Classy_Classifier_Cyborg

Nicholas Tan & Nathan Shen

Objective

We plan to build a robot that would help users remotely explore a given space and identify key objects therein.

Introduction

Our robot will be instrumental in researching, analyzing, and documenting unknown areas. In particular, these spaces may be dangerous to human life. Our solution would provide humans the safety of not having to physically explore the location, but still the benefit of gathering all the necessary information. Additionally, to preserve the data, our robot communicates with social media to give constant updates of its findings.


Robot Structure: Our robot is built out of mostly 3D printed materials. It is controlled by a Raspberry Pi and uses a Pi-camera to capture images.

Motor Control: Our robot traverses dangerous terrain using parallax continuous rotation servos.

Object detection: Our robot uses a combination of Haar training and LBP-cascade algorithms to learn what features to search for when detecting objects in its surrounding. The software libraries for computer vision were obtained from OpenCV.

Social Media Footprint: Our robot communicates with twitter and posts pictures of the objects that it has found.

Design

At first glance, the project might seem overwhelming. However, we took an incremental design approach and separated the project into three separate components:


1. OpenCV and Computer Vision

2. Servo Controls for the Robot

3. Twitter picture uploads


1. Computer Vision


Downloading OpenCV

The main component of our robot is to recognize certain objects that we wish to identify. Since neither members of the group has had any experience with computer vision or machine learning, we chose to use OpenCV since it has an established open source computer vision and machine learning software library. Thus, we set out to download OpenCV onto our RaspberryPi following a very well written guide linked below in the references [2]. The guide provided us a starting point, but because of the size of our SD card, we opted out of certain steps. As such, we will provide a brief guide(very similar to the reference) on the steps we took to download OpenCV.


Step #1: Install dependencies The first thing we need to do is to make sure that we update and upgrade and existing packages as well as updating the Raspberry Pi firmware 
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$ sudo apt-get update
$ sudo apt-get upgrade
$ sudo rpi-update
$ sudo reboot
$ sudo apt-get install build-essential git cmake pkg-config
$ sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng12-dev
$ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
$ sudo apt-get install libxvidcore-dev libx264-dev
$ sudo apt-get install libgtk2.0-dev
$ sudo apt-get install libatlas-base-dev gfortran
$ sudo apt-get install python2.7-dev python3-dev
Step #2: Download OpenCV source code At this point, we want to grab the OpenCV source code. Any version should work, but we decided to download version 3.0.0 
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$ cd ~
$ wget -O opencv.zip https://github.com/Itseez/opencv/archive/3.0.0.zip
$ unzip opencv.zip
Step #3: Compile and install OpenCV Now we are ready to compile OpenCV. The following commands setup the build and then we can run the make command with the -j4 flags, which tells the RaspberryPi to utilize all four cores for a faster compilation of OpenCV. WARNING: It takes about 2hours for the compilation to finish. However, you can pause and resume at a later time. 
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$ cd ~/opencv-3.0.0/
$ mkdir build
$ cd build
$ cmake -D CMAKE_BUILD_TYPE=RELEASE \
	-D CMAKE_INSTALL_PREFIX=/usr/local \
	-D INSTALL_C_EXAMPLES=ON \
	-D INSTALL_PYTHON_EXAMPLES=ON \
	-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-3.0.0/modules \
	-D BUILD_EXAMPLES=ON ..
$ make -j4
Assuming OpenCV compiled without error, all we need to do is install it on our system: 
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$ sudo make install
$ sudo ldconfig
Step #4: Verify that OpenCV installed correctly After typing in the cv2.__version__ command, you should get an output of the version of OpenCV that you downloaded. In our case, this was ‘3.0.0’. 
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$ python
>>> import cv2
>>> cv2.__version__
'3.0.0'

HAAR Classifier Training (Failed attempt 😞)

The first thing we had to decide was what objects we wanted to recognize. At the very beginning, we wanted to identify the iconic red solo plastic cup that is prevalent across college campuses. As such, we followed a reputable guide online link below in the references [4]. However, as you can see in the section title, we were not able to accomplish this and failed. Because of our limited memory space, instead of gathering 1000 images, we gathered approximately 100 images of non-cups as well as 100 images of cups following the guide. However, when we wanted to start training the Cascade we would receive the following error:

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===== TRAINING 0-stage =====
<BEGIN
POS count : consumed   100 : 100
Train dataset for temp stage can not be filled. Branch training terminated.
Cascade classifier can't be trained. Check the used training parameters

We believe this was because we do not have enough pictures. Alternatively, we could’ve trained the cascade on our computers. However, pressed for time and realizing that it takes approximately 1-2 days for the training to finish even if we were successful, we decided to dejectedly give up our attempts at creating our own cascade. Instead we found and used other cascades that people have already created.


