blakeblackshear.frigate/frigate/util/services.py
joshjryan d7935abc14
Set the loglevel for OpenCV ffmpeg messages to fatal (#14728)
* Set the loglevel for OpenCV ffmpeg messages to fatal

* Set OPENCV_FFMPEG_LOGLEVEL in Dockerfile
2024-11-01 20:01:38 -06:00

625 lines
18 KiB
Python

"""Utilities for services."""
import asyncio
import json
import logging
import os
import re
import signal
import subprocess as sp
import traceback
from typing import Optional
import cv2
import psutil
import py3nvml.py3nvml as nvml
import requests
from frigate.const import (
DRIVER_AMD,
DRIVER_ENV_VAR,
FFMPEG_HWACCEL_NVIDIA,
FFMPEG_HWACCEL_VAAPI,
)
from frigate.util.builtin import clean_camera_user_pass, escape_special_characters
logger = logging.getLogger(__name__)
def restart_frigate():
proc = psutil.Process(1)
# if this is running via s6, sigterm pid 1
if proc.name() == "s6-svscan":
proc.terminate()
# otherwise, just try and exit frigate
else:
os.kill(os.getpid(), signal.SIGINT)
def print_stack(sig, frame):
traceback.print_stack(frame)
def listen():
signal.signal(signal.SIGUSR1, print_stack)
def get_cgroups_version() -> str:
"""Determine what version of cgroups is enabled."""
cgroup_path = "/sys/fs/cgroup"
if not os.path.ismount(cgroup_path):
logger.debug(f"{cgroup_path} is not a mount point.")
return "unknown"
try:
with open("/proc/mounts", "r") as f:
mounts = f.readlines()
for mount in mounts:
mount_info = mount.split()
if mount_info[1] == cgroup_path:
fs_type = mount_info[2]
if fs_type == "cgroup2fs" or fs_type == "cgroup2":
return "cgroup2"
elif fs_type == "tmpfs":
return "cgroup"
else:
logger.debug(
f"Could not determine cgroups version: unhandled filesystem {fs_type}"
)
break
except Exception as e:
logger.debug(f"Could not determine cgroups version: {e}")
return "unknown"
def get_docker_memlimit_bytes() -> int:
"""Get mem limit in bytes set in docker if present. Returns -1 if no limit detected."""
# check running a supported cgroups version
if get_cgroups_version() == "cgroup2":
memlimit_path = "/sys/fs/cgroup/memory.max"
try:
with open(memlimit_path, "r") as f:
value = f.read().strip()
if value.isnumeric():
return int(value)
elif value.lower() == "max":
return -1
except Exception as e:
logger.debug(f"Unable to get docker memlimit: {e}")
return -1
def get_cpu_stats() -> dict[str, dict]:
"""Get cpu usages for each process id"""
usages = {}
docker_memlimit = get_docker_memlimit_bytes() / 1024
total_mem = os.sysconf("SC_PAGE_SIZE") * os.sysconf("SC_PHYS_PAGES") / 1024
system_cpu = psutil.cpu_percent(
interval=None
) # no interval as we don't want to be blocking
system_mem = psutil.virtual_memory()
usages["frigate.full_system"] = {
"cpu": str(system_cpu),
"mem": str(system_mem.percent),
}
for process in psutil.process_iter(["pid", "name", "cpu_percent", "cmdline"]):
pid = str(process.info["pid"])
try:
cpu_percent = process.info["cpu_percent"]
cmdline = process.info["cmdline"]
with open(f"/proc/{pid}/stat", "r") as f:
stats = f.readline().split()
utime = int(stats[13])
stime = int(stats[14])
start_time = int(stats[21])
with open("/proc/uptime") as f:
system_uptime_sec = int(float(f.read().split()[0]))
clk_tck = os.sysconf(os.sysconf_names["SC_CLK_TCK"])
