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
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
# 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.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
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
# 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
""" Read in the template image """
template_file = rospy.get_param("~template_file", "")
self.template = cv2.imread(template_file, cv.CV_LOAD_IMAGE_COLOR)
cv2.imshow("Template", self.template)
self.template = cv2.cvtColor(self.template, cv2.COLOR_RGB2GRAY)
self.surf = cv2.SURF(1000)
self.kp1, self.desc1 = self.surf.detect(self.template, None, False)
self.frame_index = 0
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.node_name = node_name
""" Read in the template image """
template_file = rospy.get_param("~template_file", "")
self.template = cv2.imread(template_file, cv.CV_LOAD_IMAGE_COLOR)
cv2.imshow("Template", self.template)
self.template = cv2.cvtColor(self.template, cv2.COLOR_RGB2GRAY)
self.surf = cv2.SURF(1000)
self.kp1, self.desc1 = self.surf.detect(self.template, None, False)
self.frame_index = 0
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.node_name = node_name
self.color_under_detect = rospy.get_param("~color_under_detect", "red")
# call masking alglorthm to get the color mask
self.mymask = Masking(color=self.color_under_detect,
shape="triangle",
masker=None,
detector=None,
matcher=None,
matching_method=None)
# 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)
cv.NamedWindow("Tracked_obj", 0)
cv.MoveWindow("Tracked_obj", 1000, 50)
# 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
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)
topicname1 = rospy.get_param("~topicname1", "/roi")
# 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
cv.NamedWindow("Backproject_Fire_Window", 0)
cv.MoveWindow("Backproject_Fire_Window", 300, 400)
# Initialize a number of variables
self.hist = None
self.track_window = None
self.show_backproj = False
self.detector_loaded = False
def __init__(self, node_name="Parking"):
ROS2OpenCV2.__init__(self, node_name)
self.debug_mode = rospy.get_param("~debug_mode",True)
self.a_maxval = rospy.get_param("~a_maxval",255)
self.a_method = rospy.get_param("~a_method",1)
self.a_threshtype = rospy.get_param("~a_threshtype",1)
self.a_block_size = rospy.get_param("~a_blocksize",15)
self.a_constant = rospy.get_param("~a_constant", 5)
self.h_rho = rospy.get_param("~h_rho",2)
self.h_theta = rospy.get_param("~h_theta",90)
self.h_thresh = rospy.get_param("~h_thresh",100)
self.h_minlinelength = rospy.get_param("~h_minlinelength",120)
self.smin = rospy.get_param("~smin", 0)
self.vmin = rospy.get_param("~vmin", 0)
self.vmax = rospy.get_param("~vmax", 254)
self.threshold = rospy.get_param("~threshold", 0)
print "image processing "
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
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)
# 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
cv.NamedWindow("Backproject", 0)
cv.MoveWindow("Backproject", 700, 600)
# Initialize a number of variables
self.hist = None
self.track_window = None
self.show_backproj = False
self.detector_loaded = False
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.node_name = node_name
# Get the paths to the cascade XML files for the Haar detectors.
# These are set in the launch file.
cascade_1 = rospy.get_param("~cascade_frontal_alt", "")
cascade_2 = rospy.get_param("~cascade_frontal_alt2", "")
cascade_3 = rospy.get_param("~cascade_profile", "")
# Initialize the Haar detectors using the cascade files
self.cascade_1 = cv2.CascadeClassifier(cascade_1)
self.cascade_2 = cv2.CascadeClassifier(cascade_2)
self.cascade_3 = cv2.CascadeClassifier(cascade_3)
# Set cascade classification parameters that tend to work well for faces
self.haar_params = dict(minSize = (20, 20),
maxSize = (150, 150),
scaleFactor = 2.0,
minNeighbors = 1,
flags = cv.CV_HAAR_DO_CANNY_PRUNING)
# Intialize the detection box
self.detect_box = None
# Initialize a couple of intermediate image variables
self.grey = None
self.small_image = None
# Track the number of hits and misses
self.hits = 0
self.misses = 0
self.hit_rate = 0
# Wait until the image topics are ready before starting
rospy.wait_for_message("input_rgb_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):
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)
#self.bin_count = 16
#self.bin_w = 24
self.bin_count = 180
self.bin_w = 2
# Initialize a number of variables
self.hist = None
self.track_window = None
self.show_backproj = False
ROS2OpenCV2.__init__(self, node_name)
self.hsv_history_sub = rospy.Subscriber("adaptive_hsv_thresholds", HSV_history, self.hsv_history_callback)
print("Init done...")
