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benchmark.py
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benchmark.py
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import datetime
import multiprocessing as mp
from statistics import mean
import numpy as np
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 = 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.")