o oi/@sddlZddlmZddlZddlmZddlmZmZmZm Z m Z ddl m Z ddl mZmZddlmZded ed ed efd d Zd%ded efddZdeded eeeeffddZGdddeZ     d&dededededed ed efd!d"ZGd#d$d$eZdS)'N)Tuple)nn)ModuleTensor concatenateeye normalize)KORNIA_CHECK_SHAPE)get_gaussian_kernel2dspatial_gradient)pikdkykxreturncCst|||}d}||kr0ttdsit_tj}||}|dur(t|}|||<|||||St|||||S)z"Return neigh2channels conv kernel.i _eye_cacheN)hasattr_get_reshape_kernelrgetrview)r rrnumel _MAX_CACHEDcacheresrK/home/ubuntu/.local/lib/python3.10/site-packages/kornia/feature/siftdesc.pyrs   rksizecCsBt|d}|t|d|}t|||d}|S)zReturn a weighted pooling kernel for SIFT descriptor. Args: ksize: kernel_size. Returns: the pooling kernel with shape :math:`(ksize, ksize)`. @g?)floattorcharangeabsger)rks_2xc2kernelrrrget_sift_pooling_kernel5s r( patch_sizenum_spatial_binscCsldt||d}||}|d}|d||dd|d}||kr1td|d|d|||fS)zReturn a tuple with SIFT parameters. Args: patch_size: the given patch size. num_spatial_bins: the ggiven number of spatial bins. Returns: ksize, stride, pad. rz Patch size zC is incompatible with requested number of spatial bins z{ for SIFT descriptor. Usually it happens when patch size is too small for num_spatial_bins specified)int ValueError)r)r*rstridepadout_sizerrrget_sift_bin_ksize_stride_padEs   r2c seZdZdZdefddZ     dd ed ed ed ededdf fdd Z de fddZ de fddZ de de fddZ ZS)SIFTDescriptoruModule which computes SIFT descriptors of given patches. Args: patch_size: Input patch size in pixels. num_ang_bins: Number of angular bins. num_spatial_bins: Number of spatial bins. clipval: clipping value to reduce single-bin dominance rootsift: if ``True``, RootSIFT (Arandjelović et. al, 2012) is computed. Returns: SIFT descriptor of the patches with shape. Shape: - Input: :math:`(B, 1, \text{num_spatial_bins}, \text{num_spatial_bins})` - Output: :math:`(B, \text{num_ang_bins * num_spatial_bins ** 2})` Example: >>> input = torch.rand(23, 1, 32, 32) >>> SIFT = SIFTDescriptor(32, 8, 4) >>> descs = SIFT(input) # 23x128 rc Cs6|jjd|jd|jd|jd|jd|jd S)N(num_ang_bins=, num_spatial_bins=z , patch_size= , rootsift= , clipval=)) __class____name__ num_ang_binsr*r)rootsiftclipvalselfrrr__repr__us zSIFTDescriptor.__repr__)r,T皙?r)r;r*r<r=Nc std|_||_||_||_||_||_|j}t|t d}t ||f||fd|_ t ||\|_|_|_t|jd}tjdd|d|df|j|jf|j|jfdd|_|jjj|dd|d|ddS) N绽|=rTrr+rF) kernel_sizer/paddingbias)super__init__epsr;r*r=r<r)r mathsqrtr gkr2 bin_ksize bin_strider0r(rConv2dsizepkweightdatacopy_reshape) r?r)r;r*r<r=kssigmanwr9rrrJs*   ,zSIFTDescriptor.