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.node_name = 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)
# 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", 100)
cv.NamedWindow("Backproject", 0)
cv.MoveWindow("Backproject", 700, 600)
# Initialize a number of variables
self.hist = None
self.track_window = None
self.show_backproj = False
# Get the paths to the cascade XML files for the Haar detectors.
# These are set in the launch file.
cascade_1 = rospy.get_param("~cascade_1", "")
cascade_2 = rospy.get_param("~cascade_2", "")
#cascade_3 = rospy.get_param("~cascade_3", "")
# Initialize the Haar detectors using the cascade files
self.cascade_1 = cv2.CascadeClassifier(cascade_1)
self.cascade_2 = cv2.CascadeClassifier(cascade_2)
#self.cascade_3 = cv2.CascadeClassifier(cascade_3)
# Set cascade parameters that tend to work well for objects.
# Can be overridden in launch file
self.LBP_scaleFactor = rospy.get_param("~LBP_scaleFactor", 1.3)
self.LBP_minNeighbors = rospy.get_param("~LBP_minNeighbors", 50)
self.LBP_minSize = rospy.get_param("~LBP_minSize", 30)
self.LBP_maxSize = rospy.get_param("~LBP_maxSize", 100)
# Store all parameters together for passing to the detector
self.LBP_params = dict(
scaleFactor=self.LBP_scaleFactor,
minNeighbors=self.LBP_minNeighbors,
# flags = cv.CV_HAAR_DO_CANNY_PRUNING,
minSize=(self.LBP_minSize, self.LBP_minSize),
maxSize=(self.LBP_maxSize, self.LBP_maxSize))
# Do we should text on the display?
self.show_text = rospy.get_param("~show_text", True)
# Intialize the detection box
self.detect_box = None
self.detector_loaded = False
self.thrsh = 3000
self.x, self.y, self.w, self.h = 10, 10, 400, 400
self.roi_pub1 = rospy.Publisher("/ObjectDetected",
String,
queue_size=10)
self.ObjectStr = "aborted"
self.Object1 = False
self.Object2 = False
def __init__(self, node_name):
ROS2OpenCV2.__init__(self, node_name)
self.node_name = 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)
# 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", 100)
cv.NamedWindow("Backproject", 0)
cv.MoveWindow("Backproject", 700, 600)
# Initialize a number of variables
self.hist = None
self.track_window = None
self.show_backproj = False
# Get the paths to the cascade XML files for the Haar detectors.
# These are set in the launch file.
cascade_1 = rospy.get_param("~cascade_1", "")
cascade_2 = rospy.get_param("~cascade_2", "")
#cascade_3 = rospy.get_param("~cascade_3", "")
# Initialize the Haar detectors using the cascade files
self.cascade_1 = cv2.CascadeClassifier(cascade_1)
self.cascade_2 = cv2.CascadeClassifier(cascade_2)
#self.cascade_3 = cv2.CascadeClassifier(cascade_3)
# Set cascade parameters that tend to work well for objects.
# Can be overridden in launch file
self.LBP_scaleFactor = rospy.get_param("~LBP_scaleFactor", 1.3)
self.LBP_minNeighbors = rospy.get_param("~LBP_minNeighbors", 50)
self.LBP_minSize = rospy.get_param("~LBP_minSize", 30)
self.LBP_maxSize = rospy.get_param("~LBP_maxSize", 100)
# Store all parameters together for passing to the detector
self.LBP_params = dict(scaleFactor = self.LBP_scaleFactor,
minNeighbors = self.LBP_minNeighbors,
# flags = cv.CV_HAAR_DO_CANNY_PRUNING,
minSize = (self.LBP_minSize, self.LBP_minSize),
maxSize = (self.LBP_maxSize, self.LBP_maxSize)
)
# Do we should text on the display?
self.show_text = rospy.get_param("~show_text", True)
# Intialize the detection box
self.detect_box = None
self.detector_loaded = False
self.thrsh =3000
self.x, self.y, self.w, self.h=10 ,10,400,400
self.roi_pub1 = rospy.Publisher("/ObjectDetected", String, queue_size=10)
self.ObjectStr = "aborted"
self.Object1=False
self.Object2=False