def calculate_laser_position(img):
print "calculating moments"
moments = cv.Moments(cv.GetMat(img))
posX = -1
posY = -1
area = cv.GetCentralMoment(moments, 0, 0)
if area > 0:
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0, 1)
posX = int(moment10 / area)
posY = int(moment01 / area)
return posX, posY, area
def main():
# captured image size, change to whatever you want
width = 320
height = 240
capture = cv.CreateCameraCapture(0)
# Over-write default captured image size
cv.SetCaptureProperty(capture,cv.CV_CAP_PROP_FRAME_WIDTH,width)
cv.SetCaptureProperty(capture,cv.CV_CAP_PROP_FRAME_HEIGHT,height)
cv.NamedWindow( "output", 1 )
cv.NamedWindow( "processed", 1 )
while True:
frame = cv.QueryFrame(capture)
cv.Smooth(frame, frame, cv.CV_BLUR, 3)
imgColorProcessed = ColorProcess(frame)
mat = cv.GetMat(imgColorProcessed)
# Calculating the moments
moments = cv.Moments(mat, 0)
area = cv.GetCentralMoment(moments, 0, 0)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0,1)
# Finding a big enough blob
if(area > 60000):
# Calculating the center postition of the blob
posX = int(moment10 / area)
posY = int(moment01 / area)
# check slave status and send coordinates
state = readData()
if state == 1:
sendData(posX)
sendData(posY)
print 'x: ' + str(posX) + ' y: ' + str(posY)
# update video windows
cv.ShowImage("processed", imgColorProcessed)
cv.ShowImage("output", frame)
if cv.WaitKey(10) >= 0:
break
return;
def drawCircles(cont,img):
li = None
if len(cont) != 0:
moments = cv.Moments(cont,1)
moments10 = cv.GetSpatialMoment(moments,1,0)
moments01 = cv.GetSpatialMoment(moments,0,1)
area = cv.GetCentralMoment(moments,0,0)
if area > 0:
x=(int)(moment10/PnB.area)
y=(int)(moment01/PnB.area)
rad = (int)(math.ceil(math.sqrt(PnB.area/3.14)))
cv.Circle(img,(x,y),rad,(255,255,255,0),1,8,0)
li = [x,y,img,area]
return li
def run(self):
while True:
img = cv.QueryFrame(self.capture)
#blur the source image to reduce color noise
cv.Smooth(img, img, cv.CV_BLUR, 3)
#convert the image to hsv(Hue, Saturation, Value) so its
#easier to determine the color to track(hue)
hsv_img = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, hsv_img, cv.CV_BGR2HSV)
#limit all pixels that don't match our criteria, in this case we are
#looking for purple but if you want you can adjust the first value in
#both turples which is the hue range(120,140). OpenCV uses 0-180 as
#a hue range for the HSV color model
thresholded_img = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
cv.InRangeS(hsv_img, (120, 80, 80), (140, 255, 255),
thresholded_img)
#determine the objects moments and check that the area is large
#enough to be our object
moments = cv.Moments(cv.GetMat(thresholded_img), 0)
area = cv.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if (area > 10000):
#determine the x and y coordinates of the center of the object
#we are tracking by dividing the 1, 0 and 0, 1 moments by the area
x = cv.GetSpatialMoment(moments, 1, 0) / area
y = cv.GetSpatialMoment(moments, 0, 1) / area
#print 'x: ' + str(x) + ' y: ' + str(y) + ' area: ' + str(area)
#create an overlay to mark the center of the tracked object
overlay = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.Circle(overlay, (int(x), int(y)), 2, (255, 255, 255), 20)
#cv.Add(img, overlay, img)
#add the thresholded image back to the img so we can see what was
#left after it was applied
#cv.Merge(thresholded_img, None, None, None, img)
#display the image
cv.ShowImage(color_tracker_window, img)
if cv.WaitKey(10) == 27:
break
cv.NamedWindow("thresh")
tmp = cv.QueryFrame(capture)
imgScribble = cv.CreateImage(cv.GetSize(tmp), 8,
3) #Image that will contain lines
