parser.add_argument('--flow_x_prefix', type=str, help="prefix of x direction flow images", default='flow_x')
parser.add_argument('--flow_y_prefix', type=str, help="prefix of y direction flow images", default='flow_y')
parser.add_argument('--num_split', type=int, default=3)
parser.add_argument('--out_list_path', type=str, default='data/')
parser.add_argument('--shuffle', action='store_true', default=False)
args = parser.parse_args()
dataset = args.dataset
frame_path = args.frame_path
rgb_p = args.rgb_prefix
flow_x_p = args.flow_x_prefix
flow_y_p = args.flow_y_prefix
num_split = args.num_split
out_path = args.out_list_path
shuffle = args.shuffle
# operation
print('processing dataset {}'.format(dataset))
split_tp = parse_split_file(dataset)
f_info = parse_directory(frame_path, rgb_p, flow_x_p, flow_y_p)
print('writing list files for training/testing')
for i in xrange(max(num_split, len(split_tp))):
lists = build_split_list(split_tp, f_info, i, shuffle)
open(os.path.join(out_path, '{}_rgb_train_split_{}.txt'.format(dataset, i+1)), 'w').writelines(lists[0][0])
open(os.path.join(out_path, '{}_rgb_val_split_{}.txt'.format(dataset, i+1)), 'w').writelines(lists[0][1])
open(os.path.join(out_path, '{}_flow_train_split_{}.txt'.format(dataset, i+1)), 'w').writelines(lists[1][0])
open(os.path.join(out_path, '{}_flow_val_split_{}.txt'.format(dataset, i+1)), 'w').writelines(lists[1][1])
frame_path = args.frame_path
rgb_p = args.rgb_prefix
flow_x_p = args.flow_x_prefix
flow_y_p = args.flow_y_prefix
num_split = args.num_split
out_path = args.out_list_path
shuffle = args.shuffle
# operation
print 'processing dataset {}'.format(dataset)
split_tp = parse_split_file(dataset)
f_info = parse_directory(frame_path, rgb_p, flow_x_p, flow_y_p)
print 'writing list files for training/testing'
for i in xrange(max(num_split, len(split_tp))):
lists = build_split_list(split_tp, f_info, i, shuffle)
open(
os.path.join(out_path,
'{}_rgb_train_split_{}.txt'.format(dataset, i + 1)),
'w').writelines(lists[0][0])
open(
os.path.join(out_path,
'{}_rgb_val_split_{}.txt'.format(dataset, i + 1)),
'w').writelines(lists[0][1])
open(
os.path.join(out_path,
'{}_flow_train_split_{}.txt'.format(dataset, i + 1)),
'w').writelines(lists[1][0])
open(
os.path.join(out_path,
'{}_flow_val_split_{}.txt'.format(dataset, i + 1)),
import sys
sys.path.append('/tsn_caffe')
import pickle
import argparse
import os.path
from pyActionRecog import parse_directory, build_split_list
parser = argparse.ArgumentParser(
description="Create training and testing splits")
parser.add_argument("split_file")
parser.add_argument('--shuffle', action='store_true', default=False)
args = parser.parse_args()
data_dir = '/generated/data'
frame_dir = '/generated/frames'
with open(args.split_file) as f:
split_tp = pickle.load(f)
# operation
f_info = parse_directory(frame_dir, 'img_', 'flow_x', 'flow_y')
print 'writing list files for training/testing'
lists = build_split_list(split_tp, f_info, 0, args.shuffle)
open(os.path.join(data_dir, 'rgb_train_split.txt'),
'w').writelines(lists[0][0])
open(os.path.join(data_dir, 'rgb_val_split.txt'), 'w').writelines(lists[0][1])
open(os.path.join(data_dir, 'flow_train_split.txt'),
'w').writelines(lists[1][0])
open(os.path.join(data_dir, 'flow_val_split.txt'), 'w').writelines(lists[1][1])
