def main_random_videos(database_path,video_list,class_name, tf_dst_folder):
# videos_base_path = argv[1]
# class_name = argv[2]
# tf_dst_folder = argv[3]
# class_name = 'all'
# database_path='/data/DL/Adversarial/ActivityNet/Crawler/Kinetics/database/val/'
# tf_dst_folder='/home/ubadmin/pony/database/Kinetics/tfrecord_uint8/val/all_cls_shuffle/'
database_path='/data/DL/Adversarial/database/UCF-101/video/'
tf_dst_folder='/home/ubadmin/pony/database/UCF101/tfrecord_uint8/train/all_cls_shuffle/'
class_name='all'
video_list='/data/DL/Adversarial/database/UCF-101/ucfTrainTestlist/trainlist01.txt'
num_videos_in_single_tfrecord=50
# import pdb
# pdb.set_trace()
# videos_base_path = '/data/DL/Adversarial/ActivityNet/Crawler/Kinetics/database/val/'
# class_name ='hula hooping'
# tf_dst_folder = '/data/DL/Adversarial/ActivityNet/Crawler/Kinetics/database/tfrecord_uint8/val/'
# if class_name=='all':
# classes_list =listdir(videos_base_path)
# else:
# classes_list =[class_name]
if not os.path.exists(tf_dst_folder):
os.makedirs(tf_dst_folder)
n_frames = 40
ucf_classes = [x.strip() for x in open('/data/DL/Adversarial/kinetics-i3d/data/label_map_ucf_101.txt')]
# ucf =ki3du.load_kinetics_classes()
videos_list_path=[x.strip() for x in open(video_list)]
# videos_list_path=glob.glob(database_path + '*/*.avi')
random.shuffle(videos_list_path)
k=0
i=0
#%%
for v in videos_list_path:
if i%num_videos_in_single_tfrecord ==0:
if k>0:
writer.close()
target_folder =os.path.join(tf_dst_folder)
if not os.path.exists(target_folder):
os.makedirs(target_folder)
train_filename = os.path.join(target_folder,'UCF101_N_{}_{:04}.tfrecords'.format(num_videos_in_single_tfrecord, k) ) # address to save the TFRecords file
# open the TFRecords file
writer = tf.python_io.TFRecordWriter(train_filename)
k+=1
gt_cls_id = v.split(' ')[-1]
vid_path=v.split(' ')[0]
cls = vid_path.split('/')[0]
cls_id = ucf_classes.index(cls)
vid_name=vid_path.split('/')[1]
assert(cls_id ==int(gt_cls_id)-1)
# vid_path = v
video_full_path = os.path.join(database_path,vid_name)
if os.path.exists(video_full_path)==False:
continue
# try:
frames,_ = img_tool.video_to_image_and_of(video_full_path,target_fps=25,
n_steps=40)
# frames = skvideo.io.vread(vid_path)
# frames = frames.astype('float32') / 128. - 1
# except:
# os.remove(vid_path)
# continue
# if frames.shape[0] < 40:
# continue
# #frames = np.pad(frames, ((0, _SAMPLE_VIDEO_FRAMES-frames.shape[0]),(0,0),(0,0),(0,0)),'wrap')
# else:
# frames=frames[-40:]
# prob = sess.run(softmax,feed_dict={rgb_input:frames})
# top_id = prob.argmax()
# if cls_id!=top_id:
# continue
feature = {'train/label': img_tool._int64_feature(cls_id),
'train/video': img_tool._bytes_feature(frames.tobytes())}
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
i+=1
writer.close()
def main(argv, arc):
videos_base_path = argv[1]
class_name = argv[2]
tf_dst_folder = argv[3]
# videos_base_path = '/data/DL/Adversarial/ActivityNet/Crawler/Kinetics/database/val/'
# class_name ='hula hooping'
# tf_dst_folder = '/data/DL/Adversarial/ActivityNet/Crawler/Kinetics/database/tfrecord_uint8/val/'
if class_name == 'all':
classes_list = listdir(videos_base_path)
else:
classes_list = [class_name]
if not os.path.exists(tf_dst_folder):
os.makedirs(tf_dst_folder)
n_frames = ki3du._SAMPLE_VIDEO_FRAMES
kinetics_classes = ki3du.load_kinetics_classes()
for c in classes_list:
videos_list = os.path.join(videos_base_path, c)
if not os.path.exists(videos_list):
print('{} not exist'.format(videos_list))
