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Add unit testes

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This commit is contained in:
Tuan Nghia Nguyen
2026-04-05 14:30:43 +10:00
parent fed40b0c90
commit f57ed78763
12 changed files with 1013 additions and 216 deletions
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5
.claude/settings.local.json
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@@ -9,7 +9,10 @@
"Bash(cp \"C:/Projects/ANLS/ANSLIB/ANSCustomFireNSmokeDetection/dllmain.cpp\" \"C:/Projects/CLionProjects/ANSCustomModels/ANSCustomFireNSmokeDetection/dllmain.cpp\")",
"Bash(cp \"C:/Projects/ANLS/ANSLIB/ANSCustomFireNSmokeDetection/framework.h\" \"C:/Projects/CLionProjects/ANSCustomModels/ANSCustomFireNSmokeDetection/framework.h\")",
"Bash(cp \"C:/Projects/ANLS/ANSLIB/ANSCustomFireNSmokeDetection/pch.h\" \"C:/Projects/CLionProjects/ANSCustomModels/ANSCustomFireNSmokeDetection/pch.h\")",
"Bash(cp \"C:/Projects/ANLS/ANSLIB/ANSCustomFireNSmokeDetection/pch.cpp\" \"C:/Projects/CLionProjects/ANSCustomModels/ANSCustomFireNSmokeDetection/pch.cpp\")"
"Bash(cp \"C:/Projects/ANLS/ANSLIB/ANSCustomFireNSmokeDetection/pch.cpp\" \"C:/Projects/CLionProjects/ANSCustomModels/ANSCustomFireNSmokeDetection/pch.cpp\")",
"Read(//c/ANSLibs/opencv/x64/vc17/**)",
"Read(//c/ANSLibs/opencv/**)",
"Bash(find C:/ANSLibs -name *.dll -type f)"
]
}
}

