Add a canny preprocessor node.
This commit is contained in:
comfyanonymous
parent
6f914fb77d
commit
bdba394290
|
|
@ -0,0 +1,299 @@
|
|||
#From https://github.com/kornia/kornia
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
def get_canny_nms_kernel(device=None, dtype=None):
|
||||
"""Utility function that returns 3x3 kernels for the Canny Non-maximal suppression."""
|
||||
return torch.tensor(
|
||||
[
|
||||
[[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]],
|
||||
[[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]],
|
||||
[[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]],
|
||||
[[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]],
|
||||
[[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
|
||||
[[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
|
||||
[[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
|
||||
[[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
|
||||
],
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
|
||||
|
||||
def get_hysteresis_kernel(device=None, dtype=None):
|
||||
"""Utility function that returns the 3x3 kernels for the Canny hysteresis."""
|
||||
return torch.tensor(
|
||||
[
|
||||
[[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]],
|
||||
[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]],
|
||||
[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]],
|
||||
[[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]],
|
||||
[[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
|
||||
[[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
|
||||
[[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
|
||||
[[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
|
||||
],
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
)
|
||||
|
||||
def gaussian_blur_2d(img, kernel_size, sigma):
|
||||
ksize_half = (kernel_size - 1) * 0.5
|
||||
|
||||
x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
|
||||
|
||||
pdf = torch.exp(-0.5 * (x / sigma).pow(2))
|
||||
|
||||
x_kernel = pdf / pdf.sum()
|
||||
x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
|
||||
|
||||
kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
|
||||
kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
|
||||
|
||||
padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
|
||||
|
||||
img = torch.nn.functional.pad(img, padding, mode="reflect")
|
||||
img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3])
|
||||
|
||||
return img
|
||||
|
||||
def get_sobel_kernel2d(device=None, dtype=None):
|
||||
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)
|
||||
kernel_y = kernel_x.transpose(0, 1)
|
||||
return torch.stack([kernel_x, kernel_y])
|
||||
|
||||
def spatial_gradient(input, normalized: bool = True):
|
||||
r"""Compute the first order image derivative in both x and y using a Sobel operator.
|
||||
.. image:: _static/img/spatial_gradient.png
|
||||
Args:
|
||||
input: input image tensor with shape :math:`(B, C, H, W)`.
|
||||
mode: derivatives modality, can be: `sobel` or `diff`.
|
||||
order: the order of the derivatives.
|
||||
normalized: whether the output is normalized.
|
||||
Return:
|
||||
the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`.
|
||||
.. note::
|
||||
See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
|
||||
filtering_edges.html>`__.
|
||||
Examples:
|
||||
>>> input = torch.rand(1, 3, 4, 4)
|
||||
>>> output = spatial_gradient(input) # 1x3x2x4x4
|
||||
>>> output.shape
|
||||
torch.Size([1, 3, 2, 4, 4])
|
||||
"""
|
||||
# KORNIA_CHECK_IS_TENSOR(input)
|
||||
# KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])
|
||||
|
||||
# allocate kernel
|
||||
kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype)
|
||||
if normalized:
|
||||
kernel = normalize_kernel2d(kernel)
|
||||
|
||||
# prepare kernel
|
||||
b, c, h, w = input.shape
|
||||
tmp_kernel = kernel[:, None, ...]
|
||||
|
||||
# Pad with "replicate for spatial dims, but with zeros for channel
|
||||
spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2]
|
||||
out_channels: int = 2
|
||||
padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate')
|
||||
out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1)
|
||||
return out.reshape(b, c, out_channels, h, w)
|
||||
|
||||
def rgb_to_grayscale(image, rgb_weights = None):
|
||||
r"""Convert a RGB image to grayscale version of image.
|
||||
|
||||
.. image:: _static/img/rgb_to_grayscale.png
|
||||
|
||||
The image data is assumed to be in the range of (0, 1).
|
||||
|
||||
Args:
|
||||
image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`.
|
||||
rgb_weights: Weights that will be applied on each channel (RGB).
|
||||
The sum of the weights should add up to one.
|
||||
Returns:
|
||||
grayscale version of the image with shape :math:`(*,1,H,W)`.
|
||||
|
||||
.. note::
|
||||
See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
|
||||
color_conversions.html>`__.
|
||||
|
||||
Example:
|
||||
>>> input = torch.rand(2, 3, 4, 5)
|
||||
>>> gray = rgb_to_grayscale(input) # 2x1x4x5
|
||||
"""
|
||||
|
||||
if len(image.shape) < 3 or image.shape[-3] != 3:
|
||||
raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}")
|
||||
|
||||
if rgb_weights is None:
|
||||
# 8 bit images
|
||||
if image.dtype == torch.uint8:
|
||||
rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8)
|
||||
# floating point images
|
||||
elif image.dtype in (torch.float16, torch.float32, torch.float64):
|
||||
rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype)
|
||||
else:
|
||||
raise TypeError(f"Unknown data type: {image.dtype}")
|
||||
else:
|
||||
# is tensor that we make sure is in the same device/dtype
|
||||
rgb_weights = rgb_weights.to(image)
|
||||
|
||||
# unpack the color image channels with RGB order
|
||||
r: Tensor = image[..., 0:1, :, :]
|
||||
g: Tensor = image[..., 1:2, :, :]
|
||||
b: Tensor = image[..., 2:3, :, :]
|
||||
|
||||
w_r, w_g, w_b = rgb_weights.unbind()
|
||||
return w_r * r + w_g * g + w_b * b
|
||||
|
||||
def canny(
|
||||
input,
|
||||
low_threshold = 0.1,
|
||||
high_threshold = 0.2,
|
||||
kernel_size = 5,
|
||||
sigma = 1,
|
||||
hysteresis = True,
|
||||
eps = 1e-6,
|
||||
):
|
||||
r"""Find edges of the input image and filters them using the Canny algorithm.
