'''
@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'):
        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, args.sr_model, args.sr_scale, args.tile_size, device=device)
        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