potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-29 23:33:05 +04:00
Code Packages Releases Wiki Activity

Address more comments

Browse Source 
Use map to manage unique marker size candidate trees.
Avoid code duplication.
Add a test to show double detection with overlapping dictionaries.
Generalize to marker sizes of not only predefined dictionaries.
This commit is contained in:
Benjamin Knecht
2025-03-04 15:24:03 +01:00
parent 3084f950cf
commit 1aa658fa75
2 changed files with 84 additions and 85 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/objdetect/src/aruco/aruco_detector.cpp
+33 -56
View File
@@ -10,7 +10,7 @@
#include "apriltag/apriltag_quad_thresh.hpp"
#include "aruco_utils.hpp"
#include <cmath>
#include <unordered_set>
#include <map>
namespace cv {
namespace aruco {
@@ -752,44 +752,20 @@ struct ArucoDetector::ArucoDetectorImpl {
/// STEP 3: Corner refinement :: use corner subpix
if (detectorParams.cornerRefinementMethod == (int)CORNER_REFINE_SUBPIX) {
CV_Assert(detectorParams.cornerRefinementWinSize > 0 && detectorParams.cornerRefinementMaxIterations > 0 &&
detectorParams.cornerRefinementMinAccuracy > 0);
// Do subpixel estimation. In Aruco3 start on the lowest pyramid level and upscale the corners
parallel_for_(Range(0, (int)candidates.size()), [&](const Range& range) {
const int begin = range.start;
const int end = range.end;
for (int i = begin; i < end; i++) {
if (detectorParams.useAruco3Detection) {
const float scale_init = (float) grey_pyramid[closest_pyr_image_idx].cols / grey.cols;
findCornerInPyrImage(scale_init, closest_pyr_image_idx, grey_pyramid, Mat(candidates[i]), detectorParams);
} else {
int cornerRefinementWinSize = std::max(1, cvRound(detectorParams.relativeCornerRefinmentWinSize*
getAverageModuleSize(candidates[i], dictionary.markerSize, detectorParams.markerBorderBits)));
cornerRefinementWinSize = min(cornerRefinementWinSize, detectorParams.cornerRefinementWinSize);
cornerSubPix(grey, Mat(candidates[i]), Size(cornerRefinementWinSize, cornerRefinementWinSize), Size(-1, -1),
TermCriteria(TermCriteria::MAX_ITER | TermCriteria::EPS,
detectorParams.cornerRefinementMaxIterations,
detectorParams.cornerRefinementMinAccuracy));
}
}
});
performCornerSubpixRefinement(grey, grey_pyramid, closest_pyr_image_idx, candidates, dictionary);
}
} else if (DictionaryMode::Multi == dictMode) {
unordered_set<int> uniqueMarkerSizes;
map<size_t, vector<MarkerCandidateTree>> candidatesPerDictionarySize;
for (const Dictionary& dictionary : dictionaries) {
uniqueMarkerSizes.insert(dictionary.markerSize);
candidatesPerDictionarySize.emplace(dictionary.markerSize, vector<MarkerCandidateTree>());
}
// create at max 4 marker candidate trees for each dictionary size
vector<vector<MarkerCandidateTree>> candidatesPerDictionarySize = {{}, {}, {}, {}};
for (int markerSize : uniqueMarkerSizes) {
// min marker size is 4, so subtract 4 to get index
const auto dictionarySizeIndex = markerSize - 4;
// create candidate trees for each dictionary size
for (auto& candidatesTreeEntry : candidatesPerDictionarySize) {
// copy candidates
vector<vector<Point2f>> candidatesCopy = candidates;
vector<vector<Point> > contoursCopy = contours;
candidatesPerDictionarySize[dictionarySizeIndex] = filterTooCloseCandidates(candidatesCopy, contoursCopy, markerSize);
candidatesTreeEntry.second = filterTooCloseCandidates(candidatesCopy, contoursCopy, candidatesTreeEntry.first);
}
candidates.clear();
contours.clear();
@@ -797,10 +773,9 @@ struct ArucoDetector::ArucoDetectorImpl {
/// STEP 2: Check candidate codification (identify markers)
int dictIndex = 0;
for (const Dictionary& currentDictionary : dictionaries) {
const auto dictionarySizeIndex = currentDictionary.markerSize - 4;