Recognizing and Labeling Target Objects

After our failed attempt at creating our own OpenCV cascade, we settled on three target objects cascades that we found: silverware, banana, and phones. The following XML files for the objects can be found at the end of the code appendices section. At this point, we created a program that will stream video from the PiCamera to the device that we used to SSH into the Raspberry Pi. In addition, the program also draws boxes around the target object, in the case of Code Block 1, a phone. Although, we say the video is streaming, it is actually transmitting picture frames and thus, the output video has a noticeable delay.


2. Servo Control


The parallax servo motors have specific pulse width modulation(PWM) functions listed below:

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p = GPIO.PWM(channel, frequency)    #to create a PWM instance
p.start(dc)                         #where dc is the duty cycle (0.0 <= dc <= 100.0)
p.ChangeFrequency(freq)             #where freq is the new frequency in Hz
p.ChangeDutyCycle(dc)               #where 0.0 <= dc <= 100.0
p.stop()                            #to stop PWM

An important thing to understand from the datasheet is that the servos need 20ms of low voltage between pulses. In addition, depending on the duration of a high pulse, between 1.3-1.7ms, it will determine the speed of the rotations of the motor. For example, with a high pulse of 1.5ms the servo will be stopped. As we decreases from 1.5ms, the servo will gradually rotate faster in the clockwise direction, and likewise as we increase from 1.5ms, the servo will gradually rotate faster in the counterclockwise direction. Knowing this information, we don’t want to hard code the frequency and duty cycle of the PWM parameters into our program since this is bad coding practice and very tedious. Therefore we created two functions that we will use for the rest of the lab shown below:

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p.ChangeFrequency(1000/(21.5+speed*0.01))
p.ChangeDutyCycle((1.5+speed*0.01)/(21.5+speed*0.01)*100)

We utilize a variable called “speed” which ranges between -20 and 20. Now we can control the speed and direction of our servos by changing one variable. More explicitly, speed = 0 is a stopped servo, speed = 20 is a servo rotating counterclockwise at full speed, and speed = -20 is a servo rotating clockwise at full speed. Using all of the information we learned above, we created a python application named servo_control.py as seen in Code Block 2.


3. Twitter Interface


At first, we wanted to be able to interact with an Instagram account. However, we found the API to be too tedious to figure out. As a result, we decided to interact with a twitter account using Python scripts. This is because there is already a Python library called tweepy which does a lot of the API work for us. We follow a useful guide for uploading pictures found in references [8].


Create an ‘App’


First thing we need to do is create an app for the twitter API. Once we log into our twitter account, we hover over our avatar and click My Applications, then click Create a new Application. We then complete the form and Create Your Twitter Application. Now we hover over our avatar and click on My Applications -> Permissions and make sure that it is set to “Read and Write” access.


Create Access Token


In order for our program to talk to twitter, it needs a way of authentication. The system is uses is called OAuth, and it uses access tokens. Click on Create My Access Token, which will create tokens that we will copy into our program later.


Install tweepy


Now all we need to do is install tweepy, which is a python extension, so it is easy to install.

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$ sudo apt-get update
$ sudo apt-get install python-dev python-pip
$ sudo pip install -U pip
$ sudo pip install tweepy

Create and Write a Python Script


Now, all we got to do is insert the consumer keys and access tokens that we created earlier for OAuth into the python script that we got from the guide [8], which is replicated in Code Block 3.


