Update app.py

app.py

@@ -4,18 +4,125 @@ os.system("wget https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/mo
 os.system("wget https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/ParseNet-latest.pth -P .")
 os.system("wget https://public-vigen-video.oss-cn-shanghai.aliyuncs.com/robin/models/rrdb_realesrnet_psnr.pth -P .")
 
-import random
 import gradio as gr
-from PIL import Image
-import torch
 torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', 'lincoln.jpg')
 torch.hub.download_url_to_file('https://upload.wikimedia.org/wikipedia/commons/5/50/Albert_Einstein_%28Nobel%29.png', 'einstein.png')
+
+'''
+@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 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
+        
+  
+    
+    model = "GPEN-BFR-512"
+    
+    key = None
+    size = 512
+    channel_multiplier = 2
+    narrow = 1
+    use_sr = False
+    use_cuda = False
+    sr_model = 'rrdb_realesrnet_psnr'
+    indir = "."
+    outdir = "."
+    
+    
+    faceenhancer = FaceEnhancement(size=size, model=model, use_sr=use_sr, sr_model=sr_model, channel_multiplier=channel_multiplier, narrow=narrow, key=key, device='cuda' if args.use_cuda else 'cpu')
+    
+    
+    
+  
 
 def inference(img):
+    im = cv2.imread(file, cv2.IMREAD_COLOR) 
+    img, orig_faces, enhanced_faces = faceenhancer.process(im)
+    
+    return enhanced_faces[0]
     os.system("python face_enhancement.py --model GPEN-BFR-512 --size 512 --channel_multiplier 2 --narrow 1 --use_sr --indir examples/imgs --outdir examples/outs-BFR2")
     return "examples/outs-BFR2/"