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import os
import cv2
import math
import random
import numpy as np
from skimage import transform
class Augmentation:
def __init__(self,
is_train=True,
aug_prob=1.0,
image_size=256,
crop_op=True,
std_lmk_5pts=None,
target_face_scale=1.0,
flip_rate=0.5,
flip_mapping=None,
random_shift_sigma=0.05,
random_rot_sigma=math.pi/180*18,
random_scale_sigma=0.1,
random_gray_rate=0.2,
random_occ_rate=0.4,
random_blur_rate=0.3,
random_gamma_rate=0.2,
random_nose_fusion_rate=0.2):
self.is_train = is_train
self.aug_prob = aug_prob
self.crop_op = crop_op
self._flip = Flip(flip_mapping, flip_rate)
if self.crop_op:
self._cropMatrix = GetCropMatrix(
image_size=image_size,
target_face_scale=target_face_scale,
align_corners=True)
else:
self._alignMatrix = GetAlignMatrix(
image_size=image_size,
target_face_scale=target_face_scale,
std_lmk_5pts=std_lmk_5pts)
self._randomGeometryMatrix = GetRandomGeometryMatrix(
target_shape=(image_size, image_size),
from_shape=(image_size, image_size),
shift_sigma=random_shift_sigma,
rot_sigma=random_rot_sigma,
scale_sigma=random_scale_sigma,
align_corners=True)
self._transform = Transform(image_size=image_size)
self._randomTexture = RandomTexture(
random_gray_rate=random_gray_rate,
random_occ_rate=random_occ_rate,
random_blur_rate=random_blur_rate,
random_gamma_rate=random_gamma_rate,
random_nose_fusion_rate=random_nose_fusion_rate)
def process(self, img, lmk, lmk_5pts=None, scale=1.0, center_w=0, center_h=0, is_train=True):
if self.is_train and random.random() < self.aug_prob:
img, lmk, lmk_5pts, center_w, center_h = self._flip.process(img, lmk, lmk_5pts, center_w, center_h)
matrix_geoaug = self._randomGeometryMatrix.process()
if self.crop_op:
matrix_pre = self._cropMatrix.process(scale, center_w, center_h)
else:
matrix_pre = self._alignMatrix.process(lmk_5pts)
matrix = matrix_geoaug @ matrix_pre
aug_img, aug_lmk = self._transform.process(img, lmk, matrix)
aug_img = self._randomTexture.process(aug_img)
else:
if self.crop_op:
matrix = self._cropMatrix.process(scale, center_w, center_h)
else:
matrix = self._alignMatrix.process(lmk_5pts)
aug_img, aug_lmk = self._transform.process(img, lmk, matrix)
return aug_img, aug_lmk, matrix
class GetCropMatrix:
def __init__(self, image_size, target_face_scale, align_corners=False):
self.image_size = image_size
self.target_face_scale = target_face_scale
self.align_corners = align_corners
def _compose_rotate_and_scale(self, angle, scale, shift_xy, from_center, to_center):
cosv = math.cos(angle)
sinv = math.sin(angle)
fx, fy = from_center
tx, ty = to_center
acos = scale * cosv
asin = scale * sinv
a0 = acos
a1 = -asin
a2 = tx - acos * fx + asin * fy + shift_xy[0]
b0 = asin
b1 = acos
b2 = ty - asin * fx - acos * fy + shift_xy[1]
rot_scale_m = np.array([
[a0, a1, a2],
[b0, b1, b2],
[0.0, 0.0, 1.0]
], np.float32)
return rot_scale_m
def process(self, scale, center_w, center_h):
if self.align_corners:
to_w, to_h = self.image_size-1, self.image_size-1
else:
to_w, to_h = self.image_size, self.image_size
rot_mu = 0
scale_mu = self.image_size / (scale * self.target_face_scale * 200.0)
shift_xy_mu = (0, 0)
matrix = self._compose_rotate_and_scale(
rot_mu, scale_mu, shift_xy_mu,
from_center=[center_w, center_h],
to_center=[to_w/2.0, to_h/2.0])
return matrix
class GetAlignMatrix:
def __init__(self, image_size, target_face_scale, std_lmk_5pts):
"""
points in std_lmk_5pts range from -1 to 1.
