def __init__ (self):
self.li_m1=list()
self.li_0=list()
self.li_1=list()
self.li_2=list()
#path to the directory of the num's directories
pathname = os.path.dirname(sys.argv[0])
fullpath = os.path.abspath(pathname)
dirs=glob.glob(fullpath+'/number_example/'+'*')
if len(dirs)==0:
raise TreatmentError('number_example folder not found, cannot proceed to recognition', 'recognition_number')
for folder in dirs:
im=cv2.imread(folder+'/-1.jpg',0)
im=toBinary(im)
self.li_m1.append(im)
im=cv2.imread(folder+'/0.jpg', 0)
im=toBinary(im)
self.li_0.append(im)
im=cv2.imread(folder+'/1.jpg', 0)
im=toBinary(im)
self.li_1.append(im)
im=cv2.imread(folder+'/2.jpg', 0)
im=toBinary(im)
self.li_2.append(im)
def track_number_callback(self, im, arg):
pub, msg = arg
target_pos=(400, 210)
try:
bridge = CvBridge()
#convert ROS image to opencv matrix
cv_image = bridge.imgmsg_to_cv2(im, "bgr8")
hsv = cv2.cvtColor(cv_image, cv2.COLOR_BGR2HSV)
lowredl = np.array([0, 0, 255])
lowredu = np.array([0, 255, 255])
upredl = np.array([150, 0, 255])
upredu = np.array([179, 255, 255])
mlow = cv2.inRange(hsv, lowredl, lowredu)
mup = cv2.inRange(hsv, upredl, upredu)
m=cv2.bitwise_or(mlow,mup)
res=cv2.bitwise_and(cv_image,cv_image, mask= m)
res=cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
retval, binary=cv2.threshold(res,127,255,cv2.THRESH_BINARY)
binary=toBinary(binary)
red_pos=self.average_pixel_pos(binary)
print red_pos
except CvBridgeError, e:
rospy.loginfo(e)
raise rospy.ServiceException("Tracking Failed")
def execute(self, userdata):
im=userdata.im_input
#extracted the interessting part of the image (the one with the number)
box, boderless=extraction(im)
extracted=im[box[0]:box[2], box[1]:box[3]]
#resizing the image to make an image with an area of 30000 pixels^2
area=30000
y, x=extracted.shape
print x*y
try:
r=float(x)/y
ny=int(np.sqrt(area/r))
except ZeroDivisionError:
print 'Wrong dimension of extraction: x=%d, y=%d'%(x,y)
return 'fail'
if x*y<400 and x*2>y:
print 'Wrong area: x=%d, y=%d'%(x,y)
return 'fail'
nx=int(r*ny)
extracted=cv2.resize(extracted, (nx, ny))
#binary thresholding requested after resize
extracted=toBinary(extracted)
#Debuging print
#cv2.imshow('Extracted', extracted)
#cv2.waitKey(5)
userdata.im_output=extracted
return 'succeed'
def execute(self, userdata):
im=userdata.im_input
#Debug visualization
#cv2.imshow('Input', im)
#cv2.waitKey(5)
#working in the HSV color model to extract the red pixel
hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
#first threshold to red extraction
lowredl = np.array([0, 0, 255])
lowredu = np.array([0, 255, 255])
#second threshold to red extraction
upredl = np.array([150, 0, 255])
upredu = np.array([179, 255, 255])
mlow = cv2.inRange(hsv, lowredl, lowredu)
mup = cv2.inRange(hsv, upredl, upredu)
#trying diffrent type of extraction, if the process failed three time, call the luminosity correction process
if self.extraction_step==3:
m=cv2.bitwise_or(mlow,mup)
res=cv2.bitwise_and(im,im, mask= m)
elif self.extraction_step==2:
res=cv2.bitwise_and(im,im, mask= mup)
elif self.extraction_step==1:
res=cv2.bitwise_and(im,im, mask= mlow)
elif self.extraction_step==0:
if self.isGPRed:
return 'fail_after_LC'
else:
self.extraction_step=3
return 'fail'
#make the thresholded image binary
res=cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
retval, res=cv2.threshold(res,127,255,cv2.THRESH_BINARY)
res=toBinary(res)
self.extraction_step-=1
userdata.im_output=res
return 'extracted'
def image_compare(im1, im2):
try:
y1, x1=im1.shape
y2, x2=im2.shape
except ValueError:
print "Not a binary image"
raise ValueError("Not a binary image")
else:
img2=cv2.resize(im2,( x1, y1))
img2=toBinary(img2)
hist = cv2.calcHist([im1],[0],None,[256],[0,256])
img2[im1==img2]=255
img2[im1!=img2]=0
hist2 = cv2.calcHist([img2],[0],None,[256],[0,256])
return float(hist2[255])/hist[255]*100
hist2 = cv2.calcHist([img2],[0],None,[256],[0,256])
return float(hist2[255])/hist[255]*100
#compare each image in l with im
def mult_image_compare(l, im):
average=0.;
for i in l:
try:
average+=image_compare(i, im)
except ValueError, e:
print 'Error in mult_image_compare: ', e
raise ValueError(e)
return average/len(l)
if __name__=='__main__':
f='resultCV/final_test2.jpg'
im=cv2.imread(f, 0)
im=toBinary(im)
ic=ImageComparator()
print ic.identify(im)