__init__cCs |jjSN)rSrTdetachr>rrrget_pooling_kernels z!SIFTDescriptor.get_pooling_kernelcCs |jSr\)rNr]r>rrrget_weighting_kernels z#SIFTDescriptor.get_weighting_kernelinputc stddjjgjd}jjj_td}|dddddf}|dddddf}t ||||j }t ||j dt }|j |||j}tj|dt }t|} || } | jdjd| || |tfdd tdjDd} | |d } t| d d } t| d tj} t| d d } jrt t| dd j } | S)NB1rdiffr+r?c:g|]}|kj|kjqSr)rStodtype.0ibo0_bigbo1_bigr`r?wo0_bigwo1_bigrr s,z*SIFTDescriptor.forward..rpg)r r)shaperSrfrgdevicer r!rMrKatan2r rN expand_astype_asr r;floorrrangerrclampr=r<) r?r`ragradsgxgymagorio_bigbo0_big_wo1_big_ang_binsrrkrforwards:         zSIFTDescriptor.forwardrArBr,TrC)r: __module__ __qualname____doc__strr@r-boolr rJrr^r_r __classcell__rrr[rr3]s0 !r3rArBr,TrCr`r;r<r=cCst||||||S)zdCompute the sift descriptor. See :class: `~kornia.feature.SIFTDescriptor` for details. )r3)r`r)r;r*r<r=rrr sift_describes rcseZdZdZdefddZ      dd ed ed ed ededededdffdd Z de fddZ de de fddZ Z S)DenseSIFTDescriptoruModule, which computes SIFT descriptor densely over the image. Args: num_ang_bins: Number of angular bins. (8 is default) num_spatial_bins: Number of spatial bins per descriptor (4 is default). You might want to set odd number and relevant padding to keep feature map size spatial_bin_size: Size of a spatial bin in pixels (4 is default) clipval: clipping value to reduce single-bin dominance rootsift: (bool) if True, RootSIFT (Arandjelović et. al, 2012) is computed stride: default 1 padding: default 0 Returns: Tensor: DenseSIFT descriptor of the image Shape: - Input: (B, 1, H, W) - Output: (B, num_ang_bins * num_spatial_bins ** 2, (H+padding)/stride, (W+padding)/stride) Examples:: >>> input = torch.rand(2, 1, 200, 300) >>> SIFT = DenseSIFTDescriptor() >>> descs = SIFT(input) # 2x128x194x294 rcCs>|jjd|jd|jd|jd|jd|jd|jdS)Nr4r5z, spatial_bin_size=r6z , stride=r7r8)r9r:r;r*spatial_bin_sizer<r/r=r>rrrr@s zDenseSIFTDescriptor.__repr__rBr,TrCr+r;r*rr<r=r/rGNc s6td|_||_||_||_||_||_||_||_ t |jd }| d| dd|d|dtjdd|d|dfdd|dd|ddfd } | jj|j| |_t||| } | d | tj|||d||f|j|jfd|j |j fd } | jj|j| |_|j|_dS) NrDrE_bin_pooling_kernel_weightr+r)r+r+Fr)rFr/rHrG_poolingconv_weight)rIrJrKr;r*rr=r<r/r0r(r register_bufferrWrRrrQrTrUrVrbin_pooling_kernelrr PoolingConvr]_pooling_kernel) r?r;r*rr<r=r/rGrZrPwrr[rrrJsD $    zDenseSIFTDescriptor.__init__cCs|jSr\)rr>rrrr^4sz&DenseSIFTDescriptor.get_pooling_kernelr`cstgd\}}}}jjj_jjj_td}|dddddf}|dddddf}t ||||j } t ||j dt } t j| dt } t| } | | } | jdjd| | | | tfddtdjDd}|}t|dd d dt j}t|dd d }jrt t|dd j }|S) N)rarbHWrcrr+rrdcrer)rrfrgrhrkrrrpMs &z/DenseSIFTDescriptor.forward..r)dimrsrr)r rRrrfrgrurr r!rMrKrvr r r;ryrrzrclamp_r=r<)r?r`_B_CH_W_Hr|r}r~rrrrrr out_no_normoutrrkrr8s8      zDenseSIFTDescriptor.forward)rBr,r,TrCr+r+)r:rrrrr@r-rr rJrr^rrrrr[rrs:  2r)rr)rLtypingrr!r kornia.corerrrrrkornia.core.checkr kornia.filtersr r kornia.geometry.conversionsr r-rr(r2r3rr rrrrrrsB     q