def make_ucf_video_names(root_path, list_path):
video_names, labels = load_video_list(list_path)
count = 0
final_labels = []
final_videos = []
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])
#print(video_names[i])
if not os.path.exists(video_path):
print('%s does not exist!!!' % (video_path))
continue
n_frames_file_path = os.path.join(video_path, 'n_frames')
if not os.path.exists(n_frames_file_path):
print('%s does not exist n_frames!!!' % (video_path))
continue
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
print('%s has 0 frame!!!' % (video_path))
continue
final_videos.append(video_path)
final_labels.append(labels[i])
count += 1
print('Load %d videos' % (count))
return final_videos, final_labels
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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 + '.avi'):
continue
frame_path = os.path.join("/nfs_mount/datasets/UCF101/UCF-101/frames_fps25_256", video_names[i])
n_frames_root = "/nfs_mount/code/ananth/code/Verisk/3D-ResNets-PyTorch/datasets/nframes"
n_frames_file_path = os.path.join(n_frames_root, video_names[i], 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames < 80:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
## if n_samples_for_each_video == 1:
## this is to create 1 sample from each video that works for both training and test video
## need to change this to above if statement if actual evaluation is done on ucf101
## n_samples_for_each_video is different for training set and val set
if True:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = int(max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1))))
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
return dataset, idx_to_class
def make_dataset(
root_path, annotation_path, subset, n_samples_for_each_video, sample_duration
):
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):
print("video path not exists: ", video_path)
continue
n_frames_file_path = os.path.join(video_path, "n_frames")
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
"video": video_path,
"segment": [begin_t, end_t],
"n_frames": n_frames,
"video_id": video_names[i].split("/")[1],
}
if len(annotations) != 0:
sample["label"] = class_to_idx[annotations[i]["label"]]
else:
sample["label"] = -1
if n_samples_for_each_video == 1:
sample["frame_indices"] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(
1,
math.ceil(
(n_frames - 1 - sample_duration)
/ (n_samples_for_each_video - 1)
),
)
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j["frame_indices"] = list(
range(j, min(n_frames + 1, j + sample_duration))
)
dataset.append(sample_j)
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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
audio_path = os.path.join(video_path,
os.path.basename(video_path + '.wav'))
if not os.path.exists(audio_path):
continue
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(
1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(1, n_frames, int(step)):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
return dataset, idx_to_class
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, n_samples_for_each_video,
sample_duration):
data = load_annotation_data(annotation_path) # json 파일 load
video_names, annotations = get_video_names_and_annotations(data, subset) # video_names = [ 'label/'key(영상이름)', 'label/key' ... ], annotations = [ value['annotations'], ... ]
class_to_idx = get_class_labels(data) # class_to_idx = { 'a' : 0 , 'b' : 1 , 'c' : 2 ...}
idx_to_class = {}
for name, label in class_to_idx.items():
idx_to_class[label] = name # idx_to_class = { 0 : 'a' , 1 : 'b' , 2 : 'c' ...}
dataset = [] # 각 비디오 영상에 대한 샘플들 저장.
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]) # root_path/label/videoname 그냥 폴더를 뜻함.
if not os.path.exists(video_path):
continue
n_frames_file_path = os.path.join(video_path, 'n_frames') # n_frames 폴더에 접근 n_frames는 n_frames_ucf101_hmdb51.py로 생성.
n_frames = int(load_value_file(n_frames_file_path)) # n_frames 는 영상 폴더 안에 있는 이미지 수.
if n_frames <= 0:
continue
begin_t = 1 # 이미지의 시작
end_t = n_frames # 이미지의 끝
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1] # 영상 이름.
