300 lines
11 KiB
Python
300 lines
11 KiB
Python
#From https://github.com/kornia/kornia
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import math
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import torch
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import torch.nn.functional as F
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import fcbh.model_management
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def get_canny_nms_kernel(device=None, dtype=None):
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"""Utility function that returns 3x3 kernels for the Canny Non-maximal suppression."""
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return torch.tensor(
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[
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[[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]],
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[[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]],
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[[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]],
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[[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]],
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[[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
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[[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
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[[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
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[[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
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],
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device=device,
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dtype=dtype,
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)
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def get_hysteresis_kernel(device=None, dtype=None):
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"""Utility function that returns the 3x3 kernels for the Canny hysteresis."""
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return torch.tensor(
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[
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[[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]],
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[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]],
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[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]],
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[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]],
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[[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
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[[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
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[[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
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[[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
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],
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device=device,
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dtype=dtype,
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)
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def gaussian_blur_2d(img, kernel_size, sigma):
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ksize_half = (kernel_size - 1) * 0.5
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x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
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pdf = torch.exp(-0.5 * (x / sigma).pow(2))
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x_kernel = pdf / pdf.sum()
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x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
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kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
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kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
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padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
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img = torch.nn.functional.pad(img, padding, mode="reflect")
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img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3])
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return img
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def get_sobel_kernel2d(device=None, dtype=None):
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kernel_x = torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=device, dtype=dtype)
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kernel_y = kernel_x.transpose(0, 1)
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return torch.stack([kernel_x, kernel_y])
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def spatial_gradient(input, normalized: bool = True):
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r"""Compute the first order image derivative in both x and y using a Sobel operator.
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.. image:: _static/img/spatial_gradient.png
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Args:
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input: input image tensor with shape :math:`(B, C, H, W)`.
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mode: derivatives modality, can be: `sobel` or `diff`.
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order: the order of the derivatives.
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normalized: whether the output is normalized.
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Return:
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the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`.
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.. note::
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See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
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filtering_edges.html>`__.
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Examples:
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>>> input = torch.rand(1, 3, 4, 4)
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>>> output = spatial_gradient(input) # 1x3x2x4x4
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>>> output.shape
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torch.Size([1, 3, 2, 4, 4])
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"""
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# KORNIA_CHECK_IS_TENSOR(input)
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# KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])
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# allocate kernel
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kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype)
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if normalized:
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kernel = normalize_kernel2d(kernel)
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# prepare kernel
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b, c, h, w = input.shape
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tmp_kernel = kernel[:, None, ...]
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# Pad with "replicate for spatial dims, but with zeros for channel
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spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2]
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out_channels: int = 2
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padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate')
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out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1)
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return out.reshape(b, c, out_channels, h, w)
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def rgb_to_grayscale(image, rgb_weights = None):
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r"""Convert a RGB image to grayscale version of image.
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.. image:: _static/img/rgb_to_grayscale.png
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The image data is assumed to be in the range of (0, 1).
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Args:
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image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`.
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rgb_weights: Weights that will be applied on each channel (RGB).
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The sum of the weights should add up to one.
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Returns:
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grayscale version of the image with shape :math:`(*,1,H,W)`.
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.. note::
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See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
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color_conversions.html>`__.
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Example:
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>>> input = torch.rand(2, 3, 4, 5)
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>>> gray = rgb_to_grayscale(input) # 2x1x4x5
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"""
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if len(image.shape) < 3 or image.shape[-3] != 3:
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raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}")
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if rgb_weights is None:
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# 8 bit images
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if image.dtype == torch.uint8:
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rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8)
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# floating point images
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elif image.dtype in (torch.float16, torch.float32, torch.float64):
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rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype)
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else:
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raise TypeError(f"Unknown data type: {image.dtype}")
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else:
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# is tensor that we make sure is in the same device/dtype
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rgb_weights = rgb_weights.to(image)
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# unpack the color image channels with RGB order
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r: Tensor = image[..., 0:1, :, :]
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g: Tensor = image[..., 1:2, :, :]
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b: Tensor = image[..., 2:3, :, :]
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w_r, w_g, w_b = rgb_weights.unbind()
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return w_r * r + w_g * g + w_b * b
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def canny(
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input,
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low_threshold = 0.1,
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high_threshold = 0.2,
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kernel_size = 5,
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sigma = 1,
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hysteresis = True,
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eps = 1e-6,
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):
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r"""Find edges of the input image and filters them using the Canny algorithm.
