原创博客:转载请标明出处:http://www.cnblogs.com/zxouxuewei/
关键点：是多个方向上亮度变化强的区域。
opencv:版本是2.4.
光学流函数：calcOpticalFlowPyrLK()。（关键点侦测器使用goodFeaturesToTrack()）二者结合。
相应的启动文件为：lk_tracker.launch
首先确保你的kinect驱动或者uvc相机驱动能正常启动：（如果你使用的是kinect，请运行openni驱动）
roslaunch openni_launch openni.launch
如果你没有安装kinect深度相机驱动，请看我前面的博文。
然后运行下面的launch文件：
roslaunch rbx1_vision good_features.launch
当视频出现时，通过鼠标画矩形将图像中的某个对象框住。这个矩形表示所选的区域，你会看到这个区域中会出现一些绿色的小圆点，他们是goodFeaturesToTrack()。侦测器在该区域中发现的关键点。然后试着移动你所选择的区域，你会看到光学流函数：calcOpticalFlowPyrLK()跟踪关键点。
以下是我的运行结果：
移动后：
下面让我们来看看代码：主要是lk_tracker.py脚本
#!/usr/bin/env python """ lk_tracker.py - Version 1.1 2013-12-20 Based on the OpenCV lk_track.py demo code Created for the Pi Robot Project: http://www.pirobot.org Copyright (c) 2011 Patrick Goebel. All rights reserved. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details at: http://www.gnu.org/licenses/gpl.html """ import rospy import cv2 import cv2.cv as cv import numpy as np from rbx1_vision.good_features import GoodFeatures class LKTracker(GoodFeatures): def __init__(self, node_name): super(LKTracker, self).__init__(node_name) self.show_text = rospy.get_param("~show_text", True) self.feature_size = rospy.get_param("~feature_size", 1) # LK parameters self.lk_winSize = rospy.get_param("~lk_winSize", (10, 10)) self.lk_maxLevel = rospy.get_param("~lk_maxLevel", 2) self.lk_criteria = rospy.get_param("~lk_criteria", (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 20, 0.01)) self.lk_params = dict( winSize = self.lk_winSize, maxLevel = self.lk_maxLevel, criteria = self.lk_criteria) self.detect_interval = 1 self.keypoints = None self.detect_box = None self.track_box = None self.mask = None self.grey = None self.prev_grey = None def process_image(self, cv_image): try: # If we don't yet have a detection box (drawn by the user # with the mouse), keep waiting if self.detect_box is None: return cv_image # Create a greyscale version of the image self.grey = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY) # Equalize the grey histogram to minimize lighting effects self.grey = cv2.equalizeHist(self.grey) # If we haven't yet started tracking, set the track box to the # detect box and extract the keypoints within it if self.track_box is None or not self.is_rect_nonzero(self.track_box): self.track_box = self.detect_box self.keypoints = self.get_keypoints(self.grey, self.track_box) else: if self.prev_grey is None: self.prev_grey = self.grey # Now that have keypoints, track them to the next frame # using optical flow self.track_box = self.track_keypoints(self.grey, self.prev_grey) # Process any special keyboard commands for this module if self.keystroke != -1: try: cc = chr(self.keystroke & 255).lower() if cc == 'c': # Clear the current keypoints self.keypoints = None self.track_box = None self.detect_box = None except: pass self.prev_grey = self.grey except: pass return cv_image def track_keypoints(self, grey, prev_grey): # We are tracking points between the previous frame and the # current frame img0, img1 = prev_grey, grey # Reshape the current keypoints into a numpy array required # by calcOpticalFlowPyrLK() p0 = np.float32([p for p in self.keypoints]).reshape(-1, 1, 2) # Calculate the optical flow from the previous frame to the current frame p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **self.lk_params) # Do the reverse calculation: from the current frame to the previous frame try: p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **self.lk_params) # Compute the distance between corresponding points in the two flows d = abs(p0-p0r).reshape(-1, 2).max(-1) # If the distance between pairs of points is < 1 pixel, set # a value in the "good" array to True, otherwise False good = d < 1 # Initialize a list to hold new keypoints new_keypoints = list() # Cycle through all current and new keypoints and only keep # those that satisfy the "good" condition above for (x, y), good_flag in zip(p1.reshape(-1, 2), good): if not good_flag: continue new_keypoints.append((x, y)) # Draw the keypoint on the image cv2.circle(self.marker_image, (x, y), self.feature_size, (0, 255, 0, 0), cv.CV_FILLED, 8, 0) # Set the global keypoint list to the new list self.keypoints = new_keypoints # Convert the keypoints list to a numpy array keypoints_array = np.float32([p for p in self.keypoints]).reshape(-1, 1, 2) # If we have enough points, find the best fit ellipse around them if len(self.keypoints) > 6: track_box = cv2.fitEllipse(keypoints_array) else: # Otherwise, find the best fitting rectangle track_box = cv2.boundingRect(keypoints_array) except: track_box = None return track_box if __name__ == '__main__': try: node_name = "lk_tracker" LKTracker(node_name) rospy.spin() except KeyboardInterrupt: print "Shutting down LK Tracking node." cv.DestroyAllWindows()