content: from SimpleCV.base import np, itertools
  try:
      import cv2
  except ImportError:
      pass
  
  def lkTracker(img, bb, ts, oldimg, **kwargs):
      """
      **DESCRIPTION**
      
      (Dev Zone)
  
      Tracking the object surrounded by the bounding box in the given
      image using Lucas Kanade based Optical Flow method.
  
      Warning: Use this if you know what you are doing. Better have a 
      look at Image.track()
  
      **PARAMETERS**
  
      * *img* - Image - Image to be tracked.
      * *bb*  - tuple - Bounding Box tuple (x, y, w, h)
      * *ts*  - TrackSet - SimpleCV.Features.TrackSet.
      * *oldimg* - Image - Previous Image.
  
      Optional PARAMETERS:
      (docs from http://docs.opencv.org/)
  
      maxCorners    - Maximum number of corners to return in goodFeaturesToTrack. 
                      If there are more corners than are found, the strongest of 
                      them is returned.
                  
      qualityLevel  - Parameter characterizing the minimal accepted quality of image corners. 
                      The parameter value is multiplied by the best corner quality measure, 
                      which is the minimal eigenvalue or the Harris function response. 
                      The corners with the quality measure less than the product are rejected.
                      For example, if the best corner has the quality measure = 1500, 
                      and the qualityLevel=0.01 , then all the corners with the quality measure 
                      less than 15 are rejected. 
                    
      minDistance   - Minimum possible Euclidean distance between the returned corners.
  
      blockSize     - Size of an average block for computing a derivative covariation matrix over each pixel neighborhood.
  
      winSize       - size of the search window at each pyramid level.
  
      maxLevel      - 0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), 
                      if set to 1, two levels are used, and so on
  
      **RETURNS**
  
      SimpleCV.Features.Tracking.LKTracker
  
      **HOW TO USE**
  
      >>> cam = Camera()
      >>> ts = []
      >>> img = cam.getImage()
      >>> bb = (100, 100, 300, 300) # get BB from somewhere
      >>> ts = lkTracker(img, bb, ts, img, maxCorners=4000, qualityLevel=0.5, winSize=(12,12))
      >>> while (some_condition_here):
          ... img = cam.getImage()
          ... bb = ts[-1].bb
          ... prevImg = img
          ... ts = lkTracker(img, bb, ts, prevImg, maxCorners=4000, qualityLevel=0.5, winSize=(12,12))
          ... ts[-1].drawBB()
          ... img.show()
  
      This is too much confusing. Better use
      Image.track() method.
  
      READ MORE:
  
      LK (Lucas Kanade) Tracker:
      It is based on LK Optical Flow. It calculates Optical flow in frame1 to frame2 
      and also in frame2 to frame1 and using back track error, filters out false
      positives.
      """
      maxCorners = 4000
      qualityLevel = 0.08
      minDistance = 2
      blockSize = 3
      winSize = (10, 10)
      maxLevel = 10
      for key in kwargs:
          if key == 'maxCorners':
              maxCorners = kwargs[key]
          elif key == 'quality':
              qualityLevel = kwargs[key]
          elif key == 'minDistance':
              minDistance = kwargs[key]
          elif key == 'blockSize':
              blockSize = kwargs[key]
          elif key == 'winSize':
              winSize = kwargs[key]
          elif key == maxLevel:
              maxLevel = kwargs[key]
  
      bb = (int(bb[0]), int(bb[1]), int(bb[2]), int(bb[3]))
      img1 = img.crop(bb[0],bb[1],bb[2],bb[3])
      g = img1.getGrayNumpyCv2()
      pt = cv2.goodFeaturesToTrack(g, maxCorners = maxCorners, qualityLevel = qualityLevel,
                                  minDistance = minDistance, blockSize = blockSize)
      if type(pt) == type(None):
          print "no points"
          track = LK(img, bb, pt)
          return track
  
      for i in xrange(len(pt)):
          pt[i][0][0] = pt[i][0][0]+bb[0]
          pt[i][0][1] = pt[i][0][1]+bb[1]
  
      p0 = np.float32(pt).reshape(-1, 1, 2)
      oldg = oldimg.getGrayNumpyCv2()
      newg = img.getGrayNumpyCv2()
      p1, st, err = cv2.calcOpticalFlowPyrLK(oldg, newg, p0, None, winSize  = winSize,
                                             maxLevel = maxLevel,
                                             criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
      p0r, st, err = cv2.calcOpticalFlowPyrLK(newg, oldg, p1, None, winSize  = winSize,
                                              maxLevel = maxLevel,
                                              criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
  
      d = abs(p0-p0r).reshape(-1, 2).max(-1)
      good = d < 1
      new_pts=[]
      for pts, val in itertools.izip(p1, good):
          if val:
              new_pts.append([pts[0][0], pts[0][1]])
      if ts[-1:]:
          old_pts = ts[-1].pts
          if type(old_pts) == type(None):
              old_pts = new_pts
      else:
          old_pts = new_pts
      dx=[]
      dy=[]
      for p1, p2 in itertools.izip(old_pts, new_pts):
          dx.append(p2[0]-p1[0])
          dy.append(p2[1]-p1[1])
  
      if not dx or not dy:
          track = LK(img, bb, new_pts)
          return track
  
      cen_dx = round(sum(dx)/len(dx))/3
      cen_dy = round(sum(dy)/len(dy))/3
  
      bb1 = [bb[0]+cen_dx, bb[1]+cen_dy, bb[2], bb[3]]
      if bb1[0] <= 0:
          bb1[0] = 1
      if bb1[0]+bb1[2] >= img.width:
          bb1[0] = img.width - bb1[2] - 1
      if bb1[1]+bb1[3] >= img.height:
          bb1[1] = img.height - bb1[3] - 1
      if bb1[1] <= 0:
          bb1[1] = 1
  
      track = LKTrack(img, bb1, new_pts)    
      return track
  
  from SimpleCV.Tracking import LKTrack
  