352
ANSCustomFireNSmokeDetection/ANSCustomFireNSmoke.cpp
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@@ -56,7 +56,109 @@ void ANSCustomFS::ResetDetectionState() {
ResetDetectedArea();
_retainDetectedArea = 0;
_isFireNSmokeDetected = false;
_trackHistory.clear();
}
// --- Tracker-based voting functions ---
void ANSCustomFS::UpdateTrackHistory(int trackId, int classId, const cv::Rect& bbox) {
auto it = _trackHistory.find(trackId);
if (it == _trackHistory.end()) {
TrackRecord record;
record.trackId = trackId;
record.classId = classId;
record.bboxHistory.push_back(bbox);
record.detectedCount = 1;
record.totalFrames = 1;
record.confirmed = false;
_trackHistory[trackId] = std::move(record);
}
else {
auto& record = it->second;
record.bboxHistory.push_back(bbox);
record.detectedCount++;
record.totalFrames++;
// Slide the window: keep only VOTE_WINDOW entries in bbox history
while (static_cast<int>(record.bboxHistory.size()) > VOTE_WINDOW) {
record.bboxHistory.pop_front();
record.detectedCount = std::max(0, record.detectedCount - 1);
}
// Update confirmed status
if (record.detectedCount >= VOTE_THRESHOLD) {
record.confirmed = true;
}
}
}
bool ANSCustomFS::IsTrackConfirmed(int trackId) const {
auto it = _trackHistory.find(trackId);
if (it == _trackHistory.end()) return false;
return it->second.detectedCount >= VOTE_THRESHOLD;
}
bool ANSCustomFS::HasBboxMovement(int trackId) const {
auto it = _trackHistory.find(trackId);
if (it == _trackHistory.end()) return false;
const auto& history = it->second.bboxHistory;
if (history.size() < 2) return false;
const cv::Rect& earliest = history.front();
const cv::Rect& latest = history.back();
// Calculate center positions
float cx1 = earliest.x + earliest.width / 2.0f;
float cy1 = earliest.y + earliest.height / 2.0f;
float cx2 = latest.x + latest.width / 2.0f;
float cy2 = latest.y + latest.height / 2.0f;
// Average size for normalization
float avgWidth = (earliest.width + latest.width) / 2.0f;
float avgHeight = (earliest.height + latest.height) / 2.0f;
if (avgWidth < 1.0f || avgHeight < 1.0f) return false;
// Position change relative to average size
float posChange = std::sqrt(
std::pow((cx2 - cx1) / avgWidth, 2) +
std::pow((cy2 - cy1) / avgHeight, 2)
);
// Size change relative to average area
float area1 = static_cast<float>(earliest.area());
float area2 = static_cast<float>(latest.area());
float avgArea = (area1 + area2) / 2.0f;
float sizeChange = (avgArea > 0) ? std::abs(area2 - area1) / avgArea : 0.0f;
return (posChange > BBOX_CHANGE_THRESHOLD) || (sizeChange > BBOX_CHANGE_THRESHOLD);
}
void ANSCustomFS::AgeTracks(const std::unordered_set<int>& detectedTrackIds) {
auto it = _trackHistory.begin();
while (it != _trackHistory.end()) {
if (detectedTrackIds.find(it->first) == detectedTrackIds.end()) {
// Track was NOT detected this frame
it->second.totalFrames++;
// Remove stale tracks that haven't been seen recently
if (it->second.totalFrames > VOTE_WINDOW &&
it->second.detectedCount == 0) {
it = _trackHistory.erase(it);
continue;
}
// Age out the sliding window (add a "miss" frame)
if (static_cast<int>(it->second.bboxHistory.size()) >= VOTE_WINDOW) {
it->second.bboxHistory.pop_front();
it->second.detectedCount = std::max(0, it->second.detectedCount - 1);
}
}
++it;
}
}
// --- End voting functions ---
void ANSCustomFS::UpdateNoDetectionCondition()
{
_isRealFireFrame = false;
@@ -120,152 +222,6 @@ void ANSCustomFS::GetModelParameters() {
_readROIs = true;
}
}
std::vector<ANSCENTER::Object> ANSCustomFS::ProcessExistingDetectedArea(
const cv::Mat& frame,
const std::string& camera_id,
const std::vector<cv::Rect>& fireNSmokeRects,
cv::Mat& draw)
{
#ifdef FNS_DEBUG
cv::rectangle(draw, _detectedArea, cv::Scalar(255, 255, 0), 2); // Cyan
#endif
// Run detection on ROI (no clone - just a view into frame)
cv::Mat activeROI = frame(_detectedArea);
std::vector<ANSCENTER::Object> detectedObjects;
_detector->RunInference(activeROI, camera_id.c_str(), detectedObjects);
if (detectedObjects.empty()) {
UpdateNoDetectionCondition();
return {};
}
std::vector<ANSCENTER::Object> output;
output.reserve(detectedObjects.size());
for (auto& detectedObj : detectedObjects) {
ProcessDetectedObject(frame, detectedObj, camera_id, fireNSmokeRects, output, draw);
}
if (output.empty()) {
UpdateNoDetectionCondition();
}
return output;
}
bool ANSCustomFS::ProcessDetectedObject(
const cv::Mat& frame,
ANSCENTER::Object& detectedObj,
const std::string& camera_id,
const std::vector<cv::Rect>& fireNSmokeRects,
std::vector<ANSCENTER::Object>& output, cv::Mat& draw)
{
// Adjust coordinates to frame space
detectedObj.box.x += _detectedArea.x;
detectedObj.box.y += _detectedArea.y;
detectedObj.cameraId = camera_id;
// Check exclusive ROI overlap
if (IsROIOverlapping(detectedObj.box, _exclusiveROIs, INCLUSIVE_IOU_THRESHOLD)) {
return false;
}
// Check confidence threshold
if (detectedObj.confidence <= _detectionScoreThreshold) {
UpdateNoDetectionCondition();
return false;
}
// Check if fire or smoke
if (!IsFireOrSmoke(detectedObj.classId, detectedObj.confidence)) {
UpdateNoDetectionCondition();
return false;
}
// Check for reflection
cv::Mat objectMask = frame(detectedObj.box);
if (detectReflection(objectMask)) {
UpdateNoDetectionCondition();
return false;
}
// Check area overlap
float areaOverlap = calculateIoU(_detectedArea, detectedObj.box);
if (areaOverlap >= MAX_AREA_OVERLAP) {
UpdateNoDetectionCondition();
return false;
}
#ifdef FNS_DEBUG
cv::Scalar color = (detectedObj.classId == 0) ?
cv::Scalar(0, 255, 255) : cv::Scalar(255, 0, 255); // Yellow/Purple
cv::rectangle(draw, detectedObj.box, color, 2);
#endif
// Check motion correlation
if (!ValidateMotionCorrelation(fireNSmokeRects)) {
UpdateNoDetectionCondition();
return false;
}
if (!IsOverlapping(detectedObj, fireNSmokeRects, 0)) {
UpdateNoDetectionCondition();
return false;
}
// Filter validation
if (!ValidateWithFilter(frame, detectedObj, camera_id, output, draw))
{
return false;
}
return true;
}
bool ANSCustomFS::ValidateWithFilter(
const cv::Mat& frame,
const ANSCENTER::Object& detectedObj,
const std::string& camera_id,
std::vector<ANSCENTER::Object>& output, cv::Mat& draw)
{
// Skip filter check after sufficient confirmation frames
if (_realFireCheck > FILTER_VERIFICATION_FRAMES) {
output.push_back(detectedObj);
UpdatePositiveDetection();
return true;
}
// Run filter inference
std::vector<ANSCENTER::Object> filteredObjects;
_filter->RunInference(frame, camera_id.c_str(), filteredObjects);
std::vector<ANSCENTER::Object> excludedObjects;
for (const auto& filteredObj : filteredObjects) {
if (EXCLUDED_FILTER_CLASSES.find(filteredObj.classId) == EXCLUDED_FILTER_CLASSES.end()) {
excludedObjects.push_back(filteredObj);
#ifdef FNS_DEBUG
cv::rectangle(draw, filteredObj.box, cv::Scalar(0, 255, 0), 2);
cv::putText(draw, filteredObj.className,
cv::Point(filteredObj.box.x, filteredObj.box.y - 10),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 255, 0), 2);
#endif
}
}
// Check if detection overlaps with excluded objects
if (excludedObjects.empty() || !IsOverlapping(detectedObj, excludedObjects, 0)) {
output.push_back(detectedObj);
UpdatePositiveDetection();
_realFireCheck++;
return true;
}
else {
// Decrement but don't go negative
_realFireCheck = std::max(0, _realFireCheck - 1);
_isRealFireFrame = (_realFireCheck > 0);
return false;
}
}
std::vector<ANSCENTER::Object> ANSCustomFS::FindNewDetectedArea(
const cv::Mat& frame,
@@ -576,6 +532,12 @@ bool ANSCustomFS::Initialize(const std::string& modelDirectory, float detectionS
return false;
}
// Enable ByteTrack tracker on the detector for persistent track IDs
int trackerResult = _detector->SetTracker(0 /*BYTETRACK*/, 1 /*enable*/);
if (trackerResult != 1) {
std::cerr << "ANSCustomFS::Initialize: Warning - Failed to enable ByteTrack tracker." << std::endl;
}
// Load filter model (COCO general object detector for false positive filtering)
float filterScoreThreshold = 0.25f;
float filterConfThreshold = 0.5f;
@@ -1031,7 +993,7 @@ std::vector<CustomObject> ANSCustomFS::RunInference(const cv::Mat& input, const
}
}
// New helper function to process detected area
// Stage A: Process existing detected area with tracker-based voting
std::vector<ANSCENTER::Object> ANSCustomFS::ProcessExistingDetectedArea(
const cv::Mat& frame,
const std::string& camera_id,
@@ -1041,18 +1003,19 @@ std::vector<ANSCENTER::Object> ANSCustomFS::ProcessExistingDetectedArea(
cv::Mat activeROI = frame(_detectedArea);
// Detect movement and objects
std::vector<ANSCENTER::Object> movementObjects = FindMovementObjects(frame, camera_id, draw);
// Run detector on ROI — tracker assigns persistent trackIds
std::vector<ANSCENTER::Object> detectedObjects;
_detector->RunInference(activeROI, camera_id.c_str(), detectedObjects);
if (detectedObjects.empty()) {
// Age all existing tracks (missed frame for all)
AgeTracks({});
UpdateNoDetectionCondition();
return output;
}
const bool skipMotionCheck = (_motionSpecificity < 0.0f) || (_motionSpecificity >= 1.0f);
const bool validMovement = !movementObjects.empty() && movementObjects.size() < MAX_MOTION_TRACKING;
// Collect detected track IDs this frame for aging
std::unordered_set<int> detectedTrackIds;
for (auto& detectedObj : detectedObjects) {
// Adjust coordinates to full frame
@@ -1060,84 +1023,75 @@ std::vector<ANSCENTER::Object> ANSCustomFS::ProcessExistingDetectedArea(
detectedObj.box.y += _detectedArea.y;
detectedObj.cameraId = camera_id;
// Skip if overlapping with exclusive ROIs
// 1. Exclusive ROI check — skip if overlapping exclusion zones
if (IsROIOverlapping(detectedObj.box, _exclusiveROIs, INCLUSIVE_IOU_THRESHOLD)) {
continue;
}
// Check confidence thresholds
// 2. Confidence check — fire >= threshold, smoke >= smoke threshold
const bool isValidFire = (detectedObj.classId == 0) && (detectedObj.confidence >= _detectionScoreThreshold);
const bool isValidSmoke = (detectedObj.classId == 2) && (detectedObj.confidence >= _smokeDetetectionThreshold);
if (!isValidFire && !isValidSmoke) {
UpdateNoDetectionCondition();
continue;
}
// Check area overlap
const float area_threshold = calculateIoU(_detectedArea, detectedObj.box);
if (area_threshold >= MAX_AREA_OVERLAP) {
UpdateNoDetectionCondition();
continue;
}
// 3. Update track history with this detection
int trackId = detectedObj.trackId;
detectedTrackIds.insert(trackId);
UpdateTrackHistory(trackId, detectedObj.classId, detectedObj.box);
#ifdef FNS_DEBUG
// Draw detection with track info
cv::Scalar color = (detectedObj.classId == 0) ? cv::Scalar(0, 255, 255) : cv::Scalar(255, 0, 255);
cv::rectangle(draw, detectedObj.box, color, 2);
auto trackIt = _trackHistory.find(trackId);
if (trackIt != _trackHistory.end()) {
std::string label = "T" + std::to_string(trackId) + " " +
std::to_string(trackIt->second.detectedCount) + "/" +
std::to_string(VOTE_THRESHOLD);
cv::putText(draw, label,
cv::Point(detectedObj.box.x, detectedObj.box.y - 10),
cv::FONT_HERSHEY_SIMPLEX, 0.5, color, 2);
}
#endif
// Check motion
if (!skipMotionCheck && !validMovement) {
UpdateNoDetectionCondition();
// 4. Voting check — require consistent detection across frames
if (!IsTrackConfirmed(trackId)) {
continue;
}
if (!skipMotionCheck && !IsOverlapping(detectedObj, movementObjects, 0)) {
UpdateNoDetectionCondition();
// 5. Movement check — verify bounding box is changing (not static false positive)
if (!HasBboxMovement(trackId)) {
continue;
}
// Process valid detection
if (!ProcessValidDetection(frame, camera_id, draw, detectedObj, output)) {
// 6. COCO filter — exclude detections that overlap with known non-fire objects
std::vector<ANSCENTER::Object> excludedObjects = FindExcludedObjects(frame, camera_id, draw);
if (!excludedObjects.empty() && IsOverlapping(detectedObj, excludedObjects, 0)) {
// Detection overlaps with a known object — not fire/smoke
_realFireCheck = std::max(0, _realFireCheck - 1);
if (_realFireCheck <= 0) {
_isRealFireFrame = false;
}
continue;
}
// All checks passed — confirmed detection
AddConfirmedDetection(detectedObj, output);
_realFireCheck++;
}
// Age out tracks not seen this frame
AgeTracks(detectedTrackIds);
if (output.empty()) {
UpdateNoDetectionCondition();
}
return output;
}
bool ANSCustomFS::ProcessValidDetection(
const cv::Mat& frame,
const std::string& camera_id,
cv::Mat& draw,
ANSCENTER::Object& detectedObj,
std::vector<ANSCENTER::Object>& output)
{
if (_realFireCheck > FILTERFRAMES) {
AddConfirmedDetection(detectedObj, output);
return true;
}
std::vector<ANSCENTER::Object> excludedObjects = FindExcludedObjects(frame, camera_id, draw);
if (excludedObjects.empty()) {
AddConfirmedDetection(detectedObj, output);
return true;
}
if (!IsOverlapping(detectedObj, excludedObjects, 0)) {
AddConfirmedDetection(detectedObj, output);
_realFireCheck++;
return true;
}
_realFireCheck = std::max(0, _realFireCheck - 1);
if (_realFireCheck <= 0) {
_isRealFireFrame = false;
}
return false;
}
void ANSCustomFS::AddConfirmedDetection(ANSCENTER::Object& detectedObj, std::vector<ANSCENTER::Object>& output) {
output.push_back(std::move(detectedObj));
_isFireNSmokeDetected = true;

