def apply_transform(args, batch_queue, aug_queue):
np.random.seed(int(time.time()))
while True:
augs = batch_queue.get()
if augs is None:
break
x, y = augs
o_aug, l_aug = transform(x, y, args.fliplr, args.rotate, args.norm,
args.ortho_side, args.ortho_side, 3,
args.label_side, args.label_side)
aug_queue.put((o_aug, l_aug))
aug_queue.put(None)
def apply_transform(args, batch_queue, aug_queue):
np.random.seed(int(time.time()))
while True:
augs = batch_queue.get()
if augs is None:
break
x, y = augs
o_aug, l_aug = transform(
x, y, args.fliplr, args.rotate, args.norm, args.ortho_side,
args.ortho_side, 3, args.label_side, args.label_side)
aug_queue.put((o_aug, l_aug))
aug_queue.put(None)
(args.ortho_original_side, args.ortho_original_side, 3))
l_patch = np.fromstring(l_val, dtype=np.uint8).reshape(
(args.label_original_side, args.label_original_side, 1))
o_batch.append(o_patch)
l_batch.append(l_patch)
ortho_cur.next()
label_cur.next()
st = time.time()
o_batch = np.asarray(o_batch, dtype=np.uint8)
l_batch = np.asarray(l_batch, dtype=np.uint8)
o_aug, l_aug = transform(
o_batch, l_batch, args.fliplr, args.rotate, args.norm, args.ortho_side,
args.ortho_side, 3, args.label_side, args.label_side)
print(time.time() - st, 'sec', o_aug.shape, l_aug.shape)
for i, (o, l) in enumerate(zip(o_aug, l_aug)):
o = o.transpose((1, 2, 0))
o = o - o.min()
o = o / o.max()
o *= 255
l = l.reshape(-1)
l = np.hstack([l == 0, l == 1, l == 2])
l = l.reshape(
(3, 16, 16)).transpose((1, 2, 0)).astype(np.uint8) * 255
(args.ortho_original_side, args.ortho_original_side, 3))
l_patch = np.fromstring(l_val, dtype=np.uint8).reshape(
(args.label_original_side, args.label_original_side, 1))
o_batch.append(o_patch)
l_batch.append(l_patch)
ortho_cur.next()
label_cur.next()
st = time.time()
o_batch = np.asarray(o_batch, dtype=np.uint8)
l_batch = np.asarray(l_batch, dtype=np.uint8)
o_aug, l_aug = transform(
o_batch, l_batch, args.fliplr, args.rotate, args.norm, args.ortho_side,
args.ortho_side, 3, args.label_side, args.label_side)
print(time.time() - st, 'sec', o_aug.shape, l_aug.shape)
for i, (o, l) in enumerate(zip(o_aug, l_aug)):
o = o.transpose((1, 2, 0))
o = o - o.min()
#nan_check = np.isfinite(o).all()
#if not nan_check: print('NaN value encountered.') # Culprit matrix is '+str(o)+' .')
#all_zeros = not o.any()
#if all_zeros: print('An all zero matrix was encountered.') # Culprit matrix is '+str(o)+' .')
o = o / o.max()