File size: 5,116 Bytes
b5beb60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | from abc import abstractmethod
from ..smp import *
class VideoBaseDataset:
MODALITY = 'VIDEO'
def __init__(self,
dataset='MMBench-Video',
pack=False,
nframe=0,
fps=-1):
try:
import decord
except Exception as e:
logging.critical(f'{type(e)}: {e}')
logging.critical('Please install decord via `pip install decord`.')
self.dataset_name = dataset
ret = self.prepare_dataset(dataset)
assert ret is not None
lmu_root = LMUDataRoot()
self.frame_root = osp.join(lmu_root, 'images', dataset)
os.makedirs(self.frame_root, exist_ok=True)
self.frame_tmpl = 'frame-{}-of-{}.jpg'
self.frame_tmpl_fps = 'frame-{}-of-{}-{}fps.jpg'
self.data_root = ret['root']
self.data_file = ret['data_file']
self.data = load(self.data_file)
if 'index' not in self.data:
self.data['index'] = np.arange(len(self.data))
assert 'question' in self.data and 'video' in self.data
videos = list(set(self.data['video']))
videos.sort()
self.videos = videos
self.pack = pack
self.nframe = nframe
self.fps = fps
if self.fps > 0 and self.nframe > 0:
raise ValueError('fps and nframe should not be set at the same time')
if self.fps <= 0 and self.nframe <= 0:
raise ValueError('fps and nframe should be set at least one valid value')
def __len__(self):
return len(self.videos) if self.pack else len(self.data)
def __getitem__(self, idx):
if self.pack:
assert idx < len(self.videos)
sub_data = self.data[self.data['video'] == self.videos[idx]]
return sub_data
else:
assert idx < len(self.data)
return dict(self.data.iloc[idx])
def frame_paths(self, video):
frame_root = osp.join(self.frame_root, video)
os.makedirs(frame_root, exist_ok=True)
return [osp.join(frame_root, self.frame_tmpl.format(i, self.nframe)) for i in range(1, self.nframe + 1)]
def frame_paths_fps(self, video, num_frames):
frame_root = osp.join(self.frame_root, video)
os.makedirs(frame_root, exist_ok=True)
return [osp.join(frame_root,
self.frame_tmpl_fps.format(i, num_frames, self.fps)) for i in range(1, num_frames + 1)]
def save_video_frames(self, video):
if self.fps > 0:
vid_path = osp.join(self.data_root, video + '.mp4')
vid = decord.VideoReader(vid_path)
# 计算视频的总帧数和总时长
total_frames = len(vid)
video_fps = vid.get_avg_fps()
total_duration = total_frames / video_fps
# 计算需要提取的总帧数
required_frames = int(total_duration * self.fps)
# 计算提取帧的间隔
step_size = video_fps / self.fps
# 计算提取帧的索引
indices = [int(i * step_size) for i in range(required_frames)]
# 提取帧并保存
frame_paths = self.frame_paths_fps(video, len(indices))
flag = np.all([osp.exists(p) for p in frame_paths])
if flag:
return frame_paths
images = [vid[i].asnumpy() for i in indices]
images = [Image.fromarray(arr) for arr in images]
for im, pth in zip(images, frame_paths):
if not osp.exists(pth):
im.save(pth)
return frame_paths
else:
frame_paths = self.frame_paths(video)
flag = np.all([osp.exists(p) for p in frame_paths])
if flag:
return frame_paths
vid_path = osp.join(self.data_root, video + '.mp4')
vid = decord.VideoReader(vid_path)
step_size = len(vid) / (self.nframe + 1)
indices = [int(i * step_size) for i in range(1, self.nframe + 1)]
images = [vid[i].asnumpy() for i in indices]
images = [Image.fromarray(arr) for arr in images]
for im, pth in zip(images, frame_paths):
if not osp.exists(pth):
im.save(pth)
return frame_paths
# Return a list of dataset names that are supported by this class, can override
@classmethod
def supported_datasets(cls):
return ['MMBench-Video', 'Video-MME', 'MVBench', 'MVBench_MP4', 'LongVideoBench', 'WorldSense', 'VDC', 'MovieChat1k']
# Given the prediction file, return the evaluation results in the format of a dictionary or pandas dataframe
@abstractmethod
def evaluate(self, eval_file, **judge_kwargs):
pass
@abstractmethod
def build_prompt(self, idx):
pass
@abstractmethod
def prepare_dataset(self, dataset):
# The prepare_dataset function should return a dictionary containing:
# `root` (directory that containing video files)
# `data_file` (the TSV dataset file)
pass
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