41
ANSCustomFireNSmokeDetection/ANSCustomFireNSmoke.h
View File

@@ -1,6 +1,7 @@
#include "ANSLIB.h"
#include <deque>
#include <unordered_set>
#include <unordered_map>
#define RETAINFRAMES 80
#define FILTERFRAMES 10
@@ -13,6 +14,22 @@ class CUSTOM_API ANSCustomFS : public IANSCustomClass
int priority;
ImageSection(const cv::Rect& r) : region(r), priority(0) {}
};
// Track record for voting-based detection confirmation
struct TrackRecord {
int trackId{ 0 };
int classId{ 0 }; // fire=0, smoke=2
std::deque<cv::Rect> bboxHistory; // bounding box history within window
int detectedCount{ 0 }; // frames detected in sliding window
int totalFrames{ 0 }; // total frames since track appeared
bool confirmed{ false }; // passed voting threshold
};
// Voting mechanism constants
static constexpr int VOTE_WINDOW = 15;
static constexpr int VOTE_THRESHOLD = 8;
static constexpr float BBOX_CHANGE_THRESHOLD = 0.05f;
private:
using ANSLIBPtr = std::unique_ptr<ANSCENTER::ANSLIB, decltype(&ANSCENTER::ANSLIB::Destroy)>;
@@ -74,6 +91,9 @@ private:
float _smokeDetetectionThreshold{ 0 };
float _motionSpecificity{ 0 };
// Tracker-based voting state
std::unordered_map<int, TrackRecord> _trackHistory;
cv::Rect GenerateMinimumSquareBoundingBox(const std::vector<ANSCENTER::Object>& detectedObjects, int minSize = 640);
void UpdateNoDetectionCondition();
bool detectStaticFire(std::deque<cv::Mat>& frameQueue);
@@ -101,7 +121,6 @@ private:
void ResetDetectionState();
void GetModelParameters();
std::vector<ANSCENTER::Object> ProcessExistingDetectedArea(const cv::Mat& frame, const std::string& camera_id, cv::Mat& draw);
bool ProcessValidDetection(const cv::Mat& frame, const std::string& camera_id, cv::Mat& draw, ANSCENTER::Object& detectedObj, std::vector<ANSCENTER::Object>& output);
void AddConfirmedDetection(ANSCENTER::Object& detectedObj, std::vector<ANSCENTER::Object>& output);
#ifdef FNS_DEBUG
void DisplayDebugFrame(cv::Mat& draw) {
@@ -117,21 +136,11 @@ private:
int getLowestPriorityRegion();
cv::Rect getRegionByPriority(int priority);
std::vector<ANSCENTER::Object> ProcessExistingDetectedArea(
const cv::Mat& frame,
const std::string& camera_id,
const std::vector<cv::Rect>& fireNSmokeRects, cv::Mat& draw);
bool ProcessDetectedObject(
const cv::Mat& frame,
ANSCENTER::Object& detectedObj,
const std::string& camera_id,
const std::vector<cv::Rect>& fireNSmokeRects,
std::vector<ANSCENTER::Object>& output, cv::Mat& draw);
bool ValidateWithFilter(
const cv::Mat& frame,
const ANSCENTER::Object& detectedObj,
const std::string& camera_id,
std::vector<ANSCENTER::Object>& output, cv::Mat& draw);
// Tracker-based voting methods
void UpdateTrackHistory(int trackId, int classId, const cv::Rect& bbox);
bool IsTrackConfirmed(int trackId) const;
bool HasBboxMovement(int trackId) const;
void AgeTracks(const std::unordered_set<int>& detectedTrackIds);
std::vector<ANSCENTER::Object> FindNewDetectedArea(
const cv::Mat& frame,
const std::string& camera_id, cv::Mat& draw);

