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import math
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .gaussian import gaussian_blur2d
from .kernels import get_canny_nms_kernel, get_hysteresis_kernel
from .sobel import spatial_gradient
def rgb_to_grayscale(image, rgb_weights = None):
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 = image[..., 0:1, :, :]
g = image[..., 1:2, :, :]
b = 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: torch.Tensor,
low_threshold: float = 0.1,
high_threshold: float = 0.2,
kernel_size: Tuple[int, int] = (5, 5),
sigma: Tuple[float, float] = (1, 1),
hysteresis: bool = True,
eps: float = 1e-6,
) -> Tuple[torch.Tensor, torch.Tensor]:
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])
"""
if not isinstance(input, torch.Tensor):
raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}")
if not len(input.shape) == 4:
raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {input.shape}")
if low_threshold > high_threshold:
raise ValueError(
"Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: {}>{}".format(
low_threshold, high_threshold
)
)
if low_threshold < 0 and low_threshold > 1:
raise ValueError(f"Invalid input threshold. low_threshold should be in range (0,1). Got: {low_threshold}")
if high_threshold < 0 and high_threshold > 1:
raise ValueError(f"Invalid input threshold. high_threshold should be in range (0,1). Got: {high_threshold}")
device: torch.device = input.device
dtype: torch.dtype = input.dtype
# To Grayscale
if input.shape[1] == 3:
input = rgb_to_grayscale(input)
# Gaussian filter
blurred: torch.Tensor = gaussian_blur2d(input, kernel_size, sigma)
# Compute the gradients
gradients: torch.Tensor = spatial_gradient(blurred, normalized=False)
# Unpack the edges
gx: torch.Tensor = gradients[:, :, 0]
gy: torch.Tensor = gradients[:, :, 1]
# Compute gradient magnitude and angle
magnitude: torch.Tensor = torch.sqrt(gx * gx + gy * gy + eps)
angle: torch.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: torch.Tensor = get_canny_nms_kernel(device, dtype)
nms_magnitude: torch.Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2)
# Get the indices for both directions
positive_idx: torch.Tensor = (angle / 45) % 8
positive_idx = positive_idx.long()
negative_idx: torch.Tensor = ((angle / 45) + 4) % 8
negative_idx = negative_idx.long()
# Apply the non-maximum suppression to the different directions
channel_select_filtered_positive: torch.Tensor = torch.gather(nms_magnitude, 1, positive_idx)
channel_select_filtered_negative: torch.Tensor = torch.gather(nms_magnitude, 1, negative_idx)
channel_select_filtered: torch.Tensor = torch.stack(
[channel_select_filtered_positive, channel_select_filtered_negative], 1
)
is_max: torch.Tensor = channel_select_filtered.min(dim=1)[0] > 0.0
magnitude = magnitude * is_max
# Threshold
edges: torch.Tensor = F.threshold(magnitude, low_threshold, 0.0)
low: torch.Tensor = magnitude > low_threshold
high: torch.Tensor = magnitude > high_threshold
edges = low * 0.5 + high * 0.5
edges = edges.to(dtype)
# Hysteresis
if hysteresis:
edges_old: torch.Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype)
hysteresis_kernels: torch.Tensor = get_hysteresis_kernel(device, dtype)
while ((edges_old - edges).abs() != 0).any():
weak: torch.Tensor = (edges == 0.5).float()
strong: torch.Tensor = (edges == 1).float()
hysteresis_magnitude: torch.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(nn.Module):
r"""Module that finds edges of the input image and filters them using the Canny algorithm.
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)`.
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])
"""
def __init__(
self,
low_threshold: float = 0.1,
high_threshold: float = 0.2,
kernel_size: Tuple[int, int] = (5, 5),
sigma: Tuple[float, float] = (1, 1),
hysteresis: bool = True,
eps: float = 1e-6,
) -> None:
super().__init__()
if low_threshold > high_threshold:
raise ValueError(
"Invalid input thresholds. low_threshold should be\
smaller than the high_threshold. Got: {}>{}".format(
low_threshold, high_threshold
)
)
if low_threshold < 0 or low_threshold > 1:
raise ValueError(f"Invalid input threshold. low_threshold should be in range (0,1). Got: {low_threshold}")
if high_threshold < 0 or high_threshold > 1:
raise ValueError(f"Invalid input threshold. high_threshold should be in range (0,1). Got: {high_threshold}")
# Gaussian blur parameters
self.kernel_size = kernel_size
self.sigma = sigma
# Double threshold
self.low_threshold = low_threshold
self.high_threshold = high_threshold
# Hysteresis
self.hysteresis = hysteresis
self.eps: float = eps
def __repr__(self) -> str:
return ''.join(
(
f'{type(self).__name__}(',
', '.join(
f'{name}={getattr(self, name)}' for name in sorted(self.__dict__) if not name.startswith('_')
),
')',
)
)
def forward(self, input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
return canny(
input, self.low_threshold, self.high_threshold, self.kernel_size, self.sigma, self.hysteresis, self.eps
) |