File size: 10,941 Bytes
f6588d9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 | """Frozen true-time frame extraction and JPEG materialization."""
from __future__ import annotations
from bisect import bisect_left, bisect_right
from collections.abc import Sequence
from dataclasses import dataclass, replace
from fractions import Fraction
from hashlib import sha256
from pathlib import Path
from typing import Any
NATIVE_FRAME_STRIDE = 5
SELECTED_FRAME_COUNT = 256
DECORD_NUM_THREADS = 1
JPEG_QUALITY = 95
DECORD_PTS_MAX_ABS_ERROR_S = 0.005
@dataclass(frozen=True)
class StrideFrame:
"""One native frame retained by the frozen full-video stride."""
preselection_ordinal: int
clip_stride_ordinal: int
source_frame_ordinal: int
pts: int
time_base: Fraction
source_time: Fraction
def _fraction_json(value: Fraction) -> dict[str, int]:
return {"numerator": value.numerator, "denominator": value.denominator}
def _sha256_file(path: Path) -> str:
return sha256(path.read_bytes()).hexdigest()
def endpoint_inclusive_indices(count: int) -> list[int]:
"""Select 256 evenly spaced positions while retaining both endpoints."""
if count < SELECTED_FRAME_COUNT:
raise ValueError(f"need at least {SELECTED_FRAME_COUNT} frames")
denominator = SELECTED_FRAME_COUNT - 1
rounding = denominator // 2
return [
(index * (count - 1) + rounding) // denominator
for index in range(SELECTED_FRAME_COUNT)
]
def build_full_stride_ledger(
source_path: Path,
) -> tuple[dict[str, int], list[StrideFrame]]:
"""Map every fifth decoded source frame to its true PyAV PTS."""
import av
import decord
reader = decord.VideoReader(
str(source_path),
ctx=decord.cpu(0),
num_threads=DECORD_NUM_THREADS,
)
declared_frame_count = len(reader)
ledger: list[StrideFrame] = []
decoded_count = 0
last_source_time: Fraction | None = None
container = av.open(str(source_path), mode="r")
try:
if not container.streams.video:
raise ValueError(f"source has no video stream: {source_path}")
stream = container.streams.video[0]
stream.codec_context.thread_count = 1
for source_ordinal, frame in enumerate(container.decode(video=0)):
decoded_count = source_ordinal + 1
if frame.pts is None or frame.time_base is None:
raise ValueError(f"frame {source_ordinal} has no true PTS/time_base")
time_base = Fraction(
frame.time_base.numerator,
frame.time_base.denominator,
)
source_time = int(frame.pts) * time_base
if last_source_time is not None and source_time < last_source_time:
raise ValueError(
"PyAV presentation PTS regressed at source ordinal "
f"{source_ordinal}"
)
last_source_time = source_time
if source_ordinal % NATIVE_FRAME_STRIDE == 0:
preselection_ordinal = source_ordinal // NATIVE_FRAME_STRIDE
ledger.append(
StrideFrame(
preselection_ordinal=preselection_ordinal,
clip_stride_ordinal=preselection_ordinal,
source_frame_ordinal=source_ordinal,
pts=int(frame.pts),
time_base=time_base,
source_time=source_time,
)
)
finally:
container.close()
if decoded_count != declared_frame_count:
raise ValueError(
"Decord/PyAV frame-count mismatch prevents ordinal mapping: "
f"decord={declared_frame_count}, pyav={decoded_count}"
)
expected_stride_count = (
declared_frame_count + NATIVE_FRAME_STRIDE - 1
) // NATIVE_FRAME_STRIDE
if len(ledger) != expected_stride_count:
raise AssertionError(
"decoded stride ledger length does not match native frame count"
)
return {
"decord_native_frame_count": declared_frame_count,
"pyav_decoded_frame_count": decoded_count,
"official_full_video_preselection_count": expected_stride_count,
}, ledger
def clip_stride_ledger(
full_ledger: Sequence[StrideFrame],
clip_start_s: Fraction,
clip_end_s: Fraction,
*,
source_times: Sequence[Fraction] | None = None,
) -> list[StrideFrame]:
"""Apply inclusive clip bounds and renumber retained clip positions."""
if clip_end_s < clip_start_s:
raise ValueError("clip end precedes clip start")
if source_times is None:
start = bisect_left(
full_ledger,
clip_start_s,
key=lambda entry: entry.source_time,
)
stop = bisect_right(
full_ledger,
clip_end_s,
key=lambda entry: entry.source_time,
)
else:
if len(source_times) != len(full_ledger):
raise ValueError("source_times and full_ledger must have equal length")
if any(
source_times[index] > source_times[index + 1]
for index in range(len(source_times) - 1)
):
raise ValueError("source_times must be monotonically non-decreasing")
if any(
value != entry.source_time
for value, entry in zip(source_times, full_ledger, strict=True)
):
raise ValueError("source_times must correspond exactly to full_ledger")
start = bisect_left(source_times, clip_start_s)
stop = bisect_right(source_times, clip_end_s)
clipped = [
replace(entry, clip_stride_ordinal=index)
for index, entry in enumerate(full_ledger[start:stop])
]
if len(clipped) < SELECTED_FRAME_COUNT:
raise ValueError(
f"clip has only {len(clipped)} official stride frames; "
f"need {SELECTED_FRAME_COUNT}"
)
return clipped
def build_stride_ledger(
source_path: Path,
clip_start_s: Fraction,
clip_end_s: Fraction,
) -> tuple[dict[str, int], list[StrideFrame]]:
"""Build the full true-time ledger, then apply inclusive clip bounds."""