}
if len(annotations) != 0:
sample['label'] = class_to_idx[int(annotations[i]['label'])] # calss_to_idx(해당 라벨값) => 해당 라벨의 인덱스 값 반환. hi 라벨의 인덱스는 0이면 0반환.
else:
sample['label'] = -1
if n_samples_for_each_video == 1: # 이것만 생각 하기.
sample['frame_indices'] = list(range(1, n_frames + 1)) # num of input frame
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) / # sample_duration => input frame
(n_samples_for_each_video - 1))) # ex) (50 - 1 - 16) / (3 - 1) = 16.5 ceil => 17
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration))) # rnrksquffh frame dmf sksnsek.
dataset.append(sample_j)
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i][:-14].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
return dataset, idx_to_class
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
def make_untrimmed_dataset(root_path, annotation_path, subset,
n_samples_for_each_video, sample_duration):
data = load_annotation_data(annotation_path)
video_names, _ = 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)):
print("loading2 ...")
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
fps_file_path = os.path.join(video_path, 'fps')
fps = load_value_file(fps_file_path)
begin_t = 1
end_t = get_end_t(video_path)
n_frames = end_t - begin_t
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'fps': fps,
'video_id': video_names[i][2:]
}
if n_samples_for_each_video >= 1:
step = max(
1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(begin_t, end_t, step):
sample_j = copy.deepcopy(sample)
frame_indices = list(range(j, j + sample_duration))
frame_indices = modify_frame_indices(sample_j['video'],
frame_indices)
if len(frame_indices) < 16:
continue
sample_j['frame_indices'] = frame_indices
dataset.append(sample_j)
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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 tqdm(range(len(video_names))):
video_name = video_names[i].split('___')[0]
video_path = os.path.join(root_path, video_name)
assert os.path.exists(video_path), f"File {video_path} does not exist !"
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
assert n_frames > 0
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_name # TODO check usage of video_id
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
return dataset, idx_to_class
def make_untrimmed_dataset(root_path, annotation_path, subset,
n_samples_for_each_video, sample_duration):
data = load_annotation_data(annotation_path)
video_names, _ = 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
fps_file_path = os.path.join(video_path, 'fps')
fps = load_value_file(fps_file_path)
begin_t = 1
end_t = get_end_t(video_path)
n_frames = end_t - begin_t
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'fps': fps,
'video_id': video_names[i][2:]
}
if n_samples_for_each_video >= 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(begin_t, end_t, step):
sample_j = copy.deepcopy(sample)
frame_indices = list(range(j, j + sample_duration))
frame_indices = modify_frame_indices(sample_j['video'],
frame_indices)
if len(frame_indices) < 16:
continue
sample_j['frame_indices'] = frame_indices
dataset.append(sample_j)
return dataset, idx_to_class
def make_data_set(
self,
paths: dict,
annotation_path: str,
subset: str
):
# アノテーションファイルからjsonやcsvオブジェクトを取得する
entry = self.load_annotation_data(annotation_path)
# 動画の名前とその属するクラスのそれぞれのリストを取得
video_names, annotations = self.get_video_names_and_annotations(entry, subset)
# クラスをidへ割り振る
class_to_idx = self.get_class_labels(entry)
# idからクラスがわかるようにする
idx_to_class = {}
for name, label in class_to_idx.items():
idx_to_class[label] = name
video_information = []
for i in range(len(video_names)):
# 1000毎に経過報告をする
if i % 1000 == 0:
print('data_set loading [{}/{}]'.format(i, len(video_names)))
full_paths = {}
n_frames = 0
for path in paths:
full_path = os.path.join(path, video_names[i])
assert os.path.exists(full_path), 'No such file :{}'.format(full_path)
full_paths[full_path] = paths[path]
n_frames_file_path = os.path.join(full_path, 'n_frames')
if os.path.exists(n_frames_file_path):
n_frames = int(load_value_file(n_frames_file_path))
assert n_frames > 0
video_info = {
'paths': full_paths,
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1],
'label': class_to_idx[annotations[i]['label']],
'frame_indices': list(range(1, n_frames + 1))
}