6
CMakeLists.txt
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@@ -7,3 +7,9 @@ set(CMAKE_CXX_STANDARD_REQUIRED ON)
add_subdirectory(ANSCustomHelmetDetection)
add_subdirectory(ANSCustomFireNSmokeDetection)
add_subdirectory(ANSCustomWeaponDetection)
# Unit & integration tests (Google Test)
option(BUILD_TESTS "Build unit tests" ON)
if(BUILD_TESTS)
add_subdirectory(tests)
endif()

31
tests/CMakeLists.txt Normal file
View File

@@ -0,0 +1,31 @@
project(ANSCustomModels_Tests LANGUAGES CXX)
# ---------- Google Test (fetched once, shared by all sub-projects) ----------
include(FetchContent)
FetchContent_Declare(
googletest
GIT_REPOSITORY https://github.com/google/googletest.git
GIT_TAG v1.14.0
)
set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
FetchContent_MakeAvailable(googletest)
enable_testing()
# ---------- Common paths (propagated to sub-projects via variables) ----------
set(ANSLIB_INCLUDE_DIR "C:/Projects/ANLS/ANSLIB/ANSLIB" CACHE PATH "")
set(OPENCV_INCLUDE_DIR "C:/ANSLibs/opencv/include" CACHE PATH "")
set(ANSLIB_LIB_DIR "C:/ProgramData/ANSCENTER/Shared" CACHE PATH "")
set(OPENCV_LIB_DIR "C:/ANSLibs/opencv/x64/vc17/lib" CACHE PATH "")
set(OPENCV_BIN_DIR "C:/ProgramData/ANSCENTER/Shared" CACHE PATH "")
set(TEST_COMMON_DIR "${CMAKE_CURRENT_SOURCE_DIR}" CACHE PATH "")
# ---------- Place all test .exe files alongside the DLLs they need ----------
# This ensures custom model DLLs (built by sibling projects) land in the same
# directory as the test executables so Windows can find them at runtime.
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/bin" CACHE PATH "" FORCE)
# ---------- Sub-project test executables ----------
add_subdirectory(FireNSmokeDetection)
add_subdirectory(HelmetDetection)
add_subdirectory(WeaponDetection)

54
tests/FireNSmokeDetection/CMakeLists.txt Normal file
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@@ -0,0 +1,54 @@
project(FireNSmokeDetection_Tests LANGUAGES CXX)
add_executable(${PROJECT_NAME}
FireNSmokeDetectionTest.cpp
)
target_compile_features(${PROJECT_NAME} PRIVATE cxx_std_17)
target_compile_definitions(${PROJECT_NAME} PRIVATE
WIN32_LEAN_AND_MEAN
NOMINMAX
$<$<CONFIG:Debug>:_DEBUG>
$<$<CONFIG:Release>:NDEBUG>
)
target_include_directories(${PROJECT_NAME} PRIVATE
${TEST_COMMON_DIR}
${ANSLIB_INCLUDE_DIR}
${OPENCV_INCLUDE_DIR}
${CMAKE_SOURCE_DIR}/ANSCustomFireNSmokeDetection
)
target_link_directories(${PROJECT_NAME} PRIVATE
${ANSLIB_LIB_DIR}
${OPENCV_LIB_DIR}
)
target_link_libraries(${PROJECT_NAME} PRIVATE
gtest
gtest_main
ANSLIB
opencv_world4130
ANSCustomFireNSmokeDetection
)
if(MSVC)
target_compile_options(${PROJECT_NAME} PRIVATE /W3 /sdl /permissive-)
endif()
# Copy required DLLs next to the test executable so Windows can find them
add_custom_command(TARGET ${PROJECT_NAME} POST_BUILD
# ANSLIB.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${ANSLIB_LIB_DIR}/ANSLIB.dll"
"$<TARGET_FILE_DIR:${PROJECT_NAME}>"
# OpenCV DLL
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${OPENCV_BIN_DIR}/opencv_world4130.dll"
"$<TARGET_FILE_DIR:${PROJECT_NAME}>"
COMMENT "Copying runtime DLLs for ${PROJECT_NAME}"
)
include(GoogleTest)
gtest_discover_tests(${PROJECT_NAME} DISCOVERY_MODE PRE_TEST)

