File size: 1,650 Bytes
d056e0b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
import torch
import torch.nn as nn
from torchvision.transforms import ToTensor, ToPILImage
from PIL import Image
class SobelOperator(nn.Module):
def __init__(self, device="cuda"):
super(SobelOperator, self).__init__()
self.device = device
self.edge_conv_x = nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False).to(
self.device
)
self.edge_conv_y = nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False).to(
self.device
)
sobel_kernel_x = torch.tensor(
[[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=self.device
)
sobel_kernel_y = torch.tensor(
[[-1.0, -2.0, -1.0], [0.0, 0.0, 0.0], [1.0, 2.0, 1.0]], device=self.device
)
self.edge_conv_x.weight = nn.Parameter(sobel_kernel_x.view((1, 1, 3, 3)))
self.edge_conv_y.weight = nn.Parameter(sobel_kernel_y.view((1, 1, 3, 3)))
@torch.no_grad()
def forward(self, image: Image.Image, low_threshold: float, high_threshold: float):
# Convert PIL image to PyTorch tensor
image_gray = image.convert("L")
image_tensor = ToTensor()(image_gray).unsqueeze(0).to(self.device)
# Compute gradients
edge_x = self.edge_conv_x(image_tensor)
edge_y = self.edge_conv_y(image_tensor)
edge = torch.sqrt(edge_x**2 + edge_y**2)
# Apply thresholding
edge = edge / edge.max() # Normalize to 0-1
edge[edge >= high_threshold] = 1.0
edge[edge <= low_threshold] = 0.0
# Convert the result back to a PIL image
return ToPILImage()(edge.squeeze(0).cpu())
|