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| 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))) | |
| 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()) | |