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import os
import random
import sys
if os.getcwd() not in sys.path:
sys.path.insert(0, os.getcwd())
import albumentations as alb
import cv2
import numpy as np
from imgaug import augmenters as iaa
from package_utils.bi_online_generation import blendImages, random_erode_dilate
from package_utils.deepfake_mask import dynamic_blend, random_get_hull
from package_utils.image_utils import load_image
from package_utils.transform import randaffine
from PIL import Image
from skimage import transform as sktransform
from ..common import ParameterStore
from ..pipelines.geo_transform import get_source_transforms
def gen_SBI(img, landmark, **kwargs):
"""
This function is adapted to process SBI generation for both image-level and video-level
"""
index = kwargs.get("index")
assert index is not None
debug = kwargs.get("debug") or False
param_store_ins = ParameterStore.get_instance()
use_lms68 = param_store_ins.get_parameters("use_lms68") or False
data_type = param_store_ins.get_parameters("data_type")
assert data_type is not None
if (
(data_type == "image" and np.random.rand() < 0.25)
or (data_type == "video" and index == 0 and np.random.rand() < 0.25)
or use_lms68
):
landmark = landmark[:68]
if not param_store_ins.has_key("use_lms68") and data_type == "video":
param_store_ins.add_parameters("use_lms68", True)
# Getting ConvexHull
mask, hull_type = random_get_hull(
landmark, img, hull_type=param_store_ins.get_parameters("hull_type")
)
if index == 0 and data_type == "video":
param_store_ins.add_parameters("hull_type", hull_type)
# For debugging
if index is not None and debug:
# Image.fromarray(img).save(f'samples/debugs/BG_{index}.jpg')
Image.fromarray((mask * 255).astype(np.uint8)).save(
f"samples/debugs/ConvexHull_{index}.jpg"
)
source = img.copy()
rand_value = param_store_ins.get_parameters("rand_value") or np.random.rand()
if index == 0 and data_type == "video":
param_store_ins.add_parameters("rand_value", rand_value)
if rand_value < 0.5:
if data_type == "video":
if index == 0:
transform = get_source_transforms(data_type=data_type)
data = transform(image=source.astype(np.uint8))
source = data["image"]
replay_params = data["replay"]
param_store_ins.add_parameters("s_replay_params", replay_params)
else:
replay_params = param_store_ins.get_parameters("s_replay_params")
data = alb.ReplayCompose.replay(
replay_params, image=source.astype(np.uint8)
)
source = data["image"]
else:
source = get_source_transforms()(image=source.astype(np.uint8))["image"]
else:
if data_type == "video":
if index == 0:
transform = get_source_transforms(data_type=data_type)
data = transform(image=img.astype(np.uint8))
img = data["image"]
replay_params = data["replay"]
param_store_ins.add_parameters("i_replay_params", replay_params)
else:
replay_params = param_store_ins.get_parameters("i_replay_params")
data = alb.ReplayCompose.replay(
replay_params, image=img.astype(np.uint8)
)
img = data["image"]
else:
img = get_source_transforms()(image=img.astype(np.uint8))["image"]
# if index is not None and debug:
# Image.fromarray(source.astype(np.uint8)).save(f'samples/debugs/FG_{index}.jpg')
if data_type == "image":
source, mask, _ = randaffine(source, mask[:, :, 0])
else:
