def main():
chainer.config.train = False
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained_model', default='camvid')
parser.add_argument('image')
args = parser.parse_args()
model = SegNetBasic(
n_class=len(camvid_label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
labels = model.predict([img])
label = labels[0]
fig = plot.figure()
ax1 = fig.add_subplot(1, 2, 1)
vis_image(img, ax=ax1)
ax2 = fig.add_subplot(1, 2, 2)
vis_label(label, camvid_label_names, camvid_label_colors, ax=ax2)
plot.show()
def main():
chainer.config.train = False
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained_model', default='camvid')
parser.add_argument('image')
args = parser.parse_args()
model = SegNetBasic(
n_class=len(camvid_label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
labels = model.predict([img])
label = labels[0]
fig = plot.figure()
ax1 = fig.add_subplot(1, 2, 1)
vis_image(img, ax=ax1)
ax2 = fig.add_subplot(1, 2, 2)
vis_semantic_segmentation(
label, camvid_label_names, camvid_label_colors, ax=ax2)
plot.show()
def main():
chainer.config.train = False
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--pretrained-model')
parser.add_argument('--dataset', choices=('camvid', ), default='camvid')
parser.add_argument('image')
args = parser.parse_args()
if args.dataset == 'camvid':
if args.pretrained_model is None:
args.pretrained_model = 'camvid'
label_names = camvid_label_names
colors = camvid_label_colors
model = SegNetBasic(n_class=len(label_names),
pretrained_model=args.pretrained_model)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
img = utils.read_image(args.image, color=True)
labels = model.predict([img])
label = labels[0]
fig = plt.figure()
ax1 = fig.add_subplot(1, 2, 1)
vis_image(img, ax=ax1)
ax2 = fig.add_subplot(1, 2, 2)
# Do not overlay the label image on the color image
vis_semantic_segmentation(None, label, label_names, colors, ax=ax2)
plt.show()
class TestSegNetBasic(unittest.TestCase):
def setUp(self):
self.n_class = 10
self.link = SegNetBasic(n_class=self.n_class)
def check_call(self):
xp = self.link.xp
x = chainer.Variable(
xp.random.uniform(low=-1, high=1,
size=(2, 3, 128, 160)).astype(np.float32))
y = self.link(x)
self.assertIsInstance(y, chainer.Variable)
self.assertIsInstance(y.data, xp.ndarray)
self.assertEqual(y.shape, (2, self.n_class, 128, 160))
def test_call_cpu(self):
self.check_call()
@attr.gpu
def test_call_gpu(self):
self.link.to_gpu()
self.check_call()
def check_predict(self):
hs = np.random.randint(128, 160, size=(2, ))
ws = np.random.randint(128, 160, size=(2, ))
imgs = [
np.random.uniform(size=(3, hs[0], ws[0])).astype(np.float32),
np.random.uniform(size=(3, hs[1], ws[1])).astype(np.float32),
]
labels = self.link.predict(imgs)
self.assertEqual(len(labels), 2)
for i in range(2):
self.assertIsInstance(labels[i], np.ndarray)
self.assertEqual(labels[i].shape, (hs[i], ws[i]))
self.assertEqual(labels[i].dtype, np.int64)
def test_predict_cpu(self):
self.check_predict()
@attr.gpu
def test_predict_gpu(self):
self.link.to_gpu()
self.check_predict()
