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# Copyright (c) 2025 Ye Liu. Licensed under the BSD-3-Clause License.
import random
import re
import decord
import nncore
import numpy as np
import pysrt
import torch
from decord import VideoReader
from PIL import Image
def load_image(path):
image = Image.open(path).convert('RGB')
image = torch.from_numpy(np.array(image)).unsqueeze(0)
return image
def load_video(path, sample_frames=-1):
frame_mode = nncore.is_dir(path)
if frame_mode:
paths = nncore.ls(path, ext=('jpg', 'png'), join_path=True)
paths.sort(key=lambda p: int(re.sub(r'^\D*', '', nncore.pure_name(p))))
vlen = len(paths)
else:
decord.bridge.set_bridge('torch')
vr = VideoReader(path, num_threads=1)
vlen = len(vr)
if sample_frames > 0 and vlen > sample_frames:
inds = np.arange(0, vlen, (vlen - 1) / (sample_frames - 1))[:sample_frames].round().astype(int).tolist()
assert len(inds) == sample_frames
else:
inds = list(range(vlen))
if frame_mode:
images = [paths[i] for i in inds]
frames = torch.cat([load_image(i) for i in images])
else:
frames = vr.get_batch(inds)
images = [Image.fromarray(t.numpy()) for t in frames]
return frames, images
def load_frames(paths, sample_frames=-1, sample_type='uniform', sample_for_llm_only=False):
assert sample_type in ('uniform', 'random')
vlen = len(paths)
if isinstance(sample_frames, str):
sep = [int(n) for n in sample_frames.split(',')]
assert len(sep) in (1, 2)
sample_frames = int(random.randint(*sep)) if len(sep) > 1 else int(sep[0])
# NOTE: some videos and images are shorter than sample_frames
if sample_frames > 0 and vlen > sample_frames:
if sample_type == 'uniform':
inds = np.arange(0, vlen, (vlen - 1) / (sample_frames - 1))[:sample_frames].round().astype(int).tolist()
else:
seps = np.arange(0, vlen, (vlen - 1) / sample_frames)[:sample_frames + 1].round().astype(int).tolist()
inds = [random.choice(range(sep, max(sep + 1, seps[i + 1]))) for i, sep in enumerate(seps[:-1])]
assert len(inds) == sample_frames
else:
inds = list(range(len(paths)))
if sample_for_llm_only:
frames = torch.cat([load_image(p) for p in paths])
else:
frames = torch.cat([load_image(paths[i]) for i in inds])
paths = [paths[i] for i in inds]
return frames, paths, inds
def load_frames_with_inds(path,
keep,
single_frame_mode=False,
sample_frames=-1,
sample_type='uniform',
sample_for_llm_only=False,
num_threads=0):
assert sample_type in ('uniform', 'random')
frame_mode = nncore.is_dir(path)
if frame_mode:
paths = nncore.ls(path, ext='jpg', join_path=True)
paths.sort(key=lambda p: int(re.sub(r'^\D*', '', nncore.pure_name(p))))
else:
decord.bridge.set_bridge('torch')
vr = VideoReader(path, num_threads=num_threads)
if single_frame_mode:
vlen = len(paths) if frame_mode else len(vr)
assert vlen > 1 and len(keep) == 1
imap = list(range(vlen))
else:
vlen = len(keep)
imap = keep
if isinstance(sample_frames, str):
sep = [int(n) for n in sample_frames.split(',')]
assert len(sep) in (1, 2)
sample_frames = int(random.randint(*sep)) if len(sep) > 1 else int(sep[0])
# some videos and images are shorter than sample_frames
if sample_frames > 0 and vlen > sample_frames:
if sample_type == 'uniform':
inds = np.arange(0, vlen, (vlen - 1) / (sample_frames - 1))[:sample_frames].round().astype(int).tolist()
else:
seps = np.arange(0, vlen, (vlen - 1) / sample_frames)[:sample_frames + 1].round().astype(int).tolist()
inds = [random.choice(range(sep, max(sep + 1, seps[i + 1]))) for i, sep in enumerate(seps[:-1])]
if single_frame_mode:
# ensure that keep is in the sampled indices
dist = [abs(keep[0] - i) for i in inds]
inds[dist.index(min(dist))] = keep[0]
assert len(inds) == sample_frames
else:
inds = list(range(vlen))
if frame_mode:
images = [paths[imap[i]] for i in inds]
else:
img_tensor = vr.get_batch([imap[i] for i in inds])
images = [Image.fromarray(t.numpy()) for t in img_tensor]
if single_frame_mode:
frames = load_image(paths[keep[0]]) if frame_mode else vr.get_batch(keep)
elif sample_for_llm_only:
frames = torch.cat([load_image(p) for p in paths]) if frame_mode else vr.get_batch(imap)
else:
frames = torch.cat([load_image(paths[imap[i]]) for i in inds]) if frame_mode else img_tensor.clone()
return frames, images, inds
def load_frames_with_inds_keep(path,
all_frame_inds,
frame_idx,
sample_frames=-1,
sample_type='uniform',
sample_for_llm_only=False,
num_threads=0):
assert sample_type in ('uniform', 'random')
frame_mode = nncore.is_dir(path)
if frame_mode:
paths = nncore.ls(path, ext='jpg', join_path=True)
paths.sort(key=lambda p: int(re.sub(r'^\D*', '', nncore.pure_name(p))))
else:
decord.bridge.set_bridge('torch')
vr = VideoReader(path, num_threads=num_threads)
vlen = len(all_frame_inds)
imap = all_frame_inds
if isinstance(sample_frames, str):
sep = [int(n) for n in sample_frames.split(',')]
assert len(sep) in (1, 2)
sample_frames = int(random.randint(*sep)) if len(sep) > 1 else int(sep[0])
# some videos and images are shorter than sample_frames
if sample_frames > 0 and vlen > sample_frames:
if sample_type == 'uniform':
inds = np.arange(0, vlen, (vlen - 1) / (sample_frames - 1))[:sample_frames].round().astype(int).tolist()
else:
seps = np.arange(0, vlen, (vlen - 1) / sample_frames)[:sample_frames + 1].round().astype(int).tolist()
inds = [random.choice(range(sep, max(sep + 1, seps[i + 1]))) for i, sep in enumerate(seps[:-1])]
# ensure that keep is in the sampled indices
keep = all_frame_inds.index(frame_idx)
dist = [abs(keep - i) for i in inds]
inds[dist.index(min(dist))] = keep
assert len(inds) == sample_frames
else:
inds = list(range(vlen))
if frame_mode:
images = [paths[imap[i]] for i in inds]
else:
img_tensor = vr.get_batch([imap[i] for i in inds])
images = [Image.fromarray(t.numpy()) for t in img_tensor]
if sample_for_llm_only:
frames = torch.cat([load_image(p) for p in paths]) if frame_mode else vr.get_batch(imap)
else:
frames = torch.cat([load_image(paths[imap[i]]) for i in inds]) if frame_mode else img_tensor.clone()
return frames, images, inds
def load_frames_with_stride(path,
every_n_frames=4,
sample_frames=-1,
sample_type='uniform',
sample_for_llm_only=False,
num_threads=0):
assert sample_type in ('uniform', 'random')
decord.bridge.set_bridge('torch')
vr = VideoReader(path, num_threads=num_threads)
keep = list(range(0, len(vr), every_n_frames))
vlen = len(keep)
if isinstance(sample_frames, str):
sep = [int(n) for n in sample_frames.split(',')]
assert len(sep) in (1, 2)
sample_frames = int(random.randint(*sep)) if len(sep) > 1 else int(sep[0])
# some videos and images are shorter than sample_frames
if sample_frames > 0 and vlen > sample_frames:
if sample_type == 'uniform':
inds = np.arange(0, vlen, (vlen - 1) / (sample_frames - 1))[:sample_frames].round().astype(int).tolist()
else:
seps = np.arange(0, vlen, (vlen - 1) / sample_frames)[:sample_frames + 1].round().astype(int).tolist()
inds = [random.choice(range(sep, max(sep + 1, seps[i + 1]))) for i, sep in enumerate(seps[:-1])]
assert len(inds) == sample_frames
else:
inds = list(range(vlen))
img_tensor = vr.get_batch([keep[i] for i in inds])
images = [Image.fromarray(t.numpy()) for t in img_tensor]
frames = vr.get_batch(keep) if sample_for_llm_only else img_tensor.clone()
return frames, images, inds
def load_subtitle(path):
subs = pysrt.open(path)
parsed = []
for sub in subs:
s, e = sub.start.to_time(), sub.end.to_time()
s = (s.hour * 60 + s.minute) * 60 + s.second + s.microsecond / 1000000
e = (e.hour * 60 + e.minute) * 60 + e.second + e.microsecond / 1000000
parsed.append((s, e, sub.text))
return parsed
def get_duration(path, num_threads=1):
# sometimes the video is loaded as a list of frames
if isinstance(path, list):
return len(path)
vr = VideoReader(path, num_threads=num_threads)
duration = len(vr) / vr.get_avg_fps()
return duration
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