"""
self.std_lmk_5pts = (std_lmk_5pts * target_face_scale + 1) * \
np.array([image_size, image_size], np.float32) / 2.0
def process(self, lmk_5pts):
assert lmk_5pts.shape[-2:] == (5, 2)
tform = transform.SimilarityTransform()
tform.estimate(lmk_5pts, self.std_lmk_5pts)
return tform.params
class GetRandomGeometryMatrix:
def __init__(self, target_shape, from_shape,
shift_sigma=0.1, rot_sigma=18*math.pi/180, scale_sigma=0.1,
shift_mu=0.0, rot_mu=0.0, scale_mu=1.0,
shift_normal=True, rot_normal=True, scale_normal=True,
align_corners=False):
self.target_shape = target_shape
self.from_shape = from_shape
self.shift_config = (shift_mu, shift_sigma, shift_normal)
self.rot_config = (rot_mu, rot_sigma, rot_normal)
self.scale_config = (scale_mu, scale_sigma, scale_normal)
self.align_corners = align_corners
def _compose_rotate_and_scale(self, angle, scale, shift_xy, from_center, to_center):
cosv = math.cos(angle)
sinv = math.sin(angle)
fx, fy = from_center
tx, ty = to_center
acos = scale * cosv
asin = scale * sinv
a0 = acos
a1 = -asin
a2 = tx - acos * fx + asin * fy + shift_xy[0]
b0 = asin
b1 = acos
b2 = ty - asin * fx - acos * fy + shift_xy[1]
rot_scale_m = np.array([
[a0, a1, a2],
[b0, b1, b2],
[0.0, 0.0, 1.0]
], np.float32)
return rot_scale_m
def _random(self, mu_sigma_normal, size=None):
mu, sigma, is_normal = mu_sigma_normal
if is_normal:
return np.random.normal(mu, sigma, size=size)
else:
return np.random.uniform(low=mu-sigma, high=mu+sigma, size=size)
def process(self):
if self.align_corners:
from_w, from_h = self.from_shape[1]-1, self.from_shape[0]-1
to_w, to_h = self.target_shape[1]-1, self.target_shape[0]-1
else:
from_w, from_h = self.from_shape[1], self.from_shape[0]
to_w, to_h = self.target_shape[1], self.target_shape[0]
if self.shift_config[:2] != (0.0, 0.0) or \
self.rot_config[:2] != (0.0, 0.0) or \
self.scale_config[:2] != (1.0, 0.0):
shift_xy = self._random(self.shift_config, size=[2]) * \
min(to_h, to_w)
rot_angle = self._random(self.rot_config)
scale = self._random(self.scale_config)
matrix_geoaug = self._compose_rotate_and_scale(
rot_angle, scale, shift_xy,
from_center=[from_w/2.0, from_h/2.0],
to_center=[to_w/2.0, to_h/2.0])
return matrix_geoaug
class Transform:
def __init__(self, image_size):
self.image_size = image_size
def _transformPoints2D(self, points, matrix):
"""
points (nx2), matrix (3x3) -> points (nx2)
"""
dtype = points.dtype
# nx3
points = np.concatenate([points, np.ones_like(points[:, [0]])], axis=1)
points = points @ np.transpose(matrix)
points = points[:, :2] / points[:, [2, 2]]
return points.astype(dtype)
def _transformPerspective(self, image, matrix):
"""
image, matrix3x3 -> transformed_image
"""
return cv2.warpPerspective(
image, matrix,
dsize=(self.image_size, self.image_size),
flags=cv2.INTER_LINEAR, borderValue=0)
def process(self, image, landmarks, matrix):
t_landmarks = self._transformPoints2D(landmarks, matrix)
t_image = self._transformPerspective(image, matrix)
return t_image, t_landmarks
class RandomTexture:
def __init__(self, random_gray_rate=0, random_occ_rate=0, random_blur_rate=0, random_gamma_rate=0, random_nose_fusion_rate=0):
self.random_gray_rate = random_gray_rate
self.random_occ_rate = random_occ_rate
self.random_blur_rate = random_blur_rate
self.random_gamma_rate = random_gamma_rate
self.random_nose_fusion_rate = random_nose_fusion_rate
