Spaces:
Paused
Paused
File size: 8,063 Bytes
b5d59e5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
'''
@paper: GAN Prior Embedded Network for Blind Face Restoration in the Wild (CVPR2021)
@author: yangxy (yangtao9009@gmail.com)
'''
import os
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from videoretalking.third_part.GPEN.face_detect.data import cfg_re50
from videoretalking.third_part.GPEN.face_detect.layers.functions.prior_box import PriorBox
from videoretalking.third_part.GPEN.face_detect.utils.nms.py_cpu_nms import py_cpu_nms
import cv2
from videoretalking.third_part.GPEN.face_detect.facemodels.retinaface import RetinaFace
from videoretalking.third_part.GPEN.face_detect.utils.box_utils import decode, decode_landm
import time
import torch.nn.functional as F
class RetinaFaceDetection(object):
def __init__(self, base_dir, device='cuda', network='RetinaFace-R50'):
torch.set_grad_enabled(False)
cudnn.benchmark = True
self.pretrained_path = os.path.join(base_dir, network+'.pth')
self.device = device #torch.cuda.current_device()
self.cfg = cfg_re50
self.net = RetinaFace(cfg=self.cfg, phase='test')
self.load_model()
self.net = self.net.to(device)
self.mean = torch.tensor([[[[104]], [[117]], [[123]]]]).to(device)
def check_keys(self, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(self.net.state_dict().keys())
used_pretrained_keys = model_keys & ckpt_keys
unused_pretrained_keys = ckpt_keys - model_keys
missing_keys = model_keys - ckpt_keys
assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
return True
def remove_prefix(self, state_dict, prefix):
''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
return {f(key): value for key, value in state_dict.items()}
def load_model(self, load_to_cpu=False):
#if load_to_cpu:
# pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage)
#else:
# pretrained_dict = torch.load(self.pretrained_path, map_location=lambda storage, loc: storage.cuda())
pretrained_dict = torch.load(self.pretrained_path, map_location=torch.device('cpu'))
if "state_dict" in pretrained_dict.keys():
pretrained_dict = self.remove_prefix(pretrained_dict['state_dict'], 'module.')
else:
pretrained_dict = self.remove_prefix(pretrained_dict, 'module.')
self.check_keys(pretrained_dict)
self.net.load_state_dict(pretrained_dict, strict=False)
self.net.eval()
def detect(self, img_raw, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False):
img = np.float32(img_raw)
im_height, im_width = img.shape[:2]
ss = 1.0
# tricky
if max(im_height, im_width) > 1500:
ss = 1000.0/max(im_height, im_width)
img = cv2.resize(img, (0,0), fx=ss, fy=ss)
im_height, im_width = img.shape[:2]
scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
img -= (104, 117, 123)
img = img.transpose(2, 0, 1)
img = torch.from_numpy(img).unsqueeze(0)
img = img.to(self.device)
scale = scale.to(self.device)
with torch.no_grad():
loc, conf, landms = self.net(img) # forward pass
priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]])
scale1 = scale1.to(self.device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, nms_threshold)
# keep = nms(dets, nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:keep_top_k, :]
landms = landms[:keep_top_k, :]
# sort faces(delete)
'''
fscores = [det[4] for det in dets]
sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index
tmp = [landms[idx] for idx in sorted_idx]
landms = np.asarray(tmp)
'''
landms = landms.reshape((-1, 5, 2))
landms = landms.transpose((0, 2, 1))
landms = landms.reshape(-1, 10, )
return dets/ss, landms/ss
def detect_tensor(self, img, resize=1, confidence_threshold=0.9, nms_threshold=0.4, top_k=5000, keep_top_k=750, save_image=False):
im_height, im_width = img.shape[-2:]
ss = 1000/max(im_height, im_width)
img = F.interpolate(img, scale_factor=ss)
im_height, im_width = img.shape[-2:]
scale = torch.Tensor([im_width, im_height, im_width, im_height]).to(self.device)
img -= self.mean
loc, conf, landms = self.net(img) # forward pass
priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
priors = priorbox.forward()
priors = priors.to(self.device)
prior_data = priors.data
boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
boxes = boxes * scale / resize
boxes = boxes.cpu().numpy()
scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2], img.shape[3], img.shape[2],
img.shape[3], img.shape[2]])
scale1 = scale1.to(self.device)
landms = landms * scale1 / resize
landms = landms.cpu().numpy()
# ignore low scores
inds = np.where(scores > confidence_threshold)[0]
boxes = boxes[inds]
landms = landms[inds]
scores = scores[inds]
# keep top-K before NMS
order = scores.argsort()[::-1][:top_k]
boxes = boxes[order]
landms = landms[order]
scores = scores[order]
# do NMS
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
keep = py_cpu_nms(dets, nms_threshold)
# keep = nms(dets, nms_threshold,force_cpu=args.cpu)
dets = dets[keep, :]
landms = landms[keep]
# keep top-K faster NMS
dets = dets[:keep_top_k, :]
landms = landms[:keep_top_k, :]
# sort faces(delete)
'''
fscores = [det[4] for det in dets]
sorted_idx = sorted(range(len(fscores)), key=lambda k:fscores[k], reverse=False) # sort index
tmp = [landms[idx] for idx in sorted_idx]
landms = np.asarray(tmp)
'''
landms = landms.reshape((-1, 5, 2))
landms = landms.transpose((0, 2, 1))
landms = landms.reshape(-1, 10, )
return dets/ss, landms/ss
|