Things Used In This Project
- Raspberry Pi 2 Model B
- Raspberry Pi Camera module
- Arduino Ultrasonic Sensor
- SparkFun Dual H-Bridge motor drivers L298
- DC motor (generic)
- Breadboard (generic)
- Connectors/ Wires
Software apps and online services:
Hand tools and fabrication machines:
The major drawback in today’s surveillance rests on the involvement of human operators which can easily be distracted, so we need a system which can autonomously monitor regions continuously, making decisions while identifying unwanted or obnoxious things and respond accordingly. Object tracking using computer vision is crucial in achieving automated surveillance.
We made this project in order to build a basic ball tracking car. Here, a bot uses camera to take frames and do image processing to track down the ball. The features of the ball such as color, shape, size can be used.
But our objective was to make a basic prototype for such a bot which can sense color and shape and follow it. The robot tries to find a color which is hard coded, if it finds a ball of that color it follows it.
We have chosen raspberry pi as micro-controller for this project as it gives great flexibility to use Raspberry Pi camera module and allows to code in Python which is very user friendly and OpenCV library, for image analysis.
For controlling the motors, We have used an H-Bridge to switch from clockwise to counter-clockwise or to stop the motors. This we have integrated via code when direction and speed has to be controlled in different obstacle situations.
Crucial thing while detecting images frame by frame was to avoid any frame drops as then the bot can go into a limbo state if the bot is unable to predict direction of ball after few frame drops. Even if it manage the frame drops then also if the ball goes out of scope of the camera, it will go into a limbo state, in that case, then we have made my bot take a 360 degree view of it's environment till the ball comes back in the scope of the camera and then start moving in it's direction.
For the image analysis, we taking each frame and then masking it with the color needed. Then for noise reduction, we eroding the noise and dilating the major blobs. Then we find all the contours and find the largest among them and bound it in a rectangle. And show the rectangle on the main image and find the coordinates of the center of the rectangle.We have attached the algorithm (pseudo-code) of the image analysis part and demonstrated this part in the video also.
Finally bot tries to bring the coordinates of the ball to the center of its imaginary coordinate axis. This is how robo works.
Pseudo code for Computer Vision
# import the necessary packages from picamera.array import PiRGBArray #As there is a resolution problem in raspberry pi, will not be able to capture frames by VideoCapture from picamera import PiCamera import RPi.GPIO as GPIO import time import cv2 import cv2.cv as cv import numpy as np #hardware work GPIO.setmode(GPIO.BOARD) GPIO_TRIGGER1 = 29 #Left ultrasonic sensor GPIO_ECHO1 = 31 GPIO_TRIGGER2 = 36 #Front ultrasonic sensor GPIO_ECHO2 = 37 GPIO_TRIGGER3 = 33 #Right ultrasonic sensor GPIO_ECHO3 = 35 MOTOR1B=18 #Left Motor MOTOR1E=22 MOTOR2B=21 #Right Motor MOTOR2E=19 LED_PIN=13 #If it finds the ball, then it will light up the led # Set pins as output and input GPIO.setup(GPIO_TRIGGER1,GPIO.OUT) # Trigger GPIO.setup(GPIO_ECHO1,GPIO.IN) # Echo GPIO.setup(GPIO_TRIGGER2,GPIO.OUT) # Trigger GPIO.setup(GPIO_ECHO2,GPIO.IN) GPIO.setup(GPIO_TRIGGER3,GPIO.OUT) # Trigger GPIO.setup(GPIO_ECHO3,GPIO.IN) GPIO.setup(LED_PIN,GPIO.OUT) # Set trigger to False (Low) GPIO.output(GPIO_TRIGGER1, False) GPIO.output(GPIO_TRIGGER2, False) GPIO.output(GPIO_TRIGGER3, False) # Allow module to settle def sonar(GPIO_TRIGGER,GPIO_ECHO): start=0 stop=0 # Set pins as output and input GPIO.setup(GPIO_TRIGGER,GPIO.OUT) # Trigger GPIO.setup(GPIO_ECHO,GPIO.IN) # Echo # Set trigger to False (Low) GPIO.output(GPIO_TRIGGER, False) # Allow module to settle time.sleep(0.01) #while distance > 5: #Send 10us pulse to trigger GPIO.output(GPIO_TRIGGER, True) time.sleep(0.00001) GPIO.output(GPIO_TRIGGER, False) begin = time.time() while GPIO.input(GPIO_ECHO)==0 and time.time()<begin+0.05: start = time.time() while GPIO.input(GPIO_ECHO)==1 and time.time()<begin+0.1: stop = time.time() # Calculate pulse length elapsed = stop-start # Distance pulse travelled in that time is time # multiplied by the speed of sound (cm/s) distance = elapsed * 34000 # That was the distance there and back so halve the value distance = distance / 2 print "Distance : %.1f" % distance # Reset GPIO settings return distance GPIO.setup(MOTOR1B, GPIO.OUT) GPIO.setup(MOTOR1E, GPIO.OUT) GPIO.setup(MOTOR2B, GPIO.OUT) GPIO.setup(MOTOR2E, GPIO.OUT) def