192
tests/FireNSmokeDetection/FireNSmokeDetectionTest.cpp Normal file
View File

@@ -0,0 +1,192 @@
#include "TestCommon.h"
#include "ANSCustomFireNSmoke.h"
// ===========================================================================
// Unit Tests — no model files required
// ===========================================================================
class FireNSmokeUnitTest : public ::testing::Test {
protected:
ANSCustomFS detector;
};
TEST_F(FireNSmokeUnitTest, EmptyFrameReturnsNoDetections) {
cv::Mat empty;
auto results = detector.RunInference(empty);
EXPECT_TRUE(results.empty());
}
TEST_F(FireNSmokeUnitTest, TinyFrameReturnsNoDetections) {
cv::Mat tiny = TestUtils::CreateTestFrame(5, 5);
auto results = detector.RunInference(tiny);
EXPECT_TRUE(results.empty());
}
TEST_F(FireNSmokeUnitTest, UninitializedDetectorReturnsNoDetections) {
cv::Mat frame = TestUtils::CreateTestFrame(640, 480);
auto results = detector.RunInference(frame);
EXPECT_TRUE(results.empty());
}
TEST_F(FireNSmokeUnitTest, RunInferenceWithCameraId) {
cv::Mat frame = TestUtils::CreateTestFrame(640, 480);
auto results = detector.RunInference(frame, "test_cam_01");
EXPECT_TRUE(results.empty());
}
TEST_F(FireNSmokeUnitTest, ConfigureParametersReturnsValidConfig) {
CustomParams params;
bool result = detector.ConfigureParameters(params);
EXPECT_TRUE(result);
// Should have ExclusiveROIs ROI config
ASSERT_FALSE(params.ROI_Config.empty());
EXPECT_EQ(params.ROI_Config[0].Name, "ExclusiveROIs");
EXPECT_TRUE(params.ROI_Config[0].Rectangle);
EXPECT_FALSE(params.ROI_Config[0].Polygon);
EXPECT_FALSE(params.ROI_Config[0].Line);
EXPECT_EQ(params.ROI_Config[0].MinItems, 0);
EXPECT_EQ(params.ROI_Config[0].MaxItems, 20);
// Should have SmokeScore and Sensitivity parameters
ASSERT_GE(params.Parameters.size(), 2u);
bool hasSmokeScore = false;
bool hasSensitivity = false;
for (const auto& p : params.Parameters) {
if (p.Name == "SmokeScore") {
hasSmokeScore = true;
EXPECT_EQ(p.DataType, "float");
EXPECT_EQ(p.MaxValue, 1);
EXPECT_EQ(p.MinValue, 0);
}
if (p.Name == "Sensitivity") {
hasSensitivity = true;
EXPECT_EQ(p.DataType, "float");
}
}
EXPECT_TRUE(hasSmokeScore) << "Missing SmokeScore parameter";
EXPECT_TRUE(hasSensitivity) << "Missing Sensitivity parameter";
}
TEST_F(FireNSmokeUnitTest, DestroySucceeds) {
EXPECT_TRUE(detector.Destroy());
}
TEST_F(FireNSmokeUnitTest, DestroyCanBeCalledMultipleTimes) {
EXPECT_TRUE(detector.Destroy());
EXPECT_TRUE(detector.Destroy());
}
TEST_F(FireNSmokeUnitTest, InitializeWithInvalidDirectoryFails) {
std::string labelMap;
bool result = detector.Initialize("C:\\NonExistent\\Path\\Model", 0.5f, labelMap);
EXPECT_FALSE(result);
}
TEST_F(FireNSmokeUnitTest, OptimizeBeforeInitializeReturnsFalse) {
EXPECT_FALSE(detector.OptimizeModel(true));
}
// ===========================================================================
// Integration Tests — require model files on disk
// ===========================================================================
class FireNSmokeIntegrationTest : public ::testing::Test {
protected:
ANSCustomFS detector;
std::string labelMap;
std::vector<std::string> classes;
void SetUp() override {
if (!TestConfig::ModelExists(TestConfig::FIRE_SMOKE_MODEL_DIR)) {
GTEST_SKIP() << "Fire/Smoke model not found at: " << TestConfig::FIRE_SMOKE_MODEL_DIR;
}
bool ok = detector.Initialize(TestConfig::FIRE_SMOKE_MODEL_DIR, 0.5f, labelMap);
ASSERT_TRUE(ok) << "Failed to initialize Fire/Smoke detector";
classes = TestUtils::ParseLabelMap(labelMap);
}
void TearDown() override {
detector.Destroy();
}
};
TEST_F(FireNSmokeIntegrationTest, InitializeProducesLabelMap) {
EXPECT_FALSE(labelMap.empty());
EXPECT_FALSE(classes.empty());
}
TEST_F(FireNSmokeIntegrationTest, InferenceOnSolidFrameReturnsNoDetections) {
cv::Mat frame = TestUtils::CreateTestFrame(1920, 1080);
auto results = detector.RunInference(frame, "test_cam");
EXPECT_TRUE(results.empty()) << "Solid gray frame should not trigger fire/smoke";
}
TEST_F(FireNSmokeIntegrationTest, InferenceOnSmallFrame) {
cv::Mat frame = TestUtils::CreateTestFrame(320, 240);
auto results = detector.RunInference(frame, "test_cam");
SUCCEED();
}
TEST_F(FireNSmokeIntegrationTest, InferenceOnLargeFrame) {
cv::Mat frame = TestUtils::CreateTestFrame(3840, 2160);
auto results = detector.RunInference(frame, "test_cam");
SUCCEED();
}
TEST_F(FireNSmokeIntegrationTest, DetectionResultFieldsAreValid) {
if (!TestConfig::VideoExists(TestConfig::FIRE_SMOKE_VIDEO)) {
GTEST_SKIP() << "Fire/Smoke test video not found";
}
cv::VideoCapture cap(TestConfig::FIRE_SMOKE_VIDEO);
ASSERT_TRUE(cap.isOpened());
bool detectionFound = false;
for (int i = 0; i < 300 && !detectionFound; i++) {
cv::Mat frame;
if (!cap.read(frame)) break;
auto results = detector.RunInference(frame, "test_cam");
for (const auto& obj : results) {
detectionFound = true;
EXPECT_GE(obj.confidence, 0.0f);
EXPECT_LE(obj.confidence, 1.0f);
EXPECT_GE(obj.box.width, 0);
EXPECT_GE(obj.box.height, 0);
EXPECT_TRUE(obj.classId == 0 || obj.classId == 2)
<< "Expected fire (0) or smoke (2), got classId=" << obj.classId;
}
}
cap.release();
}
TEST_F(FireNSmokeIntegrationTest, PerformanceBenchmark) {
if (!TestConfig::VideoExists(TestConfig::FIRE_SMOKE_VIDEO)) {
GTEST_SKIP() << "Fire/Smoke test video not found";
}
auto [totalDetections, avgMs] = TestUtils::RunVideoFrames(detector, TestConfig::FIRE_SMOKE_VIDEO, 100);
ASSERT_GE(totalDetections, 0) << "Video could not be opened";
std::cout << "[FireNSmoke] 100 frames: avg=" << avgMs << "ms/frame, "
<< "detections=" << totalDetections << std::endl;
EXPECT_LT(avgMs, 200.0) << "Average inference time exceeds 200ms";
}
TEST_F(FireNSmokeIntegrationTest, ThreadSafetyConcurrentInference) {
cv::Mat frame1 = TestUtils::CreateTestFrame(640, 480, cv::Scalar(100, 100, 100));
cv::Mat frame2 = TestUtils::CreateTestFrame(640, 480, cv::Scalar(200, 200, 200));
std::vector<CustomObject> results1, results2;
std::thread t1([&]() { results1 = detector.RunInference(frame1, "cam_1"); });
std::thread t2([&]() { results2 = detector.RunInference(frame2, "cam_2"); });
t1.join();
t2.join();
SUCCEED();
}

