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'''
@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
@author: yangxy ([email protected])
'''
import os
import cv2
import glob
import time
import math
import numpy as np
from PIL import Image, ImageDraw
import __init_paths
from face_model.face_gan import FaceGAN
# modified by yangxy
def brush_stroke_mask(img, color=(255,255,255)):
min_num_vertex = 8
max_num_vertex = 28
mean_angle = 2*math.pi / 5
angle_range = 2*math.pi / 15
min_width = 12
max_width = 80
def generate_mask(H, W, img=None):
average_radius = math.sqrt(H*H+W*W) / 8
mask = Image.new('RGB', (W, H), 0)
if img is not None: mask = img #Image.fromarray(img)
for _ in range(np.random.randint(1, 4)):
num_vertex = np.random.randint(min_num_vertex, max_num_vertex)
angle_min = mean_angle - np.random.uniform(0, angle_range)
angle_max = mean_angle + np.random.uniform(0, angle_range)
angles = []
vertex = []
for i in range(num_vertex):
if i % 2 == 0:
angles.append(2*math.pi - np.random.uniform(angle_min, angle_max))
else:
angles.append(np.random.uniform(angle_min, angle_max))
h, w = mask.size
vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h))))
for i in range(num_vertex):
r = np.clip(
np.random.normal(loc=average_radius, scale=average_radius//2),
0, 2*average_radius)
new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)
new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)
vertex.append((int(new_x), int(new_y)))
draw = ImageDraw.Draw(mask)
width = int(np.random.uniform(min_width, max_width))
draw.line(vertex, fill=color, width=width)
for v in vertex:
draw.ellipse((v[0] - width//2,
v[1] - width//2,
v[0] + width//2,
v[1] + width//2),
fill=color)
return mask
width, height = img.size
mask = generate_mask(height, width, img)
return mask
class FaceInpainting(object):
def __init__(self, base_dir='./', size=1024, model=None, channel_multiplier=2):
self.facegan = FaceGAN(base_dir, size, model, channel_multiplier)
# make sure the face image is well aligned. Please refer to face_enhancement.py
def process(self, brokenf):
# complete the face
out = self.facegan.process(brokenf)
return out
if __name__=='__main__':
model = {'name':'GPEN-Inpainting-1024', 'size':1024}
indir = 'examples/ffhq-10'
outdir = 'examples/outs-inpainting'
os.makedirs(outdir, exist_ok=True)
faceinpainter = FaceInpainting(size=model['size'], model=model['name'], channel_multiplier=2)
files = sorted(glob.glob(os.path.join(indir, '*.*g')))
for n, file in enumerate(files[:]):
filename = os.path.basename(file)
originf = cv2.imread(file, cv2.IMREAD_COLOR)
brokenf = np.asarray(brush_stroke_mask(Image.fromarray(originf)))
completef = faceinpainter.process(brokenf)
originf = cv2.resize(originf, completef.shape[:2])
brokenf = cv2.resize(brokenf, completef.shape[:2])
cv2.imwrite(os.path.join(outdir, '.'.join(filename.split('.')[:-1])+'.jpg'), np.hstack((brokenf, completef, originf)))
if n%10==0: print(n, file)
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