if config["view"]["regions_threshold"]:
cv2.imshow("ThresholdView", thresh)
key = cv2.waitKey(1) & 0xFF
# If the `q` key is pressed, break from the loop;
if key == ord("q"):
break
# Clear the stream in preparation for the next frame or loop loaded file;
frameCounter += 1
if config["debug"]["use_file"] == False:
rawCapture.truncate(0)
if config["debug"]["use_file"] and config["debug"]["file_loop"]:
if frameCounter == (rawCapture.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT) / 2):
frameCounter = 1
rawCapture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, 0)
# Start filtering right framesets from cache (when not analysing) for persistency;
if config["debug"]["print_messages"]:
print "[MESSAGE] Done, recorded " + str(len(framesCache)) + " datasets!"
print "[MESSAGE] Start filtering recorded information."
if not config["debug"]["file_loop"]:
if config["debug"]["print_frames_cache"]:
print json.dumps(framesCache)
if config["debug"]["print_messages"]:
print "[MESSAGE] Filtering motion with persistency lower than " + str(config["capture"]["frames"]) + " frames..."
if len(framesCache) > 0:
tempFramesCache = {}
for attribute, value in framesCache.iteritems():
if len(value) >= config["capture"]["frames"]:
tempFramesCache[attribute] = value
def main():
import cv2
import numpy as np
import time
import rpi
from picamera.array import PiRGBArray
from picamera import PiCamera
#set the GPIO pins of raspberry pi.
GPIO.setmode (GPIO.BCM)
GPIO.setwarnings (False)
#enable
GPIO.setup(16, GPIO.OUT)
GPIO.setup(20, GPIO.OUT)
#setting the GPIO pin as Output
GPIO.setup (24, GPIO.OUT)
GPIO.setup (23, GPIO.OUT)
GPIO.setup (27, GPIO.OUT)
GPIO.setup (22, GPIO.OUT)
#GPIO.PWM( pin, frequency ) it generates software PWM
PWMR = GPIO.PWM (24, 100)
PWMR1 = GPIO.PWM (23, 100)
PWML = GPIO.PWM (27, 100)
PWML1 = GPIO.PWM (22, 100)
#Starts PWM at 0% dutycycle
PWMR.start (0)
PWMR1.start (0)
PWML.start (0)
PWML1.start (0)
#enable pins of the motor
GPIO.output(16, GPIO.HIGH)
GPIO.output(20, GPIO.HIGH)
camera = PiCamera()
capture = PiRGBArray(camera,(640,480))
camera.capture(capture, format="bgr")
image = capture.array
capture.set(3,320.0) #set the size
capture.set(4,240.0) #set the size
capture.set(5,15) #set the frame rate
#set the resolution of the video to be captured
camera.resolution = (640,480)
#set the framerate
cam.framerate = 30
for i in range(0,2):
flag, trash = capture.read() #starting unwanted null value
while cv2.waitKey(1) != 27:
flag, frame = capture.read() #read the video in frames
gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)#convert each frame to grayscale.
blur=cv2.GaussianBlur(gray,(5,5),0)#blur the grayscale image
ret,th1 = cv2.threshold(blur,35,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)#using threshold remave noise
ret1,th2 = cv2.threshold(th1,127,255,cv2.THRESH_BINARY_INV)# invert the pixels of the image frame
_,contours, hierarchy = cv2.findContours(th2,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) #find the contours
# cv2.drawContours(frame,contours,-1,(0,255,0),3)
# cv2.imshow('frame',frame) #show video
for cnt in contours:
if cnt is not None:
area = cv2.contourArea(cnt)# find the area of contour
if area>=500 : #If detected area of contour is greater than 500 then it indicates Zone Indicator and thus further searches for contours shapes
# find moment and centroid of detect contours and then search the loop for appropriate image for zone indicator to overlay
M = cv2.moments(cnt)
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
rect = cv2.minAreaRect(cnt)
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(frame,[box],0,(0,0,255),2)
if box>=5000
#motor stop code
pixel = frame [cy,cx]
if pixel[0] != 255 and pixel[1] != 255 and pixel[2] != 255:
x,y,w,h = cv2.boundingRect(cnt) #compute bounding rectangle of each contour
if pixel[0] == 254 :
mycolor='BLUE' #.. I have BLUE Contour
if pixel[1] == 127 :
mycolor='GREEN' #.. I have Green Contour put Red Flower
overlay_image = cv2.resize(image_green,(h,w))
if pixel[2] == 254 :
mycolor='RED' #.. I have RED Contour
overlay_image = cv2.resize(image_red,(h,w))
try:
if len(approx)==3:
myshape= "Triangle"
if len(approx)==4:
myshape= "Quadrilateral"
if len(approx)==5:
myshape= "Pentagon"
if len(approx)==6:
myshape= "Hexgaon"
if len(approx) > 7:
myshape= "CIRCLE"
except:
print 'ERROR IN PROCESSING'
#Approx polyDP is used to remove shadow (noise) and thus enable us to detect pure path to follow
approx = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)
frame[y:y+w,x:x+h,:] = blend_transparent(frame[y:y+w,x:x+h,:], overlay_image) # this will call the blend function to overlay
time.sleep(5) #motor will stop for 5 seconds and overlay will start
#To detect color and shape and choosing proper image
if mycolor=='BLUE':
if myshape=="Triangle":
overlay_image = cv2.resize('tulipblue.png',(h,w))
elif myshape== "Square":
overlay_image = cv2.resize('hydrangeablue.png',(h,w))
elif myshape=="Circle":
overlay_image = cv2.resize('orchid.png',(h,w))
if mycolor == 'GREEN':
if myshape=="Triangle":
overlay_image = cv2.resize('hydrangeayellow.png',(h,w))
elif myshape== "Square":
overlay_image = cv2.resize('sunflower.png',(h,w))
elif myshape=="Circle":
overlay_image = cv2.resize('lily-double.png',(h,w))
if mycolor == 'RED':
if myshape=="Triangle":
overlay_image = cv2.resize('tulipred.png',(h,w))
elif myshape== "Square":
overlay_image = cv2.resize('gerber.png',(h,w))
elif myshape=="Circle":
overlay_image = cv2.resize('carnation.png',(h,w))
# If Zone Indicator is not detected then Search for contour's position and decide to move accordingly
# If cx is less than 150 means centroid detect to left so take LEFT turn
else cx<=150:
l=(cx*100/160)
PWMR.start (0)
PWML.start (0)
PWMR1.ChangeDutyCycle (100)
PWML1.ChangeDutyCycle (abs(l-25))
time.sleep(.08)
# If cx is greater than 170 means centroid detect to right so take RIGHT turn
elif cx>=170:
r=((320-cx)*100/160)
PWMR.start (0)
PWML.start (0)
PWMR1.ChangeDutyCycle (abs(r-25))
PWML1.ChangeDutyCycle (100)
time.sleep(.08)
# If in between this range then take a slight LEFT
elif cx>151 and cx<169:
PWMR.start (0)
PWML.start (0)
PWMR1.ChangeDutyCycle (96)
PWML1.ChangeDutyCycle (100)
time.sleep(.3)
