mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
ebf4e43ced
All Dict, List were converted to dict, list, see: https://mypy.readthedocs.io/en/stable/builtin_types.html#generic-types
264 lines
8.3 KiB
Python
264 lines
8.3 KiB
Python
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
|
|
import tflite_runtime.interpreter as tflite
|
|
from setproctitle import setproctitle
|
|
from tflite_runtime.interpreter import load_delegate
|
|
|
|
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
|
|
|
|
|
|
class LocalObjectDetector(ObjectDetector):
|
|
def __init__(self, tf_device=None, model_path=None, num_threads=3, labels=None):
|
|
self.fps = EventsPerSecond()
|
|
if labels is None:
|
|
self.labels = {}
|
|
else:
|
|
self.labels = load_labels(labels)
|
|
|
|
device_config = {"device": "usb"}
|
|
if not tf_device is None:
|
|
device_config = {"device": tf_device}
|
|
|
|
edge_tpu_delegate = None
|
|
|
|
if tf_device != "cpu":
|
|
try:
|
|
logger.info(f"Attempting to load TPU as {device_config['device']}")
|
|
edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
|
|
logger.info("TPU found")
|
|
self.interpreter = tflite.Interpreter(
|
|
model_path=model_path or "/edgetpu_model.tflite",
|
|
experimental_delegates=[edge_tpu_delegate],
|
|
)
|
|
except ValueError:
|
|
logger.error(
|
|
"No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
|
|
)
|
|
raise
|
|
else:
|
|
logger.warning(
|
|
"CPU detectors are not recommended and should only be used for testing or for trial purposes."
|
|
)
|
|
self.interpreter = tflite.Interpreter(
|
|
model_path=model_path or "/cpu_model.tflite", num_threads=num_threads
|
|
)
|
|
|
|
self.interpreter.allocate_tensors()
|
|
|
|
self.tensor_input_details = self.interpreter.get_input_details()
|
|
self.tensor_output_details = self.interpreter.get_output_details()
|
|
|
|
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):
|
|
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
|
|
self.interpreter.invoke()
|
|
|
|
boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
|
|
class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
|
|
scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
|
|
count = int(
|
|
self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
|
|
)
|
|
|
|
detections = np.zeros((20, 6), np.float32)
|
|
|
|
for i in range(count):
|
|
if scores[i] < 0.4 or i == 20:
|
|
break
|
|
detections[i] = [
|
|
class_ids[i],
|
|
float(scores[i]),
|
|
boxes[i][0],
|
|
boxes[i][1],
|
|
boxes[i][2],
|
|
boxes[i][3],
|
|
]
|
|
|
|
return detections
|
|
|
|
|
|
def run_detector(
|
|
name: str,
|
|
detection_queue: mp.Queue,
|
|
out_events: dict[str, mp.Event],
|
|
avg_speed,
|
|
start,
|
|
model_path,
|
|
model_shape,
|
|
tf_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(
|
|
tf_device=tf_device, model_path=model_path, 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_shape[0], model_shape[1], 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 EdgeTPUProcess:
|
|
def __init__(
|
|
self,
|
|
name,
|
|
detection_queue,
|
|
out_events,
|
|
model_path,
|
|
model_shape,
|
|
tf_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_path = model_path
|
|
self.model_shape = model_shape
|
|
self.tf_device = tf_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_path,
|
|
self.model_shape,
|
|
self.tf_device,
|
|
self.num_threads,
|
|
),
|
|
)
|
|
self.detect_process.daemon = True
|
|
self.detect_process.start()
|
|
|
|
|
|
class RemoteObjectDetector:
|
|
def __init__(self, name, labels, detection_queue, event, model_shape):
|
|
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_shape[0], model_shape[1], 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()
|