# if use_lms68:
# seq_distortion = kwargs.get('distortion')
# img_h, img_w, img_c = mask.shape
# aug_size = param_store_ins.get_parameters('aug_size') or random.randint(int(img_h*0.8), int(img_h/0.8))
# mask = sktransform.resize(mask,(aug_size,aug_size),preserve_range=True) # resize mask before deformation
# mask = seq_distortion.augment_image(mask)
# mask, ksize, rand_erode = random_erode_dilate(mask,
# ksize=param_store_ins.get_parameters('ksize'),
# rand_erode=param_store_ins.get_parameters('rand_erode')) # mask of shape (H,W,3)
# mask = sktransform.resize(mask,(img_h,img_w),preserve_range=True) # getting back mask
# mask = mask[:,:,0]
# # filte empty mask after deformation
# if np.sum(mask) == 0 :
# raise ValueError('Deformed mask has no facial region for blending!!!')
# if not param_store_ins.has_key('ksize'):
# param_store_ins.add_parameters('ksize', ksize)
# if not param_store_ins.has_key('rand_erode'):
# param_store_ins.add_parameters('rand_erode', rand_erode)
# if not param_store_ins.has_key('aug_size'):
# param_store_ins.add_parameters('aug_size', aug_size)
# else:
if index == 0:
source, mask, fg_replay_params = randaffine(
source, mask[:, :, 0], index=index, data_type=data_type
)
param_store_ins.add_parameters(
"f_replay_params", fg_replay_params["f_replay_params"]
)
param_store_ins.add_parameters(
"g_replay_params", fg_replay_params["g_replay_params"]
)
else:
f_replay_params = param_store_ins.get_parameters("f_replay_params")
g_replay_params = param_store_ins.get_parameters("g_replay_params")
source, mask, _ = randaffine(
source,
mask[:, :, 0],
index=index,
data_type=data_type,
f_replay_params=f_replay_params,
g_replay_params=g_replay_params,
) # mask of shape (H, W)
# Getting Deformed ConvexHull
if index is not None and debug:
Image.fromarray((mask * 255).astype(np.uint8)).save(
f"samples/debugs/Deformed_ConvexHull_{index}.jpg"
)
if data_type == "image":
img_blended, mask, _ = dynamic_blend(source, img, mask)
else:
# use_BI = param_store_ins.get_parameters('use_BI') or np.random.rand() > 0.5
# if not param_store_ins.has_key('use_BI'):
# param_store_ins.add_parameters('use_BI', use_BI)
# if use_lms68:
# if index == 0:
# img_blended, mask, blending_params = blendImages(source,
# img,
# mask*255)
# param_store_ins.add_parameters('blending_params', blending_params)
# else:
# img_blended, mask, _ = blendImages(source,
# img,
# mask*255,
# **param_store_ins.get_parameters('blending_params'))
# mask = mask[:,:,0:1]
# else:
if index == 0:
img_blended, mask, blending_params = dynamic_blend(source, img, mask)
param_store_ins.add_parameters("blending_params", blending_params)
else:
blending_params = param_store_ins.get_parameters("blending_params")
img_blended, mask, _ = dynamic_blend(source, img, mask, **blending_params)
img_blended = img_blended.astype(np.uint8)
img = img.astype(np.uint8)
return img, img_blended, mask
def gen_target(background_face, background_landmark, margin=[20, 20], **kwargs):
index = kwargs.get("index")
assert index is not None
if isinstance(background_face, str):
background_face = load_image(background_face)
background_face, face_img, mask_f = gen_SBI(
background_face, background_landmark, **kwargs
)
mask_f = (1 - mask_f) * mask_f * 4
mask_r = np.zeros((mask_f.shape[0], mask_f.shape[1], 1))
margin_x, margin_y = margin
H, W = len(face_img), len(face_img[0])
face_img = face_img[margin_y : (H - margin_y), margin_x : (W - margin_x), :]
background_face = background_face[
margin_y : (H - margin_y), margin_x : (W - margin_x), :
]
mask_f = mask_f[margin_y : (H - margin_y), margin_x : (W - margin_x), :]
mask_r = mask_r[margin_y : (H - margin_y), margin_x : (W - margin_x), :]
mask_f, mask_r = np.repeat(mask_f, 3, 2), np.repeat(mask_r, 3, 2)
mask_f, mask_r = (mask_f * 255).astype(np.uint8), (mask_r * 255).astype(np.uint8)
