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Merge pull request #26221 from asmorkalov:as/refactor_multiview_interface

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Reworked multiview calibration interface #26221

- Use InputArray / OutputArray
- Use enum for camera type
- Sort parameters according guidelines
- Made more outputs optional
- Introduce flags and added tests for intrinsics and extrinsics guess.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Alexander Smorkalov
2024-10-01 16:53:16 +03:00
committed by GitHub
parent 6c743f8e4b
commit 3bcab8db0a
8 changed files with 362 additions and 214 deletions
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samples/cpp/multiview_calibration_sample.cpp
+10 -11
View File
@@ -13,17 +13,16 @@
// ! [detectPointsAndCalibrate_signature]
static void detectPointsAndCalibrate (cv::Size pattern_size, float pattern_distance, const std::string &pattern_type,
const std::vector<bool> &is_fisheye, const std::vector<std::string> &filenames,
const std::vector<uchar> &models, const std::vector<std::string> &filenames,
const cv::String* dict_path=nullptr)
// ! [detectPointsAndCalibrate_signature]
{
// ! [calib_init]
std::vector<cv::Point3f> board (pattern_size.area());
const int num_cameras = (int)is_fisheye.size();
const int num_cameras = (int)models.size();
std::vector<std::vector<cv::Mat>> image_points_all;
std::vector<cv::Size> image_sizes;
std::vector<cv::Mat> Ks, distortions, Ts, Rs;
cv::Mat rvecs0, tvecs0, errors_mat, output_pairs;
if (pattern_type == "checkerboard" || pattern_type == "charuco") {
for (int i = 0; i < pattern_size.height; i++) {
for (int j = 0; j < pattern_size.width; j++) {
@@ -162,9 +161,9 @@ static void detectPointsAndCalibrate (cv::Size pattern_size, float pattern_dista
std::vector<std::vector<cv::Point3f>> objPoints(num_frames, board);
// ! [multiview_calib]
const double rmse = calibrateMultiview(objPoints, image_points_all, image_sizes, visibility,
Rs, Ts, Ks, distortions, cv::noArray(), cv::noArray(),
is_fisheye, errors_mat, output_pairs);
models, Rs, Ts, Ks, distortions);
// ! [multiview_calib]
std::cout << "average RMSE over detection mask " << rmse << "\n";
for (int c = 0; c < (int)Rs.size(); c++) {
@@ -219,13 +218,13 @@ int main (int argc, char **argv) {
CV_Assert(pattern_size_count == 1);
pattern_size.height = std::stoi(temp_str);
std::vector<bool> is_fisheye;
std::vector<uchar> models;
const cv::String is_fisheye_str = parser.get<cv::String>("is_fisheye");
for (char i : is_fisheye_str) {
if (i == '0') {
is_fisheye.push_back(false);
models.push_back(cv::CALIB_MODEL_PINHOLE);
} else if (i == '1') {
is_fisheye.push_back(true);
models.push_back(cv::CALIB_MODEL_FISHEYE);
}
}
const cv::String filenames_str = parser.get<cv::String>("filenames");
@@ -240,13 +239,13 @@ int main (int argc, char **argv) {
}
}
filenames.emplace_back(temp_str);
CV_CheckEQ(filenames.size(), is_fisheye.size(), "filenames size must be equal to number of cameras!");
CV_CheckEQ(filenames.size(), models.size(), "filenames size must be equal to number of cameras!");
if (parser.has("board_dict_path")) {
cv::String board_dict_path = parser.get<cv::String>("board_dict_path");
detectPointsAndCalibrate (pattern_size, parser.get<float>("pattern_distance"), parser.get<cv::String>("pattern_type"), is_fisheye, filenames, &board_dict_path);
detectPointsAndCalibrate (pattern_size, parser.get<float>("pattern_distance"), parser.get<cv::String>("pattern_type"), models, filenames, &board_dict_path);
} else {
detectPointsAndCalibrate (pattern_size, parser.get<float>("pattern_distance"), parser.get<cv::String>("pattern_type"), is_fisheye, filenames);
detectPointsAndCalibrate (pattern_size, parser.get<float>("pattern_distance"), parser.get<cv::String>("pattern_type"), models, filenames);
}
return 0;
}