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Python OpenCV识别行人入口进出人数统计
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前言
这篇博客针对《Python OpenCV识别行人入口进出人数统计》编写代码，功能包括了入口行人识别，人数统计。代码整洁，规则，易读。应用推荐首选。
文章目录
一、所需工具软件
二、使用步骤
1. 引入库
2. 识别特征图像
3. 运行结果
三、在线协助
一、所需工具软件
1. Python3.6以上
2. Pycharm代码编辑器
3. OpenCV, Numpy库
二、使用步骤
1.引入库
代码如下（示例）：
-
#导入需要的包
-
import numpy as np
-
import cv2
-
import Person
-
import time
2.识别特征图像
代码如下（示例）：
-
video=cv2.VideoCapture(
"counting_test.avi")
-
#输出视频
-
fourcc = cv2.VideoWriter_fourcc(*
'XVID')#输出视频制编码
-
out = cv2.VideoWriter(
'output.avi',fourcc,
20.0, (
640,
480))
-
-
w = video.get(
3)
-
h = video.get(
4)
-
print(
"视频的原宽度为:")
-
print(
int(w))
-
print(
"视频的原高度为:")
-
area = h*w
-
print(
int(h))
-
areaTHreshold = area/
500
-
print(
'Area Threshold', areaTHreshold)
-
-
#计算画线的位置
-
line_up =
int(
1*(h/
4))
-
line_down =
int(
2.7*(h/
4))
-
up_limit =
int(
.5*(h/
4))
-
down_limit =
int(
3.2*(h/
4))
-
print (
"Red line y:",str(line_down))
-
print (
"Green line y:", str(line_up))
-
-
pt5 = [
0, up_limit]
-
pt6 = [w, up_limit]
-
pts_L3 = np.
array([pt5,pt6], np.int32)
-
pts_L3 = pts_L3.reshape((
-1,
1,
2))
-
pt7 = [
0, down_limit]
-
pt8 = [w, down_limit]
-
pts_L4 = np.
array([pt7,pt8], np.int32)
-
pts_L4 = pts_L4.reshape((
-1,
1,
2))
-
#背景剔除
-
# fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True)
-
fgbg = cv2.createBackgroundSubtractorKNN()
-
#用于后面形态学处理的核
-
kernel = np.ones((
3,
3),np.uint8)
-
kerne2 = np.ones((
5,
5),np.uint8)
-
kerne3 = np.ones((
11,
11),np.uint8)
-
-
while(video.isOpened()):
-
ret,frame=video.read()
-
if frame is None:
-
break
-
#应用背景剔除
-
gray = cv2.GaussianBlur(frame, (
31,
31),
0)
-
#cv2.imshow(
'GaussianBlur', frame)
-
#cv2.imshow(
'GaussianBlur', gray)
-
fgmask = fgbg.apply(gray)
-
fgmask2 = fgbg.apply(gray)
-
-
try:
-
#***************************************************************
-
#二值化
-
ret,imBin= cv2.threshold(fgmask,
200,
255,cv2.THRESH_BINARY)
-
ret,imBin2 = cv2.threshold(fgmask2,
200,
255,cv2.THRESH_BINARY)
-
#cv2.imshow(
'imBin', imBin2)
-
#开操作(腐蚀->膨胀)消除噪声
-
mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kerne3)
-
mask2 = cv2.morphologyEx(imBin2, cv2.MORPH_OPEN, kerne3)
-
#闭操作(膨胀->腐蚀)将区域连接起来
-
mask = cv2.morphologyEx(mask , cv2.MORPH_CLOSE, kerne3)
-
mask2 = cv2.morphologyEx(mask2, cv2.MORPH_CLOSE, kerne3)
-
#cv2.imshow(
'closing_mask', mask2)
-
#*************************************************************
-
except:
-
print(
'EOF')
-
print (
'IN:',cnt_in+count_in)
-
print (
'OUT:',cnt_in+count_in)
-
break
-
-
#找到边界
-
_mask2,contours0, hierarchy = cv2.findContours(mask2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
for cnt in contours0:
-
rect = cv2.boundingRect(cnt)#矩形边框
-
area=cv2.contourArea(cnt)#每个矩形框的面积
-
if area>areaTHreshold:
-
#************************************************
-
#moments里包含了许多有用的信息
-
M=cv2.moments(cnt)
-
cx=
int(M[
'm10']/M[
'm00'])#计算重心
-
cy=
int(M[
'm01']/M[
'm00'])
-
x, y, w, h = cv2.boundingRect(cnt)
#x,y为矩形框左上方点的坐标，w为宽，h为高
-
new=True
-
if cy in range(up_limit,down_limit):
-
for i in persons:
-
if
abs(cx-i.getX())<=w and
abs(cy-i.getY())<=h:
-
new=False
-
i.updateCoords(cx,cy)
-
if i.going_UP(line_down,line_up)==True:
-
# cv2.circle(frame, (cx, cy),
5, line_up_color,
-1)
-
# img = cv2.rectangle(frame, (x, y), (x + w, y + h), line_up_color, 2)
-
if w>
80:
-
count_in=w/
40
-
print(
"In:执行了/60")
-
time.strftime(
"%c"))
-
elif i.going_DOWN(line_down,line_up)==True:
-
# cv2.circle(frame, (cx, cy),
5, (
0,
0,
255),
-1)
-
# img = cv2.rectangle(frame, (x, y), (x + w, y + h), line_down_color, 2)
-
time.strftime(
"%c"))
-
break
-
#状态为
1表明
-
if i.getState() ==
'1':
-
if i.getDir() ==
'down' and i.getY() > down_limit:
-
i.setDone()
-
elif i.getDir() ==
'up' and i.getY() < up_limit:
-
i.setDone()
-
if i.timedOut():
-
# 已经记过数且超出边界将其移出persons队列
-
index = persons.index(i)
-
persons.pop(index)
-
del i # 清楚内存中的第i个人
-
if new == True:
-
p = Person.MyPerson(pid, cx, cy, max_p_age)
-
persons.append(p)
-
pid +=
1
-
-
print(
"进入的总人数为:")
-
print(cnt_in)
-
print(
"出去的总人数为:")
-
print(cnt_out)
-
video.release();
-
cv2.destroyAllWindows()
3.运行结果如下：
三、在线协助：
如需安装运行环境或远程调试，见文章底部微信名片，由专业技术人员远程协助！
转载：https://blog.csdn.net/alicema1111/article/details/128407152
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