4. Integration


Computer Vision + Servo


Now that we have finished all the separate components of the project, we are finally ready to integrate them all together. We chose to integrate the computer vision and servo part first as seen in Code Block 4. In regards to the computer vision, we took phone.py and duplicated the function calls for banana and silverware. The main parts that we added in was for our program to take a picture when it found one of the target items. We also set a limit so that it will only take one picture of a target item every 5 seconds. This prevents our program from taking multiple pictures continuously of the same object. In servo_control.py, we already wrote functions that can control the speed, all we needed to do is change the speed when there is a keyboard input.


Computer Vision + Servo + Twitter


Finally, we include Twitter into our program. Essentially, when the program takes the picture, we include a timestamp and also include a hashtag of the item. We then upload the picture, using what we had from tweetpic.py. Then completed integration of the project can be found in Code Block 5.

Results

Figure 1: Schematic of our Robot


Figure 2: Picture of our PiCamera attached to the Raspberry Pi


Figure 3: Pictures of our Robot in Action


Figure 4: Pictures of the target items being identified


Figure 5: Pictures being uploaded successfully to Twitter


Conclusion

Our team has accomplished a working prototype of our robot. We are able to control the robot’s movements remotely as well as identify a few classes of objects as well as successfully post pictures of the objects to twitter. However, there is a lot of room for improvement. Our learning algorithms perform best in a controlled environment. Specifically, they work best when the object is found on a blank, white background. Additionally, our robot movement control is not fully remote. We are able to control our robot over a long ethernet wire. This may be sufficient if the robot only needs to travel to the next room, but will not be optimal when the robot is miles away. We are also able to control the robot over wifi with a wifi dongle, but there is a significant latency issue which affects the commands to the robot as well as lags the video feed.


For future work, we would focus on expanding on the robot prototype in terms of movement as well as recognition. We would train models for more objects such flames, firearms, or other life-threatening objects. This would allow the robot to help those humans in dangerous situations. Additionally, we would build arms to give the robot the ability to interact with the objects that were recognized or the environment around it.

Contributions

This project was a joint effort with work being done together or concurrently. In terms of focus, Nathan concentrated more on the Computer Vision and OpenCV aspect of the project. Nicholas concentrated more on the Robot Control aspect of the project. However, both members helped each other when needed and the project couldn’t have been finished without both members.

Parts List

Name Purchase Link Cost Quantity
Raspberry Pi Camera Amazon $19.99 1
Adafruit Flex Cable Amazon $4.99 1
Wi-Fi USB Adapter Amazon $8.99 1
Parallax Continuous Rotation Motor Amazon $13.99 2

Code Appendix

Code Block 1: 
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#   Program:    Final Project (phone.py)
#   Author:     Nathan Shen (nds64)     Nicholas Tan (nt325)
#   Date:       5/18/2016

### Imports ###################################################################

from picamera.array import PiRGBArray
from picamera import PiCamera
import time
import cv2
import os
import pygame

# Setup the camera
camera = PiCamera()
camera.resolution = ( 320, 240 )
camera.framerate = 40
rawCapture = PiRGBArray( camera, size=( 320, 240 ) )

# Load the cascade files for detecting faces and phones
phone_cascade = cv2.CascadeClassifier( 'phone_cascade = cv2.CascadeClassifier( '/home/pi/opencv-3.0.0/data/nds64_cascade/phone_cascade.xml' )' )

### Main ######################################################################

# Capture frames from the camera
for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):

    image = frame.array

    # Look for phones in the image using the loaded cascade file
    phones = phone_cascade.detectMultiScale(gray)
    
    # Draw a rectangle around every phone
    for (x,y,w,h) in phones:
        cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 255, 0, 0 ), 2 )
        cv2.putText( image, "Phone", ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 255, 255 ), 2 )

    cv2.imshow( "Frame", image )
    cv2.waitKey( 1 )

