369 lines
15 KiB
C++
369 lines
15 KiB
C++
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#include "Movienet.h"
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#include <filesystem>
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namespace ANSCENTER {
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// Extract input size from _A<digit> suffix just before the file extension.
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// _A0 / _A1 → 172 _A2 → 224 _A3 → 256 _A4 → 290 _A5 → 320
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// Examples: Violence0305_A5.onnx → 320
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// MyModel0908_A4.onnx → 290
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// Returns 0 if the suffix is not recognised.
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static int GetMovinetSizeFromFilename(const std::string& filename)
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{
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static constexpr int variantSize[] = {
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172, // A0
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172, // A1
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224, // A2
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256, // A3
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290, // A4
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320 // A5
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};
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// Strip extension — find last '.'
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const size_t dotPos = filename.rfind('.');
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if (dotPos == std::string::npos || dotPos < 3) return 0;
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// Expect 3-char suffix just before the dot: _A<digit>
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const char chUnderscore = filename[dotPos - 3];
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const char chA = filename[dotPos - 2];
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const char chDigit = filename[dotPos - 1];
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if (chUnderscore == '_' &&
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(chA == 'A' || chA == 'a') &&
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chDigit >= '0' && chDigit <= '5')
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{
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return variantSize[chDigit - '0'];
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}
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return 0;
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}
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// Scan folder for any .onnx whose stem ends with _A0.._A5 (highest first).
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// Returns the full path + matching input dimensions.
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// If modelName is provided (e.g. "Violence0305_A2"), uses that exact model
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// and resolves dimensions from its _A<digit> suffix.
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// Falls back to "movinet.onnx" with 172×172 if nothing matches.
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static std::string ResolveMovinetModel(const std::string& folder,
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int& outWidth, int& outHeight,
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const std::string& modelName = "")
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{
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// If a specific model name was given, use it directly
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if (!modelName.empty()) {
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std::string fname = modelName;
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// Append .onnx if not already present
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if (fname.size() < 5 || fname.substr(fname.size() - 5) != ".onnx") {
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fname += ".onnx";
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}
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std::string fullPath = CreateFilePath(folder, fname);
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int sz = GetMovinetSizeFromFilename(fname);
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if (sz > 0) {
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outWidth = sz;
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outHeight = sz;
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std::cout << "ANSMOVIENET: Using specified model '" << fname << "' with input size " << sz << "x" << sz << "\n";
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}
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else {
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// Model name given but no recognized _A<digit> suffix — use default
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outWidth = 172;
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outHeight = 172;
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std::cout << "ANSMOVIENET: Using specified model '" << fname << "' but failed to detect input size from filename. Defaulting to 172x172.\n";
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}
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return fullPath;
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}
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// No model name specified — scan folder, highest variant first
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static const std::string suffixes[] = { "_A5", "_A4", "_A3", "_A2", "_A1", "_A0" };
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try {
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namespace fs = std::filesystem;
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if (fs::is_directory(folder)) {
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for (const auto& suffix : suffixes) {
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for (const auto& entry : fs::directory_iterator(folder)) {
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if (!entry.is_regular_file()) continue;
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const std::string fname = entry.path().filename().string();
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// Must be .onnx
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if (fname.size() < 5) continue;
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std::string ext = fname.substr(fname.size() - 5);
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// Case-insensitive .onnx check
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for (auto& c : ext) c = static_cast<char>(std::tolower(static_cast<unsigned char>(c)));
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if (ext != ".onnx") continue;
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// Check if stem ends with the current suffix
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const std::string stem = fname.substr(0, fname.size() - 5);
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if (stem.size() >= suffix.size() &&
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stem.compare(stem.size() - suffix.size(), suffix.size(), suffix) == 0)
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{
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int sz = GetMovinetSizeFromFilename(fname);
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if (sz > 0) {
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outWidth = sz;
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outHeight = sz;
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return entry.path().string();
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}
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}
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}
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}
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}
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}
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catch (...) {
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// Filesystem error — fall through to legacy
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std::cout << "ANSMOVIENET: Error scanning model folder '. Falling back to default model.\n";
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}
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// Legacy fallback
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outWidth = 172;
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outHeight = 172;
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return CreateFilePath(folder, "movinet.onnx");
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}
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bool ANSMOVIENET::Initialize(std::string licenseKey, ModelConfig modelConfig, const std::string& modelZipFilePath, const std::string& modelZipPassword, std::string& labelMap) {
