3j`S SSKrSSKJrJrJrJr SSKrSSKJs J r SSK J r J r SSKJrJr SSKJr SSKJr S\R*S\R*4S jrS\R*S\R*4S jrS\R*S\R*4S jrS\R*S \R*S\R*4S jrS\R*S \R*S\R*4SjrS/S\R*S\\R*S\\R*S\R*4SjjrS\R*S\\\R*4S\R*4SjrS0S\R*S\S\R*4SjjrS\R*S\R*4SjrS1S\R*S\ S\R*4Sjjr!S2S\R*S\ S\\\ \\ 44Sjjr"S\R*S\R*S\R*4Sjr#S\R*S\R*S\R*4S jr$S3S!\R*S\R*S"\ S\R*4S#jjr%S4S!\R*S\R*S"\ S$\&S\R*4 S%jjr'S4S!\R*S\R*S"\ S$\&S\R*4 S&jjr(S2S\R*S\R*S'\ S\R*4S(jjr)S\R*S\R*4S)jr*S*\R*S\R*4S+jr+S,\R*S-\R*S\R*4S.jr,g)5N)ListOptionalTupleUnion)KORNIA_CHECK_LAFKORNIA_CHECK_SHAPE)angle_to_rotation_matrixconvert_points_from_homogeneous)transform_points)pyrdownLAFreturnc[U5 SnUSSS2SS24USSS2SS24-USSS2SS24USSS2SS24-- U-nUR5R5$)zReturn a scale of the LAFs. Args: LAF: :math:`(B, N, 2, 3)` Returns: scale :math:`(B, N, 1, 1)` Example: >>> input = torch.ones(1, 5, 2, 3) # BxNx2x3 >>> output = get_laf_scale(input) # BxNx1x1 g|=.r)rabssqrt)r epsouts L/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/feature/laf.py get_laf_scalersS C c1Q3!m s3!QqS=1 1CQqS!A# 4FSRSTURUWXYZWZ]I[4[ [^a aC 779>> c&[U5 USnU$)a:Return a center (keypoint) of the LAFs. The convention is that center of 5-pixel image (coordinates from 0 to 4) is 2, and not 2.5. Args: LAF: :math:`(B, N, 2, 3)` Returns: xy :math:`(B, N, 2)` Example: >>> input = torch.ones(1, 5, 2, 3) # BxNx2x3 >>> output = get_laf_center(input) # BxNx2 .r)r)r rs rget_laf_centerr2s S f+C Jrc[U5 [R"USUS5n[R"U5R S5$)zReturn orientation of the LAFs, in degrees. Args: LAF: :math:`(B, N, 2, 3)` Returns: angle in degrees :math:`(B, N, 1)` Example: >>> input = torch.ones(1, 5, 2, 3) # BxNx2x3 >>> output = get_laf_orientation(input) # BxNx1 ).rr).rr)rtorchatan2rad2deg unsqueeze)r angle_rads rget_laf_orientationr#Gs>S C NC N;I == # - -b 11rangles_degreescL[U5 URSSup#[U5RX#-SS5nUR 5n[ R "USS2SS2SS2SS24RX#-SS5U5RX#SS5USS2SS2SS2SS24'U$)a'Apply additional rotation to the LAFs. Compared to `set_laf_orientation`, the resulting rotation is original LAF orientation plus angles_degrees. Args: LAF: :math:`(B, N, 2, 3)` angles_degrees: :math:`(B, N, 1)` in degrees. Returns: LAF oriented with angles :math:`(B, N, 2, 3)` Nr)rshaper viewclonerbmmreshape)r r$BNrotmatout_lafs r rotate_lafr/ZsS 99Ra=DA %n 5 : :15!Q GFiikG!IIc!QBQB,&7&?&?q!