import datetime
import logging
import multiprocessing as mp
import os
import queue
import signal
import threading
from abc import ABC, abstractmethod
import numpy as np
from setproctitle import setproctitle
from frigate.detectors import create_detector
from frigate.detectors.detector_config import InputTensorEnum
from frigate.util.builtin import EventsPerSecond, load_labels
from frigate.util.image import SharedMemoryFrameManager
from frigate.util.services import listen
logger = logging.getLogger(__name__)
class ObjectDetector(ABC):
@abstractmethod
def detect(self, tensor_input, threshold=0.4):
pass
def tensor_transform(desired_shape):
# Currently this function only supports BHWC permutations
if desired_shape == InputTensorEnum.nhwc:
return None
elif desired_shape == InputTensorEnum.nchw:
return (0, 3, 1, 2)
class LocalObjectDetector(ObjectDetector):
def __init__(
self,
detector_config=None,
labels=None,
):
self.fps = EventsPerSecond()
if labels is None:
self.labels = {}
else:
self.labels = load_labels(labels)
if detector_config:
self.input_transform = tensor_transform(detector_config.model.input_tensor)
else:
self.input_transform = None
self.detect_api = create_detector(detector_config)
def detect(self, tensor_input, threshold=0.4):
detections = []
raw_detections = self.detect_raw(tensor_input)
for d in raw_detections:
if int(d[0]) < 0 or int(d[0]) >= len(self.labels):
logger.warning(f"Raw Detect returned invalid label: {d}")
continue
if d[1] < threshold:
break
detections.append(
(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
)
self.fps.update()
return detections
def detect_raw(self, tensor_input):
if self.input_transform:
tensor_input = np.transpose(tensor_input, self.input_transform)
return self.detect_api.detect_raw(tensor_input=tensor_input)
def run_detector(
name: str,
detection_queue: mp.Queue,
out_events: dict[str, mp.Event],
avg_speed,
start,
detector_config,
):
threading.current_thread().name = f"detector:{name}"
logger = logging.getLogger(f"detector.{name}")
logger.info(f"Starting detection process: {os.getpid()}")
setproctitle(f"frigate.detector.{name}")
listen()
stop_event = mp.Event()
def receiveSignal(signalNumber, frame):
stop_event.set()
signal.signal(signal.SIGTERM, receiveSignal)
signal.signal(signal.SIGINT, receiveSignal)
frame_manager = SharedMemoryFrameManager()
object_detector = LocalObjectDetector(detector_config=detector_config)
outputs = {}
for name in out_events.keys():
out_shm = mp.shared_memory.SharedMemory(name=f"out-{name}", create=False)
out_np = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
outputs[name] = {"shm": out_shm, "np": out_np}
while not stop_event.is_set():
try:
connection_id = detection_queue.get(timeout=1)
except queue.Empty:
continue
input_frame = frame_manager.get(
connection_id,
(1, detector_config.model.height, detector_config.model.width, 3),
)
if input_frame is None:
logger.warning(f"Failed to get frame {connection_id} from SHM")
continue
# detect and send the output
start.value = datetime.datetime.now().timestamp()
detections = object_detector.detect_raw(input_frame)
duration = datetime.datetime.now().timestamp() - start.value
frame_manager.close(connection_id)
outputs[connection_id]["np"][:] = detections[:]
out_events[connection_id].set()
start.value = 0.0
avg_speed.value = (avg_speed.value * 9 + duration) / 10
logger.info("Exited detection process...")
class ObjectDetectProcess:
def __init__(
self,
name,
detection_queue,
out_events,
detector_config,
):
self.name = name
self.out_events = out_events
self.detection_queue = detection_queue
self.avg_inference_speed = mp.Value("d", 0.01)
self.detection_start = mp.Value("d", 0.0)
self.detect_process = None
self.detector_config = detector_config
self.start_or_restart()
def stop(self):
# if the process has already exited on its own, just return
if self.detect_process and self.detect_process.exitcode:
return
self.detect_process.terminate()
logging.info("Waiting for detection process to exit gracefully...")
self.detect_process.join(timeout=30)
if self.detect_process.exitcode is None:
logging.info("Detection process didn't exit. Force killing...")
self.detect_process.kill()
self.detect_process.join()
logging.info("Detection process has exited...")
def start_or_restart(self):
self.detection_start.value = 0.0
if (self.detect_process is not None) and self.detect_process.is_alive():
self.stop()
self.detect_process = mp.Process(
target=run_detector,
name=f"detector:{self.name}",
args=(
self.name,
self.detection_queue,
self.out_events,
self.avg_inference_speed,
self.detection_start,
self.detector_config,
),
)
self.detect_process.daemon = True
self.detect_process.start()
class RemoteObjectDetector:
def __init__(self, name, labels, detection_queue, event, model_config, stop_event):
self.labels = labels
self.name = name
self.fps = EventsPerSecond()
self.detection_queue = detection_queue
self.event = event
self.stop_event = stop_event
self.shm = mp.shared_memory.SharedMemory(name=self.name, create=False)
self.np_shm = np.ndarray(
(1, model_config.height, model_config.width, 3),
dtype=np.uint8,
buffer=self.shm.buf,
)
self.out_shm = mp.shared_memory.SharedMemory(
name=f"out-{self.name}", create=False
)
self.out_np_shm = np.ndarray((20, 6), dtype=np.float32, buffer=self.out_shm.buf)
def detect(self, tensor_input, threshold=0.4):
detections = []
if self.stop_event.is_set():
return detections
# copy input to shared memory
self.np_shm[:] = tensor_input[:]
self.event.clear()
self.detection_queue.put(self.name)
result = self.event.wait(timeout=5.0)
# if it timed out
if result is None:
return detections
for d in self.out_np_shm:
if d[1] < threshold:
break
detections.append(
(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
)
self.fps.update()
return detections
def cleanup(self):
self.shm.unlink()
self.out_shm.unlink()