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fixed cv::remap() crach when the neural network did not detect any features (#1488)

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This commit is contained in:
Borong Yuan
2025-04-28 01:53:00 +08:00
committed by GitHub
parent 8cbee58f07
commit def99328ce
1 changed files with 28 additions and 24 deletions
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52
corelib/src/camera/CameraDepthAI.cpp
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@@ -843,34 +843,38 @@ SensorData CameraDepthAI::captureImage(SensorCaptureInfo * info)
std::vector<cv::Point> kpts;
cv::findNonZero(scores > threshold_, kpts);
std::vector<cv::KeyPoint> keypoints;
for(auto& kpt : kpts)
{
float response = scores.at<float>(kpt);
keypoints.emplace_back(cv::KeyPoint(kpt, 8, -1, response));
}
cv::Mat coarse_desc(25, 40, CV_32FC(256), local_descriptor_map.data());
if(detectFeatures_ == 2)
coarse_desc.forEach<cv::Vec<float, 256>>([&](cv::Vec<float, 256>& descriptor, const int position[]) -> void {
if(!kpts.empty()){
std::vector<cv::KeyPoint> keypoints;
for(auto& kpt : kpts)
{
float response = scores.at<float>(kpt);
keypoints.emplace_back(cv::KeyPoint(kpt, 8, -1, response));
}
cv::Mat coarse_desc(25, 40, CV_32FC(256), local_descriptor_map.data());
if(detectFeatures_ == 2)
coarse_desc.forEach<cv::Vec<float, 256>>([&](cv::Vec<float, 256>& descriptor, const int position[]) -> void {
cv::normalize(descriptor, descriptor);
});
cv::Mat mapX(keypoints.size(), 1, CV_32FC1);
cv::Mat mapY(keypoints.size(), 1, CV_32FC1);
for(size_t i=0; i<keypoints.size(); ++i)
{
mapX.at<float>(i) = (keypoints[i].pt.x - (targetSize_.width-1)/2) * 40/targetSize_.width + (40-1)/2;
mapY.at<float>(i) = (keypoints[i].pt.y - (targetSize_.height-1)/2) * 25/targetSize_.height + (25-1)/2;
}
cv::Mat map1, map2, descriptors;
cv::convertMaps(mapX, mapY, map1, map2, CV_16SC2);
cv::remap(coarse_desc, descriptors, map1, map2, cv::INTER_LINEAR);
descriptors.forEach<cv::Vec<float, 256>>([&](cv::Vec<float, 256>& descriptor, const int position[]) -> void {
cv::normalize(descriptor, descriptor);
});
cv::Mat mapX(keypoints.size(), 1, CV_32FC1);
cv::Mat mapY(keypoints.size(), 1, CV_32FC1);
for(size_t i=0; i<keypoints.size(); ++i)
{
mapX.at<float>(i) = (keypoints[i].pt.x - (targetSize_.width-1)/2) * 40/targetSize_.width + (40-1)/2;
mapY.at<float>(i) = (keypoints[i].pt.y - (targetSize_.height-1)/2) * 25/targetSize_.height + (25-1)/2;
}
cv::Mat map1, map2, descriptors;
cv::convertMaps(mapX, mapY, map1, map2, CV_16SC2);
cv::remap(coarse_desc, descriptors, map1, map2, cv::INTER_LINEAR);
descriptors.forEach<cv::Vec<float, 256>>([&](cv::Vec<float, 256>& descriptor, const int position[]) -> void {
cv::normalize(descriptor, descriptor);
});
descriptors = descriptors.reshape(1);
descriptors = descriptors.reshape(1);
data.setFeatures(keypoints, std::vector<cv::Point3f>(), descriptors);
}
data.setFeatures(keypoints, std::vector<cv::Point3f>(), descriptors);
if(detectFeatures_ == 3)
data.addGlobalDescriptor(GlobalDescriptor(1, cv::Mat(1, global_descriptor.size(), CV_32FC1, global_descriptor.data()).clone()));
}