def make_dataset(root_path, annotation_path, subset):
data = load_annotation_data(annotation_path)
video_names, annotations = get_video_names_and_annotations(data, subset)
class_to_idx = get_class_labels(data)
idx_to_class = {}
for name, label in class_to_idx.items():
idx_to_class[label] = name
dataset = []
for i in range(len(video_names)):
if i % 1000 == 0:
print('dataset loading [{}/{}]'.format(i, len(video_names)))
video_path = os.path.join(root_path, video_names[i])
if not os.path.exists(video_path):
continue
n_frames_file_path = os.path.join(video_path, 'number_Frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
frame_indices_file_path = os.path.join(video_path, 'frames_name')
frame_indices = load_list_file(frame_indices_file_path)
sample = {
'video': video_path,
'n_frames': n_frames,
'frame_indices': frame_indices,
'video_id': video_names[i]
}
class_indexs = annotations[i]['label']
if not '-' in class_indexs:
temp_label = np.zeros(len(class_to_idx))
temp_label[int(class_indexs)] = 1
#temp_label = int(class_indexs)
#temp_map = np.zeros(len(class_to_idx))
#temp_map[int(class_indexs)] = 1
sample['label'] = temp_label
#sample['map'] = temp_map
else:
temp = class_indexs.split('-')
temp_label = np.zeros(len(class_to_idx))
for class_index in temp:
temp_label[int(class_index)] = 1
#temp_label= int(temp[0])
#temp_map = np.zeros(len(class_to_idx))
#temp_map[int(temp[0])] = 1
sample['label'] = temp_label
#sample['map'] = temp_map
dataset.append(sample)
return dataset
def make_dataset(root_path, annotation_path, subset):
data = load_annotation_data(annotation_path)
video_names, annotations = get_video_names_and_annotations(data, subset)
class_to_idx = get_class_labels(data)
idx_to_class = {}
for name, label in class_to_idx.items():
idx_to_class[label] = name
dataset = []
#test_file = open('/DATA/disk1/qzb/datasets/FCVID/test_files_' + subset + '.txt', 'w')
for i in range(len(video_names)):
if i % 1000 == 0:
print('dataset loading [{}/{}]'.format(i, len(video_names)))
video_path = os.path.join(root_path, video_names[i])
if not os.path.exists(video_path):
continue
n_frames_file_path = os.path.join(video_path, 'number_Frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
frame_indices_file_path = os.path.join(video_path, 'frames_name')
frame_indices = load_list_file(frame_indices_file_path)
sample = {
'video': video_path,
'n_frames': n_frames,
'frame_indices': frame_indices,
'video_id': video_names[i]
}
#ipdb.set_trace()
class_indexs = annotations[i]['label']
if not '-' in class_indexs:
temp_label = np.zeros(len(class_to_idx))
temp_label[int(class_indexs)] = 1
sample['label'] = temp_label
else:
temp = class_indexs.split('-')
temp_label = np.zeros(len(class_to_idx))
temp_label[int(temp[0])] = 1
#for class_index in temp:
# temp_label[int(class_index)] = 1
sample['label'] = temp_label
dataset.append(sample)
#test_file.write(sample['video_id'] + ' ' + class_indexs + '\n')
#test_file.close()
return dataset