def main():
print("sms hi")
os.system('gpspipe -r -d -l -o /home/pi/smartcane/sms/date1.txt')
# set api key, api secret
api_key = "NCS4QACAQPBEDUMG"
api_secret = "XBGIKE2OTK0SJEDL86QTWXXQXYUMHBNU"
imgname='./image.jpg'
camera = picamera.PiCamera()
camera.vflip=True
camera.hflip=True
os.system("mplayer voicefile/camera.mp3")
capture = PiRGBArray(camera)
camera.capture(capture,format='rgb',use_video_port=True)
capture = Image.fromarray(capture.array)
capture.save(imgname)
camera.close()
#flag.initFlag()
time.sleep(10)
params = dict()
if gps.convert('date1.txt') == 1:
params['type'] = 'mms' # Message type ( sms, lms, mms, ata )
params['to'] = '010-6473-4451'
params['from'] = '01064734451'
params['text'] = 'http://maps.google.com/maps?z=11&t=k&q=37.558088+126.998222'
params["image"] = imgname # image for MMS. type must be set as "MMS"
else:
latresult,longresult = gps.convert('date1.txt')
params['type'] = 'mms' # Message type ( sms, lms, mms, ata )
params['to'] = '010-6473-4451'
params['from'] = '01064734451'
params['text'] = 'http://maps.google.com/maps?z=11&t=k&q=' + latresult +'+'+longresult # Message
params["image"] = imgname # image for MMS. type must be set as "MMS"
## 4 params(to, from, type, text) are mandat?ory. must be filled
cool = Message(api_key, api_secret)
try:
response = cool.send(params)
print("Success Count : %s" % response['success_count'])
print("Error Count : %s" % response['error_count'])
print("Group ID : %s" % response['group_id'])
os.system("mplayer /home/pi/Desktop/smartcane/voicefile/mms.MP3")
if "error_list" in response:
print("Error List : %s" % response['error_list'])
except CoolsmsException as e:
print("Error Code : %s" % e.code)
print("Error Message : %s" % e.msg)
sys.exit()
def main():
imgname=os.path.join(dirname,'blockimage.jpg')
camera = picamera.PiCamera()
camera.vflip=True
camera.hflip=True
os.system("mplayer voicefile/camera.mp3")
capture = PiRGBArray(camera)
camera.capture(capture,format='rgb',use_video_port=True)
capture = Image.fromarray(capture.array)
capture.save(imgname)
camera.close()
img = cv2.imread(target)
img = cv2.resize(img, (width,height), interpolation=cv2.INTER_AREA)
blur = cv2.GaussianBlur(img,(3,3),0)
blurfilename = os.path.join(dirname,'blur.jpg')
cv2.imwrite(blurfilename,blur)
frame = cv2.imread(blurfilename)
#hsv??
img_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
img_h, img_s, img_v = cv2.split(img_hsv)
avgValue = int(avgHSV(img_v))
#??? ?? ??
lower_white = np.array([0,0,avgValue+45])
upper_white = np.array([180,20,255])
mask = cv2.inRange(img_hsv, lower_white, upper_white)
# Bitwise-AND mask and original image
img_result = cv2.bitwise_and(frame,frame, mask= mask)
img_yellow_name = os.path.join(dirname,'img_yellow.jpg')
cv2.imwrite( img_yellow_name, img_result )
img = cv2.imread(img_yellow_name)
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#dilation
kernel = np.ones((6, 6), np.uint8)
dilation = cv2.dilate(imgray, kernel, iterations=1)
#opening
kernel = np.ones((7, 7), np.uint8)
opening = cv2.morphologyEx(dilation, cv2.MORPH_OPEN,kernel)
#Thresholding
ret, thr = cv2.threshold(opening, 20, 255, 0)
cv2.imshow('dddd',thr)
_, contours, _ = cv2.findContours(thr, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for con in contours:
detect(con,img)
#??? ?? ?? ?? ??
rectangle_sorted = sorted(rectangles, key=lambda area: area[0], reverse =True)
img_detect_name = os.path.join(dirname,'detectimg.jpg')
cv2.imwrite(img_detect_name, img)
img1_name = os.path.join(target)
img2_name = os.path.join(dirname,'detectimg.jpg')
img_original =cv2.imread(img1_name)
img_original = cv2.resize(img_original,(width,height) , interpolation=cv2.INTER_AREA)
img_detect =cv2.imread(img2_name)
x_list=[]
y_list=[]
x2_list=[]
y2_list=[]
for i,rect in enumerate(rectangle_sorted):
##rect is composed of size of rectangle(index 0) and 4 coords(index 2)
for j in range(len(rect[1])):
cv2.putText(img_detect,str(j),(rect[1][j][0],rect[1][j][1]),cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,1,(255,255,255))
if rect[1][0][0]!=0:
x_list.append(rect[1][0][0])
y_list.append(rect[1][0][1])
x2_list.append(rect[1][2][0])
y2_list.append(rect[1][2][1])
else:
x_list.append(rect[1][0][0])
y_list.append(rect[1][0][1])
x2_list.append(rect[1][2][0])
y2_list.append(rect[1][2][1])
if len(x_list) != 0:
gradient1=0;intercept1=0
gradient2=0;intercept2=0
gradient1, intercept1 = linear_reg(x_list,y_list)
gradient2, intercept2 = linear_reg(x2_list,y2_list)
x_intersect = (intercept2-intercept1)/(gradient1-gradient2)
y_interscet = x_intersect*gradient1+intercept1
y_mid = 500
x_mid = y_mid*(gradient1+gradient2)-(intercept1*gradient2) - (gradient1*intercept2)
x_mid /=2*gradient1*gradient2
x_third = [x_intersect,x_mid]
y_third = [y_interscet,y_mid]
gradient3,intercept3 = linear_reg(x_third,y_third)
if not np.isnan(gradient1):
if not np.isnan(gradient2):
img_detect = draw_line(img_detect,gradient1,intercept1,1)
img_detect = draw_line(img_detect,gradient2,intercept2,2)
img_detect = draw_line(img_detect,gradient3,intercept3,3)
print(gradient1,gradient2)
if not np.isnan(gradient3):
degree = int(math.degrees(math.atan(gradient3)))
direction_result = "mplayer voicefile/"+getDirection(degree)+"_clockwise.mp3"
os.system(direction_result)
print(direction_result)
#if gradient1 >0 and gradient2>0:
#print("Left side")
#os.system("mplayer voicefile/left.mp3")
#elif gradient1 <0 and gradient2>0:
#print("Center")
#os.system("mplayer voicefile/front.mp3")
#elif gradient1 <0 and gradient2<0:
#print("Right side")
#os.system("mplayer voicefile/right.mp3")
else:
print("NO CROSSWALK")
os.system("mplayer voicefile/nodetect.mp3")
cv2.imshow('original',img_original)
cv2.imshow('detect',img_detect)
cv2.imwrite('detectimg.jpg',img_detect)
cv2.waitKey(0)
cv2.destroyAllWindows()