video_information.append(video_info)
return video_information, idx_to_class
def _make_dataset(i, root_path, video_names, annotations, class_to_idx,
n_samples_for_each_video, sample_duration):
dataset = []
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):
return dataset
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
return dataset
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[0]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(
1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
return dataset
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
##load the json file as data to read labels and id of an arbitrary video
data = load_annotation_data(annotation_path)
##return a file that contains video names in the format of id/label and single list "annotation" of its labels
video_names = get_video_names_and_annotations(data, subset)
#give index to every category in kinetics-400
class_to_idx = get_class_labels(data)
idx_to_class = {}
for name, label in class_to_idx.items():
idx_to_class[label] = name
i = 0
dataset = []
for key, label in range(video_names):
if i % 1000 == 0:
print('dataset loading [{}/{}]'.format(i, len(video_names)))
video_path = os.path.join(root_path, label, key)
if not os.path.exists(video_path):
print("cannot find" + label + key)
##get the count of frames of the video
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i][:-14],
'label': class_to_idx[label]
}
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
return dataset, idx_to_class
def make_dataset(video_names, labels, n_samples_for_each_video):
dataset = []
for i in range(len(video_names)):
video_path = video_names[i]
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'frame_indices': list(range(begin_t, end_t + 1)),
'label': labels[i]
}
for j in range(n_samples_for_each_video):
sample_j = copy.deepcopy(sample)
dataset.append(sample_j)
return dataset
def make_dataset(yfcc_root, yfcc_results, target_classes,
n_samples_for_each_video, topk):
dataset = []
res = pd.read_pickle(yfcc_results)
video_list = []
label_list = []
count = 0
for i, c in enumerate(target_classes):
video_list.extend(res[c][:topk])
#print(res['segment'][c])
label_list.extend([i] * topk)
for i in range(len(video_list)):
video_path = os.path.join(yfcc_root, video_list[i][:-4])
if not os.path.exists(video_path):
print("%s does not exist!!!!" % video_path)
continue
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames == 0:
print("%s n_frames=0!!!!" % video_path)
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'frame_indices': list(range(begin_t, end_t + 1)),
'label': label_list[i]
}
for j in range(n_samples_for_each_video):
sample_j = copy.deepcopy(sample)
dataset.append(sample_j)
count = count + 1
print('Load %d yfcc videos' % (count))
return dataset
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
# 生成数据列表，列表中的元素为{'video':video_path, 'segment':[begin_t, end_t],\
# 'n_frames':n_frames, 'video_id':video_names, 'label':, 'frame_indices'}
# 其中frmae_indices表示所选取的frame的索引值。
# sample_duration： 采样的间隔
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, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
# 表示的意思应该是选取视频中所有帧的样本
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(
1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
# because of optical flow, we modify the beginning is 2.
for j in range(2, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
'''
dst_data:
---|labels: all labels
---|database: contains train_database and val_database.
database:
---|key: avi_video_name
---|subset: training or validation
---|annotations:
---|label: class_name
'''
# loading json
data = load_annotation_data(annotation_path)
# video_names is {label}/{video_name}, annotations is dict {'label', label}
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, list, contains N samples, and eav
# sample
# ---|video: the path of video contains a lot of images
# ---|segment: [1, n_frames]
# ---|n_frames: number of frames
# ---|video_id: avi_name
# ---|label: class_id
# ---|frame_indices: the indices of frames used in model
dataset = []
for i in range(len(video_names)):
if i % 1000 == 0:
print('dataset loading [{}/{}]'.format(i, len(video_names)))
# the image in video_path is a single frame of the video.
video_path = os.path.join(root_path, video_names[i])
if not os.path.exists(video_path):