forward(): GPIO.output(MOTOR1B, GPIO.HIGH) GPIO.output(MOTOR1E, GPIO.LOW) GPIO.output(MOTOR2B, GPIO.HIGH) GPIO.output(MOTOR2E, GPIO.LOW) def reverse(): GPIO.output(MOTOR1B, GPIO.LOW) GPIO.output(MOTOR1E, GPIO.HIGH) GPIO.output(MOTOR2B, GPIO.LOW) GPIO.output(MOTOR2E, GPIO.HIGH) def rightturn(): GPIO.output(MOTOR1B,GPIO.LOW) GPIO.output(MOTOR1E,GPIO.HIGH) GPIO.output(MOTOR2B,GPIO.HIGH) GPIO.output(MOTOR2E,GPIO.LOW) def leftturn(): GPIO.output(MOTOR1B,GPIO.HIGH) GPIO.output(MOTOR1E,GPIO.LOW) GPIO.output(MOTOR2B,GPIO.LOW) GPIO.output(MOTOR2E,GPIO.HIGH) def stop(): GPIO.output(MOTOR1E,GPIO.LOW) GPIO.output(MOTOR1B,GPIO.LOW) GPIO.output(MOTOR2E,GPIO.LOW) GPIO.output(MOTOR2B,GPIO.LOW) #Image analysis work def segment_colour(frame): #returns only the red colors in the frame hsv_roi = cv2.cvtColor(frame, cv2.cv.CV_BGR2HSV) mask_1 = cv2.inRange(hsv_roi, np.array([160, 160,10]), np.array([190,255,255])) ycr_roi=cv2.cvtColor(frame,cv2.cv.CV_BGR2YCrCb) mask_2=cv2.inRange(ycr_roi, np.array((0.,165.,0.)), np.array((255.,255.,255.))) mask = mask_1 | mask_2 kern_dilate = np.ones((8,8),np.uint8) kern_erode = np.ones((3,3),np.uint8) mask= cv2.erode(mask,kern_erode) #Eroding mask=cv2.dilate(mask,kern_dilate) #Dilating #cv2.imshow('mask',mask) return mask def find_blob(blob): #returns the red colored circle largest_contour=0 cont_index=0 contours, hierarchy = cv2.findContours(blob, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) for idx, contour in enumerate(contours): area=cv2.contourArea(contour) if (area >largest_contour) : largest_contour=area cont_index=idx #if res>15 and res<18: # cont_index=idx r=(0,0,2,2) if len(contours) > 0: r = cv2.boundingRect(contours[cont_index]) return r,largest_contour def target_hist(frame): hsv_img=cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) hist=cv2.calcHist([hsv_img],,None,,[0,255]) return hist #CAMERA CAPTURE #initialize the camera and grab a reference to the raw camera capture camera = PiCamera() camera.resolution = (160, 120) camera.framerate = 16 rawCapture = PiRGBArray(camera, size=(160, 120)) # allow the camera to warmup time.sleep(0.001) # capture frames from the camera for image in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True): #grab the raw NumPy array representing the image, then initialize the timestamp and occupied/unoccupied text frame = image.array frame=cv2.flip(frame,1) global centre_x global centre_y centre_x=0. centre_y=0. hsv1 = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) mask_red=segment_colour(frame) #masking red the frame loct,area=find_blob(mask_red) x,y,w,h=loct #distance coming from front ultrasonic sensor distanceC = sonar(GPIO_TRIGGER2,GPIO_ECHO2) #distance coming from right ultrasonic sensor distanceR = sonar(GPIO_TRIGGER3,GPIO_ECHO3) #distance coming from left ultrasonic sensor distanceL = sonar(GPIO_TRIGGER1,GPIO_ECHO1) if (w*h) < 10: found=0 else: found=1 simg2 = cv2.rectangle(frame, (x,y), (x+w,y+h), 255,2) centre_x=x+((w)/2) centre_y=y+((h)/2) cv2.circle(frame,(int(centre_x),int(centre_y)),3,(0,110,255),-1) centre_x-=80 centre_y=6--centre_y print centre_x,centre_y initial=400 flag=0 GPIO.output(LED_PIN,GPIO.LOW) if(found==0): #if the ball is not found and the last time it sees ball in which direction, it will start to rotate in that direction if flag==0: rightturn() time.sleep(0.05) else: leftturn() time.sleep(0.05) stop() time.sleep(0.0125) elif(found==1): if(area<initial): if(distanceC<10): #if ball is too far but it detects something in front of it,then it avoid it and reaches the ball. if distanceR>=8: rightturn() time.sleep(0.00625) stop() time.sleep(0.0125) forward() time.sleep(0.00625) stop() time.sleep(0.0125) #while found==0: leftturn() time.sleep(0.00625) elif distanceL>=8: leftturn() time.sleep(0.00625) stop() time.sleep(0.0125) forward() time.sleep(0.00625) stop() time.sleep(0.0125) rightturn() time.sleep(0.00625) stop() time.sleep(0.0125) else: stop() time.sleep(0.01) else: #otherwise it move forward forward() time.sleep(0.00625) elif(area>=initial): initial2=6700 if(area<initial2): if(distanceC>10): #it brings coordinates of ball to center of camera's imaginary axis. if(centre_x<=-20 or centre_x>=20): if(centre_x<0): flag=0 rightturn() time.sleep(0.025) elif(centre_x>0): flag=1 leftturn() time.sleep(0.025) forward() time.sleep(0.00003125) stop() time.sleep(0.00625) else: stop() time.sleep(0.01) else: #if it founds the ball and it is too close it lights up the led. GPIO.output(LED_PIN,GPIO.HIGH) time.sleep(0.1) stop() time.sleep(0.1) #cv2.imshow("draw",frame) rawCapture.truncate(0) # clear the stream in preparation for the next frame if(cv2.waitKey(1) & 0xff == ord('q')): break GPIO.cleanup() #free all the GPIO pins
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