    # Clear the stream in preparation for the next frame
    rawCapture.truncate( 0 )

Code Block 2: 
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#   Program:	Final Project(servo_control.py)
#   Author:		Nathan Shen (nds64)		Nicholas Tan (nt325)
#   Date:		5/18/2016

from __future__ import division
import time
import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BCM)
GPIO.setup(5, GPIO.OUT)
GPIO.setup(17, GPIO.IN, pull_up_down=GPIO.PUD_UP)
speed = 0 			  #speed ranges between -20 and 20 (increments of 2)

p = GPIO.PWM(5, 46.5) #pin 5 frequency 50Hz
p.start(7)
programrun = 1

while (programrun):
	for speed in xrange(-20, 22, 2):
		p.ChangeFrequency(1000/(21.5+speed*0.01))
		p.ChangeDutyCycle((1.5+speed*0.01)/(21.5+speed*0.01)*100)
		time.sleep(3)
	speed = 0
	p.ChangeDutyCycle(100.0*(1.5+speed)/(21.5+speed))
	p.ChangeFrequency(1000.0/(21.5+speed))
	time.sleep(10)
	if(True or not GPIO.input(17)):
		programrun = 0
		GPIO.cleanup()
		p.stop()

Code Block 3: 
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#   Program:    Final Project (tweetpic.py)
#   Author:     Nathan Shen (nds64)     Nicholas Tan (nt325)
#   Date:       5/18/2016

#!/usr/bin/env python2.7
import tweepy
from subprocess import call
from datetime import datetime
 
i = datetime.now()               #take time and date for filename
now = i.strftime('%Y%m%d-%H%M%S')
photo_name = now + '.jpg'
cmd = 'raspistill -t 500 -w 1024 -h 768 -o /home/pi/' + photo_name 
call ([cmd], shell=True)         #shoot the photo

# Consumer keys and access tokens, used for OAuth
consumer_key = 'copy your consumer key here'
consumer_secret = 'copy your consumer secret here'
access_token = 'copy your access token here'
access_token_secret = 'copy your access token secret here'

# OAuth process, using the keys and tokens
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
 
# Creation of the actual interface, using authentication
api = tweepy.API(auth)

# Send the tweet with photo
photo_path = '/home/pi/' + photo_name
status = 'Photo auto-tweet from Pi: ' + i.strftime('%Y/%m/%d %H:%M:%S') 
api.update_with_media(photo_path, status=status)

Code Block 4: 
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#   Program:    Final Project (robot_control.py)
#   Author:     Nathan Shen (nds64)     Nicholas Tan (nt325)
#   Date:       5/18/2016

from __future__ import division
from picamera.array import PiRGBArray
from picamera import PiCamera
import RPi.GPIO as GPIO
import time
import cv2
import os

### Setup #####################################################################
# Setup the camera
camera = PiCamera()
camera.resolution = ( 640, 480 )
camera.framerate = 20
camera.vflip = True
camera.hflip = True
camera.led = False
camera.brightness = 60
rawCapture = PiRGBArray( camera, size=( 640, 480 ) )

# Load a cascade file for detecting phones, silverware, and banana
phone_cascade = cv2.CascadeClassifier( '/home/pi/opencv-3.0.0/data/nds64_cascade/phone_cascade.xml' )
silver_cascade = cv2.CascadeClassifier( '/home/pi/opencv-3.0.0/data/lbpcascades/lbpcascade_silverware.xml' )
banana_cascade = cv2.CascadeClassifier( '/home/pi/opencv-3.0.0/data/nds64_cascade/banana_classifier.xml' )

# Setup GPIO pins for motors
GPIO.setmode(GPIO.BCM)
GPIO.setup(5, GPIO.OUT)
GPIO.setup(6, GPIO.OUT)
speed = 0

l = GPIO.PWM(5, 46.5) #pin 5 stop/ left motor
r = GPIO.PWM(6, 46.5) #pin 6 stop/ right motor
lspeed = 0
rspeed = 0

# Setup picture counters
silver_counter = 0
phone_counter = 0
banana_counter = 0
s_start = time.time()
p_start = time.time()

### Main ######################################################################

# Capture frames from the camera
for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):
    image = frame.array
    # Use the cascade file we loaded to detect items
    silver = silver_cascade.detectMultiScale( image )
    phone  = phone_cascade.detectMultiScale ( image )
    banana = banana_cascade.detectMultiScale( image )