continue
# n_frames_file_path saves the number of frames in this video.
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id':
video_names[i].split('/')[1] # video_name, and [0] is class_name
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(
1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(2, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
# idx_to_class: indice to class name.
return dataset, idx_to_class
def make_dataset(root_path,
annotation_path,
subset,
n_samples_for_each_video,
sample_duration,
debug=True):
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 = []
debug_len = len(video_names)
if debug:
debug_len = 500
for i in tqdm(range(debug_len)): # 所有数据集
# for i in range(500): # 使用1000个作为测试
# 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, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = int(
max(
1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1))))
else:
step = int(sample_duration)
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
# print('video_names.size-{}:'.format(subset), len(dataset))
# print(dataset[0])
# print(idx_to_class)
return dataset, idx_to_class
def make_dataset(opt, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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 = []
total_number_of_clips = 0
gflop_per_clip = 8.5
for i in range(len(video_names)):
#video_path = os.path.join(opt.video_path, video_names[i])+ ".mp4"
video_path = os.path.join(opt.video_path,video_names[i]) + ".mp4"
if i % 1000 == 0:
print('dataset loading [{}/{}]'.format(i, len(video_names)))
j_path = os.path.join(opt.jpeg_path, video_names[i])
n_frames_file_path = os.path.join(j_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
i_path = os.path.join(opt.iframe_path, video_names[i])
m_path = os.path.join(opt.mv_path, video_names[i])
r_path = os.path.join(opt.residual_path, video_names[i])
if not os.path.exists(opt.jpeg_path):
continue
begin_t = 1
end_t = n_frames
sample = {
'video' : video_path,
'jpeg_path': j_path,
'iframe_path': i_path,
'mv_path': m_path,
'residual_path': r_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
#dataset.append(sample)
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:###teesting
if subset == 'training':
step = int(sample_duration/1)
else:
step = int(sample_duration/2)
for j in range(0, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
total_number_of_clips += 1
if n_samples_for_each_video == 0:
num_of_videos = len(video_names)
avg_clips_per_video = round(total_number_of_clips/num_of_videos,2)
tot_gflops=round(gflop_per_clip*avg_clips_per_video,2)
print("Number of videos:",num_of_videos)
print("Number of clips:",total_number_of_clips)
print("Avarage amount of clips per video:",avg_clips_per_video)
print("Number of GFlos for each video in avarage will be,(true to mfnet only):",tot_gflops)
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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
if os.path.exists(os.path.join(os.path.dirname(annotation_path), 'cache', 'dataset_{}.json'.format(subset))):
with open(os.path.join(os.path.dirname(annotation_path), 'cache', 'dataset_{}.json'.format(subset)), 'r') as f:
dataset = json.load(f)
else:
dataset = []
for i in range(len(video_names)):
if subset == 'training':
video_path = os.path.join(root_path, 'train', video_names[i])
else:
video_path = os.path.join(root_path, 'val', video_names[i])
if not os.path.exists(video_path):
continue
if i % 1000 == 0:
print('dataset loading [{}/{}]'.format(i, len(video_names)))
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i][:-14].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = int(sample_duration / 2)
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
if not os.path.exists(os.path.join(os.path.dirname(annotation_path), 'cache')):
os.makedirs(os.path.join(os.path.dirname(annotation_path), 'cache'))
with open(os.path.join(os.path.dirname(annotation_path), 'cache', 'dataset_{}.json'.format(subset)), 'w') as f:
json.dump(dataset, f)
return dataset, idx_to_class
def make_dataset2(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
import pickle
cache_path = os.path.join(
root_path, '../cache',
'%s-%s-%s-%s.pkl' % (os.path.basename(annotation_path).replace(