continue
video_list_path = glob.glob(videos_list + '*/*.mp4')
k = 0
for i, v in enumerate(video_list_path):
if i % 100 == 0:
if i > 0:
writer.close()
k += 1
target_folder = os.path.join(tf_dst_folder, c)
if not os.path.exists(target_folder):
os.makedirs(target_folder)
train_filename = os.path.join(
target_folder, 'kinetics_{}_{:04}.tfrecords'.format(
c, k)) # address to save the TFRecords file
# open the TFRecords file
writer = tf.python_io.TFRecordWriter(train_filename)
cls = c
cls_id = kinetics_classes.index(cls)
vid_path = v
if os.path.exists(v) == False:
continue
try:
frames = skvideo.io.vread(vid_path)
# frames = frames.astype('float32') / 128. - 1
except:
os.remove(vid_path)
continue
if frames.shape[0] < ki3du._SAMPLE_VIDEO_FRAMES:
continue
#frames = np.pad(frames, ((0, _SAMPLE_VIDEO_FRAMES-frames.shape[0]),(0,0),(0,0),(0,0)),'wrap')
else:
frames = frames[-ki3du._SAMPLE_VIDEO_FRAMES:]
# prob = sess.run(softmax,feed_dict={rgb_input:frames})
# top_id = prob.argmax()
# if cls_id!=top_id:
# continue
feature = {
'train/label': img_tool._int64_feature(cls_id),
'train/video': img_tool._bytes_feature(frames.tobytes())
}
example = tf.train.Example(features=tf.train.Features(
feature=feature))
writer.write(example.SerializeToString())
writer.close()
def main_random_videos(database_path, class_name, tf_dst_folder):
#%%
# videos_base_path = argv[1]
# class_name = argv[2]
# tf_dst_folder = argv[3]
# class_name = 'all'
# database_path='/data/DL/Adversarial/ActivityNet/Crawler/Kinetics/database/val/'
# tf_dst_folder='/home/ubadmin/pony/database/Kinetics/tfrecord_uint8/val/all_cls_shuffle/'
num_videos_in_single_tfrecord = 50
# import pdb
# pdb.set_trace()
# videos_base_path = '/data/DL/Adversarial/ActivityNet/Crawler/Kinetics/database/val/'
# class_name ='hula hooping'
# tf_dst_folder = '/data/DL/Adversarial/ActivityNet/Crawler/Kinetics/database/tfrecord_uint8/val/'
# if class_name=='all':
# classes_list =listdir(videos_base_path)
# else:
# classes_list =[class_name]
if not os.path.exists(tf_dst_folder):
os.makedirs(tf_dst_folder)
n_frames = ki3du._SAMPLE_VIDEO_FRAMES
kinetics_classes = ki3du.load_kinetics_classes()
videos_list_path = glob.glob(database_path + '*/*.mp4')
random.shuffle(videos_list_path)
k = 0
i = 0
#%%
for v in videos_list_path:
if i % num_videos_in_single_tfrecord == 0:
if k > 0:
writer.close()
target_folder = os.path.join(tf_dst_folder)
if not os.path.exists(target_folder):
os.makedirs(target_folder)
train_filename = os.path.join(
target_folder, 'kinetics_N_{}_{:04}.tfrecords'.format(
num_videos_in_single_tfrecord,
k)) # address to save the TFRecords file
# open the TFRecords file
writer = tf.python_io.TFRecordWriter(train_filename)
k += 1
cls = v.split('/')[-2]
cls_id = kinetics_classes.index(cls)
vid_path = v
if os.path.exists(v) == False:
continue
try:
frames = skvideo.io.vread(vid_path)
# frames = frames.astype('float32') / 128. - 1
except:
os.remove(vid_path)
continue
if frames.shape[0] < ki3du._SAMPLE_VIDEO_FRAMES:
continue
#frames = np.pad(frames, ((0, _SAMPLE_VIDEO_FRAMES-frames.shape[0]),(0,0),(0,0),(0,0)),'wrap')
else:
frames = frames[-ki3du._SAMPLE_VIDEO_FRAMES:]
# prob = sess.run(softmax,feed_dict={rgb_input:frames})
# top_id = prob.argmax()
# if cls_id!=top_id:
# continue
feature = {
'train/label': img_tool._int64_feature(cls_id),
'train/video': img_tool._bytes_feature(frames.tobytes())
}
example = tf.train.Example(features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
i += 1
#%%
writer.close()