{"action":"created","comment":{"url":"https://api.github.com/repos/opencv/opencv/pulls/comments/72829741","id":72829741,"diff_hunk":"@@ -1574,6 +1574,53 @@ CV_EXPORTS_W  int estimateAffine3D(InputArray src, InputArray dst,\n                                    OutputArray out, OutputArray inliers,\n                                    double ransacThreshold = 3, double confidence = 0.99);\n \n+/** @brief Computes an optimal affine transformation between two 2D point sets.\n+\n+@param src First input 2D point set.\n+@param dst Second input 2D point set.\n+@param out Output 2D affine transformation matrix \\f$2 \\times 3\\f$ .\n+@param inliers Output vector indicating which points are inliers.\n+@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as\n+an inlier.\n+@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything\n+between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation\n+significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.\n+\n+The function estimates an optimal 2D affine transformation between two 2D point sets using the\n+RANSAC algorithm.\n+ */\n+CV_EXPORTS_W int estimateAffine2D(InputArray src, InputArray dst,\n+                                  OutputArray out, OutputArray inliers,\n+                                  double ransacThreshold = 3, double confidence = 0.99);\n+\n+/** @brief Computes an optimal limited affine transformation with 4 degrees of freedom between\n+two 2D point sets.\n+\n+@param src First input 2D point set.\n+@param dst Second input 2D point set.\n+@param out Output 2D affine transformation (4 degrees of freedom) matrix \\f$2 \\times 3\\f$ .\n+@param inliers Output vector indicating which points are inliers.\n+@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as\n+an inlier.\n+@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything\n+between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation\n+significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.\n+\n+The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to\n+combinations of translation, rotation, and uniform scaling. Uses the RANSAC algorithm for robust\n+estimation.\n+\n+Estimated transformation matrix is:\n+\\f[ \\begin{bmatrix} \\cos(\\theta)s & -\\sin(\\theta)s & tx \\\\\n+                \\sin(\\theta)s & \\cos(\\theta)s & ty\n+\\end{bmatrix} \\f]\n+Where \\f$ \\theta \\f$ is the rotation angle, \\f$ s \\f$ the scaling factor and \\f$ tx, ty \\f$ are\n+translations in \\f$ x, y \\f$ axes respectively.\n+*/\n+CV_EXPORTS_W int estimateAffinePartial2D(InputArray src, InputArray dst,","path":"modules/calib3d/include/opencv2/calib3d.hpp","position":47,"original_position":47,"commit_id":"08d9ec47a7fe5cd05a72a7f79347d7d83dd58b6b","original_commit_id":"08d9ec47a7fe5cd05a72a7f79347d7d83dd58b6b","user":{"login":"mself","id":6620480,"avatar_url":"https://avatars.githubusercontent.com/u/6620480?v=3","gravatar_id":"","url":"https://api.github.com/users/mself","html_url":"https://github.com/mself","followers_url":"https://api.github.com/users/mself/followers","following_url":"https://api.github.com/users/mself/following{/other_user}","gists_url":"https://api.github.com/users/mself/gists{/gist_id}","starred_url":"https://api.github.com/users/mself/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mself/subscriptions","organizations_url":"https://api.github.com/users/mself/orgs","repos_url":"https://api.github.com/users/mself/repos","events_url":"https://api.github.com/users/mself/events{/privacy}","received_events_url":"https://api.github.com/users/mself/received_events","type":"User","site_admin":false},"body":"The existing (non-robust) function is called estimateRigidTransform, but that seems poor since AFAIK a rigid transform should not include scaling and would only be 3 DOF rather than 4.  One other option besides \"partial\" would be something like **estimateAffine4DOF()**, for example.  That's more explicit than \"partial\".","created_at":"2016-07-29T17:29:31Z","updated_at":"2016-07-29T17:29:31Z","html_url":"https://github.com/opencv/opencv/pull/6933#discussion_r72829741","pull_request_url":"https://api.github.com/repos/opencv/opencv/pulls/6933","_links":{"self":{"href":"https://api.github.com/repos/opencv/opencv/pulls/comments/72829741"},"html":{"href":"https://github.com/opencv/opencv/pull/6933#discussion_r72829741"},"pull_request":{"href":"https://api.github.com/repos/opencv/opencv/pulls/6933"}}},"pull_request":{"url":"https://api.github.com/repos/opencv/opencv/pulls/6933","id":77737744,"html_url":"https://github.com/opencv/opencv/pull/6933","diff_url":"https://github.com/opencv/opencv/pull/6933.diff","patch_url":"https://github.com/opencv/opencv/pull/6933.patch","issue_url":"https://api.github.com/repos/opencv/opencv/issues/6933","number":6933,"state":"open","locked":false,"title":"[GSOC] New camera model for stitching 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GSOC project.\r\n\r\n# goals\r\n\r\n## new camera model\r\n- [x]  robust fuctions for affine transform estimations\r\n    - [ ] add support for least median robust method\r\n    - [x] Levenberg–Marquardt algorithm-based refining for affine estimation functions\r\n- [x] tests and docs for ^\r\n- [x] perf tests for ^\r\n- [x] affine matcher\r\n- [x] affine estimator \r\n- [x] affine warper\r\n- tests for affine stitching\r\n     - [x] basic affine stitching integration test\r\n     - [x] tests for affine matcher\r\n     - [ ] integration tests on real-word scans\r\n     - [x] affine warper tests\r\n- [x] integrating with high level API (Stitcher)\r\n- [x] stitching_detailed sample\r\n- [x] stitching simple sample\r\n\r\n- [ ] stitching tutorial for high level API\r\n- [x] fix existing stitching tests with SURF (SURF was disabled)\r\n\r\n## parallel feature finding\r\n- [x] parallel API in feature finder\r\n- [x] tests (incl. perf tests) \r\n- [x] integrating with 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