def main(_):
assert FLAGS.out
assert FLAGS.db and os.path.exists(FLAGS.db)
picpac_config = dict(
seed=2016,
#loop=True,
shuffle=True,
reshuffle=True,
#resize_width=256,
#resize_height=256,
round_div=FLAGS.stride,
batch=1,
split=1,
split_fold=0,
annotate='json',
channels=FLAGS.channels,
stratify=True,
pert_color1=20,
pert_angle=20,
pert_min_scale=0.8,
pert_max_scale=1.2,
#pad=False,
pert_hflip=True,
pert_vflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
stream = picpac.ImageStream(FLAGS.db,
perturb=False,
loop=False,
**picpac_config)
gal = Gallery(FLAGS.out)
cc = 0
with Model(FLAGS.model, name=FLAGS.name, prob=True) as model:
for images, _, _ in stream:
#images *= 600.0/1500
#images -= 800
#images *= 3000 /(2000-800)
_, H, W, _ = images.shape
if FLAGS.max_size:
if max(H, W) > FLAGS.max_size:
continue
if FLAGS.patch:
stch = Stitcher(images, FLAGS.patch)
probs = stch.stitch(model.apply(stch.split()))
else:
probs = model.apply(images)
cc += 1
save(gal.next(), images, probs)
if FLAGS.max and cc >= FLAGS.max:
break
gal.flush()
pass
