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import numpy as np
def deform_part(landmarks, part_inds, scale_y=1., scale_x=1., shift_ver=0., shift_horiz=0.):
""" deform facial part landmarks - matching ibug annotations of 68 landmarks """
landmarks_part = landmarks[part_inds, :].copy()
part_mean = np.mean(landmarks_part, 0)
landmarks_norm = landmarks_part - part_mean
landmarks_deform = landmarks_norm.copy()
landmarks_deform[:, 1] = scale_x * landmarks_deform[:, 1]
landmarks_deform[:, 0] = scale_y * landmarks_deform[:, 0]
landmarks_deform = landmarks_deform + part_mean
landmarks_deform = landmarks_deform + shift_ver * np.array([1, 0]) + shift_horiz * np.array([0, 1])
deform_shape = landmarks.copy()
deform_shape[part_inds] = landmarks_deform
return deform_shape
def deform_mouth(lms, p_scale=0, p_shift=0, pad=5):
""" deform mouth landmarks - matching ibug annotations of 68 landmarks """
jaw_line_inds = np.arange(0, 17)
nose_inds = np.arange(27, 36)
mouth_inds = np.arange(48, 68)
part_inds = mouth_inds.copy()
# find part spatial limitations
jaw_pad = 4
x_max = np.max(lms[part_inds, 1]) + (np.max(lms[jaw_line_inds[jaw_pad:-jaw_pad], 1]) - np.max(
lms[part_inds, 1])) * 0.5 - pad
x_min = np.min(lms[jaw_line_inds[jaw_pad:-jaw_pad], 1]) + (np.min(lms[part_inds, 1]) - np.min(
lms[jaw_line_inds[jaw_pad:-jaw_pad], 1])) * 0.5 + pad
y_min = np.max(lms[nose_inds, 0]) + (np.min(lms[part_inds, 0]) - np.max(lms[nose_inds, 0])) * 0.5
max_jaw = np.minimum(np.max(lms[jaw_line_inds, 0]), lms[8, 0])
y_max = max_jaw - (max_jaw - np.max(lms[part_inds, 0])) * 0.5 - pad
# scale facial feature
scale = np.random.rand()
if p_scale > 0.5 and scale > 0.5:
part_mean = np.mean(lms[part_inds, :], 0)
lms_part_norm = lms[part_inds, :] - part_mean
part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
scale_max_y = np.minimum(
(y_min - part_mean[0]) / part_y_bound_min,
(y_max - part_mean[0]) / part_y_bound_max)
scale_max_y = np.minimum(scale_max_y, 1.2)
scale_max_x = np.minimum(
(x_min - part_mean[1]) / part_x_bound_min,
(x_max - part_mean[1]) / part_x_bound_max)
scale_max_x = np.minimum(scale_max_x, 1.2)
scale_y = np.random.uniform(0.7, scale_max_y)
scale_x = np.random.uniform(0.7, scale_max_x)
lms_def_scale = deform_part(lms, part_inds, scale_y=scale_y, scale_x=scale_x, shift_ver=0., shift_horiz=0.)
# check for spatial errors
error = check_deformation_spatial_errors(lms_def_scale, part_inds, pad=pad)
if error:
lms_def_scale = lms.copy()
else:
lms_def_scale = lms.copy()
# shift facial feature
if p_shift > 0.5 and (np.random.rand() > 0.5 or not scale):
part_mean = np.mean(lms_def_scale[part_inds, :], 0)
lms_part_norm = lms_def_scale[part_inds, :] - part_mean
part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
shift_x = np.random.uniform(x_min - (part_mean[1] + part_x_bound_min),
x_max - (part_mean[1] + part_x_bound_max))
shift_y = np.random.uniform(y_min - (part_mean[0] + part_y_bound_min),
y_max - (part_mean[0] + part_y_bound_max))
lms_def = deform_part(lms_def_scale, part_inds, scale_y=1., scale_x=1., shift_ver=shift_y, shift_horiz=shift_x)
error = check_deformation_spatial_errors(lms_def, part_inds, pad=pad)
if error:
lms_def = lms_def_scale.copy()
else:
lms_def = lms_def_scale.copy()
return lms_def
def deform_nose(lms, p_scale=0, p_shift=0, pad=5):
""" deform nose landmarks - matching ibug annotations of 68 landmarks """
nose_inds = np.arange(27, 36)
