zhicao/dreamzero main

PYTHON

BENIGN

Mal-ecule

Size 17.4 KB download

First seen 17 days ago

Analyzed 2 days ago

Package pkg:huggingface/zhicao/dreamzero@main

Ecosystem huggingface

Source huggingface.co

video_utils.py python

1	import json
2	import subprocess	Imports Python's subprocess module
3
4	import av
5	import cv2	Imports a Python raster imaging library
6	import numpy as np	Imports the NumPy array library
7	import torchvision
8
9	# Import decord with graceful fallback
10	try:
11	import decord
12
13	DECORD_AVAILABLE = True
14	except ImportError:
15	DECORD_AVAILABLE = False
16
17	try:
18	import torchcodec
19
20	TORCHCODEC_AVAILABLE = True
21	exce…

36:6	… "json",
37	video_path,
38	]
39
40	try:
41	output = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode("utf-8")
42	probe_data = json.loads(output)
43	stream = probe_data["streams"][0]
44
45	# Parse frame rate (comes as fraction like "15/1")
46	if "/" in…

66:5	… raise ValueError(f"Failed to get video info for {video_path}: {e}")
67
68
69	def _extract_frames_ffmpeg(video_path: str, frame_indices: list[int]) -> np.ndarray:	Module built on the numeric Python stack
70	"""Extract specific frames using ffmpeg."""
71	frames = []
72
73	for idx in frame_indices:
74	# Use ffmpeg to extract a specific frame
75	cmd = [
76	"ffm…

128:8	… # Default fallback frame
129	frames.append(np.zeros((480, 640, 3), dtype=np.uint8))	Module built on the numeric Python stack
130
131	return np.array(frames)
132
133
134	def _extract_frames_at_timestamps_ffmpeg(video_path: str, timestamps: list[float]) -> np.ndarray:
135	"""Extract fram…

244:45	…)
245	)
246
247	# Generate timestamps
248	timestamps = np.arange(actual_frames) / fps	Module built on the numeric Python stack
249
250	return frames, timestamps
251
252	except subprocess.CalledProcessError as e:
253	raise ValueErro…

Micro-behaviors

66:5	… raise ValueError(f"Failed to get video info for {video_path}: {e}")
67
68
69	def _extract_frames_ffmpeg(video_path: str, frame_indices: list[int]) -> np.ndarray:
70	"""Extract specific frames using ffmpeg."""
71	frames = []
72
73	for idx in frame_indices:
74	# Use ffmpeg to extract a specific frame
75	cmd = [
76	"ffm…

1	import json
2	import subprocess
3
4	import av
5	import cv2
6	import numpy as np
7	import torchvision
8
9	# Import decord with graceful fallback
10	try:
11	import decord
12
13	DECORD_AVAILABLE = True
14	except ImportError:
15	DECORD_AVAILABLE = False
16
17	try:
18	import torchcodec
19
20	TORCHCODEC_AVAILABLE = True
21	exce…

128:8	… # Default fallback frame
129	frames.append(np.zeros((480, 640, 3), dtype=np.uint8))
130
131	return np.array(frames)
132
133
134	def _extract_frames_at_timestamps_ffmpeg(video_path: str, timestamps: list[float]) -> np.ndarray:
135	"""Extract fram…

244:45	…)
245	)
246
247	# Generate timestamps
248	timestamps = np.arange(actual_frames) / fps
249
250	return frames, timestamps
251
252	except subprocess.CalledProcessError as e:
253	raise ValueErro…

347:22	…tamps to valid range to avoid RuntimeError
348	timestamps = np.clip(timestamps, min_pts, max_pts)
349
350	# Correct float precision issues in timestamps
351	# E.g. for 5fps video: [1.0, 1.20…

36:6	… "json",
37	video_path,
38	]
39
40	try:
41	output = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode("utf-8")
42	probe_data = json.loads(output)
43	stream = probe_data["streams"][0]
44
45	# Parse frame rate (comes as fraction like "15/1")
46	if "/" in…

1	import json
2	import subprocess
3
4	import av
5	import cv2
6	import numpy as np
7	import torchvision
8
9	# Import decord with graceful fallback
10	try:
11	import decord
12
13	DECORD_AVAILABLE = True
14	except ImportError:
15	DECORD_AVAILABLE = False
16
17	try:
18	import torchcodec
19
20	TORCHCODEC_AVAILABLE = True
21	exce…

1	import json
2	import subprocess
3
4	import av
5	import cv2
6	import numpy as np
7	import torchvision
8
9	# Import decord with graceful fallback
10	try:
11	import decord
12
13	DECORD_AVAILABLE = True
14	except ImportError:
15	DECORD_AVAILABLE = False
16
17	try:
18	import torchcodec
19
20	TORCHCODEC_AVAILABLE = True
21	exce…

300:54	…s.
301
302	Args:
303	video_path (str): Path to the video file.
304	timestamps (list[int] \| np.ndarray): Timestamps to retrieve frames for, in …

50:28	…eam["r_frame_rate"])
51
52	# Get frame count and duration
53	nb_frames = int(stream.get("nb_frames", 0))
54	duration = float(stream.ge…

66:5	… raise ValueError(f"Failed to get video info for {video_path}: {e}")
67
68
69	def _extract_frames_ffmpeg(video_path: str, frame_indices: list[int]) -> np.ndarray:
70	"""Extract specific frames using ffmpeg."""
71	frames = []
72
73	for idx in frame_indices:
74	# Use ffmpeg to extract a specific frame
75	cmd = [
76	"ffm…

340:25	…tion range from first and last frames
341	# This is a robust way to get valid timestamp range without depending on specific metadata attributes

SHA-256	719dbfbc8d7ab6f965bf431393d54fae3f8014905db2f84258e7149138cd101d
Filename	video_utils.py
Package	zhicao/dreamzero
Version	main
PURL	pkg:huggingface/zhicao/dreamzero@main

Source	forager
Feed	huggingface.co
Ecosystem	huggingface
Domain	huggingface.co
URL	https://huggingface.co/zhicao/dreamzero

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277:7	…if video_backend == "opencv":
278	frames = []
279	cap = cv2.VideoCapture(video_path, **video_backend_kwargs)
280	for idx in indices:
281	cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
282	ret, frame = cap.read()
283	if not ret:
284	raise ValueError(f"Unable to read frame at index {idx}…

326:62	… num_ffmpeg_threads=0
327	)
328
329	# https://docs.pytorch.org/torchcodec/stable/generated/torchcodec.decoders…