o oi#*@sUddlZddlZddlmZmZddlZddlmZddlmZm Z ddl m Z ddl m Z ddlmZmZmZmZmZmZmZiZeeefed <d ed <Gd d d ejZGdddejZGdddejZdS)N)DictOptional)nn)KORNIA_CHECK_LAFKORNIA_CHECK_SHAPE)get_gaussian_kernel2d)SpatialGradient)ellipse_to_lafextract_patches_from_pyramidget_laf_orientation get_laf_scale make_upright scale_lafset_laf_orientationurlszEhttps://github.com/ducha-aiki/affnet/raw/master/pretrained/AffNet.pthaffnetcsTeZdZdZddededdffdd Zdefd d Zd e j de j fd dZ Z S)PatchAffineShapeEstimatoraModule, which estimates the second moment matrix of the patch gradients. The method determines the affine shape of the local feature as in :cite:`baumberg2000`. Args: patch_size: the input image patch size. eps: for safe division. 绽|= patch_sizeepsreturnNcsTt||_tdd|_||_t|jtd}t |j|jf||fd|_ dS)Nsobelr g@T) super__init__rrgradientrfloatmathsqrtr weighting)selfrrsigma __class__O/home/ubuntu/.local/lib/python3.10/site-packages/kornia/feature/affine_shape.pyr6s  z"PatchAffineShapeEstimator.__init__cCs|jjd|jd|jdS)N (patch_size=z, eps=))r$__name__rrr!r%r%r&__repr__>sz"PatchAffineShapeEstimator.__repr__patchcCs6t|gd|j|j|j|_|||j}|dddddf}|dddddf}tj|dj ddj ddd||j ddj ddd|dj ddj dddgdd}||j k j ddddk|j}t gd |j|jddd }|d |||}||jdddd}|S) zRun forward. Args: patch: :math:`(B, 1, H, W)` Returns: torch.Tensor: ellipse_shape :math:`(B, 1, 3)` B1HWNrr dimTr4keepdim)?gr7r7)rr todtypedevicertorchcatpowmeanrrsumtensorviewmax)r!r,gradsgxgy ellipse_shapebad_maskcircular_shaper%r%r&forwardAs" $ (z!PatchAffineShapeEstimator.forward)rr) r) __module__ __qualname____doc__intrrstrr+r<TensorrJ __classcell__r%r%r#r&r+s  rc sfeZdZdZ ddedeejdeddffd d Z de fd d Z d e j de j de j fddZZS)LAFAffineShapeEstimatorawModule, which extracts patches using input images and local affine frames (LAFs). Then runs :class:`~kornia.feature.PatchAffineShapeEstimator` on patches to estimate LAFs shape. Then original LAF shape is replaced with estimated one. The original LAF orientation is not preserved, so it is recommended to first run LAFAffineShapeEstimator and then LAFOrienter, Args: patch_size: the input image patch size. affine_shape_detector: Patch affine shape estimator, :class:`~kornia.feature.PatchAffineShapeEstimator`. preserve_orientation: if True, the original orientation is preserved. NTraffine_shape_detectorpreserve_orientationrcsBt||_|pt|j|_||_|rtjdtdddSdS)Nz`LAFAffineShapeEstimator` default behaviour is changed and now it does preserve original LAF orientation. Make sure your code accounts for this.r2 stacklevel) rrrrrTrUwarningswarnDeprecationWarning)r!rrTrUr#r%r&rus  z LAFAffineShapeEstimator.__init__cCs&|jjd|jd|jd|jdS)Nr'z, affine_shape_detector=z, preserve_orientation=r()r$r)rrTrUr*r%r%r&r+s z LAFAffineShapeEstimator.__repr__lafimgc Cst|t|gd|jdd\}}|j}t|t||ddd||}||}tj |dddd d|gdd ||d }t |} |j rNt |} t|} t | } t| | | } |j ret| | } | S) Run forward. Args: laf: :math:`(B, N, 2, 3)` img: :math:`(B, 1, H, W)` Returns: LAF_out: :math:`(B, N, 2, 3)` r-Nr2Tr r8).r2r3)rrshaperr rrBrTr<r= unsqueezer rUr r rr) r!r[r\r.NPSpatchesrGellipses scale_origori_origlaf_out ellipse_scaler%r%r&rJs   0 zLAFAffineShapeEstimator.forward)rSNT)r)rKrLrMrNrrModuleboolrrOr+r<rPrJrQr%r%r#r&rRes$rRcsleZdZdZddededdffdd Zedd ejd e dejfd dZ dejdejdejfddZ Z S)LAFAffNetShapeEstimatora]Module, which extracts patches using input images and local affine frames (LAFs). Then runs AffNet on patches to estimate LAFs shape. This is based on the original code from paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"". See :cite:`AffNet2018` for more details. Then original LAF shape is replaced with estimated one. The original LAF orientation is not preserved, so it is recommended to first run LAFAffineShapeEstimator and then LAFOrienter. Args: pretrained: Download and set pretrained weights to the model. FT pretrainedrUrNcs|tttjddddddtjdddttjdddddddtjdddttjdddd dddtjdddttjdddddddtjdddttjdd dd dddtjd ddttjd d dddddtjd ddttd tjd dd dd ddtt d|_ d|_ |rt j jtdt dd}|j|ddd||_|rtjdtd d|dS)Nr r8F) kernel_sizepaddingbias)affine)rostriderprqrSr2@g?rTrcpu) map_location state_dict)strictz`LAFAffNetShapeEstimator` default behaviour is changed and now it does preserve original LAF orientation. Make sure your code accounts for this.rV)rrr SequentialConv2d BatchNorm2dReLUDropoutTanhAdaptiveAvgPool2dfeaturesrr<hubload_state_dict_from_urlrr;load_state_dictrUrXrYrZeval)r!rmrUpretrained_dictr#r%r&rsJ        z LAFAffNetShapeEstimator.__init__ư>xrcCs,tj|ddd\}}||||S)zNormalize the input by batch.)r^Tr5)r<std_meandetach)rrspmpr%r%r&_normalize_inputsz(LAFAffNetShapeEstimator._normalize_inputr[r\cCs|t|t|gd|jdd\}}|j}t|t||ddd||}|||dd}t j d|ddd f dddd |ddd f dddgdd }t j |dddf dddd|dddf dddgdd } t j || gdd  ||dd} t j | |ddddddddfgdd } t |} |j rt|} t | }tt| | |}|j rt|| }|S) r]r-Nr2Tr^r r8r7rr3)rrr`rr rrBrrr<r=reshaper rUr rr)r!r[r\r.rbrcrdxya1a2new_laf_no_centernew_lafrfrgrirhr%r%r&rJs$ FB. zLAFAffNetShapeEstimator.forward)FT)r) r)rKrLrMrkr staticmethodr<rPrrrJrQr%r%r#r&rls *$rl)rrXtypingrrr<rkornia.core.checkrrkornia.filters.kernelsrkornia.filters.sobelrr[r r r r rrrrrO__annotations__rjrrRrlr%r%r%r&s    $ :E