left_eye_inds = np.arange(36, 42)
right_eye_inds = np.arange(42, 48)
mouth_inds = np.arange(48, 68)
part_inds = nose_inds.copy()
# find part spatial limitations
x_max = np.max(lms[part_inds[:4], 1]) + (np.min(lms[right_eye_inds, 1]) - np.max(lms[part_inds[:4], 1])) * 0.5 - pad
x_min = np.max(lms[left_eye_inds, 1]) + (np.min(lms[part_inds[:4], 1]) - np.max(lms[left_eye_inds, 1])) * 0.5 + pad
max_brows = np.max(lms[21:23, 0])
y_min = np.min(lms[part_inds, 0]) + (max_brows - np.min(lms[part_inds, 0])) * 0.5
min_mouth = np.min(lms[mouth_inds, 0])
y_max = np.max(lms[part_inds, 0]) + (np.max(lms[part_inds, 0]) - min_mouth) * 0 - pad
# scale facial feature
scale = np.random.rand()
if p_scale > 0.5 and scale > 0.5:
part_mean = np.mean(lms[part_inds, :], 0)
lms_part_norm = lms[part_inds, :] - part_mean
part_y_bound_min = np.min(lms_part_norm[:, 0])
part_y_bound_max = np.max(lms_part_norm[:, 0])
scale_max_y = np.minimum(
(y_min - part_mean[0]) / part_y_bound_min,
(y_max - part_mean[0]) / part_y_bound_max)
scale_y = np.random.uniform(0.7, scale_max_y)
scale_x = np.random.uniform(0.7, 1.5)
lms_def_scale = deform_part(lms, part_inds, scale_y=scale_y, scale_x=scale_x, shift_ver=0., shift_horiz=0.)
error1 = check_deformation_spatial_errors(lms_def_scale, part_inds[:4], pad=pad)
error2 = check_deformation_spatial_errors(lms_def_scale, part_inds[4:], pad=pad)
error = error1 + error2
if error:
lms_def_scale = lms.copy()
else:
lms_def_scale = lms.copy()
# shift facial feature
if p_shift > 0.5 and (np.random.rand() > 0.5 or not scale):
part_mean = np.mean(lms_def_scale[part_inds, :], 0)
lms_part_norm = lms_def_scale[part_inds, :] - part_mean
part_x_bound_min = np.min(lms_part_norm[:4], 0)
part_x_bound_max = np.max(lms_part_norm[:4], 0)
part_y_bound_min = np.min(lms_part_norm[:, 0])
part_y_bound_max = np.max(lms_part_norm[:, 0])
shift_x = np.random.uniform(x_min - (part_mean[1] + part_x_bound_min),
x_max - (part_mean[1] + part_x_bound_max))
shift_y = np.random.uniform(y_min - (part_mean[0] + part_y_bound_min),
y_max - (part_mean[0] + part_y_bound_max))
lms_def = deform_part(lms_def_scale, part_inds, scale_y=1., scale_x=1., shift_ver=shift_y, shift_horiz=shift_x)
error1 = check_deformation_spatial_errors(lms_def, part_inds[:4], pad=pad)
error2 = check_deformation_spatial_errors(lms_def, part_inds[4:], pad=pad)
error = error1 + error2
if error:
lms_def = lms_def_scale.copy()
else:
lms_def = lms_def_scale.copy()
return lms_def
def deform_eyes(lms, p_scale=0, p_shift=0, pad=10):
""" deform eyes + eyebrows landmarks - matching ibug annotations of 68 landmarks """
nose_inds = np.arange(27, 36)
left_eye_inds = np.arange(36, 42)
right_eye_inds = np.arange(42, 48)
left_brow_inds = np.arange(17, 22)
right_brow_inds = np.arange(22, 27)
part_inds_right = np.hstack((right_brow_inds, right_eye_inds))
part_inds_left = np.hstack((left_brow_inds, left_eye_inds))
# find part spatial limitations
# right eye+eyebrow
x_max_right = np.max(lms[part_inds_right, 1]) + (lms[16, 1] - np.max(lms[part_inds_right, 1])) * 0.5 - pad
x_min_right = np.max(lms[nose_inds[:4], 1]) + (np.min(lms[part_inds_right, 1]) - np.max(
lms[nose_inds[:4], 1])) * 0.5 + pad
y_max_right = np.max(lms[part_inds_right, 0]) + (lms[33, 0] - np.max(lms[part_inds_right, 0])) * 0.25 - pad
y_min_right = 2 * pad
# left eye+eyebrow
x_max_left = np.max(lms[part_inds_left, 1]) + (np.min(lms[nose_inds[:4], 1]) - np.max(