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.. image:: _static/img/canny.png
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Args:
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input: input image tensor with shape :math:`(B,C,H,W)`.
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low_threshold: lower threshold for the hysteresis procedure.
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high_threshold: upper threshold for the hysteresis procedure.
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kernel_size: the size of the kernel for the gaussian blur.
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sigma: the standard deviation of the kernel for the gaussian blur.
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hysteresis: if True, applies the hysteresis edge tracking.
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Otherwise, the edges are divided between weak (0.5) and strong (1) edges.
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eps: regularization number to avoid NaN during backprop.
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Returns:
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- the canny edge magnitudes map, shape of :math:`(B,1,H,W)`.
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- the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`.
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.. note::
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See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
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canny.html>`__.
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Example:
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>>> input = torch.rand(5, 3, 4, 4)
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>>> magnitude, edges = canny(input) # 5x3x4x4
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>>> magnitude.shape
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torch.Size([5, 1, 4, 4])
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>>> edges.shape
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torch.Size([5, 1, 4, 4])
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"""
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# KORNIA_CHECK_IS_TENSOR(input)
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# KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])
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# KORNIA_CHECK(
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# low_threshold <= high_threshold,
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# "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: "
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# f"{low_threshold}>{high_threshold}",
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# )
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# KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}')
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# KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}')
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device = input.device
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dtype = input.dtype
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# To Grayscale
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if input.shape[1] == 3:
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input = rgb_to_grayscale(input)
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# Gaussian filter
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blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma)
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# Compute the gradients
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gradients: Tensor = spatial_gradient(blurred, normalized=False)
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# Unpack the edges
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gx: Tensor = gradients[:, :, 0]
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gy: Tensor = gradients[:, :, 1]
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# Compute gradient magnitude and angle
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magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps)
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angle: Tensor = torch.atan2(gy, gx)
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# Radians to Degrees
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angle = 180.0 * angle / math.pi
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# Round angle to the nearest 45 degree
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angle = torch.round(angle / 45) * 45
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# Non-maximal suppression
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nms_kernels: Tensor = get_canny_nms_kernel(device, dtype)
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nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2)
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# Get the indices for both directions
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positive_idx: Tensor = (angle / 45) % 8
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positive_idx = positive_idx.long()
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negative_idx: Tensor = ((angle / 45) + 4) % 8
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negative_idx = negative_idx.long()
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# Apply the non-maximum suppression to the different directions
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channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx)
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channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx)
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channel_select_filtered: Tensor = torch.stack(
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[channel_select_filtered_positive, channel_select_filtered_negative], 1
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)
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is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0
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magnitude = magnitude * is_max
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# Threshold
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edges: Tensor = F.threshold(magnitude, low_threshold, 0.0)
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low: Tensor = magnitude > low_threshold
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high: Tensor = magnitude > high_threshold
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edges = low * 0.5 + high * 0.5
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edges = edges.to(dtype)
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# Hysteresis
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if hysteresis:
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edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype)
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hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype)
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while ((edges_old - edges).abs() != 0).any():
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weak: Tensor = (edges == 0.5).float()
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strong: Tensor = (edges == 1).float()
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hysteresis_magnitude: Tensor = F.conv2d(
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edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2
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)
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hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype)
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hysteresis_magnitude = hysteresis_magnitude * weak + strong
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edges_old = edges.clone()
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edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5
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edges = hysteresis_magnitude
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return magnitude, edges
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class Canny:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"image": ("IMAGE",),
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"low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}),
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"high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01})
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}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "detect_edge"
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CATEGORY = "image/preprocessors"
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def detect_edge(self, image, low_threshold, high_threshold):
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output = canny(image.to(fcbh.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
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img_out = output[1].cpu().repeat(1, 3, 1, 1).movedim(1, -1)
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return (img_out,)
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NODE_CLASS_MAPPINGS = {
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"Canny": Canny,
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}
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