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# Counting Objects In Movement Using Raspberry PI & OpenCV

What do you think in using Raspberry Pi & OpenCV to count moving objects that go in and out of a certain zone?

BeginnerFull instructions provided2 hours58,018

## Things used in this project

### Hardware components

 Raspberry Pi 3 Model B
×1
 Webcam An ordinary USB webcam (any model Linux-compatible fits well here)
×1
 Power Source for Raspberry PI A power-source (5V/3A recommended) with micro-USB connection. Some smartphone and tablet chargers fits well here too (in this case, observe if it's output current is compatible to what recommended for Raspberry PI)
×1

 OpenCV

## Code

### Source-code

Python
Project's source-code
```import datetime
import math
import cv2
import numpy as np

#global variables
width = 0
height = 0
EntranceCounter = 0
ExitCounter = 0

#Check if an object in entering in monitored zone
def CheckEntranceLineCrossing(y, CoorYEntranceLine, CoorYExitLine):
AbsDistance = abs(y - CoorYEntranceLine)

if ((AbsDistance <= 2) and (y < CoorYExitLine)):
return 1
else:
return 0

#Check if an object in exitting from monitored zone
def CheckExitLineCrossing(y, CoorYEntranceLine, CoorYExitLine):
AbsDistance = abs(y - CoorYExitLine)

if ((AbsDistance <= 2) and (y > CoorYEntranceLine)):
return 1
else:
return 0

camera = cv2.VideoCapture(0)

#force 640x480 webcam resolution
camera.set(3,640)
camera.set(4,480)

ReferenceFrame = None

#The webcam maybe get some time / captured frames to adapt to ambience lighting. For this reason, some frames are grabbed and discarted.
for i in range(0,20):

while True:
height = np.size(Frame,0)
width = np.size(Frame,1)

#if cannot grab a frame, this program ends here.
if not grabbed:
break

#gray-scale convertion and Gaussian blur filter applying
GrayFrame = cv2.cvtColor(Frame, cv2.COLOR_BGR2GRAY)
GrayFrame = cv2.GaussianBlur(GrayFrame, (21, 21), 0)

if ReferenceFrame is None:
ReferenceFrame = GrayFrame
continue

#Background subtraction and image binarization
FrameDelta = cv2.absdiff(ReferenceFrame, GrayFrame)
FrameThresh = cv2.threshold(FrameDelta, BinarizationThreshold, 255, cv2.THRESH_BINARY)[1]

#Dilate image and find all the contours
FrameThresh = cv2.dilate(FrameThresh, None, iterations=2)
_, cnts, _ = cv2.findContours(FrameThresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

QttyOfContours = 0

#plot reference lines (entrance and exit lines)
CoorYEntranceLine = (height / 2)-OffsetRefLines
CoorYExitLine = (height / 2)+OffsetRefLines
cv2.line(Frame, (0,CoorYEntranceLine), (width,CoorYEntranceLine), (255, 0, 0), 2)
cv2.line(Frame, (0,CoorYExitLine), (width,CoorYExitLine), (0, 0, 255), 2)

#check all found countours
for c in cnts:
#if a contour has small area, it'll be ignored
if cv2.contourArea(c) < MinCountourArea:
continue

QttyOfContours = QttyOfContours+1

#draw an rectangle "around" the object
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(Frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

#find object's centroid
CoordXCentroid = (x+x+w)/2
CoordYCentroid = (y+y+h)/2
ObjectCentroid = (CoordXCentroid,CoordYCentroid)
cv2.circle(Frame, ObjectCentroid, 1, (0, 0, 0), 5)

if (CheckEntranceLineCrossing(CoordYCentroid,CoorYEntranceLine,CoorYExitLine)):
EntranceCounter += 1

if (CheckExitLineCrossing(CoordYCentroid,CoorYEntranceLine,CoorYExitLine)):
ExitCounter += 1

print "Total countours found: "+str(QttyOfContours)

#Write entrance and exit counter values on frame and shows it
cv2.putText(Frame, "Entrances: {}".format(str(EntranceCounter)), (10, 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (250, 0, 1), 2)
cv2.putText(Frame, "Exits: {}".format(str(ExitCounter)), (10, 70),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.imshow("Original Frame", Frame)
cv2.waitKey(1);

# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()
```

## Credits

### Pedro Henrique Fonseca Bertoleti

8 projects • 50 followers
Hi there! My name is Pedro Bertoleti. I am aboslutely crazy about: - Electronics - Embedded software design - Technology