kh8ddlZddlmZddlmZGddZy)N)distance_matrix)check_nDcNeZdZdZdZdZedZdZdZ dZ e dZ y ) ThinPlateSplineTransformulThin-plate spline transformation. Given two matching sets of points, source and destination, this class estimates the thin-plate spline (TPS) transformation which transforms each point in source into its destination counterpart. Attributes ---------- src : (N, 2) array_like Coordinates of control points in source image. References ---------- .. [1] Bookstein, Fred L. "Principal warps: Thin-plate splines and the decomposition of deformations," IEEE Transactions on pattern analysis and machine intelligence 11.6 (1989): 567–585. DOI:`10.1109/34.24792` https://user.engineering.uiowa.edu/~aip/papers/bookstein-89.pdf Examples -------- >>> import skimage as ski Define source and destination control points such that they simulate rotating by 90 degrees and generate a meshgrid from them: >>> src = np.array([[0, 0], [0, 5], [5, 5], [5, 0]]) >>> dst = np.array([[5, 0], [0, 0], [0, 5], [5, 5]]) Estimate the transformation: >>> tps = ski.transform.ThinPlateSplineTransform() >>> tps.estimate(src, dst) True Appyling the transformation to `src` approximates `dst`: >>> np.round(tps(src)) array([[5., 0.], [0., 0.], [0., 5.], [5., 5.]]) Create a meshgrid to apply the transformation to: >>> grid = np.meshgrid(np.arange(5), np.arange(5)) >>> grid[1] array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2], [3, 3, 3, 3, 3], [4, 4, 4, 4, 4]]) >>> coords = np.vstack([grid[0].ravel(), grid[1].ravel()]).T >>> transformed = tps(coords) >>> np.round(transformed[:, 1]).reshape(5, 5).astype(int) array([[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]) c.d|_d|_d|_y)NF) _estimated_spline_mappingssrcselfs /home/developers/rajanand/mypropertyqr-fmb-refixing-v2/venv/lib/python3.12/site-packages/skimage/transform/_thin_plate_splines.py__init__z!ThinPlateSplineTransform.__init__Gs $c|j d}t|tj|}|jdk7s|j ddk7r d}t||j |}|j||}|S)a*Estimate the transformation from a set of corresponding points. Parameters ---------- coords : (N, 2) array_like x, y coordinates to transform Returns ------- transformed_coords: (N, D) array Destination coordinates zOTransformation is undefined, define it by calling `estimate` before applying itrz%Input `coords` must have shape (N, 2))r ValueErrornparrayndimshape_radial_distance_spline_function)r coordsmsg radial_disttransformed_coordss r__call__z!ThinPlateSplineTransform.__call__Ls  (% S/ !&! ;;! v||A!39CS/ !++F3 !226;G!!rctd)Nz Not supported)NotImplementedErrorr s rinversez ThinPlateSplineTransform.inversejs !/22rczt|ddt|dd|jddks|jddkr d}t||j|jk7r'd|jd |j}t|||_|j\}}t ||}|j |}t jt j|d f|g}|dz} t j| | ft j } || d |d |f<|| d |d d f<|j| d d d |f<t j|t j|d z|fg} t jj| | |_y#t jj $rYywxYw)aEstimate optimal spline mappings between source and destination points. Parameters ---------- src : (N, 2) array_like Control points at source coordinates. dst : (N, 2) array_like Control points at destination coordinates. Returns ------- success: bool True indicates that the estimation was successful. Notes ----- The number N of source and destination points must match. rr )arg_namedstrz,Need at least 3 points in in `src` and `dst`z'Shape of `src` and `dst` didn't match, z != r)dtypeNFT)rrrr r_radial_basis_kernelrhstackoneszerosfloat32Tvstacklinalgsolver LinAlgError) r r r$rnddistKPn_plus_3LVs restimatez!ThinPlateSplineTransform.estimatens& a%(a%( 99Qrg)rwherelog)r_smallr_sqUs rr(z-ThinPlateSplineTransform._radial_basis_kernels?!t HHQ#XsD266$-+@$@ ArN) __name__ __module__ __qualname____doc__rrpropertyr!r:rr staticmethodr(rrrrsJ=~ "<33-^0 "rr)numpyr scipy.spatialr _shared.utilsrrrNrrrRs)$uur