    # Draw a rectangle around 1 silverware
    for ( x, y, w, h ) in silver:
        cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 100, 255, 100 ), 2 )
        cv2.putText( image, "Silverware", ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )
        if( (time.time() - s_start) > 5): # no more than 1 saved picture in 5 seconds
            cv2.imwrite('silverware' + str(silver_counter) + '.jpg', image)
            silver_counter+=1
            s_start = time.time()
            print "silver picture"
        break
    # Draw a rectangle around 1 phone
    for ( x, y, w, h ) in phone:
        cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 100, 255, 100 ), 2 )
        cv2.putText( image, "Phone", ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )
        if( (time.time() - p_start) > 5): # no more than 1 saved picture in 5 seconds
            cv2.imwrite('phone' + str(phone_counter) + '.jpg', image)
            phone_counter+=1
            p_start = time.time()
            print "phone picture"
        break
    # Draw a rectangle around 1 banana
    for ( x, y, w, h ) in banana:
        cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 100, 255, 100 ), 2 )
        cv2.putText( image, "Banana", ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )
        if( (time.time() - s_start) > 5): # no more than 1 saved picture in 5 seconds
            cv2.imwrite('banana' + str(banana_counter) + '.jpg', image)
            banana_counter+=1
            s_start = time.time()
            print "banana picture"
        break


    # Show the frame
    cv2.imshow( "Frame", image )
    key = cv2.waitKey( 500 ) & 0xFF
    # Clear the stream in preparation for the next frame
    rawCapture.truncate( 0 )
    if key == ord("q"):     # quit the program
        l.stop()
        r.stop()
        GPIO.cleanup()
        break
    elif key == ord("w"):   # go forward
        l.start(7)
        r.start(7)
        lspeed = 5
        rspeed = -6
        l.ChangeFrequency(1000/(21.5+lspeed*0.01)) #speed ranges between -20 and 20
        l.ChangeDutyCycle((1.5+lspeed*0.01)/(21.5+lspeed*0.01)*100)
        r.ChangeFrequency(1000/(21.5+rspeed*0.01))
        r.ChangeDutyCycle((1.5+rspeed*0.01)/(21.5+rspeed*0.01)*100)
    elif key == ord("s"):   # go backwards
        l.start(7)
        r.start(7)
        lspeed = -5
        rspeed = 6
        l.ChangeFrequency(1000/(21.5+lspeed*0.01)) #speed ranges between -20 and 20
        l.ChangeDutyCycle((1.5+lspeed*0.01)/(21.5+lspeed*0.01)*100)
        r.ChangeFrequency(1000/(21.5+rspeed*0.01))
        r.ChangeDutyCycle((1.5+rspeed*0.01)/(21.5+rspeed*0.01)*100)
    elif key == ord("a"):   # turn left
        l.start(7)
        r.start(7)
        lspeed = -2
        rspeed = -2
        l.ChangeFrequency(1000/(21.5+lspeed*0.01)) #speed ranges between -20 and 20
        l.ChangeDutyCycle((1.5+lspeed*0.01)/(21.5+lspeed*0.01)*100)
        r.ChangeFrequency(1000/(21.5+rspeed*0.01))
        r.ChangeDutyCycle((1.5+rspeed*0.01)/(21.5+rspeed*0.01)*100)
    elif key == ord("d"):   # turn right
        l.start(7)
        r.start(7)
        lspeed = 2
        rspeed = 2
        l.ChangeFrequency(1000/(21.5+lspeed*0.01)) #speed ranges between -20 and 20
        l.ChangeDutyCycle((1.5+lspeed*0.01)/(21.5+lspeed*0.01)*100)
        r.ChangeFrequency(1000/(21.5+rspeed*0.01))
        r.ChangeDutyCycle((1.5+rspeed*0.01)/(21.5+rspeed*0.01)*100)
    else:                   # stop the robot
        lspeed = 0
        rspeed = 0
        l.stop()
        r.stop()

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#   Program:    Final Project (robot_twitter.py)
#   Author:     Nathan Shen (nds64)     Nicholas Tan (nt325)
#   Date:       5/18/2016

#!/usr/bin/env python2.7
from __future__ import division
from picamera.array import PiRGBArray
from subprocess import call
from datetime import datetime
from picamera import PiCamera
import tweepy
import RPi.GPIO as GPIO
import time
import cv2
import os

