Files
frigate/frigate/log.py
T
Josh HawkinsandGitHub 52f50a7396
CI / AMD64 Build (push) Canceled after 0s
CI / AMD64 Smoke Test (push) Canceled after 0s
CI / ARM Build (push) Canceled after 0s
CI / Jetson Jetpack 6 (push) Canceled after 0s
CI / AMD64 Extra Build (push) Canceled after 0s
CI / ARM Extra Build (push) Canceled after 0s
CI / Synaptics Build (push) Canceled after 0s
CI / Assemble and push default build (push) Canceled after 0s
Tweaks (#24418)
* don't display audio transcription provider message as health notice

* show remote provider for audio transcription in health pane

* adjust trigger and notifications messages to be consistent with the rest of the settings UI

* disable save buttons when there are no changes in config editor

* fix audio manager crash when a camera is added at runtime

The audio processor and the camera maintainer both poll the same `add` config update on their own one second timers, and the maintainer is what creates `camera_metrics[name]`. When the audio processor got there first it looked the new camera up before that entry existed, and the `KeyError` took down the whole `frigate.audio_manager` process. Whether it happens depends purely on which poll fires first, so cloning a camera from the UI fails or succeeds at random. `spawn_if_needed` now skips a camera whose metrics aren't there yet and picks it up on the next poll, the same way it already waits on a late ffmpeg update.

`AudioEventMaintainer` holds the `CameraMetrics` object now instead of indexing the manager dict on every audio chunk, which drops the IPC round trips and means a removed camera can't `KeyError` out of `detect_audio` after the maintainer pops the entry. The audio process is also registered with the watchdog, since a crash there previously left audio detection dead for every camera until a full restart, and it now receives the shared `DataProcessorMetrics` so `AudioTranscriptionRealTimeProcessor` gets the same type as the other real time processors.

* fix stationary max_frames dropping other tracked objects

When `max_frames` was set for a label, deregistering one object rebuilt norfair's list with a filter that kept an object only if it was both not the target and already on its way out, so every other healthy object of that label was dropped along with it. Any car leaving the frame took the rest of the cars with it and they came back as new tracked objects a few frames later. The filter now removes only the target, and objects that are expiring are still reaped by norfair on the next update.

* fix test

* fix skip_motion_threshold permanently disabling motion detection

The skip check returned before the two `accumulateWeighted` calls at the end of `detect`, so a skipped frame never made it into the background and setting `calibrating` there only picked a faster alpha for calls that never ran. `avg_frame` starts as an all zero image and a normally lit scene differs from black across nearly the whole frame, so the cameras I tested measure 0.84 to 0.98 against it. Any `skip_motion_threshold` below that number skips the first frame, leaves the background black, and skips every frame after it. Motion detection is dead for that camera until the setting is removed or Frigate restarts, with no motion boxes, no motion recordings, and no regions for the tracker since the detector stays calibrating.

Startup isn't the only way in. `update_mask` zeroes the background on any motion config change, and once a camera has calibrated the first IR switch or PTZ move freezes the background on the old scene, so it can't transition to the new one, which is the case the option exists for. The frame is now blended in before the early return at the same 0.2 alpha the calibrating path uses elsewhere, so a large scene change is still suppressed while the background catches up, about a second on a 5 fps camera, and then motion comes back.

* dump ffmpeg logs on every restart

The record watchdog restarted ffmpeg without flushing its `LogPipe`, so a camera whose recording segments went stale never showed a single line of ffmpeg output. The dump now happens in `start_or_restart_ffmpeg` right after the stop, which covers the stale record path, the record crash path, and the audio restart. `reset_capture_thread` and the audio `log_and_restart` fallback keep their own dumps since both pass `ffmpeg_process=None`.

* dump ffmpeg logs once per restart

The audio restart path dumped the log pipe itself before calling the helper, so the restart dump printed a second "last 100 lines" heading over an already drained deque and split the tail that `stop_ffmpeg` flushed into its own section. The heading is now only printed when there's something under it, and the audio path leaves the dump to the restart so each failure produces one section.