&LfU]]^_deghiGAq"1"bqbL Nrc[U5 URSSup#[U5RU5n[ XU- 5$)zChange the orientation of the LAFs. Args: LAF: :math:`(B, N, 2, 3)` angles_degrees: :math:`(B, N, 1)` in degrees. Returns: LAF oriented with angles :math:`(B, N, 2, 3)` Nr)rr&r# reshape_asr/)r r$_B_Noris rset_laf_orientationr5osBS YYr]FB c " - -n =C cC/ 00rxyscaler4c [U/SQ5 URnURnURSSupVUc[R "XVSSX4S9nUc[R "XVSX4S9n[U/SQ5 [U/SQ5 [R"[URS55URS5/SS 9n[Xq5nU$) aZCreate a LAF from keypoint center, scale and orientation. Useful to create kornia LAFs from OpenCV keypoints. Args: xy: :math:`(B, N, 2)`. scale: :math:`(B, N, 1, 1)`. If not provided, scale = 1.0 is assumed ori: angle in degrees :math:`(B, N, 1)`. If not provided orientation = 0 is assumed Returns: LAF :math:`(B, N, 2, 3)` )r+r,2Nrr)devicedtype)r+r,1r<)r+r,r<rdim) rr:r;r&roneszeroscatr squeezer! scale_laf) r6r7r4r:r;r+r, unscaled_laflafs rlaf_from_center_scale_orirFs r?+ YYF HHE 88BQu23sO,996s{{2GVXIYZ`bcL L (C JrrE scale_coefc [U[[R45(d[ S[ U535e[ U5 USS2SS2SS2SS24n[R"X-USS2SS2SS2SS24/SS9$)aMultiplies region part of LAF ([:, :, :2, :2]) by a scale_coefficient. So the center, shape and orientation of the local feature stays the same, but the region area changes. Args: laf: :math:`(B, N, 2, 3)` scale_coef: broadcastable torch.Tensor or float. Returns: LAF :math:`(B, N, 2, 3)` Example: >>> input = torch.ones(1, 5, 2, 3) # BxNx2x3 >>> scale = 0.5 >>> output = scale_laf(input, scale) # BxNx2x3 z0scale_coef should be float or torch.Tensor. Got Nrr=) isinstancefloatrTensor TypeErrortyperrA)rErGcenterless_lafs rrCrCs~$ j5%,,"7 8 8J4PZK[J\]^^SArr2A2&N 99j13q!Q{3CD! LLrrc N[U5 [U5nUn[R"USSS2SS24S-USSS2SS24S--5U-n[R"XB- R 5[R "U5/SS9n[R"USSS2SS24USSS2SS24-USSS2SS24USSS2SS24--XB-- X$- R 5/SS9n[R"[R"XV/SS9USSS2SS24/SS9n[Xs5$)aRectify the affine matrix, so that it becomes upright. Args: laf: :math:`(B, N, 2, 3)` eps: for safe division. Returns: laf: :math:`(B, N, 2, 3)` Example: >>> input = torch.ones(1, 5, 2, 3) # BxNx2x3 >>> output = make_upright(input) # BxNx2x3 .rrrrIr=N)rrrrrA contiguous zeros_likerC)rErdetr7b2a2laf1_elllaf2_elllaf_unit_scales r make_uprightrXsjS  C E ::c#qsAaC-(A-C1acM0Ba0GG H3 NDyy4:113U5E5Ec5JKQRSHyy#qsAaC- 3sAaC1}#55C1acM8JSQTVWXYVY[\]^[^Q^M_8__dhdn o Z # # %   HYY 8*>A FCQRTUVWTWKHXY_`aN ^ ++rellsc [U/SQ5 URupnUSSS24R5R5n[R "U5nUSSS24R5R5nUSSS24XF-R S5- n[R"XEXv/SS 9RXSS5R5n[R"XSS S24RXSS 5/SS 9n U $) aConvert ellipse regions to LAF format. Ellipse (a, b, c) and upright covariance matrix [a11 a12; 0 a22] are connected by inverse matrix square root: A = invsqrt([a b; b c]). See also https://github.com/vlfeat/vlfeat/blob/master/toolbox/sift/vl_frame2oell.m Args: ells: torch.Tensor :math:`(B, N, 5)` of ellipses in Oxford format [x y a b c]. Returns: LAF :math:`(B, N, 2, 3)` Example: >>> input = torch.ones(1, 10, 5) # BxNx5 >>> output = ellipse_to_laf(input) # BxNx2x3 )r+r,5.rrI& .