1. Page 1
Visual Based Product Identification
System

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Visual Based Product
Identification System
Project Supervisor-Mr.Mahanama Dharmawardhana
Project Group-P 16
Group Members-E/11/252 (Madushanka,W.F.R)
E/11/267 (Muthukumaranage,M.S.P)

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Title & Outlines
• Introduction
• Details of Our Solution
• Experimental Results
• Conclusion
• Back-up Slides

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Introduction(Overall Project)

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Solution Methodology
• Conveyor design
• Install operating system openCV and other necessary
packages to raspberry pi minicomputer
• Colour detection method
• Shape detection method
• Data send method

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Machine Vision System
 Computer
Hardware+Software
 Cameras
 lighting
Vision systems can be
thought of as
computers with eyes
that can identify,
inspect and
communicate critical
information.
 Eliminate Defects
 Improve Quality
 Automate Production
 Track & Identify Parts
 Reduce Cost
 Image Captured
 Stored In Memory
 Algorithmically
Compared
Visual Based
Product
Identification
System

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Project Objectives
 Build a system that can detect, recognize objects according to the
Colour
Shape
 The whole process should be done in real time
 Should be used ease & efficient algorithms

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Software Part & Hardware Part
Hardware
Lighting system
Web Camera
Raspberry-pi Computer
Monitor
Conveyor
Software
OpenCV
Python
Numpy

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OpenCV
OpenCV
 Speed
 Resource-saving
 Cost
 Portability
Open source
computer vision
library
 Core module.
 Imgproc module
 Highgui module
 Feature 2D module
 Calib 3D module
 Library is written in
C & C++
 Runs under
linux,windows
 Provides interfaces
Python,Ruby,Matlab
,etc

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Programing Software Development
Colour
Detection
Use contours methodShape
Detection
Use RGB to HSV conversion
method

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Colour Detection
STEPS
1. Capture image
2. Convert from BGR to HSV color-space
3. Threshold the HSV image for a range of color
4. Show the mask image.

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RGB to HSV Conversion Method
RGB
In terms of Hue ,Saturation and ValueHSV
In terms of the amount of RED,GREEN,BLUE present

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RGB to HSV Conversion Method
Hue –Represents colour type
Range 0 to 255
Saturation –Represents the vibrancy of the colour
Range 0 to 255
Value –Represents brightness of the colour
0 – Completely DARK
255 – Fully BRIGHT

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Experimental Result
Blue Colour Detection RedColour Detection
Green Colour Detection

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Shape Detection
STEPS
1. Capture the image
2. Get an image after 30 frames
3. Delete the camera
4. Threshold the image
5. Find contours
6. Approximate contours
7. Show correct shape

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Experimental Result
• Triangle Identification

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Square Identification

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Circle Identification

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Raspbery pi Circuit
Raspberry pi
Circuit
Monitor
Keyboard
Mouse
Small credit card
size single board
computer
Low cost
Simplicity
Easy to handle
 Rasbian Jessie used
as operating system
 OpenCV,
Python,Numpy
should be installed

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Raspberry –pi Circuit
5v Micro usb
HDMI port
CSI Camera
connector Ethernet socket
Usb Ports
Micro SD card slot

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Software Installation
• Installing OpenCV 3 on Raspbian Jessie
Installing OpenCV 3 is a multi-step (and even time consuming) process requiring you to
install many dependencies and pre-requisites.
– Step #1: Install dependencies
– Step #2: Grab the OpenCV source code
– Step #3: Setup Python
– Step #4: Compile and install OpenCV
– Step #5: Finishing the install
– Step #6: Verifying your OpenCV 3 install

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SSH via direct Ethernet cable
• What is the meaning of SSH?
Secure Shell (SSH) is a UNIX-based command
interface and protocol for securely getting access to
a remote computer.
• What is the use of SSH server?
An SSH server is a software program which uses
the secure shell protocol to accept connections from
remote computers. SFTP/SCP file transfers and
remote terminal connections are popular use cases
for an SSH server.

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Conveyor Designing

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Backup Slides

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OpenCV
Since Version 2.2, the OpenCV library is divided into several modules.
1.The opencv_core module that contains the core functionalities of the library, in
particular, basic data structures and arithmetic functions
2.The opencv_imgproc module that contains the main image processing functions
3.The opencv_highgui module that contains the image and video reading and writing
functions along with some user interface functions
4.The opencv_features2d module that contains the feature point detectors and
descriptors and the feature point matching framework
5.The opencv_calib3d module that contains the camera calibration, two-view
geometry estimation, and stereo function

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6.The opencv_video module that contains the motion estimation, feature
tracking, and
foreground extraction functions and classes
7.The opencv_obj detect module that contains the object detection functions
such as
the face and people detectors

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Colour Detectionimport cv2
import numpy as np
cap= cv2.VideoCapture(0)
while True :
_,frame =cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower_blue = np.array([0 , 100, 100])
upper_blue = np.array([ 130,255, 255])
lower_red =np.array([100,150,0])
upper_red =np.array([255,255,150])
lower_green =np.array([100,0,100])
upper_green=np.array([255,150,255])
mask = cv2.inRange(hsv,lower_blue, upper_blue)
mask2= cv2.inRange(hsv,lower_red,upper_red)
mask3=cv2.inRange(hsv,lower_green,upper_green)
output = cv2.bitwise_and(frame,frame,mask =
mask)
cv2.imshow('frame',frame)
cv2.imshow('mask',mask)
cv2.imshow('mask2',mask2)
cv2.imshow('mask3',mask3)
k = cv2.waitKey(5) & 0xFF
if k== 27:
break

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Shape Detection
• import numpy as np
import cv2
#import cv2.cv as cv
cap = cv2.VideoCapture(0)
#Capture the image
def get_image():
retval, img = cap.read()
return img
#Get an image after 30 frames
for i in xrange(30):
img = get_image()
print("Takingggg....")
#Save the image
file = "D:/tttt/test.png"
cv2.imwrite(file, img)
#Delete the camera
del(cap)
final_img = cv2.imread("D:/tttt/test.png")
cv2.imshow("final", final_img)
gray = cv2.imread("D:/tttt/test.png",0)

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#identifying the object sandaru.ny@gmail.com
ret,thresh=cv2.threshold(gray,127,255,1)
img,contours,h=cv2.findContours(thresh,1,2)
for cnt in contours:
apporx=cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)
print len(apporx)
if len(apporx)==3:
print"Triangle"
cv2.drawContours(img,[cnt],0,255,-1)
cv2.imshow('Triangle',img)
elif len(apporx)==4:
print"Square"
cv2.drawContours(img,[cnt],0,(0,255,0),-1)
cv2.imshow('Square',img)
elif len(apporx)>15:
print"Circle"
cv2.drawContours(img,[cnt],0,(0,0,255),-1)
cv2.imshow('Circle',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

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Raspberry-pi Circuit Description

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Alternative Methods
Colour detection -Colour based blob detection
Shape detection –Shape based block detection