# lower_bound = [0.5,0.75,1,1]
# fake_intensity = np.random.uniform(lower_bound[np.random.randint(len(lower_bound))], 1.)
fake_intensity = np.random.uniform(0.5, 1.0)
return face_img, mask_f, background_face, mask_r, fake_intensity
def reorder_landmark(landmark):
landmark_add = np.zeros((13, 2))
for idx, idx_l in enumerate([77, 75, 76, 68, 69, 70, 71, 80, 72, 73, 79, 74, 78]):
landmark_add[idx] = landmark[idx_l]
landmark[68:] = landmark_add
return landmark
def sbi_hflip(img, mask=None, landmark=None, bbox=None):
H, W = img.shape[:2]
if landmark is not None:
landmark = landmark.copy()
if bbox is not None:
bbox = bbox.copy()
if landmark is not None:
landmark_new = np.zeros_like(landmark)
landmark_new[:17] = landmark[:17][::-1]
landmark_new[17:27] = landmark[17:27][::-1]
landmark_new[27:31] = landmark[27:31]
landmark_new[31:36] = landmark[31:36][::-1]
landmark_new[36:40] = landmark[42:46][::-1]
landmark_new[40:42] = landmark[46:48][::-1]
landmark_new[42:46] = landmark[36:40][::-1]
landmark_new[46:48] = landmark[40:42][::-1]
landmark_new[48:55] = landmark[48:55][::-1]
landmark_new[55:60] = landmark[55:60][::-1]
landmark_new[60:65] = landmark[60:65][::-1]
landmark_new[65:68] = landmark[65:68][::-1]
if len(landmark) == 68:
pass
elif len(landmark) == 81:
landmark_new[68:81] = landmark[68:81][::-1]
else:
raise NotImplementedError
landmark_new[:, 0] = W - landmark_new[:, 0]
else:
landmark_new = None
if bbox is not None:
bbox_new = np.zeros_like(bbox)
bbox_new[0, 0] = bbox[1, 0]
bbox_new[1, 0] = bbox[0, 0]
bbox_new[:, 0] = W - bbox_new[:, 0]
bbox_new[:, 1] = bbox[:, 1].copy()
if len(bbox) > 2:
bbox_new[2, 0] = W - bbox[3, 0]
bbox_new[2, 1] = bbox[3, 1]
bbox_new[3, 0] = W - bbox[2, 0]
bbox_new[3, 1] = bbox[2, 1]
bbox_new[4, 0] = W - bbox[4, 0]
bbox_new[4, 1] = bbox[4, 1]
bbox_new[5, 0] = W - bbox[6, 0]
bbox_new[5, 1] = bbox[6, 1]
bbox_new[6, 0] = W - bbox[5, 0]
bbox_new[6, 1] = bbox[5, 1]
else:
bbox_new = None
if mask is not None:
mask = mask[:, ::-1]
else:
mask = None
img = img[:, ::-1].copy()
return img, mask, landmark_new, bbox_new
def BI_postprocessing(img, face_img, mask):
param_store_ins = ParameterStore.get_instance()
face_img = Image.fromarray(face_img)
img = Image.fromarray(img)
# randomly downsample after BI pipeline
rand_val_post = param_store_ins.get_parameters("rand_val_post") or random.randint(
0, 1
)
if rand_val_post:
aug_size = param_store_ins.get_parameters("post_aug_size") or random.randint(
64, 317
)
rand_resize = param_store_ins.get_parameters("rand_resize") or random.randint(
0, 1
)
if rand_resize:
face_img = face_img.resize((aug_size, aug_size), Image.BILINEAR)
img = img.resize((aug_size, aug_size), Image.BILINEAR)
else:
face_img = face_img.resize((aug_size, aug_size), Image.NEAREST)
img = img.resize((aug_size, aug_size), Image.NEAREST)
if not param_store_ins.has_key("post_aug_size"):
param_store_ins.add_parameters("post_aug_size", aug_size)
if not param_store_ins.has_key("rand_resize"):
param_store_ins.add_parameters("rand_resize", rand_resize)
if not param_store_ins.has_key("rand_val_post"):
param_store_ins.add_parameters("rand_val_post", rand_val_post)
face_img = face_img.resize((317, 317), Image.BILINEAR)
img = img.resize((317, 317), Image.BILINEAR)
face_img = np.array(face_img)
img = np.array(img)
soft_margin = param_store_ins.get_parameters("soft_margin") or np.random.randint(
-30, 30
)
if not param_store_ins.has_key("soft_margin"):
param_store_ins.add_parameters("soft_margin", soft_margin)
face_img = face_img[
30 + soft_margin : (287 + soft_margin),
30 + soft_margin : (287 + soft_margin),
:,
]
img = img[
30 + soft_margin : (287 + soft_margin),
30 + soft_margin : (287 + soft_margin),
:,
]
mask = mask[
30 + soft_margin : (287 + soft_margin),
30 + soft_margin : (287 + soft_margin),
:,
]
return img, face_img, mask
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