|
||||
.. image:: _static/img/canny.png
|
||||
Args:
|
||||
input: input image tensor with shape :math:`(B,C,H,W)`.
|
||||
low_threshold: lower threshold for the hysteresis procedure.
|
||||
high_threshold: upper threshold for the hysteresis procedure.
|
||||
kernel_size: the size of the kernel for the gaussian blur.
|
||||
sigma: the standard deviation of the kernel for the gaussian blur.
|
||||
hysteresis: if True, applies the hysteresis edge tracking.
|
||||
Otherwise, the edges are divided between weak (0.5) and strong (1) edges.
|
||||
eps: regularization number to avoid NaN during backprop.
|
||||
Returns:
|
||||
- the canny edge magnitudes map, shape of :math:`(B,1,H,W)`.
|
||||
- the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`.
|
||||
.. note::
|
||||
See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
|
||||
canny.html>`__.
|
||||
Example:
|
||||
>>> input = torch.rand(5, 3, 4, 4)
|
||||
>>> magnitude, edges = canny(input) # 5x3x4x4
|
||||
>>> magnitude.shape
|
||||
torch.Size([5, 1, 4, 4])
|
||||
>>> edges.shape
|
||||
torch.Size([5, 1, 4, 4])
|
||||
"""
|
||||
# KORNIA_CHECK_IS_TENSOR(input)
|
||||
# KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])
|
||||
# KORNIA_CHECK(
|
||||
# low_threshold <= high_threshold,
|
||||
# "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: "
|
||||
# f"{low_threshold}>{high_threshold}",
|
||||
# )
|
||||
# KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}')
|
||||
# KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}')
|
||||
|
||||
device = input.device
|
||||
dtype = input.dtype
|
||||
|
||||
# To Grayscale
|
||||
if input.shape[1] == 3:
|
||||
input = rgb_to_grayscale(input)
|
||||
|
||||
# Gaussian filter
|
||||
blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma)
|
||||
|
||||
# Compute the gradients
|
||||
gradients: Tensor = spatial_gradient(blurred, normalized=False)
|
||||
|
||||
# Unpack the edges
|
||||
gx: Tensor = gradients[:, :, 0]
|
||||
gy: Tensor = gradients[:, :, 1]
|
||||
|
||||
# Compute gradient magnitude and angle
|
||||
magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps)
|
||||
angle: Tensor = torch.atan2(gy, gx)
|
||||
|
||||
# Radians to Degrees
|
||||
angle = 180.0 * angle / math.pi
|
||||
|
||||
# Round angle to the nearest 45 degree
|
||||
angle = torch.round(angle / 45) * 45
|
||||
|
||||
# Non-maximal suppression
|
||||
nms_kernels: Tensor = get_canny_nms_kernel(device, dtype)
|
||||
nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2)
|
||||
|
||||
# Get the indices for both directions
|
||||
positive_idx: Tensor = (angle / 45) % 8
|
||||
positive_idx = positive_idx.long()
|
||||
|
||||
negative_idx: Tensor = ((angle / 45) + 4) % 8
|
||||
negative_idx = negative_idx.long()
|
||||
|
||||
# Apply the non-maximum suppression to the different directions
|
||||
channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx)
|
||||
channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx)
|
||||
|
||||
channel_select_filtered: Tensor = torch.stack(
|
||||
[channel_select_filtered_positive, channel_select_filtered_negative], 1
|
||||
)
|
||||
|
||||
is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0
|
||||
|
||||
magnitude = magnitude * is_max
|
||||
|
||||
# Threshold
|
||||
edges: Tensor = F.threshold(magnitude, low_threshold, 0.0)
|
||||
|
||||
low: Tensor = magnitude > low_threshold
|
||||
high: Tensor = magnitude > high_threshold
|
||||
|
||||
edges = low * 0.5 + high * 0.5
|
||||
edges = edges.to(dtype)
|
||||
|
||||
# Hysteresis
|
||||
if hysteresis:
|
||||
edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype)
|
||||
hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype)
|
||||
|
||||
while ((edges_old - edges).abs() != 0).any():
|
||||
weak: Tensor = (edges == 0.5).float()
|
||||
strong: Tensor = (edges == 1).float()
|
||||
|
||||
hysteresis_magnitude: Tensor = F.conv2d(
|
||||
edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2
|
||||
)
|
||||
hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype)
|
||||
hysteresis_magnitude = hysteresis_magnitude * weak + strong
|
||||
|
||||
edges_old = edges.clone()
|
||||
edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5
|
||||
|
||||
edges = hysteresis_magnitude
|
||||
|
||||
return magnitude, edges
|
||||
|
||||
|
||||
class Canny:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {"image": ("IMAGE",),
|
||||
"low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}),
|
||||
"high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01})
|
||||
}}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "detect_edge"
|
||||
|
||||
CATEGORY = "image/preprocessors"
|
||||
|
||||
def detect_edge(self, image, low_threshold, high_threshold):
|
||||
output = canny(image.movedim(-1, 1), low_threshold, high_threshold)
|
||||
img_out = output[1].repeat(1, 3, 1, 1).movedim(1, -1)
|
||||
return (img_out,)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"Canny": Canny,
|
||||
}
|
||||
1
nodes.py
1
nodes.py
|
|
@ -1562,4 +1562,5 @@ def init_custom_nodes():
|
|||
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_model_merging.py"))
|
||||
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_tomesd.py"))
|
||||
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_clip_sdxl.py"))
|
||||
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_canny.py"))
|
||||
load_custom_nodes()
|
||||
|
|
|
|||
Loading…
Reference in New Issue