mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-02-15 07:35:27 +03:00
30a3c6dc86
In preparation to switch the multiprocessing startup method away from "fork", we cannot rely on os.fork cloning the log state at fork time. Instead, we have to set up logging before we run the business logic of each process.
110 lines
3.1 KiB
Python
Executable File
110 lines
3.1 KiB
Python
Executable File
import datetime
|
|
import multiprocessing as mp
|
|
from statistics import mean
|
|
|
|
import numpy as np
|
|
|
|
import frigate.util as util
|
|
from frigate.config import DetectorTypeEnum
|
|
from frigate.object_detection import (
|
|
ObjectDetectProcess,
|
|
RemoteObjectDetector,
|
|
load_labels,
|
|
)
|
|
|
|
my_frame = np.expand_dims(np.full((300, 300, 3), 1, np.uint8), axis=0)
|
|
labels = load_labels("/labelmap.txt")
|
|
|
|
######
|
|
# Minimal same process runner
|
|
######
|
|
# object_detector = LocalObjectDetector()
|
|
# tensor_input = np.expand_dims(np.full((300,300,3), 0, np.uint8), axis=0)
|
|
|
|
# start = datetime.datetime.now().timestamp()
|
|
|
|
# frame_times = []
|
|
# for x in range(0, 1000):
|
|
# start_frame = datetime.datetime.now().timestamp()
|
|
|
|
# tensor_input[:] = my_frame
|
|
# detections = object_detector.detect_raw(tensor_input)
|
|
# parsed_detections = []
|
|
# for d in detections:
|
|
# if d[1] < 0.4:
|
|
# break
|
|
# parsed_detections.append((
|
|
# labels[int(d[0])],
|
|
# float(d[1]),
|
|
# (d[2], d[3], d[4], d[5])
|
|
# ))
|
|
# frame_times.append(datetime.datetime.now().timestamp()-start_frame)
|
|
|
|
# duration = datetime.datetime.now().timestamp()-start
|
|
# print(f"Processed for {duration:.2f} seconds.")
|
|
# print(f"Average frame processing time: {mean(frame_times)*1000:.2f}ms")
|
|
|
|
|
|
def start(id, num_detections, detection_queue, event):
|
|
object_detector = RemoteObjectDetector(
|
|
str(id), "/labelmap.txt", detection_queue, event
|
|
)
|
|
start = datetime.datetime.now().timestamp()
|
|
|
|
frame_times = []
|
|
for x in range(0, num_detections):
|
|
start_frame = datetime.datetime.now().timestamp()
|
|
object_detector.detect(my_frame)
|
|
frame_times.append(datetime.datetime.now().timestamp() - start_frame)
|
|
|
|
duration = datetime.datetime.now().timestamp() - start
|
|
object_detector.cleanup()
|
|
print(f"{id} - Processed for {duration:.2f} seconds.")
|
|
print(f"{id} - FPS: {object_detector.fps.eps():.2f}")
|
|
print(f"{id} - Average frame processing time: {mean(frame_times)*1000:.2f}ms")
|
|
|
|
|
|
######
|
|
# Separate process runner
|
|
######
|
|
# event = mp.Event()
|
|
# detection_queue = mp.Queue()
|
|
# edgetpu_process = EdgeTPUProcess(detection_queue, {'1': event}, 'usb:0')
|
|
|
|
# start(1, 1000, edgetpu_process.detection_queue, event)
|
|
# print(f"Average raw inference speed: {edgetpu_process.avg_inference_speed.value*1000:.2f}ms")
|
|
|
|
####
|
|
# Multiple camera processes
|
|
####
|
|
camera_processes = []
|
|
|
|
events = {}
|
|
for x in range(0, 10):
|
|
events[str(x)] = mp.Event()
|
|
detection_queue = mp.Queue()
|
|
edgetpu_process_1 = ObjectDetectProcess(
|
|
detection_queue, events, DetectorTypeEnum.edgetpu, "usb:0"
|
|
)
|
|
edgetpu_process_2 = ObjectDetectProcess(
|
|
detection_queue, events, DetectorTypeEnum.edgetpu, "usb:1"
|
|
)
|
|
|
|
for x in range(0, 10):
|
|
camera_process = util.Process(
|
|
target=start, args=(x, 300, detection_queue, events[str(x)])
|
|
)
|
|
camera_process.daemon = True
|
|
camera_processes.append(camera_process)
|
|
|
|
start_time = datetime.datetime.now().timestamp()
|
|
|
|
for p in camera_processes:
|
|
p.start()
|
|
|
|
for p in camera_processes:
|
|
p.join()
|
|
|
|
duration = datetime.datetime.now().timestamp() - start_time
|
|
print(f"Total - Processed for {duration:.2f} seconds.")
|