a dX @sddlmZmZddlZddlmmZgdZej ej dddZ dej ej eej eee e e e e ej d d d Zdej ej eej eee e e e e ej d ddZdej ej eej eee e e e e ej d ddZdej ej eej eee e e e e ej d ddZdej ej eej eee e e e e ej d ddZdej ej eej eee e e e e ej d ddZd ej ej eej eee e e e e ej d ddZdS)!)ListOptionalN)dilationerosionopeningclosinggradienttop_hat bottom_hat)kernelreturncCs8|\}}tj|||j|jd}|||d||S)N)dtypedevice)sizetorchZeyer rview)r hwre/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/kornia/morphology/morphology.py_neight2channels_like_kernel s rgeodesic@unfold) tensorr structuring_elementorigin border_type border_valuemax_valenginer cCst|tjstdt|t|jdkr>td|t|tjs\tdt|t|jdkr|td||j\}} |dur|d| dg}|d| |dd|d ||d dg} |d kr| }d }t j || ||d } |durt |} | | |d k<n| } | | |d k<|d krv| d|d d| d} t| | dd\} } t| d\} } n|dkr|\}}}}| jdd\}}t|}t j| ||d|||d | dd djdd\} } | ||||} ntd|d| |S)aReturn the dilated image applying the same kernel in each channel. .. image:: _static/img/dilation.png The kernel must have 2 dimensions. Args: tensor: Image with shape :math:`(B, C, H, W)`. kernel: Positions of non-infinite elements of a flat structuring element. Non-zero values give the set of neighbors of the center over which the operation is applied. Its shape is :math:`(k_x, k_y)`. For full structural elements use torch.ones_like(structural_element). structuring_element: Structuring element used for the grayscale dilation. It may be a non-flat structuring element. origin: Origin of the structuring element. Default: ``None`` and uses the center of the structuring element as origin (rounding towards zero). border_type: It determines how the image borders are handled, where ``border_value`` is the value when ``border_type`` is equal to ``constant``. Default: ``geodesic`` which ignores the values that are outside the image when applying the operation. border_value: Value to fill past edges of input if ``border_type`` is ``constant``. max_val: The value of the infinite elements in the kernel. engine: convolution is faster and less memory hungry, and unfold is more stable numerically Returns: Dilated image with shape :math:`(B, C, H, W)`. .. note:: See a working example `here `__. Example: >>> tensor = torch.rand(1, 3, 5, 5) >>> kernel = torch.ones(3, 3) >>> dilated_img = dilation(tensor, kernel) &Input type is not a torch.Tensor. Got 'Input size must have 4 dimensions. Got 'Kernel type is not a torch.Tensor. Got (Kernel size must have 2 dimensions. Got Nrrrconstantmodevaluer)rr convolutionpaddingZbiasdimengine * is unknown, use 'convolution' or 'unfold') isinstancerTensor TypeErrortypelenshape ValueErrorr4Fpad zeros_likeclonermaxZfliprrconv2drNotImplementedErrorZview_as)rr rrrr r!r"se_hse_wpad_eoutput neighborhood_BCHWZh_padZw_padreshape_kernelrrrrsJ-   ,    $ rcCst|tjstdt|t|jdkr>td|t|tjs\tdt|t|jdkr|td||j\}} |dur|d| dg}|d| |dd|d ||d dg} |d kr|}d }t j || ||d } |durt |} | | |d k<n| } | | |d k<|d krn| d|d d| d} t| | d\} } t| d\} } n|dkr|\}}}}| jdd\}}t|}t j| ||d|||d | d djdd\} } | ||||} ntd|d| S)aReturn the eroded image applying the same kernel in each channel. .. image:: _static/img/erosion.png The kernel must have 2 dimensions. Args: tensor: Image with shape :math:`(B, C, H, W)`. kernel: Positions of non-infinite elements of a flat structuring element. Non-zero values give the set of neighbors of the center over which the operation is applied. Its shape is :math:`(k_x, k_y)`. For full structural elements use torch.ones_like(structural_element). structuring_element (torch.Tensor, optional): Structuring element used for the grayscale dilation. It may be a non-flat structuring element. origin: Origin of the structuring element. Default: ``None`` and uses the center of the structuring element as origin (rounding towards zero). border_type: It determines how the image borders are handled, where ``border_value`` is the value when ``border_type`` is equal to ``constant``. Default: ``geodesic`` which ignores the values that are outside the image when applying the operation. border_value: Value to fill past edges of input if border_type is ``constant``. max_val: The value of the infinite elements in the kernel. engine: ``convolution`` is faster and less memory hungry, and ``unfold`` is more stable numerically Returns: Eroded image