def __init__(self, model_path=None):
if model_path is None:
model_path = '../../../external/data/models/face_parsing/face_parsing_79999_iter.pth'
self.net = BiSeNet(n_classes=19)
self.net.load_state_dict(torch.load(model_path))
self.net.eval()
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
def __init__(self, model_path=None):
if model_path is None:
# REVIEW this is a terrible default
model_path = '../../../external/data/models/face_parsing/face_parsing_79999_iter.pth'
self.net = BiSeNet(n_classes=19)
self.net.load_state_dict(torch.load(model_path))
self.net.eval()
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
self.label_by_idx = collections.OrderedDict(
[(-1, 'unlabeled'), (0, 'background'), (1, 'skin'),
(2, 'l_brow'), (3, 'r_brow'), (4, 'l_eye'), (5, 'r_eye'),
(6, 'eye_g (eye glasses)'), (7, 'l_ear'), (8, 'r_ear'), (9, 'ear_r (ear ring)'),
(10, 'nose'), (11, 'mouth'), (12, 'u_lip'), (13, 'l_lip'),
(14, 'neck'), (15, 'neck_l (necklace)'), (16, 'cloth'),
(17, 'hair'), (18, 'hat')])
self.idx_by_label = {v: k for k,v in self.label_by_idx.iteritems()}
class FaceParsing(object):
def __init__(self, model_path=None):
if model_path is None:
model_path = '../../../external/data/models/face_parsing/face_parsing_79999_iter.pth'
self.net = BiSeNet(n_classes=19)
self.net.load_state_dict(torch.load(model_path))
self.net.eval()
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
def to_tensor(self, images):
# images : N,H,W,C numpy.array
return self.transform(images)
def parse_face(self, images, device=0):
# images : list of PIL Images
# device : which CUDA device to run on
#
# returns parsings : list of PIL Images
# move the network to the correct device
self.net.to('cuda:{}'.format(device))
assert all(im.size[0] == im.size[1] for im in images)
in_sizes = [im.size[0] for im in images] # im is square
pt_images = []
for img in images:
# seems to work best with images around 512
img = img.resize((512, 512), Image.BILINEAR)
img = self.to_tensor(img)
pt_images.append(img)
pt_images = torch.stack(pt_images, dim=0)
# move the data to the device
pt_images = pt_images.to('cuda:{}'.format(device))
out = self.label(pt_images)
parsings = out.squeeze(0).cpu().numpy().argmax(1).astype(np.uint8)
parsings = [
Image.fromarray(parsing).resize((in_size, in_size))
for parsing, in_size in zip(parsings, in_sizes)
]
return parsings # list of PIL Images
def label(self, pt_images):
# N,H,W,C torch.tensor
with torch.no_grad():
return self.net(pt_images)[0]
def load_face_parser(cpu=False):
from face_parsing.model import BiSeNet
face_parser = BiSeNet(n_classes=19)
if not cpu:
face_parser.cuda()
face_parser.load_state_dict(
torch.load('motion-co-seg/face_parsing/cp/79999_iter.pth'))
else:
face_parser.load_state_dict(
torch.load('motion-co-seg/face_parsing/cp/79999_iter.pth',
map_location=torch.device('cpu')))
face_parser.eval()
mean = torch.Tensor(np.array([0.485, 0.456, 0.406],
dtype=np.float32)).view(1, 3, 1, 1)
std = torch.Tensor(np.array([0.229, 0.224, 0.225],
dtype=np.float32)).view(1, 3, 1, 1)
if not cpu:
face_parser.mean = mean.cuda()
face_parser.std = std.cuda()
else:
face_parser.mean = mean
face_parser.std = std
return face_parser
class FaceParsing(object):
def __init__(self, model_path=None):
if model_path is None:
# REVIEW this is a terrible default
model_path = '../../../external/data/models/face_parsing/face_parsing_79999_iter.pth'
self.net = BiSeNet(n_classes=19)
self.net.load_state_dict(torch.load(model_path))
self.net.eval()
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
self.label_by_idx = collections.OrderedDict(
[(-1, 'unlabeled'), (0, 'background'), (1, 'skin'),
(2, 'l_brow'), (3, 'r_brow'), (4, 'l_eye'), (5, 'r_eye'),
(6, 'eye_g (eye glasses)'), (7, 'l_ear'), (8, 'r_ear'), (9, 'ear_r (ear ring)'),
(10, 'nose'), (11, 'mouth'), (12, 'u_lip'), (13, 'l_lip'),
(14, 'neck'), (15, 'neck_l (necklace)'), (16, 'cloth'),
(17, 'hair'), (18, 'hat')])
self.idx_by_label = {v: k for k,v in self.label_by_idx.iteritems()}
def to_tensor(self, images):
# images : N,H,W,C numpy.array
return self.transform(images)
def label_for_idx(idx):
return self.label_by_idx[idx]
def idx_for_label(label):
return self.idx_by_label[label]
def parse_face(self, images, device=0):
# images : list of PIL Images
# device : which CUDA device to run on
#
# returns parsings : list of PIL Images
#
# 0 - background, 1 - skin, 2 - l_brow, 3 - r_brow, 4 - l_eye, 5 - r_eye, 6 - eye_g (eye glasses),
# 7 - l_ear, 8 - r_ear, 9 - ear_r (ear ring), 10 - nose, 11 - mouth, 12 - u_lip, 13 - l_lip,
# 14 - neck, 15 - neck_l (necklace), 16 - cloth, 17 - hair, 18 - hat
# move the network to the correct device
self.net.to('cuda:{}'.format(device))
assert all(im.size[0] == im.size[1] for im in images)
in_sizes = [im.size[0] for im in images] # im is square
pt_images = []
for img in images:
# seems to work best with images around 512
img = img.resize((512, 512), Image.BILINEAR)
img = self.to_tensor(img)
pt_images.append(img)
pt_images = torch.stack(pt_images, dim=0)
# move the data to the device
pt_images = pt_images.to('cuda:{}'.format(device))
out = self.label(pt_images)
parsings = out.cpu().numpy().argmax(axis=1).astype(np.uint8)
parsings = [Image.fromarray(parsing).resize((in_size, in_size))
for parsing, in_size in zip(parsings, in_sizes)]
return parsings # list of PIL Images
def label(self, pt_images):
# N,H,W,C torch.tensor
with torch.no_grad():
return self.net(pt_images)[0]
def load_face_parser(cpu=False):
from face_parsing.model import BiSeNet
face_parser = BiSeNet(n_classes=19)
# print(os.path.dirname(os.path.realpath(__file__)))
# print(os.listdir())
# print(os.getcwd())
if not cpu:
face_parser.cuda()
face_parser.load_state_dict(torch.load(f'{os.path.dirname(os.path.realpath(__file__))}/face_parsing/cp/79999_iter.pth'))
else:
face_parser.load_state_dict(torch.load(f'{os.path.dirname(os.path.realpath(__file__))}/face_parsing/cp/79999_iter.pth', map_location=torch.device('cpu')))
face_parser.eval()
mean = torch.Tensor(np.array([0.485, 0.456, 0.406], dtype=np.float32)).view(1, 3, 1, 1)
std = torch.Tensor(np.array([0.229, 0.224, 0.225], dtype=np.float32)).view(1, 3, 1, 1)
if not cpu:
face_parser.mean = mean.cuda()
face_parser.std = std.cuda()
else:
face_parser.mean = mean
face_parser.std = std
return face_parser