a dw& @sddlmZmZddlZdejejejeeejejfdddZdejejeejddd Zdejejejeejd d d Zdejejejejeejd ddZ dejeee eeeeje e eeejejfd ddZ dS))OptionalTupleN绽|=)valuesbinssigmaepsilonreturncCst|tjstdt|t|tjs>> x = torch.rand(1, 10) >>> bins = torch.torch.linspace(0, 255, 128) >>> hist = histogram(x, bins, bandwidth=torch.tensor(0.9)) >>> hist.shape torch.Size([1, 128]) r )r"r)r)rr*rr_r r r! histogramZsr,)x1x2rr*rr c Cs>t|d|||\}}t|d|||\}}t||}|S)arEstimate the 2d histogram of the input tensor. The calculation uses kernel density estimation which requires a bandwidth (smoothing) parameter. Args: x1: Input tensor to compute the histogram with shape :math:`(B, D1)`. x2: Input tensor to compute the histogram with shape :math:`(B, D2)`. bins: The number of bins to use the histogram :math:`(N_{bins})`. bandwidth: Gaussian smoothing factor with shape shape [1]. epsilon: A scalar, for numerical stability. Default: 1e-10. Returns: Computed histogram of shape :math:`(B, N_{bins}), N_{bins})`. Examples: >>> x1 = torch.rand(2, 32) >>> x2 = torch.rand(2, 32) >>> bins = torch.torch.linspace(0, 255, 128) >>> hist = histogram2d(x1, x2, bins, bandwidth=torch.tensor(0.9)) >>> hist.shape torch.Size([2, 128, 128]) r )r"rr() r-r.rr*rr+r#r$rr r r! histogram2dus r/o@F triangular) imageminmaxn_binsr*centers return_pdfkernelepsr c Cs|dur(t|tjs(tdt|d|durPt|tjsPtdt|d|durt|jdkr|dkrtd|jdt|t stdt|dt|t std t|dt|t std t|d|durt|t std t|dt|t s(td t|d|dur>|||}|durh||tj ||j |jd d}|ddddd}t|d||} |dkrtd| d} nn|dvr| dk| j} |dkrd| | } n*|dkrt| | } nd| d| } ntd|dtj| ddddd} |rtj| ddd|} | | }|dkrx| } |}n"|dkr| d} |d}| |fS|dkr| } n|dkr| d} | t| fS)aQEstimate the histogram of the input image(s). The calculation uses triangular kernel density estimation. Args: image: Input tensor to compute the histogram with shape :math:`(H, W)`, :math:`(C, H, W)` or :math:`(B, C, H, W)`. min: Lower end of the interval (inclusive). max: Upper end of the interval (inclusive). Ignored when :attr:`centers` is specified. n_bins: The number of histogram bins. Ignored when :attr:`centers` is specified. bandwidth: Smoothing factor. If not specified or equal to -1, :math:`(bandwidth = (max - min) / n_bins)`. centers: Centers of the bins with shape :math:`(n_bins,)`. If not specified or empty, it is calculated as centers of equal width bins of [min, max] range. return_pdf: If True, also return probability densities for each bin. kernel: kernel to perform kernel density estimation ``(`triangular`, `gaussian`, `uniform`, `epanechnikov`)``. Returns: Computed histogram of shape :math:`(bins)`, :math:`(C, bins)`, :math:`(B, C, bins)`. Computed probability densities of shape :math:`(bins)`, :math:`(C, bins)`, :math:`(B, C, bins)`, if return_pdf is ``True``. Tensor of zeros with shape of the histogram otherwise. Nz,Input image type is not a torch.Tensor. Got .z.Bins' centers type is not a torch.Tensor. Got rr zABins' centers must be a torch.Tensor of the shape (n_bins,). Got z3Type of lower end of the range is not a float. Got z3Type of upper end of the range is not a float. Got z*Type of number of bins is not an int. Got z#Bandwidth type is not a float. Got z#Return_pdf type is not a bool. Got )devicedtypeg?r%Zgaussianr r )r3uniformZ epanechnikovr3g?r?zJKernel must be 'triangular', 'gaussian', 'uniform' or 'epanechnikov'. Got )r%rT)rZkeepdimr )rrrrrlenrrrfloatintboolZaranger=r>ZreshapeabsrrtoZ ones_likerZpermuteZsqueezeZ zeros_like)r4r5r6r7r*r8r9r:r;urmaskhistrrr r r!image_histogram2ds`("                 rJ)r)r)r)r)r0r1r2NNFr3r)typingrrrrrBr"r(r,r/rCrDstrrJr r r r!sB .&  $