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project(HelmetDetection_Tests LANGUAGES CXX)
add_executable(${PROJECT_NAME}
HelmetDetectionTest.cpp
)
target_compile_features(${PROJECT_NAME} PRIVATE cxx_std_17)
target_compile_definitions(${PROJECT_NAME} PRIVATE
WIN32_LEAN_AND_MEAN
NOMINMAX
$<$<CONFIG:Debug>:_DEBUG>
$<$<CONFIG:Release>:NDEBUG>
)
target_include_directories(${PROJECT_NAME} PRIVATE
${TEST_COMMON_DIR}
${ANSLIB_INCLUDE_DIR}
${OPENCV_INCLUDE_DIR}
${CMAKE_SOURCE_DIR}/ANSCustomHelmetDetection
)
target_link_directories(${PROJECT_NAME} PRIVATE
${ANSLIB_LIB_DIR}
${OPENCV_LIB_DIR}
)
target_link_libraries(${PROJECT_NAME} PRIVATE
gtest
gtest_main
ANSLIB
opencv_world4130
ANSCustomHelmetDetection
)
if(MSVC)
target_compile_options(${PROJECT_NAME} PRIVATE /W3 /sdl /permissive-)
endif()
# Copy required DLLs next to the test executable so Windows can find them
add_custom_command(TARGET ${PROJECT_NAME} POST_BUILD
# ANSLIB.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${ANSLIB_LIB_DIR}/ANSLIB.dll"
"$<TARGET_FILE_DIR:${PROJECT_NAME}>"
# OpenCV DLL
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${OPENCV_BIN_DIR}/opencv_world4130.dll"
"$<TARGET_FILE_DIR:${PROJECT_NAME}>"
COMMENT "Copying runtime DLLs for ${PROJECT_NAME}"
)
include(GoogleTest)
gtest_discover_tests(${PROJECT_NAME} DISCOVERY_MODE PRE_TEST)

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#include "TestCommon.h"
#include "ANSCustomCodeHelmetDetection.h"
// ===========================================================================
// Unit Tests — no model files required
// ===========================================================================
class HelmetUnitTest : public ::testing::Test {
protected:
ANSCustomHMD detector;
};
TEST_F(HelmetUnitTest, EmptyFrameReturnsNoDetections) {
cv::Mat empty;
auto results = detector.RunInference(empty);
EXPECT_TRUE(results.empty());
}
TEST_F(HelmetUnitTest, TinyFrameReturnsNoDetections) {
cv::Mat tiny = TestUtils::CreateTestFrame(5, 5);
auto results = detector.RunInference(tiny);
EXPECT_TRUE(results.empty());
}
TEST_F(HelmetUnitTest, UninitializedDetectorReturnsNoDetections) {
cv::Mat frame = TestUtils::CreateTestFrame(640, 480);
auto results = detector.RunInference(frame);
EXPECT_TRUE(results.empty());
}
TEST_F(HelmetUnitTest, RunInferenceWithCameraId) {
cv::Mat frame = TestUtils::CreateTestFrame(640, 480);
auto results = detector.RunInference(frame, "test_cam_01");
EXPECT_TRUE(results.empty());
}
TEST_F(HelmetUnitTest, ConfigureParametersReturnsValidConfig) {
CustomParams params;
bool result = detector.ConfigureParameters(params);
EXPECT_TRUE(result);
}
TEST_F(HelmetUnitTest, DestroySucceeds) {
EXPECT_TRUE(detector.Destroy());
}
TEST_F(HelmetUnitTest, DestroyCanBeCalledMultipleTimes) {
EXPECT_TRUE(detector.Destroy());
EXPECT_TRUE(detector.Destroy());
}
TEST_F(HelmetUnitTest, InitializeWithInvalidDirectoryFails) {
std::string labelMap;
bool result = detector.Initialize("C:\\NonExistent\\Path\\Model", 0.5f, labelMap);
EXPECT_FALSE(result);
}
TEST_F(HelmetUnitTest, OptimizeBeforeInitializeReturnsFalse) {
EXPECT_FALSE(detector.OptimizeModel(true));
}
// ===========================================================================
// Integration Tests — require model files on disk
// ===========================================================================
class HelmetIntegrationTest : public ::testing::Test {
protected:
ANSCustomHMD detector;
std::string labelMap;
std::vector<std::string> classes;
void SetUp() override {
if (!TestConfig::ModelExists(TestConfig::HELMET_MODEL_DIR)) {
GTEST_SKIP() << "Helmet model not found at: " << TestConfig::HELMET_MODEL_DIR;
}
bool ok = detector.Initialize(TestConfig::HELMET_MODEL_DIR, 0.6f, labelMap);
ASSERT_TRUE(ok) << "Failed to initialize Helmet detector";
classes = TestUtils::ParseLabelMap(labelMap);
}
void TearDown() override {
detector.Destroy();
}
};
TEST_F(HelmetIntegrationTest, InitializeProducesLabelMap) {
EXPECT_FALSE(labelMap.empty());
EXPECT_FALSE(classes.empty());
}
TEST_F(HelmetIntegrationTest, InferenceOnSolidFrameReturnsNoDetections) {
cv::Mat frame = TestUtils::CreateTestFrame(1920, 1080);
auto results = detector.RunInference(frame, "test_cam");
EXPECT_TRUE(results.empty()) << "Solid gray frame should not trigger helmet detection";
}
TEST_F(HelmetIntegrationTest, InferenceOnSmallFrame) {
cv::Mat frame = TestUtils::CreateTestFrame(320, 240);
auto results = detector.RunInference(frame, "test_cam");
SUCCEED();
}
TEST_F(HelmetIntegrationTest, InferenceOnLargeFrame) {
cv::Mat frame = TestUtils::CreateTestFrame(3840, 2160);
auto results = detector.RunInference(frame, "test_cam");
SUCCEED();
}
TEST_F(HelmetIntegrationTest, DetectionResultFieldsAreValid) {
if (!TestConfig::VideoExists(TestConfig::HELMET_VIDEO)) {
GTEST_SKIP() << "Helmet test video not found";
}
cv::VideoCapture cap(TestConfig::HELMET_VIDEO);
ASSERT_TRUE(cap.isOpened());
bool detectionFound = false;
for (int i = 0; i < 300 && !detectionFound; i++) {
cv::Mat frame;
if (!cap.read(frame)) break;
auto results = detector.RunInference(frame, "test_cam");
for (const auto& obj : results) {
detectionFound = true;
EXPECT_GE(obj.confidence, 0.0f);
EXPECT_LE(obj.confidence, 1.0f);
EXPECT_GE(obj.box.width, 0);
EXPECT_GE(obj.box.height, 0);
EXPECT_GE(obj.classId, 0);
}
}
cap.release();
}
TEST_F(HelmetIntegrationTest, PerformanceBenchmark) {
if (!TestConfig::VideoExists(TestConfig::HELMET_VIDEO)) {
GTEST_SKIP() << "Helmet test video not found";
}
auto [totalDetections, avgMs] = TestUtils::RunVideoFrames(detector, TestConfig::HELMET_VIDEO, 100);
ASSERT_GE(totalDetections, 0) << "Video could not be opened";
std::cout << "[Helmet] 100 frames: avg=" << avgMs << "ms/frame, "
<< "detections=" << totalDetections << std::endl;
EXPECT_LT(avgMs, 200.0) << "Average inference time exceeds 200ms";
}
TEST_F(HelmetIntegrationTest, ThreadSafetyConcurrentInference) {
cv::Mat frame1 = TestUtils::CreateTestFrame(640, 480, cv::Scalar(100, 100, 100));
cv::Mat frame2 = TestUtils::CreateTestFrame(640, 480, cv::Scalar(200, 200, 200));
std::vector<CustomObject> results1, results2;
std::thread t1([&]() { results1 = detector.RunInference(frame1, "cam_1"); });
std::thread t2([&]() { results2 = detector.RunInference(frame2, "cam_2"); });
t1.join();
t2.join();
SUCCEED();
}

