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updating loop closure test

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matlabbe
2026-06-08 22:18:23 -07:00
parent e52a4378c7
commit 8ca4f6c4ac
2 changed files with 45 additions and 27 deletions
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corelib/src/Features2d.cpp
+1 -4
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@@ -1978,11 +1978,8 @@ void FAST::parseParameters(const ParametersMap & parameters)
bool FAST::isGpuAvailable() const
{
#ifdef HAVE_OPENCV_CUDAFEATURES2D
return cv::cuda::getCudaEnabledDeviceCount() > 0;
#else
// Not implemented
return false;
#endif
}
std::vector<cv::KeyPoint> FAST::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
corelib/test/test_rtabmap_integration.cpp
+44 -23
View File
@@ -1978,6 +1978,13 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
static_cast<Feature2D::Type>(Parameters::defaultKpDetectorStrategy());
int detectorsTested = 0;
// Tracks whether any detector × variant across the whole test produced
// a bad signature that still carried non-empty word-keypoints. Asserted
// at the end so the BadSignatures path is verified at least once
// regardless of which detectors are available in this build.
// The idea is that we should test that in case of bad signatures,
// the keypoints AND descriptors should be generated (#1717).
bool sawBadSigWithKpts = false;
for(int strategy = Feature2D::kFeatureSurf; strategy < Feature2D::kFeatureEnd; ++strategy)
{
const Feature2D::Type detectorType = static_cast<Feature2D::Type>(strategy);
@@ -2057,7 +2064,8 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
params[Parameters::kMemSTMSize()] = "20";
params[Parameters::kKpTfIdfLikelihoodUsed()] = tfIdfUsed ? "true" : "false";
params[Parameters::kKpMaxFeatures()] = "500";
params[Parameters::kKpBadSignRatio()] = "0.25";
params[Parameters::kKpBadSignRatio()] = "0.1";
params[Parameters::kKpNndrRatio()] = "0.8";
// SIFT-specific: lower the contrast threshold so more keypoints
// survive on the low-texture frames in data/samples.
params[Parameters::kSIFTContrastThreshold()] = "0.01";
@@ -2095,8 +2103,6 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
case Feature2D::kFeatureSurf: params[Parameters::kSURFGpuVersion()] = "true"; break;
case Feature2D::kFeatureSift: params[Parameters::kSIFTGpu()] = "true"; break;
case Feature2D::kFeatureOrb: params[Parameters::kORBGpu()] = "true"; break;
case Feature2D::kFeatureFastBrief:
case Feature2D::kFeatureFastFreak: params[Parameters::kFASTGpu()] = "true"; break;
case Feature2D::kFeatureGfttFreak:
case Feature2D::kFeatureGfttBrief:
case Feature2D::kFeatureGfttOrb:
@@ -2155,7 +2161,14 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
<< "size mismatch on signature ("
<< lastSig->getWordsKpts().size() << " vs "
<< lastSig->getWordsDescriptors().rows << ")";
if(lastSig->isBadSignature()) ++badSignatureCount;
if(lastSig->isBadSignature())
{
++badSignatureCount;
if(!lastSig->getWordsKpts().empty())
{
sawBadSigWithKpts = true;
}
}
FrameStat s;
s.queryRow = i - 1;
@@ -2186,16 +2199,21 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
<< detectorLabel << " expected " << kNumFrames
<< " frames from " << samplesDir << ", got " << i;
// Mem/BadSignaturesIgnored=false should reveal at least one bad
// signature during the 84-frame run -- except for SuperPoint
// (Magicleap) on GPU, whose dense features always yield >= 1
// valid word per frame on this dataset, so no frame ever turns
// into a bad signature.
if(detectorType != Feature2D::kFeatureSuperPointTorch)
std::cerr << "[" << detectorLabel
<< "] bad signatures: " << badSignatureCount
<< " (Mem/BadSignaturesIgnored=false)\n";
// Pick the descriptor type from the last in-memory signature to
// drive the recall-floor logic below (binary vs float bucket).
bool binaryDescriptors = false;
{
EXPECT_GE(badSignatureCount, 1)
<< detectorLabel << " expected at least 1 bad signature with "
<< "Mem/BadSignaturesIgnored=false; got " << badSignatureCount;
const Signature * lastSig =
rtabmap.getMemory()->getLastWorkingSignature(false);
if(lastSig && !lastSig->getWordsDescriptors().empty())
{
binaryDescriptors =
lastSig->getWordsDescriptors().type() == CV_8U;
}
}
rtabmap.close();
@@ -2312,16 +2330,11 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
<< ", FP=" << acceptedFp << ")\n";
}
const bool isCornerBased =
detectorType == Feature2D::kFeatureBrisk ||
detectorType == Feature2D::kFeatureOrb ||
detectorType == Feature2D::kFeatureGfttFreak ||
detectorType == Feature2D::kFeatureGfttBrief ||
detectorType == Feature2D::kFeatureGfttOrb ||
detectorType == Feature2D::kFeatureGfttDaisy ||
detectorType == Feature2D::kFeatureFastFreak ||
detectorType == Feature2D::kFeatureFastBrief;
const float recallFloor = isCornerBased ? 0.5f : 0.9f;
// Binary-descriptor detectors (Hamming-distance BoW: ORB, BRIEF,
// FREAK, BRISK) carry less per-keypoint discrimination than float
// descriptors (SIFT, SURF, KAZE, DAISY, SuperPoint) on this 84-img
// low-texture set, so they get a looser recall@100%P floor.
const float recallFloor = binaryDescriptors ? 0.5f : 0.9f;
EXPECT_GE(recallAt100p, recallFloor)
<< detectorLabel << " recall@100%P=" << recallAt100p
<< " is below " << recallFloor
@@ -2331,4 +2344,12 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
} // end for(tfIdfUsed)
}
ASSERT_GT(detectorsTested, 0) << "no Features2D detector was available in this build";
// Across every detector × variant we exercised, at least one bad
// signature must have been observed with non-empty word-keypoints --
// otherwise the BadSignatures path was never genuinely triggered by
// the data and the test setup has drifted away from validating it.
EXPECT_TRUE(sawBadSigWithKpts)
<< "no bad signature with non-empty words-keypoints was seen "
<< "across any detector; data/samples or the BoW pipeline may "
<< "no longer be exercising the BadSignatures path";
}