>rr=Nr) rr&rrrrRclampstackr'inverserA) rYr+r,_a11a12a22a21Ars rellipse_to_lafrhs&t_-jjGA! sAaCx.    # # %C   3 C sAaCx.    # # %C sAaCx.CI,,T2 2C Ss(b166qQBJJLA ))QS"1"W **1A67Q ?C Jrn_ptsc [U5 UR5up# n[R"[R"[R "SS[ R-US- 55RS5[R"[R "SS[ R-US- 55RS5[R"US- S5/SS9n[R"[R"/SQ5RSS5U/SS9RS5RX#-US5nURUR5RUR 5n[R"/SQ5RSSS5RX#-SS5n[R"URSSS5URUR5RUR 5/SS9n[R""XuR%SSS55R%SSS5n['URX#US55$)aConvert LAFs to boundary points of the regions + center. Used for local features visualization, see visualize_laf function. Args: LAF: :math:`(B, N, 2, 3)` n_pts: number of points to output. Returns: torch.Tensor of boundary points LAF: :math:`(B, N, n_pts, 2)` rrrrr=)rk?rI)rsizerrAsinlinspacemathpir!cosr?tensorr'expandtor:r;r)permuter ) r rir+r,rbptsauxHLAFpts_hs rlaf_to_boundary_pointsr{sSJA!Q )) IIennQDGG UQY? @ J J2 N IIennQDGG UQY? @ J J2 N JJuqy! $   C ))U\\/2771=sC K U UVW X _ _`a`eglno pC &&    *C ,, ' , ,Q1 5 < >#  $$rc 6[U5 UR5u p#n[US- 5n[US- 5n[Xe5n[R "SSSSUR URS9U- nSU- US'SU- US'URU5U-$)aNormalize LAFs to [0,1] scale from pixel scale. See below: B,N,H,W = images.size() MIN_SIZE = min(H - 1, W -1) [a11 a21 x] [a21 a22 y] becomes: [a11/MIN_SIZE a21/MIN_SIZE x/(W-1)] [a21/MIN_SIZE a22/MIN_SIZE y/(H-1)] Args: LAF: :math:`(B, N, 2, 3)` images: :math:`(B, CH, H, W)` Returns: the denormalized LAF: :math:`(B, N, 2, 3)`, scale in image percentage (0, 1) rrrIrrlrrrrs r normalize_lafr\s(SJAqQ q1uB q1uB2{H ::aAq #** E PDRxDRxD >>#  $$rimgPSc[U5 UR5up4 nUR5upVpx[X5n [R"U R X4-SS5X4-XbU/SS9n SU SSS2S4R 5-[US - 5- S - U SSS2S4'SU SSS2S 4R 5-[US - 5- S - U SSS2S 4'U $) zGenerate affine grid. Args: img: image torch.Tensor of shape :math:`(B, CH, H, W)`. LAF: laf with shape :math:`(B, N, 2, 3)`. PS: patch size to be extracted. Returns: grid :math:`(B*N, PS, PS, 2)` rrIF) align_corners@.Nrrrl)rrmrF affine_gridr'r(rK) rr rr+r,rbchrr LAF_renormgrids r'generate_patch_grid_from_normalized_LAFr{sSJA!Q((*KA1!