OpenCV/Python:用于实时面部识别的多线程 [英] OpenCV / Python : multi-threading for live facial recognition
问题描述
我正在使用OpenCv和Dlib执行人脸识别(带有地标)，并通过网络摄像头流进行直播.语言是 Python .它可以在我的Macbook笔记本电脑上正常工作，但我需要它从24/7全天候台式计算机运行.该计算机是运行Debian Jessie的PCIntel®Core™2 Quad CPU Q6600 @ 2.40GHz 32bit. 性能大幅下降:由于处理，延迟了10秒！
I'm using OpenCv and Dlib to execute facial recognition w/ landmarks, live from the webcam stream. The language is Python. It works fine on my macbook laptop, but I need it to run from a desktop computer 24/7. The computer is a PC Intel® Core™2 Quad CPU Q6600 @ 2.40GHz 32bit running Debian Jessie. The drop in performance is drastic : there is a 10 seconds delay due to processing !
因此，我研究了多线程以提高性能:
I therefore looked into multi-threading to gain performance :
- 我首先通过OpenCv尝试了示例代码，结果很棒！所有四个内核的命中率均达到100％，并且性能要好得多.
- 然后我用我的代码替换了帧处理代码，这根本没有提高性能！只有一个核心达到100％，其他核心保持非常低. 我什至认为启用多线程会更糟.
我从dlib示例代码中获得了面部标志性代码.我知道它可能可以优化，但是我想了解为什么我的旧计算机无法完全使用多线程功能?
I got the facial landmark code from the dlib sample code. I know it can probably be optimized, but I want to understand why am I not able to use my (old) computer's full power with multi-threading ?
我将代码放在下面，非常感谢您阅读:)
I'll drop my code below, thanks a lot for reading :)
from __future__ import print_function
import numpy as np
import cv2
import dlib
from multiprocessing.pool import ThreadPool
from collections import deque
from common import clock, draw_str, StatValue
import video
class DummyTask:
def __init__(self, data):
self.data = data
def ready(self):
return True
def get(self):
return self.data
if __name__ == '__main__':
import sys
print(__doc__)
try:
fn = sys.argv[1]
except:
fn = 0
cap = video.create_capture(fn)
#Face detector
detector = dlib.get_frontal_face_detector()
#Landmarks shape predictor
predictor = dlib.shape_predictor("landmarks/shape_predictor_68_face_landmarks.dat")
# This is where the facial detection takes place
def process_frame(frame, t0, detector, predictor):
# some intensive computation...
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
clahe_image = clahe.apply(gray)
detections = detector(clahe_image, 1)
for k,d in enumerate(detections):
shape = predictor(clahe_image, d)
for i in range(1,68): #There are 68 landmark points on each face
cv2.circle(frame, (shape.part(i).x, shape.part(i).y), 1, (0,0,255), thickness=2)
return frame, t0
threadn = cv2.getNumberOfCPUs()
pool = ThreadPool(processes = threadn)
pending = deque()
threaded_mode = True
latency = StatValue()
frame_interval = StatValue()
last_frame_time = clock()
while True:
while len(pending) > 0 and pending[0].ready():
res, t0 = pending.popleft().get()
latency.update(clock() - t0)
draw_str(res, (20, 20), "threaded : " + str(threaded_mode))
draw_str(res, (20, 40), "latency : %.1f ms" % (latency.value*1000))
draw_str(res, (20, 60), "frame interval : %.1f ms" % (frame_interval.value*1000))
cv2.imshow('threaded video', res)
if len(pending) < threadn:
ret, frame = cap.read()
t = clock()
frame_interval.update(t - last_frame_time)
last_frame_time = t
if threaded_mode:
task = pool.apply_async(process_frame, (frame.copy(), t, detector, predictor))
else:
task = DummyTask(process_frame(frame, t, detector, predictor))
pending.append(task)
ch = cv2.waitKey(1)
if ch == ord(' '):
threaded_mode = not threaded_mode
if ch == 27:
break
cv2.destroyAllWindows()
推荐答案
性能问题归因于dlib的编译错误. 请勿使用 pip install dlib，与正确的编译相比，它出于某种原因运行非常缓慢.这样，我从几乎10秒钟的延迟变为了大约2秒钟.所以最后我不需要多线程/处理，但是我正在努力以进一步提高速度.感谢您的帮助:)
Performance issue was due to a bad compilation of dlib. Do not use pip install dlib which runs very very slowly for some reason compared to the proper compilation. I went from almost 10 seconds lag to about 2 seconds this way. So finally I didn't need multi-threading/processing, but I'm working on it to enhance the speed even more. Thanks for the help :)
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