counts, full_ledger = build_full_stride_ledger(source_path)
ledger = clip_stride_ledger(full_ledger, clip_start_s, clip_end_s)
return {**counts, "clip_preselection_count": len(ledger)}, ledger
def select_stride_frames(ledger: Sequence[StrideFrame]) -> list[StrideFrame]:
"""Apply the frozen endpoint-inclusive 256-frame selection."""
positions = endpoint_inclusive_indices(len(ledger))
selected = [ledger[position] for position in positions]
if any(
frame.clip_stride_ordinal != position
for frame, position in zip(selected, positions, strict=True)
):
raise AssertionError(
"clip stride ordinal does not match selected ledger position"
)
return selected
def decode_and_write_selected_jpegs(
source_path: Path,
selected: Sequence[StrideFrame],
frame_dir: Path,
) -> tuple[list[dict[str, Any]], dict[str, float]]:
"""Decode selected Decord ordinals and write official OpenCV JPEG bytes."""
if len(selected) != SELECTED_FRAME_COUNT:
raise ValueError(f"expected {SELECTED_FRAME_COUNT} selected frames")
import cv2
import decord
import numpy as np
reader = decord.VideoReader(
str(source_path),
ctx=decord.cpu(0),
num_threads=DECORD_NUM_THREADS,
)
source_ordinals = [frame.source_frame_ordinal for frame in selected]
raw_timestamps = reader.get_frame_timestamp(source_ordinals)
if hasattr(raw_timestamps, "asnumpy"):
raw_timestamps = raw_timestamps.asnumpy()
timestamp_array = np.asarray(raw_timestamps)
expected_timestamp_shape = (SELECTED_FRAME_COUNT, 2)
if timestamp_array.shape != expected_timestamp_shape:
raise ValueError(
f"unexpected Decord timestamp shape {timestamp_array.shape}, "
f"expected {expected_timestamp_shape}"
)
raw_frames = reader.get_batch(source_ordinals)
if hasattr(raw_frames, "asnumpy"):
raw_frames = raw_frames.asnumpy()
frames = np.asarray(raw_frames)
if (
frames.shape[0] != SELECTED_FRAME_COUNT
or frames.ndim != 4
or frames.shape[-1] != 3
):
raise ValueError(f"unexpected Decord pixel shape: {frames.shape}")
if frames.dtype != np.uint8:
raise ValueError(f"unexpected Decord pixel dtype: {frames.dtype}")
frame_dir.mkdir(parents=True, exist_ok=False)
rows: list[dict[str, Any]] = []
mapping_errors: list[float] = []
jpeg_options = [int(cv2.IMWRITE_JPEG_QUALITY), JPEG_QUALITY]
for selected_position, (entry, rgb_frame, timestamp) in enumerate(
zip(selected, frames, timestamp_array, strict=True)
):
decord_start_s = float(timestamp[0])
mapping_error = abs(decord_start_s - float(entry.source_time))
mapping_errors.append(mapping_error)
if mapping_error > DECORD_PTS_MAX_ABS_ERROR_S:
raise ValueError(
"Decord/PyAV PTS mismatch at source ordinal "
f"{entry.source_frame_ordinal}: {mapping_error:.9f}s > "
f"{DECORD_PTS_MAX_ABS_ERROR_S:.9f}s"
)
bgr_frame = cv2.cvtColor(rgb_frame, cv2.COLOR_RGB2BGR)
filename = f"frame{entry.preselection_ordinal:07d}.jpg"
output_path = frame_dir / filename
if not cv2.imwrite(str(output_path), bgr_frame, jpeg_options):
raise RuntimeError(f"OpenCV failed to write {output_path}")
encoded_ok, encoded = cv2.imencode(
".jpg",
bgr_frame,
jpeg_options,
)
if not encoded_ok or output_path.read_bytes() != encoded.tobytes():
raise RuntimeError(
f"OpenCV JPEG encoding was not byte-stable for {output_path}"
)
rows.append(
{
"selected_position": selected_position,
"preselection_ordinal": entry.preselection_ordinal,
"clip_stride_ordinal": entry.clip_stride_ordinal,
"source_frame_ordinal": entry.source_frame_ordinal,
"pts": entry.pts,
"time_base_numerator": entry.time_base.numerator,
"time_base_denominator": entry.time_base.denominator,
"source_time": _fraction_json(entry.source_time),
"decord_timestamp_start_s": decord_start_s,
"decord_pyav_abs_error_s": mapping_error,
"jpeg_relpath": filename,
"jpeg_size_bytes": output_path.stat().st_size,
"jpeg_sha256": _sha256_file(output_path),
}
)
return rows, {
"max_abs_error_s": max(mapping_errors),
"mean_abs_error_s": sum(mapping_errors) / len(mapping_errors),
"threshold_s": DECORD_PTS_MAX_ABS_ERROR_S,
}
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