process_utime_sec = utime // clk_tck
process_stime_sec = stime // clk_tck
process_start_time_sec = start_time // clk_tck
process_elapsed_sec = system_uptime_sec - process_start_time_sec
process_usage_sec = process_utime_sec + process_stime_sec
cpu_average_usage = process_usage_sec * 100 // process_elapsed_sec
with open(f"/proc/{pid}/statm", "r") as f:
mem_stats = f.readline().split()
mem_res = int(mem_stats[1]) * os.sysconf("SC_PAGE_SIZE") / 1024
if docker_memlimit > 0:
mem_pct = round((mem_res / docker_memlimit) * 100, 1)
else:
mem_pct = round((mem_res / total_mem) * 100, 1)
usages[pid] = {
"cpu": str(cpu_percent),
"cpu_average": str(round(cpu_average_usage, 2)),
"mem": f"{mem_pct}",
"cmdline": clean_camera_user_pass(" ".join(cmdline)),
}
except Exception:
continue
return usages
def get_physical_interfaces(interfaces) -> list:
if not interfaces:
return []
with open("/proc/net/dev", "r") as file:
lines = file.readlines()
physical_interfaces = []
for line in lines:
if ":" in line:
interface = line.split(":")[0].strip()
for int in interfaces:
if interface.startswith(int):
physical_interfaces.append(interface)
return physical_interfaces
def get_bandwidth_stats(config) -> dict[str, dict]:
"""Get bandwidth usages for each ffmpeg process id"""
usages = {}
top_command = ["nethogs", "-t", "-v0", "-c5", "-d1"] + get_physical_interfaces(
config.telemetry.network_interfaces
)
p = sp.run(
top_command,
encoding="ascii",
capture_output=True,
)
if p.returncode != 0:
logger.error(f"Error getting network stats :: {p.stderr}")
return usages
else:
lines = p.stdout.split("\n")
for line in lines:
stats = list(filter(lambda a: a != "", line.strip().split("\t")))
try:
if re.search(
r"(^ffmpeg|\/go2rtc|frigate\.detector\.[a-z]+)/([0-9]+)/", stats[0]
):
process = stats[0].split("/")
usages[process[len(process) - 2]] = {
"bandwidth": round(float(stats[1]) + float(stats[2]), 1),
}
except (IndexError, ValueError):
continue
return usages
def is_vaapi_amd_driver() -> bool:
# Use the explicitly configured driver, if available
driver = os.environ.get(DRIVER_ENV_VAR)
if driver:
return driver == DRIVER_AMD
# Otherwise, ask vainfo what is has autodetected
p = vainfo_hwaccel()
if p.returncode != 0:
logger.error(f"Unable to poll vainfo: {p.stderr}")
return False
else:
output = p.stdout.decode("unicode_escape").split("\n")
# VA Info will print out the friendly name of the driver
return any("AMD Radeon Graphics" in line for line in output)
def get_amd_gpu_stats() -> dict[str, str]:
"""Get stats using radeontop."""
radeontop_command = ["radeontop", "-d", "-", "-l", "1"]
p = sp.run(
radeontop_command,
encoding="ascii",
capture_output=True,
)
if p.returncode != 0:
logger.error(f"Unable to poll radeon GPU stats: {p.stderr}")
return None
else:
usages = p.stdout.split(",")
results: dict[str, str] = {}
for hw in usages:
if "gpu" in hw:
results["gpu"] = f"{hw.strip().split(' ')[1].replace('%', '')}%"
elif "vram" in hw:
results["mem"] = f"{hw.strip().split(' ')[1].replace('%', '')}%"
return results
def get_intel_gpu_stats() -> dict[str, str]:
"""Get stats using intel_gpu_top."""
def get_stats_manually(output: str) -> dict[str, str]:
"""Find global stats via regex when json fails to parse."""
