PyDetector: adding check if returned descriptors are empty (#1715)

* PyDetector: adding check if returned descriptors are empty (addressing #1714)

* Throwing error instead of asserting if keypoints and descriptors mistmatch

* ading error instead of warning

* lets do warnings instead (could be possible that images are blanck)

corelib/src/Features2d.cpp

@@ -887,8 +887,17 @@ cv::Mat Feature2D::generateDescriptors(
 		UASSERT(!image.empty());
 		UASSERT(image.type() == CV_8UC1);
 		descriptors = generateDescriptorsImpl(image, keypoints);
-		UASSERT_MSG(descriptors.rows == (int)keypoints.size(), uFormat("descriptors=%d, keypoints=%d", descriptors.rows, (int)keypoints.size()).c_str());
+		if(descriptors.rows != (int)keypoints.size())
-		UDEBUG("Descriptors extracted = %d, remaining kpts=%d", descriptors.rows, (int)keypoints.size());
+		{
+			UWARN("Descriptor extraction returned %d rows for %d keypoints — "
+					"clearing keypoints to keep them in sync.",
+					descriptors.rows, (int)keypoints.size());
+			keypoints.clear();
+			descriptors = cv::Mat();
+		}
+		else {
+			UDEBUG("Descriptors extracted = %d, remaining kpts=%d", descriptors.rows, (int)keypoints.size());
+		}
 	}
 	return descriptors;
 }

corelib/src/python/PyDetector.cpp

@@ -202,18 +202,30 @@ std::vector<cv::KeyPoint> PyDetector::generateKeypointsImpl(const cv::Mat & imag
 
 					arrayPtr = reinterpret_cast<PyArrayObject*>(descPtr);
 					int nDesc = PyArray_SHAPE(arrayPtr)[0];
-					UASSERT(nDesc = nKpts);
 					int dim = PyArray_SHAPE(arrayPtr)[1];
 					type = PyArray_TYPE(arrayPtr);
 					UDEBUG("Desc array %dx%d (type=%d)", nDesc, dim, type);
-					UASSERT_MSG(type == NPY_FLOAT, uFormat("Returned matches should type FLOAT=11, received type=%d", type).c_str());
 
-					c_out = reinterpret_cast<float*>(PyArray_DATA(arrayPtr));
+					if(nDesc != nKpts || dim <= 0)
-					for (int i = 0, kpt_idx = 0; i < nDesc*dim; i+=dim, kpt_idx++)
 					{
-						if(keep_kpt[kpt_idx]) {
+						UWARN("Python detector returned mismatched arrays: "
-							cv::Mat descriptor = cv::Mat(1, dim, CV_32FC1, &c_out[i]).clone();
+								"%d keypoints vs %d descriptors (dim=%d). "
-							descriptors_.push_back(descriptor);
+								"Returning empty features.",
+								nKpts, nDesc, dim);
+						keypoints.clear();
+						descriptors_ = cv::Mat();
+					}
+					else
+					{
+						UASSERT_MSG(type == NPY_FLOAT, uFormat("Returned matches should type FLOAT=11, received type=%d", type).c_str());
+
+						c_out = reinterpret_cast<float*>(PyArray_DATA(arrayPtr));
+						for (int i = 0, kpt_idx = 0; i < nDesc*dim; i+=dim, kpt_idx++)
+						{
+							if(keep_kpt[kpt_idx]) {
+								cv::Mat descriptor = cv::Mat(1, dim, CV_32FC1, &c_out[i]).clone();
+								descriptors_.push_back(descriptor);
+							}
 						}
 					}
 				}

corelib/src/superpoint_torch/SuperPoint.cc

@@ -144,7 +144,12 @@ std::vector<cv::KeyPoint> SPDetector::detect(const cv::Mat &img, const cv::Mat &
 	UASSERT(img.type() == CV_8UC1);
 	UASSERT(mask.empty() || (mask.type() == CV_8UC1 && img.cols == mask.cols && img.rows == mask.rows));
 	detected_ = false;
-	if(model_)
+	if(!model_)
+	{
+		UERROR("No model is loaded!");
+		return std::vector<cv::KeyPoint>();
+	}
+	try
 	{
 		torch::NoGradGuard no_grad_guard;
 		auto x = torch::from_blob(img.data, {1, 1, img.rows, img.cols}, torch::kByte);
@@ -199,9 +204,9 @@ std::vector<cv::KeyPoint> SPDetector::detect(const cv::Mat &img, const cv::Mat &
 		detected_ = true;
 		return keypoints;
 	}
-	else
+	catch(const std::exception & e)
 	{
-		UERROR("No model is loaded!");
+		UERROR("SPDetector::detect() threw: %s", e.what());
 		return std::vector<cv::KeyPoint>();
 	}
 }