File size: 4,447 Bytes
95ed4d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
'''
@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
@author: yangxy (yangtao9009@gmail.com)
'''
import os
import cv2
import glob
import time
import numpy as np
from PIL import Image
import __init_paths
from retinaface.retinaface_detection import RetinaFaceDetection
from face_model.face_gan import FaceGAN
from sr_model.real_esrnet import RealESRNet
from align_faces import warp_and_crop_face, get_reference_facial_points
class FaceEnhancement(object):
def __init__(self, base_dir='./', size=512, out_size=None, model=None, channel_multiplier=2, narrow=1, key=None, device='cpu', u=False):
self.facedetector = RetinaFaceDetection(base_dir, device)
self.facegan = FaceGAN(base_dir, size, out_size, model, channel_multiplier, narrow, key, device=device)
self.srmodel = RealESRNet(base_dir, 'realesrnet', 2, 0, device=device)
self.use_sr = u
self.size = size
self.out_size = size if out_size==None else out_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, aligned=False):
orig_faces, enhanced_faces = [], []
if aligned:
ef = self.facegan.process(img)
orig_faces.append(img)
enhanced_faces.append(ef)
if self.use_sr:
ef = self.srmodel.process(ef)
return ef, orig_faces, enhanced_faces
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)
height, width = img.shape[:2]
full_mask = np.zeros((height, width), dtype=np.float32)
full_img = np.zeros(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
ef = self.facegan.process(of)
orig_faces.append(of)
enhanced_faces.append(ef)
tmp_mask = self.mask
tmp_mask = cv2.resize(tmp_mask, (self.size, self.size))
tmp_mask = cv2.warpAffine(tmp_mask, tfm_inv, (width, height), flags=3)
if min(fh, fw)<100: # gaussian filter for small faces
ef = cv2.filter2D(ef, -1, self.kernel)
if self.size!=self.out_size:
ef = cv2.resize(ef, (self.size, self.size))
tmp_img = cv2.warpAffine(ef, 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)]
full_mask = full_mask[:, :, np.newaxis]
if self.use_sr and img_sr is not None:
img = cv2.convertScaleAbs(img_sr*(1-full_mask) + full_img*full_mask)
else:
img = cv2.convertScaleAbs(img*(1-full_mask) + full_img*full_mask)
return img, orig_faces, enhanced_faces
|