def run(self):
hist = cv2.createHist([180], cv2.CV_HIST_ARRAY, [(0,180)], 1 )
backproject_mode = True
while True:
frame = cv2.QueryFrame( self.capture )
# Convert to HSV and keep the hue
hsv = cv2.createImage(cv2.GetSize(frame), 8, 3)
cv2.cvtColor(frame, hsv, cv2.CV_BGR2HSV)
self.hue = cv2.createImage(cv2.GetSize(frame), 8, 1)
cv2.split(hsv, self.hue, None, None, None)
# Compute back projection
backproject = cv2.createImage(cv2.GetSize(frame), 8, 1)
cv2.calcArrBackProject( [self.hue], backproject, hist )
# Run the cam-shift (if the a window is set and != 0)
if self.track_window and is_rect_nonzero(self.track_window):
crit = ( cv2.CV_TERMCRIT_EPS | cv2.CV_TERMCRIT_ITER, 10, 1)
(iters, (area, value, rect), track_box) = cv2.camShift(backproject, self.track_window, crit) #Call the camshift !!
self.track_window = rect #Put the current rectangle as the tracked area
# If mouse is pressed, highlight the current selected rectangle and recompute histogram
if self.drag_start and is_rect_nonzero(self.selection):
sub = cv2.getSubRect(frame, self.selection) #Get specified area
#Make the effect of background shadow when selecting a window
save = cv2.cloneMat(sub)
cv2.convertScale(frame, frame, 0.5)
cv2.copy(save, sub)
#Draw temporary rectangle
x,y,w,h = self.selection
cv2.rectangle(frame, (x,y), (x+w,y+h), (255,255,255))
#Take the same area but in hue image to calculate histogram
sel = cv2.getSubRect(self.hue, self.selection )
cv2.calcArrHist( [sel], hist, 0)
#Used to rescale the histogram with the max value (to draw it later on)
(_, max_val, _, _) = cv2.getMinMaxHistValue( hist)
if max_val != 0:
cv2.convertScale(hist.bins, hist.bins, 255. / max_val)
elif self.track_window and is_rect_nonzero(self.track_window): #If window set draw an elipseBox
cv2.ellipseBox( frame, track_box, cv2.CV_RGB(255,0,0), 3, cv2.CV_AA, 0 )
cv2.showImage( "CamShiftDemo", frame )
cv2.showImage( "Backprojection", backproject)
cv2.showImage( "Histogram", self.hue_histogram_as_image(hist))
c = cv2.waitKey(7) % 0x100
if c == 27:
break
def set_frame(self,frame):
#This function sets the picture to a desired frame size
#self - object of this class
#frame - captured frame
jpegPIL = Image.fromstring("RGB",(640,480),frame,"jpeg","RGB","raw")
cv_im = cv2.createImage((640,480),cv.IPL_DEPTH_8U,3)
cv2.setData(cv_im,jpegPIL.tostring())
cv2.showImage(self.name,cv_im)
def DoCanny(img, lowThresh, highThresh, aperature):
""" from chapter 2 of the book "Learning OpenCV: Computer Vision with the OpenCV Library", ISBN-10: 0596516134
also found on http://www.beechtreetech.com/dev/opencv-exercises-in-python.aspx -> example 2.6 """
gray = cv2.createImage(cvSize(cvGetSize(img).width, cvGetSize(img).height), IPL_DEPTH_8U, 1)
cv2.cvtColor(img,gray,cv2.CV_BGR2GRAY)
if (gray.nChannels != 1):
return False
out = cv2.Canny(gray, lowThresh, highThresh, aperature)
return out
def hue_histogram_as_image(self, hist):
""" Returns a nice representation of a hue histogram """
histimg_hsv = cv2.createImage( (320,200), 8, 3)
mybins = cv2.cloneMatND(hist.bins) #Contain all values
cv2.log(mybins, mybins) #Calculate logarithm of all values (so there are all above 0)
(_, hi, _, _) = cv2.MinMaxLoc(mybins)
cv2.convertScale(mybins, mybins, 255. / hi) #Rescale all element to get the highest at 255
w,h = cv2.getSize(histimg_hsv)
hdims = cv2.getDims(mybins)[0]
for x in range(w):
xh = (180 * x) / (w - 1) # hue sweeps from 0-180 across the image
val = int(mybins[int(hdims * x / w)] * h / 255)
cv2.rectangle( histimg_hsv, (x, 0), (x, h-val), (xh,255,64), -1)
cv2.rectangle( histimg_hsv, (x, h-val), (x, h), (xh,255,255), -1)
histimg = cv2.createImage( (320,200), 8, 3) #Convert image from hsv to RGB
cv2.cvtColor(histimg_hsv, histimg, cv2.CV_HSV2BGR)
return histimg
def run(self):
while True:
img = self.capture.read()
#blur the source image to reduce color noise
cv2.medianBlur(img, 5)
cv2.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 = cv2.createImage(cv2.getSize(img), 8, 3)
cv2.cvtColor(img, hsv_img, 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(thresholded_img, 0)
area = cv.GetCentralMoment(moments, 0, 0)
#there can be noise in the video so ignore objects with small areas
if (area > 100000):
#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, (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)
if cv.WaitKey(10) == 27:
break
def hue_histogram_as_image(self, hist):
""" Returns a nice representation of a hue histogram """
histimg_hsv = cv2.createImage((320, 200), 8, 3)
mybins = cv2.cloneMatND(hist.bins)
cv2.log(mybins, mybins)
(_, hi, _, _) = cv2.minMaxLoc(mybins)
cv2.convertScale(mybins, mybins, 255. / hi)
w,h = cv2.getSize(histimg_hsv)
hdims = cv2.getDims(mybins)[0]
for x in range(w):
xh = (180 * x) / (w - 1) # hue sweeps from 0-180 across the image
val = int(mybins[int(hdims * x / w)] * h / 255)
cv2.rectangle(histimg_hsv, (x, 0), (x, h-val), (xh,255,64), -1)
cv2.rectangle(histimg_hsv, (x, h-val), (x, h), (xh,255,255), -1)
histimg = cv2.cvtColor(histimg_hsv, cv2.COLOR_HSV2BGR)
return histimg