with shape :math:`(B, C, H, W)`. .. note:: See a working example `here `__. Example: >>> tensor = torch.rand(1, 3, 5, 5) >>> kernel = torch.ones(5, 5) >>> output = erosion(tensor, kernel) r#r$r%r&r'r(Nrrrr)r*rr-r.r/r0r1r3r5r6)r7rr8r9r:r;r<r=r4r>r?r@rArminrrrCrrD)rr rrrr r!r"rErFrGrHrIrJrKrLrMrNZHpadZWpadrOrrrrmsJ-   ,      rc Cst|tjstdt|t|jdkr>td|t|tjs\tdt|t|jdkr|td|t t |||||||d|||||||dS) aReturn the opened image, (that means, dilation after an erosion) applying the same kernel in each channel. .. image:: _static/img/opening.png The kernel must have 2 dimensions. Args: tensor: Image with shape :math:`(B, C, H, W)`. kernel: Positions of non-infinite elements of a flat structuring element. Non-zero values give the set of neighbors of the center over which the operation is applied. Its shape is :math:`(k_x, k_y)`. For full structural elements use torch.ones_like(structural_element). structuring_element: Structuring element used for the grayscale dilation. It may be a non-flat structuring element. origin: Origin of the structuring element. Default: ``None`` and uses the center of the structuring element as origin (rounding towards zero). border_type: It determines how the image borders are handled, where ``border_value`` is the value when ``border_type`` is equal to ``constant``. Default: ``geodesic`` which ignores the values that are outside the image when applying the operation. border_value: Value to fill past edges of input if ``border_type`` is ``constant``. max_val: The value of the infinite elements in the kernel. engine: convolution is faster and less memory hungry, and unfold is more stable numerically Returns: torch.Tensor: Opened image with shape :math:`(B, C, H, W)`. .. note:: See a working example `here `__. Example: >>> tensor = torch.rand(1, 3, 5, 5) >>> kernel = torch.ones(3, 3) >>> opened_img = opening(tensor, kernel) r#r$r%r&r'r(r rrrr r!r rrrr r!r") r7rr8r9r:r;r<r=r4rrrr rrrr r!r"rrrrs4-   rc Cst|tjstdt|t|jdkr>td|t|tjs\tdt|t|jdkr|td|t t |||||||d|||||||dS) aReturn the closed image, (that means, erosion after a dilation) applying the same kernel in each channel. .. image:: _static/img/closing.png The kernel must have 2 dimensions. Args: tensor: Image with shape :math:`(B, C, H, W)`. kernel: Positions of non-infinite elements of a flat structuring element. Non-zero values give the set of neighbors of the center over which the operation is applied. Its shape is :math:`(k_x, k_y)`. For full structural elements use torch.ones_like(structural_element). structuring_element: Structuring element used for the grayscale dilation. It may be a non-flat structuring element. origin: Origin of the structuring element. Default is None and uses the center of the structuring element as origin (rounding towards zero). border_type: It determines how the image borders are handled, where ``border_value`` is the value when ``border_type`` is equal to ``constant``. Default: ``geodesic`` which ignores the values that are outside the image when applying the operation. border_value: Value to fill past edges of input if ``border_type`` is ``constant``. max_val: The value of the infinite elements in the kernel. engine: convolution is faster and less memory hungry, and unfold is more stable numerically Returns: Closed image with shape :math:`(B, C, H, W)`. .. note:: See a working example `here `__. Example: >>> tensor = torch.rand(1, 3, 5, 5) >>> kernel = torch.ones(3, 3) >>> closed_img = closing(tensor, kernel) r#r$r%r&r'r(rQrR) r7rr8r9r:r;r<r=r4rrrSrrrrs4-   rc Cs0t||||||||dt||||||||dS)aReturn the morphological gradient of an image. .. image:: _static/img/gradient.png That means, (dilation - erosion) applying the same kernel in each channel. The kernel must have 2 dimensions. Args: tensor: Image with shape :math:`(B, C, H, W)`. kernel: Positions of non-infinite elements of a flat structuring element. Non-zero values give the set of neighbors of the center over which the operation is applied. Its shape is :math:`(k_x, k_y)`. For full structural elements use torch.ones_like(structural_element). structuring_element: Structuring element used for the grayscale dilation. It may be a non-flat structuring element. origin: Origin of the structuring element. Default is None and uses the