*J ==15rr7JZ_ `DDaO1133eAElBSHDaODaO1133eAElBSHDaO Kr normalize_lafs_before_extractionc [U5 U(a [X5nOUnUR5upVpxUR5up n/n [U 5Hn [ X U S-XLU S-U5R UR 5n UR RS:Xa]U R[R"X U S-RU RS5XgU5U RSS5SSS95 MU R[R"X U S-RU RS5XgU5U SSS95 M [R"U SS 9RXXbU5$) aExtract patches defined by LAFs from image torch.Tensor. No smoothing applied, huge aliasing (better use extract_patches_from_pyramid). Args: img: images, LAFs are detected in :math:`(B, CH, H, W)`. laf: :math:`(B, N, 2, 3)`. PS: patch size. normalize_lafs_before_extraction: if True, lafs are normalized to image size. Returns: patches with shape :math:`(B, N, CH, PS,PS)`. rmpsrrr@F padding_moderborderr=)rrrmrangerrur:rNappendr grid_samplertr_rrAr')rrErrnlafrbrrrr+r,rirs rextract_patches_simplersI"S'S&((*KA1JA!Q C 1X6sq1u~tPQTUPUXZ[^^_b_i_ij ::??e # JJ AEN))$))A,qAJJr1%!("'   JJ AEN))$))A,qA4V^ns " 99Sa % %aBB 77rc [U5 U(a [X5nOUnUR5upV nUR5upxpS[[ X@55-[ U5- n [ X5U-n U R5RS[SU S- 5S9R5n [R"XVXX$RURS9nUn[SU 5n[U5GHfnUR5unnnn[U5HnU UU:HR!USSS5n[#UUUS-UUUS-U5nURR$S:Xa@[&R("UUUS-R+UUUU5URSS5S S S 9nO/[&R("UUUS-R+UUUU5US S S 9n[R,"UUR/UR5UU5UU'M UUS- :dGM*[1U5n[ URS 5URS55U:dGMf U$ U$)awExtract patches defined by LAFs from image torch.Tensor. Patches are extracted from appropriate pyramid level. Args: img: images, LAFs are detected in :math:`(B, CH, H, W)`. laf: :math:`(B, N, 2, 3)`. PS: patch size. normalize_lafs_before_extraction: if True, lafs are normalized to image size. Returns: patches with shape :math:`(B, N, CH, PS,PS)`. rrkrr)rmaxrrrr@FrrrrI)rrrmrrrKrlog2r_rlongrr@r;r:rr'rrNrrrtwhererur )rrErrrr+r,rbrrrr7 max_levelpyr_idxrcur_img num_levels cur_pyr_levelch_lh_lw_lr level_maskrpatchess rextract_patches_from_pyramidrs5"S'S&JA!Q((*KA1 - :; ;eBi GEA RIjjl  Sc!Y].C DIIKG ++aBBjj MCGQ "Jz* #LLN4cqA!!* 5;;Aq!QGJ:71q1u;MtTUXY\]X]`bcD~~""e+--AA&--asC@JJr1%!("' --AA&--asC@$U]mr[[WZZ -CSVLCF :> )g&G7<<?GLLO4r9 J/+. Jrrc[U5 UR5u p4n[US5nUSU:USXR- :*-USU:-USXB- :*-nURSS9SnU$)aACheck if the LAF is touching or partly outside the image boundary. Returns the mask of LAFs, which are fully inside the image, i.e. valid. Args: laf: :math:`(B, N, 2, 3)`. images: images, lafs are detected in :math:`(B, CH, H, W)`. border: additional border. Returns: mask with shape :math:`(B, N)`. r~rrr=r)rrmr{r)rErrrbrrrwgood_lafs_masks rlaf_is_inside_imagersSJAqQ b )C V 3v;!