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improving features2d tests

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This commit is contained in:
matlabbe
2026-06-03 22:55:23 -07:00
parent 06dfe92d02
commit cb50b6f21b
6 changed files with 508 additions and 105 deletions
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corelib/test/test_rtabmap_integration.cpp
+67 -17
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@@ -1645,30 +1645,28 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
const std::string superpointRpautratWeights = std::string(RTABMAP_TEST_DATA_ROOT) + "/tests/superpoint_v6_from_tf.pth";
const std::string superpointRpautratModel = std::string(RTABMAP_TEST_DATA_ROOT) + "/tests/superpoint_pytorch.py";
// Two BoW likelihood variants: the rtabmap default (raw word-count
// likelihood) and the TF-IDF-weighted variant. They produce different
// hypothesis distributions, so each detector is exercised under both.
const std::vector<bool> tfIdfVariants = {false, true};
// One pass per detector: prefer GPU if available, otherwise CPU. To
// keep wall-clock low, the two BoW likelihood variants (default raw
// word-count vs TF-IDF-weighted) are exercised only on rtabmap's
// default detector (Kp/DetectorStrategy default = kFeatureGfttOrb).
const Feature2D::Type defaultDetector =
static_cast<Feature2D::Type>(Parameters::defaultKpDetectorStrategy());
int detectorsTested = 0;
for(bool tfIdfUsed : tfIdfVariants)
for(int strategy = Feature2D::kFeatureSurf; strategy < Feature2D::kFeatureEnd; ++strategy)
{
const Feature2D::Type detectorType = static_cast<Feature2D::Type>(strategy);
// Label includes the variant so per-detector logs / output BMPs /
// work DBs don't clobber each other across the two iterations.
const std::string detectorLabel = Feature2D::typeName(detectorType)
+ (tfIdfUsed ? "[TfIdf]" : "[Likelihood]");
const std::string typeName = Feature2D::typeName(detectorType);
if(!Feature2D::isAvailable(detectorType))
{
std::cerr << "[skip] detector " << detectorLabel << " not available in this build\n";
std::cerr << "[skip] detector " << typeName << " not available in this build\n";
continue;
}
if(detectorType == Feature2D::kFeaturePyDetector)
{
std::cerr << "[skip] detector " << detectorLabel
std::cerr << "[skip] detector " << typeName
<< " requires a user-supplied Py/DetectorPath script\n";
continue;
}
@@ -1678,7 +1676,7 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
// script writes them under data/tests/; skip cleanly if absent.
if(detectorType == Feature2D::kFeatureSuperPointTorch && !UFile::exists(superpointTorchModel))
{
std::cerr << "[skip] detector " << detectorLabel
std::cerr << "[skip] detector " << typeName
<< " missing weights: " << superpointTorchModel
<< " (run scripts/fetch_test_data.sh)\n";
continue;
@@ -1687,13 +1685,45 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
(!UFile::exists(superpointRpautratWeights) ||
!UFile::exists(superpointRpautratModel)))
{
std::cerr << "[skip] detector " << detectorLabel
std::cerr << "[skip] detector " << typeName
<< " missing assets: weights=" << superpointRpautratWeights
<< " model=" << superpointRpautratModel
<< " (run scripts/fetch_test_data.sh)\n";
continue;
}
SCOPED_TRACE(std::string("detector=") + detectorLabel);
// Probe GPU availability once per detector. Build the temp instance
// with the same asset paths the actual run uses; otherwise SuperPoint
// variants log a (harmless) load failure.
bool useGpu = false;
{
ParametersMap probeParams;
if(detectorType == Feature2D::kFeatureSuperPointTorch)
{
probeParams[Parameters::kSuperPointModelPath()] = superpointTorchModel;
}
else if(detectorType == Feature2D::kFeatureSuperPointRpautrat)
{
probeParams[Parameters::kSuperPointRpautratWeightsPath()] = superpointRpautratWeights;
probeParams[Parameters::kSuperPointRpautratModelPath()] = superpointRpautratModel;
}
std::unique_ptr<Feature2D> probe(Feature2D::create(detectorType, probeParams));
if(probe) useGpu = probe->isGpuAvailable();
}
// Run only the default likelihood variant for every detector;
// the TF-IDF variant adds a second run on the default detector
// (kFeatureGfttOrb) so the alternative likelihood path stays
// exercised without quadrupling the test runtime.
std::vector<bool> tfIdfVariants = {false};
if(detectorType == defaultDetector) tfIdfVariants.push_back(true);
for(bool tfIdfUsed : tfIdfVariants)
{
const std::string detectorLabel = typeName
+ (tfIdfUsed ? "[TfIdf]" : "[Likelihood]")
+ (useGpu ? "[GPU]" : "");
SCOPED_TRACE(std::string("detector=") + detectorLabel);
ParametersMap params;
params[Parameters::kRGBDEnabled()] = "false";
@@ -1718,17 +1748,36 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
params[Parameters::kMemBadSignaturesIgnored()] = "true";
params[Parameters::kMemRehearsalSimilarity()] = "0.20";
// Backend-specific asset paths.
// Backend-specific asset paths + GPU/CUDA toggle. `useGpu` only
// reaches here when this detector reports a usable GPU path; we
// then flip the per-detector "use GPU" parameter on.
const std::string gpuFlag = useGpu ? "true" : "false";
if(detectorType == Feature2D::kFeatureSuperPointTorch)
{
params[Parameters::kSuperPointModelPath()] = superpointTorchModel;
params[Parameters::kSuperPointCuda()] = "false";
params[Parameters::kSuperPointCuda()] = gpuFlag;
}
else if(detectorType == Feature2D::kFeatureSuperPointRpautrat)
{
params[Parameters::kSuperPointRpautratWeightsPath()] = superpointRpautratWeights;
params[Parameters::kSuperPointRpautratModelPath()] = superpointRpautratModel;
params[Parameters::kSuperPointRpautratCuda()] = "false";
params[Parameters::kSuperPointRpautratCuda()] = gpuFlag;
}
else if(useGpu)
{
switch(detectorType)
{
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:
case Feature2D::kFeatureGfttDaisy: params[Parameters::kGFTTGpu()] = "true"; break;
default: break;
}
}
const std::string workDb = workDbForCurrentTest(detectorLabel);
@@ -1908,6 +1957,7 @@ TEST_F(RtabmapIntegrationFixture, AppearanceOnly_PrecisionRecall)
<< " is below 0.9 (sortedTP=" << tp << ", sortedFP=" << fp << ")";
++detectorsTested;
} // end for(tfIdfUsed)
}
ASSERT_GT(detectorsTested, 0) << "no Features2D detector was available in this build";
}