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@ -173,13 +173,71 @@ namespace |
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} while (was); |
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} while (was); |
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} |
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} |
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} |
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} |
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// Computes rotation, translation pair for small subsets if the input data
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class TransformHypothesesGenerator |
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{ |
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public: |
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TransformHypothesesGenerator(const Mat& object_, const Mat& image_, const Mat& camera_mat_, |
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int num_points_, int subset_size_, Mat rot_matrices_, Mat transl_vectors_) |
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: object(&object_), image(&image_), camera_mat(&camera_mat_), num_points(num_points_), |
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subset_size(subset_size_), rot_matrices(rot_matrices_), transl_vectors(transl_vectors_) {} |
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void operator()(const BlockedRange& range) const |
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{ |
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// We assume that input is undistorted
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Mat empty_dist_coef; |
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// Input data for generation of the current hypothesis
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vector<int> subset_indices(subset_size); |
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Mat_<Point3f> object_subset(1, subset_size); |
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Mat_<Point2f> image_subset(1, subset_size); |
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// Current hypothesis data
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Mat rot_vec(1, 3, CV_64F); |
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Mat rot_mat(3, 3, CV_64F); |
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Mat transl_vec(1, 3, CV_64F); |
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for (int iter = range.begin(); iter < range.end(); ++iter) |
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{ |
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selectRandom(subset_size, num_points, subset_indices); |
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for (int i = 0; i < subset_size; ++i) |
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{ |
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object_subset(0, i) = object->at<Point3f>(subset_indices[i]); |
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image_subset(0, i) = image->at<Point2f>(subset_indices[i]); |
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} |
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solvePnP(object_subset, image_subset, *camera_mat, empty_dist_coef, rot_vec, transl_vec); |
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// Remember translation vector
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Mat transl_vec_ = transl_vectors.colRange(iter * 3, (iter + 1) * 3); |
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transl_vec = transl_vec.reshape(0, 1); |
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transl_vec.convertTo(transl_vec_, CV_32F); |
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// Remember rotation matrix
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Rodrigues(rot_vec, rot_mat); |
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Mat rot_mat_ = rot_matrices.colRange(iter * 9, (iter + 1) * 9).reshape(0, 3); |
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rot_mat.convertTo(rot_mat_, CV_32F); |
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} |
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} |
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const Mat* object; |
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const Mat* image; |
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const Mat* camera_mat; |
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int num_points; |
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int subset_size; |
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// Hypotheses storage (global)
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Mat rot_matrices; |
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Mat transl_vectors; |
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}; |
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} |
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} |
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void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& camera_mat, |
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void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& camera_mat, |
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const Mat& dist_coef, Mat& rvec, Mat& tvec, SolvePnpRansacParams params) |
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const Mat& dist_coef, Mat& rvec, Mat& tvec, SolvePnpRansacParams params) |
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{ |
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{ |
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CV_Assert(object.rows == 1 && object.cols > 0 && object.type() == CV_32FC3); |
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CV_Assert(object.rows == 1 && object.cols > 0 && object.type() == CV_32FC3); |
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CV_Assert(image.rows == 1 && image.cols > 1 && image.type() == CV_32FC2); |
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CV_Assert(image.rows == 1 && image.cols > 0 && image.type() == CV_32FC2); |
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CV_Assert(object.cols == image.cols); |
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CV_Assert(object.cols == image.cols); |
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CV_Assert(camera_mat.size() == Size(3, 3) && camera_mat.type() == CV_32F); |
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CV_Assert(camera_mat.size() == Size(3, 3) && camera_mat.type() == CV_32F); |
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CV_Assert(dist_coef.empty()); // We don't support undistortion for now
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CV_Assert(dist_coef.empty()); // We don't support undistortion for now
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@ -187,43 +245,14 @@ void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& cam |
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const int num_points = object.cols; |
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const int num_points = object.cols; |
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// Current hypothesis input
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// Hypotheses storage (global)
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vector<int> subset_indices(params.subset_size); |
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Mat_<Point3f> object_subset(1, params.subset_size); |
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Mat_<Point2f> image_subset(1, params.subset_size); |
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// Current hypothesis result
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Mat rot_vec(1, 3, CV_64F); |
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Mat rot_mat(3, 3, CV_64F); |
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Mat transl_vec(1, 3, CV_64F); |
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// All hypotheses results
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Mat rot_matrices(1, params.num_iters * 9, CV_32F); |
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Mat rot_matrices(1, params.num_iters * 9, CV_32F); |
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Mat transl_vectors(1, params.num_iters * 3, CV_32F); |
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Mat transl_vectors(1, params.num_iters * 3, CV_32F); |
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// Generate set of (rotation, translation) hypotheses using small subsets
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// Generate set of hypotheses using small subsets of the input data
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// of the input data
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TransformHypothesesGenerator body(object, image, camera_mat, num_points, |
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for (int iter = 0; iter < params.num_iters; ++iter) // TODO TBB?
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params.subset_size, rot_matrices, transl_vectors); |
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{ |
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parallel_for(BlockedRange(0, params.num_iters), body); |
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selectRandom(params.subset_size, num_points, subset_indices); |
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for (int i = 0; i < params.subset_size; ++i) |
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{ |
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object_subset(0, i) = object.at<Point3f>(subset_indices[i]); |
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image_subset(0, i) = image.at<Point2f>(subset_indices[i]); |
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} |
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solvePnP(object_subset, image_subset, camera_mat, dist_coef, rot_vec, transl_vec); |
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// Remember translation vector
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Mat transl_vec_ = transl_vectors.colRange(iter * 3, (iter + 1) * 3); |
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transl_vec = transl_vec.reshape(0, 1); |
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transl_vec.convertTo(transl_vec_, CV_32F); |
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// Remember rotation matrix
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Rodrigues(rot_vec, rot_mat); |
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Mat rot_mat_ = rot_matrices.colRange(iter * 9, (iter + 1) * 9).reshape(0, 3); |
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rot_mat.convertTo(rot_mat_, CV_32F); |
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} |
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// Compute scores (i.e. number of inliers) for each hypothesis
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// Compute scores (i.e. number of inliers) for each hypothesis
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GpuMat d_object(object); |
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GpuMat d_object(object); |
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@ -241,7 +270,7 @@ void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& cam |
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int num_inliers = static_cast<int>(best_score); |
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int num_inliers = static_cast<int>(best_score); |
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// Extract the best hypothesis data
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// Extract the best hypothesis data
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rot_mat = rot_matrices.colRange(best_idx.x * 9, (best_idx.x + 1) * 9).reshape(0, 3); |
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Mat rot_mat = rot_matrices.colRange(best_idx.x * 9, (best_idx.x + 1) * 9).reshape(0, 3); |
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Rodrigues(rot_mat, rvec); |
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Rodrigues(rot_mat, rvec); |
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rvec = rvec.reshape(0, 1); |
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rvec = rvec.reshape(0, 1); |
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tvec = transl_vectors.colRange(best_idx.x * 3, (best_idx.x + 1) * 3).clone(); |
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tvec = transl_vectors.colRange(best_idx.x * 3, (best_idx.x + 1) * 3).clone(); |
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@ -278,3 +307,4 @@ void cv::gpu::solvePnpRansac(const Mat& object, const Mat& image, const Mat& cam |
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#endif |
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#endif |
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