DISABLED by default — concurrent graph launches + uploads cause GPU deadlock on WDDM
// -- Multi-GPU pool data ---------------------------------------------------

10
tests/ANSLPR-UnitTest/ANSLPR-UnitTest.cpp
View File

@@ -814,8 +814,8 @@ static void ALPRWorkerThread(int taskId,
g_log.add(prefix + " Empty frame (count=" + std::to_string(emptyFrames) + ")");
}
if (emptyFrames > 300) {
g_log.add(prefix + " Too many empty frames, attempting reconnect...");
ReconnectRTSP(&rtspClient);
g_log.add(prefix + " Too many empty frames (reconnect disabled for long test)");
// ReconnectRTSP(&rtspClient); // Disabled for VRAM stability testing
emptyFrames = 0;
}
streamLock.unlock();
@@ -1222,9 +1222,9 @@ int ANSLPR_MultiGPU_StressTest() {
g_log.add(buf);
printf("%s\n", buf);
} else if (currentGpu != streamPreferredGpu[s]) {
// Decoder is active on wrong GPU — reconnect to move it
// Decoder is active on wrong GPU — reconnect disabled for VRAM stability testing
SetRTSPHWDecoding(&rtspClients[s], 7, streamPreferredGpu[s]);
ReconnectRTSP(&rtspClients[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)",
@@ -1279,7 +1279,7 @@ int ANSLPR_MultiGPU_StressTest() {
// CUDA cleanup (cuArrayDestroy, cuMemFree) while inference is running.
// This is the exact scenario that triggers the nvcuda64 SRW lock deadlock.
// =========================================================================
std::atomic<bool> chaosEnabled{true};
std::atomic<bool> chaosEnabled{false}; // Disabled for VRAM stability long test
std::thread chaosThread([&]() {
std::mt19937 rng(std::random_device{}());