116
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#pragma once
#include <gtest/gtest.h>
#include <opencv2/opencv.hpp>
#include <string>
#include <vector>
#include <sstream>
#include <chrono>
#include <filesystem>
#include "ANSLIB.h"
// ---------------------------------------------------------------------------
// Model directory paths — update these to match your local environment
// ---------------------------------------------------------------------------
namespace TestConfig {
inline const std::string FIRE_SMOKE_MODEL_DIR =
"C:\\Programs\\DemoAssets\\ModelsForANSVIS\\ANS_FireSmoke_v2.0";
inline const std::string HELMET_MODEL_DIR =
"C:\\Programs\\DemoAssets\\ModelsForANSVIS\\ANS_Helmet(GPU)_v1.0";
inline const std::string WEAPON_MODEL_DIR =
"C:\\Programs\\DemoAssets\\ModelsForANSVIS\\ANS_WeaponDetection(GPU)_1.0";
inline const std::string FIRE_SMOKE_VIDEO =
"C:\\Programs\\DemoAssets\\Videos\\FireNSmoke\\ANSFireFull.mp4";
inline const std::string HELMET_VIDEO =
"C:\\Programs\\DemoAssets\\Videos\\Helmet\\HM2.mp4";
inline const std::string WEAPON_VIDEO =
"C:\\Programs\\DemoAssets\\Videos\\Weapon\\AK47 Glock.mp4";
// Check if model directory exists
inline bool ModelExists(const std::string& path) {
return std::filesystem::exists(path) && std::filesystem::is_directory(path);
}
// Check if video file exists
inline bool VideoExists(const std::string& path) {
return std::filesystem::exists(path);
}
} // namespace TestConfig
// ---------------------------------------------------------------------------
// Helper utilities
// ---------------------------------------------------------------------------
namespace TestUtils {
// Parse comma-separated label map into vector of class names
inline std::vector<std::string> ParseLabelMap(const std::string& labelMap) {
std::vector<std::string> classes;
std::stringstream ss(labelMap);
std::string item;
while (std::getline(ss, item, ',')) {
classes.push_back(item);
}
return classes;
}
// Create a solid-color test frame (no model required)
inline cv::Mat CreateTestFrame(int width, int height, cv::Scalar color = cv::Scalar(128, 128, 128)) {
return cv::Mat(height, width, CV_8UC3, color);
}
// Create a frame with a bright red/orange region to simulate fire-like colors
inline cv::Mat CreateFireLikeFrame(int width, int height) {
cv::Mat frame(height, width, CV_8UC3, cv::Scalar(50, 50, 50));
cv::Rect fireRegion(width / 4, height / 4, width / 2, height / 2);
frame(fireRegion) = cv::Scalar(0, 80, 255); // BGR: orange-red
return frame;
}
// Create a frame with a gray haze to simulate smoke-like colors
inline cv::Mat CreateSmokeLikeFrame(int width, int height) {
cv::Mat frame(height, width, CV_8UC3, cv::Scalar(30, 30, 30));
cv::Rect smokeRegion(width / 4, height / 4, width / 2, height / 2);
frame(smokeRegion) = cv::Scalar(180, 180, 190); // BGR: light gray
return frame;
}
// Measure inference time in milliseconds
template <typename Func>
double MeasureMs(Func&& func) {
auto start = std::chrono::high_resolution_clock::now();
func();
auto end = std::chrono::high_resolution_clock::now();
return std::chrono::duration<double, std::milli>(end - start).count();
}
// Run N frames of video through a detector, return (totalDetections, avgMs)
template <typename Detector>
std::pair<int, double> RunVideoFrames(Detector& detector, const std::string& videoPath, int maxFrames) {
cv::VideoCapture cap(videoPath);
if (!cap.isOpened()) return { -1, 0.0 };
int totalDetections = 0;
double totalMs = 0.0;
int frameCount = 0;
while (frameCount < maxFrames) {
cv::Mat frame;
if (!cap.read(frame)) break;
double ms = MeasureMs([&]() {
auto results = detector.RunInference(frame);
totalDetections += static_cast<int>(results.size());
});
totalMs += ms;
frameCount++;
}
cap.release();
double avgMs = (frameCount > 0) ? totalMs / frameCount : 0.0;
return { totalDetections, avgMs };
}
} // namespace TestUtils

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project(WeaponDetection_Tests LANGUAGES CXX)
add_executable(${PROJECT_NAME}
WeaponDetectionTest.cpp
)
target_compile_features(${PROJECT_NAME} PRIVATE cxx_std_17)
target_compile_definitions(${PROJECT_NAME} PRIVATE
WIN32_LEAN_AND_MEAN
NOMINMAX
$<$<CONFIG:Debug>:_DEBUG>
$<$<CONFIG:Release>:NDEBUG>
)
target_include_directories(${PROJECT_NAME} PRIVATE
${TEST_COMMON_DIR}
${ANSLIB_INCLUDE_DIR}
${OPENCV_INCLUDE_DIR}
${CMAKE_SOURCE_DIR}/ANSCustomWeaponDetection
)
target_link_directories(${PROJECT_NAME} PRIVATE
${ANSLIB_LIB_DIR}
${OPENCV_LIB_DIR}
)
target_link_libraries(${PROJECT_NAME} PRIVATE
gtest
gtest_main
ANSLIB
opencv_world4130
ANSCustomWeaponDetection
)
if(MSVC)
target_compile_options(${PROJECT_NAME} PRIVATE /W3 /sdl /permissive-)
endif()
# Copy required DLLs next to the test executable so Windows can find them
add_custom_command(TARGET ${PROJECT_NAME} POST_BUILD
# ANSLIB.dll
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${ANSLIB_LIB_DIR}/ANSLIB.dll"
"$<TARGET_FILE_DIR:${PROJECT_NAME}>"
# OpenCV DLL
COMMAND ${CMAKE_COMMAND} -E copy_if_different
"${OPENCV_BIN_DIR}/opencv_world4130.dll"
"$<TARGET_FILE_DIR:${PROJECT_NAME}>"
COMMENT "Copying runtime DLLs for ${PROJECT_NAME}"
)
include(GoogleTest)
gtest_discover_tests(${PROJECT_NAME} DISCOVERY_MODE PRE_TEST)