lms[part_inds_left, 1])) * 0.5 - pad
x_min_left = lms[0, 1] + (np.min(lms[part_inds_left, 1]) - lms[0, 1]) * 0.5 + pad
y_max_left = np.max(lms[part_inds_left, 0]) + (lms[33, 0] - np.max(lms[part_inds_left, 0])) * 0.25 - pad
y_min_left = 2 * pad
# scale facial feature
scale = np.random.rand()
if p_scale > 0.5 and scale > 0.5:
# right eye+eyebrow
part_mean = np.mean(lms[part_inds_right, :], 0)
lms_part_norm = lms[part_inds_right, :] - part_mean
part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
scale_max_y = np.minimum(
(y_min_right - part_mean[0]) / part_y_bound_min,
(y_max_right - part_mean[0]) / part_y_bound_max)
scale_max_y_right = np.minimum(scale_max_y, 1.5)
scale_max_x = np.minimum(
(x_min_right - part_mean[1]) / part_x_bound_min,
(x_max_right - part_mean[1]) / part_x_bound_max)
scale_max_x_right = np.minimum(scale_max_x, 1.5)
# left eye+eyebrow
part_mean = np.mean(lms[part_inds_left, :], 0)
lms_part_norm = lms[part_inds_left, :] - part_mean
part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
scale_max_y = np.minimum(
(y_min_left - part_mean[0]) / part_y_bound_min,
(y_max_left - part_mean[0]) / part_y_bound_max)
scale_max_y_left = np.minimum(scale_max_y, 1.5)
scale_max_x = np.minimum(
(x_min_left - part_mean[1]) / part_x_bound_min,
(x_max_left - part_mean[1]) / part_x_bound_max)
scale_max_x_left = np.minimum(scale_max_x, 1.5)
scale_max_x = np.minimum(scale_max_x_left, scale_max_x_right)
scale_max_y = np.minimum(scale_max_y_left, scale_max_y_right)
scale_y = np.random.uniform(0.8, scale_max_y)
scale_x = np.random.uniform(0.8, scale_max_x)
lms_def_scale = deform_part(lms, part_inds_right, scale_y=scale_y, scale_x=scale_x, shift_ver=0.,
shift_horiz=0.)
lms_def_scale = deform_part(lms_def_scale.copy(), part_inds_left, scale_y=scale_y, scale_x=scale_x,
shift_ver=0., shift_horiz=0.)
error1 = check_deformation_spatial_errors(lms_def_scale, part_inds_right, pad=pad)
error2 = check_deformation_spatial_errors(lms_def_scale, part_inds_left, pad=pad)
error = error1 + error2
if error:
lms_def_scale = lms.copy()
else:
lms_def_scale = lms.copy()
# shift facial feature
if p_shift > 0.5 and (np.random.rand() > 0.5 or not scale):
y_min_right = np.maximum(0.8 * np.min(lms_def_scale[part_inds_right, 0]), pad)
y_min_left = np.maximum(0.8 * np.min(lms_def_scale[part_inds_left, 0]), pad)
# right eye
part_mean = np.mean(lms_def_scale[part_inds_right, :], 0)
lms_part_norm = lms_def_scale[part_inds_right, :] - part_mean
part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
shift_x = np.random.uniform(x_min_right - (part_mean[1] + part_x_bound_min),
x_max_right - (part_mean[1] + part_x_bound_max))
shift_y = np.random.uniform(y_min_right - (part_mean[0] + part_y_bound_min),
y_max_right - (part_mean[0] + part_y_bound_max))
lms_def_right = deform_part(lms_def_scale, part_inds_right, scale_y=1., scale_x=1., shift_ver=shift_y,
shift_horiz=shift_x)
error1 = check_deformation_spatial_errors(lms_def_right, part_inds_right, pad=pad)
if error1:
lms_def_right = lms_def_scale.copy()
# left eye
part_mean = np.mean(lms_def_scale[part_inds_left, :], 0)
lms_part_norm = lms_def_scale[part_inds_left, :] - part_mean
part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
shift_x = np.random.uniform(x_min_left - (part_mean[1] + part_x_bound_min),
x_max_left - (part_mean[1] + part_x_bound_max))
shift_y = np.random.uniform(y_min_left - (part_mean[0] + part_y_bound_min),