reading = "".join(output)
results: dict[str, str] = {}
# render is used for qsv
render = []
for result in re.findall(r'"Render/3D/0":{[a-z":\d.,%]+}', reading):
packet = json.loads(result[14:])
single = packet.get("busy", 0.0)
render.append(float(single))
if render:
render_avg = sum(render) / len(render)
else:
render_avg = 1
# video is used for vaapi
video = []
for result in re.findall(r'"Video/\d":{[a-z":\d.,%]+}', reading):
packet = json.loads(result[10:])
single = packet.get("busy", 0.0)
video.append(float(single))
if video:
video_avg = sum(video) / len(video)
else:
video_avg = 1
results["gpu"] = f"{round((video_avg + render_avg) / 2, 2)}%"
results["mem"] = "-%"
return results
intel_gpu_top_command = [
"timeout",
"0.5s",
"intel_gpu_top",
"-J",
"-o",
"-",
"-s",
"1",
]
p = sp.run(
intel_gpu_top_command,
encoding="ascii",
capture_output=True,
)
# timeout has a non-zero returncode when timeout is reached
if p.returncode != 124:
logger.error(f"Unable to poll intel GPU stats: {p.stderr}")
return None
else:
output = "".join(p.stdout.split())
try:
data = json.loads(f"[{output}]")
except json.JSONDecodeError:
return get_stats_manually(output)
results: dict[str, str] = {}
render = {"global": []}
video = {"global": []}
for block in data:
global_engine = block.get("engines")
if global_engine:
render_frame = global_engine.get("Render/3D/0", {}).get("busy")
video_frame = global_engine.get("Video/0", {}).get("busy")
if render_frame is not None:
render["global"].append(float(render_frame))
if video_frame is not None:
video["global"].append(float(video_frame))
clients = block.get("clients", {})
if clients and len(clients):
for client_block in clients.values():
key = client_block["pid"]
if render.get(key) is None:
render[key] = []
video[key] = []
client_engine = client_block.get("engine-classes", {})
render_frame = client_engine.get("Render/3D", {}).get("busy")
video_frame = client_engine.get("Video", {}).get("busy")
if render_frame is not None:
render[key].append(float(render_frame))
if video_frame is not None:
video[key].append(float(video_frame))
if render["global"]:
results["gpu"] = (
f"{round(((sum(render['global']) / len(render['global'])) + (sum(video['global']) / len(video['global']))) / 2, 2)}%"
)
results["mem"] = "-%"
if len(render.keys()) > 1:
results["clients"] = {}
for key in render.keys():
if key == "global" or not render[key] or not video[key]:
continue
results["clients"][key] = (
f"{round(((sum(render[key]) / len(render[key])) + (sum(video[key]) / len(video[key]))) / 2, 2)}%"
)
return results
def try_get_info(f, h, default="N/A"):
try:
if h:
v = f(h)
else:
v = f()
except nvml.NVMLError_NotSupported:
v = default
return v
def get_nvidia_gpu_stats() -> dict[int, dict]:
results = {}
try:
nvml.nvmlInit()
deviceCount = nvml.nvmlDeviceGetCount()
for i in range(deviceCount):
handle = nvml.nvmlDeviceGetHandleByIndex(i)
meminfo = try_get_info(nvml.nvmlDeviceGetMemoryInfo, handle)
util = try_get_info(nvml.nvmlDeviceGetUtilizationRates, handle)
enc = try_get_info(nvml.nvmlDeviceGetEncoderUtilization, handle)
dec = try_get_info(nvml.nvmlDeviceGetDecoderUtilization, handle)
pstate = try_get_info(nvml.nvmlDeviceGetPowerState, handle, default=None)
if util != "N/A":
gpu_util = util.gpu
else:
gpu_util = 0
if meminfo != "N/A":
gpu_mem_util = meminfo.used / meminfo.total * 100
else:
gpu_mem_util = -1
if enc != "N/A":
enc_util = enc[0]
else:
enc_util = -1
if dec != "N/A":
dec_util = dec[0]
else:
dec_util = -1
results[i] = {
"name": nvml.nvmlDeviceGetName(handle),
"gpu": gpu_util,
"mem": gpu_mem_util,
"enc": enc_util,
"dec": dec_util,
"pstate": pstate or "unknown",
}
except Exception:
pass
finally:
return results
def get_jetson_stats() -> dict[int, dict]:
results = {}
try:
results["mem"] = "-" # no discrete gpu memory
with open("/sys/devices/gpu.0/load", "r") as f:
gpuload = float(f.readline()) / 10
results["gpu"] = f"{gpuload}%"
except Exception:
return None
return results
def ffprobe_stream(ffmpeg, path: str) -> sp.CompletedProcess:
"""Run ffprobe on stream."""
