回复 2楼 wp231957
#! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import sys
sys.path.append('../')
import math
from timeit import time
import warnings
import numpy as np
from PIL import Image
from yolo import YOLO
from deep_sort import preprocessing
from deep_sort import nn_matching
from deep_sort.detection import Detection
from deep_sort.tracker import Tracker
from tools import generate_detections as gdet
from yolov3.yolo2 import YOLO as YOLO2
# import pymysql
import utils
import operator
import json
import line
import queue
import os
import cv2
import gc
from multiprocessing import Process, Manager
import tensorflow as tf
import os
# conn=pymysql.connect(host='120.24.15.17',user='root',passwd='123456',db='track',port=3306,charset='gbk')
info_queue = queue.Queue()
warnings# 向共享缓冲栈中写入数据:
def write(stack, cam, top) :
"""
:param cam: 摄像头参数
:param stack: Manager.list对象
:param top: 缓冲栈容量
:return: None
"""
print('Process to write: %s' % os.getpid())
cap = cv2.VideoCapture(cam)
while True:
_, img = cap.read()
if _:
stack.append(img)
# 每到一定容量清空一次缓冲栈
# 利用gc库，手动清理内存垃圾，防止内存溢出
if len(stack) >= top:
del stack[:]
gc.collect()
def read(stack) :
print('Process to read: %s' % os.getpid())
yolo = YOLO()
# Definition of the parameters
max_cosine_distance = 0.3
nn_budget = None
nms_max_overlap = 1.0
# deep_sort
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)
max_boxs = 0
face = ['A']
# face = []
# cur1 = conn.cursor()
# 获取一个游标
# sql = "select * from worker"
# # cur1.execute(sql)
# data = cur1.fetchall()
# for d in data:
#
# 注意int类型需要使用str函数转义
#
name = str(d[1])
#
#
face.append(name)
# cur1.close()
# 关闭游标
yolo2 = YOLO2()
#目标上一帧的点
history = {}
#id和标签的字典
person = {}
#赋予新标签的id列表
change = []
while True:
if len(stack) != 0:
frame = stack.pop()
t1 = time.time()
localtime = time.asctime(time.localtime(time.time()))
# 进行安全措施检测
#frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
img = Image.fromarray(frame)
#img.save('frame.jpg')
frame, wear = yolo2.detect_image(img)
frame = np.array(frame)
# frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
# 获取警戒线
transboundaryline = line.readline()
utils.draw(frame, transboundaryline)
# image = Image.fromarray(frame)
image = Image.fromarray(frame[..., ::-1])
# bgr to rgb
boxs = yolo.detect_image(image)
# print("box_num",len(boxs))
features = encoder(frame, boxs)
# score to 1.0 here).
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
if len(boxs) > max_boxs:
max_boxs = len(boxs)
# Call the tracker
tracker.predict()
tracker.update(detections)
#一帧信息
info = {}
target = []
for track in tracker.tracks:
#一帧中的目标
per_info = {}
if not track.is_confirmed() or track.time_since_update > 1:
continue
if track.track_id not in person:
person[track.track_id] = str(track.track_id)
bbox = track.to_tlbr()
PointX = bbox[0] + ((bbox[2] - bbox[0]) / 2)
PointY = bbox[3]
dis = int(PointX) - 1200
try:
if dis<15:
if track.track_id not in change:
person[track.track_id] = face.pop(0)
change.append(track.track_id)
except:
print('非法入侵')
#当前目标
if track.track_id not in change:
per_info['worker_id'] = 'unknow'+str(track.track_id)
else:
per_info['worker_id'] = person[track.track_id]
#当前目标坐标
yoloPoint = (int(PointX), int(PointY))
per_info['current_point'] = yoloPoint
# 卡尔曼滤波预测
if per_info['worker_id'] not in utils.KalmanNmae:
utils.myKalman(per_info['worker_id'])
if per_info['worker_id'] not in utils.lmp:
utils.setLMP(per_info['worker_id'])
cpx, cpy = utils.predict(yoloPoint[0], yoloPoint[1], per_info['worker_id'])
if cpx[0] == 0.0 or cpy[0] == 0.0:
cpx[0] = yoloPoint[0]
cpy[0] = yoloPoint[1]
per_info['next_point'] = (int(cpx), int(cpy))
# 写入安全措施情况
wear_dic = {}
per_info['wear'] = 'safe wear'
if len(wear) > 0:
for w in wear:
wear_dis = int(math.sqrt(pow(w[0] - yoloPoint[0], 2) + pow(w[1] - yoloPoint[1], 2)))
wear_dic[wear_dis] = w
wear_dic = sorted(wear_dic.items(), key=operator.itemgetter(0), reverse=False)
if wear_dic[0][0] < 180:
if wear[wear_dic[0][1]] == 1:
per_info['wear'] = 'no_helmet'
elif wear[wear_dic[0][1]] == 2:
per_info['wear'] = 'no work cloths'
elif wear[wear_dic[0][1]] == 3:
per_info['wear'] = 'unsafe wear'
# 写入越线情况
if per_info['worker_id'] in history:
per_info['transboundary'] = 'no'
# print(transboundaryline)
line1 = [per_info['next_point'], history[per_info['worker_id']]]
a = line.IsIntersec2(transboundaryline, line1)
if a == '有交点':
print('越线提醒')
per_info['transboundary'] = 'yes'
history[per_info['worker_id']] = per_info['current_point']
#print(per_info)
#画目标框
#cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 255, 255), 2)
cv2.putText(frame, per_info['worker_id'], (int(bbox[0]), int(bbox[1])), 0, 5e-3 * 200, (0, 255, 0), 2)
target.append(per_info)
info['time'] = localtime
#info['frame'] = str(img.tolist()).encode('base64')
info['frame'] = 'frame'
info['target'] = target
#写入josn
info_json = json.dumps(info)
info_queue.put(info_json)
getInfo(info_queue)
cv2.imshow("img", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
def getInfo(queue):
a = queue.get()
print(a)
if __name__ == '__main__':
#配置GPU显存
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
# 指定第一块GPU可用
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.5
# 程序最多只能占用指定gpu50%的显存
config.gpu_options.allow_growth = True
# 程序按需申请内存
session = tf.Session(config=config)
# 父进程创建缓冲栈，并传给各个子进程：
q = Manager().list()
pw = Process(target=write, args=(q, "rtsp://admin:yfzx2019@192.168.10.6:554/h264/ch1/main/av_stream", 100))
pr = Process(target=read, args=(q,))
# 启动子进程pw，写入:
pw.start()
# 启动子进程pr，读取:
pr.start()
# 等待pr结束:
pr.join()
# pw进程里是死循环，无法等待其结束，只能强行终止:
pw.terminate()