y_max_left - (part_mean[0] + part_y_bound_max))
lms_def = deform_part(lms_def_right.copy(), part_inds_left, scale_y=1., scale_x=1., shift_ver=shift_y,
shift_horiz=shift_x)
error2 = check_deformation_spatial_errors(lms_def, part_inds_left, pad=pad)
if error2:
lms_def = lms_def_right.copy()
else:
lms_def = lms_def_scale.copy()
return lms_def
def deform_scale_face(lms, p_scale=0, pad=5, image_size=256):
""" change face landmarks scale & aspect ratio - matching ibug annotations of 68 landmarks """
part_inds = np.arange(68)
# find spatial limitations
x_max = np.max(lms[part_inds, 1]) + (image_size - np.max(lms[part_inds, 1])) * 0.5 - pad
x_min = np.min(lms[part_inds, 1]) * 0.5 + pad
y_min = 2 * pad
y_max = np.max(lms[part_inds, 0]) + (image_size - np.max(lms[part_inds, 0])) * 0.5 - pad
if p_scale > 0.5:
part_mean = np.mean(lms[part_inds, :], 0)
lms_part_norm = lms[part_inds, :] - part_mean
part_y_bound_min, part_x_bound_min = np.min(lms_part_norm, 0)
part_y_bound_max, part_x_bound_max = np.max(lms_part_norm, 0)
scale_max_y = np.minimum(
(y_min - part_mean[0]) / part_y_bound_min,
(y_max - part_mean[0]) / part_y_bound_max)
scale_max_y = np.minimum(scale_max_y, 1.2)
scale_max_x = np.minimum(
(x_min - part_mean[1]) / part_x_bound_min,
(x_max - part_mean[1]) / part_x_bound_max)
scale_max_x = np.minimum(scale_max_x, 1.2)
scale_y = np.random.uniform(0.6, scale_max_y)
scale_x = np.random.uniform(0.6, scale_max_x)
lms_def_scale = deform_part(lms, part_inds, scale_y=scale_y, scale_x=scale_x, shift_ver=0., shift_horiz=0.)
# check for spatial errors
error2 = np.sum(lms_def_scale >= image_size) + np.sum(lms_def_scale < 0)
error1 = len(np.unique((lms_def_scale).astype('int'), axis=0)) != len(lms_def_scale)
error = error1 + error2
if error:
lms_def_scale = lms.copy()
else:
lms_def_scale = lms.copy()
return lms_def_scale
def deform_face_geometric_style(lms, p_scale=0, p_shift=0):
""" deform facial landmarks - matching ibug annotations of 68 landmarks """
lms = deform_scale_face(lms.copy(), p_scale=p_scale, pad=0)
lms = deform_nose(lms.copy(), p_scale=p_scale, p_shift=p_shift, pad=0)
lms = deform_mouth(lms.copy(), p_scale=p_scale, p_shift=p_shift, pad=0)
lms = deform_eyes(lms.copy(), p_scale=p_scale, p_shift=p_shift, pad=0)
return lms
def get_bounds(lms):
part_y_bound_min, part_x_bound_min = np.min(lms,0)
part_y_bound_max, part_x_bound_max = np.max(lms,0)
return np.array([[part_x_bound_min, part_x_bound_max], [part_y_bound_min, part_y_bound_max]])
def part_intersection(part_to_check, points_to_compare, pad=0):
points_to_compare = np.round(points_to_compare.copy())
check_bounds = np.round(get_bounds(part_to_check))
check_bounds[:, 0] += pad
check_bounds[:, 1] -= pad
inds_y = np.where(np.logical_and(points_to_compare[:,0] > check_bounds[1,0], points_to_compare[:,0]<check_bounds[1,1]))
inds_x = np.where(np.logical_and(points_to_compare[:,1] > check_bounds[0,0], points_to_compare[:,1]<check_bounds[0,1]))
return np.intersect1d(inds_y, inds_x)
def check_deformation_spatial_errors(def_landmarks, part_inds,pad=0):
""" check for spatial errors in deformed landmarks"""
part_to_check = def_landmarks[part_inds, :].copy()
points_to_compare = np.delete(def_landmarks, part_inds,axis=0).reshape(-1,2)
inter_inds = part_intersection(part_to_check,points_to_compare, pad=pad)
out = len(inter_inds) > 0
return out
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