def main():
base_dir = 'exper/voc12'
gpu_id, model_name, iteration_num, phase, subset_dataset = process_arguments(sys.argv)
if phase == 1:
model_path = os.path.join(base_dir, 'model', model_name, 'train_iter_{}.caffemodel')
elif phase == 2:
model_path = os.path.join(base_dir, 'model', model_name, 'train2_iter_{}.caffemodel')
if subset_dataset:
net_path = os.path.join(model_name, 'deploy4.prototxt')
class_names = ['bird', 'bottle', 'chair'] # CHANGE
class_ids = get_id_classes(class_names)
file_names = load_test_data(os.path.join(base_dir, 'list_subset/val_id.txt'))
images_path = os.path.join(base_dir, 'data/images_orig')
labels_path = os.path.join(base_dir, 'data/labels_sub_orig')
else:
net_path = os.path.join(model_name, 'deploy21.prototxt')
class_ids = range(1,21)
file_names = load_test_data(os.path.join(base_dir, 'list/val_id.txt'))
images_path = os.path.join(base_dir, 'data/images_orig')
labels_path = os.path.join(base_dir, 'data/labels_orig')
lut = create_lut(class_ids)
images, labels = create_full_paths(file_names, images_path, labels_path)
test_net(net_path, model_path.format(iteration_num), images, labels, lut, gpu_id)
def convert2lmdb(path_src, src_imgs, ext, path_dst, class_ids, preprocess_mode,
im_sz, data_mode):
if os.path.isdir(path_dst):
print('DB ' + path_dst + ' already exists.\n'
'Skip creating ' + path_dst + '.',
file=sys.stderr)
return None
if data_mode == 'label':
lut = create_lut(class_ids)
db = lmdb.open(path_dst, map_size=int(1e12))
with db.begin(write=True) as in_txn:
for idx, img_name in enumerate(src_imgs):
#img = imread(os.path.join(path_src + img_name)+ext)
img = np.array(Image.open(os.path.join(path_src + img_name) + ext))
img = img.astype(np.uint8)
if data_mode == 'label':
img = preprocess_label(img, lut, preprocess_mode, im_sz)
elif data_mode == 'image':
img = preprocess_image(img, preprocess_mode, im_sz)
img_dat = caffe.io.array_to_datum(img)
in_txn.put('{:0>10d}'.format(idx), img_dat.SerializeToString())
def main():
iteration_num = process_arguments(sys.argv)
prototxt = 'TVG_CRFRNN_COCO_VOC_TEST_3_CLASSES.prototxt'
caffemodel = 'models/train_iter_{}.caffemodel'
class_names = ['bird', 'bottle', 'chair']
class_ids = get_id_classes(class_names)
lut = create_lut(class_ids)
file_names = load_test_data()
images, labels = create_full_paths(file_names, 'images', 'labels')
test_net(prototxt, caffemodel.format(iteration_num), images, labels, lut)
def main():
##
ext = '.png'
class_names = ['bird', 'bottle', 'chair']
##
input_path, output_path, list_file, subset_data_file = process_arguments(sys.argv)
clear_subset_list_logs(subset_data_file)
class_ids = get_id_classes(class_names)
lut = create_lut(class_ids)
with open(list_file, 'rb') as f:
for img_name in f:
img_name = img_name.strip()
img = contain_class(os.path.join(input_path, img_name)+ext, class_ids, lut)
if img != None:
log_image(img_name, subset_data_file)
imsave(os.path.join(output_path, img_name)+ext, img)
def main():
##
ext = '.png'
class_names = ['bird', 'bottle', 'chair']
##
input_path, output_path, list_file, subset_data_file = process_arguments(
sys.argv)
clear_subset_list_logs(subset_data_file)
class_ids = get_id_classes(class_names)
lut = create_lut(class_ids)
with open(list_file, 'rb') as f:
for img_name in f:
img_name = img_name.strip()
img = contain_class(
os.path.join(input_path, img_name) + ext, class_ids, lut)
if img != None:
log_image(img_name, subset_data_file)
imsave(os.path.join(output_path, img_name) + ext, img)
def convert2lmdb(path_src, src_imgs, ext, path_dst, class_ids, preprocess_mode, im_sz, data_mode):
if os.path.isdir(path_dst):
print('DB ' + path_dst + ' already exists.\n'
'Skip creating ' + path_dst + '.', file=sys.stderr)
return None
if data_mode == 'label':
lut = create_lut(class_ids)
db = lmdb.open(path_dst, map_size=int(1e12))
with db.begin(write=True) as in_txn:
for idx, img_name in enumerate(src_imgs):
#img = imread(os.path.join(path_src + img_name)+ext)
img = np.array(Image.open(os.path.join(path_src + img_name)+ext))
img = img.astype(np.uint8)
if data_mode == 'label':
img = preprocess_label(img, lut, preprocess_mode, im_sz)
elif data_mode == 'image':
img = preprocess_image(img, preprocess_mode, im_sz)
img_dat = caffe.io.array_to_datum(img)
in_txn.put('{:0>10d}'.format(idx), img_dat.SerializeToString())