// temporary variable to store the current candidates
vector<vector<Point2f>> currentCandidates;
identifyCandidates(grey, grey_pyramid, candidatesPerDictionarySize[dictionarySizeIndex], currentCandidates, contours,
identifyCandidates(grey, grey_pyramid, candidatesPerDictionarySize.at(currentDictionary.markerSize), currentCandidates, contours,
ids, currentDictionary, rejectedImgPoints);
if (_dictIndices.needed()) {
dictIndices.insert(dictIndices.end(), currentCandidates.size(), dictIndex);
@@ -808,29 +783,7 @@ struct ArucoDetector::ArucoDetectorImpl {
/// STEP 3: Corner refinement :: use corner subpix
if (detectorParams.cornerRefinementMethod == (int)CORNER_REFINE_SUBPIX) {
CV_Assert(detectorParams.cornerRefinementWinSize > 0 && detectorParams.cornerRefinementMaxIterations > 0 &&
detectorParams.cornerRefinementMinAccuracy > 0);
// Do subpixel estimation. In Aruco3 start on the lowest pyramid level and upscale the corners
parallel_for_(Range(0, (int)currentCandidates.size()), [&](const Range& range) {
const int begin = range.start;
const int end = range.end;
for (int i = begin; i < end; i++) {
if (detectorParams.useAruco3Detection) {
const float scale_init = (float) grey_pyramid[closest_pyr_image_idx].cols / grey.cols;
findCornerInPyrImage(scale_init, closest_pyr_image_idx, grey_pyramid, Mat(currentCandidates[i]), detectorParams);
}
else {
int cornerRefinementWinSize = std::max(1, cvRound(detectorParams.relativeCornerRefinmentWinSize*
getAverageModuleSize(currentCandidates[i], currentDictionary.markerSize, detectorParams.markerBorderBits)));
cornerRefinementWinSize = min(cornerRefinementWinSize, detectorParams.cornerRefinementWinSize);
cornerSubPix(grey, Mat(currentCandidates[i]), Size(cornerRefinementWinSize, cornerRefinementWinSize), Size(-1, -1),
TermCriteria(TermCriteria::MAX_ITER | TermCriteria::EPS,
detectorParams.cornerRefinementMaxIterations,
detectorParams.cornerRefinementMinAccuracy));
}
}
});
performCornerSubpixRefinement(grey, grey_pyramid, closest_pyr_image_idx, currentCandidates, currentDictionary);
}
candidates.insert(candidates.end(), currentCandidates.begin(), currentCandidates.end());
dictIndex++;
@@ -1105,6 +1058,30 @@ struct ArucoDetector::ArucoDetectorImpl {
}
}
void performCornerSubpixRefinement(const Mat& grey, const vector<Mat>& grey_pyramid, int closest_pyr_image_idx, const vector<vector<Point2f>>& candidates, const Dictionary& dictionary) const {
CV_Assert(detectorParams.cornerRefinementWinSize > 0 && detectorParams.cornerRefinementMaxIterations > 0 &&
detectorParams.cornerRefinementMinAccuracy > 0);
// Do subpixel estimation. In Aruco3 start on the lowest pyramid level and upscale the corners
parallel_for_(Range(0, (int)candidates.size()), [&](const Range& range) {
const int begin = range.start;
const int end = range.end;
for (int i = begin; i < end; i++) {
if (detectorParams.useAruco3Detection) {
const float scale_init = (float) grey_pyramid[closest_pyr_image_idx].cols / grey.cols;
findCornerInPyrImage(scale_init, closest_pyr_image_idx, grey_pyramid, Mat(candidates[i]), detectorParams);
} else {
int cornerRefinementWinSize = std::max(1, cvRound(detectorParams.relativeCornerRefinmentWinSize*
getAverageModuleSize(candidates[i], dictionary.markerSize, detectorParams.markerBorderBits)));
cornerRefinementWinSize = min(cornerRefinementWinSize, detectorParams.cornerRefinementWinSize);
cornerSubPix(grey, Mat(candidates[i]), Size(cornerRefinementWinSize, cornerRefinementWinSize), Size(-1, -1),
TermCriteria(TermCriteria::MAX_ITER | TermCriteria::EPS,
detectorParams.cornerRefinementMaxIterations,
detectorParams.cornerRefinementMinAccuracy));
}
}
});
}
};
ArucoDetector::ArucoDetector(const Dictionary &_dictionary,