o oi'@sddlmZmZddlZddlmZd'dedededed eeeff d d Zd'd ed eded efddZd'dedededed ef ddZ d'dededededed ef ddZ        d(dededed e deed!eed"e d#e d$ed eeeffd%d&ZdS)))OptionalTupleN)Tensor绽|=valuesbinssigmaepsilonreturncCs t|tstdt|t|tstdt|t|ts*tdt||dks8td|j|dksFtd|j|dksTtd |j||dd}t d || d }tj |dd }tj |dd d|}||}||fS) aCalculate the marginal probability distribution function of the input based on the number of histogram bins. Args: values: shape [BxNx1]. bins: shape [NUM_BINS]. sigma: shape [1], gaussian smoothing factor. epsilon: scalar, for numerical stability. Returns: Tuple[Tensor, Tensor]: - Tensor: shape [BxN]. - Tensor: shape [BxNxNUM_BINS]. z'Input values type is not a Tensor. Got z%Input bins type is not a Tensor. Got z&Input sigma type is not a Tensor. Got z/Input values must be a of the shape BxNx1. Got z0Input bins must be a of the shape NUM_BINS. Got rz*Input sigma must be a of the shape 1. Got dim) isinstancer TypeErrortyper ValueErrorshape unsqueezetorchexppowmeansum)rrrr residuals kernel_valuespdf normalizationr L/home/ubuntu/.local/lib/python3.10/site-packages/kornia/enhance/histogram.py marginal_pdfs$      r"kernel_values1kernel_values2cCst|tstdt|t|tstdt||dks*td|j|dks8td|j|j|jkrJtd|jd|jt| dd |}tj |d d  d dd|}||}|S) aCCalculate the joint probability distribution function of the input tensors based on the number of histogram bins. Args: kernel_values1: shape [BxNxNUM_BINS]. kernel_values2: shape [BxNxNUM_BINS]. epsilon: scalar, for numerical stability. Returns: shape [BxNUM_BINSxNUM_BINS]. z/Input kernel_values1 type is not a Tensor. Got z/Input kernel_values2 type is not a Tensor. Got r z5Input kernel_values1 must be a of the shape BxN. Got z5Input kernel_values2 must be a of the shape BxN. Got zGInputs kernel_values1 and kernel_values2 must have the same shape. Got z and r r)r rr) rrrrrrrrmatmul transposerview)r#r$r joint_kernel_valuesrrr r r! joint_pdfDs(     r*x bandwidthcCst|d|||\}}|S)aEstimate the histogram of the input tensor. The calculation uses kernel density estimation which requires a bandwidth (smoothing) parameter. Args: x: Input tensor to compute the histogram with shape :math:`(B, D)`. bins: The number of bins to use the histogram :math:`(N_{bins})`. bandwidth: Gaussian smoothing factor with shape shape [1]. epsilon: A scalar, for numerical stability. Returns: Computed histogram of shape :math:`(B, N_{bins})`. Examples: >>> x = torch.rand(1, 10) >>> bins = torch.torch.linspace(0, 255, 128) >>> hist = histogram(x, bins, bandwidth=torch.tensor(0.9)) >>> hist.shape torch.Size([1, 128]) r)r"r)r+rr,r r_r r r! histogramisr.x1x2c Cs>t|d|||\}}t|d|||\}}t||}|S)asEstimate the 2d histogram of the input tensor. The calculation uses kernel density estimation which requires a bandwidth (smoothing) parameter. Args: x1: Input tensor to compute the histogram with shape :math:`(B, D1)`. x2: Input tensor to compute the histogram with shape :math:`(B, D2)`. bins: The number of bins to use the histogram :math:`(N_{bins})`. bandwidth: Gaussian smoothing factor with shape shape [1]. epsilon: A scalar, for numerical stability. Default: 1e-10. Returns: Computed histogram of shape :math:`(B, N_{bins}), N_{bins})`. Examples: >>> x1 = torch.rand(2, 32) >>> x2 = torch.rand(2, 32) >>> bins = torch.torch.linspace(0, 255, 128) >>> hist = histogram2d(x1, x2, bins, bandwidth=torch.tensor(0.9)) >>> hist.shape torch.Size([2, 128, 128]) r)r"rr*) r/r0rr,r r-r#r$rr r r! histogram2ds r1o@F triangularimageminmaxn_binscenters return_pdfkernelepsc Cs|durt|tstdt|d|dur&t|ts&tdt|d|dur@t|jdkr@|dkr@td|jdt|tsOtdt|dt|ts^td t|dt|t smtd t|d|durt|tstd t|dt|t std t|d|dur|||}|dur||t j ||j |jd d}|ddddd}t |d||} |dkrt d| d} n/|dvr| dk| j} |dkrd| | } n|dkr| } nd| d| } ntd|dt j| ddddd} |rDt j| ddd|} | | }|dkr/| } |}| |fS|dkr@| d} |d}| |fS|dkrP| } n |dkr\| d} | t | fS)aEstimate 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`)``. eps: epsilon for numerical stability. 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 Tensor. Got .z(Bins' centers type is not a Tensor. Got rr z;Bins' centers must be a 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%gaussianr r)r5uniform epanechnikovr5g?rBzJKernel must be 'triangular', 'gaussian', 'uniform' or 'epanechnikov'. Got )r%rT)rkeepdimr )rrrrlenrrrfloatintboolraranger?r@reshapeabsrrtorpermutesqueeze zeros_like)r6r7r8r9r,r:r;r<r=urmaskhistrrr r r!image_histogram2dsb*"          rT)r)r2r3r4NNFr5r)typingrrr kornia.corerrGr"r*r.r1rHrIstrrTr r r r!sH (+ %$"