center of the structuring element as origin (rounding towards zero). border_type: It determines how the image borders are handled, where ``border_value`` is the value when ``border_type`` is equal to ``constant``. Default: ``geodesic`` which ignores the values that are outside the image when applying the operation. border_value: Value to fill past edges of input if ``border_type`` is ``constant``. max_val: The value of the infinite elements in the kernel. engine: convolution is faster and less memory hungry, and unfold is more stable numerically Returns: Gradient image with shape :math:`(B, C, H, W)`. .. note:: See a working example `here `__. Example: >>> tensor = torch.rand(1, 3, 5, 5) >>> kernel = torch.ones(3, 3) >>> gradient_img = gradient(tensor, kernel) rR)rrrSrrrrgs*. rc Cst|tjstdt|t|jdkr>td|t|tjs\tdt|t|jdkr|td||t ||||||||dS)a+Return the top hat transformation of an image. .. image:: _static/img/top_hat.png That means, (image - opened_image) applying the same kernel in each channel. The kernel must have 2 dimensions. See :func:`~kornia.morphology.opening` for details. Args: tensor: Image with shape :math:`(B, C, H, W)`. kernel: Positions of non-infinite elements of a flat structuring element. Non-zero values give the set of neighbors of the center over which the operation is applied. Its shape is :math:`(k_x, k_y)`. For full structural elements use torch.ones_like(structural_element). structuring_element: Structuring element used for the grayscale dilation. It may be a non-flat structuring element. origin: Origin of the structuring element. Default: ``None`` and uses the center of the structuring element as origin (rounding towards zero). border_type: It determines how the image borders are handled, where ``border_value`` is the value when ``border_type`` is equal to ``constant``. Default: ``geodesic`` which ignores the values that are outside the image when applying the operation. border_value: Value to fill past edges of input if ``border_type`` is ``constant``. max_val: The value of the infinite elements in the kernel. engine: convolution is faster and less memory hungry, and unfold is more stable numerically Returns: Top hat transformed image with shape :math:`(B, C, H, W)`. .. note:: See a working example `here `__. Example: >>> tensor = torch.rand(1, 3, 5, 5) >>> kernel = torch.ones(3, 3) >>> top_hat_img = top_hat(tensor, kernel) r#r$r%r&r'r(rR) r7rr8r9r:r;r<r=r4rrSrrrr s$0  r c Cst|tjstdt|t|jdkr>td|t|tjs\tdt|t|jdkr|td|t ||||||||d|S)a7Return the bottom hat transformation of an image. .. image:: _static/img/bottom_hat.png That means, (closed_image - image) applying the same kernel in each channel. The kernel must have 2 dimensions. See :func:`~kornia.morphology.closing` for details. Args: tensor: Image with shape :math:`(B, C, H, W)`. kernel: Positions of non-infinite elements of a flat structuring element. Non-zero values give the set of neighbors of the center over which the operation is applied. Its shape is :math:`(k_x, k_y)`. For full structural elements use torch.ones_like(structural_element). structuring_element: Structuring element used for the grayscale dilation. It may be a non-flat structuring element. origin: Origin of the structuring element. Default: ``None`` and uses the center of the structuring element as origin (rounding towards zero). border_type: It determines how the image borders are handled, where ``border_value`` is the value when ``border_type`` is equal to ``constant``. Default: ``geodesic`` which ignores the values that are outside the image when applying the operation. border_value: Value to fill past edges of input if ``border_type`` is ``constant``. max_val: The value of the infinite elements in the kernel. engine: convolution is faster and less memory hungry, and unfold is more stable numerically Returns: Top hat transformed image with shape :math:`(B, C, H, W)`. .. note:: See a working example `here `__. Example: >>> tensor = torch.rand(1, 3, 5, 5) >>> kernel = torch.ones(3, 3) >>> bottom_hat_img = bottom_hat(tensor, kernel) r#r$r%r&r'r(rR) r7rr8r9r:r;r<r=r4rrSrrrr s*0   r )NNrrrr)NNrrrr)NNrrrr)NNrrrr)NNrrrr)NNrrrr)NNrrrr)typingrrrZtorch.nn.functionalnnZ functionalr>__all__r8rintstrfloatrrrrrr r rrrrs   a  b  P  Q  F  K