clean_path = escape_special_characters(path)
ffprobe_cmd = [
ffmpeg.ffprobe_path,
"-timeout",
"1000000",
"-print_format",
"json",
"-show_entries",
"stream=codec_long_name,width,height,bit_rate,duration,display_aspect_ratio,avg_frame_rate",
"-loglevel",
"quiet",
clean_path,
]
return sp.run(ffprobe_cmd, capture_output=True)
def vainfo_hwaccel(device_name: Optional[str] = None) -> sp.CompletedProcess:
"""Run vainfo."""
ffprobe_cmd = (
["vainfo"]
if not device_name
else ["vainfo", "--display", "drm", "--device", f"/dev/dri/{device_name}"]
)
return sp.run(ffprobe_cmd, capture_output=True)
def get_nvidia_driver_info() -> dict[str, any]:
"""Get general hardware info for nvidia GPU."""
results = {}
try:
nvml.nvmlInit()
deviceCount = nvml.nvmlDeviceGetCount()
for i in range(deviceCount):
handle = nvml.nvmlDeviceGetHandleByIndex(i)
driver = try_get_info(nvml.nvmlSystemGetDriverVersion, None, default=None)
cuda_compute = try_get_info(
nvml.nvmlDeviceGetCudaComputeCapability, handle, default=None
)
vbios = try_get_info(nvml.nvmlDeviceGetVbiosVersion, handle, default=None)
results[i] = {
"name": nvml.nvmlDeviceGetName(handle),
"driver": driver or "unknown",
"cuda_compute": cuda_compute or "unknown",
"vbios": vbios or "unknown",
}
except Exception:
pass
finally:
return results
def auto_detect_hwaccel() -> str:
"""Detect hwaccel args by default."""
try:
cuda = False
vaapi = False
resp = requests.get("http://127.0.0.1:1984/api/ffmpeg/hardware", timeout=3)
if resp.status_code == 200:
data: dict[str, list[dict[str, str]]] = resp.json()
for source in data.get("sources", []):
if "cuda" in source.get("url", "") and source.get("name") == "OK":
cuda = True
if "vaapi" in source.get("url", "") and source.get("name") == "OK":
vaapi = True
except requests.RequestException:
pass
if cuda:
logger.info("Automatically detected nvidia hwaccel for video decoding")
return FFMPEG_HWACCEL_NVIDIA
if vaapi:
logger.info("Automatically detected vaapi hwaccel for video decoding")
return FFMPEG_HWACCEL_VAAPI
logger.warning(
"Did not detect hwaccel, using a GPU for accelerated video decoding is highly recommended"
)
return ""
async def get_video_properties(
ffmpeg, url: str, get_duration: bool = False
) -> dict[str, any]:
async def calculate_duration(video: Optional[any]) -> float:
duration = None
if video is not None:
# Get the frames per second (fps) of the video stream
fps = video.get(cv2.CAP_PROP_FPS)
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
if fps and total_frames:
duration = total_frames / fps
# if cv2 failed need to use ffprobe
if duration is None:
p = await asyncio.create_subprocess_exec(
ffmpeg.ffprobe_path,
"-v",
"error",
"-show_entries",
"format=duration",
"-of",
"default=noprint_wrappers=1:nokey=1",
f"{url}",
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE,
)
await p.wait()
if p.returncode == 0:
result = (await p.stdout.read()).decode()
else:
result = None
if result:
try:
duration = float(result.strip())
except ValueError:
duration = -1
else:
duration = -1
return duration
width = height = 0
try:
# Open the video stream using OpenCV
video = cv2.VideoCapture(url)
# Check if the video stream was opened successfully
if not video.isOpened():
video = None
except Exception:
video = None
result = {}
if get_duration:
result["duration"] = await calculate_duration(video)
if video is not None:
# Get the width of frames in the video stream
width = video.get(cv2.CAP_PROP_FRAME_WIDTH)
# Get the height of frames in the video stream
height = video.get(cv2.CAP_PROP_FRAME_HEIGHT)
# Get the stream encoding
fourcc_int = int(video.get(cv2.CAP_PROP_FOURCC))
fourcc = (
chr((fourcc_int >> 0) & 255)
+ chr((fourcc_int >> 8) & 255)
+ chr((fourcc_int >> 16) & 255)
+ chr((fourcc_int >> 24) & 255)
)
# Release the video stream
video.release()
result["width"] = round(width)
result["height"] = round(height)
result["fourcc"] = fourcc
return result