self.texture_augs = (
(self.add_occ, self.random_occ_rate),
(self.add_blur, self.random_blur_rate),
(self.add_gamma, self.random_gamma_rate),
(self.add_nose_fusion, self.random_nose_fusion_rate)
)
def add_gray(self, image):
assert image.ndim == 3 and image.shape[-1] == 3
image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
image = np.tile(np.expand_dims(image, -1), [1, 1, 3])
return image
def add_occ(self, image):
h, w, c = image.shape
rh = 0.2 + 0.6 * random.random() # [0.2, 0.8]
rw = rh - 0.2 + 0.4 * random.random()
cx = int((h - 1) * random.random())
cy = int((w - 1) * random.random())
dh = int(h / 2 * rh)
dw = int(w / 2 * rw)
x0 = max(0, cx - dw // 2)
y0 = max(0, cy - dh // 2)
x1 = min(w - 1, cx + dw // 2)
y1 = min(h - 1, cy + dh // 2)
image[y0:y1+1, x0:x1+1] = 0
return image
def add_blur(self, image):
blur_kratio = 0.05 * random.random()
blur_ksize = int((image.shape[0] + image.shape[1]) / 2 * blur_kratio)
if blur_ksize > 1:
image = cv2.blur(image, (blur_ksize, blur_ksize))
return image
def add_gamma(self, image):
if random.random() < 0.5:
gamma = 0.25 + 0.75 * random.random()
else:
gamma = 1.0 + 3.0 * random.random()
image = (((image / 255.0) ** gamma) * 255).astype("uint8")
return image
def add_nose_fusion(self, image):
h, w, c = image.shape
nose = np.array(bytearray(os.urandom(h * w * c)), dtype=image.dtype).reshape(h, w, c)
alpha = 0.5 * random.random()
image = (1 - alpha) * image + alpha * nose
return image.astype(np.uint8)
def process(self, image):
image = image.copy()
if random.random() < self.random_occ_rate:
image = self.add_occ(image)
if random.random() < self.random_blur_rate:
image = self.add_blur(image)
if random.random() < self.random_gamma_rate:
image = self.add_gamma(image)
if random.random() < self.random_nose_fusion_rate:
image = self.add_nose_fusion(image)
"""
orders = list(range(len(self.texture_augs)))
random.shuffle(orders)
for order in orders:
if random.random() < self.texture_augs[order][1]:
image = self.texture_augs[order][0](image)
"""
if random.random() < self.random_gray_rate:
image = self.add_gray(image)
return image
class Flip:
def __init__(self, flip_mapping, random_rate):
self.flip_mapping = flip_mapping
self.random_rate = random_rate
def process(self, image, landmarks, landmarks_5pts, center_w, center_h):
if random.random() >= self.random_rate or self.flip_mapping is None:
return image, landmarks, landmarks_5pts, center_w, center_h
# COFW
if landmarks.shape[0] == 29:
flip_offset = 0
# 300W, WFLW
elif landmarks.shape[0] in (68, 98):
flip_offset = -1
else:
flip_offset = -1
h, w, _ = image.shape
#image_flip = cv2.flip(image, 1)
image_flip = np.fliplr(image).copy()
landmarks_flip = landmarks.copy()
for i, j in self.flip_mapping:
landmarks_flip[i] = landmarks[j]
landmarks_flip[j] = landmarks[i]
landmarks_flip[:, 0] = w + flip_offset - landmarks_flip[:, 0]
if landmarks_5pts is not None:
flip_mapping = ([0, 1], [3, 4])
landmarks_5pts_flip = landmarks_5pts.copy()
for i, j in flip_mapping:
landmarks_5pts_flip[i] = landmarks_5pts[j]
landmarks_5pts_flip[j] = landmarks_5pts[i]
landmarks_5pts_flip[:, 0] = w + flip_offset - landmarks_5pts_flip[:, 0]
else:
landmarks_5pts_flip = None
center_w = w + flip_offset - center_w
return image_flip, landmarks_flip, landmarks_5pts_flip, center_w, center_h
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