parser.add_argument('--train-coords', type=bool_str, default=False, help='train landmark coordinate regression')
parser.add_argument('--lr-lm-coords', default=0.001, type=float, help='learning rate for landmark coordinate regression')
# visualization
parser.add_argument('--show-random-faces', default=False, action='store_true')
parser.add_argument('--wait', default=10, type=int)
args = parser.parse_args()
args.color = True
args.dataset_train = args.dataset
args.dataset_val = args.dataset
lmcfg.config_landmarks(args.dataset[0])
if args.eval:
log.info('Switching to evaluation mode...')
args.train_ae = False
args.batchsize_eval = 10
args.wait = 0
args.workers = 0
args.print_freq_eval = 1
args.epochs = 1
if args.benchmark:
log.info('Switching to benchmark mode...')
args.eval = True
args.train_ae = False
args.batchsize_eval = 50
result['image_mod'] = crop_occ
if self.return_landmark_heatmaps and self.crop_type != 'fullsize':
result['lm_heatmaps'] = create_landmark_heatmaps(
result['landmarks'], self.landmark_sigma, self.landmark_ids)
return result
if __name__ == '__main__':
from utils.nn import Batch, to_numpy
import utils.common
from landmarks import lmconfig as lmcfg
utils.common.init_random(3)
lmcfg.config_landmarks('wflw')
ds = WFLW(train=True, deterministic=True, use_cache=True, daug=0)
# ds.filter_labels({'pose':0, 'blur':0, 'occlusion':1})
dl = td.DataLoader(ds, batch_size=1, shuffle=False, num_workers=0)
cfg.WITH_LANDMARK_LOSS = False
for data in dl:
batch = Batch(data, gpu=False)
images = vis._to_disp_images(batch.images, denorm=True)
# lms = lmutils.convert_landmarks(to_numpy(batch.landmarks), lmutils.LM98_TO_LM68)
lms = batch.landmarks
images = vis.add_landmarks_to_images(images,
lms,
draw_wireframe=False,
def __init__(self, root_dir=cfg.W300_ROOT, train=True,
transform=None, color=True, start=None, max_samples=None,
deterministic=None, align_face_orientation=cfg.CROP_ALIGN_ROTATION,
crop_type='tight', test_split='challenging', detect_face=False, use_cache=True,
crop_source='bb_detector', daug=0, return_modified_images=False,
return_landmark_heatmaps=False, landmark_sigma=3, landmark_ids=range(68), **kwargs):
assert(crop_type in ['fullsize', 'tight','loose'])
test_split = test_split.lower()
assert(test_split in ['common', 'challenging', '300w', 'full'])
assert(crop_source in W300.CROP_SOURCES)
lmcfg.config_landmarks('300w')
self.start = start
self.max_samples = max_samples
self.use_cache = use_cache
self.crop_source = crop_source
self.return_landmark_heatmaps = return_landmark_heatmaps
self.return_modified_images = return_modified_images
self.landmark_sigma = landmark_sigma
self.landmark_ids = landmark_ids
self.root_dir = root_dir
self.local_root_dir = cfg.W300_ROOT_LOCAL
self.color = color
self.transform = transform
self.fullsize_img_dir = os.path.join(self.root_dir, 'images')
self.align_face_orientation = align_face_orientation
self.detect_face = detect_face
self.crop_type = crop_type
self.cropped_img_dir = os.path.join(cfg.W300_ROOT_LOCAL, 'crops', crop_source)
self.feature_dir_cnn = os.path.join(cfg.W300_ROOT_LOCAL, 'features_cnn')
self.feature_dir_of = os.path.join(cfg.W300_ROOT_LOCAL, 'features_of')
self.bounding_box_dir = os.path.join(cfg.W300_ROOT, 'Bounding Boxes')
self.split = 'train' if train else test_split
self.build_annotations(self.split)
print("Num. images: {}".format(len(self)))
# limit number of samples
st,nd = 0, None
if start is not None:
st = start
if max_samples is not None:
nd = st+max_samples
self.annotations = self.annotations[st:nd]
if deterministic is None:
deterministic = not train
if self.crop_type == 'tight':
self.transform = ds_utils.build_transform(deterministic, True, daug)
elif self.crop_type == 'fullsize':
self.transform = lambda x:x
from utils.face_extractor import FaceExtractor
self.face_extractor = FaceExtractor()
transforms = [fp.CenterCrop(cfg.INPUT_SIZE)]
transforms += [fp.ToTensor() ]
transforms += [fp.Normalize([0.518, 0.418, 0.361], [1, 1, 1])] # VGGFace(2)
self.crop_to_tensor = tf.Compose(transforms)