parser.add_argument('-f', '--flip', dest='flip', action='store_true')
parser.add_argument('-de',
'--denoise',
dest='denoise',
action='store_true')
return parser.parse_args()
args = get_args()
u.set_gray(args.gray)
u.set_cs(args.cs)
# Load the data and add an axis to the end of y
# so that it is three-dimensional
X, y = u.ips()
y = np.expand_dims(y, axis=3)
if args.test:
X, y = X[:100], y[:100]
if args.jcs and X.shape[-1] == 2:
X = np.expand_dims(X[:, :, :, 1], axis=3)
if args.flip:
X = np.append(X, [np.fliplr(x) for x in X], axis=0)
y = np.append(y, [np.fliplr(y) for y in y], axis=0)
if len(X.shape) == 3:
X = np.expand_dims(X, axis=3)
from iou import iou_metric
import numpy as np
model = u.load_unet()
all_ids = u.img_ids(train=True)
batch_size = 64
i = 0
id_batch = all_ids[i * batch_size:(i + 1) * batch_size]
ious = dict()
while i * batch_size < len(all_ids) - 1:
batch, y = u.ips(for_ids=id_batch)
if len(batch.shape) == 3:
batch = np.expand_dims(batch, axis=3)
p = model.predict(batch, batch_size=batch_size)[:, :, :, 0]
for img_id, true, pred in zip(id_batch, y, p):
ious[img_id] = iou_metric(true, pred)
i += 1
id_batch = all_ids[i * batch_size:(i + 1) * batch_size]
ious = sorted(ious, key=lambda kv: kv[1])
with open('ious.csv', 'w') as f:
for iou_pair in ious:
print('%s:%f' % (iou_pair[0], iou_pair[1]))
f.write('%s:%f\n' % (iou_pair[0], iou_pair[1]))
action='store_true')
parser.add_argument('-dp', '--depth', dest='depth', action='store_true')
parser.add_argument('-pp', '--prepro', dest='prepro', action='store_true')
return parser.parse_args()
args = get_args()
u.set_gray(args.gray)
u.set_cs(args.cs)
u.set_depth(args.depth)
# Load the data and add an axis to the end of y
# so that it is three-dimensional
if args.depth:
X_w_depths, y = u.ips()
X, depths = X_w_depths
depths = depths.astype(np.float32)
mu, sigma = np.mean(depths), np.std(depths)
depths = (depths - mu) / sigma
else:
X, y = u.ips()
y = np.expand_dims(y, axis=3)
if args.test:
X, y = X[:100], y[:100]
if args.depth: depths = depths[:100]
if args.jcs and X.shape[-1] == 2:
model = model_from_json(json)
model.load_weights(join('models', name, 'model.h5'))
num_chan = model.layers[0].input_shape[-1]
if num_chan == 3:
u.set_gray(False)
u.set_cs(False)
elif num_chan == 2:
u.set_cs(True)
else:
u.set_cs(False)
ids = u.img_ids(train=True)
ids = np.random.choice(ids, args.num, replace=False)
X, y = u.ips(for_ids=ids)
if len(X.shape) == 3:
X = np.expand_dims(X, axis=3)
u.unsilence()
print(X.shape, y.shape)
y_pred = model.predict(X)[:, :, :, 0]
if (X[0].shape)[2] == 2 or (X[0].shape)[2] == 1:
X = X[:, :, :, 0]
for x, true, pred in zip(X, y, y_pred):
if args.threshold >= 0.0:
pred = (pred > args.threshold).astype(np.uint8)