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bool result = ANSODBase::Initialize(licenseKey, modelConfig, modelZipFilePath, modelZipPassword, labelMap);
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labelMap = "Face";
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_licenseValid = true;
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std::vector<std::string> labels{ labelMap };
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if (!_licenseValid) return false;
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try {
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_modelConfig = modelConfig;
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_modelConfig.modelType = ModelType::MOVIENET;
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_modelConfig.detectionType = DetectionType::CLASSIFICATION;
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// Auto-detect model variant and matching input size
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int detectedW = 0, detectedH = 0;
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std::string onnxModel = ResolveMovinetModel(_modelFolder, detectedW, detectedH);
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_modelConfig.inpHeight = detectedH;
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_modelConfig.inpWidth = detectedW;
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if (_modelConfig.modelMNSThreshold < 0.2)
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_modelConfig.modelMNSThreshold = 0.5;
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if (_modelConfig.modelConfThreshold < 0.2)
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_modelConfig.modelConfThreshold = 0.5;
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if (_isInitialized) {
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_movienet_detector.reset();
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_isInitialized = false;
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}
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unsigned int numThreads = 1;
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this->_movienet_detector = std::make_unique<MOVINET>(
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onnxModel, TEMPORAL_LENGTH, detectedW, detectedH, 3, numThreads);
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_isInitialized = true;
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return true;
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}
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catch (const std::exception& e) {
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this->_logger.LogFatal("ANSMOVIENET::Initialize", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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bool ANSMOVIENET::LoadModel(const std::string& modelZipFilePath, const std::string& modelZipPassword) {
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try {
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bool result = ANSODBase::LoadModel(modelZipFilePath, modelZipPassword);
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if (!result) return false;
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int detectedW = 0, detectedH = 0;
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std::string onnxModel = ResolveMovinetModel(_modelFolder, detectedW, detectedH);
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_modelConfig.inpWidth = detectedW;
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_modelConfig.inpHeight = detectedH;
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unsigned int numThreads = 1;
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_movienet_detector = std::make_unique<MOVINET>(
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onnxModel, TEMPORAL_LENGTH, detectedW, detectedH, 3, numThreads);
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_isInitialized = true;
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return _isInitialized;
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSMOVIENET::LoadModel", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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bool ANSMOVIENET::LoadModelFromFolder(std::string licenseKey, ModelConfig modelConfig, std::string modelName, std::string className, const std::string& modelFolder, std::string& labelMap) {
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try {
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bool result = ANSODBase::LoadModelFromFolder(licenseKey, modelConfig, modelName, className, modelFolder, labelMap);
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if (!result) return false;
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_modelConfig = modelConfig;
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_modelConfig.modelType = ModelType::MOVIENET;
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_modelConfig.detectionType = DetectionType::CLASSIFICATION;
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// Resolve model path and input dimensions.
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// If modelName is given (e.g. "Violence0305_A2"), uses that exact model;
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// otherwise scans the folder for the best _A<digit> variant.
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int detectedW = 0, detectedH = 0;
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std::string onnxModel = ResolveMovinetModel(modelFolder, detectedW, detectedH, modelName);
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_modelConfig.inpWidth = detectedW;
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_modelConfig.inpHeight = detectedH;
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if (_modelConfig.modelMNSThreshold < 0.2)
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_modelConfig.modelMNSThreshold = 0.5;
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if (_modelConfig.modelConfThreshold < 0.2)
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_modelConfig.modelConfThreshold = 0.5;
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if (_isInitialized) {
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_movienet_detector.reset();
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_isInitialized = false;
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}
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unsigned int numThreads = 1;
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this->_movienet_detector = std::make_unique<MOVINET>(
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onnxModel, TEMPORAL_LENGTH, detectedW, detectedH, 3, numThreads);
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_isInitialized = true;
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return _isInitialized;
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSMOVIENET::LoadModel", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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bool ANSMOVIENET::OptimizeModel(bool fp16, std::string& optimizedModelFolder) {
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if (FileExist(_modelFilePath)) {
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optimizedModelFolder = GetParentFolder(_modelFilePath);
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this->_logger.LogDebug("ANSMOVIENET::OptimizeModel", "This model is optimized. No need other optimization.", __FILE__, __LINE__);
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return true;
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}
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else {
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optimizedModelFolder = "";
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this->_logger.LogFatal("ANSMOVIENET::OptimizeModel", "This model is not exist. Please check the model path again.", __FILE__, __LINE__);
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return false;
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}
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}
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ANSMOVIENET::~ANSMOVIENET() {
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try {
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Destroy();
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSMOVIENET::Destroy", e.what(), __FILE__, __LINE__);
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}
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}
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bool ANSMOVIENET::Destroy() {
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try {
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std::lock_guard<std::recursive_mutex> lock(_mutex);
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_cameraQueues.clear();
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_globalFrameCounter = 0;
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_movienet_detector.reset();
