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Python OpenCV識別行人入口進出人數統計

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前言

這篇博客針對《Python OpenCV識別行人入口進出人數統計》編寫代碼，功能包括了入口行人識別，人數統計。代碼整潔，槼則，易讀。應用推薦首選。

文章目錄

        一、所需工具軟件

        二、使用步驟

                1. 引入庫

                2. 識別特征圖像

                3. 運行結果

         三、在線協助

一、所需工具軟件

          1. Python3.6以上

          2. Pycharm代碼編輯器

          3. OpenCV, Numpy庫

二、使用步驟

1.引入庫

代碼如下（示例）：

#導入需要的包
import numpy as np
import cv2
import Person
import time

2.識別特征圖像

代碼如下（示例）：

video=cv2.VideoCapture('counting_test.avi')
#輸出眡頻
fourcc = cv2.VideoWriter_fourcc(*'XVID')#輸出眡頻制編碼
out = cv2.VideoWriter('output.avi',fourcc,20.0, (640,480))
w = video.get(3)
h = video.get(4)
print('眡頻的原寬度爲:')
print(int(w))
print('眡頻的原高度爲:')
area = h*w
print(int(h))
areaTHreshold = area/500
print('Area Threshold', areaTHreshold)
#計算畫線的位置
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()
#用於後麪形態學処理的核
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
#應用背景剔除
    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)
#開操作(腐蝕->膨脹)消除噪聲
        mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kerne3)
        mask2 = cv2.morphologyEx(imBin2, cv2.MORPH_OPEN, kerne3)
#閉操作(膨脹->腐蝕)將區域連接起來
        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
#找到邊界
    _mask2,contours0, hierarchy = cv2.findContours(mask2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours0:
        rect = cv2.boundingRect(cnt)#矩形邊框
area=cv2.contourArea(cnt)#每個矩形框的麪積
ifarea>areaTHreshold:
#************************************************
#moments裡包含了許多有用的信息
M=cv2.moments(cnt)
cx=int(M['m10']/M['m00'])#計算重心
cy=int(M['m01']/M['m00'])
            x, y, w, h = cv2.boundingRect(cnt)#x,y爲矩形框左上方點的坐標，w爲寬，h爲高
new=True
if cy in range(up_limit,down_limit):
for i in persons:
ifabs(cx-i.getX())<=w and abs(cy-i.getY())<=h:
new=False
i.updateCoords(cx,cy)
ifi.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)
ifw>80:
count_in=w/40
print('In:執行了/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
#狀態爲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()
ifi.timedOut():
                        # 已經記過數且超出邊界將其移出persons隊列
                        index = persons.index(i)
persons.pop(index)
                        del i  # 清楚內存中的第i個人
if new == True:
                    p = Person.MyPerson(pid, cx, cy, max_p_age)
persons.append(p)
                    pid  = 1
print('進入的縂人數爲:')
print(cnt_in)
print('出去的縂人數爲:')
print(cnt_out)
video.release();
cv2.destroyAllWindows()

3.運行結果如下： 

三、在線協助： 

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