| import cv2 |
| from PIL import Image |
|
|
| import numpy as np |
| import torch |
| import torch.nn.functional as F |
|
|
|
|
| def gaussian_kernel(kernel_size=3, sigma=1.0, channels=3): |
| x_coord = torch.arange(kernel_size) |
| gaussian_1d = torch.exp(-(x_coord - (kernel_size - 1) / 2) ** 2 / (2 * sigma ** 2)) |
| gaussian_1d = gaussian_1d / gaussian_1d.sum() |
| gaussian_2d = gaussian_1d[:, None] * gaussian_1d[None, :] |
| kernel = gaussian_2d[None, None, :, :].repeat(channels, 1, 1, 1) |
| return kernel |
|
|
|
|
| def gaussian_filter(latents, kernel_size=3, sigma=1.0): |
| channels = latents.shape[1] |
| kernel = gaussian_kernel(kernel_size, sigma, channels).to(latents.device, latents.dtype) |
| blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels) |
| return blurred_latents |
|
|
|
|
| def make_coord(shape, ranges=None, flatten=True, device='cpu'): |
| |
| coord_seqs = [] |
| for i, n in enumerate(shape): |
| if ranges is None: |
| v0, v1 = -1, 1 |
| else: |
| v0, v1 = ranges[i] |
| r = (v1 - v0) / (2 * n) |
| seq = v0 + r + (2 * r) * torch.arange(n, device=device).float() |
| coord_seqs.append(seq) |
| ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1) |
| if flatten: |
| ret = ret.view(-1, ret.shape[-1]) |
| return ret |
|
|
|
|
| def apply_canny_detection(image_np, low_threshold=100, high_threshold=200): |
| gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) |
| filtered_image = cv2.Canny(gray_image, low_threshold, high_threshold) |
| return filtered_image |
