File size: 9,338 Bytes
f60a6c1 |
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 |
from tempfile import NamedTemporaryFile
from logging import getLogger
from pathlib import Path
from collections import OrderedDict
from threading import RLock
from contextlib import contextmanager
from cv2 import (
CAP_PROP_FRAME_COUNT,
CAP_PROP_POS_FRAMES,
COLOR_BGR2GRAY,
COLOR_HSV2BGR,
NORM_MINMAX,
VideoCapture,
calcOpticalFlowFarneback,
cartToPolar,
cvtColor,
normalize,
)
from numpy import ndarray, pi, zeros_like
from scorevision.utils.settings import get_settings
from scorevision.utils.async_clients import get_async_client
logger = getLogger(__name__)
@contextmanager
def open_video(path: Path) -> VideoCapture:
logger.info(f"Attempting to open video: {path}")
if not path.exists():
raise FileNotFoundError
if not path.is_file():
raise ValueError("Path is not a file")
video = VideoCapture(str(path))
if not video.isOpened():
video.release()
raise ValueError("Could not open video")
try:
yield video
finally:
video.release()
def background_temporal_differencing(
video_path: Path, frame_numbers: list[int]
) -> tuple[dict[int, ndarray], dict[int, ndarray]]:
logger.info(
f"Computing Background Temporal Differencing for frame_numbers {frame_numbers} using Dense Optical Flow..."
)
images, flow_images = {}, {}
with open_video(path=video_path) as video:
if not video.isOpened():
raise IOError(f"Cannot open video: {video_path}")
max_frame_number = int(video.get(CAP_PROP_FRAME_COUNT))
prev_frame, prev_gray = None, None
for frame_number in range(max_frame_number):
ok, frame = video.read()
if not ok:
logger.error(f"Error reading frame {frame_number}")
continue
images[frame_number] = frame
gray = cvtColor(frame, COLOR_BGR2GRAY)
if frame_number in frame_numbers and prev_gray is not None:
flow = calcOpticalFlowFarneback(
prev_gray,
gray,
None,
pyr_scale=0.5,
levels=3,
winsize=15,
iterations=3,
poly_n=5,
poly_sigma=1.2,
flags=0,
)
mag, ang = cartToPolar(flow[..., 0], flow[..., 1])
hsv = zeros_like(prev_frame)
hsv[..., 0] = ang * 180 / pi / 2
hsv[..., 1] = 255
hsv[..., 2] = normalize(mag, None, 0, 255, NORM_MINMAX)
rgb = cvtColor(hsv, COLOR_HSV2BGR)
flow_images[frame_number] = rgb
prev_gray = gray
prev_frame = frame
return images, flow_images
async def download_video(
url: str, frame_numbers: list[int]
) -> tuple[str, dict[int, ndarray], dict[int, ndarray]]:
settings = get_settings()
session = await get_async_client()
async with session.get(url) as response:
if response.status != 200:
txt = await response.text()
raise RuntimeError(f"Download failed {response.status}: {txt[:200]}")
data = await response.read()
with NamedTemporaryFile(prefix="sv_video_", suffix=".mp4") as f:
f.write(data)
frames, flows = background_temporal_differencing(
video_path=Path(f.name), frame_numbers=frame_numbers
)
name = url.split("/")[-1]
return name, frames, flows
class FrameStore:
"""Lazy frame/flow accessor backed by a cached MP4 on disk."""
