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import time
from pathlib import Path
from typing import Iterable
from typing import NamedTuple
from typing import Optional
from typing import Tuple
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from skimage import transform as skt
from .scrfd_detect import SCRFD
# ---frontal
src = np.array(
[
[39.730, 51.138],
[72.270, 51.138],
[56.000, 68.493],
[42.463, 87.010],
[69.537, 87.010],
],
dtype=np.float32,
)
class alignFace:
def __init__(self) -> None:
self.src_map = src
def estimate_norm(self, lmk, image_size=112):
assert lmk.shape == (5, 2)
tform = skt.SimilarityTransform()
src_ = self.src_map * image_size / 112
tform.estimate(lmk, src_)
M = tform.params[0:2, :]
return M
def align_face(
self, img: np.ndarray, key_points: np.ndarray, crop_size: int
) -> Tuple[Iterable[np.ndarray], Iterable[np.ndarray]]:
transform_matrix = self.estimate_norm(key_points, crop_size)
align_img = cv2.warpAffine(img, transform_matrix, (crop_size, crop_size), borderValue=0.0)
return align_img, transform_matrix
class Detection(NamedTuple):
bbox: Optional[np.ndarray]
score: Optional[np.ndarray]
key_points: Optional[np.ndarray]
class FaceDetector:
def __init__(
self,
model_path: Path,
det_thresh: float = 0.5,
det_size: Tuple[int, int] = (640, 640),
mode: str = "None",
device: str = "cuda",
):
self.det_thresh = det_thresh
self.mode = mode
self.device = device
self.handler = SCRFD(str(model_path), device=self.device, det_thresh=det_thresh)
ctx_id = -1 if device == "cpu" else 0
self.handler.prepare(ctx_id, input_size=det_size)
def __call__(self, img: np.ndarray, max_num: int = 0) -> Detection:
bboxes, kpss = self.handler.detect(img, max_num=max_num, metric="default")
if bboxes.shape[0] == 0:
return Detection(None, None, None)
return Detection(bboxes[..., :-1], bboxes[..., -1], kpss)
def tensor2img(tensor):
tensor = tensor.detach().cpu().numpy()
img = tensor.transpose(0, 2, 3, 1)[0]
img = np.clip(img * 255, 0.0, 255.0).astype(np.uint8)
return img
def inverse_transform_batch(mat: torch.Tensor, device="cuda") -> torch.Tensor:
# inverse the Affine transformation matrix
inv_mat = torch.zeros_like(mat).to(device)
div1 = mat[:, 0, 0] * mat[:, 1, 1] - mat[:, 0, 1] * mat[:, 1, 0]
inv_mat[:, 0, 0] = mat[:, 1, 1] / div1
inv_mat[:, 0, 1] = -mat[:, 0, 1] / div1
inv_mat[:, 0, 2] = -(mat[:, 0, 2] * mat[:, 1, 1] - mat[:, 0, 1] * mat[:, 1, 2]) / div1
div2 = mat[:, 0, 1] * mat[:, 1, 0] - mat[:, 0, 0] * mat[:, 1, 1]
inv_mat[:, 1, 0] = mat[:, 1, 0] / div2
inv_mat[:, 1, 1] = -mat[:, 0, 0] / div2
inv_mat[:, 1, 2] = -(mat[:, 0, 2] * mat[:, 1, 0] - mat[:, 0, 0] * mat[:, 1, 2]) / div2
return inv_mat
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
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