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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。
rbx1/rbx1_vision/nodes/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 rospy import cv2 from cv2 import cv as cv from rbx1_vision.ros2opencv2 import ROS2OpenCV2 from std_msgs.msg import String from sensor_msgs.msg import Image import numpy as np class 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()