def show_pairs(images, features, pairs):
dists = np.sqrt(np.sum((features[0] - features[1])**2, axis=1))
ds_utils.denormalize(images[0])
ds_utils.denormalize(images[1])
images[1] = vis.add_error_to_images(images[1],
dists,
size=2.0,
thickness=2,
vmin=0,
vmax=1)
images[1] = vis.add_id_to_images(images[1],
pairs.numpy(),
size=1.2,
thickness=2,
color=(1, 0, 1))
thresh = 0.4
corrects = (dists < thresh) == pairs.cpu().numpy()
colors = [(0, 1, 0) if c else (1, 0, 0) for c in corrects]
images[1] = vis.add_cirle_to_images(images[1], colors)
images[0] = vis._to_disp_images(images[0])
img_rows = [
vis.make_grid(imgs,
fx=0.75,
fy=0.75,
nCols=len(dists),
normalize=False) for imgs in images
]
vis.vis_square(img_rows, nCols=1, normalize=False)
return np.concatenate(sizes)
if __name__ == '__main__':
import torch
from utils import vis
from utils.nn import Batch
from datasets import ds_utils
from datasets import affectnet
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
train = True
datasets = [
affectnet.AffectNet(train=train, max_samples=1000),
# vggface2.VggFace2(train=train, max_samples=1000),
]
multi_ds = MultiFaceDataset(datasets, train=True, max_samples=5000)
print(multi_ds)
dl = td.DataLoader(multi_ds, batch_size=40, shuffle=False, num_workers=0)
for data in dl:
batch = Batch(data, gpu=False)
inputs = batch.images.clone()
ds_utils.denormalize(inputs)
imgs = vis.add_landmarks_to_images(inputs.numpy(),
batch.landmarks.numpy())
# imgs = vis.add_pose_to_images(inputs.numpy(), batch.poses.numpy())
# imgs = vis.add_emotion_to_images(imgs, batch.emotions.numpy())
vis.vis_square(imgs, nCols=20, fx=0.6, fy=0.6, normalize=False)
args = parser.parse_args()
extract_features(st=args.st, nd=args.nd)
if __name__ == '__main__':
# extract_main()
# create_annotations(split='dev', num_ids=500)
# extract_crops()
# read_openface_csvs()
# exit()
from utils import vis, face_processing
ds = VoxCeleb(train=True, max_samples=50000, use_cache=True)
print(ds)
dl = td.DataLoader(ds, batch_size=40, shuffle=False, num_workers=0)
from utils.nn import Batch
for data in dl:
batch = Batch(data)
print(batch.ids)
ds_utils.denormalize(batch.images)
# vis.show_images_in_batch(batch.images.detach().cpu())
vis.vis_square(batch.images.detach().cpu(),
fx=0.7,
fy=0.7,
normalize=False)
# print(item)