本教程我將分享幾個簡單步驟解釋如何使用OpenCV進行Python對象計數。
需要安裝一些軟件：
- Python 3
- OpennCV
1.瞭解Opencv從攝像頭獲得視頻的Python腳本
import cv2, time
#1. Create an object.Zero for external camera
video=cv2. VideoCapture(0)
#1. a variable
a=0
while True:
\ta=a+1
\t#3. Create frame object
\tcheck, frame = video.read()
\tprint(check)
\tprint(frame) # Reprsenting image
\t#6. converting to grascale
\tgray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
\t#4. shadow the frame
\tcv2.imshow("Capturing", gray)
\t#5. for press any key to out (milisecond)
\t#cv2.waitKey(0)
\t#7. for playing
\tkey=cv2.waitKey(1)
\t
\tif key==ord('q'):
\t\tbreak
\t
print (a)
#2. Shutdown the camera
video.release()
cv2.destroyAllWindows
2.加載視頻
現在我們將開始逐步學習這個車輛計數教程。第一步是打開我們將在本教程中使用的視頻錄製。Python示例代碼如下：
import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
while(cap.isOpened()):
ret, frame = cap.read() #read a frame
try:
cv2.imshow('Frame',frame)
except:
#if there are no more frames to show...
print('EOF')
break
#Abort and exit with 'Q' or ESC
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows
3. 在視頻窗口中繪圖
這部分非常簡單，因為我們只在視頻上顯示文字或畫線。
使用Python代碼在視頻文件中顯示文本如下：
import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
w = cap.get(3) #get width
h = cap.get(4) #get height
mx = int(w/2)
my = int(h/2)
count = 0
while(cap.isOpened()):
ret, frame = cap.read() #read a frame
try:
count = count + 1
text = "Statistika UII " + str(count)
cv2.putText(frame, text ,(mx,my),cv2.FONT_HERSHEY_SIMPLEX
,1,(255,255,255),1,cv2.LINE_AA)
cv2.imshow('Frame',frame)
except:
#if there are no more frames to show...
print('EOF')
break
#Abort and exit with 'Q' or ESC
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows
除了顯示文字，我們還可以繪製線條，圓圈等。OpenCV有許多繪製幾何形狀的方法
import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
while(cap.isOpened()):
ret, frame = cap.read() #read a frame
try:
cv2.imshow('Frame',frame)
frame2 = frame
except:
#if there are no more frames to show...
print('EOF')
break
line1 = np.array([[100,100],[300,100],[350,200]], np.int32).reshape((-1,1,2))
line2 = np.array([[400,50],[450,300]], np.int32).reshape((-1,1,2))
frame2 = cv2.polylines(frame2,[line1],False,(255,0,0),thickness=2)
frame2 = cv2.polylines(frame2,[line2],False,(0,0,255),thickness=1)
cv2.imshow('Frame 2',frame2)
#Abort and exit with 'Q' or ESC
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows
4.背景分離
此方法通過區分背景和對象（前景）的移動來分離對象。該方法非常廣泛地用於進入或離房間計數，交通信息系統中車輛統計，訪客數量等。
import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True) #Create the background substractor
while(cap.isOpened()):
ret, frame = cap.read() #read a frame
fgmask = fgbg.apply(frame) #Use the substractor
try:
cv2.imshow('Frame',frame)
cv2.imshow('Background Substraction',fgmask)
except:
#if there are no more frames to show...