'.json', ''), subset, n_samples_for_each_video, sample_duration))
if os.path.isfile(cache_path):
print('pickle load from', cache_path)
with open(cache_path, 'rb') as f:
cache = pickle.load(f)
dataset = cache['dataset']
idx_to_class = cache['idx_to_class']
else:
print('pickle dump to', cache_path)
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, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(
1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
cache = {
'dataset': dataset,
'idx_to_class': idx_to_class,
}
with open(cache_path, 'wb') as f:
pickle.dump(cache, f, protocol=pickle.HIGHEST_PROTOCOL)
return dataset, idx_to_class
def make_untrimmed_dataset(root_path, scores_dump_path, annotation_path, subset,
n_samples_for_each_video, window_size, window_stride):
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 = []
videos = {}
for i in range(len(video_names)):
if i != 0 and i % 1000 == 0:
print('dataset loading [{}/{}]'.format(i, len(video_names)))
# break # early exit
annotation = annotations[i]
label = int(annotation['label'])
begin_t = annotation['start_frame']
end_t = annotation['end_frame']
clip = {
'label': label,
'segment': [begin_t, end_t],
'video_id': video_names[i]
}
video = get_video_from_clip(video_names[i])
if not video in videos:
videos[video] = []
videos[video].append(clip)
i = 0
# dump_dir = os.path.join(root_path, scores_dump_path)
for video in videos:
video_path = os.path.join(root_path, video)
if not os.path.exists(video_path) or os.path.exists(scores_dump_path + "/" + video):
print("Skipping video ", video_path, scores_dump_path)
continue
n_frames_file_path = os.path.join(video_path, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
window_start = 1
window_end = window_start + window_size
idx = 1
while window_end < n_frames:
sample = {
'video': video_path,
'segment': [window_start, window_end],
'video_id': video,
'window_id': idx}
frame_indices = list(range(window_start, window_end))
frame_indices = modify_frame_indices(
sample['video'], frame_indices)
sample['frame_indices'] = frame_indices
# sample['label'] = -1 # Not computed yet
sample['label'] = get_untrimmed_label(
videos[video], window_start, window_end)
if len(frame_indices) == window_size:
dataset.append(sample)
window_start += window_stride
window_end = window_start + window_size
idx += 1
if i % 10 == 0:
print('dataset loading [{}/{}]'.format(i, len(videos)))
i += 1
print("Make untrimmed dataset")
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration, sample_stride):
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, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1],
'frame_indices': []
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
clip_length = sample_duration * sample_stride
# print('='*80)
if n_samples_for_each_video <= 0:
n_samples_for_cur_video = math.ceil(
n_frames / clip_length) # sample times for current video
else:
n_samples_for_cur_video = n_samples_for_each_video
if n_samples_for_cur_video == 1:
step = n_frames
else:
step = max(1, (n_frames - clip_length) /
(n_samples_for_cur_video - 1))
for i in range(n_samples_for_cur_video):
sample_i = copy.deepcopy(sample)
if step < clip_length:
random_offset = random.randint(0, sample_stride - 1)
else:
random_offset = random.randint(
0, math.floor(step - clip_length + sample_stride - 1))
start_frame = math.floor(i * step + random_offset)
for j in range(sample_duration):
sample_i['frame_indices'].append(start_frame % n_frames + 1)
start_frame += sample_stride
dataset.append(sample_i)
# print('sample:', video_path, n_frames, sample_i['frame_indices'][0], len(sample_i['frame_indices']) )
print('total samples:', len(dataset))
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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
class_map_save(class_to_idx)
# deb_image_loader = get_default_image_loader() ## debug ######################
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, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 80:
print('Skipping too short video {}'.format(video_path))
continue
if n_frames > 300:
print('Skipping too long video {}'.format(video_path))
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
# 'video_id': video_names[i][:-14].split('/')[1]
'video_id': video_names[i].split('/')[1]
