frigate/benchmark.py

import os
from statistics import mean
import multiprocessing as mp
import numpy as np
import datetime
from frigate.edgetpu import LocalObjectDetector, EdgeTPUProcess, 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()
    detections = 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 = EdgeTPUProcess(detection_queue, events, 'usb:0')
edgetpu_process_2 = EdgeTPUProcess(detection_queue, events, 'usb:1')

for x in range(0, 10):
  camera_process = mp.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.")