### Setup #####################################################################
# Setup the camera
camera = PiCamera()
camera.resolution = ( 320, 240 )
camera.framerate = 20
camera.vflip = True
camera.hflip = True
camera.led = False
camera.brightness = 60
rawCapture = PiRGBArray( camera, size=( 320, 240 ) )

# Load a cascade file for detecting phones, silverware, and banana
phone_cascade = cv2.CascadeClassifier( '/home/pi/opencv-3.0.0/data/nds64_cascade/phone_cascade.xml' )
silver_cascade = cv2.CascadeClassifier( '/home/pi/opencv-3.0.0/data/lbpcascades/lbpcascade_silverware.xml' )
banana_cascade = cv2.CascadeClassifier( '/home/pi/opencv-3.0.0/data/nds64_cascade/banana_classifier.xml' )

# Setup GPIO pins for motors
GPIO.setmode(GPIO.BCM)
GPIO.setup(5, GPIO.OUT)
GPIO.setup(6, GPIO.OUT)

l = GPIO.PWM(5, 46.5) #pin 5 stop/ left motor
r = GPIO.PWM(6, 46.5) #pin 6 stop/ right motor

# Setup speed for motor
freq = []
duty = []
for x in xrange(-20, 21):
    freq.append(1000/(21.5+x*0.01))
    duty.append((1.5+x*0.01)/(21.5+x*0.01)*100)


# Setup picture counters
silver_counter = 0
phone_counter = 0
banana_counter = 0
s_start = time.time()
p_start = time.time()

### Twitter Setup ###############################################################
# Consumer keys and access tokens, used for OAuth
consumer_key = 'in4fmLsJiAuGKhfox2V1smZpy'
consumer_secret = '9HsPpMZrOjYt9de2qCB7HaR2C80LfMH92VcpEpgV9hJE2lsIbQ'
access_token = '732682805533114368-jKZxGQD9fe0o7Xk6GQM5nTmplhl3H0f'
access_token_secret = 'TZyCq4gGnmwtqifhEas1B3t2MDydUBUf969xmz2uQjPZX'

# OAuth process, using the keys and tokens
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)

# Creation of the actual interface, using authentication
api = tweepy.API(auth)

# The path to picture
photo_path = '/home/pi/Project/'
photo_time = datetime.now()

### Main ######################################################################

# Capture frames from the camera
for frame in camera.capture_continuous( rawCapture, format="bgr", use_video_port=True ):
    image = frame.array
    # Use the cascade file we loaded to detect items
    silver = silver_cascade.detectMultiScale( image )
    phone  = phone_cascade.detectMultiScale ( image )
    banana = banana_cascade.detectMultiScale( image )

    # Draw a rectangle around 1 silverware
    for ( x, y, w, h ) in silver:
        cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 100, 255, 100 ), 2 )
        cv2.putText( image, "Silverware", ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )
        if( (time.time() - s_start) > 5): # no more than 1 saved picture in 5 seconds
            photo_name = 'silverware' + str(silver_counter) + '.jpg'
            cv2.imwrite(photo_name, image)
            status = 'Photo auto-tweet from Pi: ' + photo_time.strftime('%Y/%m/%d %H:%M:%S') + ' #silverware'
            api.update_with_media(photo_path + photo_name, status=status)
            silver_counter+=1
            s_start = time.time()
            print "silver picture"
        break
    # Draw a rectangle around 1 phone
    for ( x, y, w, h ) in phone:
        cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 100, 255, 100 ), 2 )
        cv2.putText( image, "Phone", ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )
        if( (time.time() - p_start) > 5): # no more than 1 saved picture in 5 seconds
            photo_name = 'phone' + str(phone_counter) + '.jpg'
            cv2.imwrite(photo_name, image)
            status = 'Photo auto-tweet from Pi: ' + photo_time.strftime('%Y/%m/%d %H:%M:%S') + ' #phone'
            api.update_with_media(photo_path + photo_name, status=status)
            phone_counter+=1
            p_start = time.time()
            print "phone picture"
        break
    # Draw a rectangle around 1 banana
    for ( x, y, w, h ) in banana:
        cv2.rectangle( image, ( x, y ), ( x + w, y + h ), ( 100, 255, 100 ), 2 )
        cv2.putText( image, "Banana", ( x, y ), cv2.FONT_HERSHEY_SIMPLEX, 0.5, ( 0, 0, 255 ), 2 )
        if( (time.time() - s_start) > 5): # no more than 1 saved picture in 5 seconds
            photo_name = 'banana' + str(banana_counter) + '.jpg'
            cv2.imwrite(photo_name, image)
            status = 'Photo auto-tweet from Pi: ' + photo_time.strftime('%Y/%m/%d %H:%M:%S') + ' #banana'
            api.update_with_media(photo_path + photo_name, status=status)
            banana_counter+=1
            s_start = time.time()
            print "banana picture"
        break