* keep all logpipe dumps consistent
2026-09-20 12:45:24 -06:00

368 lines
11 KiB
Python

# In log.py
import atexit
import io
import logging
import os
import sys
import threading
from collections import deque
from collections.abc import Callable, Generator
from contextlib import contextmanager
from enum import Enum
from functools import wraps
from logging.handlers import QueueHandler, QueueListener
from multiprocessing.managers import SyncManager
from queue import Empty, Queue
from typing import Any, TypeVar, cast
from frigate.util.builtin import clean_camera_user_pass
# lets a decorator keep the signature of the function it wraps
_F = TypeVar("_F", bound=Callable[..., Any])
LOG_HANDLER = logging.StreamHandler()
LOG_HANDLER.setFormatter(
logging.Formatter(
"[%(asctime)s] %(name)-30s %(levelname)-8s: %(message)s",
"%Y-%m-%d %H:%M:%S",
)
)
# filter out norfair warning
LOG_HANDLER.addFilter(
lambda record: (
not record.getMessage().startswith("You are using a scalar distance function")
)
)
# filter out tflite logging
LOG_HANDLER.addFilter(
lambda record: (
"Created TensorFlow Lite XNNPACK delegate for CPU." not in record.getMessage()
)
)
class LogLevel(str, Enum):
debug = "debug"
info = "info"
warning = "warning"
error = "error"
critical = "critical"
log_listener: QueueListener | None = None
log_queue: Queue | None = None
def setup_logging(manager: SyncManager) -> None:
global log_listener, log_queue
log_queue = manager.Queue()
log_listener = QueueListener(log_queue, LOG_HANDLER, respect_handler_level=True)
atexit.register(_stop_logging)
log_listener.start()
logging.basicConfig(
level=logging.INFO,
handlers=[],
force=True,
)
logging.getLogger().addHandler(QueueHandler(log_listener.queue))
def _stop_logging() -> None:
global log_listener
if log_listener is not None:
log_listener.stop()
log_listener = None
def apply_log_levels(default: str, log_levels: dict[str, LogLevel]) -> None:
logging.getLogger().setLevel(default)
log_levels = {
"absl": LogLevel.error,
"httpx": LogLevel.error,
"h5py": LogLevel.error,
"keras": LogLevel.error,
"matplotlib": LogLevel.error,
"tensorflow": LogLevel.error,
"tensorflow.python": LogLevel.error,
"werkzeug": LogLevel.error,
"ws4py": LogLevel.error,
"PIL": LogLevel.warning,
"numba": LogLevel.warning,
"google_genai.models": LogLevel.warning,
**log_levels,
}
for log, level in log_levels.items():
logging.getLogger(log).setLevel(level.value.upper())
# When a multiprocessing.Process exits, python tries to flush stdout and stderr. However, if the
# process is created after a thread (for example a logging thread) is created and the process fork
# happens while an internal lock is held, the stdout/err flush can cause a deadlock.
#
# https://github.com/python/cpython/issues/91776
def reopen_std_streams() -> None:
sys.stdout = os.fdopen(1, "w")
sys.stderr = os.fdopen(2, "w")
os.register_at_fork(after_in_child=reopen_std_streams)
# based on https://codereview.stackexchange.com/a/17959
class LogPipe(threading.Thread):
def __init__(self, log_name: str, level: int = logging.ERROR):
"""Setup the object with a logger and start the thread"""
super().__init__(daemon=False)
self.logger = logging.getLogger(log_name)
self.level = level
self.deque: deque[str] = deque(maxlen=100)
self.fdRead, self.fdWrite = os.pipe()
self.pipeReader = os.fdopen(self.fdRead)
self.start()
def cleanup_log(self, log: str) -> str:
"""Cleanup the log line to remove sensitive info and string tokens."""
log = clean_camera_user_pass(log).strip("\n")
return log
def fileno(self) -> int:
"""Return the write file descriptor of the pipe"""
return self.fdWrite
def run(self) -> None:
"""Run the thread, logging everything."""
for line in iter(self.pipeReader.readline, ""):
self.deque.append(self.cleanup_log(line))
self.pipeReader.close()
def dump(self) -> None:
if not self.deque:
return
self.logger.log(
self.level,
"The following ffmpeg logs include the last 100 lines prior to exit.",
)
while len(self.deque) > 0:
self.logger.log(self.level, self.deque.popleft())
def close(self) -> None:
"""Close the write end of the pipe."""
os.close(self.fdWrite)
class LogRedirect(io.StringIO):
"""
A custom file-like object to capture stdout and process it.
It extends io.StringIO to capture output and then processes it
line by line.
"""
def __init__(self, logger_instance: logging.Logger, level: int):
super().__init__()
self.logger = logger_instance
self.log_level = level
self._line_buffer: list[str] = []
def write(self, s: Any) -> int:
if not isinstance(s, str):
s = str(s)
self._line_buffer.append(s)
# Process output line by line if a newline is present
if "\n" in s:
full_output = "".join(self._line_buffer)
lines = full_output.splitlines(keepends=True)
self._line_buffer = []
for line in lines:
if line.endswith("\n"):
self._process_line(line.rstrip("\n"))
else:
self._line_buffer.append(line)
return len(s)
def _process_line(self, line: str) -> None:
self.logger.log(self.log_level, line)
def flush(self) -> None:
if self._line_buffer:
full_output = "".join(self._line_buffer)
self._line_buffer = []
if full_output: # Only process if there's content
self._process_line(full_output)
def __enter__(self) -> "LogRedirect":
"""Context manager entry point."""
return self
def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
"""Context manager exit point. Ensures buffered content is flushed."""
self.flush()
@contextmanager
def __redirect_fd_to_queue(queue: Queue[str]) -> Generator[None, None, None]:
"""Redirect file descriptor 1 (stdout) to a pipe and capture output in a queue."""
stdout_fd = os.dup(1)
read_fd, write_fd = os.pipe()
os.dup2(write_fd, 1)
os.close(write_fd)
stop_event = threading.Event()
def reader() -> None:
"""Read from pipe and put lines in queue until stop_event is set."""
try:
with os.fdopen(read_fd, "r") as pipe:
while not stop_event.is_set():
line = pipe.readline()
if not line: # EOF
break
queue.put(line.strip())
except OSError as e:
queue.put(f"Reader error: {e}")
finally:
if not stop_event.is_set():
stop_event.set()
reader_thread = threading.Thread(target=reader, daemon=False)
reader_thread.start()
try:
yield
finally:
os.dup2(stdout_fd, 1)
os.close(stdout_fd)
stop_event.set()
reader_thread.join(timeout=1.0)
try:
os.close(read_fd)
except OSError:
pass
def redirect_output_to_logger(logger: logging.Logger, level: int) -> Callable[[_F], _F]:
"""Decorator to redirect both Python sys.stdout/stderr and C-level stdout to logger."""
def decorator(func: _F) -> _F:
@wraps(func)
def wrapper(*args: Any, **kwargs: Any) -> Any:
queue: Queue[str] = Queue()
log_redirect = LogRedirect(logger, level)
old_stdout = sys.stdout
old_stderr = sys.stderr
sys.stdout = log_redirect
sys.stderr = log_redirect
try:
# Redirect C-level stdout
with __redirect_fd_to_queue(queue):
result = func(*args, **kwargs)
finally:
# Restore Python stdout/stderr
sys.stdout = old_stdout
sys.stderr = old_stderr
log_redirect.flush()
# Log C-level output from queue
while True:
try:
logger.log(level, queue.get_nowait())
except Empty:
break
return result
return cast(_F, wrapper)
return decorator
def suppress_os_output(func: Callable) -> Callable:
"""
A decorator that suppresses all output (stdout and stderr)
at the operating system file descriptor level for the decorated function.
This is useful for silencing noisy C/C++ libraries.
Note: This is a Unix-specific solution using os.dup2 and os.pipe.
It temporarily redirects file descriptors 1 (stdout) and 2 (stderr)
to a non-read pipe, effectively discarding their output.
"""
@wraps(func)
def wrapper(*args: tuple, **kwargs: dict[str, Any]) -> Any:
# Save the original file descriptors for stdout (1) and stderr (2)
original_stdout_fd = os.dup(1)
original_stderr_fd = os.dup(2)
# Create dummy pipes. We only need the write ends to redirect to.
# The data written to these pipes will be discarded as nothing
# will read from the read ends.
devnull_read_fd, devnull_write_fd = os.pipe()
try:
# Redirect stdout (FD 1) and stderr (FD 2) to the write end of our dummy pipe
os.dup2(devnull_write_fd, 1) # Redirect stdout to devnull pipe
os.dup2(devnull_write_fd, 2) # Redirect stderr to devnull pipe
# Execute the original function
result = func(*args, **kwargs)
finally:
# Restore original stdout and stderr file descriptors (1 and 2)
# This is crucial to ensure normal printing resumes after the decorated function.
os.dup2(original_stdout_fd, 1)
os.dup2(original_stderr_fd, 2)
# Close all duplicated and pipe file descriptors to prevent resource leaks.
# It's important to close the read end of the dummy pipe too,
# as nothing is explicitly reading from it.
os.close(original_stdout_fd)
os.close(original_stderr_fd)
os.close(devnull_read_fd)
os.close(devnull_write_fd)
return result
return wrapper
@contextmanager
def suppress_stderr_during(operation_name: str) -> Generator[None, None, None]:
"""
Context manager to suppress stderr output during a specific operation.
Useful for silencing LLVM debug output, CUDA messages, and other native
library logging that cannot be controlled via Python logging or environment
variables. Completely redirects file descriptor 2 (stderr) to /dev/null.
Usage:
with suppress_stderr_during("model_conversion"):
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
Args:
operation_name: Name of the operation for debugging purposes
"""
original_stderr_fd = os.dup(2)
devnull = os.open(os.devnull, os.O_WRONLY)
try:
os.dup2(devnull, 2)
yield
finally:
os.dup2(original_stderr_fd, 2)
os.close(devnull)
os.close(original_stderr_fd)