print('EOF')
break
#Abort and exit with 'Q' or ESC
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows
在圖像中，黑色的圖像為背景，而白色的圖像是檢測的對象。
5.形態轉換
圖像處理中的形態學，即數學形態學(mathematical Morphology），是圖像處理中應用最為廣泛的技術之一，主要用於從圖像中提取對錶達和描繪區域形狀有意義的圖像分量，使後續的識別工作能夠抓住目標對象最為本質〈最具區分能力－most discriminative）的形狀特徵，如邊界和連通區域等。同時像細化、像素化和修剪毛刺等技術也常應用於圖像的預處理和後處理中，成為圖像增強技術的有力補充。
經常使用的形態學操作：包括腐蝕、膨脹， 以及開、閉運算。
膨脹： 輸出像素的值是所有輸入像素值中的最大值。在二值圖像中，如果領域中有一個像素值為1，則輸出像素值為1。如下圖
腐蝕：輸出像素的值是所有輸入像素值中的最小值，在二值圖像中，若果領域中有一個像素值為0，則輸出像素值為0，看下圖：
膨脹和腐蝕的Python實現如下：
import cv2
import numpy as np
img = cv2.imread("carcount.png")
ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
kernel = np.ones((3,3),np.uint8)
erosion = cv2.erode(img,kernel,iterations = 1)
dilation = cv2.dilate(img,kernel,iterations = 1)
cv2.imwrite("erode.png",erosion)
cv2.imwrite("dilate.png",dilation)
查看侵蝕和擴張的結果如下圖：
開運算：先腐蝕再膨脹，可以去掉目標外的孤立點。目標外的孤立點是和目標像素值一樣的點，而非背景像素點，即為1而非0（0表示選取的空洞或背景像素值）。使用腐蝕，背景擴展，該孤立點被腐蝕掉，但是腐蝕會導致目標區域縮小一圈，因此需要再進行膨脹操作，將目標區域擴展回原來大小。所以，要使用開運算去除目標外的孤立點。
閉運算：先膨脹再腐蝕，可以去掉目標內的孔。目標內的孔，屬於周圍都是值為1，內部空洞值為0.目的是去除周圍都是1的像素中間的0值。閉運算首先進行膨脹操作，目標區域擴張一圈，將目標區域的0去除，但是目標區域同時也會向外擴張一圈，因此需要使用腐蝕操作，使得圖像中的目標區域恢復到之前的大小。
代碼實現如下：
import cv2
import numpy as np
img = cv2.imread("carcount.png")
ret,thresh1 = cv2.threshold(img,200,255,cv2.THRESH_BINARY)
kernel = np.ones((5,5),np.uint8)
opening = cv2.morphologyEx(thresh1, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(thresh1, cv2.MORPH_CLOSE, kernel)
cv2.imwrite("carcount_closing.png",closing)
cv2.imwrite("carcount_opening.png",opening)
6.尋找輪廓
到目前為止，我們已經過濾了視頻流文件，然後我們將檢測移動對象上的輪廓。
import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True) #Create the background substractor
kernelOp = np.ones((3,3),np.uint8)
kernelCl = np.ones((11,11),np.uint8)
while(cap.isOpened()):
ret, frame = cap.read() #read a frame
fgmask = fgbg.apply(frame) #Use the substractor
try:
ret,imBin= cv2.threshold(fgmask,200,255,cv2.THRESH_BINARY)
#Opening (erode->dilate)
mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kernelOp)
#Closing (dilate -> erode)
mask = cv2.morphologyEx(mask , cv2.MORPH_CLOSE, kernelCl)
except:
#if there are no more frames to show...
print('EOF')
break
_, contours0, hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for cnt in contours0:
cv2.drawContours(frame, cnt, -1, (0,255,0), 3, 8)
cv2.imshow('Frame',frame)
#Abort and exit with 'Q' or ESC
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows
7.定義對象
這是一個非常有趣的部分，我們將輪廓分類為車輛對象。此定義以小紅點標記。Python實現如下：
import numpy as np
import cv2
cap = cv2.VideoCapture('traf.mp4') #Open video file
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True) #Create the background substractor
kernelOp = np.ones((3,3),np.uint8)
kernelCl = np.ones((11,11),np.uint8)
areaTH = 500
while(cap.isOpened()):
ret, frame = cap.read() #read a frame
fgmask = fgbg.apply(frame) #Use the substractor
try:
ret,imBin= cv2.threshold(fgmask,200,255,cv2.THRESH_BINARY)
#Opening (erode->dilate)
mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kernelOp)
#Closing (dilate -> erode)
mask = cv2.morphologyEx(mask , cv2.MORPH_CLOSE, kernelCl)
except:
#if there are no more frames to show...
