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import cv2
import numpy as np
import torch
from skimage import transform as skt
from typing import Iterable, Tuple
src1 = np.array(
[
[51.642, 50.115],
[57.617, 49.990],
[35.740, 69.007],
[51.157, 89.050],
[57.025, 89.702],
],
dtype=np.float32,
)
# <--left
src2 = np.array(
[
[45.031, 50.118],
[65.568, 50.872],
[39.677, 68.111],
[45.177, 86.190],
[64.246, 86.758],
],
dtype=np.float32,
)
# ---frontal
src3 = 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,
)
# -->right
src4 = np.array(
[
[46.845, 50.872],
[67.382, 50.118],
[72.737, 68.111],
[48.167, 86.758],
[67.236, 86.190],
],
dtype=np.float32,
)
# -->right profile
src5 = np.array(
[
[54.796, 49.990],
[60.771, 50.115],
[76.673, 69.007],
[55.388, 89.702],
[61.257, 89.050],
],
dtype=np.float32,
)
src = np.array([src1, src2, src3, src4, src5])
src_map = src
ffhq_src = np.array(
[
[192.98138, 239.94708],
[318.90277, 240.1936],
[256.63416, 314.01935],
[201.26117, 371.41043],
[313.08905, 371.15118],
]
)
ffhq_src = np.expand_dims(ffhq_src, axis=0)
# arcface_src = np.array(
# [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366],
# [41.5493, 92.3655], [70.7299, 92.2041]],
# dtype=np.float32)
# arcface_src = np.expand_dims(arcface_src, axis=0)
# In[66]:
# lmk is prediction; src is template
def estimate_norm(lmk, image_size=112, mode="ffhq"):
assert lmk.shape == (5, 2)
tform = skt.SimilarityTransform()
lmk_tran = np.insert(lmk, 2, values=np.ones(5), axis=1)
min_M = []
min_index = []
min_error = float("inf")
if mode == "ffhq":
# assert image_size == 112
src = ffhq_src * image_size / 512
else:
src = src_map * image_size / 112
for i in np.arange(src.shape[0]):
tform.estimate(lmk, src[i])
M = tform.params[0:2, :]
results = np.dot(M, lmk_tran.T)
results = results.T
error = np.sum(np.sqrt(np.sum((results - src[i]) ** 2, axis=1)))
if error < min_error:
min_error = error
min_M = M
min_index = i
return min_M, min_index
def norm_crop(img, landmark, image_size=112, mode="ffhq"):
if mode == "Both":
M_None, _ = estimate_norm(landmark, image_size, mode="newarc")
M_ffhq, _ = estimate_norm(landmark, image_size, mode="ffhq")
warped_None = cv2.warpAffine(
img, M_None, (image_size, image_size), borderValue=0.0
)
warped_ffhq = cv2.warpAffine(
img, M_ffhq, (image_size, image_size), borderValue=0.0
)
return warped_ffhq, warped_None
else:
M, pose_index = estimate_norm(landmark, image_size, mode)
warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0)
return warped
def square_crop(im, S):
if im.shape[0] > im.shape[1]:
height = S
width = int(float(im.shape[1]) / im.shape[0] * S)
scale = float(S) / im.shape[0]
else:
width = S
height = int(float(im.shape[0]) / im.shape[1] * S)
scale = float(S) / im.shape[1]
resized_im = cv2.resize(im, (width, height))
det_im = np.zeros((S, S, 3), dtype=np.uint8)
det_im[: resized_im.shape[0], : resized_im.shape[1], :] = resized_im
return det_im, scale
def transform(data, center, output_size, scale, rotation):
scale_ratio = scale
rot = float(rotation) * np.pi / 180.0
# translation = (output_size/2-center[0]*scale_ratio, output_size/2-center[1]*scale_ratio)
t1 = skt.SimilarityTransform(scale=scale_ratio)
cx = center[0] * scale_ratio
cy = center[1] * scale_ratio
t2 = skt.SimilarityTransform(translation=(-1 * cx, -1 * cy))
t3 = skt.SimilarityTransform(rotation=rot)
t4 = skt.SimilarityTransform(translation=(output_size / 2, output_size / 2))
t = t1 + t2 + t3 + t4
M = t.params[0:2]
cropped = cv2.warpAffine(data, M, (output_size, output_size), borderValue=0.0)
return cropped, M
def trans_points2d(pts, M):
new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
for i in range(pts.shape[0]):
pt = pts[i]
new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
new_pt = np.dot(M, new_pt)
# print('new_pt', new_pt.shape, new_pt)
new_pts[i] = new_pt[0:2]
return new_pts
def trans_points3d(pts, M):
scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1])
# print(scale)
new_pts = np.zeros(shape=pts.shape, dtype=np.float32)
for i in range(pts.shape[0]):
pt = pts[i]
new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32)
new_pt = np.dot(M, new_pt)
# print('new_pt', new_pt.shape, new_pt)
new_pts[i][0:2] = new_pt[0:2]
new_pts[i][2] = pts[i][2] * scale
return new_pts
def trans_points(pts, M):
if pts.shape[1] == 2:
return trans_points2d(pts, M)
else:
return trans_points3d(pts, M)
def inverse_transform(mat: np.ndarray) -> np.ndarray:
# inverse the Affine transformation matrix
inv_mat = np.zeros([2, 3])
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
def inverse_transform_batch(mat: torch.Tensor) -> torch.Tensor:
# inverse the Affine transformation matrix
inv_mat = torch.zeros_like(mat)
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
def align_face(
img: np.ndarray, key_points: np.ndarray, crop_size: int, mode: str = "ffhq"
) -> Tuple[Iterable[np.ndarray], Iterable[np.ndarray]]:
align_imgs = []
transforms = []
for i in range(key_points.shape[0]):
kps = key_points[i]
transform_matrix, _ = estimate_norm(kps, crop_size, mode=mode)
align_img = cv2.warpAffine(
img, transform_matrix, (crop_size, crop_size), borderValue=0.0
)
align_imgs.append(align_img)
transforms.append(transform_matrix)
return align_imgs, transforms
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