mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
224 lines
7.7 KiB
Python
224 lines
7.7 KiB
Python
import datetime
|
|
import hashlib
|
|
import logging
|
|
import multiprocessing as mp
|
|
import os
|
|
import queue
|
|
import threading
|
|
import signal
|
|
from abc import ABC, abstractmethod
|
|
from multiprocessing.connection import Connection
|
|
from typing import Dict
|
|
|
|
import numpy as np
|
|
import tflite_runtime.interpreter as tflite
|
|
from tflite_runtime.interpreter import load_delegate
|
|
|
|
from frigate.util import EventsPerSecond, SharedMemoryFrameManager, listen
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
def load_labels(path, encoding='utf-8'):
|
|
"""Loads labels from file (with or without index numbers).
|
|
Args:
|
|
path: path to label file.
|
|
encoding: label file encoding.
|
|
Returns:
|
|
Dictionary mapping indices to labels.
|
|
"""
|
|
with open(path, 'r', encoding=encoding) as f:
|
|
lines = f.readlines()
|
|
if not lines:
|
|
return {}
|
|
|
|
if lines[0].split(' ', maxsplit=1)[0].isdigit():
|
|
pairs = [line.split(' ', maxsplit=1) for line in lines]
|
|
return {int(index): label.strip() for index, label in pairs}
|
|
else:
|
|
return {index: line.strip() for index, line in enumerate(lines)}
|
|
|
|
class ObjectDetector(ABC):
|
|
@abstractmethod
|
|
def detect(self, tensor_input, threshold = .4):
|
|
pass
|
|
|
|
class LocalObjectDetector(ObjectDetector):
|
|
def __init__(self, tf_device=None, 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")
|
|
except ValueError:
|
|
logger.info("No EdgeTPU detected. Falling back to CPU.")
|
|
|
|
if edge_tpu_delegate is None:
|
|
self.interpreter = tflite.Interpreter(
|
|
model_path='/cpu_model.tflite')
|
|
else:
|
|
self.interpreter = tflite.Interpreter(
|
|
model_path='/edgetpu_model.tflite',
|
|
experimental_delegates=[edge_tpu_delegate])
|
|
|
|
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=.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 = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[0]['index']))
|
|
label_codes = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[1]['index']))
|
|
scores = np.squeeze(self.interpreter.get_tensor(self.tensor_output_details[2]['index']))
|
|
|
|
detections = np.zeros((20,6), np.float32)
|
|
for i, score in enumerate(scores):
|
|
detections[i] = [label_codes[i], score, 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, tf_device):
|
|
threading.current_thread().name = f"detector:{name}"
|
|
logger = logging.getLogger(f"detector.{name}")
|
|
logger.info(f"Starting detection process: {os.getpid()}")
|
|
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)
|
|
|
|
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 True:
|
|
if stop_event.is_set():
|
|
break
|
|
|
|
try:
|
|
connection_id = detection_queue.get(timeout=5)
|
|
except queue.Empty:
|
|
continue
|
|
input_frame = frame_manager.get(connection_id, (1,300,300,3))
|
|
|
|
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, tf_device=None):
|
|
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.tf_device = tf_device
|
|
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.tf_device))
|
|
self.detect_process.daemon = True
|
|
self.detect_process.start()
|
|
|
|
class RemoteObjectDetector():
|
|
def __init__(self, name, labels, detection_queue, event):
|
|
self.labels = load_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((1,300,300,3), 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=.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()
|