def __init__(self,
controller_ip=0,
controller_port=0,
source="kinect",
standalone=False,
filepath="default",
test=0):
self.logger = logging.getLogger("Borg.Brain.Vision.ObstacleDetector")
avm.__init__(self, controller_ip, controller_port)
self.fail_count = 0
#TODO: Remove this hack (inserted to be backwards compitable for config files that set "kinect" as source instead of "kinect_depth":
if source == "kinect":
source = "kinect_depth"
if standalone:
self.logger.setLevel("WARNING")
# if detector is run as standalone, the kinect images will be retreived
# directly from the kinect instead of from shared memory
# set standalone true, if you want to use the obstacle detector without the architecture
self.standalone = standalone
# update frequency of the behaviour
self.update_frequency = 20
# bit depth of the used depth image
self.imageDepth = 8
#tolerance when it comes to detecting obstacles (don't set it too tight)
# The higher the tollerance, the larger an obstacle should be before it is detected
# when it's to low the risk is taken it will detect the floor as an obstacle...
self.tolerance = 2.0
self.minTolerance = self.tolerance
self.maxTolerance = self.tolerance
# calibration matrix (containing floor calibrated data)
# min.cal, max.cal and avg.cal are files generated during calibrations.
# They contain the distance min, max and average distance to the floor from the kinect.
# The test files are used by obstacledetectortest.py
if test == 0:
minfile = os.environ[
'BORG'] + '/brain/src/vision/obstacledetectorutil/min.cal'
maxfile = os.environ[
'BORG'] + '/brain/src/vision/obstacledetectorutil/max.cal'
avgfile = os.environ[
'BORG'] + '/brain/src/vision/obstacledetectorutil/avg.cal'
else:
minfile = os.environ[
'BORG'] + '/brain/src/vision/obstacledetectorutil/testmin.cal'
maxfile = os.environ[
'BORG'] + '/brain/src/vision/obstacledetectorutil/testmax.cal'
avgfile = os.environ[
'BORG'] + '/brain/src/vision/obstacledetectorutil/testavg.cal'
# read the calibration files and store the content in variables (multidimensional arrays).
self.minMatrix = self.getCalibrationData(minfile)
self.maxMatrix = self.getCalibrationData(maxfile)
self.avgMatrix = self.getCalibrationData(avgfile)
# current image where obstacles should be detected in.
self.image = 0
#segmentizer, used for segmentating images into segments
self.segmentizer = Segmentizer()
# specify the source (file or kinect)
# reading from file is used for testing
# when read from kinect, the depth images are read from the shared memory, or directly from the kinect (specified in self.standalone)
self.source = source
if source == "file":
if filepath == "default":
self.path = os.environ[
'BORG'] + '/brain/data/od_images/ball_left/'
else:
self.path = filepath
self.filelist = glob.glob(os.path.join(self.path, '*.png'))
self.framenumber = 0
else:
self.vid_mem_reader = util.vidmemreader.VidMemReader([self.source])
