objectDetect.py

'''
Object detection and tracking with OpenCV
    ==> Turning a LED on detection and
    ==> Real Time tracking with Pan-Tilt servos
    Based on original tracking object code developed by Adrian Rosebrock
    Visit original post: https://www.pyimagesearch.com/2016/05/09/opencv-rpi-gpio-and-gpio-zero-on-the-raspberry-pi/
Developed by Marcelo Rovai - MJRoBot.org @ 9Feb2018
'''

# import the necessary packages
from __future__ import print_function
from imutils.video import VideoStream
import argparse
import imutils
import time
import cv2
import os
import RPi.GPIO as GPIO

#define Servos GPIOs
panServo = 27
tiltServo = 37
triggerSolenoid = 40

# initialize LED GPIO
redLed = 21
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BOARD)
GPIO.setup(redLed, GPIO.OUT)
GPIO.setup(triggerSolenoid, GPIO.OUT)
#p=GPIO.start(32)

#position servos
def positionServo (servo, angle):
    os.system("python angleServoCtrl.py " + str(servo) + " " + str(angle))
    print("[INFO] Positioning servo at GPIO {0} to {1} degrees\n".format(servo, angle))

# position servos to present object at center of the frame
def mapServoPosition (x, y):
    global panAngle
    global tiltAngle
    if (x < 220):
        panAngle += 10
        if panAngle > 140:
            panAngle = 140
        positionServo (panServo, panAngle)

    if (x > 280):
        panAngle -= 10
        if panAngle < 40:
            panAngle = 40
        positionServo (panServo, panAngle)

    if (y < 270)
         tiltAngle += 5
        if tiltAngle > 130:
            tiltAngle = 130
        positionServo (tiltServo, tiltAngle)

    if (y > 300):
        tiltAngle -= 5
        if tiltAngle < 40:
            tiltAngle = 40
        positionServo (tiltServo, tiltAngle)

# initialize the video stream and allow the camera sensor to warmup
print("[INFO] waiting for camera to warmup...")
vs = VideoStream(0).start()
time.sleep(2.0)

# define the lower and upper boundaries of the object
# to be tracked in the HSV color space
colorLower = (164, 100, 100)
colorUpper = (184, 255, 255)

# Start with LED off and solenoid off
GPIO.output(redLed, GPIO.LOW)
GPIO.output(triggerSolenoid, GPIO.LOW)
ledOn = False

# Initialize angle servos at 90-90 position
global panAngle
panAngle = 90
global tiltAngle
tiltAngle = 90

# positioning Pan/Tilt servos at initial position
positionServo (panServo, panAngle)
positionServo (tiltServo, tiltAngle)

# loop over the frames from the video stream
while True:
    # grab the next frame from the video stream, Invert 180o, resize the
    # frame, and convert it to the HSV color space
    frame = vs.read()
    frame = imutils.resize(frame, width=500)
    frame = imutils.rotate(frame, angle=180)
    frame = cv2.flip(frame,-1)
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # construct a mask for the object color, then perform
    # a series of dilations and erosions to remove any small
    # blobs left in the mask
    mask = cv2.inRange(hsv, colorLower, colorUpper)
    mask = cv2.erode(mask, None, iterations=2)
    mask = cv2.dilate(mask, None, iterations=2)
global panAngle
panAngle = 90
global tiltAngle
tiltAngle = 90

# positioning Pan/Tilt servos at initial position
positionServo (panServo, panAngle)
positionServo (tiltServo, tiltAngle)

# loop over the frames from the video stream
while True:
    # grab the next frame from the video stream, Invert 180o, resize the
    # frame, and convert it to the HSV color space
    frame = vs.read()
    frame = imutils.resize(frame, width=500)
    frame = imutils.rotate(frame, angle=180)
    frame = cv2.flip(frame,-1)
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # construct a mask for the object color, then perform
    # a series of dilations and erosions to remove any small
    # blobs left in the mask
    mask = cv2.inRange(hsv, colorLower, colorUpper)
    mask = cv2.erode(mask, None, iterations=2)
    mask = cv2.dilate(mask, None, iterations=2)

    # find contours in the mask and initialize the current
    # (x, y) center of the object
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
                            cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if imutils.is_cv2() else cnts[1]
    center = None

    # only proceed if at least one contour was found
    if len(cnts) > 0:
        # find the largest contour in the mask, then use
        # it to compute the minimum enclosing circle and
        # centroid
        c = max(cnts, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c)
        M = cv2.moments(c)
        center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))

        # only proceed if the radius meets a minimum size
        if radius > 10:
            # draw the circle and centroid on the frame,
            # then update the list of tracked points
            cv2.circle(frame, (int(x), int(y)), int(radius),
                       (0, 255, 255), 2)
            cv2.circle(frame, center, 5, (0, 0, 255), -1)

            # position Servo at center of circle
            mapServoPosition(int(x), int(y))
            if (tiltAngle == 120):
                print("\n Target Acquired FIRE FIRE FIRE \n")
                GPIO.output(triggerSolenoid, GPIO.HIGH)

            # if the led is not already on, turn the LED on
            if not ledOn:
                GPIO.output(redLed, GPIO.HIGH)
                ledOn = True

        # if the ball is not detected, turn the LED off
        elif ledOn:
            GPIO.output(redLed, GPIO.LOW)
            ledOn = False

        # show the frame to our screen
        cv2.imshow("Frame", frame)

        # if [ESC] key is pressed, stop the loop
        key = cv2.waitKey(1) & 0xFF
        if key == 27:
            break

# do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff \n")
#positionServo (panServo, 90)
positionServo (tiltServo, 90)
GPIO.cleanup()