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import numpy as np
import torch
import torch.nn.functional as F
from typing import Tuple
class SoftErosion(torch.nn.Module):
def __init__(
self, kernel_size: int = 15, threshold: float = 0.6, iterations: int = 1
):
super(SoftErosion, self).__init__()
r = kernel_size // 2
self.padding = r
self.iterations = iterations
self.threshold = threshold
# Create kernel
y_indices, x_indices = torch.meshgrid(
torch.arange(0.0, kernel_size), torch.arange(0.0, kernel_size)
)
dist = torch.sqrt((x_indices - r) ** 2 + (y_indices - r) ** 2)
kernel = dist.max() - dist
kernel /= kernel.sum()
kernel = kernel.view(1, 1, *kernel.shape)
self.register_buffer("weight", kernel)
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
for i in range(self.iterations - 1):
x = torch.min(
x,
F.conv2d(
x, weight=self.weight, groups=x.shape[1], padding=self.padding
),
)
x = F.conv2d(x, weight=self.weight, groups=x.shape[1], padding=self.padding)
mask = x >= self.threshold
x[mask] = 1.0
# add small epsilon to avoid Nans
x[~mask] /= (x[~mask].max() + 1e-7)
return x, mask
def encode_segmentation_rgb(
segmentation: np.ndarray, no_neck: bool = True
) -> np.ndarray:
parse = segmentation
# https://github.com/zllrunning/face-parsing.PyTorch/blob/master/prepropess_data.py
face_part_ids = (
[1, 2, 3, 4, 5, 6, 10, 12, 13]
if no_neck
else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
)
mouth_id = 11
# hair_id = 17
face_map = np.zeros([parse.shape[0], parse.shape[1]])
mouth_map = np.zeros([parse.shape[0], parse.shape[1]])
# hair_map = np.zeros([parse.shape[0], parse.shape[1]])
for valid_id in face_part_ids:
valid_index = np.where(parse == valid_id)
face_map[valid_index] = 255
valid_index = np.where(parse == mouth_id)
mouth_map[valid_index] = 255
# valid_index = np.where(parse==hair_id)
# hair_map[valid_index] = 255
# return np.stack([face_map, mouth_map,hair_map], axis=2)
return np.stack([face_map, mouth_map], axis=2)
def encode_segmentation_rgb_batch(
segmentation: torch.Tensor, no_neck: bool = True
) -> torch.Tensor:
# https://github.com/zllrunning/face-parsing.PyTorch/blob/master/prepropess_data.py
face_part_ids = (
[1, 2, 3, 4, 5, 6, 10, 12, 13]
if no_neck
else [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14]
)
mouth_id = 11
# hair_id = 17
segmentation = segmentation.int()
face_map = torch.zeros_like(segmentation)
mouth_map = torch.zeros_like(segmentation)
# hair_map = np.zeros([parse.shape[0], parse.shape[1]])
white_tensor = face_map + 255
for valid_id in face_part_ids:
face_map = torch.where(segmentation == valid_id, white_tensor, face_map)
mouth_map = torch.where(segmentation == mouth_id, white_tensor, mouth_map)
return torch.cat([face_map, mouth_map], dim=1)
def postprocess(
swapped_face: np.ndarray,
target: np.ndarray,
target_mask: np.ndarray,
smooth_mask: torch.nn.Module,
) -> np.ndarray:
# target_mask = cv2.resize(target_mask, (self.size, self.size))
mask_tensor = (
torch.from_numpy(target_mask.copy().transpose((2, 0, 1)))
.float()
.mul_(1 / 255.0)
.cuda()
)
face_mask_tensor = mask_tensor[0] + mask_tensor[1]
soft_face_mask_tensor, _ = smooth_mask(face_mask_tensor.unsqueeze_(0).unsqueeze_(0))
soft_face_mask_tensor.squeeze_()
soft_face_mask = soft_face_mask_tensor.cpu().numpy()
soft_face_mask = soft_face_mask[:, :, np.newaxis]
result = swapped_face * soft_face_mask + target * (1 - soft_face_mask)
result = result[:, :, ::-1] # .astype(np.uint8)
return result
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