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#include "TestCommon.h"
#include "ANSCustomCodeWeaponDetection.h"
// ===========================================================================
// Unit Tests — no model files required
// ===========================================================================
class WeaponUnitTest : public ::testing::Test {
protected:
ANSCustomWD detector;
};
TEST_F(WeaponUnitTest, EmptyFrameReturnsNoDetections) {
cv::Mat empty;
auto results = detector.RunInference(empty);
EXPECT_TRUE(results.empty());
}
TEST_F(WeaponUnitTest, TinyFrameReturnsNoDetections) {
cv::Mat tiny = TestUtils::CreateTestFrame(5, 5);
auto results = detector.RunInference(tiny);
EXPECT_TRUE(results.empty());
}
TEST_F(WeaponUnitTest, UninitializedDetectorReturnsNoDetections) {
cv::Mat frame = TestUtils::CreateTestFrame(640, 480);
auto results = detector.RunInference(frame);
EXPECT_TRUE(results.empty());
}
TEST_F(WeaponUnitTest, RunInferenceWithCameraId) {
cv::Mat frame = TestUtils::CreateTestFrame(640, 480);
auto results = detector.RunInference(frame, "test_cam_01");
EXPECT_TRUE(results.empty());
}
TEST_F(WeaponUnitTest, ConfigureParametersReturnsValidConfig) {
CustomParams params;
bool result = detector.ConfigureParameters(params);
EXPECT_TRUE(result);
}
TEST_F(WeaponUnitTest, DestroySucceeds) {
EXPECT_TRUE(detector.Destroy());
}
TEST_F(WeaponUnitTest, DestroyCanBeCalledMultipleTimes) {
EXPECT_TRUE(detector.Destroy());
EXPECT_TRUE(detector.Destroy());
}
TEST_F(WeaponUnitTest, InitializeWithInvalidDirectoryFails) {
std::string labelMap;
bool result = detector.Initialize("C:\\NonExistent\\Path\\Model", 0.5f, labelMap);
EXPECT_FALSE(result);
}
TEST_F(WeaponUnitTest, OptimizeBeforeInitializeReturnsFalse) {
EXPECT_FALSE(detector.OptimizeModel(true));
}
// ===========================================================================
// Integration Tests — require model files on disk
// ===========================================================================
class WeaponIntegrationTest : public ::testing::Test {
protected:
ANSCustomWD detector;
std::string labelMap;
std::vector<std::string> classes;
void SetUp() override {
if (!TestConfig::ModelExists(TestConfig::WEAPON_MODEL_DIR)) {
GTEST_SKIP() << "Weapon model not found at: " << TestConfig::WEAPON_MODEL_DIR;
}
bool ok = detector.Initialize(TestConfig::WEAPON_MODEL_DIR, 0.6f, labelMap);
ASSERT_TRUE(ok) << "Failed to initialize Weapon detector";
classes = TestUtils::ParseLabelMap(labelMap);
}
void TearDown() override {
detector.Destroy();
}
};
TEST_F(WeaponIntegrationTest, InitializeProducesLabelMap) {
EXPECT_FALSE(labelMap.empty());
EXPECT_FALSE(classes.empty());
}
TEST_F(WeaponIntegrationTest, InferenceOnSolidFrameReturnsNoDetections) {
cv::Mat frame = TestUtils::CreateTestFrame(1920, 1080);
auto results = detector.RunInference(frame, "test_cam");
EXPECT_TRUE(results.empty()) << "Solid gray frame should not trigger weapon detection";
}
TEST_F(WeaponIntegrationTest, InferenceOnSmallFrame) {
cv::Mat frame = TestUtils::CreateTestFrame(320, 240);
auto results = detector.RunInference(frame, "test_cam");
SUCCEED();
}
TEST_F(WeaponIntegrationTest, InferenceOnLargeFrame) {
cv::Mat frame = TestUtils::CreateTestFrame(3840, 2160);
auto results = detector.RunInference(frame, "test_cam");
SUCCEED();
}
TEST_F(WeaponIntegrationTest, DetectionResultFieldsAreValid) {
if (!TestConfig::VideoExists(TestConfig::WEAPON_VIDEO)) {
GTEST_SKIP() << "Weapon test video not found";
}
cv::VideoCapture cap(TestConfig::WEAPON_VIDEO);
ASSERT_TRUE(cap.isOpened());
bool detectionFound = false;
for (int i = 0; i < 300 && !detectionFound; i++) {
cv::Mat frame;
if (!cap.read(frame)) break;
auto results = detector.RunInference(frame, "test_cam");
for (const auto& obj : results) {
detectionFound = true;
EXPECT_GE(obj.confidence, 0.0f);
EXPECT_LE(obj.confidence, 1.0f);
EXPECT_GE(obj.box.width, 0);
EXPECT_GE(obj.box.height, 0);
EXPECT_GE(obj.classId, 0);
}
}
cap.release();
}
TEST_F(WeaponIntegrationTest, PerformanceBenchmark) {
if (!TestConfig::VideoExists(TestConfig::WEAPON_VIDEO)) {
GTEST_SKIP() << "Weapon test video not found";
}
auto [totalDetections, avgMs] = TestUtils::RunVideoFrames(detector, TestConfig::WEAPON_VIDEO, 100);
ASSERT_GE(totalDetections, 0) << "Video could not be opened";
std::cout << "[Weapon] 100 frames: avg=" << avgMs << "ms/frame, "
<< "detections=" << totalDetections << std::endl;
EXPECT_LT(avgMs, 200.0) << "Average inference time exceeds 200ms";
}
TEST_F(WeaponIntegrationTest, ThreadSafetyConcurrentInference) {
cv::Mat frame1 = TestUtils::CreateTestFrame(640, 480, cv::Scalar(100, 100, 100));
cv::Mat frame2 = TestUtils::CreateTestFrame(640, 480, cv::Scalar(200, 200, 200));
std::vector<CustomObject> results1, results2;
std::thread t1([&]() { results1 = detector.RunInference(frame1, "cam_1"); });
std::thread t2([&]() { results2 = detector.RunInference(frame2, "cam_2"); });
t1.join();
t2.join();
SUCCEED();
}