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_isInitialized = false;
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return true;
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}
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catch (std::exception& e) {
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this->_logger.LogFatal("ANSMOVIENET::Destroy", e.what(), __FILE__, __LINE__);
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return false;
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}
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}
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// Inference functions
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std::vector<Object> ANSMOVIENET::RunInference(const cv::Mat& input, const std::string& camera_id) {
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std::vector<Object> result = Inference(input, camera_id);
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if (_trackerEnabled) {
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result = ApplyTracking(result, camera_id);
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if (_stabilizationEnabled) result = StabilizeDetections(result, camera_id);
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}
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return result;
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}
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std::vector<Object> ANSMOVIENET::RunInference(const cv::Mat& input) {
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return Inference(input, "MovienetCam");
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}
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void ANSMOVIENET::CleanupStaleQueues() {
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// Called internally <20> already under lock from Inference()
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if (_cameraQueues.empty()) return;
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// 1. Remove queues not accessed for STALE_THRESHOLD frames
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for (auto it = _cameraQueues.begin(); it != _cameraQueues.end(); ) {
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int age = _globalFrameCounter - it->second.lastAccessFrame;
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if (age > STALE_THRESHOLD) {
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it = _cameraQueues.erase(it);
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}
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else {
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++it;
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}
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}
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// 2. If still over hard cap, remove oldest queues first
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if (static_cast<int>(_cameraQueues.size()) > MAX_QUEUES) {
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// Collect and sort by last access time
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std::vector<std::pair<int, std::string>> accessTimes;
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accessTimes.reserve(_cameraQueues.size());
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for (const auto& [key, state] : _cameraQueues) {
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accessTimes.emplace_back(state.lastAccessFrame, key);
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}
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std::sort(accessTimes.begin(), accessTimes.end());
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// Remove oldest until under cap
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int toRemove = static_cast<int>(_cameraQueues.size()) - MAX_QUEUES;
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for (int i = 0; i < toRemove && i < static_cast<int>(accessTimes.size()); ++i) {
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_cameraQueues.erase(accessTimes[i].second);
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}
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}
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}
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std::vector<Object> ANSMOVIENET::Inference(const cv::Mat& input, const std::string& camera_id) {
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std::lock_guard<std::recursive_mutex> lock(_mutex);
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std::vector<Object> detectedObjects;
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if (!_isInitialized || !_licenseValid || !_movienet_detector) {
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this->_logger.LogError("ANSMOVIENET::Inference",
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"Model is not initialized or license is not valid", __FILE__, __LINE__);
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return detectedObjects;
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}
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if (input.empty()) {
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this->_logger.LogError("ANSMOVIENET::Inference",
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"Input frame is empty", __FILE__, __LINE__);
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return detectedObjects;
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}
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try {
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_globalFrameCounter++;
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std::string cameraKey = camera_id.empty() ? "" : camera_id;
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auto& state = _cameraQueues[cameraKey];
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// Update access timestamp
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state.lastAccessFrame = _globalFrameCounter;
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state.frameCount++;
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// Add frame to queue
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state.frames.push_back(input.clone());
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// Maintain queue at TEMPORAL_LENGTH
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while (state.frames.size() > static_cast<size_t>(TEMPORAL_LENGTH)) {
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state.frames.pop_front();
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}
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// Run inference when:
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// 1. Full window available
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// 2. Stride condition met
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bool hasFullWindow = (state.frames.size() == static_cast<size_t>(TEMPORAL_LENGTH));
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bool strideReady = (state.frameCount == TEMPORAL_LENGTH)
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|| (state.frameCount > TEMPORAL_LENGTH
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&& (state.frameCount - TEMPORAL_LENGTH) % _inferenceStride == 0);
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if (hasFullWindow && strideReady) {
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std::pair<int, float> result;
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_movienet_detector->inference(state.frames, result);
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|
if (result.first >= 0) {
|
|||
|
|
Object obj;
|
|||
|
|
obj.classId = result.first;
|
|||
|
|
obj.className = (result.first < static_cast<int>(_classes.size())) ?
|
|||
|
|
_classes[result.first] : "Unknown";
|
|||
|
|
obj.confidence = result.second;
|
|||
|
|
obj.box = cv::Rect(0, 0, input.cols, input.rows);
|
|||
|
|
detectedObjects.push_back(obj);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Store in base class camera data
|
|||
|
|
CameraData& cameraData = GetCameraData(cameraKey);
|
|||
|
|
cameraData._detectionQueue.push_back(detectedObjects);
|
|||
|
|
if (cameraData._detectionQueue.size() > QUEUE_SIZE) {
|
|||
|
|
cameraData._detectionQueue.pop_front();
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// ----- Periodic self-cleanup -----
|
|||
|
|
if (_globalFrameCounter % CLEANUP_INTERVAL == 0) {
|
|||
|
|
CleanupStaleQueues();
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
catch (const std::exception& e) {
|
|||
|
|
this->_logger.LogError("ANSMOVIENET::Inference",
|
|||
|
|
std::string("Exception during inference: ") + e.what(), __FILE__, __LINE__);
|
|||
|
|
detectedObjects.clear();
|
|||
|
|
}
|
|||
|
|
catch (...) {
|
|||
|
|
this->_logger.LogError("ANSMOVIENET::Inference",
|
|||
|
|
"Unknown exception during inference", __FILE__, __LINE__);
|
|||
|
|
detectedObjects.clear();
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return detectedObjects;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
}
|