}
## debug begin ##############################
# print(video_path)
# video_loader(video_path,[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],deb_image_loader)
# print(".")
## debug end ##########################
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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
fps_file_path = os.path.join(video_path, 'fps')
fps = load_value_file(fps_file_path)
# if subset == 'testing':
# print(annotations)
for kk, vv in annotations[i].items():
for annotation in vv:
# print(annotation)
# print(fps)
begin_t = math.ceil(annotation[0] * fps)
end_t = math.ceil(annotation[1] * fps)
if begin_t == 0:
begin_t = 1
n_frames = end_t - begin_t
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'fps': fps,
'video_id': video_names[i][2:]
}
# if len(annotations) != 0:
# sample['label'] = class_to_idx[annotation['label']]
# else:
# sample['label'] = -1
sample['label'] = class_to_idx[kk]
if n_samples_for_each_video == 1:
frame_indices = list(range(begin_t, end_t))
frame_indices = modify_frame_indices(sample['video'],
frame_indices)
if len(frame_indices) < 16:
continue
sample['frame_indices'] = frame_indices
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(begin_t, end_t, step):
sample_j = copy.deepcopy(sample)
frame_indices = list(range(j, j + sample_duration))
frame_indices = modify_frame_indices(
sample_j['video'], frame_indices)
if len(frame_indices) < 16:
continue
sample_j['frame_indices'] = frame_indices
dataset.append(sample_j)
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration, n_test_clips_for_each_video, n_test_crops_for_each_video):
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, 'n_frames')
n_frames = int(load_value_file(n_frames_file_path))
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
'video_id': video_names[i].split('/')[1]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
# for train and validation
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
# if n_samples_for_each_video > 1:
# step = max(1,
# math.ceil((n_frames - 1 - sample_duration) /
# (n_samples_for_each_video - 1)))
# else:
# step = sample_duration
# for j in range(1, n_frames, step):
# sample_j = copy.deepcopy(sample)
# sample_j['frame_indices'] = list(
# range(j, min(n_frames + 1, j + sample_duration)))
# dataset.append(sample_j)
if n_samples_for_each_video > 1:
# for validation only
for j in range(n_samples_for_each_video):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample_j)
else:
# for test only, option n_val_samples = 0
for j in range(n_test_clips_for_each_video):
for k in range(n_test_crops_for_each_video):
step = n_frames / n_test_clips_for_each_video
start_index = int(j * step)
sample_j = copy.deepcopy(sample)
# frame_indices represents the test clip info of the video.
# frame_indices = [clip_index, n_test_clips, n_frames], clip_index is start from 0.
sample_j['frame_indices'] = [j, n_test_clips_for_each_video, n_frames]
# crop_indices represents the test crop info of the video.
# crop_indices = [crop_index, n_test_crops], crop_index is start from 0.
sample_j["crop_indices"] = [k, n_test_crops_for_each_video]
# index of test segment, from 0 to (n_test_clips * n_test_crops - 1).
sample_j["test_segment_index"] = j * n_test_crops_for_each_video + k
dataset.append(sample_j)
print("{} videos loaded ...".format(str(len(dataset))))
return dataset, idx_to_class
def make_dataset(root_path, annotation_path, subset, n_samples_for_each_video,
sample_duration):
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 = []
print("[INFO]: Jester Dataset - " + subset + " is loading...")
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,
'n_frames') # reads number of farames under each video path
n_frames = int(load_value_file(n_frames_file_path))
# n_frames = len(glob.glob(os.path.join(video_path, '*.jpg'))) # in case there is no n_frames file uncomment this line
if n_frames <= 0:
continue
begin_t = 1
end_t = n_frames
sample = {
'video': video_path,
'segment': [begin_t, end_t],
'n_frames': n_frames,
#'video_id': video_names[i].split('/')[1]
'video_id': video_names[i]
}
if len(annotations) != 0:
sample['label'] = class_to_idx[annotations[i]['label']]
else:
sample['label'] = -1
if n_samples_for_each_video == 1:
sample['frame_indices'] = list(range(1, n_frames + 1))
dataset.append(sample)
else:
if n_samples_for_each_video > 1:
step = max(
1,
math.ceil((n_frames - 1 - sample_duration) /
(n_samples_for_each_video - 1)))
else:
step = sample_duration
for j in range(1, n_frames, step):
sample_j = copy.deepcopy(sample)
sample_j['frame_indices'] = list(
range(j, min(n_frames + 1, j + sample_duration)))
dataset.append(sample_j)
return dataset, idx_to_class