def __init__(
self,
video_path: Path,
*,
max_frames: int = 64,
max_flows: int = 32,
) -> None:
self.video_path = video_path
self.video_name = video_path.name
self._frame_cache: OrderedDict[int, ndarray] = OrderedDict()
self._flow_cache: OrderedDict[int, ndarray] = OrderedDict()
self._max_frames = max_frames
self._max_flows = max_flows
self._lock = RLock()
self._capture: VideoCapture | None = None
self._current_frame_index: int | None = None
def _ensure_capture(self) -> None:
if self._capture is None:
cap = VideoCapture(str(self.video_path))
if not cap.isOpened():
raise ValueError(f"Could not open video: {self.video_path}")
self._capture = cap
def _evict_if_needed(self, cache: OrderedDict[int, ndarray], limit: int) -> None:
if limit <= 0:
return
while len(cache) > limit:
cache.popitem(last=False)
def get_frame(self, frame_number: int) -> ndarray:
with self._lock:
cached = self._frame_cache.get(frame_number)
if cached is not None:
self._frame_cache.move_to_end(frame_number)
return cached
self._ensure_capture()
if not self._capture:
raise RuntimeError("Video capture not initialised")
if (
self._current_frame_index is None
or frame_number < self._current_frame_index
):
self._capture.set(CAP_PROP_POS_FRAMES, frame_number)
elif frame_number > self._current_frame_index + 1:
self._capture.set(CAP_PROP_POS_FRAMES, frame_number)
ok, frame = self._capture.read()
if not ok or frame is None:
raise IOError(f"Failed to read frame {frame_number}")
self._current_frame_index = frame_number
result = frame.copy()
self._frame_cache[frame_number] = result
self._frame_cache.move_to_end(frame_number)
self._evict_if_needed(self._frame_cache, self._max_frames)
return result
def get_flow(self, frame_number: int) -> ndarray:
if frame_number <= 0:
raise ValueError("Optical flow requires frame_number > 0")
with self._lock:
cached = self._flow_cache.get(frame_number)
if cached is not None:
self._flow_cache.move_to_end(frame_number)
return cached
prev_frame = self.get_frame(frame_number - 1)
current_frame = self.get_frame(frame_number)
prev_gray = cvtColor(prev_frame, COLOR_BGR2GRAY)
gray = cvtColor(current_frame, COLOR_BGR2GRAY)
flow = calcOpticalFlowFarneback(
prev_gray,
gray,
None,
pyr_scale=0.5,
levels=3,
winsize=15,
iterations=3,
poly_n=5,
poly_sigma=1.2,
flags=0,
)
mag, ang = cartToPolar(flow[..., 0], flow[..., 1])
hsv = zeros_like(prev_frame)
hsv[..., 0] = ang * 180 / pi / 2
hsv[..., 1] = 255
hsv[..., 2] = normalize(mag, None, 0, 255, NORM_MINMAX)
rgb = cvtColor(hsv, COLOR_HSV2BGR)
self._flow_cache[frame_number] = rgb
self._flow_cache.move_to_end(frame_number)
self._evict_if_needed(self._flow_cache, self._max_flows)
return rgb
def close(self) -> None:
with self._lock:
if self._capture is not None:
try:
self._capture.release()
except Exception:
pass
self._capture = None
self._current_frame_index = None
def clear(self) -> None:
with self._lock:
self._frame_cache.clear()
self._flow_cache.clear()
def unlink(self) -> None:
try:
self.close()
self.video_path.unlink(missing_ok=True)
except Exception:
pass
def __del__(self) -> None:
self.close()
async def download_video_cached(
url: str,
_frame_numbers: list[int], # retained for backward compatibility
cached_path: Path | None = None,
) -> tuple[str, FrameStore]:
"""
Download the video once and reuse the cached file across retries.
When `cached_path` is provided, the file is not re-downloaded.
The returned Path should be cleaned up by the caller when no longer needed.
"""
if cached_path is None:
session = await get_async_client()
temp_path: Path | None = None
try:
async with session.get(url) as response:
if response.status != 200:
txt = await response.text()
raise RuntimeError(
f"Download failed {response.status}: {txt[:200]}"
)
with NamedTemporaryFile(
prefix="sv_video_", suffix=".mp4", delete=False
) as tmp:
async for chunk in response.content.iter_chunked(1024 * 1024):
tmp.write(chunk)
temp_path = Path(tmp.name)
except Exception:
if temp_path is not None:
temp_path.unlink(missing_ok=True)
raise
video_path = temp_path
else:
video_path = cached_path
name = url.split("/")[-1]
return name, FrameStore(video_path)
|