*#<=VPVAVW[^_e[fjkjt[tu$''A'.q1N rcv[U5 [R"USUS-USUS-US/SS9nU$)zConvert local affine frame(LAF) to alternative representation: coordinates of LAF center, LAF-x unit vector, LAF-y unit vector. Args: laf: :math:`(B, N, 2, 3)`. Returns: threepts :math:`(B, N, 2, 3)`. rr~rrr=)rrr`)rE three_ptss rlaf_to_three_pointsrsKS S[3v;6F c&k8QSVW]S^_eghI rthreeptsc`[R"USUS- USUS- US/SS9nU$)zConvert three points to local affine frame. Order is (0,0), (0, 1), (1, 0). Args: threepts: :math:`(B, N, 2, 3)`. Returns: laf :math:`(B, N, 2, 3)`. r~rrrr=)rr`)rrEs rlaf_from_three_pointsr%sK ++ & HV, ,hv.>&AQ.QS[\bScdjl C Jrtrans_01lafs_1ch[U5 [R"U5(d [S5eURUR:Xd [S5eUR SUR S:Xd [ S5eUR SS:XaUR SS:Xd [ S5eUR5up# n[U5nURSS SS 5RX#S-S 5n[X5nURX#SS 5RSS SS 5n[U5$) aiApply perspective transformations to a set of local affine frames (LAFs). Args: trans_01: torch.Tensor for perspective transformations of shape :math:`(B, 3, 3)`. lafs_1: torch.Tensor of lafs of shape :math:`(B, N, 2, 3)`. Returns: torch.Tensor of N-dimensional points of shape :math:`(B, N, 2, 3)`. Examples: >>> rng = torch.manual_seed(0) >>> lafs_1 = torch.rand(2, 4, 2, 3) # BxNx2x3 >>> lafs_1 tensor([[[[0.4963, 0.7682, 0.0885], [0.1320, 0.3074, 0.6341]], [[0.4901, 0.8964, 0.4556], [0.6323, 0.3489, 0.4017]], [[0.0223, 0.1689, 0.2939], [0.5185, 0.6977, 0.8000]], [[0.1610, 0.2823, 0.6816], [0.9152, 0.3971, 0.8742]]], [[[0.4194, 0.5529, 0.9527], [0.0362, 0.1852, 0.3734]], [[0.3051, 0.9320, 0.1759], [0.2698, 0.1507, 0.0317]], [[0.2081, 0.9298, 0.7231], [0.7423, 0.5263, 0.2437]], [[0.5846, 0.0332, 0.1387], [0.2422, 0.8155, 0.7932]]]]) >>> trans_01 = torch.eye(3).repeat(2, 1, 1) # Bx3x3 >>> trans_01.shape torch.Size([2, 3, 3]) >>> lafs_0 = perspective_transform_lafs(trans_01, lafs_1) # BxNx2x3 z Input type is not a torch.Tensorz'torch.Tensor must be in the same devicerz2Input batch size must be the same for both tensorsrrIz#Transformation should be homographyrr)rr is_tensorrMr:r& ValueErrorrmrrvr*r r'r) rrbsnrb threepts_1points_1points_0 threepts_0s rperspective_transform_lafsr7sXV ??8 $ $:;; ??fmm +ABB >>!  Q /MNN ^^B 1 $r0Ba0G>??++-KB1a$V,J!!!Q1-55ba%CH 3Hra+33Aq!Q?J  ,,r)NN)r^)2)r) )rT)-rptypingrrrrrtorch.nn.functionalnn functionalrkornia.core.checkrrkornia.geometry.conversionsr r kornia.geometry.linalgr kornia.geometry.transformr rLrrr#r/r5rFrKrCrXrhintr{rrrrboolrrrrrrrrrsh$ // Ba3-u|| ( *2U\\2ell2&ELL%,,5<<*1U\\15<<1ELL1$[_ %ell3AI%,,AW \\>M5<<MU5%,,3F-GMELLM2,ell,,%,,,B''%,,'TG GSG%,,G@>U\\>C>d3iQUVYQZFZ@[>2%%u||% %@%u||%U\\%ell%>ELLVYchcoco6bf+8 +8!LL+8.1+8Z^+8 \\+8^bf7 7!LL7.17Z^7 \\7tU\\5<<UZUaUa0 U\\ ell  ELLU\\$C-C-u||C-PUP\P\C-r