        # Show the frame
    cv2.imshow( "Frame", image )
    key = cv2.waitKey( 500 ) & 0xFF
    # Clear the stream in preparation for the next frame
    rawCapture.truncate( 0 )
    if key == ord("q"):     # quit the program
        l.stop()
        r.stop()
        GPIO.cleanup()
        break
    elif key == ord("w"):   # go forward
        lspeed = 5
        rspeed = -9
        l.ChangeFrequency(freq[20 + lspeed]) #speed ranges between -20 and 20
        r.ChangeFrequency(freq[20 + rspeed])
        l.start(duty[20 + lspeed])
        r.start(duty[20 + rspeed])
    elif key == ord("s"):   # go backwards
        lspeed = -9
        rspeed = 5
        l.ChangeFrequency(freq[20 + lspeed]) #speed ranges between -20 and 20
        r.ChangeFrequency(freq[20 + rspeed])
        l.start(duty[20 + lspeed])
        r.start(duty[20 + rspeed])
    elif key == ord("a"):   # turn left
        lspeed = -4
        rspeed = -4
        l.ChangeFrequency(freq[20 + lspeed]) #speed ranges between -20 and 20
        r.ChangeFrequency(freq[20 + rspeed])
        l.start(duty[20 + lspeed])
        r.start(duty[20 + rspeed])
    elif key == ord("d"):   # turn right
        lspeed = 2
        rspeed = 2
        l.ChangeFrequency(freq[20 + lspeed]) #speed ranges between -20 and 20
        r.ChangeFrequency(freq[20 + rspeed])
        l.start(duty[20 + lspeed])
        r.start(duty[20 + rspeed])
    else:                   # stop the robot
        lspeed = 0
        rspeed = 0
        l.stop()
        r.stop()

XML Files:


Silverware

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<?xml version="1.0"?>
<!--
    This is 12x80 detector of the silverware (forks, spoons, knives) using LBP features.
    It was created by Attila Novak during GSoC 2012.
    Note that the detector only detects vertically oriented silverware,
    so you should care of the proper image orientation
    (probably should run detector several times).
    It also assumes the "top view" when the camera optical axis is orthogonal to the table plane.
-->
<opencv_storage>
<cascade>
  <stageType>BOOST</stageType>
  <featureType>LBP</featureType>
  <height>80</height>
  <width>12</width>
  <stageParams>
    <boostType>GAB</boostType>
    <minHitRate>9.9500000476837158e-001</minHitRate>
    <maxFalseAlarm>3.0000001192092896e-001</maxFalseAlarm>
    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
    <maxDepth>1</maxDepth>
    <maxWeakCount>100</maxWeakCount></stageParams>
  <featureParams>
    <maxCatCount>256</maxCatCount>
    <featSize>1</featSize></featureParams>
  <stageNum>16</stageNum>
  <stages>
    <!-- stage 0 -->
    <_>
      <maxWeakCount>4</maxWeakCount>
      <stageThreshold>-8.2867860794067383e-002</stageThreshold>
      <weakClassifiers>
        <_>
          <internalNodes>
            0 -1 99 -268435521 -486543361 -258 1659633406 -134217857
            1702887279 -134217929 -184549377</internalNodes>
          <leafValues>
            -7.5000000000000000e-001 8.6380833387374878e-001</leafValues></_>
        <_>
          <internalNodes>
            0 -1 39 -540541017 -1060113913 -781245688 -477121697
            -1818664155 1105186857 -505961467 -152575569</internalNodes>
          <leafValues>
            -7.9976779222488403e-001 7.5056612491607666e-001</leafValues></_>
        <_>
          <internalNodes>
            0 -1 101 -479208497 -353380921 -855254781 -1566689761
            -454302869 1893310787 -271591561 -134222965</internalNodes>
          <leafValues>
            -7.1062028408050537e-001 7.7380746603012085e-001</leafValues></_>
        <_>
          <internalNodes>
            0 -1 41 -338958865 -925383977 -1438297681 -981777969
            -882901177 1913369038 -135286729 1995959223</internalNodes>
          <leafValues>
            -7.8616768121719360e-001 6.9309240579605103e-001</leafValues></_></weakClassifiers></_>
    <!-- stage 1 -->
    <_>
      <maxWeakCount>5</maxWeakCount>
      <stageThreshold>-7.7058833837509155e-001</stageThreshold>
      <weakClassifiers>
        <_>
          <internalNodes>
            0 -1 14 -34089161 -2245 1878980471 -8687769 -134316045
            1744797563 -8388737 1795146607</internalNodes>
          <leafValues>
            -6.1089491844177246e-001 7.3594772815704346e-001</leafValues></_>
        <_>
          <internalNodes>
            0 -1 32 -707274321 1896302609 1132560802 -183140351 17019099
            830472347 -1993621429 1440074510</internalNodes>
          <leafValues>
            -6.4869755506515503e-001 5.6941097974777222e-001</leafValues></_>
        <_>
          <internalNodes>
            0 -1 4 -1055898237 -104492975 -1795141251 1464975384
            -1602043461 -914358144 1111543953 -2067496448</internalNodes>
          <leafValues>
            -6.0432785749435425e-001 5.5685383081436157e-001</leafValues></_>
        <_>
          <internalNodes>
            0 -1 96 -520160401 2063466495 -65665 -134217729 -50462805
            1761476478 1693969709 1910503031</internalNodes>
          <leafValues>
            -5.6237226724624634e-001 6.2263637781143188e-001</leafValues></_>
        <_>
          <internalNodes>
            0 -1 6 -1479564374 -954482597 16859161 -799804534 268468874
            713187329 1108033665 -714619755</internalNodes>
          <leafValues>
            -6.9048601388931274e-001 5.3264212608337402e-001</leafValues></_></weakClassifiers></_>
    <!-- stage 2 -->
    <_>
      <maxWeakCount>5</maxWeakCount>
      <stageThreshold>-7.1249550580978394e-001</stageThreshold>
      <weakClassifiers>
        <_>
          <internalNodes>
            0 -1 21 -34638473 -553976197 -134217865 -159715533
            -142901385 -272629761 -8421377 -956303361</internalNodes>
          <leafValues>
            -6.4170038700103760e-001 7.0683228969573975e-001</leafValues></_>
        <_>
          <internalNodes>
            0 -1 100 -8389777 -185860353 -277 -2097152001 -161
            -209780865 -1 -529006609</internalNodes>
          <leafValues>
            -5.5270516872406006e-001 6.9983023405075073e-001</leafValues></_>
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Cellular Device

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    <!-- stage 5 -->
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    <!-- stage 6 -->
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    <!-- stage 7 -->
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    <!-- stage 8 -->
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    <!-- stage 9 -->
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    <!-- stage 10 -->
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    <!-- stage 11 -->
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    <!-- stage 12 -->
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    <!-- stage 13 -->
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    <!-- stage 14 -->
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    <!-- stage 15 -->
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    <!-- stage 16 -->
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    <!-- stage 17 -->
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    <!-- stage 18 -->
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<?xml version="1.0"?>
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    <!-- stage 1 -->
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    <!-- stage 2 -->
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    <!-- stage 4 -->
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    <!-- stage 5 -->
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    <!-- stage 14 -->
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</opencv_storage>

Works Cited

[1] Banana Classifier
[2] Download OpenCV
[3] Face Recognition
[4] Haar Classifier Training
[5] Motor Datasheet
[6] Raspberry PI Camera
[7] Twitter Webpage
[8] Uploading to Twitter

Contact

Nicholas Tan (nt325) & Nathan Shen (nds64)


We would like to thank the staff of ECE5990 for all of their hard work and support through our many struggles