posx = 0
posy = 0
while True:
frame = cv.QueryFrame(capture)
imgYellowTresh = getThresholdImage(frame) #Apply the threshold function
moments = cv.Moments(cv.GetMat(imgYellowTresh), 1)
moment10 = cv.GetSpatialMoment(moments, 1, 0)
moment01 = cv.GetSpatialMoment(moments, 0, 1)
area = cv.GetCentralMoment(moments, 0, 0) #Get the center
lastx = posx
lasty = posy
if area == 0:
posx = 0
posy = 0
else:
posx = moment10 / area
posy = moment01 / area
if lastx > 0 and lasty > 0 and posx > 0 and posy > 0: #Mean we have received coordinates to print
#Draw the line
cv.Line(imgScribble, (int(posx), int(posy)), (int(lastx), int(lasty)),
def run(self):
initialTime = 0. #sets the initial time
num_Frames = int(
cv.GetCaptureProperty(self.capture, cv.CV_CAP_PROP_FRAME_COUNT))
fps = cv.GetCaptureProperty(self.capture, cv.CV_CAP_PROP_FPS)
for ii in range(num_Frames - 8):
print('Frame: ' + str(ii) + ' of ' + str(num_Frames))
# read the ii-th frame
img = cv.QueryFrame(self.capture)
# Blur the source image to reduce color noise
cv.Smooth(img, img, cv.CV_BLUR, 3)
# Convert the image to hsv(Hue, Saturation, Value) so its
# It's easier to determine the color to track(hue)
hsv_img = cv.CreateImage(cv.GetSize(img), 8, 3)
cv.CvtColor(img, hsv_img, cv.CV_BGR2HSV)
# limit all pixels that don't match our criteria, in the is case we are
# looking for purple but if you want you can adjust the first value in
# both turples which is the hue range(120,140). OpenCV uses 0-180 as
# a hue range for the HSV color model
thresholded_img = cv.CreateImage(cv.GetSize(hsv_img), 8, 1)
# uncomment below for tracking blue
# cv.InRangeS(hsv_img, (112, 50, 50), (118, 200, 200), thresholded_img)
# tracking red
cv.InRangeS(hsv_img, (160, 150, 100), (180, 255, 255),
thresholded_img)
#determine the objects moments and check that the area is large
#enough to be our object
thresholded_img2 = cv.GetMat(thresholded_img)
moments = cv.Moments(thresholded_img2, 0)
area = cv.GetCentralMoment(moments, 0, 0)
# there can be noise in the video so ignore objects with small areas
if (area > 2500):
#determine the x and y coordinates of the center of the object
#we are tracking by dividing the 1, 0 and 0, 1 moments by the area
x = cv.GetSpatialMoment(moments, 1, 0) / area
y = cv.GetSpatialMoment(moments, 0, 1) / area
elapsedTime = ii / fps
f.write(
str(elapsedTime) + ',' + '%013.9f' % x + ',' +
'%013.9f' % y + "\n"
) #prints output to the specified output file for later use
#
# x = int(x)
# y = int(y)
#
# #create an overlay to mark the center of the tracked object
# overlay = cv.CreateImage(cv.GetSize(img), 8, 3)
#
# cv.Circle(overlay, (x, y), 2, (255, 255, 255), 20)
# cv.Add(img, overlay, img)
# #add the thresholded image back to the img so we can see what was
# #left after it was applied
# cv.Merge(thresholded_img, None, None, None, img)
#
# #display the image
# cv.ShowImage(color_tracker_window, img)
# close the data file
f.close()
thr = cv.CreateImage(cv.GetSize(frame), 8, 1)
#Change threshold value
cv.InRangeS(hsv, (h1, s1, v1), (h2, s2, v2), thr)
moments = cv.Moments(cv.GetMat(thr, 1), 0)
area = cv.GetCentralMoment(moments, 0, 0)
cv.Line(frame, (160, 0), (160, 240), (0, 0, 255), 3, 8, 0)
if (area > 10000):
x = cv.GetSpatialMoment(moments, 1, 0) / area
y = cv.GetSpatialMoment(moments, 0, 1) / area
# overlay = cv.CreateImage(cv.GetSize(frame),8,3)
cv.Circle(frame, (int(x), int(y)), 2, (255, 255, 255), 20)
# cv.Add(frame,overlay,frame)
# cv.Merge(thr,None,None,None,frame)
if (int(x) < 130):
value = 130 - int(x)
cv.PutText(frame, "Left[" + str(value) + "]", (int(x), int(y)),
font, (255, 255, 0))
x_count += 1
