mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-10-15 20:07:23 +02:00
265 lines
8.3 KiB
Python
265 lines
8.3 KiB
Python
import datetime
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import logging
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import multiprocessing as mp
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import os
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import queue
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import signal
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import threading
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from abc import ABC, abstractmethod
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from typing import Dict
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import numpy as np
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import tflite_runtime.interpreter as tflite
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from setproctitle import setproctitle
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from tflite_runtime.interpreter import load_delegate
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from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen, load_labels
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logger = logging.getLogger(__name__)
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class ObjectDetector(ABC):
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@abstractmethod
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def detect(self, tensor_input, threshold=0.4):
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pass
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class LocalObjectDetector(ObjectDetector):
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def __init__(self, tf_device=None, model_path=None, num_threads=3, labels=None):
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self.fps = EventsPerSecond()
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if labels is None:
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self.labels = {}
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else:
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self.labels = load_labels(labels)
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device_config = {"device": "usb"}
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if not tf_device is None:
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device_config = {"device": tf_device}
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edge_tpu_delegate = None
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if tf_device != "cpu":
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try:
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logger.info(f"Attempting to load TPU as {device_config['device']}")
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edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
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logger.info("TPU found")
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self.interpreter = tflite.Interpreter(
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model_path=model_path or "/edgetpu_model.tflite",
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experimental_delegates=[edge_tpu_delegate],
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)
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except ValueError:
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logger.error(
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"No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
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)
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raise
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else:
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logger.warning(
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"CPU detectors are not recommended and should only be used for testing or for trial purposes."
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)
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self.interpreter = tflite.Interpreter(
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model_path=model_path or "/cpu_model.tflite", num_threads=num_threads
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)
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self.interpreter.allocate_tensors()
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self.tensor_input_details = self.interpreter.get_input_details()
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self.tensor_output_details = self.interpreter.get_output_details()
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def detect(self, tensor_input, threshold=0.4):
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detections = []
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raw_detections = self.detect_raw(tensor_input)
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for d in raw_detections:
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if d[1] < threshold:
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break
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detections.append(
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(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
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)
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self.fps.update()
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return detections
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def detect_raw(self, tensor_input):
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self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
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self.interpreter.invoke()
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boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
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class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
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scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
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count = int(
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self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
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)
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detections = np.zeros((20, 6), np.float32)
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for i in range(count):
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if scores[i] < 0.4 or i == 20:
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break
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detections[i] = [
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class_ids[i],
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float(scores[i]),
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boxes[i][0],
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boxes[i][1],
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boxes[i][2],
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boxes[i][3],
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]
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return detections
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def run_detector(
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name: str,
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detection_queue: mp.Queue,
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out_events: Dict[str, mp.Event],
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avg_speed,
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start,
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model_path,
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model_shape,
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tf_device,
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num_threads,
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):
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threading.current_thread().name = f"detector:{name}"
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logger = logging.getLogger(f"detector.{name}")
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logger.info(f"Starting detection process: {os.getpid()}")
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setproctitle(f"frigate.detector.{name}")
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listen()
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stop_event = mp.Event()
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def receiveSignal(signalNumber, frame):
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stop_event.set()
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signal.signal(signal.SIGTERM, receiveSignal)
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signal.signal(signal.SIGINT, receiveSignal)
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frame_manager = SharedMemoryFrameManager()
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object_detector = LocalObjectDetector(
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tf_device=tf_device, model_path=model_path, num_threads=num_threads
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)
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outputs = {}
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for name in out_events.keys():
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out_shm = mp.shared_memory.SharedMemory(name=f"out-{name}", create=False)
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out_np = np.ndarray((20, 6), dtype=np.float32, buffer=out_shm.buf)
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outputs[name] = {"shm": out_shm, "np": out_np}
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while not stop_event.is_set():
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try:
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connection_id = detection_queue.get(timeout=5)
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except queue.Empty:
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continue
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input_frame = frame_manager.get(
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connection_id, (1, model_shape[0], model_shape[1], 3)
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)
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if input_frame is None:
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continue
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# detect and send the output
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start.value = datetime.datetime.now().timestamp()
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detections = object_detector.detect_raw(input_frame)
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duration = datetime.datetime.now().timestamp() - start.value
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outputs[connection_id]["np"][:] = detections[:]
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out_events[connection_id].set()
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start.value = 0.0
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avg_speed.value = (avg_speed.value * 9 + duration) / 10
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class EdgeTPUProcess:
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def __init__(
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self,
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name,
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detection_queue,
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out_events,
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model_path,
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model_shape,
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tf_device=None,
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num_threads=3,
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):
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self.name = name
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self.out_events = out_events
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self.detection_queue = detection_queue
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self.avg_inference_speed = mp.Value("d", 0.01)
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self.detection_start = mp.Value("d", 0.0)
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self.detect_process = None
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self.model_path = model_path
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self.model_shape = model_shape
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self.tf_device = tf_device
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self.num_threads = num_threads
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self.start_or_restart()
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def stop(self):
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self.detect_process.terminate()
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logging.info("Waiting for detection process to exit gracefully...")
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self.detect_process.join(timeout=30)
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if self.detect_process.exitcode is None:
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logging.info("Detection process didnt exit. Force killing...")
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self.detect_process.kill()
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self.detect_process.join()
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def start_or_restart(self):
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self.detection_start.value = 0.0
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if (not self.detect_process is None) and self.detect_process.is_alive():
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self.stop()
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self.detect_process = mp.Process(
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target=run_detector,
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name=f"detector:{self.name}",
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args=(
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self.name,
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self.detection_queue,
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self.out_events,
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self.avg_inference_speed,
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self.detection_start,
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self.model_path,
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self.model_shape,
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self.tf_device,
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self.num_threads,
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),
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)
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self.detect_process.daemon = True
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self.detect_process.start()
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class RemoteObjectDetector:
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def __init__(self, name, labels, detection_queue, event, model_shape):
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self.labels = labels
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self.name = name
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self.fps = EventsPerSecond()
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self.detection_queue = detection_queue
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self.event = event
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self.shm = mp.shared_memory.SharedMemory(name=self.name, create=False)
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self.np_shm = np.ndarray(
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(1, model_shape[0], model_shape[1], 3), dtype=np.uint8, buffer=self.shm.buf
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)
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self.out_shm = mp.shared_memory.SharedMemory(
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name=f"out-{self.name}", create=False
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)
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self.out_np_shm = np.ndarray((20, 6), dtype=np.float32, buffer=self.out_shm.buf)
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def detect(self, tensor_input, threshold=0.4):
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detections = []
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# copy input to shared memory
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self.np_shm[:] = tensor_input[:]
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self.event.clear()
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self.detection_queue.put(self.name)
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result = self.event.wait(timeout=10.0)
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# if it timed out
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if result is None:
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return detections
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for d in self.out_np_shm:
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if d[1] < threshold:
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break
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detections.append(
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(self.labels[int(d[0])], float(d[1]), (d[2], d[3], d[4], d[5]))
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)
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self.fps.update()
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return detections
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def cleanup(self):
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self.shm.unlink()
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self.out_shm.unlink()
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