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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 torch
import torch.backends.cudnn as cudnn
import numpy as np
from data import cfg_re50
from layers.functions.prior_box import PriorBox
from utils.nms.py_cpu_nms import py_cpu_nms
import cv2
from facemodels.retinaface import RetinaFace
from 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, 'weights', 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==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)
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
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