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main_get_detect.py
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from __future__ import print_function
import os
import torch
import torch.backends.cudnn as cudnn
# import numpy as np
from utils.timer import Timer
import glob
import PIL.Image as Image
import random
from interface import compare_vector, Detect, Reid
import cv2
# import multiprocessing
from multiprocessing import Process, Queue
import threading
import time
# import matplotlib.pyplot as plt
import logging
import uuid
import numpy as np
import sys
import signal
import time
from mysql import *
_t = {'detect': Timer(), 'reid': Timer()}
def quit(signum, frame):
for i in processes:
os.kill(i.pid, signal.SIGTERM)
sys.exit()
class ReceiveProcess(Process):
def __init__(self, ip, frame):
super(ReceiveProcess, self).__init__()
self.ip = ip
self.frame = frame
def run(self):
cap = cv2.VideoCapture(self.ip)
ret, frame = cap.read()
if self.frame.full():
self.frame.get()
self.frame.put((frame, ret, datetime.now()))
while True:
for _ in range(25):
ret, frame = cap.read()
while frame is None:
logging.warning("Missing frame from ", self.ip)
cap = cv2.VideoCapture(self.ip)
ret, frame = cap.read()
ret, frame = cap.read()
if self.frame.full():
self.frame.get()
self.frame.put((frame, ret, datetime.now()))
while frame is None:
logging.warning("Missing frame from ", self.ip)
cap = cv2.VideoCapture(self.ip)
ret, frame = cap.read()
if self.frame.full():
self.frame.get()
self.frame.put((frame, ret, datetime.now()))
# class ReceiveThread(threading.Thread):
# def __init__(self, ip, frame):
# threading.Thread.__init__(self)
# self.ip = ip
# self.frame = frame
# def run(self):
# cap = cv2.VideoCapture(self.ip)
# ret, frame = cap.read()
# self.frame.append((frame, ret))
# while True:
# ret, frame = cap.read()
# self.frame[0] = (frame, ret)
# while frame is None:
# print("Missing frame from ", self.ip)
# cap = cv2.VideoCapture(self.ip)
# ret, frame = cap.read()
# self.frame[0] = (frame, ret)
def predict(frame):
img_raw = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
img = frame
## 计时器，tic开始计时，toc结束计时
_t['detect'].tic()
## 检测测试帧中的行人，返回bbox
bbox = detector.get_bbox(img)
## 结束行人检测的计时器，开启重识别计时器
_t['detect'].toc()
## 临时变量
# img_for_vis = np.asarray(img_raw)
# img_for_vis = cv2.cvtColor(img_for_vis, cv2.COLOR_BGR2RGB)
## 对当前帧中的所有进行进行重识别
for b in bbox:
## 获取识别到的行人的特征向量
img = img_raw.crop((b[0], b[1], b[2], b[3]))
if (b[3] - b[1]) / (b[2] - b[0]) < 1:
continue
# _t['reid'].tic()
# f = reidor.generate_feature(img)
# _t['reid'].toc()
img_for_vis = np.asarray(img)
img_for_vis = cv2.cvtColor(img_for_vis, cv2.COLOR_BGR2RGB)
cv2.imwrite('./raw_data/'+str(time.time())+'.jpg', img_for_vis)
print("Detect!")
if __name__ == '__main__':
torch.set_grad_enabled(False)
cudnn.benchmark = True
device = torch.device("cpu" if False else "cuda")
## 日志文件
logging.basicConfig(level=logging.INFO, # 控制台打印的日志级别
filename='recognition.log',
filemode='a', ##模式，有w和a，w就是写模式，每次都会重新写日志，覆盖之前的日志
# a是追加模式，默认如果不写的话，就是追加模式
format=
'%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'
# 日志格式
)
host, user, password, database, charset = get_mysql_info()
if not os.path.exists('./raw_data/'):
os.mkdir('./raw_data/')
# mysql = Mysql(host, user, password, database, charset)
# 摄像头地址
# ipcamera = ["rtsp://admin:admin12345@10.141.5.141/Streaming/Channels/1",
# "rtsp://admin:admin12345@10.141.5.142/Streaming/Channels/1",
# "rtsp://admin:admin12345@10.141.5.142/Streaming/Channels/1",
# "rtsp://admin:admin12345@10.141.5.142/Streaming/Channels/1",
# "rtsp://admin:admin12345@10.141.5.142/Streaming/Channels/1",
# "rtsp://admin:admin12345@10.141.5.142/Streaming/Channels/1",
# "rtsp://admin:admin12345@10.141.5.142/Streaming/Channels/1",
# "rtsp://admin:admin12345@10.141.5.142/Streaming/Channels/1"]
file_name = 'ip.txt'
fo = open(file_name, "r")
ipcamera_all = fo.readlines()
ipcamera = []
ipcamera_ip = []
ipcamera_channel = []
for i in range(len(ipcamera_all)):
temp = ipcamera_all[i].replace('\n', '').split(' ')
ipcamera.append(temp[0])
ipcamera_ip.append(temp[1])
ipcamera_channel.append(temp[2])
## 初始化检测模型和重识别模型
detector = Detect('./weights/Final_FaceBoxes.pth', device)
reidor = Reid('./weights/model_best.pth', device)
## 开启监督线程
processes = []
frames_list = []
# print(ipcamera)
for i, ip in enumerate(ipcamera):
frames = Queue(5)
frames_list.append(frames)
process = ReceiveProcess(ip, frames)
processes.append(process)
for process in processes:
process.start()
signal.signal(signal.SIGINT, quit)
signal.signal(signal.SIGTERM, quit)
while True:
for cid_idx, frames in enumerate(frames_list):
frame, ret, time_create = frames.get()
predict(frame)