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GPEN
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'''
@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 argparse
import numpy as np
from PIL import Image
import __init_paths
from face_detect.retinaface_detection import RetinaFaceDetection
from face_parse.face_parsing import FaceParse
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, model=None, use_sr=True, sr_model=None, channel_multiplier=2, narrow=1, key=None, device='cuda'):
        self.facedetector = RetinaFaceDetection(base_dir, device)
        self.facegan = FaceGAN(base_dir, size, model, channel_multiplier, narrow, key, device=device)
        self.srmodel =  RealESRNet(base_dir, sr_model, device=device)
        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):
        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)
        
        orig_faces, enhanced_faces = [], []
        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 = self.mask_postprocess(self.faceparser.process(ef)[0]/255.)
            tmp_mask = cv2.resize(tmp_mask, ef.shape[:2])
            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)
            
            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
        

if __name__=='__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--model', type=str, default='GPEN-BFR-512', help='GPEN model')
    parser.add_argument('--key', type=str, default=None, help='key of GPEN model')
    parser.add_argument('--size', type=int, default=512, help='resolution of GPEN')
    parser.add_argument('--channel_multiplier', type=int, default=2, help='channel multiplier of GPEN')
    parser.add_argument('--narrow', type=float, default=1, help='channel narrow scale')
    parser.add_argument('--use_sr', action='store_true', help='use sr or not')
    parser.add_argument('--use_cuda', action='store_true', help='use cuda or not')
    parser.add_argument('--sr_model', type=str, default='rrdb_realesrnet_psnr', help='SR model')
    parser.add_argument('--sr_scale', type=int, default=2, help='SR scale')
    parser.add_argument('--indir', type=str, default='examples/imgs', help='input folder')
    parser.add_argument('--outdir', type=str, default='results/outs-BFR', help='output folder')
    args = parser.parse_args()

    #model = {'name':'GPEN-BFR-512', 'size':512, 'channel_multiplier':2, 'narrow':1}
    #model = {'name':'GPEN-BFR-256', 'size':256, 'channel_multiplier':1, 'narrow':0.5}
    
    os.makedirs(args.outdir, exist_ok=True)

    faceenhancer = FaceEnhancement(size=args.size, model=args.model, use_sr=args.use_sr, sr_model=args.sr_model, channel_multiplier=args.channel_multiplier, narrow=args.narrow, key=args.key, device='cuda' if args.use_cuda else 'cpu')

    files = sorted(glob.glob(os.path.join(args.indir, '*.*g')))
    for n, file in enumerate(files[:]):
        filename = os.path.basename(file)
        
        im = cv2.imread(file, cv2.IMREAD_COLOR) # BGR
        if not isinstance(im, np.ndarray): print(filename, 'error'); continue
        #im = cv2.resize(im, (0,0), fx=2, fy=2) # optional

        img, orig_faces, enhanced_faces = faceenhancer.process(im)
        
        im = cv2.resize(im, img.shape[:2][::-1])
        cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_COMP.jpg'), np.hstack((im, img)))
        cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_GPEN.jpg'), img)
        
        for m, (ef, of) in enumerate(zip(enhanced_faces, orig_faces)):
            of = cv2.resize(of, ef.shape[:2])
            cv2.imwrite(os.path.join(args.outdir, '.'.join(filename.split('.')[:-1])+'_face%02d'%m+'.jpg'), np.hstack((of, ef)))
        
        if n%10==0: print(n, filename)