def main():
parser = argparse.ArgumentParser()
parser.add_argument('raw_path')
parser.add_argument('model_path')
args = parser.parse_args()
raw_path = args.raw_path
model_path = args.model_path
mask_path = raw_to_mask_path(raw_path)
raw = imread(raw_path)
mask = imread(mask_path)
y_min, x_min, y_max, x_max = mask_to_roi(mask)
im = raw[y_min:y_max, x_min:x_max]
model = VGG_mini_ABN()
serializers.load_hdf5(model_path, model)
im = resize(im, (128, 128), preserve_range=True)
x_data = np.array([im_to_blob(im)], dtype=np.float32)
x = Variable(x_data, volatile=True)
model.train = False
y = model(x)
y_data = y.data
print(OBJECT_CLASSES[np.argmax(y_data[0])])
def dataset_to_xt_data(dataset, crop_roi):
"""Convert dataset to x_data and t_data"""
x_data = []
for raw_path in dataset.filenames:
raw = im_preprocess(imread(raw_path))
if crop_roi:
mask_path = raw_to_mask_path(raw_path)
roi = mask_to_roi(im_preprocess(imread(mask_path)))
raw = raw[roi[0]:roi[2], roi[1]:roi[3]]
raw = resize(raw, (128, 128), preserve_range=True)
x_data.append(im_to_blob(raw))
x_data = np.array(x_data, dtype=np.float32)
t_data = dataset.target.astype(np.int32)
return x_data, t_data
def test_mask_to_roi():
mask = doll_mask()
roi = apc_od.mask_to_roi(mask)
assert_tuple_equal(roi, (208, 216, 264, 392))