print('EOF')
break
_, contours0, hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for cnt in contours0:
cv2.drawContours(frame, cnt, -1, (0,255,0), 3, 8)
area = cv2.contourArea(cnt)
print (area)
if area > areaTH:
#################
# TRACKING #
#################
M = cv2.moments(cnt)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
x,y,w,h = cv2.boundingRect(cnt)
cv2.circle(frame,(cx,cy), 5, (0,0,255), -1)
img = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
cv2.imshow('Frame',frame)
#Abort and exit with 'Q' or ESC
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows
8.移動方向
您已經知道我們的視頻上有什麼對象，現在您想知道它們往哪裡移動（如：向上/向下）。在第一幀中，您需要將檢測到的ID對象保存初始位置。然後，在下一幀中，要繼續跟蹤對象，必須將幀中對象的輪廓與首次出現時的ID匹配，並保存該對象的座標。然後，在對象跨越視頻的邊界（或一定量的限制）之後，您可以使用存儲的位置來評估它是向上或是向下移動。
import numpy as np
import cv2
import Car
import time
cap = cv2.VideoCapture('peopleCounter.avi') #Open video file
fgbg = cv2.createBackgroundSubtractorMOG2(detectShadows = True) #Create the background substractor
kernelOp = np.ones((3,3),np.uint8)
kernelCl = np.ones((11,11),np.uint8)
#Variables
font = cv2.FONT_HERSHEY_SIMPLEX
cars = []
max_p_age = 5
pid = 1
areaTH = 500
while(cap.isOpened()):
ret, frame = cap.read() #read a frame
fgmask = fgbg.apply(frame) #Use the substractor
try:
ret,imBin= cv2.threshold(fgmask,200,255,cv2.THRESH_BINARY)
#Opening (erode->dilate)
mask = cv2.morphologyEx(imBin, cv2.MORPH_OPEN, kernelOp)
#Closing (dilate -> erode)
mask = cv2.morphologyEx(mask , cv2.MORPH_CLOSE, kernelCl)
except:
#if there are no more frames to show...
print('EOF')
break
_, contours0, hierarchy = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for cnt in contours0:
cv2.drawContours(frame, cnt, -1, (0,255,0), 3, 8)
area = cv2.contourArea(cnt)
if area > areaTH:
#################
# TRACKING #
#################
M = cv2.moments(cnt)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
x,y,w,h = cv2.boundingRect(cnt)
new = True
for i in cars:
if abs(x-i.getX()) <= w and abs(y-i.getY()) <= h:
# the object is close to one that was already detected before
new = False
i.updateCoords(cx,cy) #Update coordinates on the object and resets age
break
if new == True:
p = Car.MyCar(pid,cx,cy, max_p_age)
cars.append(p)
pid += 1
cv2.circle(frame,(cx,cy), 5, (0,0,255), -1)
img = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)
cv2.drawContours(frame, cnt, -1, (0,255,0), 3)
for i in cars:
if len(i.getTracks()) >= 2:
pts = np.array(i.getTracks(), np.int32)
pts = pts.reshape((-1,1,2))
frame = cv2.polylines(frame,[pts],False,i.getRGB())
if i.getId() == 9:
print (str(i.getX()), ',', str(i.getY()))
cv2.putText(frame, str(i.getId()),(i.getX(),i.getY()),font,0.3,i.getRGB(),1,cv2.LINE_AA)
cv2.imshow('Frame',frame)
#Abort and exit with 'Q' or ESC
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release() #release video file
cv2.destroyAllWindows() #close all openCV windows
9.計數
你之前的部分已經知道如何檢測對象運動的方法。現在，我們必須看到這個列表並確定對象是否在我們的視頻中向上或下降。要做到這一點，首先將創造兩條線，這將顯示什麼時候來評估對象的方向(line_up，line_down)。而且還會有兩行界限，告訴我們什麼時候停止跟蹤物體(up_limit，down_limit)。
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