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import cv2
import numpy as np
######### face enhancement
from videoretalking.third_part.GPEN.face_parse.face_parsing import FaceParse
from videoretalking.third_part.GPEN.face_detect.retinaface_detection import RetinaFaceDetection
from videoretalking.third_part.GPEN.face_parse.face_parsing import FaceParse
from videoretalking.third_part.GPEN.face_model.face_gan import FaceGAN
# from sr_model.real_esrnet import RealESRNet
from videoretalking.third_part.GPEN.align_faces import warp_and_crop_face, get_reference_facial_points
from videoretalking.utils.inference_utils import Laplacian_Pyramid_Blending_with_mask
class FaceEnhancement(object):
def __init__(self, base_dir='./', size=512, model=None, use_sr=True, sr_model=None, channel_multiplier=2, narrow=1, device='cuda'):
self.facedetector = RetinaFaceDetection(base_dir, device)
self.facegan = FaceGAN(base_dir, size, model, channel_multiplier, narrow, device=device)
# self.srmodel = RealESRNet(base_dir, sr_model, device=device)
self.srmodel=None
self.faceparser = FaceParse(base_dir, device=device)
self.use_sr = use_sr
self.size = size
self.threshold = 0.9
# the mask for pasting restored faces back
self.mask = np.zeros((512, 512), np.float32)
cv2.rectangle(self.mask, (26, 26), (486, 486), (1, 1, 1), -1, cv2.LINE_AA)
self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11)
self.mask = cv2.GaussianBlur(self.mask, (101, 101), 11)
self.kernel = np.array((
[0.0625, 0.125, 0.0625],
[0.125, 0.25, 0.125],
[0.0625, 0.125, 0.0625]), dtype="float32")
# get the reference 5 landmarks position in the crop settings
default_square = True
inner_padding_factor = 0.25
outer_padding = (0, 0)
self.reference_5pts = get_reference_facial_points(
(self.size, self.size), inner_padding_factor, outer_padding, default_square)
def mask_postprocess(self, mask, thres=20):
mask[:thres, :] = 0; mask[-thres:, :] = 0
mask[:, :thres] = 0; mask[:, -thres:] = 0
mask = cv2.GaussianBlur(mask, (101, 101), 11)
mask = cv2.GaussianBlur(mask, (101, 101), 11)
return mask.astype(np.float32)
def process(self, img, ori_img, bbox=None, face_enhance=True, possion_blending=False):
if self.use_sr:
img_sr = self.srmodel.process(img)
if img_sr is not None:
img = cv2.resize(img, img_sr.shape[:2][::-1])
facebs, landms = self.facedetector.detect(img.copy())
orig_faces, enhanced_faces = [], []
height, width = img.shape[:2]
full_mask = np.zeros((height, width), dtype=np.float32)
full_img = np.zeros(ori_img.shape, dtype=np.uint8)
for i, (faceb, facial5points) in enumerate(zip(facebs, landms)):
if faceb[4]<self.threshold: continue
fh, fw = (faceb[3]-faceb[1]), (faceb[2]-faceb[0])
facial5points = np.reshape(facial5points, (2, 5))
of, tfm_inv = warp_and_crop_face(img, facial5points, reference_pts=self.reference_5pts, crop_size=(self.size, self.size))
# enhance the face
if face_enhance:
ef = self.facegan.process(of)
else:
ef = of
orig_faces.append(of)
enhanced_faces.append(ef)
# print(ef.shape)
# tmp_mask = self.mask
'''
0: 'background' 1: 'skin' 2: 'nose'
3: 'eye_g' 4: 'l_eye' 5: 'r_eye'
6: 'l_brow' 7: 'r_brow' 8: 'l_ear'
9: 'r_ear' 10: 'mouth' 11: 'u_lip'
12: 'l_lip' 13: 'hair' 14: 'hat'
15: 'ear_r' 16: 'neck_l' 17: 'neck'
18: 'cloth'
'''
# no ear, no neck, no hair&hat, only face region
mm = [0, 255, 255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 0, 0, 0, 0, 0, 0]
mask_sharp = self.faceparser.process(ef, mm)[0]/255.
tmp_mask = self.mask_postprocess(mask_sharp)
tmp_mask = cv2.resize(tmp_mask, ef.shape[:2])
mask_sharp = cv2.resize(mask_sharp, ef.shape[:2])
tmp_mask = cv2.warpAffine(tmp_mask, tfm_inv, (width, height), flags=3)
mask_sharp = cv2.warpAffine(mask_sharp, tfm_inv, (width, height), flags=3)
if min(fh, fw)<100: # gaussian filter for small faces
ef = cv2.filter2D(ef, -1, self.kernel)
if face_enhance:
tmp_img = cv2.warpAffine(ef, tfm_inv, (width, height), flags=3)
else:
tmp_img = cv2.warpAffine(of, tfm_inv, (width, height), flags=3)
mask = tmp_mask - full_mask
full_mask[np.where(mask>0)] = tmp_mask[np.where(mask>0)]
full_img[np.where(mask>0)] = tmp_img[np.where(mask>0)]
mask_sharp = cv2.GaussianBlur(mask_sharp, (0,0), sigmaX=1, sigmaY=1, borderType = cv2.BORDER_DEFAULT)
full_mask = full_mask[:, :, np.newaxis]
mask_sharp = mask_sharp[:, :, np.newaxis]
if self.use_sr and img_sr is not None:
img = cv2.convertScaleAbs(img_sr*(1-full_mask) + full_img*full_mask)
elif possion_blending is True:
if bbox is not None:
y1, y2, x1, x2 = bbox
mask_bbox = np.zeros_like(mask_sharp)
mask_bbox[y1:y2 - 5, x1:x2] = 1
full_img, ori_img, full_mask = [cv2.resize(x,(512,512)) for x in (full_img, ori_img, np.float32(mask_sharp * mask_bbox))]
else:
full_img, ori_img, full_mask = [cv2.resize(x,(512,512)) for x in (full_img, ori_img, full_mask)]
img = Laplacian_Pyramid_Blending_with_mask(full_img, ori_img, full_mask, 6)
img = np.clip(img, 0 ,255)
img = np.uint8(cv2.resize(img, (width, height)))
else:
img = cv2.convertScaleAbs(ori_img*(1-full_mask) + full_img*full_mask)
img = cv2.convertScaleAbs(ori_img*(1-mask_sharp) + img*mask_sharp)
return img, orig_faces, enhanced_faces |