o oiD(@sUddlmZmZddlZddlmmZddlmZddlm Z ddl m Z m Z ddl mZmZmZddlmZdd lmZmZmZiZeeefed <d ed <Gd ddejZGdddejZGdddejZGdddejZdS))DictOptionalN)nn)pi)KORNIA_CHECK_LAFKORNIA_CHECK_SHAPE)SpatialGradientget_gaussian_discrete_kernel1dget_gaussian_kernel2d)rad2deg)extract_patches_from_pyramidget_laf_orientationset_laf_orientationurlszEhttps://github.com/ducha-aiki/affnet/raw/master/pretrained/OriNet.pthorinetc@s,eZdZdZdejdejdejfddZdS)PassLAFzSDummy module to use instead of local feature orientation or affine shape estimator.lafimgreturncCs|S)zRun forward. Args: laf: :math:`(B, N, 2, 3)` img: :math:`(B, 1, H, W)` Returns: LAF, unchanged :math:`(B, N, 2, 3)` )selfrrrrN/home/ubuntu/.local/lib/python3.10/site-packages/kornia/feature/orientation.pyforward&s zPassLAF.forwardN)__name__ __module__ __qualname____doc__torchTensorrrrrrr#s rc sXeZdZdZddedededd ffd d Zdefd d Zde j de j fddZ Z S) PatchDominantGradientOrientationa(Module, which estimates the dominant gradient orientation of the given patches, in radians. Zero angle points towards right. Args: patch_size: size of the (square) input patch. num_angular_bins: number of histogram bins. eps: for safe division, and arctan. $:0yE> patch_sizenum_angular_binsepsrNcst||_||_tdd|_||_tjddddddd|_ t t dd|j j dd<Wdn1s:wYt|jd }t|j|jf||fd |_dS) Nsobelr Fcircular) kernel_sizepaddingbias padding_modeg?g@T)super__init__r$ num_ang_binsrgradientr&rConv1dangular_smoothrno_gradr weightfloatr weighting)rr$r%r&sigma __class__rrr0@s   z)PatchDominantGradientOrientation.__init__cCs&|jjd|jd|jd|jdS)N (patch_size=z, num_ang_bins=z, eps=))r;rr$r1r&rrrr__repr__Ls$z)PatchDominantGradientOrientation.__repr__patchcCst|gd|\}}}}||jks||jks|dkr.td|jd|jd||j|j|j|_|j|j|j|_| |}|dddddf}|dddddf}t |||||j |j} t |||j dt} t|j| d tdt} t | } | | } | |j} | d|j}d | | }| | } g}td|jD]}t| |k|j|||k|j| d }||qt |dd d|j}||d |j}|d\}}|j|d|j}|d|j}t |d|d dd }|}t |d|d dd }d ||||d|}dt||j|t|jt }|S) Run forward. Args: patch: :math:`(B, 1, H, W)` Returns: angle in radians: :math:`(B)` )B1HWr z#input shape should be must be [Bx1xxz]. Got Nrg@g?)r r g?)rsizer$ TypeErrorr8todtypedevicer4r2rsqrtr&atan2rr7r1floorrangeFadaptive_avg_pool2dappendcatviewmaxgatherreshape)rr@_CHrErDgradsgxgymagorio_bigbo0_bigwo1_bigbo1_bigwo0_big ang_bins_listi ang_bins_iang_binsvaluesindices indices_left indices_rightleftcenterrightc_subpixanglerrrrQsJ  "   ( (z(PatchDominantGradientOrientation.forward)r!r"r#) rrrrintr7r0strr?rrr __classcell__rrr:rr 4s   r csfeZdZdZddededdffdd Zedd ej dedej fd d Z dej dej fddZ Z S)OriNetaNetwork, which estimates the canonical orientation of the given 32x32 patches, in radians. Zero angle points towards right. 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. Args: pretrained: Download and set pretrained weights to the model. eps: to avoid division by zero in atan2. Returns: Angle in radians. Shape: - Input: (B, 1, 32, 32) - Output: (B) Examples: >>> input = torch.rand(16, 1, 32, 32) >>> orinet = OriNet() >>> angle = orinet(input) # 16 Fr# pretrainedr&rNcsbtttjddddddtjdddttjdddddddtjdddttjdddd dddtjdddttjdddddddtjdddttjdd dd dddtjd ddttjd d dddddtjd ddttd tjd d d ddd dtt d|_ ||_ |rt j jtdt dd}|j|ddd|dS)Nr F)r+r,r-)affine)r+strider,r-r!r)@g?Trcpu) map_location state_dict)strict)r/r0r SequentialConv2d BatchNorm2dReLUDropoutTanhAdaptiveAvgPool2dfeaturesr&rhubload_state_dict_from_urlrrLload_state_dicteval)rrvr&pretrained_dictr:rrr0s<        zOriNet.__init__ư>rFcCs,tj|ddd\}}||||S)z4Utility function that normalizes the input by batch.)rGT)dimkeepdim)rstd_meandetach)rFr&spmprrr_normalize_inputszOriNet._normalize_inputr@cCsJ|||dd}t|dddfd|dddf|j}|S)rArGr)Nrr#r )rrrUrrNr&)rr@xyrqrrrrs .zOriNet.forward)Fr#)r) rrrrboolr7r0 staticmethodrrrrrtrrr:rrus !ruc sfeZdZdZ ddededeejddffd d Zde fd d Z d e j de j de j fddZ ZS) LAFOrienteraModule, which extracts patches using input images and local affine frames (LAFs). Then runs :class:`~kornia.feature.PatchDominantGradientOrientation` or :class:`~kornia.feature.OriNet` on patches and then rotates the LAFs by the estimated angles Args: patch_size: num_angular_bins: angle_detector: Patch orientation estimator, e.g. :class:`~kornia.feature.PatchDominantGradientOrientation` or OriNet. r!r"Nr$r%angle_detectorrcs@t||_||_||durt|j|j|_dS||_dS)N)r/r0r$r1r r)rr$r%rr:rrr0s  zLAFOrienter.__init__cCs|jjd|jd|jdS)Nr<z, angle_detector=r=)r;rr$rr>rrrr?szLAFOrienter.__repr__rrc Cst|t|gd|d|dkr%td|dd|d|jdd\}}t|||jdd|j|j}||||}t | |}t |t ||}|S) zRun forward. Args: laf: :math:`(B, N, 2, 3)` img: :math:`(B, 1, H, W)` Returns: LAF_out: :math:`(B, N, 2, 3)` )rBCrDrErz2Batch size of laf and img should be the same. Got z, Nr)rGr ) rrrH ValueErrorshaper r$rUrrview_asrr ) rrrrBNpatchesangles_radians prev_anglelaf_outrrrrs   zLAFOrienter.forward)r!r"N)rrrrrrrrModuler0rsr?rrrrtrrr:rrs $r)typingrrrtorch.nn.functionalr functionalrQkornia.constantsrkornia.core.checkrrkornia.filtersrr r kornia.geometryr rr rrrrs__annotations__rrr rurrrrrs   QR