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颜色追踪块CamShift滤波器。

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 camshift.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，试着移动所选的区域。

以下是我的实验结果：

看看代码：启动文件为：camshift.launch。

///camshift.py节点代码：

#!/usr/bin/env python""" camshift_node.py - Version 1.1 2013-12-20    Modification of the ROS OpenCV Camshift example using cv_bridge and publishing the ROI    coordinates to the /roi topic.   """import rospyimport cv2from cv2 import cv as cvfrom rbx1_vision.ros2opencv2 import ROS2OpenCV2from std_msgs.msg import Stringfrom sensor_msgs.msg import Imageimport numpy as npclass CamShiftNode(ROS2OpenCV2):    def __init__(self, node_name):        ROS2OpenCV2.__init__(self, node_name)        self.node_name = node_name                # The minimum saturation of the tracked color in HSV space,        # as well as the min and max value (the V in HSV) and a         # threshold on the backprojection probability image.        self.smin = rospy.get_param("~smin", 85)        self.vmin = rospy.get_param("~vmin", 50)        self.vmax = rospy.get_param("~vmax", 254)        self.threshold = rospy.get_param("~threshold", 50)                               # Create a number of windows for displaying the histogram,        # parameters controls, and backprojection image        cv.NamedWindow("Histogram", cv.CV_WINDOW_NORMAL)        cv.MoveWindow("Histogram", 700, 50)        cv.NamedWindow("Parameters", 0)        cv.MoveWindow("Parameters", 700, 325)        cv.NamedWindow("Backproject", 0)        cv.MoveWindow("Backproject", 700, 600)                # Create the slider controls for saturation, value and threshold        cv.CreateTrackbar("Saturation", "Parameters", self.smin, 255, self.set_smin)        cv.CreateTrackbar("Min Value", "Parameters", self.vmin, 255, self.set_vmin)        cv.CreateTrackbar("Max Value", "Parameters", self.vmax, 255, self.set_vmax)        cv.CreateTrackbar("Threshold", "Parameters", self.threshold, 255, self.set_threshold)                # Initialize a number of variables        self.hist = None        self.track_window = None        self.show_backproj = False        # These are the callbacks for the slider controls    def set_smin(self, pos):        self.smin = pos            def set_vmin(self, pos):        self.vmin = pos                def set_vmax(self, pos):       self.vmax = pos           def set_threshold(self, pos):        self.threshold = pos    # The main processing function computes the histogram and backprojection    def process_image(self, cv_image):        try:            # First blur the image            frame = cv2.blur(cv_image, (5, 5))                        # Convert from RGB to HSV space            hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)                        # Create a mask using the current saturation and value parameters            mask = cv2.inRange(hsv, np.array((0., self.smin, self.vmin)), np.array((180., 255., self.vmax)))                        # If the user is making a selection with the mouse,             # calculate a new histogram to track            if self.selection is not None:                x0, y0, w, h = self.selection                x1 = x0 + w                y1 = y0 + h                self.track_window = (x0, y0, x1, y1)                hsv_roi = hsv[y0:y1, x0:x1]                mask_roi = mask[y0:y1, x0:x1]                self.hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )                cv2.normalize(self.hist, self.hist, 0, 255, cv2.NORM_MINMAX);                self.hist = self.hist.reshape(-1)                self.show_hist()                if self.detect_box is not None:                self.selection = None                        # If we have a histogram, track it with CamShift            if self.hist is not None:                # Compute the backprojection from the histogram                backproject = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)                                # Mask the backprojection with the mask created earlier                backproject &= mask                    # Threshold the backprojection                ret, backproject = cv2.threshold(backproject, self.threshold, 255, cv.CV_THRESH_TOZERO)                    x, y, w, h = self.track_window                if self.track_window is None or w <= 0 or h <=0:                    self.track_window = 0, 0, self.frame_width - 1, self.frame_height - 1                                # Set the criteria for the CamShift algorithm                term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )                                # Run the CamShift algorithm                self.track_box, self.track_window = cv2.CamShift(backproject, self.track_window, term_crit)                    # Display the resulting backprojection                cv2.imshow("Backproject", backproject)        except:            pass        return cv_image            def show_hist(self):        bin_count = self.hist.shape[0]        bin_w = 24        img = np.zeros((256, bin_count*bin_w, 3), np.uint8)        for i in xrange(bin_count):            h = int(self.hist[i])            cv2.rectangle(img, (i*bin_w+2, 255), ((i+1)*bin_w-2, 255-h), (int(180.0*i/bin_count), 255, 255), -1)        img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)        cv2.imshow('Histogram', img)            def hue_histogram_as_image(self, hist):            """ Returns a nice representation of a hue histogram """            histimg_hsv = cv.CreateImage((320, 200), 8, 3)                        mybins = cv.CloneMatND(hist.bins)            cv.Log(mybins, mybins)            (_, hi, _, _) = cv.MinMaxLoc(mybins)            cv.ConvertScale(mybins, mybins, 255. / hi)                w,h = cv.GetSize(histimg_hsv)            hdims = cv.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, cv.CV_HSV2BGR)                        return histimg         if __name__ == '__main__':    try:        node_name = "camshift"        CamShiftNode(node_name)        try:            rospy.init_node(node_name)        except:            pass        rospy.spin()    except KeyboardInterrupt:        print "Shutting down vision node."        cv.DestroyAllWindows()