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 grey_histogram(img, nBins=64): """ Returns a one dimension histogram for the given image The image is expected to have one channel, 8 bits depth nBins can be defined between 1 and 255 """ hist_size = [nBins] # h_ranges = [0, 255] hist = cv2.createHist(hist_size, cv2.CV_HIST_ARRAY, [[0, 255]], 1) cv2.calcHist([img], hist) return hist