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
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
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.match_threshold = rospy.get_param("~match_threshold", 0.7)
self.find_multiple_targets = rospy.get_param("~find_multiple_targets", False)
self.n_pyr = rospy.get_param("~n_pyr", 2)
self.min_template_size = rospy.get_param("~min_template_size", 25)
self.scale_factor = rospy.get_param("~scale_factor", 1.2)
self.scale_and_rotate = rospy.get_param("~scale_and_rotate", False)
self.use_depth_for_detection = rospy.get_param("~use_depth_for_detection", False)
self.fov_width = rospy.get_param("~fov_width", 1.094)
self.fov_height = rospy.get_param("~fov_height", 1.094)
self.max_object_size = rospy.get_param("~max_object_size", 0.28)
# Intialize the detection box
self.detect_box = None
self.detector_loaded = False
rospy.loginfo("Waiting for video topics to become available...")
# Wait until the image topics are ready before starting
rospy.wait_for_message("input_rgb_image", Image)
if self.use_depth_for_detection:
rospy.wait_for_message("input_depth_image", Image)
rospy.loginfo("Ready.")
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.match_threshold = rospy.get_param("~match_threshold", 0.7)
self.find_multiple_targets = rospy.get_param("~find_multiple_targets", False)
self.n_pyr = rospy.get_param("~n_pyr", 2)
self.min_template_size = rospy.get_param("~min_template_size", 25)
self.scale_factor = rospy.get_param("~scale_factor", 1.2)
self.scale_and_rotate = rospy.get_param("~scale_and_rotate", False)
self.use_depth_for_detection = rospy.get_param("~use_depth_for_detection", False)
self.fov_width = rospy.get_param("~fov_width", 1.094)
self.fov_height = rospy.get_param("~fov_height", 1.094)
self.max_object_size = rospy.get_param("~max_object_size", 0.28)
# Intialize the detection box
self.detect_box = None
self.detector_loaded = False
rospy.loginfo("Waiting for video topics to become available...")
# Wait until the image topics are ready before starting
rospy.wait_for_message("input_rgb_image", Image)
if self.use_depth_for_detection:
rospy.wait_for_message("input_depth_image", Image)
rospy.loginfo("Ready.")
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 Customized to load pre-saved color
self.home_folder = expanduser("~")
self.hist_file = self.home_folder + '/.arlobot/self_hist.p'
self.window_file = self.home_folder + '/.arlobot/track_window.p'
print self.window_file, self.hist_file
#self.hist = None
self.hist = pickle.load(open(self.hist_file, "rb"))
#self.track_window = None
self.track_window = pickle.load(open(self.window_file, "rb"))
self.show_backproj = False
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.matchPercentage = rospy.get_param("~matchPercentage", 70)
self.findMultipleTargets = rospy.get_param("~findMultipleTargets",
False)
self.numMaxima = rospy.get_param("~numMaxima", 1)
self.numDownPyrs = rospy.get_param("~numDownPyrs", 2)
self.searchExpansion = rospy.get_param("~searchExpansion", 15)
self.use_depth_for_detection = rospy.get_param(
"~use_depth_for_detection", False)
self.fov_width = rospy.get_param("~fov_width", 1.094)
self.fov_height = rospy.get_param("~fov_height", 1.094)
self.max_object_size = rospy.get_param("~max_object_size", 0.28)
self.foundPointsList = list()
self.confidencesList = list()
# Intialize the detection box
self.detect_box = None
# Initialize a couple of intermediate image variables
self.grey = None
self.small_image = None
# What kind of detector do we want to load
self.detector_type = "template"
self.detector_loaded = False
rospy.loginfo("Waiting for video topics to become available...")
# Wait until the image topics are ready before starting
rospy.wait_for_message("input_rgb_image", Image)
if self.use_depth_for_detection:
rospy.wait_for_message("input_depth_image", Image)
rospy.loginfo("Ready.")
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.matchPercentage = rospy.get_param("~matchPercentage", 70)
self.findMultipleTargets = rospy.get_param("~findMultipleTargets", False)
self.numMaxima = rospy.get_param("~numMaxima", 1)
self.numDownPyrs = rospy.get_param("~numDownPyrs", 2)
self.searchExpansion = rospy.get_param("~searchExpansion", 15)
self.use_depth_for_detection = rospy.get_param("~use_depth_for_detection", False)
self.fov_width = rospy.get_param("~fov_width", 1.094)
self.fov_height = rospy.get_param("~fov_height", 1.094)
self.max_object_size = rospy.get_param("~max_object_size", 0.28)
self.foundPointsList = list()
self.confidencesList = list()
# Intialize the detection box
self.detect_box = None
# Initialize a couple of intermediate image variables
self.grey = None
self.small_image = None
# What kind of detector do we want to load
self.detector_type = "template"
self.detector_loaded = False
rospy.loginfo("Waiting for video topics to become available...")
# Wait until the image topics are ready before starting
rospy.wait_for_message("input_rgb_image", Image)
if self.use_depth_for_detection:
rospy.wait_for_message("input_depth_image", Image)
rospy.loginfo("Ready.")
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.CreateTracker('Saturation','Parameters',self.smin,255，self.set_smin)
cv.CreateTracker('Min Value','Parameters',self.vmin,255,self.set_vmin)
cv.CreateTracker('Max Value','Parameter',self.vmax,255,self.set_vmax)
cv.CreateTracker('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):
# First blur the image
frame = cv2.blur(cv_image,(5,5))
# Convert from RGB to HSV spare
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 = x1 + 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, tracking 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 backproject
ret,baackproject = 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 algorith,
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)
return cv_image