blakeblackshear.frigate/frigate/object_detection.py

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.config import DetectorTypeEnum, InputTensorEnum
from frigate.detectors.edgetpu_tfl import EdgeTpuTfl
from frigate.detectors.cpu_tfl import CpuTfl

from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen, load_labels

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,
        det_type=DetectorTypeEnum.cpu,
        det_device=None,
        model_config=None,
        num_threads=3,
        labels=None,
    ):
        self.fps = EventsPerSecond()
        if labels is None:
            self.labels = {}
        else:
            self.labels = load_labels(labels)

        if model_config:
            self.input_transform = tensor_transform(model_config.input_tensor)
        else:
            self.input_transform = None

        if det_type == DetectorTypeEnum.edgetpu:
            self.detect_api = EdgeTpuTfl(
                det_device=det_device, model_config=model_config
            )
        else:
            logger.warning(
                "CPU detectors are not recommended and should only be used for testing or for trial purposes."
            )
            self.detect_api = CpuTfl(model_config=model_config, num_threads=num_threads)

    def detect(self, tensor_input, threshold=0.4):
        detections = []

        raw_detections = self.detect_raw(tensor_input)

        for d in raw_detections:
            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,
    model_config,
    det_type,
    det_device,
    num_threads,
):
    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(
        det_type=det_type,
        det_device=det_device,
        model_config=model_config,
        num_threads=num_threads,
    )

    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=5)
        except queue.Empty:
            continue
        input_frame = frame_manager.get(
            connection_id, (1, model_config.height, model_config.width, 3)
        )

        if input_frame is None:
            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
        outputs[connection_id]["np"][:] = detections[:]
        out_events[connection_id].set()
        start.value = 0.0

        avg_speed.value = (avg_speed.value * 9 + duration) / 10


class ObjectDetectProcess:
    def __init__(
        self,
        name,
        detection_queue,
        out_events,
        model_config,
        det_type=None,
        det_device=None,
        num_threads=3,
    ):
        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.model_config = model_config
        self.det_type = det_type
        self.det_device = det_device
        self.num_threads = num_threads
        self.start_or_restart()

    def stop(self):
        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 didnt exit. Force killing...")
            self.detect_process.kill()
            self.detect_process.join()

    def start_or_restart(self):
        self.detection_start.value = 0.0
        if (not self.detect_process is 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.model_config,
                self.det_type,
                self.det_device,
                self.num_threads,
            ),
        )
        self.detect_process.daemon = True
        self.detect_process.start()


class RemoteObjectDetector:
    def __init__(self, name, labels, detection_queue, event, model_config):
        self.labels = labels
        self.name = name
        self.fps = EventsPerSecond()
        self.detection_queue = detection_queue
        self.event = 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 = []

        # copy input to shared memory
        self.np_shm[:] = tensor_input[:]
        self.event.clear()
        self.detection_queue.put(self.name)
        result = self.event.wait(timeout=10.0)

        # 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()